pandas 1.4.2

NotesParametersReturns
reindex_like(self: 'NDFrameT', other, method: 'str | None' = None, copy: 'bool_t' = True, limit=None, tolerance=None) -> 'NDFrameT'

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.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...) .

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.

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.

Return an object with matching indices as other object.

See Also

DataFrame.reindex

Change to new indices or expand indices.

DataFrame.reset_index

Remove row labels or move them to new columns.

DataFrame.set_index

Set row labels.

Examples

This example is valid syntax, but we were not able to check execution
>>> 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'))
This example is valid syntax, but we were not able to check execution
>>> 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
This example is valid syntax, but we were not able to check execution
>>> 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']))
This example is valid syntax, but we were not able to check execution
>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium
This example is valid syntax, but we were not able to check execution
>>> 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
See :

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File: /pandas/core/generic.py#4126
type: <class 'function'>
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