Pandas Series: fillna() function

Last update on September 15 2022 12:57:50 (UTC/GMT +8 hours)

Fill NA/NaN values using the specified method

The fillna() function is used to fill NA/NaN values using the specified method.

Syntax:

Series.fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
value	Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.	scalar, dict, Series, or DataFrame	Required
method	Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.	{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None} Default Value: None	Required
axis	Axis along which to fill missing values.	{0 or ‘index’}	Required
inplace	If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).	bool Default Value: False	Required
limit	If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.	int Default Value: None	Required
downcast	A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).	dict Default Value: None	Required

Returns: Series- Object with missing values filled.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [5, np.nan, np.nan, 6],
                   [np.nan, 4, np.nan, 5]],
                 columns=list('PQRS'))
df

Output:

   P	 Q	 R	S
0	NaN	2.0	NaN	0
1	3.0	4.0	NaN	1
2	5.0	NaN	NaN	6
3	NaN	4.0	NaN	5

Example - Replace all NaN elements with 0s:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [5, np.nan, np.nan, 6],
                   [np.nan, 4, np.nan, 5]],
                 columns=list('PQRS'))
df.fillna(0)

Output:

   P	Q	R	S
0	0.0	2.0	0.0	0
1	3.0	4.0	0.0	1
2	5.0	0.0	0.0	6
3	0.0	4.0	0.0	5

Example - We can also propagate non-null values forward or backward:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [5, np.nan, np.nan, 6],
                   [np.nan, 4, np.nan, 5]],
                 columns=list('PQRS'))
df.fillna(method='ffill')

Output:

   P	Q	R	S
0	NaN	2.0	NaN	0
1	3.0	4.0	NaN	1
2	5.0	4.0	NaN	6
3	5.0	4.0	NaN	5

Example - Replace all NaN elements in column ‘P’, ‘Q’, ‘R’, and ‘S’, with 0, 1, 2, and 3 respectively:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [5, np.nan, np.nan, 6],
                   [np.nan, 4, np.nan, 5]],
                 columns=list('PQRS'))
values = {'P': 0, 'Q': 1, 'R': 2, 'S': 3}
df.fillna(value=values)

Output:

   P	Q	R	S
0	0.0	2.0	2.0	0
1	3.0	4.0	2.0	1
2	5.0	1.0	2.0	6
3	0.0	4.0	2.0	5

Example - Only replace the first NaN element:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                   [3, 4, np.nan, 1],
                   [5, np.nan, np.nan, 6],
                   [np.nan, 4, np.nan, 5]],
                 columns=list('PQRS'))
values = {'P': 0, 'Q': 1, 'R': 2, 'S': 3}
df.fillna(value=values, limit=1)

Output:

   P	Q	R	S
0	0.0	2.0	2.0	0
1	3.0	4.0	NaN	1
2	5.0	1.0	NaN	6
3	NaN	4.0	NaN	5

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