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N-dimensional analogue of DataFrame. Store multi-dimensional in a
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----------
data : BlockManager
axes : list
copy : bool, default False
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Private helper function to create a DataFrame with specific manager.
Parameters
----------
typ : {"block", "array"}
copy : bool, default True
Only controls whether the conversion from Block->ArrayManager
copies the 1D arrays (to ensure proper/contiguous memory layout).
Returns
-------
DataFrame
New DataFrame using specified manager type. Is not guaranteed
to be a copy or not.
)�typr� �r� )r� r� �_constructor_from_mgrr� � __finalize__)r� r� r� �new_mgrs r� � _as_managerzNDFrame._as_manager< s< � �& �T�Y�Y�C�d�;���)�)�'�� � �)�E�R�R�SW�X�Xr� c �T � | j | � }t j ||� |S )a�
Construct a new object of this type from a Manager object and axes.
Parameters
----------
mgr : Manager
Must have the same ndim as cls.
axes : list[Index]
Notes
-----
The axes must match mgr.axes, but are required for future-proofing
in the event that axes are refactored out of the Manager objects.
)�__new__r� r� )r� r� r� �objs r� � _from_mgrzNDFrame._from_mgrS s' � �" �k�k�#� ������c�"��
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Dictionary of global attributes of this dataset.
.. warning::
attrs is experimental and may change without warning.
See Also
--------
DataFrame.flags : Global flags applying to this object.
Notes
-----
Many operations that create new datasets will copy ``attrs``. Copies
are always deep so that changing ``attrs`` will only affect the
present dataset. ``pandas.concat`` copies ``attrs`` only if all input
datasets have the same ``attrs``.
Examples
--------
For Series:
>>> ser = pd.Series([1, 2, 3])
>>> ser.attrs = {"A": [10, 20, 30]}
>>> ser.attrs
{'A': [10, 20, 30]}
For DataFrame:
>>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
>>> df.attrs = {"A": [10, 20, 30]}
>>> df.attrs
{'A': [10, 20, 30]}
)r� �r� s r� �attrsz
NDFrame.attrsk s � �H �{�{�r� c �$ � t |� | _ y �N)�dictr� )r� �values r� r� z
NDFrame.attrs� s
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Get the properties associated with this pandas object.
The available flags are
* :attr:`Flags.allows_duplicate_labels`
See Also
--------
Flags : Flags that apply to pandas objects.
DataFrame.attrs : Global metadata applying to this dataset.
Notes
-----
"Flags" differ from "metadata". Flags reflect properties of the
pandas object (the Series or DataFrame). Metadata refer to properties
of the dataset, and should be stored in :attr:`DataFrame.attrs`.
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags
<Flags(allows_duplicate_labels=True)>
Flags can be get or set using ``.``
>>> df.flags.allows_duplicate_labels
True
>>> df.flags.allows_duplicate_labels = False
Or by slicing with a key
>>> df.flags["allows_duplicate_labels"]
False
>>> df.flags["allows_duplicate_labels"] = True
)r� r� s r� �flagsz
NDFrame.flags� s � �N �{�{�r� )r� r� c �f � | j |xr t � �� }|�||j d< |S )a�
Return a new object with updated flags.
Parameters
----------
copy : bool, default False
Specify if a copy of the object should be made.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
allows_duplicate_labels : bool, optional
Whether the returned object allows duplicate labels.
Returns
-------
Series or DataFrame
The same type as the caller.
See Also
--------
DataFrame.attrs : Global metadata applying to this dataset.
DataFrame.flags : Global flags applying to this object.
Notes
-----
This method returns a new object that's a view on the same data
as the input. Mutating the input or the output values will be reflected
in the other.
This method is intended to be used in method chains.
"Flags" differ from "metadata". Flags reflect properties of the
pandas object (the Series or DataFrame). Metadata refer to properties
of the dataset, and should be stored in :attr:`DataFrame.attrs`.
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
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Return the special character free column resolvers of a dataframe.
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ndarray.ndim : Number of array dimensions.
Examples
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>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
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ndarray.size : Number of elements in the array.
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4
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Assign desired index to given axis.
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labels : list-like, Index
The values for the new index.
axis : %(axes_single_arg)s, default 0
The axis to update. The value 0 identifies the rows. For `Series`
this parameter is unused and defaults to 0.
copy : bool, default True
Whether to make a copy of the underlying data.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
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future version of pandas.
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Returns
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An object of type %(klass)s.
See Also
--------
%(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s.
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.. deprecated:: 2.1.0
``swapaxes`` is deprecated and will be removed.
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Examples
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Please see examples for :meth:`DataFrame.transpose`.
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Return {klass} with requested index / column level(s) removed.
Parameters
----------
level : int, str, or list-like
If a string is given, must be the name of a level
If list-like, elements must be names or positional indexes
of levels.
axis : {{0 or 'index', 1 or 'columns'}}, default 0
Axis along which the level(s) is removed:
* 0 or 'index': remove level(s) in column.
* 1 or 'columns': remove level(s) in row.
For `Series` this parameter is unused and defaults to 0.
Returns
-------
{klass}
{klass} with requested index / column level(s) removed.
Examples
--------
>>> df = pd.DataFrame([
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
... ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1 c d
level_2 e f
a b
1 2 3 4
5 6 7 8
9 10 11 12
>>> df.droplevel('a')
level_1 c d
level_2 e f
b
2 3 4
6 7 8
10 11 12
>>> df.droplevel('level_2', axis=1)
level_1 c d
a b
1 2 3 4
5 6 7 8
9 10 11 12
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Squeeze 1 dimensional axis objects into scalars.
Series or DataFrames with a single element are squeezed to a scalar.
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Series. Otherwise the object is unchanged.
This method is most useful when you don't know if your
object is a Series or DataFrame, but you do know it has just a single
column. In that case you can safely call `squeeze` to ensure you have a
Series.
Parameters
----------
axis : {0 or 'index', 1 or 'columns', None}, default None
A specific axis to squeeze. By default, all length-1 axes are
squeezed. For `Series` this parameter is unused and defaults to `None`.
Returns
-------
DataFrame, Series, or scalar
The projection after squeezing `axis` or all the axes.
See Also
--------
Series.iloc : Integer-location based indexing for selecting scalars.
DataFrame.iloc : Integer-location based indexing for selecting Series.
Series.to_frame : Inverse of DataFrame.squeeze for a
single-column DataFrame.
Examples
--------
>>> primes = pd.Series([2, 3, 5, 7])
Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0 2
dtype: int64
>>> even_primes.squeeze()
2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1 3
2 5
3 7
dtype: int64
>>> odd_primes.squeeze()
1 3
2 5
3 7
dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
a b
0 1 2
1 3 4
Slicing a single column will produce a DataFrame with the columns
having only one value:
>>> df_a = df[['a']]
>>> df_a
a
0 1
1 3
So the columns can be squeezed down, resulting in a Series:
>>> df_a.squeeze('columns')
0 1
1 3
Name: a, dtype: int64
Slicing a single row from a single column will produce a single
scalar DataFrame:
>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
a
0 1
Squeezing the rows produces a single scalar Series:
>>> df_0a.squeeze('rows')
a 1
Name: 0, dtype: int64
Squeezing all axes will project directly into a scalar:
>>> df_0a.squeeze()
1
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Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
Value to set the axis name attribute.
index, columns : scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like or functions transformations to
apply to that axis' values.
Note that the ``columns`` parameter is not allowed if the
object is a Series. This parameter only apply for DataFrame
type objects.
Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index``
and/or ``columns``.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to rename. For `Series` this parameter is unused and defaults to 0.
copy : bool, default None
Also copy underlying data.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
inplace : bool, default False
Modifies the object directly, instead of creating a new Series
or DataFrame.
Returns
-------
Series, DataFrame, or None
The same type as the caller or None if ``inplace=True``.
See Also
--------
Series.rename : Alter Series index labels or name.
DataFrame.rename : Alter DataFrame index labels or name.
Index.rename : Set new names on index.
Notes
-----
``DataFrame.rename_axis`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
The first calling convention will only modify the names of
the index and/or the names of the Index object that is the columns.
In this case, the parameter ``copy`` is ignored.
The second calling convention will modify the names of the
corresponding index if mapper is a list or a scalar.
However, if mapper is dict-like or a function, it will use the
deprecated behavior of modifying the axis *labels*.
We *highly* recommend using keyword arguments to clarify your
intent.
Examples
--------
**Series**
>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0 dog
1 cat
2 monkey
dtype: object
>>> s.rename_axis("animal")
animal
0 dog
1 cat
2 monkey
dtype: object
**DataFrame**
>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
... "num_arms": [0, 0, 2]},
... ["dog", "cat", "monkey"])
>>> df
num_legs num_arms
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("animal")
>>> df
num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
**MultiIndex**
>>> df.index = pd.MultiIndex.from_product([['mammal'],
... ['dog', 'cat', 'monkey']],
... names=['type', 'name'])
>>> df
limbs num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(index={'type': 'class'})
limbs num_legs num_arms
class name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(columns=str.upper)
LIMBS num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
�r r Nr� Fr� z,Use `.rename` to alter labels with a mapper.r� T)r rX r r �
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Set the name(s) of the axis.
Parameters
----------
name : str or list of str
Name(s) to set.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to set the label. The value 0 or 'index' specifies index,
and the value 1 or 'columns' specifies columns.
inplace : bool, default False
If `True`, do operation inplace and return None.
copy:
Whether to make a copy of the result.
Returns
-------
Series, DataFrame, or None
The same type as the caller or `None` if `inplace` is `True`.
See Also
--------
DataFrame.rename : Alter the axis labels of :class:`DataFrame`.
Series.rename : Alter the index labels or set the index name
of :class:`Series`.
Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`.
Examples
--------
>>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
... ["dog", "cat", "monkey"])
>>> df
num_legs
dog 4
cat 4
monkey 2
>>> df._set_axis_name("animal")
num_legs
animal
dog 4
cat 4
monkey 2
>>> df.index = pd.MultiIndex.from_product(
... [["mammal"], ['dog', 'cat', 'monkey']])
>>> df._set_axis_name(["type", "name"])
num_legs
type name
mammal dog 4
cat 4
monkey 2
r� r� r N)r r
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Test whether two objects contain the same elements.
This function allows two Series or DataFrames to be compared against
each other to see if they have the same shape and elements. NaNs in
the same location are considered equal.
The row/column index do not need to have the same type, as long
as the values are considered equal. Corresponding columns and
index must be of the same dtype.
Parameters
----------
other : Series or DataFrame
The other Series or DataFrame to be compared with the first.
Returns
-------
bool
True if all elements are the same in both objects, False
otherwise.
See Also
--------
Series.eq : Compare two Series objects of the same length
and return a Series where each element is True if the element
in each Series is equal, False otherwise.
DataFrame.eq : Compare two DataFrame objects of the same shape and
return a DataFrame where each element is True if the respective
element in each DataFrame is equal, False otherwise.
testing.assert_series_equal : Raises an AssertionError if left and
right are not equal. Provides an easy interface to ignore
inequality in dtypes, indexes and precision among others.
testing.assert_frame_equal : Like assert_series_equal, but targets
DataFrames.
numpy.array_equal : Return True if two arrays have the same shape
and elements, False otherwise.
Examples
--------
>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
1 2
0 10 20
DataFrames df and exactly_equal have the same types and values for
their elements and column labels, which will return True.
>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
1 2
0 10 20
>>> df.equals(exactly_equal)
True
DataFrames df and different_column_type have the same element
types and values, but have different types for the column labels,
which will still return True.
>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
1.0 2.0
0 10 20
>>> df.equals(different_column_type)
True
DataFrames df and different_data_type have different types for the
same values for their elements, and will return False even though
their column labels are the same values and types.
>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
1 2
0 10.0 20.0
>>> df.equals(different_data_type)
False
F)r� r r
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� }t |t t j f� r t |� S t |� r!t dt | � j � �� �| j � y)a
Return the bool of a single element Series or DataFrame.
.. deprecated:: 2.1.0
bool is deprecated and will be removed in future version of pandas.
For ``Series`` use ``pandas.Series.item``.
This must be a boolean scalar value, either True or False. It will raise a
ValueError if the Series or DataFrame does not have exactly 1 element, or that
element is not boolean (integer values 0 and 1 will also raise an exception).
Returns
-------
bool
The value in the Series or DataFrame.
See Also
--------
Series.astype : Change the data type of a Series, including to boolean.
DataFrame.astype : Change the data type of a DataFrame, including to boolean.
numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
Examples
--------
The method will only work for single element objects with a boolean value:
>>> pd.Series([True]).bool() # doctest: +SKIP
True
>>> pd.Series([False]).bool() # doctest: +SKIP
False
>>> pd.DataFrame({'col': [True]}).bool() # doctest: +SKIP
True
>>> pd.DataFrame({'col': [False]}).bool() # doctest: +SKIP
False
This is an alternative method and will only work
for single element objects with a boolean value:
>>> pd.Series([True]).item() # doctest: +SKIP
True
>>> pd.Series([False]).item() # doctest: +SKIP
False
zG.bool is now deprecated and will be removed in future version of pandasr� z0bool cannot act on a non-boolean single element T)r� r� r r� rc rU r{ r� �boolrO �bool_rg r r� )r� r5 s r� r� z NDFrame.bool0 s� � �` �
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Return a Series/DataFrame with absolute numeric value of each element.
This function only applies to elements that are all numeric.
Returns
-------
abs
Series/DataFrame containing the absolute value of each element.
See Also
--------
numpy.absolute : Calculate the absolute value element-wise.
Notes
-----
For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
:math:`\sqrt{ a^2 + b^2 }`.
Examples
--------
Absolute numeric values in a Series.
>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0 1.10
1 2.00
2 3.33
3 4.00
dtype: float64
Absolute numeric values in a Series with complex numbers.
>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0 1.56205
dtype: float64
Absolute numeric values in a Series with a Timedelta element.
>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0 1 days
dtype: timedelta64[ns]
Select rows with data closest to certain value using argsort (from
`StackOverflow <https://stackoverflow.com/a/17758115>`__).
>>> df = pd.DataFrame({
... 'a': [4, 5, 6, 7],
... 'b': [10, 20, 30, 40],
... 'c': [100, 50, -30, -50]
... })
>>> df
a b c
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
>>> df.loc[(df.c - 43).abs().argsort()]
a b c
1 5 20 50
0 4 10 100
2 6 30 -30
3 7 40 -50
r� �abs)r( )r� r� rO r� r� r� r� )r� �res_mgrs r� r� z NDFrame.abss sM � �H �)�)�/�/�"�&�&�)���)�)�'�� � �)�E�R�R� �u� S�
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Test whether a key is a level reference for a given axis.
To be considered a level reference, `key` must be a string that:
- (axis=0): Matches the name of an index level and does NOT match
a column label.
- (axis=1): Matches the name of a column level and does NOT match
an index label.
Parameters
----------
key : Hashable
Potential level name for the given axis
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
is_level : bool
Nr� )r rm r� r" �_is_label_reference)r� r) r� �axis_ints r� �_is_level_referencezNDFrame._is_level_reference� sk � �, �(�(��.��
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Test whether a key is a label reference for a given axis.
To be considered a label reference, `key` must be a string that:
- (axis=0): Matches a column label
- (axis=1): Matches an index label
Parameters
----------
key : Hashable
Potential label name, i.e. Index entry.
axis : int, default 0
Axis perpendicular to the axis that labels are associated with
(0 means search for column labels, 1 means search for index labels)
Returns
-------
is_label: bool
c 3 �. �K � | ] }|�k7 s� |�� � y �wr� rt �rH r� r� s �r� rI z.NDFrame._is_label_reference.<locals>.<genexpr> � �� �� �K�R�B�(�N�b�K�� �
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Test whether a key is a label or level reference for a given axis.
To be considered either a label or a level reference, `key` must be a
string that:
- (axis=0): Matches a column label or an index level
- (axis=1): Matches an index label or a column level
Parameters
----------
key : Hashable
Potential label or level name
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
bool
r� )r� r� )r� r) r� s r� � _is_label_or_level_referencez$NDFrame._is_label_or_level_reference s9 � �* �'�'��$�'�7�
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Check whether `key` is ambiguous.
By ambiguous, we mean that it matches both a level of the input
`axis` and a label of the other axis.
Parameters
----------
key : Hashable
Label or level name.
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns).
Raises
------
ValueError: `key` is ambiguous
c 3 �. �K � | ] }|�k7 s� |�� � y �wr� rt r� s �r� rI z:NDFrame._check_label_or_level_ambiguity.<locals>.<genexpr>7 r� r� Nc 3 �@ �K � | ] }��j | v �� � y �wr� r� r� s ��r� rI z:NDFrame._check_label_or_level_ambiguity.<locals>.<genexpr>= r� r� r )�anr )r� �columnr^ z
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|� �� �|S c c}w ) a�
Return a 1-D array of values associated with `key`, a label or level
from the given `axis`.
Retrieval logic:
- (axis=0): Return column values if `key` matches a column label.
Otherwise return index level values if `key` matches an index
level.
- (axis=1): Return row values if `key` matches an index label.
Otherwise return column level values if 'key' matches a column
level
Parameters
----------
key : Hashable
Label or level name.
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
np.ndarray or ExtensionArray
Raises
------
KeyError
if `key` matches neither a label nor a level
ValueError
if `key` matches multiple labels
r� r r� zX
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Drop labels and/or levels for the given `axis`.
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- (axis=0): If key matches a column label then drop the column.
Otherwise if key matches an index level then drop the level.
- (axis=1): If key matches an index label then drop the row.
Otherwise if key matches a column level then drop the level.
Parameters
----------
keys : str or list of str
labels or levels to drop
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
dropped: DataFrame
Raises
------
ValueError
if any `keys` match neither a label nor a level
r� z;The following keys are not valid labels or levels for axis z: Fr� r T)�dropr� r� �r� r� )r rs �maybe_make_listr� r r� r� � reset_indexr� r� r r rq r� rQ )r� �keysr� r4 � invalid_keys�levels_to_drop�labels_to_drop�droppeds r� �_drop_labels_or_levelsz NDFrame._drop_labels_or_levels� s� � �6 �$�$�T�*�� �%�%�d�+���
��4�#D�#D�Q�T�#D�#R�A�
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�� U��Xs# �E!�E!�!E&�:E&�E+� E+zClassVar[None]�__hash__c �, � t | j � S )a'
Iterate over info axis.
Returns
-------
iterator
Info axis as iterator.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> for x in df:
... print(x)
A
B
)�iterr? r� s r� �__iter__zNDFrame.__iter__� s � �" �D�O�O�$�$r� c � � | j S )a�
Get the 'info axis' (see Indexing for more).
This is index for Series, columns for DataFrame.
Returns
-------
Index
Info axis.
Examples
--------
>>> d = pd.DataFrame(data={'A': [1, 2, 3], 'B': [0, 4, 8]},
... index=['a', 'b', 'c'])
>>> d
A B
a 1 0
b 2 4
c 3 8
>>> d.keys()
Index(['A', 'B'], dtype='object')
�r? r� s r� r� z NDFrame.keys� s � �. ��� r� c # �@ K � | j D ] }|| | f�� �
y�w)z�
Iterate over (label, values) on info axis
This is index for Series and columns for DataFrame.
Returns
-------
Generator
Nr )r� �hs r� r� z
NDFrame.items s) � �� � � � � �A��T�!�W�*� � �s � c �, � t | j � S )zReturns length of info axis)r� r? r� s r� �__len__zNDFrame.__len__ s � ��4�?�?�#�#r� c � � || j v S )z#True if the key is in the info axisr )r� r) s r� � __contains__zNDFrame.__contains__ s � � �d�o�o�%�%r� c �@ � � t � fd�� j D � � S )a�
Indicator whether Series/DataFrame is empty.
True if Series/DataFrame is entirely empty (no items), meaning any of the
axes are of length 0.
Returns
-------
bool
If Series/DataFrame is empty, return True, if not return False.
See Also
--------
Series.dropna : Return series without null values.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
where (all or any) data are missing.
Notes
-----
If Series/DataFrame contains only NaNs, it is still not considered empty. See
the example below.
Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:
>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True
If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:
>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True
>>> ser_empty = pd.Series({'A' : []})
>>> ser_empty
A []
dtype: object
>>> ser_empty.empty
False
>>> ser_empty = pd.Series()
>>> ser_empty.empty
True
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��Z�1�_�%�&P�Q� Q�35��r� c �~ � ddj t t | � � � d�}t | � j � d|� d�S )N�[�,�]�(�))�join�mapr� r r� )r� �preprs r� �__repr__zNDFrame.__repr__� sA � � �C�H�H�S� �t� 4�5�6�a�8���t�*�%�%�&�a��w�a�0�0r� c �T � t j d� dk( r| j � S y)z�
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
zstyler.render.repr�latexN)r �
get_option�to_latexr� s r� � _repr_latex_zNDFrame._repr_latex_� s' � � � � � 1� 2�g� =��=�=�?� "�r� c �� � t j d� ra| j t j d� � }|j d�� }t t
|� }t
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Not a real Jupyter special repr method, but we use the same
naming convention.
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r� r � collections� OrderedDict)r� r� �as_jsons r� �_repr_data_resource_z NDFrame._repr_data_resource_� sa � � � � � 8� 9��9�9�V� .� .�/A� B�C�D��l�l�'�l�2�G��3��(�G���K�4K�4K�L� L� :r� z3.0r� � excel_writer�to_excel)�version� allowed_argsr( �storage_optionsz1.2.0)r� rE � storage_options_versionaddedr� c � � |�i }t | t � r| n| j � }ddlm} ||||||||| |
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Write {klass} to an Excel sheet.
To write a single {klass} to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.
Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.
Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
Name of sheet which will contain DataFrame.
na_rep : str, default ''
Missing data representation.
float_format : str, optional
Format string for floating point numbers. For example
``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of string is given it is
assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
index_label : str or sequence, optional
Column label for index column(s) if desired. If not specified, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
Upper left cell row to dump data frame.
startcol : int, default 0
Upper left cell column to dump data frame.
engine : str, optional
Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
via the options ``io.excel.xlsx.writer`` or
``io.excel.xlsm.writer``.
merge_cells : bool, default True
Write MultiIndex and Hierarchical Rows as merged cells.
inf_rep : str, default 'inf'
Representation for infinity (there is no native representation for
infinity in Excel).
freeze_panes : tuple of int (length 2), optional
Specifies the one-based bottommost row and rightmost column that
is to be frozen.
{storage_options}
.. versionadded:: {storage_options_versionadded}
engine_kwargs : dict, optional
Arbitrary keyword arguments passed to excel engine.
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
io.formats.style.Styler.to_excel : Add styles to Excel sheet.
Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.
Once a workbook has been saved it is not possible to write further
data without rewriting the whole workbook.
Examples
--------
Create, write to and save a workbook:
>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx") # doctest: +SKIP
To specify the sheet name:
>>> df1.to_excel("output.xlsx",
... sheet_name='Sheet_name_1') # doctest: +SKIP
If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:
>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP
... df1.to_excel(writer, sheet_name='Sheet_name_1')
... df2.to_excel(writer, sheet_name='Sheet_name_2')
ExcelWriter can also be used to append to an existing Excel file:
>>> with pd.ExcelWriter('output.xlsx',
... mode='a') as writer: # doctest: +SKIP
... df1.to_excel(writer, sheet_name='Sheet_name_3')
To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):
>>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP
Nr )�ExcelFormatter)�na_rep�cols�header� float_formatr � index_label� merge_cells�inf_rep)�
sheet_name�startrow�startcol� freeze_panes�enginerE �
engine_kwargs)r� rk �to_frame�pandas.io.formats.excelrH �write)r� rA rP rI rL r rK r rM rQ rR rT rN rO rS rE rU r� rH � formatters r� rB zNDFrame.to_excel� sy � �R � � �M� ��l�3�T�� � � ��:�"� ����%��#�#��
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r� � path_or_bufr< �compression_options)rE r[ �inferc � � ddl m} |�|dk( rd}n|�d}t j | � | xs d} |j || |||||||| |
| | |
�� S )a
Convert the object to a JSON string.
Note NaN's and None will be converted to null and datetime objects
will be converted to UNIX timestamps.
Parameters
----------
path_or_buf : str, path object, file-like object, or None, default None
String, path object (implementing os.PathLike[str]), or file-like
object implementing a write() function. If None, the result is
returned as a string.
orient : str
Indication of expected JSON string format.
* Series:
- default is 'index'
- allowed values are: {{'split', 'records', 'index', 'table'}}.
* DataFrame:
- default is 'columns'
- allowed values are: {{'split', 'records', 'index', 'columns',
'values', 'table'}}.
* The format of the JSON string:
- 'split' : dict like {{'index' -> [index], 'columns' -> [columns],
'data' -> [values]}}
- 'records' : list like [{{column -> value}}, ... , {{column -> value}}]
- 'index' : dict like {{index -> {{column -> value}}}}
- 'columns' : dict like {{column -> {{index -> value}}}}
- 'values' : just the values array
- 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}}
Describing the data, where data component is like ``orient='records'``.
date_format : {{None, 'epoch', 'iso'}}
Type of date conversion. 'epoch' = epoch milliseconds,
'iso' = ISO8601. The default depends on the `orient`. For
``orient='table'``, the default is 'iso'. For all other orients,
the default is 'epoch'.
double_precision : int, default 10
The number of decimal places to use when encoding
floating point values. The possible maximal value is 15.
Passing double_precision greater than 15 will raise a ValueError.
force_ascii : bool, default True
Force encoded string to be ASCII.
date_unit : str, default 'ms' (milliseconds)
The time unit to encode to, governs timestamp and ISO8601
precision. One of 's', 'ms', 'us', 'ns' for second, millisecond,
microsecond, and nanosecond respectively.
default_handler : callable, default None
Handler to call if object cannot otherwise be converted to a
suitable format for JSON. Should receive a single argument which is
the object to convert and return a serialisable object.
lines : bool, default False
If 'orient' is 'records' write out line-delimited json format. Will
throw ValueError if incorrect 'orient' since others are not
list-like.
{compression_options}
.. versionchanged:: 1.4.0 Zstandard support.
index : bool or None, default None
The index is only used when 'orient' is 'split', 'index', 'column',
or 'table'. Of these, 'index' and 'column' do not support
`index=False`.
indent : int, optional
Length of whitespace used to indent each record.
{storage_options}
mode : str, default 'w' (writing)
Specify the IO mode for output when supplying a path_or_buf.
Accepted args are 'w' (writing) and 'a' (append) only.
mode='a' is only supported when lines is True and orient is 'records'.
Returns
-------
None or str
If path_or_buf is None, returns the resulting json format as a
string. Otherwise returns None.
See Also
--------
read_json : Convert a JSON string to pandas object.
Notes
-----
The behavior of ``indent=0`` varies from the stdlib, which does not
indent the output but does insert newlines. Currently, ``indent=0``
and the default ``indent=None`` are equivalent in pandas, though this
may change in a future release.
``orient='table'`` contains a 'pandas_version' field under 'schema'.
This stores the version of `pandas` used in the latest revision of the
schema.
Examples
--------
>>> from json import loads, dumps
>>> df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{{
"columns": [
"col 1",
"col 2"
],
"index": [
"row 1",
"row 2"
],
"data": [
[
"a",
"b"
],
[
"c",
"d"
]
]
}}
Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
Note that index labels are not preserved with this encoding.
>>> result = df.to_json(orient="records")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
[
{{
"col 1": "a",
"col 2": "b"
}},
{{
"col 1": "c",
"col 2": "d"
}}
]
Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
>>> result = df.to_json(orient="index")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{{
"row 1": {{
"col 1": "a",
"col 2": "b"
}},
"row 2": {{
"col 1": "c",
"col 2": "d"
}}
}}
Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
>>> result = df.to_json(orient="columns")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{{
"col 1": {{
"row 1": "a",
"row 2": "c"
}},
"col 2": {{
"row 1": "b",
"row 2": "d"
}}
}}
Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
>>> result = df.to_json(orient="values")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
[
[
"a",
"b"
],
[
"c",
"d"
]
]
Encoding with Table Schema:
>>> result = df.to_json(orient="table")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4) # doctest: +SKIP
{{
"schema": {{
"fields": [
{{
"name": "index",
"type": "string"
}},
{{
"name": "col 1",
"type": "string"
}},
{{
"name": "col 2",
"type": "string"
}}
],
"primaryKey": [
"index"
],
"pandas_version": "1.4.0"
}},
"data": [
{{
"index": "row 1",
"col 1": "a",
"col 2": "b"
}},
{{
"index": "row 2",
"col 1": "c",
"col 2": "d"
}}
]
}}
r )�jsonr8 �iso�epoch)rZ r� r9 � date_format�double_precision� force_ascii� date_unit�default_handler�lines� compressionr �indentrE �mode)� pandas.ior^ r �is_nonnegative_intr< )r� rZ r9 ra rb rc rd re rf rg r rh rE ri r^ s r� r< zNDFrame.to_json| s| � �P #� � �6�W�#4� �K�
�
�!�K��!�!�&�)���1���|�|�#���#� -�#� � +��#��� +�� �
�
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Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Format (HDF) is self-describing, allowing an
application to interpret the structure and contents of a file with
no outside information. One HDF file can hold a mix of related objects
which can be accessed as a group or as individual objects.
In order to add another DataFrame or Series to an existing HDF file
please use append mode and a different a key.
.. warning::
One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
but the type of the subclass is lost upon storing.
For more information see the :ref:`user guide <io.hdf5>`.
Parameters
----------
path_or_buf : str or pandas.HDFStore
File path or HDFStore object.
key : str
Identifier for the group in the store.
mode : {'a', 'w', 'r+'}, default 'a'
Mode to open file:
- 'w': write, a new file is created (an existing file with
the same name would be deleted).
- 'a': append, an existing file is opened for reading and
writing, and if the file does not exist it is created.
- 'r+': similar to 'a', but the file must already exist.
complevel : {0-9}, default None
Specifies a compression level for data.
A value of 0 or None disables compression.
complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
Specifies the compression library to be used.
These additional compressors for Blosc are supported
(default if no compressor specified: 'blosc:blosclz'):
{'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
'blosc:zlib', 'blosc:zstd'}.
Specifying a compression library which is not available issues
a ValueError.
append : bool, default False
For Table formats, append the input data to the existing.
format : {'fixed', 'table', None}, default 'fixed'
Possible values:
- 'fixed': Fixed format. Fast writing/reading. Not-appendable,
nor searchable.
- 'table': Table format. Write as a PyTables Table structure
which may perform worse but allow more flexible operations
like searching / selecting subsets of the data.
- If None, pd.get_option('io.hdf.default_format') is checked,
followed by fallback to "fixed".
index : bool, default True
Write DataFrame index as a column.
min_itemsize : dict or int, optional
Map column names to minimum string sizes for columns.
nan_rep : Any, optional
How to represent null values as str.
Not allowed with append=True.
dropna : bool, default False, optional
Remove missing values.
data_columns : list of columns or True, optional
List of columns to create as indexed data columns for on-disk
queries, or True to use all columns. By default only the axes
of the object are indexed. See
:ref:`Query via data columns<io.hdf5-query-data-columns>`. for
more information.
Applicable only to format='table'.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
encoding : str, default "UTF-8"
See Also
--------
read_hdf : Read from HDF file.
DataFrame.to_orc : Write a DataFrame to the binary orc format.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
DataFrame.to_sql : Write to a SQL table.
DataFrame.to_feather : Write out feather-format for DataFrames.
DataFrame.to_csv : Write out to a csv file.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
... index=['a', 'b', 'c']) # doctest: +SKIP
>>> df.to_hdf('data.h5', key='df', mode='w') # doctest: +SKIP
We can add another object to the same file:
>>> s = pd.Series([1, 2, 3, 4]) # doctest: +SKIP
>>> s.to_hdf('data.h5', key='s') # doctest: +SKIP
Reading from HDF file:
>>> pd.read_hdf('data.h5', 'df') # doctest: +SKIP
A B
a 1 4
b 2 5
c 3 6
>>> pd.read_hdf('data.h5', 's') # doctest: +SKIP
0 1
1 2
2 3
3 4
dtype: int64
r )�pytables) ri � complevel�complib�append�formatr � min_itemsize�nan_rep�dropna� data_columnsr~ �encodingN)rj rn rl )r� rZ r) ri ro rp rq rr r rs rt ru rv r~ rw rn s r� rl zNDFrame.to_hdf�
sH � �H '� ��� � � �� �����%���%�� � �
r� )r� r( �con�to_sqlc
�D � ddl m}
|
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S )a�
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be
newly created, appended to, or overwritten.
Parameters
----------
name : str
Name of SQL table.
con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
Using SQLAlchemy makes it possible to use any DB supported by that
library. Legacy support is provided for sqlite3.Connection objects. The user
is responsible for engine disposal and connection closure for the SQLAlchemy
connectable. See `here <https://docs.sqlalchemy.org/en/20/core/connections.html>`_.
If passing a sqlalchemy.engine.Connection which is already in a transaction,
the transaction will not be committed. If passing a sqlite3.Connection,
it will not be possible to roll back the record insertion.
schema : str, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the table already exists.
* fail: Raise a ValueError.
* replace: Drop the table before inserting new values.
* append: Insert new values to the existing table.
index : bool, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table. Creates a table index for this column.
index_label : str or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, optional
Specify the number of rows in each batch to be written at a time.
By default, all rows will be written at once.
dtype : dict or scalar, optional
Specifying the datatype for columns. If a dictionary is used, the
keys should be the column names and the values should be the
SQLAlchemy types or strings for the sqlite3 legacy mode. If a
scalar is provided, it will be applied to all columns.
method : {None, 'multi', callable}, optional
Controls the SQL insertion clause used:
* None : Uses standard SQL ``INSERT`` clause (one per row).
* 'multi': Pass multiple values in a single ``INSERT`` clause.
* callable with signature ``(pd_table, conn, keys, data_iter)``.
Details and a sample callable implementation can be found in the
section :ref:`insert method <io.sql.method>`.
Returns
-------
None or int
Number of rows affected by to_sql. None is returned if the callable
passed into ``method`` does not return an integer number of rows.
The number of returned rows affected is the sum of the ``rowcount``
attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable which may not
reflect the exact number of written rows as stipulated in the
`sqlite3 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount>`__ or
`SQLAlchemy <https://docs.sqlalchemy.org/en/20/core/connections.html#sqlalchemy.engine.CursorResult.rowcount>`__.
.. versionadded:: 1.4.0
Raises
------
ValueError
When the table already exists and `if_exists` is 'fail' (the
default).
See Also
--------
read_sql : Read a DataFrame from a table.
Notes
-----
Timezone aware datetime columns will be written as
``Timestamp with timezone`` type with SQLAlchemy if supported by the
database. Otherwise, the datetimes will be stored as timezone unaware
timestamps local to the original timezone.
Not all datastores support ``method="multi"``. Oracle, for example,
does not support multi-value insert.
References
----------
.. [1] https://docs.sqlalchemy.org
.. [2] https://www.python.org/dev/peps/pep-0249/
Examples
--------
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
>>> df.to_sql(name='users', con=engine)
3
>>> from sqlalchemy import text
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
An `sqlalchemy.engine.Connection` can also be passed to `con`:
>>> with engine.begin() as connection:
... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
... df1.to_sql(name='users', con=connection, if_exists='append')
2
This is allowed to support operations that require that the same
DBAPI connection is used for the entire operation.
>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql(name='users', con=engine, if_exists='append')
2
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
(0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
(1, 'User 7')]
Overwrite the table with just ``df2``.
>>> df2.to_sql(name='users', con=engine, if_exists='replace',
... index_label='id')
2
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 6'), (1, 'User 7')]
Use ``method`` to define a callable insertion method to do nothing
if there's a primary key conflict on a table in a PostgreSQL database.
>>> from sqlalchemy.dialects.postgresql import insert
>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
... # "a" is the primary key in "conflict_table"
... data = [dict(zip(keys, row)) for row in data_iter]
... stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
... result = conn.execute(stmt)
... return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_nothing) # doctest: +SKIP
0
For MySQL, a callable to update columns ``b`` and ``c`` if there's a conflict
on a primary key.
>>> from sqlalchemy.dialects.mysql import insert
>>> def insert_on_conflict_update(table, conn, keys, data_iter):
... # update columns "b" and "c" on primary key conflict
... data = [dict(zip(keys, row)) for row in data_iter]
... stmt = (
... insert(table.table)
... .values(data)
... )
... stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
... result = conn.execute(stmt)
... return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append", method=insert_on_conflict_update) # doctest: +SKIP
2
Specify the dtype (especially useful for integers with missing values).
Notice that while pandas is forced to store the data as floating point,
the database supports nullable integers. When fetching the data with
Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
A
0 1.0
1 NaN
2 2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql(name='integers', con=engine, index=False,
... dtype={"A": Integer()})
3
>>> with engine.connect() as conn:
... conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]
r )�sql)�schema� if_existsr rM � chunksizer� rb )rj r{ ry ) r� r( rx r| r} r rM r~ r� rb r{ s r� ry zNDFrame.to_sql9 s< � �h "��z�z� � � �� ��#� ��� �
�
r� �path� to_picklec �* � ddl m} || ||||�� y)a�
Pickle (serialize) object to file.
Parameters
----------
path : str, path object, or file-like object
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a binary ``write()`` function. File path where
the pickled object will be stored.
{compression_options}
protocol : int
Int which indicates which protocol should be used by the pickler,
default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
values are 0, 1, 2, 3, 4, 5. A negative value for the protocol
parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html.
{storage_options}
See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Examples
--------
>>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}}) # doctest: +SKIP
>>> original_df # doctest: +SKIP
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> original_df.to_pickle("./dummy.pkl") # doctest: +SKIP
>>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP
>>> unpickled_df # doctest: +SKIP
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
r )r� )rg �protocolrE N)�pandas.io.pickler� )r� r rg r� rE r� s r� r� zNDFrame.to_pickle s � �~ /�� � �#� � +�
r� � to_clipboardc �< � ddl m} |j | f||d�|�� y)a�
Copy object to the system clipboard.
Write a text representation of object to the system clipboard.
This can be pasted into Excel, for example.
Parameters
----------
excel : bool, default True
Produce output in a csv format for easy pasting into excel.
- True, use the provided separator for csv pasting.
- False, write a string representation of the object to the clipboard.
sep : str, default ``'\t'``
Field delimiter.
**kwargs
These parameters will be passed to DataFrame.to_csv.
See Also
--------
DataFrame.to_csv : Write a DataFrame to a comma-separated values
(csv) file.
read_clipboard : Read text from clipboard and pass to read_csv.
Notes
-----
Requirements for your platform.
- Linux : `xclip`, or `xsel` (with `PyQt4` modules)
- Windows : none
- macOS : none
This method uses the processes developed for the package `pyperclip`. A
solution to render any output string format is given in the examples.
Examples
--------
Copy the contents of a DataFrame to the clipboard.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',') # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6
We can omit the index by passing the keyword `index` and setting
it to false.
>>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
Using the original `pyperclip` package for any string output format.
.. code-block:: python
import pyperclip
html = df.style.to_html()
pyperclip.copy(html)
r )�
clipboards)�excel�sepN)rj r� r� )r� r� r� r r� s r� r� zNDFrame.to_clipboarde s$ � �P )� �
� � ��E�E�s�E�f�Er� c � � t d� }| j dk( r|j j | � S |j j | � S )at
Return an xarray object from the pandas object.
Returns
-------
xarray.DataArray or xarray.Dataset
Data in the pandas structure converted to Dataset if the object is
a DataFrame, or a DataArray if the object is a Series.
See Also
--------
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Notes
-----
See the `xarray docs <https://xarray.pydata.org/en/stable/>`__
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4
>>> df.to_xarray() # doctest: +SKIP
<xarray.Dataset>
Dimensions: (index: 4)
Coordinates:
* index (index) int64 32B 0 1 2 3
Data variables:
name (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 32B 389.0 24.0 80.5 nan
num_legs (index) int64 32B 2 2 4 4
>>> df['max_speed'].to_xarray() # doctest: +SKIP
<xarray.DataArray 'max_speed' (index: 4)>
array([389. , 24. , 80.5, nan])
Coordinates:
* index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot',
... 'falcon', 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15
>>> df_multiindex.to_xarray() # doctest: +SKIP
<xarray.Dataset>
Dimensions: (date: 2, animal: 2)
Coordinates:
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
speed (date, animal) int64 350 18 361 15
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Render object to a LaTeX tabular, longtable, or nested table.
Requires ``\usepackage{{booktabs}}``. The output can be copy/pasted
into a main LaTeX document or read from an external file
with ``\input{{table.tex}}``.
.. versionchanged:: 2.0.0
Refactored to use the Styler implementation via jinja2 templating.
Parameters
----------
buf : str, Path or StringIO-like, optional, default None
Buffer to write to. If None, the output is returned as a string.
columns : list of label, optional
The subset of columns to write. Writes all columns by default.
header : bool or list of str, default True
Write out the column names. If a list of strings is given,
it is assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
na_rep : str, default 'NaN'
Missing data representation.
formatters : list of functions or dict of {{str: function}}, optional
Formatter functions to apply to columns' elements by position or
name. The result of each function must be a unicode string.
List must be of length equal to the number of columns.
float_format : one-parameter function or str, optional, default None
Formatter for floating point numbers. For example
``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
both result in 0.1234 being formatted as 0.12.
sparsify : bool, optional
Set to False for a DataFrame with a hierarchical index to print
every multiindex key at each row. By default, the value will be
read from the config module.
index_names : bool, default True
Prints the names of the indexes.
bold_rows : bool, default False
Make the row labels bold in the output.
column_format : str, optional
The columns format as specified in `LaTeX table format
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3
columns. By default, 'l' will be used for all columns except
columns of numbers, which default to 'r'.
longtable : bool, optional
Use a longtable environment instead of tabular. Requires
adding a \usepackage{{longtable}} to your LaTeX preamble.
By default, the value will be read from the pandas config
module, and set to `True` if the option ``styler.latex.environment`` is
`"longtable"`.
.. versionchanged:: 2.0.0
The pandas option affecting this argument has changed.
escape : bool, optional
By default, the value will be read from the pandas config
module and set to `True` if the option ``styler.format.escape`` is
`"latex"`. When set to False prevents from escaping latex special
characters in column names.
.. versionchanged:: 2.0.0
The pandas option affecting this argument has changed, as has the
default value to `False`.
encoding : str, optional
A string representing the encoding to use in the output file,
defaults to 'utf-8'.
decimal : str, default '.'
Character recognized as decimal separator, e.g. ',' in Europe.
multicolumn : bool, default True
Use \multicolumn to enhance MultiIndex columns.
The default will be read from the config module, and is set
as the option ``styler.sparse.columns``.
.. versionchanged:: 2.0.0
The pandas option affecting this argument has changed.
multicolumn_format : str, default 'r'
The alignment for multicolumns, similar to `column_format`
The default will be read from the config module, and is set as the option
``styler.latex.multicol_align``.
.. versionchanged:: 2.0.0
The pandas option affecting this argument has changed, as has the
default value to "r".
multirow : bool, default True
Use \multirow to enhance MultiIndex rows. Requires adding a
\usepackage{{multirow}} to your LaTeX preamble. Will print
centered labels (instead of top-aligned) across the contained
rows, separating groups via clines. The default will be read
from the pandas config module, and is set as the option
``styler.sparse.index``.
.. versionchanged:: 2.0.0
The pandas option affecting this argument has changed, as has the
default value to `True`.
caption : str or tuple, optional
Tuple (full_caption, short_caption),
which results in ``\caption[short_caption]{{full_caption}}``;
if a single string is passed, no short caption will be set.
label : str, optional
The LaTeX label to be placed inside ``\label{{}}`` in the output.
This is used with ``\ref{{}}`` in the main ``.tex`` file.
position : str, optional
The LaTeX positional argument for tables, to be placed after
``\begin{{}}`` in the output.
Returns
-------
str or None
If buf is None, returns the result as a string. Otherwise returns None.
See Also
--------
io.formats.style.Styler.to_latex : Render a DataFrame to LaTeX
with conditional formatting.
DataFrame.to_string : Render a DataFrame to a console-friendly
tabular output.
DataFrame.to_html : Render a DataFrame as an HTML table.
Notes
-----
As of v2.0.0 this method has changed to use the Styler implementation as
part of :meth:`.Styler.to_latex` via ``jinja2`` templating. This means
that ``jinja2`` is a requirement, and needs to be installed, for this method
to function. It is advised that users switch to using Styler, since that
implementation is more frequently updated and contains much more
flexibility with the output.
Examples
--------
Convert a general DataFrame to LaTeX with formatting:
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
... age=[26, 45],
... height=[181.23, 177.65]))
>>> print(df.to_latex(index=False,
... formatters={"name": str.upper},
... float_format="{:.1f}".format,
... )) # doctest: +SKIP
\begin{tabular}{lrr}
\toprule
name & age & height \\
\midrule
RAPHAEL & 26 & 181.2 \\
DONATELLO & 45 & 177.7 \\
\bottomrule
\end{tabular}
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Render object to a LaTeX tabular, longtable, or nested table.
Uses the ``Styler`` implementation with the following, ordered, method chaining:
.. code-block:: python
styler = Styler(DataFrame)
styler.hide(**hide)
styler.relabel_index(**relabel_index)
styler.format(**format)
styler.format_index(**format_index)
styler.to_latex(buf=buf, **render_kwargs)
Parameters
----------
buf : str, Path or StringIO-like, optional, default None
Buffer to write to. If None, the output is returned as a string.
hide : dict, list of dict
Keyword args to pass to the method call of ``Styler.hide``. If a list will
call the method numerous times.
relabel_index : dict, list of dict
Keyword args to pass to the method of ``Styler.relabel_index``. If a list
will call the method numerous times.
format : dict, list of dict
Keyword args to pass to the method call of ``Styler.format``. If a list will
call the method numerous times.
format_index : dict, list of dict
Keyword args to pass to the method call of ``Styler.format_index``. If a
list will call the method numerous times.
render_kwargs : dict
Keyword args to pass to the method call of ``Styler.to_latex``.
Returns
-------
str or None
If buf is None, returns the result as a string. Otherwise returns None.
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Write object to a comma-separated values (csv) file.
Parameters
----------
path_or_buf : str, path object, file-like object, or None, default None
String, path object (implementing os.PathLike[str]), or file-like
object implementing a write() function. If None, the result is
returned as a string. If a non-binary file object is passed, it should
be opened with `newline=''`, disabling universal newlines. If a binary
file object is passed, `mode` might need to contain a `'b'`.
sep : str, default ','
String of length 1. Field delimiter for the output file.
na_rep : str, default ''
Missing data representation.
float_format : str, Callable, default None
Format string for floating point numbers. If a Callable is given, it takes
precedence over other numeric formatting parameters, like decimal.
columns : sequence, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of strings is given it is
assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
index_label : str or sequence, or False, default None
Column label for index column(s) if desired. If None is given, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the object uses MultiIndex. If
False do not print fields for index names. Use index_label=False
for easier importing in R.
mode : {{'w', 'x', 'a'}}, default 'w'
Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control
the file opening. Typical values include:
- 'w', truncate the file first.
- 'x', exclusive creation, failing if the file already exists.
- 'a', append to the end of file if it exists.
encoding : str, optional
A string representing the encoding to use in the output file,
defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
is a non-binary file object.
{compression_options}
May be a dict with key 'method' as compression mode
and other entries as additional compression options if
compression mode is 'zip'.
Passing compression options as keys in dict is
supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.
quoting : optional constant from csv module
Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
will treat them as non-numeric.
quotechar : str, default '\"'
String of length 1. Character used to quote fields.
lineterminator : str, optional
The newline character or character sequence to use in the output
file. Defaults to `os.linesep`, which depends on the OS in which
this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
.. versionchanged:: 1.5.0
Previously was line_terminator, changed for consistency with
read_csv and the standard library 'csv' module.
chunksize : int or None
Rows to write at a time.
date_format : str, default None
Format string for datetime objects.
doublequote : bool, default True
Control quoting of `quotechar` inside a field.
escapechar : str, default None
String of length 1. Character used to escape `sep` and `quotechar`
when appropriate.
decimal : str, default '.'
Character recognized as decimal separator. E.g. use ',' for
European data.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
{storage_options}
Returns
-------
None or str
If path_or_buf is None, returns the resulting csv format as a
string. Otherwise returns None.
See Also
--------
read_csv : Load a CSV file into a DataFrame.
to_excel : Write DataFrame to an Excel file.
Examples
--------
Create 'out.csv' containing 'df' without indices
>>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']}})
>>> df.to_csv('out.csv', index=False) # doctest: +SKIP
Create 'out.zip' containing 'out.csv'
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(method='zip',
... archive_name='out.csv') # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
... compression=compression_opts) # doctest: +SKIP
To write a csv file to a new folder or nested folder you will first
need to create it using either Pathlib or os:
>>> from pathlib import Path # doctest: +SKIP
>>> filepath = Path('folder/subfolder/out.csv') # doctest: +SKIP
>>> filepath.parent.mkdir(parents=True, exist_ok=True) # doctest: +SKIP
>>> df.to_csv(filepath) # doctest: +SKIP
>>> import os # doctest: +SKIP
>>> os.makedirs('folder/subfolder', exist_ok=True) # doctest: +SKIP
>>> df.to_csv('folder/subfolder/out.csv') # doctest: +SKIP
)�framerK r rI rL r� )r� r� rw r~ rg r� r rM ri r~ r� ra r� r� rE )r� rk rV r� r� r� )r� rZ r� rI rL r rK r rM ri rw rg r� r� r� r~ ra r� r� r� r~ rE r� rY s r� r� zNDFrame.to_csv� s� � �~ ��l�3�T�� � � ��&�����%��
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Parameters
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clear : bool, default False
Clear the item cache.
verify_is_copy : bool, default True
Provide is_copy checks.
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Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
axis : {0 or 'index', 1 or 'columns', None}, default 0
The axis on which to select elements. ``0`` means that we are
selecting rows, ``1`` means that we are selecting columns.
For `Series` this parameter is unused and defaults to 0.
**kwargs
For compatibility with :meth:`numpy.take`. Has no effect on the
output.
Returns
-------
same type as caller
An array-like containing the elements taken from the object.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by labels.
DataFrame.iloc : Select a subset of a DataFrame by positions.
numpy.take : Take elements from an array along an axis.
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> df
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to
our selected indices 0 and 3. That's because we are selecting the 0th
and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3])
name class max_speed
0 falcon bird 389.0
1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1)
class max_speed
0 bird 389.0
2 bird 24.0
3 mammal 80.5
1 mammal NaN
We may take elements using negative integers for positive indices,
starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2])
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
rt r� r r� Nr� z1.take requires a sequence of integers, not slice.zPassing a slice to zq.take is deprecated and will raise in a future version. Use `obj[slicer]` or pass a sequence of integers instead.r� T�r� �verifyr� �takera ) �nv�
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attribute to keep track of the parent dataframe (using in indexing
for the SettingWithCopyWarning).
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See the docstring of `take` for full explanation of the parameters.
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Return cross-section from the Series/DataFrame.
This method takes a `key` argument to select data at a particular
level of a MultiIndex.
Parameters
----------
key : label or tuple of label
Label contained in the index, or partially in a MultiIndex.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis to retrieve cross-section on.
level : object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate
which levels are used. Levels can be referred by label or position.
drop_level : bool, default True
If False, returns object with same levels as self.
Returns
-------
Series or DataFrame
Cross-section from the original Series or DataFrame
corresponding to the selected index levels.
See Also
--------
DataFrame.loc : Access a group of rows and columns
by label(s) or a boolean array.
DataFrame.iloc : Purely integer-location based indexing
for selection by position.
Notes
-----
`xs` can not be used to set values.
MultiIndex Slicers is a generic way to get/set values on
any level or levels.
It is a superset of `xs` functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`.
Examples
--------
>>> d = {'num_legs': [4, 4, 2, 2],
... 'num_wings': [0, 0, 2, 2],
... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
... 'animal': ['cat', 'dog', 'bat', 'penguin'],
... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
num_legs num_wings
class animal locomotion
mammal cat walks 4 0
dog walks 4 0
bat flies 2 2
bird penguin walks 2 2
Get values at specified index
>>> df.xs('mammal')
num_legs num_wings
animal locomotion
cat walks 4 0
dog walks 4 0
bat flies 2 2
Get values at several indexes
>>> df.xs(('mammal', 'dog', 'walks'))
num_legs 4
num_wings 0
Name: (mammal, dog, walks), dtype: int64
Get values at specified index and level
>>> df.xs('cat', level=1)
num_legs num_wings
class locomotion
mammal walks 4 0
Get values at several indexes and levels
>>> df.xs(('bird', 'walks'),
... level=[0, 'locomotion'])
num_legs num_wings
animal
penguin 2 2
Get values at specified column and axis
>>> df.xs('num_wings', axis=1)
class animal locomotion
mammal cat walks 0
dog walks 0
bat flies 2
bird penguin walks 2
Name: num_wings, dtype: int64
z7list keys are not supported in xs, pass a tuple insteadNzIndex must be a MultiIndex)r* �
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||j �� }|j | � }|dk7 xs |j }|j | |�� |S )zp
Construct a slice of this container.
Slicing with this method is *always* positional.
r� r� r r ) r� rz r r� r� � get_slicer� r� r� r r� )r� r r� r� rv �is_copys r� r zNDFrame._slice s� � � �%��'�4��e��4�'��+�+�D�1���)�)�%�%�e�$�%�7���+�+�G�'�,�,�+�G���$�$�T�*�� �!�)�.�v�������D�w��/��
r� c �T � |sd | _ y |�J �t j |� | _ y r� )r� �weakref�ref)r� r r� s r� r� zNDFrame._set_is_copy s& � �� �D�M��?� "�?� #�K�K�� ,�D�Mr� c �@ � | j r| j d�� y)aV
Check if we are a view, have a cacher, and are of mixed type.
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Should be called just near setting a value
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single-dtype meaning that the cacher should be updated following
setting.
r� r� F)r� r� r� s r� �%_check_is_chained_assignment_possiblez-NDFrame._check_is_chained_assignment_possible# s � � �=�=� � $� $�z� $� 2�r� c �. � t � s
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Parameters
----------
t : str, the type of setting error
force : bool, default False
If True, then force showing an error.
validate if we are doing a setitem on a chained copy.
It is technically possible to figure out that we are setting on
a copy even WITH a multi-dtyped pandas object. In other words, some
blocks may be views while other are not. Currently _is_view will ALWAYS
return False for multi-blocks to avoid having to handle this case.
df = DataFrame(np.arange(0,9), columns=['count'])
df['group'] = 'b'
# This technically need not raise SettingWithCopy if both are view
# (which is not generally guaranteed but is usually True. However,
# this is in general not a good practice and we recommend using .loc.
df.iloc[0:5]['group'] = 'a'
Nzmode.chained_assignmentr� z�
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copya
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copyr� r� r� )r r r� r r4 r� r� �gc�
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| j |= y# t
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C3 �$ C0�/C0�3 C?�>C?c �L � |r"| j j s t d� �y y )NzQCannot specify 'inplace=True' when 'self.flags.allows_duplicate_labels' is False.)r� r� r )r� r� s r� r� z2NDFrame._check_inplace_and_allows_duplicate_labels� s+ � � �4�:�:�=�=� �A��
� >�7r� c �F � | | S # t t t f$ r |cY S w xY w)aB
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
Parameters
----------
key : object
Returns
-------
same type as items contained in object
Examples
--------
>>> df = pd.DataFrame(
... [
... [24.3, 75.7, "high"],
... [31, 87.8, "high"],
... [22, 71.6, "medium"],
... [35, 95, "medium"],
... ],
... columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
... index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )
>>> df
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df.get(["temp_celsius", "windspeed"])
temp_celsius windspeed
2014-02-12 24.3 high
2014-02-13 31.0 high
2014-02-14 22.0 medium
2014-02-15 35.0 medium
>>> ser = df['windspeed']
>>> ser.get('2014-02-13')
'high'
If the key isn't found, the default value will be used.
>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'
>>> ser.get('2014-02-10', '[unknown]')
'[unknown]'
)r r �
IndexError)r� r) �defaults r� re z NDFrame.get� s- � �l ���9� ���*�j�1� ��N� �s � � � c �. � | j j S )z:Return boolean indicating if self is view of another array)r� �is_viewr� s r� r zNDFrame._is_view� s � � �y�y� � � r� c �f � |j | j ||||�� } | j di |��S )a�
Return an object with matching indices as other object.
Conform the object to the same index on all axes. Optional
filling logic, placing NaN in locations having no value
in the previous index. A new object is produced unless the
new index is equivalent to the current one and copy=False.
Parameters
----------
other : Object of the same data type
Its row and column indices are used to define the new indices
of this object.
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
Method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* None (default): don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap.
copy : bool, default True
Return a new object, even if the passed indexes are the same.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
limit : int, default None
Maximum number of consecutive labels to fill for inexact matches.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations must
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
Series or DataFrame
Same type as caller, but with changed indices on each axis.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
DataFrame.reindex : Change to new indices or expand indices.
Notes
-----
Same as calling
``.reindex(index=other.index, columns=other.columns,...)``.
Examples
--------
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
... [31, 87.8, 'high'],
... [22, 71.6, 'medium'],
... [35, 95, 'medium']],
... columns=['temp_celsius', 'temp_fahrenheit',
... 'windspeed'],
... index=pd.date_range(start='2014-02-12',
... end='2014-02-15', freq='D'))
>>> df1
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df2 = pd.DataFrame([[28, 'low'],
... [30, 'low'],
... [35.1, 'medium']],
... columns=['temp_celsius', 'windspeed'],
... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
... '2014-02-15']))
>>> df2
temp_celsius windspeed
2014-02-12 28.0 low
2014-02-13 30.0 low
2014-02-15 35.1 medium
>>> df2.reindex_like(df1)
temp_celsius temp_fahrenheit windspeed
2014-02-12 28.0 NaN low
2014-02-13 30.0 NaN low
2014-02-14 NaN NaN NaN
2014-02-15 35.1 NaN medium
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axis : int or axis name
level : int or level name, default None
For MultiIndex
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
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Replace self internals with result.
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Prefix labels with string `prefix`.
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Parameters
----------
prefix : str
The string to add before each label.
axis : {0 or 'index', 1 or 'columns', None}, default None
Axis to add prefix on
.. versionadded:: 2.0.0
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_suffix: Suffix row labels with string `suffix`.
DataFrame.add_suffix: Suffix column labels with string `suffix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_prefix('item_')
item_0 1
item_1 2
item_2 3
item_3 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6
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Suffix labels with string `suffix`.
For Series, the row labels are suffixed.
For DataFrame, the column labels are suffixed.
Parameters
----------
suffix : str
The string to add after each label.
axis : {0 or 'index', 1 or 'columns', None}, default None
Axis to add suffix on
.. versionadded:: 2.0.0
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_prefix: Prefix row labels with string `prefix`.
DataFrame.add_prefix: Prefix column labels with string `prefix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_suffix('_item')
0_item 1
1_item 2
2_item 3
3_item 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6
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add_suffixzNDFrame.add_suffixo sK �� �v
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Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. `mergesort` and `stable` are the only stable algorithms. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
key : callable, optional
Apply the key function to the values
before sorting. This is similar to the `key` argument in the
builtin :meth:`sorted` function, with the notable difference that
this `key` function should be *vectorized*. It should expect a
``Series`` and return a Series with the same shape as the input.
It will be applied to each column in `by` independently.
Returns
-------
DataFrame or None
DataFrame with sorted values or None if ``inplace=True``.
See Also
--------
DataFrame.sort_index : Sort a DataFrame by the index.
Series.sort_values : Similar method for a Series.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... 'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3 col4
1 A 1 1 B
0 A 2 0 a
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3 col4
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
3 NaN 8 4 D
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3 col4
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
Sorting with a key function
>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Natural sort with the key argument,
using the `natsort <https://github.com/SethMMorton/natsort>` package.
>>> df = pd.DataFrame({
... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
... "value": [10, 20, 30, 40, 50]
... })
>>> df
time value
0 0hr 10
1 128hr 20
2 72hr 30
3 48hr 40
4 96hr 50
>>> from natsort import index_natsorted
>>> df.sort_values(
... by="time",
... key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
time value
0 0hr 10
3 48hr 40
2 72hr 30
4 96hr 50
1 128hr 20
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Conform {klass} to new index with optional filling logic.
Places NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
``copy=False``.
Parameters
----------
{optional_reindex}
method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}}
Method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* None (default): don't fill gaps
* pad / ffill: Propagate last valid observation forward to next
valid.
* backfill / bfill: Use next valid observation to fill gap.
* nearest: Use nearest valid observations to fill gap.
copy : bool, default True
Return a new object, even if the passed indexes are the same.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level.
fill_value : scalar, default np.nan
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value.
limit : int, default None
Maximum number of consecutive elements to forward or backward fill.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
{klass} with changed index.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
``DataFrame.reindex`` supports two calling conventions
* ``(index=index_labels, columns=column_labels, ...)``
* ``(labels, axis={{'index', 'columns'}}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}},
... index=index)
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned ``NaN``.
>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404.0 0.07
Iceweasel NaN NaN
Comodo Dragon NaN NaN
IE10 404.0 0.08
Chrome 200.0 0.02
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
increasing or decreasing, we cannot use arguments to the keyword
``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
Safari 404 0.07
Iceweasel 0 0.00
Comodo Dragon 0 0.00
IE10 404 0.08
Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing')
http_status response_time
Safari 404 0.07
Iceweasel missing missing
Comodo Dragon missing missing
IE10 404 0.08
Chrome 200 0.02
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent'])
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
Or we can use "axis-style" keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns")
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
To further illustrate the filling functionality in
``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}},
... index=date_index)
>>> df2
prices
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider
date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
The index entries that did not have a value in the original data frame
(for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
options.
For example, to back-propagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
>>> df2.reindex(date_index2, method='bfill')
prices
2009-12-29 100.0
2009-12-30 100.0
2009-12-31 100.0
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
(at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
in the original dataframe, use the ``fillna()`` method.
See the :ref:`user guide <basics.reindexing>` for more.
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Subset the dataframe rows or columns according to the specified index labels.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
Keep labels from axis which are in items.
like : str
Keep labels from axis for which "like in label == True".
regex : str (regular expression)
Keep labels from axis for which re.search(regex, label) == True.
axis : {0 or 'index', 1 or 'columns', None}, default None
The axis to filter on, expressed either as an index (int)
or axis name (str). By default this is the info axis, 'columns' for
DataFrame. For `Series` this parameter is unused and defaults to `None`.
Returns
-------
same type as input object
See Also
--------
DataFrame.loc : Access a group of rows and columns
by label(s) or a boolean array.
Notes
-----
The ``items``, ``like``, and ``regex`` parameters are
enforced to be mutually exclusive.
``axis`` defaults to the info axis that is used when indexing
with ``[]``.
Examples
--------
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
... index=['mouse', 'rabbit'],
... columns=['one', 'two', 'three'])
>>> df
one two three
mouse 1 2 3
rabbit 4 5 6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
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Return the first `n` rows.
This function returns the first `n` rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
For negative values of `n`, this function returns all rows except
the last `|n|` rows, equivalent to ``df[:n]``.
If n is larger than the number of rows, this function returns all rows.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
same type as caller
The first `n` rows of the caller object.
See Also
--------
DataFrame.tail: Returns the last `n` rows.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first `n` lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
For negative values of `n`
>>> df.head(-3)
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
N�r r| r� �r� �ns r� r; z NDFrame.head� s4 � �R � ��9�9�R�a�=�%�%�'� '��y�y��!�}� r� c �� � t � r@|dk( r | j dd j � S | j | d j � S |dk( r| j dd S | j | d S )a�
Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of `n`, this function returns all rows except
the first `|n|` rows, equivalent to ``df[|n|:]``.
If n is larger than the number of rows, this function returns all rows.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
type of caller
The last `n` rows of the caller object.
See Also
--------
DataFrame.head : The first `n` rows of the caller object.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail()
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last `n` lines (three in this case)
>>> df.tail(3)
animal
6 shark
7 whale
8 zebra
For negative values of `n`
>>> df.tail(-3)
animal
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
r Nr� r� s r� �tailz NDFrame.tail so � �R � ��A�v��y�y��1�~�*�*�,�,��9�9�a�R�S�>�&�&�(� (� ��6��9�9�Q�q�>� !��y�y�!���~� r� �replacec � � |�d}| j |� }| j | }t j |� } t j
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Return a random sample of items from an axis of object.
You can use `random_state` for reproducibility.
Parameters
----------
n : int, optional
Number of items from axis to return. Cannot be used with `frac`.
Default = 1 if `frac` = None.
frac : float, optional
Fraction of axis items to return. Cannot be used with `n`.
replace : bool, default False
Allow or disallow sampling of the same row more than once.
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index. Index
values in weights not found in sampled object will be ignored and
index values in sampled object not in weights will be assigned
weights of zero.
If called on a DataFrame, will accept the name of a column
when axis = 0.
Unless weights are a Series, weights must be same length as axis
being sampled.
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
Infinite values not allowed.
random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional
If int, array-like, or BitGenerator, seed for random number generator.
If np.random.RandomState or np.random.Generator, use as given.
.. versionchanged:: 1.4.0
np.random.Generator objects now accepted
axis : {0 or 'index', 1 or 'columns', None}, default None
Axis to sample. Accepts axis number or name. Default is stat axis
for given data type. For `Series` this parameter is unused and defaults to `None`.
ignore_index : bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
.. versionadded:: 1.3.0
Returns
-------
Series or DataFrame
A new object of same type as caller containing `n` items randomly
sampled from the caller object.
See Also
--------
DataFrameGroupBy.sample: Generates random samples from each group of a
DataFrame object.
SeriesGroupBy.sample: Generates random samples from each group of a
Series object.
numpy.random.choice: Generates a random sample from a given 1-D numpy
array.
Notes
-----
If `frac` > 1, `replacement` should be set to `True`.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
... 'num_wings': [2, 0, 0, 0],
... 'num_specimen_seen': [10, 2, 1, 8]},
... index=['falcon', 'dog', 'spider', 'fish'])
>>> df
num_legs num_wings num_specimen_seen
falcon 2 2 10
dog 4 0 2
spider 8 0 1
fish 0 0 8
Extract 3 random elements from the ``Series`` ``df['num_legs']``:
Note that we use `random_state` to ensure the reproducibility of
the examples.
>>> df['num_legs'].sample(n=3, random_state=1)
fish 0
spider 8
falcon 2
Name: num_legs, dtype: int64
A random 50% sample of the ``DataFrame`` with replacement:
>>> df.sample(frac=0.5, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
An upsample sample of the ``DataFrame`` with replacement:
Note that `replace` parameter has to be `True` for `frac` parameter > 1.
>>> df.sample(frac=2, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
falcon 2 2 10
falcon 2 2 10
fish 0 0 8
dog 4 0 2
fish 0 0 8
dog 4 0 2
Using a DataFrame column as weights. Rows with larger value in the
`num_specimen_seen` column are more likely to be sampled.
>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
num_legs num_wings num_specimen_seen
falcon 2 2 10
fish 0 0 8
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Apply chainable functions that expect Series or DataFrames.
Parameters
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func : function
Function to apply to the {klass}.
``args``, and ``kwargs`` are passed into ``func``.
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``data_keyword`` is a string indicating the keyword of
``callable`` that expects the {klass}.
*args : iterable, optional
Positional arguments passed into ``func``.
**kwargs : mapping, optional
A dictionary of keyword arguments passed into ``func``.
Returns
-------
the return type of ``func``.
See Also
--------
DataFrame.apply : Apply a function along input axis of DataFrame.
DataFrame.map : Apply a function elementwise on a whole DataFrame.
Series.map : Apply a mapping correspondence on a
:class:`~pandas.Series`.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
Series, DataFrames or GroupBy objects.
Examples
--------
Constructing a income DataFrame from a dictionary.
>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=['Salary', 'Others'])
>>> df
Salary Others
0 8000 1000.0
1 9500 NaN
2 5000 2000.0
Functions that perform tax reductions on an income DataFrame.
>>> def subtract_federal_tax(df):
... return df * 0.9
>>> def subtract_state_tax(df, rate):
... return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
... new_rate = rate + rate_increase
... return df * (1 - new_rate)
Instead of writing
>>> subtract_national_insurance(
... subtract_state_tax(subtract_federal_tax(df), rate=0.12),
... rate=0.05,
... rate_increase=0.02) # doctest: +SKIP
You can write
>>> (
... df.pipe(subtract_federal_tax)
... .pipe(subtract_state_tax, rate=0.12)
... .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
Salary Others
0 5892.48 736.56
1 6997.32 NaN
2 3682.80 1473.12
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``national_insurance`` takes its data as ``df``
in the second argument:
>>> def subtract_national_insurance(rate, df, rate_increase):
... new_rate = rate + rate_increase
... return df * (1 - new_rate)
>>> (
... df.pipe(subtract_federal_tax)
... .pipe(subtract_state_tax, rate=0.12)
... .pipe(
... (subtract_national_insurance, 'df'),
... rate=0.05,
... rate_increase=0.02
... )
... )
Salary Others
0 5892.48 736.56
1 6997.32 NaN
2 3682.80 1473.12
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Propagate metadata from other to self.
Parameters
----------
other : the object from which to get the attributes that we are going
to propagate
method : str, optional
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was called.
.. warning::
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Return the dtypes in the DataFrame.
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with mixed types are stored with the ``object`` dtype. See
:ref:`the User Guide <basics.dtypes>` for more.
Returns
-------
pandas.Series
The data type of each column.
Examples
--------
>>> df = pd.DataFrame({'float': [1.0],
... 'int': [1],
... 'datetime': [pd.Timestamp('20180310')],
... 'string': ['foo']})
>>> df.dtypes
float float64
int int64
datetime datetime64[ns]
string object
dtype: object
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Cast a pandas object to a specified dtype ``dtype``.
Parameters
----------
dtype : str, data type, Series or Mapping of column name -> data type
Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to
cast entire pandas object to the same type. Alternatively, use a
mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is
a numpy.dtype or Python type to cast one or more of the DataFrame's
columns to column-specific types.
copy : bool, default True
Return a copy when ``copy=True`` (be very careful setting
``copy=False`` as changes to values then may propagate to other
pandas objects).
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
errors : {'raise', 'ignore'}, default 'raise'
Control raising of exceptions on invalid data for provided dtype.
- ``raise`` : allow exceptions to be raised
- ``ignore`` : suppress exceptions. On error return original object.
Returns
-------
same type as caller
See Also
--------
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to a numeric type.
numpy.ndarray.astype : Cast a numpy array to a specified type.
Notes
-----
.. versionchanged:: 2.0.0
Using ``astype`` to convert from timezone-naive dtype to
timezone-aware dtype will raise an exception.
Use :meth:`Series.dt.tz_localize` instead.
Examples
--------
Create a DataFrame:
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1 int64
col2 int64
dtype: object
Cast all columns to int32:
>>> df.astype('int32').dtypes
col1 int32
col2 int32
dtype: object
Cast col1 to int32 using a dictionary:
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object
Create a series:
>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
dtype: int32
>>> ser.astype('int64')
0 1
1 2
dtype: int64
Convert to categorical type:
>>> ser.astype('category')
0 1
1 2
dtype: category
Categories (2, int32): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
... categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0 1
1 2
dtype: category
Categories (2, int64): [2 < 1]
Create a series of dates:
>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0 2020-01-01
1 2020-01-02
2 2020-01-03
dtype: datetime64[ns]
Fr� zFOnly the Series name can be used for the key in Series dtype mappings.r r8 r� zJOnly a column name can be used for the key in a dtype mappings argument. 'z' not found in columns.N)ro r� r� )r� r� r~ z': Error while type casting for column 'r^ c 3 �< �K � | ] }|j �k( �� � y �wr� r� )rH r r� s �r� rI z!NDFrame.astype.<locals>.<genexpr>� s �� �� � 9�'*�� � �U�"�9�r� )r� r~ r� r� ra rR )#r ra r r� r( r r� �pandasr� r� r r5 r r! r� �iatro r� r r� rq rb rh r� rj r� r� �arraysr� r� r� r� r� r
r8 )r� r� r� r~ �new_typer� � dtype_ser�col_name�resultsr' r� �cdt�res_col�ex�_�serr� r� rv s ` r� r� zNDFrame.astype? s� �� �r �'�)��D� �� ��y�y�A�~��u�:��>�T�Y�Y�e�%;�"�<�� � !����+���{�{�8�T�6�:�:�
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(� &�e�
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Make a copy of this object's indices and data.
When ``deep=True`` (default), a new object will be created with a
copy of the calling object's data and indices. Modifications to
the data or indices of the copy will not be reflected in the
original object (see notes below).
When ``deep=False``, a new object will be created without copying
the calling object's data or index (only references to the data
and index are copied). Any changes to the data of the original
will be reflected in the shallow copy (and vice versa).
.. note::
The ``deep=False`` behaviour as described above will change
in pandas 3.0. `Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that the "shallow" copy
is that is returned with ``deep=False`` will still avoid making
an eager copy, but changes to the data of the original will *no*
longer be reflected in the shallow copy (or vice versa). Instead,
it makes use of a lazy (deferred) copy mechanism that will copy
the data only when any changes to the original or shallow copy is
made.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
Parameters
----------
deep : bool, default True
Make a deep copy, including a copy of the data and the indices.
With ``deep=False`` neither the indices nor the data are copied.
Returns
-------
Series or DataFrame
Object type matches caller.
Notes
-----
When ``deep=True``, data is copied but actual Python objects
will not be copied recursively, only the reference to the object.
This is in contrast to `copy.deepcopy` in the Standard Library,
which recursively copies object data (see examples below).
While ``Index`` objects are copied when ``deep=True``, the underlying
numpy array is not copied for performance reasons. Since ``Index`` is
immutable, the underlying data can be safely shared and a copy
is not needed.
Since pandas is not thread safe, see the
:ref:`gotchas <gotchas.thread-safety>` when copying in a threading
environment.
When ``copy_on_write`` in pandas config is set to ``True``, the
``copy_on_write`` config takes effect even when ``deep=False``.
This means that any changes to the copied data would make a new copy
of the data upon write (and vice versa). Changes made to either the
original or copied variable would not be reflected in the counterpart.
See :ref:`Copy_on_Write <copy_on_write>` for more information.
Examples
--------
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a 1
b 2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a 1
b 2
dtype: int64
**Shallow copy versus default (deep) copy:**
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.
>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True
Deep copy has own copy of data and index.
>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False
Updates to the data shared by shallow copy and original is reflected
in both (NOTE: this will no longer be true for pandas >= 3.0);
deep copy remains unchanged.
>>> s.iloc[0] = 3
>>> shallow.iloc[1] = 4
>>> s
a 3
b 4
dtype: int64
>>> shallow
a 3
b 4
dtype: int64
>>> deep
a 1
b 2
dtype: int64
Note that when copying an object containing Python objects, a deep copy
will copy the data, but will not do so recursively. Updating a nested
data object will be reflected in the deep copy.
>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0 [10, 2]
1 [3, 4]
dtype: object
>>> deep
0 [10, 2]
1 [3, 4]
dtype: object
**Copy-on-Write is set to true**, the shallow copy is not modified
when the original data is changed:
>>> with pd.option_context("mode.copy_on_write", True):
... s = pd.Series([1, 2], index=["a", "b"])
... copy = s.copy(deep=False)
... s.iloc[0] = 100
... s
a 100
b 2
dtype: int64
>>> copy
a 1
b 2
dtype: int64
r� r� r� ra )r� r� r[ r� r� r� )r� r� r� s r� r� z NDFrame.copy sW � �j �y�y�~�~�4�~�(�� � � � ��)�)�$�T�Y�Y�)�?�L�L� �� M�
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Parameters
----------
memo, default None
Standard signature. Unused
Tr� r )r� �memos r� � __deepcopy__zNDFrame.__deepcopy__� s � � �y�y�d�y�#�#r� c � � | j j |�� }| j ||j �� }|j | d�� S )a�
Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped
columns, leaving non-object and unconvertible
columns unchanged. The inference rules are the
same as during normal Series/DataFrame construction.
Parameters
----------
copy : bool, default True
Whether to make a copy for non-object or non-inferable columns
or Series.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
Returns
-------
same type as input object
See Also
--------
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to numeric type.
convert_dtypes : Convert argument to best possible dtype.
Examples
--------
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
A
1 1
2 2
3 3
>>> df.dtypes
A object
dtype: object
>>> df.infer_objects().dtypes
A int64
dtype: object
r r� �
infer_objectsra )r� �convertr� r� r� )r� r� r� r� s r� r� zNDFrame.infer_objects� sK � �r �)�)�#�#��#�.���(�(��w�|�|�(�D��� � ��_� �=�=r� c � � t |� | j j ||||||�� }| j ||j �� }|j | d�� S )a�
Convert columns to the best possible dtypes using dtypes supporting ``pd.NA``.
Parameters
----------
infer_objects : bool, default True
Whether object dtypes should be converted to the best possible types.
convert_string : bool, default True
Whether object dtypes should be converted to ``StringDtype()``.
convert_integer : bool, default True
Whether, if possible, conversion can be done to integer extension types.
convert_boolean : bool, defaults True
Whether object dtypes should be converted to ``BooleanDtypes()``.
convert_floating : bool, defaults True
Whether, if possible, conversion can be done to floating extension types.
If `convert_integer` is also True, preference will be give to integer
dtypes if the floats can be faithfully casted to integers.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
Returns
-------
Series or DataFrame
Copy of input object with new dtype.
See Also
--------
infer_objects : Infer dtypes of objects.
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to a numeric type.
Notes
-----
By default, ``convert_dtypes`` will attempt to convert a Series (or each
Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
``convert_string``, ``convert_integer``, ``convert_boolean`` and
``convert_floating``, it is possible to turn off individual conversions
to ``StringDtype``, the integer extension types, ``BooleanDtype``
or floating extension types, respectively.
For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
rules as during normal Series/DataFrame construction. Then, if possible,
convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
or floating extension type, otherwise leave as ``object``.
If the dtype is integer, convert to an appropriate integer extension type.
If the dtype is numeric, and consists of all integers, convert to an
appropriate integer extension type. Otherwise, convert to an
appropriate floating extension type.
In the future, as new dtypes are added that support ``pd.NA``, the results
of this method will change to support those new dtypes.
Examples
--------
>>> df = pd.DataFrame(
... {
... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
... }
... )
Start with a DataFrame with default dtypes.
>>> df
a b c d e f
0 1 x True h 10.0 NaN
1 2 y False i NaN 100.5
2 3 z NaN NaN 20.0 200.0
>>> df.dtypes
a int32
b object
c object
d object
e float64
f float64
dtype: object
Convert the DataFrame to use best possible dtypes.
>>> dfn = df.convert_dtypes()
>>> dfn
a b c d e f
0 1 x True h 10 <NA>
1 2 y False i <NA> 100.5
2 3 z <NA> <NA> 20 200.0
>>> dfn.dtypes
a Int32
b string[python]
c boolean
d string[python]
e Int64
f Float64
dtype: object
Start with a Series of strings and missing data represented by ``np.nan``.
>>> s = pd.Series(["a", "b", np.nan])
>>> s
0 a
1 b
2 NaN
dtype: object
Obtain a Series with dtype ``StringDtype``.
>>> s.convert_dtypes()
0 a
1 b
2 <NA>
dtype: string
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s|n|jQ |� }| | jK |||�� }|s|| |< �St |t � r6|jR | | jR k( r|| jT dd�|f< ��|| |< ��| jV jY |� }t |tZ � r&t] j^ | j` d � | }n�t |t\ jb � r-|jR jd dk( r|jg � d }n>t |t\ jb � r|jR jd dk( s tM d� �ti |� D ]j \ }}|jj dd�|f }| jj dd�|f }|jR |jR k( r|| jj dd�|f< �Y| jm ||� �l ��� |r| jo | � S | S tE |� sY|dk( r4| jp jK ||�� jp } | jH } n�| jH jK ||||�� } nat |tr � r:| j2 dk( r+| ju | jw � |� jH } nty dt# |� � �� �| j{ | | j| �� } |r| jo | � S | j | d�� S )aW
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). Values not
in the dict/Series/DataFrame will not be filled. This value cannot
be a list.
method : {{'backfill', 'bfill', 'ffill', None}}, default None
Method to use for filling holes in reindexed Series:
* ffill: propagate last valid observation forward to next valid.
* backfill / bfill: use next valid observation to fill gap.
.. deprecated:: 2.1.0
Use ffill or bfill instead.
axis : {axes_single_arg}
Axis along which to fill missing values. For `Series`
this parameter is unused and defaults to 0.
inplace : bool, default False
If True, fill in-place. Note: this will modify any
other views on this object (e.g., a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
See Also
--------
ffill : Fill values by propagating the last valid observation to next valid.
bfill : Fill values by using the next valid observation to fill the gap.
interpolate : Fill NaN values using interpolation.
reindex : Conform object to new index.
asfreq : Convert TimeSeries to specified frequency.
Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, np.nan],
... [np.nan, 3, np.nan, 4]],
... columns=list("ABCD"))
>>> df
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN NaN NaN NaN
3 NaN 3.0 NaN 4.0
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0.0
1 3.0 4.0 0.0 1.0
2 0.0 0.0 0.0 0.0
3 0.0 3.0 0.0 4.0
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.
>>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0.0
1 3.0 4.0 2.0 1.0
2 0.0 1.0 2.0 3.0
3 0.0 3.0 2.0 4.0
Only replace the first NaN element.
>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0.0
1 3.0 4.0 NaN 1.0
2 NaN 1.0 NaN 3.0
3 NaN 3.0 NaN 4.0
When filling using a DataFrame, replacement happens along
the same column names and same indices
>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
A B C D
0 0.0 2.0 0.0 0.0
1 3.0 4.0 0.0 1.0
2 0.0 0.0 0.0 NaN
3 0.0 3.0 0.0 4.0
Note that column D is not affected since it is not present in df2.
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Fill NA/NaN values by propagating the last valid observation to next valid.
Parameters
----------
axis : {axes_single_arg}
Axis along which to fill missing values. For `Series`
this parameter is unused and defaults to 0.
inplace : bool, default False
If True, fill in-place. Note: this will modify any
other views on this object (e.g., a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
limit_area : {{`None`, 'inside', 'outside'}}, default None
If limit is specified, consecutive NaNs will be filled with this
restriction.
* ``None``: No fill restriction.
* 'inside': Only fill NaNs surrounded by valid values
(interpolate).
* 'outside': Only fill NaNs outside valid values (extrapolate).
.. versionadded:: 2.2.0
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, np.nan],
... [np.nan, 3, np.nan, 4]],
... columns=list("ABCD"))
>>> df
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 NaN NaN NaN NaN
3 NaN 3.0 NaN 4.0
>>> df.ffill()
A B C D
0 NaN 2.0 NaN 0.0
1 3.0 4.0 NaN 1.0
2 3.0 4.0 NaN 1.0
3 3.0 3.0 NaN 4.0
>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0 1.0
1 1.0
2 2.0
3 3.0
dtype: float64
r r� r r� r� r� �r� rX rG r r r
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Fill NA/NaN values by propagating the last valid observation to next valid.
.. deprecated:: 2.0
{klass}.pad is deprecated. Use {klass}.ffill instead.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
Examples
--------
Please see examples for :meth:`DataFrame.ffill` or :meth:`Series.ffill`.
zPDataFrame.pad/Series.pad is deprecated. Use DataFrame.ffill/Series.ffill insteadr� r )r� r� rc rU r �r� r� r� r3 r� s r� �padz NDFrame.pad� �7 � �4 �
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Fill NA/NaN values by using the next valid observation to fill the gap.
Parameters
----------
axis : {axes_single_arg}
Axis along which to fill missing values. For `Series`
this parameter is unused and defaults to 0.
inplace : bool, default False
If True, fill in-place. Note: this will modify any
other views on this object (e.g., a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
limit_area : {{`None`, 'inside', 'outside'}}, default None
If limit is specified, consecutive NaNs will be filled with this
restriction.
* ``None``: No fill restriction.
* 'inside': Only fill NaNs surrounded by valid values
(interpolate).
* 'outside': Only fill NaNs outside valid values (extrapolate).
.. versionadded:: 2.2.0
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible).
.. deprecated:: 2.2.0
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
Examples
--------
For Series:
>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0 1.0
1 2.0
2 2.0
3 2.0
dtype: float64
>>> s.bfill(limit=1)
0 1.0
1 NaN
2 2.0
3 2.0
dtype: float64
With DataFrame:
>>> df = pd.DataFrame({{'A': [1, None, None, 4], 'B': [None, 5, None, 7]}})
>>> df
A B
0 1.0 NaN
1 NaN 5.0
2 NaN NaN
3 4.0 7.0
>>> df.bfill()
A B
0 1.0 5.0
1 4.0 5.0
2 4.0 7.0
3 4.0 7.0
>>> df.bfill(limit=1)
A B
0 1.0 5.0
1 NaN 5.0
2 4.0 7.0
3 4.0 7.0
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.. deprecated:: 2.0
{klass}.backfill is deprecated. Use {klass}.bfill instead.
Returns
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Examples
--------
Please see examples for :meth:`DataFrame.bfill` or :meth:`Series.bfill`.
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Fill NaN values using an interpolation method.
Please note that only ``method='linear'`` is supported for
DataFrame/Series with a MultiIndex.
Parameters
----------
method : str, default 'linear'
Interpolation technique to use. One of:
* 'linear': Ignore the index and treat the values as equally
spaced. This is the only method supported on MultiIndexes.
* 'time': Works on daily and higher resolution data to interpolate
given length of interval.
* 'index', 'values': use the actual numerical values of the index.
* 'pad': Fill in NaNs using existing values.
* 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'polynomial': Passed to
`scipy.interpolate.interp1d`, whereas 'spline' is passed to
`scipy.interpolate.UnivariateSpline`. These methods use the numerical
values of the index. Both 'polynomial' and 'spline' require that
you also specify an `order` (int), e.g.
``df.interpolate(method='polynomial', order=5)``. Note that,
`slinear` method in Pandas refers to the Scipy first order `spline`
instead of Pandas first order `spline`.
* 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
'cubicspline': Wrappers around the SciPy interpolation methods of
similar names. See `Notes`.
* 'from_derivatives': Refers to
`scipy.interpolate.BPoly.from_derivatives`.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Axis to interpolate along. For `Series` this parameter is unused
and defaults to 0.
limit : int, optional
Maximum number of consecutive NaNs to fill. Must be greater than
0.
inplace : bool, default False
Update the data in place if possible.
limit_direction : {{'forward', 'backward', 'both'}}, Optional
Consecutive NaNs will be filled in this direction.
If limit is specified:
* If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
* If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
'backwards'.
If 'limit' is not specified:
* If 'method' is 'backfill' or 'bfill', the default is 'backward'
* else the default is 'forward'
raises ValueError if `limit_direction` is 'forward' or 'both' and
method is 'backfill' or 'bfill'.
raises ValueError if `limit_direction` is 'backward' or 'both' and
method is 'pad' or 'ffill'.
limit_area : {{`None`, 'inside', 'outside'}}, default None
If limit is specified, consecutive NaNs will be filled with this
restriction.
* ``None``: No fill restriction.
* 'inside': Only fill NaNs surrounded by valid values
(interpolate).
* 'outside': Only fill NaNs outside valid values (extrapolate).
downcast : optional, 'infer' or None, defaults to None
Downcast dtypes if possible.
.. deprecated:: 2.1.0
``**kwargs`` : optional
Keyword arguments to pass on to the interpolating function.
Returns
-------
Series or DataFrame or None
Returns the same object type as the caller, interpolated at
some or all ``NaN`` values or None if ``inplace=True``.
See Also
--------
fillna : Fill missing values using different methods.
scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
(Akima interpolator).
scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
Bernstein basis.
scipy.interpolate.interp1d : Interpolate a 1-D function.
scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
interpolator).
scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
interpolation.
scipy.interpolate.CubicSpline : Cubic spline data interpolator.
Notes
-----
The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
methods are wrappers around the respective SciPy implementations of
similar names. These use the actual numerical values of the index.
For more information on their behavior, see the
`SciPy documentation
<https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__.
Examples
--------
Filling in ``NaN`` in a :class:`~pandas.Series` via linear
interpolation.
>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0 0.0
1 1.0
2 NaN
3 3.0
dtype: float64
>>> s.interpolate()
0 0.0
1 1.0
2 2.0
3 3.0
dtype: float64
Filling in ``NaN`` in a Series via polynomial interpolation or splines:
Both 'polynomial' and 'spline' methods require that you also specify
an ``order`` (int).
>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0 0.000000
1 2.000000
2 4.666667
3 8.000000
dtype: float64
Fill the DataFrame forward (that is, going down) along each column
using linear interpolation.
Note how the last entry in column 'a' is interpolated differently,
because there is no entry after it to use for interpolation.
Note how the first entry in column 'b' remains ``NaN``, because there
is no entry before it to use for interpolation.
>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
... (np.nan, 2.0, np.nan, np.nan),
... (2.0, 3.0, np.nan, 9.0),
... (np.nan, 4.0, -4.0, 16.0)],
... columns=list('abcd'))
>>> df
a b c d
0 0.0 NaN -1.0 1.0
1 NaN 2.0 NaN NaN
2 2.0 3.0 NaN 9.0
3 NaN 4.0 -4.0 16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
a b c d
0 0.0 NaN -1.0 1.0
1 1.0 2.0 -2.0 5.0
2 2.0 3.0 -3.0 9.0
3 2.0 4.0 -4.0 16.0
Using polynomial interpolation.
>>> df['d'].interpolate(method='polynomial', order=2)
0 1.0
1 4.0
2 9.0
3 16.0
Name: d, dtype: float64
r� z�.interpolate is deprecated and will be removed in a future version. Call result.infer_objects(copy=False) on the result instead.r� Nr\ z'downcast must be either None or 'infer'r� r r� z&'method' should be a string, not None.)r r$ r! r) z.interpolate with method=zZ is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.FTz�.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.ro z('fill_value' is not a valid keyword for z .interpolate with method from �linearz@Only `method=linear` interpolation is supported on MultiIndexes.zvCannot interpolate with all object-dtype columns in the DataFrame. Try setting at least one column to a numeric dtype.r� )rb r r3 r< r� r� r� r� r? ra rt )2r r� r� r� r r� rc rU r rX rG r r r
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Return the last row(s) without any NaNs before `where`.
The last row (for each element in `where`, if list) without any
NaN is taken.
In case of a :class:`~pandas.DataFrame`, the last row without NaN
considering only the subset of columns (if not `None`)
If there is no good value, NaN is returned for a Series or
a Series of NaN values for a DataFrame
Parameters
----------
where : date or array-like of dates
Date(s) before which the last row(s) are returned.
subset : str or array-like of str, default `None`
For DataFrame, if not `None`, only use these columns to
check for NaNs.
Returns
-------
scalar, Series, or DataFrame
The return can be:
* scalar : when `self` is a Series and `where` is a scalar
* Series: when `self` is a Series and `where` is an array-like,
or when `self` is a DataFrame and `where` is a scalar
* DataFrame : when `self` is a DataFrame and `where` is an
array-like
See Also
--------
merge_asof : Perform an asof merge. Similar to left join.
Notes
-----
Dates are assumed to be sorted. Raises if this is not the case.
Examples
--------
A Series and a scalar `where`.
>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10 1.0
20 2.0
30 NaN
40 4.0
dtype: float64
>>> s.asof(20)
2.0
For a sequence `where`, a Series is returned. The first value is
NaN, because the first element of `where` is before the first
index value.
>>> s.asof([5, 20])
5 NaN
20 2.0
dtype: float64
Missing values are not considered. The following is ``2.0``, not
NaN, even though NaN is at the index location for ``30``.
>>> s.asof(30)
2.0
Take all columns into consideration
>>> df = pd.DataFrame({'a': [10., 20., 30., 40., 50.],
... 'b': [None, None, None, None, 500]},
... index=pd.DatetimeIndex(['2018-02-27 09:01:00',
... '2018-02-27 09:02:00',
... '2018-02-27 09:03:00',
... '2018-02-27 09:04:00',
... '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']))
a b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN
Take a single column into consideration
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']),
... subset=['a'])
a b
2018-02-27 09:03:30 30.0 NaN
2018-02-27 09:04:30 40.0 NaN
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Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
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values.
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strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
{klass}
Mask of bool values for each element in {klass} that
indicates whether an element is an NA value.
See Also
--------
{klass}.isnull : Alias of isna.
{klass}.notna : Boolean inverse of isna.
{klass}.dropna : Omit axes labels with missing values.
isna : Top-level isna.
Examples
--------
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.isna()
0 False
1 False
2 True
dtype: bool
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Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.
Returns
-------
{klass}
Mask of bool values for each element in {klass} that
indicates whether an element is not an NA value.
See Also
--------
{klass}.notnull : Alias of notna.
{klass}.isna : Boolean inverse of notna.
{klass}.dropna : Omit axes labels with missing values.
notna : Top-level notna.
Examples
--------
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.notna()
0 True
1 True
2 False
dtype: bool
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Trim values at input threshold(s).
Assigns values outside boundary to boundary values. Thresholds
can be singular values or array like, and in the latter case
the clipping is performed element-wise in the specified axis.
Parameters
----------
lower : float or array-like, default None
Minimum threshold value. All values below this
threshold will be set to it. A missing
threshold (e.g `NA`) will not clip the value.
upper : float or array-like, default None
Maximum threshold value. All values above this
threshold will be set to it. A missing
threshold (e.g `NA`) will not clip the value.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Align object with lower and upper along the given axis.
For `Series` this parameter is unused and defaults to `None`.
inplace : bool, default False
Whether to perform the operation in place on the data.
*args, **kwargs
Additional keywords have no effect but might be accepted
for compatibility with numpy.
Returns
-------
Series or DataFrame or None
Same type as calling object with the values outside the
clip boundaries replaced or None if ``inplace=True``.
See Also
--------
Series.clip : Trim values at input threshold in series.
DataFrame.clip : Trim values at input threshold in dataframe.
numpy.clip : Clip (limit) the values in an array.
Examples
--------
>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
col_0 col_1
0 9 -2
1 -3 -7
2 0 6
3 -1 8
4 5 -5
Clips per column using lower and upper thresholds:
>>> df.clip(-4, 6)
col_0 col_1
0 6 -2
1 -3 -4
2 0 6
3 -1 6
4 5 -4
Clips using specific lower and upper thresholds per column:
>>> df.clip([-2, -1], [4, 5])
col_0 col_1
0 4 -1
1 -2 -1
2 0 5
3 -1 5
4 4 -1
Clips using specific lower and upper thresholds per column element:
>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0 2
1 -4
2 -1
3 6
4 3
dtype: int64
>>> df.clip(t, t + 4, axis=0)
col_0 col_1
0 6 2
1 -3 -4
2 0 3
3 6 8
4 5 3
Clips using specific lower threshold per column element, with missing values:
>>> t = pd.Series([2, -4, np.nan, 6, 3])
>>> t
0 2.0
1 -4.0
2 NaN
3 6.0
4 3.0
dtype: float64
>>> df.clip(t, axis=0)
col_0 col_1
0 9 2
1 -3 -4
2 0 6
3 6 8
4 5 3
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r� c �* � ddl m} || |||||�� S )a�
Convert time series to specified frequency.
Returns the original data conformed to a new index with the specified
frequency.
If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
is the result of transforming the original index with
:meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
will map one-to-one to the new index).
Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
last entries in the original index (see :func:`pandas.date_range`). The
values corresponding to any timesteps in the new index which were not present
in the original index will be null (``NaN``), unless a method for filling
such unknowns is provided (see the ``method`` parameter below).
The :meth:`resample` method is more appropriate if an operation on each group of
timesteps (such as an aggregate) is necessary to represent the data at the new
frequency.
Parameters
----------
freq : DateOffset or str
Frequency DateOffset or string.
method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {{'start', 'end'}}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
{klass}
{klass} object reindexed to the specified frequency.
See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='min')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({{'s': series}})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30s')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30s', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30s', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
r )�asfreq)rb �how� normalizero )�pandas.core.resampler{ )r� rK rb r| r} ro r{ s r� r{ zNDFrame.asfreq�# s' � �f 0�� � ��� �!�
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Select values at particular time of day (e.g., 9:30AM).
Parameters
----------
time : datetime.time or str
The values to select.
axis : {0 or 'index', 1 or 'columns'}, default 0
For `Series` this parameter is unused and defaults to 0.
Returns
-------
Series or DataFrame
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
between_time : Select values between particular times of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_at_time : Get just the index locations for
values at particular time of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='12h')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-09 12:00:00 2
2018-04-10 00:00:00 3
2018-04-10 12:00:00 4
>>> ts.at_time('12:00')
A
2018-04-09 12:00:00 2
2018-04-10 12:00:00 4
r �Index must be DatetimeIndex)rW r� )r r
r� r} r� �indexer_at_timer� )r� �timerW r� r r� s r� �at_timezNDFrame.at_time$ sn � �Z �<��D��$�$�T�*�����t�$���%� �/�� 9�:� :��'�'��4�'�8���&�&�w�T�&�:�:r� c �� � |�d}| j |� }| j |� }t |t � s t d� �t |� \ }}|j
||||�� }| j ||�� S )a]
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Parameters
----------
start_time : datetime.time or str
Initial time as a time filter limit.
end_time : datetime.time or str
End time as a time filter limit.
inclusive : {"both", "neither", "left", "right"}, default "both"
Include boundaries; whether to set each bound as closed or open.
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine range time on index or columns value.
For `Series` this parameter is unused and defaults to 0.
Returns
-------
Series or DataFrame
Data from the original object filtered to the specified dates range.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
r r� )�
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Resample time-series data.
Convenience method for frequency conversion and resampling of time series.
The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,
or `TimedeltaIndex`), or the caller must pass the label of a datetime-like
series/index to the ``on``/``level`` keyword parameter.
Parameters
----------
rule : DateOffset, Timedelta or str
The offset string or object representing target conversion.
axis : {{0 or 'index', 1 or 'columns'}}, default 0
Which axis to use for up- or down-sampling. For `Series` this parameter
is unused and defaults to 0. Must be
`DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
.. deprecated:: 2.0.0
Use frame.T.resample(...) instead.
closed : {{'right', 'left'}}, default None
Which side of bin interval is closed. The default is 'left'
for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
'BA', 'BQE', and 'W' which all have a default of 'right'.
label : {{'right', 'left'}}, default None
Which bin edge label to label bucket with. The default is 'left'
for all frequency offsets except for 'ME', 'YE', 'QE', 'BME',
'BA', 'BQE', and 'W' which all have a default of 'right'.
convention : {{'start', 'end', 's', 'e'}}, default 'start'
For `PeriodIndex` only, controls whether to use the start or
end of `rule`.
.. deprecated:: 2.2.0
Convert PeriodIndex to DatetimeIndex before resampling instead.
kind : {{'timestamp', 'period'}}, optional, default None
Pass 'timestamp' to convert the resulting index to a
`DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
By default the input representation is retained.
.. deprecated:: 2.2.0
Convert index to desired type explicitly instead.
on : str, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like.
level : str or int, optional
For a MultiIndex, level (name or number) to use for
resampling. `level` must be datetime-like.
origin : Timestamp or str, default 'start_day'
The timestamp on which to adjust the grouping. The timezone of origin
must match the timezone of the index.
If string, must be one of the following:
- 'epoch': `origin` is 1970-01-01
- 'start': `origin` is the first value of the timeseries
- 'start_day': `origin` is the first day at midnight of the timeseries
- 'end': `origin` is the last value of the timeseries
- 'end_day': `origin` is the ceiling midnight of the last day
.. versionadded:: 1.3.0
.. note::
Only takes effect for Tick-frequencies (i.e. fixed frequencies like
days, hours, and minutes, rather than months or quarters).
offset : Timedelta or str, default is None
An offset timedelta added to the origin.
group_keys : bool, default False
Whether to include the group keys in the result index when using
``.apply()`` on the resampled object.
.. versionadded:: 1.5.0
Not specifying ``group_keys`` will retain values-dependent behavior
from pandas 1.4 and earlier (see :ref:`pandas 1.5.0 Release notes
<whatsnew_150.enhancements.resample_group_keys>` for examples).
.. versionchanged:: 2.0.0
``group_keys`` now defaults to ``False``.
Returns
-------
pandas.api.typing.Resampler
:class:`~pandas.core.Resampler` object.
See Also
--------
Series.resample : Resample a Series.
DataFrame.resample : Resample a DataFrame.
groupby : Group {klass} by mapping, function, label, or list of labels.
asfreq : Reindex a {klass} with the given frequency without grouping.
Notes
-----
See the `user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__
for more.
To learn more about the offset strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.
Examples
--------
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='min')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: min, dtype: int64
Downsample the series into 3 minute bins and sum the values
of the timestamps falling into a bin.
>>> series.resample('3min').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3min, dtype: int64
Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
value in the resampled bucket with the label ``2000-01-01 00:03:00``
does not include 3 (if it did, the summed value would be 6, not 3).
>>> series.resample('3min', label='right').sum()
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3min, dtype: int64
To include this value close the right side of the bin interval,
as shown below.
>>> series.resample('3min', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3min, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30s').asfreq()[0:5] # Select first 5 rows
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1.0
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
Freq: 30s, dtype: float64
Upsample the series into 30 second bins and fill the ``NaN``
values using the ``ffill`` method.
>>> series.resample('30s').ffill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30s, dtype: int64
Upsample the series into 30 second bins and fill the
``NaN`` values using the ``bfill`` method.
>>> series.resample('30s').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30s, dtype: int64
Pass a custom function via ``apply``
>>> def custom_resampler(arraylike):
... return np.sum(arraylike) + 5
...
>>> series.resample('3min').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3min, dtype: int64
For DataFrame objects, the keyword `on` can be used to specify the
column instead of the index for resampling.
>>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
... periods=8,
... freq='W')
>>> df
price volume week_starting
0 10 50 2018-01-07
1 11 60 2018-01-14
2 9 40 2018-01-21
3 13 100 2018-01-28
4 14 50 2018-02-04
5 18 100 2018-02-11
6 17 40 2018-02-18
7 19 50 2018-02-25
>>> df.resample('ME', on='week_starting').mean()
price volume
week_starting
2018-01-31 10.75 62.5
2018-02-28 17.00 60.0
For a DataFrame with MultiIndex, the keyword `level` can be used to
specify on which level the resampling needs to take place.
>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
>>> df2 = pd.DataFrame(
... d2,
... index=pd.MultiIndex.from_product(
... [days, ['morning', 'afternoon']]
... )
... )
>>> df2
price volume
2000-01-01 morning 10 50
afternoon 11 60
2000-01-02 morning 9 40
afternoon 13 100
2000-01-03 morning 14 50
afternoon 18 100
2000-01-04 morning 17 40
afternoon 19 50
>>> df2.resample('D', level=0).sum()
price volume
2000-01-01 21 110
2000-01-02 22 140
2000-01-03 32 150
2000-01-04 36 90
If you want to adjust the start of the bins based on a fixed timestamp:
>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00 0
2000-10-01 23:37:00 3
2000-10-01 23:44:00 6
2000-10-01 23:51:00 9
2000-10-01 23:58:00 12
2000-10-02 00:05:00 15
2000-10-02 00:12:00 18
2000-10-02 00:19:00 21
2000-10-02 00:26:00 24
Freq: 7min, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00 0
2000-10-01 23:31:00 9
2000-10-01 23:48:00 21
2000-10-02 00:05:00 54
2000-10-02 00:22:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00 0
2000-10-01 23:35:00 18
2000-10-01 23:52:00 27
2000-10-02 00:09:00 39
2000-10-02 00:26:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00 3
2000-10-01 23:41:00 15
2000-10-01 23:58:00 45
2000-10-02 00:15:00 45
Freq: 17min, dtype: int64
If you want to adjust the start of the bins with an `offset` Timedelta, the two
following lines are equivalent:
>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17min, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17min, dtype: int64
If you want to take the largest Timestamp as the end of the bins:
>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00 0
2000-10-01 23:52:00 18
2000-10-02 00:09:00 27
2000-10-02 00:26:00 63
Freq: 17min, dtype: int64
In contrast with the `start_day`, you can use `end_day` to take the ceiling
midnight of the largest Timestamp as the end of the bins and drop the bins
not containing data:
>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00 3
2000-10-01 23:55:00 15
2000-10-02 00:12:00 45
2000-10-02 00:29:00 45
Freq: 17min, dtype: int64
r )�
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Select initial periods of time series data based on a date offset.
.. deprecated:: 2.1
:meth:`.first` is deprecated and will be removed in a future version.
Please create a mask and filter using `.loc` instead.
For a DataFrame with a sorted DatetimeIndex, this function can
select the first few rows based on a date offset.
Parameters
----------
offset : str, DateOffset or dateutil.relativedelta
The offset length of the data that will be selected. For instance,
'1ME' will display all the rows having their index within the first month.
Returns
-------
Series or DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
last : Select final periods of time series based on a date offset.
at_time : Select values at a particular time of the day.
between_time : Select values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the first 3 days:
>>> ts.first('3D')
A
2018-04-09 1
2018-04-11 2
Notice the data for 3 first calendar days were returned, not the first
3 days observed in the dataset, and therefore data for 2018-04-13 was
not returned.
zqfirst is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` insteadr� z+'first' only supports a DatetimeIndex indexr Fr� �leftrM N)r� r� rc rU r� r r} r� r� r� r r � is_on_offset�baserR r| r )r� r� �end_date�ends r� �firstz
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Select final periods of time series data based on a date offset.
.. deprecated:: 2.1
:meth:`.last` is deprecated and will be removed in a future version.
Please create a mask and filter using `.loc` instead.
For a DataFrame with a sorted DatetimeIndex, this function
selects the last few rows based on a date offset.
Parameters
----------
offset : str, DateOffset, dateutil.relativedelta
The offset length of the data that will be selected. For instance,
'3D' will display all the rows having their index within the last 3 days.
Returns
-------
Series or DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
first : Select initial periods of time series based on a date offset.
at_time : Select values at a particular time of the day.
between_time : Select values between particular times of the day.
Notes
-----
.. deprecated:: 2.1.0
Please create a mask and filter using `.loc` instead
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> ts.last('3D') # doctest: +SKIP
A
2018-04-13 3
2018-04-15 4
Notice the data for 3 last calendar days were returned, not the last
3 observed days in the dataset, and therefore data for 2018-04-11 was
not returned.
zplast is deprecated and will be removed in a future version. Please create a mask and filter using `.loc` insteadr� z*'last' only supports a DatetimeIndex indexr Fr� r� rL rM N)
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� } n� } || � S )a�
Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the
ranks of those values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Index to direct ranking.
For `Series` this parameter is unused and defaults to 0.
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
How to rank the group of records that have the same value (i.e. ties):
* average: average rank of the group
* min: lowest rank in the group
* max: highest rank in the group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups.
numeric_only : bool, default False
For DataFrame objects, rank only numeric columns if set to True.
.. versionchanged:: 2.0.0
The default value of ``numeric_only`` is now ``False``.
na_option : {'keep', 'top', 'bottom'}, default 'keep'
How to rank NaN values:
* keep: assign NaN rank to NaN values
* top: assign lowest rank to NaN values
* bottom: assign highest rank to NaN values
ascending : bool, default True
Whether or not the elements should be ranked in ascending order.
pct : bool, default False
Whether or not to display the returned rankings in percentile
form.
Returns
-------
same type as caller
Return a Series or DataFrame with data ranks as values.
See Also
--------
core.groupby.DataFrameGroupBy.rank : Rank of values within each group.
core.groupby.SeriesGroupBy.rank : Rank of values within each group.
Examples
--------
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
... 'spider', 'snake'],
... 'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
Animal Number_legs
0 cat 4.0
1 penguin 2.0
2 dog 4.0
3 spider 8.0
4 snake NaN
Ties are assigned the mean of the ranks (by default) for the group.
>>> s = pd.Series(range(5), index=list("abcde"))
>>> s["d"] = s["b"]
>>> s.rank()
a 1.0
b 2.5
c 4.0
d 2.5
e 5.0
dtype: float64
The following example shows how the method behaves with the above
parameters:
* default_rank: this is the default behaviour obtained without using
any parameter.
* max_rank: setting ``method = 'max'`` the records that have the
same values are ranked using the highest rank (e.g.: since 'cat'
and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
* NA_bottom: choosing ``na_option = 'bottom'``, if there are records
with NaN values they are placed at the bottom of the ranking.
* pct_rank: when setting ``pct = True``, the ranking is expressed as
percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
Animal Number_legs default_rank max_rank NA_bottom pct_rank
0 cat 4.0 2.5 3.0 2.5 0.625
1 penguin 2.0 1.0 1.0 1.0 0.250
2 dog 4.0 2.5 3.0 2.5 0.625
3 spider 8.0 4.0 4.0 4.0 1.000
4 snake NaN NaN NaN 5.0 NaN
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Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameters
----------
other : DataFrame or Series
join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
Type of alignment to be performed.
* left: use only keys from left frame, preserve key order.
* right: use only keys from right frame, preserve key order.
* outer: use union of keys from both frames, sort keys lexicographically.
* inner: use intersection of keys from both frames,
preserve the order of the left keys.
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or both (None).
level : int or level name, default None
Broadcast across a level, matching Index values on the
passed MultiIndex level.
copy : bool, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
fill_value : scalar, default np.nan
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value.
method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
Method to use for filling holes in reindexed Series:
- pad / ffill: propagate last valid observation forward to next valid.
- backfill / bfill: use NEXT valid observation to fill gap.
.. deprecated:: 2.1
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
.. deprecated:: 2.1
fill_axis : {axes_single_arg}, default 0
Filling axis, method and limit.
.. deprecated:: 2.1
broadcast_axis : {axes_single_arg}, default None
Broadcast values along this axis, if aligning two objects of
different dimensions.
.. deprecated:: 2.1
Returns
-------
tuple of ({klass}, type of other)
Aligned objects.
Examples
--------
>>> df = pd.DataFrame(
... [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
... [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
... columns=["A", "B", "C", "D"],
... index=[2, 3, 4],
... )
>>> df
D B E A
1 1 2 3 4
2 6 7 8 9
>>> other
A B C D
2 10 20 30 40
3 60 70 80 90
4 600 700 800 900
Align on columns:
>>> left, right = df.align(other, join="outer", axis=1)
>>> left
A B C D E
1 4 2 NaN 1 3
2 9 7 NaN 6 8
>>> right
A B C D E
2 10 20 30 40 NaN
3 60 70 80 90 NaN
4 600 700 800 900 NaN
We can also align on the index:
>>> left, right = df.align(other, join="outer", axis=0)
>>> left
D B E A
1 1.0 2.0 3.0 4.0
2 6.0 7.0 8.0 9.0
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
>>> right
A B C D
1 NaN NaN NaN NaN
2 10.0 20.0 30.0 40.0
3 60.0 70.0 80.0 90.0
4 600.0 700.0 800.0 900.0
Finally, the default `axis=None` will align on both index and columns:
>>> left, right = df.align(other, join="outer", axis=None)
>>> left
A B C D E
1 4.0 2.0 NaN 1.0 3.0
2 9.0 7.0 NaN 6.0 8.0
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
>>> right
A B C D E
1 NaN NaN NaN NaN NaN
2 10.0 20.0 30.0 40.0 NaN
3 60.0 70.0 80.0 90.0 NaN
4 600.0 700.0 800.0 900.0 NaN
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NDFrame.where'* r r� �True�Falser rL )r� rc �cond_revr( �
name_otherc � � t |d� }|r�t sGt � r=t j | � t
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Replace values where the condition is {cond_rev}.
Parameters
----------
cond : bool {klass}, array-like, or callable
Where `cond` is {cond}, keep the original value. Where
{cond_rev}, replace with corresponding value from `other`.
If `cond` is callable, it is computed on the {klass} and
should return boolean {klass} or array. The callable must
not change input {klass} (though pandas doesn't check it).
other : scalar, {klass}, or callable
Entries where `cond` is {cond_rev} are replaced with
corresponding value from `other`.
If other is callable, it is computed on the {klass} and
should return scalar or {klass}. The callable must not
change input {klass} (though pandas doesn't check it).
If not specified, entries will be filled with the corresponding
NULL value (``np.nan`` for numpy dtypes, ``pd.NA`` for extension
dtypes).
inplace : bool, default False
Whether to perform the operation in place on the data.
axis : int, default None
Alignment axis if needed. For `Series` this parameter is
unused and defaults to 0.
level : int, default None
Alignment level if needed.
Returns
-------
Same type as caller or None if ``inplace=True``.
See Also
--------
:func:`DataFrame.{name_other}` : Return an object of same shape as
self.
Notes
-----
The {name} method is an application of the if-then idiom. For each
element in the calling DataFrame, if ``cond`` is ``{cond}`` the
element is used; otherwise the corresponding element from the DataFrame
``other`` is used. If the axis of ``other`` does not align with axis of
``cond`` {klass}, the misaligned index positions will be filled with
{cond_rev}.
The signature for :func:`DataFrame.where` differs from
:func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
``np.where(m, df1, df2)``.
For further details and examples see the ``{name}`` documentation in
:ref:`indexing <indexing.where_mask>`.
The dtype of the object takes precedence. The fill value is casted to
the object's dtype, if this can be done losslessly.
Examples
--------
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0 0
1 99
2 99
3 99
4 99
dtype: int64
>>> s.mask(t, 99)
0 99
1 1
2 99
3 99
4 99
dtype: int64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> s.mask(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
>>> m = df % 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
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� S | j' |||� S ) a�
Shift index by desired number of periods with an optional time `freq`.
When `freq` is not passed, shift the index without realigning the data.
If `freq` is passed (in this case, the index must be date or datetime,
or it will raise a `NotImplementedError`), the index will be
increased using the periods and the `freq`. `freq` can be inferred
when specified as "infer" as long as either freq or inferred_freq
attribute is set in the index.
Parameters
----------
periods : int or Sequence
Number of periods to shift. Can be positive or negative.
If an iterable of ints, the data will be shifted once by each int.
This is equivalent to shifting by one value at a time and
concatenating all resulting frames. The resulting columns will have
the shift suffixed to their column names. For multiple periods,
axis must not be 1.
freq : DateOffset, tseries.offsets, timedelta, or str, optional
Offset to use from the tseries module or time rule (e.g. 'EOM').
If `freq` is specified then the index values are shifted but the
data is not realigned. That is, use `freq` if you would like to
extend the index when shifting and preserve the original data.
If `freq` is specified as "infer" then it will be inferred from
the freq or inferred_freq attributes of the index. If neither of
those attributes exist, a ValueError is thrown.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Shift direction. For `Series` this parameter is unused and defaults to 0.
fill_value : object, optional
The scalar value to use for newly introduced missing values.
the default depends on the dtype of `self`.
For numeric data, ``np.nan`` is used.
For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
For extension dtypes, ``self.dtype.na_value`` is used.
suffix : str, optional
If str and periods is an iterable, this is added after the column
name and before the shift value for each shifted column name.
Returns
-------
{klass}
Copy of input object, shifted.
See Also
--------
Index.shift : Shift values of Index.
DatetimeIndex.shift : Shift values of DatetimeIndex.
PeriodIndex.shift : Shift values of PeriodIndex.
Examples
--------
>>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
... "Col2": [13, 23, 18, 33, 48],
... "Col3": [17, 27, 22, 37, 52]}},
... index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
Col1 Col2 Col3
2020-01-01 10 13 17
2020-01-02 20 23 27
2020-01-03 15 18 22
2020-01-04 30 33 37
2020-01-05 45 48 52
>>> df.shift(periods=3)
Col1 Col2 Col3
2020-01-01 NaN NaN NaN
2020-01-02 NaN NaN NaN
2020-01-03 NaN NaN NaN
2020-01-04 10.0 13.0 17.0
2020-01-05 20.0 23.0 27.0
>>> df.shift(periods=1, axis="columns")
Col1 Col2 Col3
2020-01-01 NaN 10 13
2020-01-02 NaN 20 23
2020-01-03 NaN 15 18
2020-01-04 NaN 30 33
2020-01-05 NaN 45 48
>>> df.shift(periods=3, fill_value=0)
Col1 Col2 Col3
2020-01-01 0 0 0
2020-01-02 0 0 0
2020-01-03 0 0 0
2020-01-04 10 13 17
2020-01-05 20 23 27
>>> df.shift(periods=3, freq="D")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
>>> df.shift(periods=3, freq="infer")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
>>> df['Col1'].shift(periods=[0, 1, 2])
Col1_0 Col1_1 Col1_2
2020-01-01 10 NaN NaN
2020-01-02 20 10.0 NaN
2020-01-03 15 20.0 10.0
2020-01-04 30 15.0 20.0
2020-01-05 45 30.0 15.0
Nz�Passing a 'freq' together with a 'fill_value' silently ignores the fill_value and is deprecated. This will raise in a future version.r� r r� )�periodsrK r� ro )r� ro r� �shiftra )r r r� r� r� rc rU r� rc r� rl rV r� r
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Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index
values above or below certain thresholds.
Parameters
----------
before : date, str, int
Truncate all rows before this index value.
after : date, str, int
Truncate all rows after this index value.
axis : {0 or 'index', 1 or 'columns'}, optional
Axis to truncate. Truncates the index (rows) by default.
For `Series` this parameter is unused and defaults to 0.
copy : bool, default is True,
Return a copy of the truncated section.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
Returns
-------
type of caller
The truncated Series or DataFrame.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by label.
DataFrame.iloc : Select a subset of a DataFrame by position.
Notes
-----
If the index being truncated contains only datetime values,
`before` and `after` may be specified as strings instead of
Timestamps.
Examples
--------
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
... 'B': ['f', 'g', 'h', 'i', 'j'],
... 'C': ['k', 'l', 'm', 'n', 'o']},
... index=[1, 2, 3, 4, 5])
>>> df
A B C
1 a f k
2 b g l
3 c h m
4 d i n
5 e j o
>>> df.truncate(before=2, after=4)
A B C
2 b g l
3 c h m
4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns")
A B
1 a f
2 b g
3 c h
4 d i
5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4)
2 b
3 c
4 d
Name: A, dtype: object
The index values in ``truncate`` can be datetimes or string
dates.
>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
A
2016-01-31 23:59:56 1
2016-01-31 23:59:57 1
2016-01-31 23:59:58 1
2016-01-31 23:59:59 1
2016-02-01 00:00:00 1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
... after=pd.Timestamp('2016-01-10')).tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Because the index is a DatetimeIndex containing only dates, we can
specify `before` and `after` as strings. They will be coerced to
Timestamps before truncation.
>>> df.truncate('2016-01-05', '2016-01-10').tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Note that ``truncate`` assumes a 0 value for any unspecified time
component (midnight). This differs from partial string slicing, which
returns any partially matching dates.
>>> df.loc['2016-01-05':'2016-01-10', :].tail()
A
2016-01-10 23:59:55 1
2016-01-10 23:59:56 1
2016-01-10 23:59:57 1
2016-01-10 23:59:58 1
2016-01-10 23:59:59 1
Nr z truncate requires a sorted index)� to_datetimez
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� � S ) a�
Convert tz-aware axis to target time zone.
Parameters
----------
tz : str or tzinfo object or None
Target time zone. Passing ``None`` will convert to
UTC and remove the timezone information.
axis : {{0 or 'index', 1 or 'columns'}}, default 0
The axis to convert
level : int, str, default None
If axis is a MultiIndex, convert a specific level. Otherwise
must be None.
copy : bool, default True
Also make a copy of the underlying data.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
Returns
-------
{klass}
Object with time zone converted axis.
Raises
------
TypeError
If the axis is tz-naive.
Examples
--------
Change to another time zone:
>>> s = pd.Series(
... [1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']),
... )
>>> s.tz_convert('Asia/Shanghai')
2018-09-15 07:30:00+08:00 1
dtype: int64
Pass None to convert to UTC and get a tz-naive index:
>>> s = pd.Series([1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_convert(None)
2018-09-14 23:30:00 1
dtype: int64
c � �� t | d� s<t | � dkD r �j �� }t |� d�� �t g |�� } | S | j |� } | S )N�
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|j |
|�� }n+|dd|j fvrt d|� d�� � | ||||� }� j |xr t � � � } | j |�d
� � } | j � d �
� S )a
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a
timezone-naive Series, use :meth:`Series.dt.tz_localize`.
Parameters
----------
tz : str or tzinfo or None
Time zone to localize. Passing ``None`` will remove the
time zone information and preserve local time.
axis : {{0 or 'index', 1 or 'columns'}}, default 0
The axis to localize
level : int, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise
must be None.
copy : bool, default True
Also make a copy of the underlying data.
.. note::
The `copy` keyword will change behavior in pandas 3.0.
`Copy-on-Write
<https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
will be enabled by default, which means that all methods with a
`copy` keyword will use a lazy copy mechanism to defer the copy and
ignore the `copy` keyword. The `copy` keyword will be removed in a
future version of pandas.
You can already get the future behavior and improvements through
enabling copy on write ``pd.options.mode.copy_on_write = True``
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
When clocks moved backward due to DST, ambiguous times may arise.
For example in Central European Time (UTC+01), when going from
03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
`ambiguous` parameter dictates how ambiguous times should be
handled.
- 'infer' will attempt to infer fall dst-transition hours based on
order
- bool-ndarray where True signifies a DST time, False designates
a non-DST time (note that this flag is only applicable for
ambiguous times)
- 'NaT' will return NaT where there are ambiguous times
- 'raise' will raise an AmbiguousTimeError if there are ambiguous
times.
nonexistent : str, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST. Valid values are:
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times.
Returns
-------
{klass}
Same type as the input.
Raises
------
TypeError
If the TimeSeries is tz-aware and tz is not None.
Examples
--------
Localize local times:
>>> s = pd.Series(
... [1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00']),
... )
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00 1
dtype: int64
Pass None to convert to tz-naive index and preserve local time:
>>> s = pd.Series([1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00+02:00']))
>>> s.tz_localize(None)
2018-09-15 01:30:00 1
dtype: int64
Be careful with DST changes. When there is sequential data, pandas
can infer the DST time:
>>> s = pd.Series(range(7),
... index=pd.DatetimeIndex(['2018-10-28 01:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 03:00:00',
... '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00 0
2018-10-28 02:00:00+02:00 1
2018-10-28 02:30:00+02:00 2
2018-10-28 02:00:00+01:00 3
2018-10-28 02:30:00+01:00 4
2018-10-28 03:00:00+01:00 5
2018-10-28 03:30:00+01:00 6
dtype: int64
In some cases, inferring the DST is impossible. In such cases, you can
pass an ndarray to the ambiguous parameter to set the DST explicitly
>>> s = pd.Series(range(3),
... index=pd.DatetimeIndex(['2018-10-28 01:20:00',
... '2018-10-28 02:36:00',
... '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00 0
2018-10-28 02:36:00+02:00 1
2018-10-28 03:46:00+01:00 2
dtype: int64
If the DST transition causes nonexistent times, you can shift these
dates forward or backward with a timedelta object or `'shift_forward'`
or `'shift_backward'`.
>>> s = pd.Series(range(2),
... index=pd.DatetimeIndex(['2015-03-29 02:30:00',
... '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1h'))
2015-03-29 03:30:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
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Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding ``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe()
count 3
mean 2006-09-01 08:00:00
min 2000-01-01 00:00:00
25% 2004-12-31 12:00:00
50% 2010-01-01 00:00:00
75% 2010-01-01 00:00:00
max 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d', 'e', 'f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all') # doctest: +SKIP
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN a
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[object]) # doctest: +SKIP
object
count 3
unique 3
top a
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top d
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number]) # doctest: +SKIP
categorical object
count 3 3
unique 3 3
top f a
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[object]) # doctest: +SKIP
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
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Computes the fractional change from the immediately previous row by
default. This is useful in comparing the fraction of change in a time
series of elements.
.. note::
Despite the name of this method, it calculates fractional change
(also known as per unit change or relative change) and not
percentage change. If you need the percentage change, multiply
these values by 100.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change.
fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad'
How to handle NAs **before** computing percent changes.
.. deprecated:: 2.1
All options of `fill_method` are deprecated except `fill_method=None`.
limit : int, default None
The number of consecutive NAs to fill before stopping.
.. deprecated:: 2.1
freq : DateOffset, timedelta, or str, optional
Increment to use from time series API (e.g. 'ME' or BDay()).
**kwargs
Additional keyword arguments are passed into
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Returns
-------
Series or DataFrame
The same type as the calling object.
See Also
--------
Series.diff : Compute the difference of two elements in a Series.
DataFrame.diff : Compute the difference of two elements in a DataFrame.
Series.shift : Shift the index by some number of periods.
DataFrame.shift : Shift the index by some number of periods.
Examples
--------
**Series**
>>> s = pd.Series([90, 91, 85])
>>> s
0 90
1 91
2 85
dtype: int64
>>> s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
See the percentage change in a Series where filling NAs with last
valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85])
>>> s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
>>> s.ffill().pct_change()
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
**DataFrame**
Percentage change in French franc, Deutsche Mark, and Italian lira from
1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing
the percentage change between columns.
>>> df = pd.DataFrame({
... '2016': [1769950, 30586265],
... '2015': [1500923, 40912316],
... '2014': [1371819, 41403351]},
... index=['GOOG', 'APPL'])
>>> df
2016 2015 2014
GOOG 1769950 1500923 1371819
APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns', periods=-1)
2016 2015 2014
GOOG 0.179241 0.094112 NaN
APPL -0.252395 -0.011860 NaN
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Returns
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Returns
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type of index
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>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2
>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If all elements in Series are NA/null, returns None.
>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If Series is empty, returns None.
For DataFrame:
>>> df = pd.DataFrame({{'A': [None, None, 2], 'B': [None, 3, 4]}})
>>> df
A B
0 NaN NaN
1 NaN 3.0
2 2.0 4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2
>>> df = pd.DataFrame({{'A': [None, None, None], 'B': [None, None, None]}})
>>> df
A B
0 None None
1 None None
2 None None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If all elements in DataFrame are NA/null, returns None.
>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None
If DataFrame is empty, returns None.
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{desc}
Parameters
----------
axis : {axis_descr}
Axis for the function to be applied on.
For `Series` this parameter is unused and defaults to 0.
For DataFrames, specifying ``axis=None`` will apply the aggregation
across both axes.
.. versionadded:: 2.0.0
skipna : bool, default True
Exclude NA/null values when computing the result.
numeric_only : bool, default False
Include only float, int, boolean columns. Not implemented for Series.
{min_count}**kwargs
Additional keyword arguments to be passed to the function.
Returns
-------
{name1} or scalar{see_also}{examples}
a�
{desc}
Parameters
----------
axis : {axis_descr}
Axis for the function to be applied on.
For `Series` this parameter is unused and defaults to 0.
.. warning::
The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
in a future version this will reduce over both axes and return a scalar
To retain the old behavior, pass axis=0 (or do not pass axis).
.. versionadded:: 2.0.0
skipna : bool, default True
Exclude NA/null values when computing the result.
numeric_only : bool, default False
Include only float, int, boolean columns. Not implemented for Series.
{min_count}**kwargs
Additional keyword arguments to be passed to the function.
Returns
-------
{name1} or scalar{see_also}{examples}
a!
{desc}
Parameters
----------
axis : {axis_descr}
For `Series` this parameter is unused and defaults to 0.
.. warning::
The behavior of DataFrame.{name} with ``axis=None`` is deprecated,
in a future version this will reduce over both axes and return a scalar
To retain the old behavior, pass axis=0 (or do not pass axis).
skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
numeric_only : bool, default False
Include only float, int, boolean columns. Not implemented for Series.
Returns
-------
{name1} or {name2} (if level specified) {notes}{examples}
zg
Notes
-----
To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
default `ddof=1`)ay
Examples
--------
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
... 'age': [21, 25, 62, 43],
... 'height': [1.61, 1.87, 1.49, 2.01]}
... ).set_index('person_id')
>>> df
age height
person_id
0 21 1.61
1 25 1.87
2 62 1.49
3 43 2.01
The standard deviation of the columns can be found as follows:
>>> df.std()
age 18.786076
height 0.237417
dtype: float64
Alternatively, `ddof=0` can be set to normalize by N instead of N-1:
>>> df.std(ddof=0)
age 16.269219
height 0.205609
dtype: float64a?
Examples
--------
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
... 'age': [21, 25, 62, 43],
... 'height': [1.61, 1.87, 1.49, 2.01]}
... ).set_index('person_id')
>>> df
age height
person_id
0 21 1.61
1 25 1.87
2 62 1.49
3 43 2.01
>>> df.var()
age 352.916667
height 0.056367
dtype: float64
Alternatively, ``ddof=0`` can be set to normalize by N instead of N-1:
>>> df.var(ddof=0)
age 264.687500
height 0.042275
dtype: float64aA
{desc}
Parameters
----------
axis : {{0 or 'index', 1 or 'columns', None}}, default 0
Indicate which axis or axes should be reduced. For `Series` this parameter
is unused and defaults to 0.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
* 1 / 'columns' : reduce the columns, return a Series whose index is the
original index.
* None : reduce all axes, return a scalar.
bool_only : bool, default False
Include only boolean columns. Not implemented for Series.
skipna : bool, default True
Exclude NA/null values. If the entire row/column is NA and skipna is
True, then the result will be {empty_value}, as for an empty row/column.
If skipna is False, then NA are treated as True, because these are not
equal to zero.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
{name1} or {name2}
If level is specified, then, {name2} is returned; otherwise, {name1}
is returned.
{see_also}
{examples}z�Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element within a series or
along a Dataframe axis that is False or equivalent (e.g. zero or
empty).a� Examples
--------
**Series**
>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True
**DataFrames**
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
col1 col2
0 True True
1 True False
Default behaviour checks if values in each column all return True.
>>> df.all()
col1 True
col2 False
dtype: bool
Specify ``axis='columns'`` to check if values in each row all return True.
>>> df.all(axis='columns')
0 True
1 False
dtype: bool
Or ``axis=None`` for whether every value is True.
>>> df.all(axis=None)
False
z�See Also
--------
Series.all : Return True if all elements are True.
DataFrame.any : Return True if one (or more) elements are True.
aZ
Return cumulative {desc} over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative
{desc}.
Parameters
----------
axis : {{0 or 'index', 1 or 'columns'}}, default 0
The index or the name of the axis. 0 is equivalent to None or 'index'.
For `Series` this parameter is unused and defaults to 0.
skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
{name1} or {name2}
Return cumulative {desc} of {name1} or {name2}.
See Also
--------
core.window.expanding.Expanding.{accum_func_name} : Similar functionality
but ignores ``NaN`` values.
{name2}.{accum_func_name} : Return the {desc} over
{name2} axis.
{name2}.cummax : Return cumulative maximum over {name2} axis.
{name2}.cummin : Return cumulative minimum over {name2} axis.
{name2}.cumsum : Return cumulative sum over {name2} axis.
{name2}.cumprod : Return cumulative product over {name2} axis.
{examples}a� Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummin()
0 2.0
1 NaN
2 2.0
3 -1.0
4 -1.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummin(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the minimum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummin()
A B
0 2.0 1.0
1 2.0 NaN
2 1.0 0.0
To iterate over columns and find the minimum in each row,
use ``axis=1``
>>> df.cummin(axis=1)
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
a� Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumsum()
0 2.0
1 NaN
2 7.0
3 6.0
4 6.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumsum(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the sum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumsum()
A B
0 2.0 1.0
1 5.0 NaN
2 6.0 1.0
To iterate over columns and find the sum in each row,
use ``axis=1``
>>> df.cumsum(axis=1)
A B
0 2.0 3.0
1 3.0 NaN
2 1.0 1.0
a� Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumprod()
0 2.0
1 NaN
2 10.0
3 -10.0
4 -0.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumprod(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the product
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumprod()
A B
0 2.0 1.0
1 6.0 NaN
2 6.0 0.0
To iterate over columns and find the product in each row,
use ``axis=1``
>>> df.cumprod(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 0.0
a� Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummax()
0 2.0
1 NaN
2 5.0
3 5.0
4 5.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummax(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the maximum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummax()
A B
0 2.0 1.0
1 3.0 NaN
2 3.0 1.0
To iterate over columns and find the maximum in each row,
use ``axis=1``
>>> df.cummax(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 1.0
a2 See Also
--------
numpy.any : Numpy version of this method.
Series.any : Return whether any element is True.
Series.all : Return whether all elements are True.
DataFrame.any : Return whether any element is True over requested axis.
DataFrame.all : Return whether all elements are True over requested axis.
z�Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element within a series or
along a Dataframe axis that is True or equivalent (e.g. non-zero or
non-empty).a\ Examples
--------
**Series**
For Series input, the output is a scalar indicating whether any element
is True.
>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True
**DataFrame**
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
A B C
0 1 0 0
1 2 2 0
>>> df.any()
A True
B True
C False
dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.any(axis='columns')
0 True
1 True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
A B
0 True 1
1 False 0
>>> df.any(axis='columns')
0 True
1 False
dtype: bool
Aggregating over the entire DataFrame with ``axis=None``.
>>> df.any(axis=None)
True
`any` for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any()
Series([], dtype: bool)
a�
Examples
--------
>>> idx = pd.MultiIndex.from_arrays([
... ['warm', 'warm', 'cold', 'cold'],
... ['dog', 'falcon', 'fish', 'spider']],
... names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded animal
warm dog 4
falcon 2
cold fish 0
spider 8
Name: legs, dtype: int64
>>> s.{stat_func}()
{default_output}�stat_func_exampler� �Sum� � � )� stat_func�verb�default_output�level_output_0�level_output_1a
By default, the sum of an empty or all-NA Series is ``0``.
>>> pd.Series([], dtype="float64").sum() # min_count=0 is the default
0.0
This can be controlled with the ``min_count`` parameter. For example, if
you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
>>> pd.Series([], dtype="float64").sum(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nanrv �Max� r� �
_max_examplesru �Minr �
_min_examplesa
See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.sum : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis.a�
Examples
--------
By default, the product of an empty or all-NA Series is ``1``
>>> pd.Series([], dtype="float64").prod()
1.0
This can be controlled with the ``min_count`` parameter
>>> pd.Series([], dtype="float64").prod(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nanz�min_count : int, default 0
The required number of valid values to perform the operation. If fewer than
``min_count`` non-NA values are present the result will be NA.
c � � |dk( rd}d}d}nd}d}d}| dk( r t }t }t }t }dd i} �n�| d
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|
S )4zB
Generate the docstring for a Series/DataFrame reduction.
r� �scalarr� z {index (0)}r� z{index (0), columns (1)}r� � empty_valuer� r� r� ru z�Return the minimum of the values over the requested axis.
If you want the *index* of the minimum, use ``idxmin``. This is the equivalent of the ``numpy.ndarray`` method ``argmin``.r� r� rv z�Return the maximum of the values over the requested axis.
If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.r� zfReturn the sum of the values over the requested axis.
This is equivalent to the method ``numpy.sum``.rP z9Return the product of the values over the requested axis.rj z8Return the median of the values over the requested axis.a
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.median()
2.0
With a DataFrame
>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
a b
tiger 1 2
zebra 2 3
>>> df.median()
a 1.5
b 2.5
dtype: float64
Using axis=1
>>> df.median(axis=1)
tiger 1.5
zebra 2.5
dtype: float64
In this case, `numeric_only` should be set to `True`
to avoid getting an error.
>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
... index=['tiger', 'zebra'])
>>> df.median(numeric_only=True)
a 1.5
dtype: float64rk z6Return the mean of the values over the requested axis.aw
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.mean()
2.0
With a DataFrame
>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
a b
tiger 1 2
zebra 2 3
>>> df.mean()
a 1.5
b 2.5
dtype: float64
Using axis=1
>>> df.mean(axis=1)
tiger 1.5
zebra 2.5
dtype: float64
In this case, `numeric_only` should be set to `True` to avoid
getting an error.
>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
... index=['tiger', 'zebra'])
>>> df.mean(numeric_only=True)
a 1.5
dtype: float64rd zyReturn unbiased variance over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument.�notesrg z�Return sample standard deviation over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument.r_ z�Return unbiased standard error of the mean over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argumenta�
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.sem().round(6)
0.57735
With a DataFrame
>>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
>>> df
a b
tiger 1 2
zebra 2 3
>>> df.sem()
a 0.5
b 0.5
dtype: float64
Using axis=1
>>> df.sem(axis=1)
tiger 0.5
zebra 0.5
dtype: float64
In this case, `numeric_only` should be set to `True`
to avoid getting an error.
>>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
... index=['tiger', 'zebra'])
>>> df.sem(numeric_only=True)
a 0.5
dtype: float64rm z=Return unbiased skew over requested axis.
Normalized by N-1.a-
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s.skew()
0.0
With a DataFrame
>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
... index=['tiger', 'zebra', 'cow'])
>>> df
a b c
tiger 1 2 1
zebra 2 3 3
cow 3 4 5
>>> df.skew()
a 0.0
b 0.0
c 0.0
dtype: float64
Using axis=1
>>> df.skew(axis=1)
tiger 1.732051
zebra -1.732051
cow 0.000000
dtype: float64
In this case, `numeric_only` should be set to `True` to avoid
getting an error.
>>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
... index=['tiger', 'zebra', 'cow'])
>>> df.skew(numeric_only=True)
a 0.0
dtype: float64rl z�Return unbiased kurtosis over requested axis.
Kurtosis obtained using Fisher's definition of
kurtosis (kurtosis of normal == 0.0). Normalized by N-1.a6
Examples
--------
>>> s = pd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat 1
dog 2
dog 2
mouse 3
dtype: int64
>>> s.kurt()
1.5
With a DataFrame
>>> df = pd.DataFrame({'a': [1, 2, 2, 3], 'b': [3, 4, 4, 4]},
... index=['cat', 'dog', 'dog', 'mouse'])
>>> df
a b
cat 1 3
dog 2 4
dog 2 4
mouse 3 4
>>> df.kurt()
a 1.5
b 4.0
dtype: float64
With axis=None
>>> df.kurt(axis=None).round(6)
-0.988693
Using axis=1
>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [3, 4], 'd': [1, 2]},
... index=['cat', 'dog'])
>>> df.kurt(axis=1)
cat -6.0
dog -6.0
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