The value to fill missing values with. Note that missing values that existed in the source DataFrame will not be filled - only those that arise due to this method will be filled. By default, fill_value=None.

Return Value

A DataFrame with a new time index that respects the specified frequency.

Examples

Consider the following DataFrame:


        
        
            
                
                
                    index_date = pd.date_range("2020-12-25", periods=3, freq="T")
df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6]}, index=index_date)
df
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:01:00  2  5
2020-12-25 00:02:00  3  6

Basic usage

To set a new time index with a frequency of 30 seconds:


        
        
            
                
                
                    df.asfreq(freq="30S")
                
            
                                 A    B
2020-12-25 00:00:00  1.0  4.0
2020-12-25 00:00:30  NaN  NaN
2020-12-25 00:01:00  2.0  5.0
2020-12-25 00:01:30  NaN  NaN
2020-12-25 00:02:00  3.0  6.0

The reason why we get NaN for some rows is that their index is not present in the source DataFrame df. For instance, we get NaN for the second row because df does not contain a row with index 2020-12-25 00:00:30.

Filling methods

None

By default, method=None, which means that new values will be marked as NaN and no filling rules will be applied:


        
        
            
                
                
                    df.asfreq(freq="30S")   # method=None
                
            
                                 A    B
2020-12-25 00:00:00  1.0  4.0
2020-12-25 00:00:30  NaN  NaN
2020-12-25 00:01:00  2.0  5.0
2020-12-25 00:01:30  NaN  NaN
2020-12-25 00:02:00  3.0  6.0

ffill

To fill using the previous valid observation:


        
        
            
                
                
                    df.asfreq(freq="30S", method="ffill")
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:00:30  1  4
2020-12-25 00:01:00  2  5
2020-12-25 00:01:30  2  5
2020-12-25 00:02:00  3  6

bfill

To fill using the next valid observation:


        
        
            
                
                
                    df.asfreq(freq="30S", method="bfill")
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:00:30  2  5
2020-12-25 00:01:00  2  5
2020-12-25 00:01:30  3  6
2020-12-25 00:02:00  3  6

Normalising the index

Consider the following DataFrame:


        
        
            
                
                
                    index_date = pd.date_range("2020-12-25", periods=3, freq="T")
df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6]}, index=index_date)
df
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:01:00  2  5
2020-12-25 00:02:00  3  6

To reset the time of day to midnight, set normalize=True:


        
        
            
                
                
                    df.asfreq(freq="MIN", normalize=True)
                
            
                         A  B
2020-12-25   1  4
2020-12-25   2  5
2020-12-25   3  6

Specifying a fill value

Consider the same df as above:


        
        
            
                
                
                    df
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:01:00  2  5
2020-12-25 00:02:00  3  6

Instead of the default NaNs, we can choose the value to fill with using fill_value:


        
        
            
                
                
                    df.asfreq(freq="30S", fill_value=0)
                
            
                                 A  B
2020-12-25 00:00:00  1  4
2020-12-25 00:00:30  0  0
2020-12-25 00:01:00  2  5
2020-12-25 00:01:30  0  0
2020-12-25 00:02:00  3  6

Case when NaNs already exist in DataFrame

Consider the following DataFrame:


        
        
            
                
                
                    index_date = pd.date_range("2020-12-25", periods=3, freq="T")
df = pd.DataFrame({"A":[1,2,3],"B":[4,5,pd.np.NaN]}, index=index_date)
df
                
            
                                 A  B
2020-12-25 00:00:00  1  4.0
2020-12-25 00:01:00  2  5.0
2020-12-25 00:02:00  3  NaN

Here, our df has a NaN.

Calling the asfreq(~) method:


        
        
            
                
                
                    df.asfreq(freq="30S", fill_value=8)
                
            
                                 A  B
2020-12-25 00:00:00  1  4.0
2020-12-25 00:00:30  8  8.0
2020-12-25 00:01:00  2  5.0
2020-12-25 00:01:30  8  8.0
2020-12-25 00:02:00  3  NaN

Notice how the original NaN is not filled. - only NaN that arise due to the method will be filled.

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.asfreq.html

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