Pandas Series - fillna() function
The Pandas Series fillna() function returns is used to fill NA/NaN values using the specified method. The syntax for using this function is mentioned below:
Syntax
Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None)
Parameters
value |
Optional. Specify 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) can be used. Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. |
method |
Optional. Specify method to use for filling holes in reindexed Series. It can take values from {'backfill', 'bfill', 'pad', 'ffill', None}. Default is None. pad / ffill: use last valid observation to fill gap. backfill / bfill: use next valid observation to fill gap. |
axis |
Optional. Specify axis along which to fill missing values. It can take values from {0 or 'index'} |
inplace |
Optional. If True, fill in-place. Default: False |
limit |
Optional. 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. Default is None. |
Return Value
Returns Series with missing values filled or None if inplace=True.
Example:
In the example below, a DataFrame df is created. The fillna() function is used to fill all NA/NaN values with 0.0.
import pandas as pd import numpy as np df = pd.DataFrame({ "Bonus": [5, np.nan, 2, np.nan], "Salary": [60, np.nan, 65, 59]}, index= ["John", "Marry", "Sam", "Jo"] ) print("The DataFrame is:") print(df) #filling all NA/NaN values with 0.0 df_new = df.fillna(0) print("\ndf.fillna(0) returns:") print(df_new)
The output of the above code will be:
The DataFrame is: Bonus Salary John 5.0 60.0 Marry NaN NaN Sam 2.0 65.0 Jo NaN 59.0 df.fillna(0) returns: Bonus Salary John 5.0 60.0 Marry 0.0 0.0 Sam 2.0 65.0 Jo 0.0 59.0
Example: using method
Instead of using values, method can be used to fill NA/NaN values. Consider the example below:
import pandas as pd import numpy as np df = pd.DataFrame({ "Bonus": [5, np.nan, 2, np.nan], "Salary": [np.nan, np.nan, 65, 59]}, index= ["John", "Marry", "Sam", "Jo"] ) print("The DataFrame is:") print(df) #filling all NA/NaN values using bfill df_new1 = df.fillna(method='bfill') print("\ndf.fillna(method='bfill') returns:") print(df_new1) #filling all NA/NaN values using ffill df_new2 = df.fillna(method='ffill') print("\ndf.fillna(method='ffill') returns:") print(df_new2)
The output of the above code will be:
The DataFrame is: Bonus Salary John 5.0 NaN Marry NaN NaN Sam 2.0 65.0 Jo NaN 59.0 df.fillna(method='bfill') returns: Bonus Salary John 5.0 65.0 Marry 2.0 65.0 Sam 2.0 65.0 Jo NaN 59.0 df.fillna(method='ffill') returns: Bonus Salary John 5.0 NaN Marry 5.0 NaN Sam 2.0 65.0 Jo 2.0 59.0
Example: fill values column wise
To fill NA/NaN values column-wise, a dictionary specifying which value to use for each column can be used. Consider the example below:
import pandas as pd import numpy as np df = pd.DataFrame({ "Bonus": [5, np.nan, 2, np.nan], "Salary": [np.nan, np.nan, 65, 59]}, index= ["John", "Marry", "Sam", "Jo"] ) values = {'Bonus': 3, 'Salary': 50} print("The DataFrame is:") print(df) #filling all NA/NaN values using dictionary df_new = df.fillna(value=values) print("\ndf.fillna(value=values) returns:") print(df_new)
The output of the above code will be:
The DataFrame is: Bonus Salary John 5.0 NaN Marry NaN NaN Sam 2.0 65.0 Jo NaN 59.0 df.fillna(value=values) returns: Bonus Salary John 5.0 50.0 Marry 3.0 50.0 Sam 2.0 65.0 Jo 3.0 59.0
❮ Pandas Series - Functions