How to fill NA values of dataframe in python?

Data Science, AI and ML
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#1

Pandas DataFrame.fillna()

We can use the fillna() function to fill the null values in the dataset.

Syntax:

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

Parameters:

  • value: It is a value that is used to fill the null values, alternately a Series/dict/DataFrame.
  • method: A method that is used to fill the null values in the reindexed Series.
  • axis: It takes int or string value for rows/columns. Axis along which we need to fill missing values.
  • inplace: If it is True, it fills values at an empty place.
  • limit: It is an integer value that specifies the maximum number of consecutive forward/backward NaN value fills.
  • downcast: It takes a dict that specifies what to downcast like Float64 to int64.

Returns:

It returns an object in which the missing values are being filled.

Example1:

import pandas as pd  
# Create a dataframe  
info = pd.DataFrame(data={'x':[10,20,30,40,50,None]})  
print(info)  
# Fill null value to dataframe using 'inplace'  
info.fillna(value=0, inplace=True)  
print(info)

Output

       x
0     10.0
1     20.0
2     30.0
3     40.0
4     50.0
5     NaN
        x
0     10.0
1     20.0
2     30.0
3     40.0
4     50.0
5      0.0

Example2:
The below code is responsible for filling the DataFrame that consist some NaN values.

import pandas as pd  
# Create a dataframe  
info = pd.DataFrame([[np.nan,np.nan, 20, 0],  
[1, np.nan, 4, 1],  
[np.nan, np.nan, np.nan, 5],  
[np.nan, 20, np.nan, 2]],  
columns=list('ABCD'))  
info

Output

    A    B     C    D
0  NaN  NaN   20.0  0
1  1.0  NaN   4.0   1
2  NaN  NaN   NaN   5
3  NaN  20.0  NaN   2

Example3:
In below code, we have used the fillna function to fill in some of the NaN values only.

info = pd.DataFrame([[np.nan,np.nan, 20, 0],  
[1, np.nan, 4, 1],  
[np.nan, np.nan, np.nan, 5],  
[np.nan, 20, np.nan, 2]],  
columns=list('ABCD'))  
info  
info.fillna(0)  
info.fillna(method='ffill')  
values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}  
info.fillna(value=values)  
info.fillna(value=values, limit=1)

Output

    A    B     C    D
0  0.0  1.0   20.0  0
1  1.0  NaN   4.0   1
2  NaN  NaN   2.0   5
3  NaN  20.0  NaN   2