pandas.Series.reindex_like — pandas 0.24.0rc1 documentation (original) (raw)
Series. reindex_like(other, method=None, copy=True, limit=None, tolerance=None)[source]¶
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. 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 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. New in version 0.21.0: (list-like tolerance) |
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| Returns: | Series or DataFrame Same type as caller, but with changed indices on each axis. |
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