U
|
¬ý°dqn ã @ sv U d dl mZ i Zded< ded< ded< ded < d
|
ed < d ed < ded< ded< ded< ded< ded< ded< dS )é )Ú annotationszdict[str, str]Ú _shared_docsa¸
|
Aggregate using one or more operations over the specified axis.
|
|
Parameters
|
----------
|
func : function, str, list or dict
|
Function to use for aggregating the data. If a function, must either
|
work when passed a {klass} or when passed to {klass}.apply.
|
|
Accepted combinations are:
|
|
- function
|
- string function name
|
- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
|
- dict of axis labels -> functions, function names or list of such.
|
{axis}
|
*args
|
Positional arguments to pass to `func`.
|
**kwargs
|
Keyword arguments to pass to `func`.
|
|
Returns
|
-------
|
scalar, Series or DataFrame
|
|
The return can be:
|
|
* scalar : when Series.agg is called with single function
|
* Series : when DataFrame.agg is called with a single function
|
* DataFrame : when DataFrame.agg is called with several functions
|
|
Return scalar, Series or DataFrame.
|
{see_also}
|
Notes
|
-----
|
`agg` is an alias for `aggregate`. Use the alias.
|
|
Functions that mutate the passed object can produce unexpected
|
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
|
for more details.
|
|
A passed user-defined-function will be passed a Series for evaluation.
|
{examples}Z aggregatea
|
Compare to another {klass} and show the differences.
|
|
.. versionadded:: 1.1.0
|
|
Parameters
|
----------
|
other : {klass}
|
Object to compare with.
|
|
align_axis : {{0 or 'index', 1 or 'columns'}}, default 1
|
Determine which axis to align the comparison on.
|
|
* 0, or 'index' : Resulting differences are stacked vertically
|
with rows drawn alternately from self and other.
|
* 1, or 'columns' : Resulting differences are aligned horizontally
|
with columns drawn alternately from self and other.
|
|
keep_shape : bool, default False
|
If true, all rows and columns are kept.
|
Otherwise, only the ones with different values are kept.
|
|
keep_equal : bool, default False
|
If true, the result keeps values that are equal.
|
Otherwise, equal values are shown as NaNs.
|
|
result_names : tuple, default ('self', 'other')
|
Set the dataframes names in the comparison.
|
|
.. versionadded:: 1.5.0
|
Úcompareaâ
|
Group %(klass)s using a mapper or by a Series of columns.
|
|
A groupby operation involves some combination of splitting the
|
object, applying a function, and combining the results. This can be
|
used to group large amounts of data and compute operations on these
|
groups.
|
|
Parameters
|
----------
|
by : mapping, function, label, pd.Grouper or list of such
|
Used to determine the groups for the groupby.
|
If ``by`` is a function, it's called on each value of the object's
|
index. If a dict or Series is passed, the Series or dict VALUES
|
will be used to determine the groups (the Series' values are first
|
aligned; see ``.align()`` method). If a list or ndarray of length
|
equal to the selected axis is passed (see the `groupby user guide
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`_),
|
the values are used as-is to determine the groups. A label or list
|
of labels may be passed to group by the columns in ``self``.
|
Notice that a tuple is interpreted as a (single) key.
|
axis : {0 or 'index', 1 or 'columns'}, default 0
|
Split along rows (0) or columns (1). For `Series` this parameter
|
is unused and defaults to 0.
|
level : int, level name, or sequence of such, default None
|
If the axis is a MultiIndex (hierarchical), group by a particular
|
level or levels. Do not specify both ``by`` and ``level``.
|
as_index : bool, default True
|
For aggregated output, return object with group labels as the
|
index. Only relevant for DataFrame input. as_index=False is
|
effectively "SQL-style" grouped output.
|
sort : bool, default True
|
Sort group keys. Get better performance by turning this off.
|
Note this does not influence the order of observations within each
|
group. Groupby preserves the order of rows within each group.
|
|
.. versionchanged:: 2.0.0
|
|
Specifying ``sort=False`` with an ordered categorical grouper will no
|
longer sort the values.
|
|
group_keys : bool, default True
|
When calling apply and the ``by`` argument produces a like-indexed
|
(i.e. :ref:`a transform <groupby.transform>`) result, add group keys to
|
index to identify pieces. By default group keys are not included
|
when the result's index (and column) labels match the inputs, and
|
are included otherwise.
|
|
.. versionchanged:: 1.5.0
|
|
Warns that ``group_keys`` will no longer be ignored when the
|
result from ``apply`` is a like-indexed Series or DataFrame.
|
Specify ``group_keys`` explicitly to include the group keys or
|
not.
|
|
.. versionchanged:: 2.0.0
|
|
``group_keys`` now defaults to ``True``.
|
|
observed : bool, default False
|
This only applies if any of the groupers are Categoricals.
|
If True: only show observed values for categorical groupers.
|
If False: show all values for categorical groupers.
|
dropna : bool, default True
|
If True, and if group keys contain NA values, NA values together
|
with row/column will be dropped.
|
If False, NA values will also be treated as the key in groups.
|
|
.. versionadded:: 1.1.0
|
|
Returns
|
-------
|
%(klass)sGroupBy
|
Returns a groupby object that contains information about the groups.
|
|
See Also
|
--------
|
resample : Convenience method for frequency conversion and resampling
|
of time series.
|
|
Notes
|
-----
|
See the `user guide
|
<https://pandas.pydata.org/pandas-docs/stable/groupby.html>`__ for more
|
detailed usage and examples, including splitting an object into groups,
|
iterating through groups, selecting a group, aggregation, and more.
|
Úgroupbya?
|
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
|
|
This function is useful to massage a DataFrame into a format where one
|
or more columns are identifier variables (`id_vars`), while all other
|
columns, considered measured variables (`value_vars`), are "unpivoted" to
|
the row axis, leaving just two non-identifier columns, 'variable' and
|
'value'.
|
|
Parameters
|
----------
|
id_vars : tuple, list, or ndarray, optional
|
Column(s) to use as identifier variables.
|
value_vars : tuple, list, or ndarray, optional
|
Column(s) to unpivot. If not specified, uses all columns that
|
are not set as `id_vars`.
|
var_name : scalar
|
Name to use for the 'variable' column. If None it uses
|
``frame.columns.name`` or 'variable'.
|
value_name : scalar, default 'value'
|
Name to use for the 'value' column.
|
col_level : int or str, optional
|
If columns are a MultiIndex then use this level to melt.
|
ignore_index : bool, default True
|
If True, original index is ignored. If False, the original index is retained.
|
Index labels will be repeated as necessary.
|
|
.. versionadded:: 1.1.0
|
|
Returns
|
-------
|
DataFrame
|
Unpivoted DataFrame.
|
|
See Also
|
--------
|
%(other)s : Identical method.
|
pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
|
DataFrame.pivot : Return reshaped DataFrame organized
|
by given index / column values.
|
DataFrame.explode : Explode a DataFrame from list-like
|
columns to long format.
|
|
Notes
|
-----
|
Reference :ref:`the user guide <reshaping.melt>` for more examples.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
|
... 'B': {0: 1, 1: 3, 2: 5},
|
... 'C': {0: 2, 1: 4, 2: 6}})
|
>>> df
|
A B C
|
0 a 1 2
|
1 b 3 4
|
2 c 5 6
|
|
>>> %(caller)sid_vars=['A'], value_vars=['B'])
|
A variable value
|
0 a B 1
|
1 b B 3
|
2 c B 5
|
|
>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
|
A variable value
|
0 a B 1
|
1 b B 3
|
2 c B 5
|
3 a C 2
|
4 b C 4
|
5 c C 6
|
|
The names of 'variable' and 'value' columns can be customized:
|
|
>>> %(caller)sid_vars=['A'], value_vars=['B'],
|
... var_name='myVarname', value_name='myValname')
|
A myVarname myValname
|
0 a B 1
|
1 b B 3
|
2 c B 5
|
|
Original index values can be kept around:
|
|
>>> %(caller)sid_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
|
A variable value
|
0 a B 1
|
1 b B 3
|
2 c B 5
|
0 a C 2
|
1 b C 4
|
2 c C 6
|
|
If you have multi-index columns:
|
|
>>> df.columns = [list('ABC'), list('DEF')]
|
>>> df
|
A B C
|
D E F
|
0 a 1 2
|
1 b 3 4
|
2 c 5 6
|
|
>>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
|
A variable value
|
0 a B 1
|
1 b B 3
|
2 c B 5
|
|
>>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
|
(A, D) variable_0 variable_1 value
|
0 a B E 1
|
1 b B E 3
|
2 c B E 5
|
Zmeltax
|
|
Call ``func`` on self producing a {klass} with the same axis shape as self.
|
|
Parameters
|
----------
|
func : function, str, list-like or dict-like
|
Function to use for transforming the data. If a function, must either
|
work when passed a {klass} or when passed to {klass}.apply. If func
|
is both list-like and dict-like, dict-like behavior takes precedence.
|
|
Accepted combinations are:
|
|
- function
|
- string function name
|
- list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
|
- dict-like of axis labels -> functions, function names or list-like of such.
|
{axis}
|
*args
|
Positional arguments to pass to `func`.
|
**kwargs
|
Keyword arguments to pass to `func`.
|
|
Returns
|
-------
|
{klass}
|
A {klass} that must have the same length as self.
|
|
Raises
|
------
|
ValueError : If the returned {klass} has a different length than self.
|
|
See Also
|
--------
|
{klass}.agg : Only perform aggregating type operations.
|
{klass}.apply : Invoke function on a {klass}.
|
|
Notes
|
-----
|
Functions that mutate the passed object can produce unexpected
|
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
|
for more details.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({{'A': range(3), 'B': range(1, 4)}})
|
>>> df
|
A B
|
0 0 1
|
1 1 2
|
2 2 3
|
>>> df.transform(lambda x: x + 1)
|
A B
|
0 1 2
|
1 2 3
|
2 3 4
|
|
Even though the resulting {klass} must have the same length as the
|
input {klass}, it is possible to provide several input functions:
|
|
>>> s = pd.Series(range(3))
|
>>> s
|
0 0
|
1 1
|
2 2
|
dtype: int64
|
>>> s.transform([np.sqrt, np.exp])
|
sqrt exp
|
0 0.000000 1.000000
|
1 1.000000 2.718282
|
2 1.414214 7.389056
|
|
You can call transform on a GroupBy object:
|
|
>>> df = pd.DataFrame({{
|
... "Date": [
|
... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
|
... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
|
... "Data": [5, 8, 6, 1, 50, 100, 60, 120],
|
... }})
|
>>> df
|
Date Data
|
0 2015-05-08 5
|
1 2015-05-07 8
|
2 2015-05-06 6
|
3 2015-05-05 1
|
4 2015-05-08 50
|
5 2015-05-07 100
|
6 2015-05-06 60
|
7 2015-05-05 120
|
>>> df.groupby('Date')['Data'].transform('sum')
|
0 55
|
1 108
|
2 66
|
3 121
|
4 55
|
5 108
|
6 66
|
7 121
|
Name: Data, dtype: int64
|
|
>>> df = pd.DataFrame({{
|
... "c": [1, 1, 1, 2, 2, 2, 2],
|
... "type": ["m", "n", "o", "m", "m", "n", "n"]
|
... }})
|
>>> df
|
c type
|
0 1 m
|
1 1 n
|
2 1 o
|
3 2 m
|
4 2 m
|
5 2 n
|
6 2 n
|
>>> df['size'] = df.groupby('c')['type'].transform(len)
|
>>> df
|
c type size
|
0 1 m 3
|
1 1 n 3
|
2 1 o 3
|
3 2 m 4
|
4 2 m 4
|
5 2 n 4
|
6 2 n 4
|
Z transforma\ storage_options : dict, optional
|
Extra options that make sense for a particular storage connection, e.g.
|
host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
|
are forwarded to ``urllib.request.Request`` as header options. For other
|
URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
|
forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
|
details, and for more examples on storage options refer `here
|
<https://pandas.pydata.org/docs/user_guide/io.html?
|
highlight=storage_options#reading-writing-remote-files>`_.Zstorage_optionsa compression : str or dict, default 'infer'
|
For on-the-fly compression of the output data. If 'infer' and '%s' is
|
path-like, then detect compression from the following extensions: '.gz',
|
'.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
|
(otherwise no compression).
|
Set to ``None`` for no compression.
|
Can also be a dict with key ``'method'`` set
|
to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'tar'``} and other
|
key-value pairs are forwarded to
|
``zipfile.ZipFile``, ``gzip.GzipFile``,
|
``bz2.BZ2File``, ``zstandard.ZstdCompressor`` or
|
``tarfile.TarFile``, respectively.
|
As an example, the following could be passed for faster compression and to create
|
a reproducible gzip archive:
|
``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
|
|
.. versionadded:: 1.5.0
|
Added support for `.tar` files.Zcompression_optionsaê compression : str or dict, default 'infer'
|
For on-the-fly decompression of on-disk data. If 'infer' and '%s' is
|
path-like, then detect compression from the following extensions: '.gz',
|
'.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
|
(otherwise no compression).
|
If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
|
Set to ``None`` for no decompression.
|
Can also be a dict with key ``'method'`` set
|
to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'tar'``} and other
|
key-value pairs are forwarded to
|
``zipfile.ZipFile``, ``gzip.GzipFile``,
|
``bz2.BZ2File``, ``zstandard.ZstdDecompressor`` or
|
``tarfile.TarFile``, respectively.
|
As an example, the following could be passed for Zstandard decompression using a
|
custom compression dictionary:
|
``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
|
|
.. versionadded:: 1.5.0
|
Added support for `.tar` files.Zdecompression_optionsa$
|
Replace values given in `to_replace` with `value`.
|
|
Values of the {klass} are replaced with other values dynamically.
|
{replace_iloc}
|
|
Parameters
|
----------
|
to_replace : str, regex, list, dict, Series, int, float, or None
|
How to find the values that will be replaced.
|
|
* numeric, str or regex:
|
|
- numeric: numeric values equal to `to_replace` will be
|
replaced with `value`
|
- str: string exactly matching `to_replace` will be replaced
|
with `value`
|
- regex: regexs matching `to_replace` will be replaced with
|
`value`
|
|
* list of str, regex, or numeric:
|
|
- First, if `to_replace` and `value` are both lists, they
|
**must** be the same length.
|
- Second, if ``regex=True`` then all of the strings in **both**
|
lists will be interpreted as regexs otherwise they will match
|
directly. This doesn't matter much for `value` since there
|
are only a few possible substitution regexes you can use.
|
- str, regex and numeric rules apply as above.
|
|
* dict:
|
|
- Dicts can be used to specify different replacement values
|
for different existing values. For example,
|
``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and
|
'y' with 'z'. To use a dict in this way, the optional `value`
|
parameter should not be given.
|
- For a DataFrame a dict can specify that different values
|
should be replaced in different columns. For example,
|
``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a'
|
and the value 'z' in column 'b' and replaces these values
|
with whatever is specified in `value`. The `value` parameter
|
should not be ``None`` in this case. You can treat this as a
|
special case of passing two lists except that you are
|
specifying the column to search in.
|
- For a DataFrame nested dictionaries, e.g.,
|
``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column
|
'a' for the value 'b' and replace it with NaN. The optional `value`
|
parameter should not be specified to use a nested dict in this
|
way. You can nest regular expressions as well. Note that
|
column names (the top-level dictionary keys in a nested
|
dictionary) **cannot** be regular expressions.
|
|
* None:
|
|
- This means that the `regex` argument must be a string,
|
compiled regular expression, or list, dict, ndarray or
|
Series of such elements. If `value` is also ``None`` then
|
this **must** be a nested dictionary or Series.
|
|
See the examples section for examples of each of these.
|
value : scalar, dict, list, str, regex, default None
|
Value to replace any values matching `to_replace` with.
|
For a DataFrame a dict of values can be used to specify which
|
value to use for each column (columns not in the dict will not be
|
filled). Regular expressions, strings and lists or dicts of such
|
objects are also allowed.
|
{inplace}
|
limit : int, default None
|
Maximum size gap to forward or backward fill.
|
regex : bool or same types as `to_replace`, default False
|
Whether to interpret `to_replace` and/or `value` as regular
|
expressions. If this is ``True`` then `to_replace` *must* be a
|
string. Alternatively, this could be a regular expression or a
|
list, dict, or array of regular expressions in which case
|
`to_replace` must be ``None``.
|
method : {{'pad', 'ffill', 'bfill'}}
|
The method to use when for replacement, when `to_replace` is a
|
scalar, list or tuple and `value` is ``None``.
|
|
Returns
|
-------
|
{klass}
|
Object after replacement.
|
|
Raises
|
------
|
AssertionError
|
* If `regex` is not a ``bool`` and `to_replace` is not
|
``None``.
|
|
TypeError
|
* If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
|
* If `to_replace` is a ``dict`` and `value` is not a ``list``,
|
``dict``, ``ndarray``, or ``Series``
|
* If `to_replace` is ``None`` and `regex` is not compilable
|
into a regular expression or is a list, dict, ndarray, or
|
Series.
|
* When replacing multiple ``bool`` or ``datetime64`` objects and
|
the arguments to `to_replace` does not match the type of the
|
value being replaced
|
|
ValueError
|
* If a ``list`` or an ``ndarray`` is passed to `to_replace` and
|
`value` but they are not the same length.
|
|
See Also
|
--------
|
{klass}.fillna : Fill NA values.
|
{klass}.where : Replace values based on boolean condition.
|
Series.str.replace : Simple string replacement.
|
|
Notes
|
-----
|
* Regex substitution is performed under the hood with ``re.sub``. The
|
rules for substitution for ``re.sub`` are the same.
|
* Regular expressions will only substitute on strings, meaning you
|
cannot provide, for example, a regular expression matching floating
|
point numbers and expect the columns in your frame that have a
|
numeric dtype to be matched. However, if those floating point
|
numbers *are* strings, then you can do this.
|
* This method has *a lot* of options. You are encouraged to experiment
|
and play with this method to gain intuition about how it works.
|
* When dict is used as the `to_replace` value, it is like
|
key(s) in the dict are the to_replace part and
|
value(s) in the dict are the value parameter.
|
|
Examples
|
--------
|
|
**Scalar `to_replace` and `value`**
|
|
>>> s = pd.Series([1, 2, 3, 4, 5])
|
>>> s.replace(1, 5)
|
0 5
|
1 2
|
2 3
|
3 4
|
4 5
|
dtype: int64
|
|
>>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
|
... 'B': [5, 6, 7, 8, 9],
|
... 'C': ['a', 'b', 'c', 'd', 'e']}})
|
>>> df.replace(0, 5)
|
A B C
|
0 5 5 a
|
1 1 6 b
|
2 2 7 c
|
3 3 8 d
|
4 4 9 e
|
|
**List-like `to_replace`**
|
|
>>> df.replace([0, 1, 2, 3], 4)
|
A B C
|
0 4 5 a
|
1 4 6 b
|
2 4 7 c
|
3 4 8 d
|
4 4 9 e
|
|
>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
|
A B C
|
0 4 5 a
|
1 3 6 b
|
2 2 7 c
|
3 1 8 d
|
4 4 9 e
|
|
>>> s.replace([1, 2], method='bfill')
|
0 3
|
1 3
|
2 3
|
3 4
|
4 5
|
dtype: int64
|
|
**dict-like `to_replace`**
|
|
>>> df.replace({{0: 10, 1: 100}})
|
A B C
|
0 10 5 a
|
1 100 6 b
|
2 2 7 c
|
3 3 8 d
|
4 4 9 e
|
|
>>> df.replace({{'A': 0, 'B': 5}}, 100)
|
A B C
|
0 100 100 a
|
1 1 6 b
|
2 2 7 c
|
3 3 8 d
|
4 4 9 e
|
|
>>> df.replace({{'A': {{0: 100, 4: 400}}}})
|
A B C
|
0 100 5 a
|
1 1 6 b
|
2 2 7 c
|
3 3 8 d
|
4 400 9 e
|
|
**Regular expression `to_replace`**
|
|
>>> df = pd.DataFrame({{'A': ['bat', 'foo', 'bait'],
|
... 'B': ['abc', 'bar', 'xyz']}})
|
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
|
A B
|
0 new abc
|
1 foo new
|
2 bait xyz
|
|
>>> df.replace({{'A': r'^ba.$'}}, {{'A': 'new'}}, regex=True)
|
A B
|
0 new abc
|
1 foo bar
|
2 bait xyz
|
|
>>> df.replace(regex=r'^ba.$', value='new')
|
A B
|
0 new abc
|
1 foo new
|
2 bait xyz
|
|
>>> df.replace(regex={{r'^ba.$': 'new', 'foo': 'xyz'}})
|
A B
|
0 new abc
|
1 xyz new
|
2 bait xyz
|
|
>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
|
A B
|
0 new abc
|
1 new new
|
2 bait xyz
|
|
Compare the behavior of ``s.replace({{'a': None}})`` and
|
``s.replace('a', None)`` to understand the peculiarities
|
of the `to_replace` parameter:
|
|
>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
|
|
When one uses a dict as the `to_replace` value, it is like the
|
value(s) in the dict are equal to the `value` parameter.
|
``s.replace({{'a': None}})`` is equivalent to
|
``s.replace(to_replace={{'a': None}}, value=None, method=None)``:
|
|
>>> s.replace({{'a': None}})
|
0 10
|
1 None
|
2 None
|
3 b
|
4 None
|
dtype: object
|
|
When ``value`` is not explicitly passed and `to_replace` is a scalar, list
|
or tuple, `replace` uses the method parameter (default 'pad') to do the
|
replacement. So this is why the 'a' values are being replaced by 10
|
in rows 1 and 2 and 'b' in row 4 in this case.
|
|
>>> s.replace('a')
|
0 10
|
1 10
|
2 10
|
3 b
|
4 b
|
dtype: object
|
|
On the other hand, if ``None`` is explicitly passed for ``value``, it will
|
be respected:
|
|
>>> s.replace('a', None)
|
0 10
|
1 None
|
2 None
|
3 b
|
4 None
|
dtype: object
|
|
.. versionchanged:: 1.4.0
|
Previously the explicit ``None`` was silently ignored.
|
Úreplacea
|
Return index of first occurrence of minimum over requested axis.
|
|
NA/null values are excluded.
|
|
Parameters
|
----------
|
axis : {{0 or 'index', 1 or 'columns'}}, default 0
|
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
|
skipna : bool, default True
|
Exclude NA/null values. If an entire row/column is NA, the result
|
will be NA.
|
numeric_only : bool, default {numeric_only_default}
|
Include only `float`, `int` or `boolean` data.
|
|
.. versionadded:: 1.5.0
|
|
Returns
|
-------
|
Series
|
Indexes of minima along the specified axis.
|
|
Raises
|
------
|
ValueError
|
* If the row/column is empty
|
|
See Also
|
--------
|
Series.idxmin : Return index of the minimum element.
|
|
Notes
|
-----
|
This method is the DataFrame version of ``ndarray.argmin``.
|
|
Examples
|
--------
|
Consider a dataset containing food consumption in Argentina.
|
|
>>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
|
... 'co2_emissions': [37.2, 19.66, 1712]}},
|
... index=['Pork', 'Wheat Products', 'Beef'])
|
|
>>> df
|
consumption co2_emissions
|
Pork 10.51 37.20
|
Wheat Products 103.11 19.66
|
Beef 55.48 1712.00
|
|
By default, it returns the index for the minimum value in each column.
|
|
>>> df.idxmin()
|
consumption Pork
|
co2_emissions Wheat Products
|
dtype: object
|
|
To return the index for the minimum value in each row, use ``axis="columns"``.
|
|
>>> df.idxmin(axis="columns")
|
Pork consumption
|
Wheat Products co2_emissions
|
Beef consumption
|
dtype: object
|
Zidxmina
|
Return index of first occurrence of maximum over requested axis.
|
|
NA/null values are excluded.
|
|
Parameters
|
----------
|
axis : {{0 or 'index', 1 or 'columns'}}, default 0
|
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
|
skipna : bool, default True
|
Exclude NA/null values. If an entire row/column is NA, the result
|
will be NA.
|
numeric_only : bool, default {numeric_only_default}
|
Include only `float`, `int` or `boolean` data.
|
|
.. versionadded:: 1.5.0
|
|
Returns
|
-------
|
Series
|
Indexes of maxima along the specified axis.
|
|
Raises
|
------
|
ValueError
|
* If the row/column is empty
|
|
See Also
|
--------
|
Series.idxmax : Return index of the maximum element.
|
|
Notes
|
-----
|
This method is the DataFrame version of ``ndarray.argmax``.
|
|
Examples
|
--------
|
Consider a dataset containing food consumption in Argentina.
|
|
>>> df = pd.DataFrame({{'consumption': [10.51, 103.11, 55.48],
|
... 'co2_emissions': [37.2, 19.66, 1712]}},
|
... index=['Pork', 'Wheat Products', 'Beef'])
|
|
>>> df
|
consumption co2_emissions
|
Pork 10.51 37.20
|
Wheat Products 103.11 19.66
|
Beef 55.48 1712.00
|
|
By default, it returns the index for the maximum value in each column.
|
|
>>> df.idxmax()
|
consumption Wheat Products
|
co2_emissions Beef
|
dtype: object
|
|
To return the index for the maximum value in each row, use ``axis="columns"``.
|
|
>>> df.idxmax(axis="columns")
|
Pork co2_emissions
|
Wheat Products consumption
|
Beef co2_emissions
|
dtype: object
|
ZidxmaxN)Ú
|
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