Pandas Series: asfreq() function

Last update on September 15 2022 12:57:44 (UTC/GMT +8 hours)

Convert Pandas TimeSeries to specified frequency

The asfreq() function is used to convert TimeSeries to specified frequency.

Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified frequency. resample is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency.

Syntax:

Series.asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None)

Parameters:

Name	Description	Type/Default Value	Required / Optional
freq		DateOffset object, or string	Required
method	Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): 'pad' / 'ffill': propagate last valid observation forward to next valid 'backfill' / 'bfill': use NEXT valid observation to fill	{'backfill'/'bfill', 'pad'/'ffill'} Default Value: None	Required
how	For PeriodIndex only, see PeriodIndex.asfreq	{'start', 'end'} Default Value: end	Required
normalize	Whether to reset output index to midnight	bool Default Value: False	Required
fill_value	Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).	scalar	Optional

Returns: converted - same type as caller

Example - Start by creating a series with 4 one minute timestamps:

Python-Pandas Code:

import numpy as np
import pandas as pd
index = pd.date_range('1/1/2019', periods=4, freq='T')
series = pd.Series([0.0, None, 2.0, 3.0], index=index)
df = pd.DataFrame({'s':series})
df

Output:

                        s
2019-01-01 00:00:00	0.0
2019-01-01 00:01:00	NaN
2019-01-01 00:02:00	2.0
2019-01-01 00:03:00	3.0

Example - Upsample the series into 20 second bins:

Python-Pandas Code:

import numpy as np
import pandas as pd
index = pd.date_range('1/1/2019', periods=4, freq='T')
series = pd.Series([0.0, None, 2.0, 3.0], index=index)
df = pd.DataFrame({'s':series})
df.asfreq(freq='20S')

Output:

                        s
2019-01-01 00:00:00	0.0
2019-01-01 00:00:20	NaN
2019-01-01 00:00:40	NaN
2019-01-01 00:01:00	NaN
2019-01-01 00:01:20	NaN
2019-01-01 00:01:40	NaN
2019-01-01 00:02:00	2.0
2019-01-01 00:02:20	NaN
2019-01-01 00:02:40	NaN
2019-01-01 00:03:00	3.0

Example - Upsample again, providing a fill value:

Python-Pandas Code:

import numpy as np
import pandas as pd
index = pd.date_range('1/1/2019', periods=4, freq='T')
series = pd.Series([0.0, None, 2.0, 3.0], index=index)
df = pd.DataFrame({'s':series})
df.asfreq(freq='20S', fill_value=7.0)

Output:

                        s
2019-01-01 00:00:00	0.0
2019-01-01 00:00:20	7.0
2019-01-01 00:00:40	7.0
2019-01-01 00:01:00	NaN
2019-01-01 00:01:20	7.0
2019-01-01 00:01:40	7.0
2019-01-01 00:02:00	2.0
2019-01-01 00:02:20	7.0
2019-01-01 00:02:40	7.0
2019-01-01 00:03:00	3.0

Example - Upsample again, providing a method:

Python-Pandas Code:

import numpy as np
import pandas as pd
index = pd.date_range('1/1/2019', periods=4, freq='T')
series = pd.Series([0.0, None, 2.0, 3.0], index=index)
df = pd.DataFrame({'s':series})
df.asfreq(freq='20S', method='bfill')

Output:

                        s
2019-01-01 00:00:00	0.0
2019-01-01 00:00:20	NaN
2019-01-01 00:00:40	NaN
2019-01-01 00:01:00	NaN
2019-01-01 00:01:20	2.0
2019-01-01 00:01:40	2.0
2019-01-01 00:02:00	2.0
2019-01-01 00:02:20	3.0
2019-01-01 00:02:40	3.0
2019-01-01 00:03:00	3.0

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