Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/../api/pandas.Series.reset_index.html below:

pandas.Series.reset_index — pandas 2.3.1 documentation

Series.reset_index(level=None, *, drop=False, name=<no_default>, inplace=False, allow_duplicates=False)[source]#

Generate a new DataFrame or Series with the index reset.

This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.

Parameters:

levelint, str, tuple, or list, default optional

For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default.

dropbool, default False

Just reset the index, without inserting it as a column in the new DataFrame.

nameobject, optional

The name to use for the column containing the original Series values. Uses self.name by default. This argument is ignored when drop is True.

inplacebool, default False

Modify the Series in place (do not create a new object).

allow_duplicatesbool, default False

Allow duplicate column labels to be created.

Added in version 1.5.0.

Returns:

Series or DataFrame or None

When drop is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When drop is True, a Series is returned. In either case, if inplace=True, no value is returned.

Examples

>>> s = pd.Series([1, 2, 3, 4], name='foo',
...               index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))

Generate a DataFrame with default index.

>>> s.reset_index()
  idx  foo
0   a    1
1   b    2
2   c    3
3   d    4

To specify the name of the new column use name.

>>> s.reset_index(name='values')
  idx  values
0   a       1
1   b       2
2   c       3
3   d       4

To generate a new Series with the default set drop to True.

>>> s.reset_index(drop=True)
0    1
1    2
2    3
3    4
Name: foo, dtype: int64

The level parameter is interesting for Series with a multi-level index.

>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
...           np.array(['one', 'two', 'one', 'two'])]
>>> s2 = pd.Series(
...     range(4), name='foo',
...     index=pd.MultiIndex.from_arrays(arrays,
...                                     names=['a', 'b']))

To remove a specific level from the Index, use level.

>>> s2.reset_index(level='a')
       a  foo
b
one  bar    0
two  bar    1
one  baz    2
two  baz    3

If level is not set, all levels are removed from the Index.

>>> s2.reset_index()
     a    b  foo
0  bar  one    0
1  bar  two    1
2  baz  one    2
3  baz  two    3

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4