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.
Same as calling .reindex(index=other.index, columns=other.columns,...)
.
Its row and column indices are used to define the new indices of this object.
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.
Return a new object, even if the passed indexes are the same.
Maximum number of consecutive labels to fill for inexact matches.
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.
Same type as caller, but with changed indices on each axis.
Return an object with matching indices as other object.
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.
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],This example is valid syntax, but we were not able to check execution
... [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 mediumThis example is valid syntax, but we were not able to check execution
>>> df2 = pd.DataFrame([[28, 'low'],This example is valid syntax, but we were not able to check execution
... [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 mediumThis 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 mediumSee :
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