pandas.Series.reindex

Series.reindex(index=None, **kwargs)[source]

Conform Series to new index with optional filling logic, placing 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 (can be specified in order, or as

keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data

method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

  • 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 : boolean, 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.

New in version 0.17.0.
Returns:

reindexed : Series

Examples

Create a dataframe with some fictional data.

>>> 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.

>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...             'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari                 404           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                   404           0.08
Chrome                 200           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.

>>> 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

>>> 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

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

>>> 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
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88

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

>>> 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
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88
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 backpropagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29     100
2009-12-30     100
2009-12-31     100
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88
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.