pandas 1.4.2

ParametersReturnsBackRef
reindex(self, *args, **kwargs) -> 'Series'

Places NA/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

index : array-like, optional

New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data.

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.

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level.

fill_value : scalar, default np.NaN

Value to use for missing values. Defaults to NaN, but can be any "compatible" value.

limit : int, default None

Maximum number of consecutive elements to forward or backward fill.

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.

Returns

Series with changed index.

Conform Series to new index with optional filling logic.

See Also

DataFrame.reindex_like

Change to same indices as other DataFrame.

DataFrame.reset_index

Remove row labels or move them to new columns.

DataFrame.set_index

Set row labels.

Examples

DataFrame.reindex supports two calling conventions

We highly recommend using keyword arguments to clarify your intent.

Create a dataframe with some fictional data.

This example is valid syntax, but we were not able to check execution
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
... df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301],
...  'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...  index=index)
... df http_status response_time Firefox 200 0.04 Chrome 200 0.02 Safari 404 0.07 IE10 404 0.08 Konqueror 301 1.00

Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN .

This example is valid syntax, but we were not able to check execution
>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...  'Chrome']
... df.reindex(new_index) http_status response_time Safari 404.0 0.07 Iceweasel NaN NaN Comodo Dragon NaN NaN IE10 404.0 0.08 Chrome 200.0 0.02

We can fill in the missing values by passing a value to the keyword fill_value . Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

This example is valid syntax, but we were not able to check execution
>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02
This example is valid syntax, but we were not able to check execution
>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

This example is valid syntax, but we were not able to check execution
>>> df.reindex(columns=['http_status', 'user_agent'])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use "axis-style" keyword arguments

This example is valid syntax, but we were not able to check execution
>>> df.reindex(['http_status', 'user_agent'], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex , we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).

This example is valid syntax, but we were not able to check execution
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
... df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
...  index=date_index)
... df2 prices 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0

Suppose we decide to expand the dataframe to cover a wider date range.

This example is valid syntax, but we were not able to check execution
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
... df2.reindex(date_index2) prices 2009-12-29 NaN 2009-12-30 NaN 2009-12-31 NaN 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN

The index entries that did not have a value in the original data frame (for example, '2009-12-29') are by default filled with NaN . If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

This example is valid syntax, but we were not able to check execution
>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29   100.0
2009-12-30   100.0
2009-12-31   100.0
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

Please note that the NaN value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna() method.

See the user guide <basics.reindexing> for more.

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.indexes.base.Index.reindex pandas.core.series.Series.asfreq pandas.core.series.Series.drop pandas.core.series.Series.fillna

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