pandas.DataFrame.reindex¶
- DataFrame.reindex(index=None, columns=None, **kwargs)¶
Conform DataFrame 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, columns : 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.
.. versionadded:: 0.17.0
Returns: reindexed : DataFrame
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.