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Pandas.fillna()

Pandas.fillna() with What is Python Pandas, Reading Multiple Files, Null values, Multiple index, Application, Application Basics, Resampling, Plotting the data, Moving windows functions, Series, Read the file, Data operations, Filter Data etc.

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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
Next TopicDataFrame.replace()




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