DOC: update the DataFrame.reindex_like docstring (#22775)

Author: math-and-data

pandas/core/frame.py

@@ -3924,22 +3924,36 @@ def shift(self, periods=1, freq=None, axis=0):
  def set_index(self, keys, drop=True, append=False, inplace=False,
  verify_integrity=False):
  """
+ Set the DataFrame index using existing columns.
+
  Set the DataFrame index (row labels) using one or more existing
- columns. By default yields a new object.
+ columns. The index can replace the existing index or expand on it.
 
  Parameters
  ----------
- keys : column label or list of column labels / arrays
- drop : boolean, default True
- Delete columns to be used as the new index
- append : boolean, default False
- Whether to append columns to existing index
- inplace : boolean, default False
- Modify the DataFrame in place (do not create a new object)
- verify_integrity : boolean, default False
+ keys : label or list of label
+ Name or names of the columns that will be used as the index.
+ drop : bool, default True
+ Delete columns to be used as the new index.
+ append : bool, default False
+ Whether to append columns to existing index.
+ inplace : bool, default False
+ Modify the DataFrame in place (do not create a new object).
+ verify_integrity : bool, default False
  Check the new index for duplicates. Otherwise defer the check until
  necessary. Setting to False will improve the performance of this
- method
+ method.
+
+ Returns
+ -------
+ DataFrame
+ Changed row labels.
+
+ See Also
+ --------
+ DataFrame.reset_index : Opposite of set_index.
+ DataFrame.reindex : Change to new indices or expand indices.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
 
  Returns
  -------
@@ -3949,22 +3963,23 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
  --------
  >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
  ... 'year': [2012, 2014, 2013, 2014],
- ... 'sale':[55, 40, 84, 31]})
- month sale year
- 0 1 55 2012
- 1 4 40 2014
- 2 7 84 2013
- 3 10 31 2014
+ ... 'sale': [55, 40, 84, 31]})
+ >>> df
+ month year sale
+ 0 1 2012 55
+ 1 4 2014 40
+ 2 7 2013 84
+ 3 10 2014 31
 
  Set the index to become the 'month' column:
 
  >>> df.set_index('month')
- sale year
+ year sale
  month
- 1 55 2012
- 4 40 2014
- 7 84 2013
- 10 31 2014
+ 1 2012 55
+ 4 2014 40
+ 7 2013 84
+ 10 2014 31
 
  Create a multi-index using columns 'year' and 'month':
 
@@ -4072,22 +4087,22 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
  def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
  col_fill=''):
  """
- For DataFrame with multi-level index, return new DataFrame with
- labeling information in the columns under the index names, defaulting
- to 'level_0', 'level_1', etc. if any are None. For a standard index,
- the index name will be used (if set), otherwise a default 'index' or
- 'level_0' (if 'index' is already taken) will be used.
+ Reset the index, or a level of it.
+
+ Reset the index of the DataFrame, and use the default one instead.
+ If the DataFrame has a MultiIndex, this method can remove one or more
+ levels.
 
  Parameters
  ----------
  level : int, str, tuple, or list, default None
  Only remove the given levels from the index. Removes all levels by
- default
- drop : boolean, default False
+ default.
+ drop : bool, default False
  Do not try to insert index into dataframe columns. This resets
  the index to the default integer index.
- inplace : boolean, default False
- Modify the DataFrame in place (do not create a new object)
+ inplace : bool, default False
+ Modify the DataFrame in place (do not create a new object).
  col_level : int or str, default 0
  If the columns have multiple levels, determines which level the
  labels are inserted into. By default it is inserted into the first
@@ -4098,13 +4113,20 @@ def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
 
  Returns
  -------
- resetted : DataFrame
+ DataFrame
+ DataFrame with the new index.
+
+ See Also
+ --------
+ DataFrame.set_index : Opposite of reset_index.
+ DataFrame.reindex : Change to new indices or expand indices.
+ DataFrame.reindex_like : Change to same indices as other DataFrame.
 
  Examples
  --------
- >>> df = pd.DataFrame([('bird',  389.0),
- ... ('bird',  24.0),
- ... ('mammal',  80.5),
+ >>> df = pd.DataFrame([('bird', 389.0),
+ ... ('bird', 24.0),
+ ... ('mammal', 80.5),
  ... ('mammal', np.nan)],
  ... index=['falcon', 'parrot', 'lion', 'monkey'],
  ... columns=('class', 'max_speed'))