pandas.DataFrame.reindex_like的使用说明

参考链接：https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex_like.html#pandas.DataFrame.reindex_like

pandas.DataFrame.reindex_like¶

DataFrame.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

otherObject 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.

copybool, default True

Return a new object, even if the passed indexes are the same.

limitint, default None

Maximum number of consecutive labels to fill for inexact matches.

toleranceoptional

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.

 

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

跟这个效果一样reindex(index=other.index, columns=other.columns,...).

 

这个是今天学习的4个方法中最简单的一个，其实就是去学习另外一个的df对象的index与columns。直接抄书中说明。

In [146]: df1                                                                                               
Out[146]: 
            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

In [147]: df2                                                                                               
Out[147]: 
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium

In [148]: df2.reindex_like(df1)                                                                             
Out[148]: 
            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

In [149]:     

　　从代码中，可以清晰的看到df2，完全使用了df1的index与columns信息，并且在缺省的信息下，使用了NaN数据。