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Filling NaN values using a dictionary involves replacing the NaN values with values from a dictionary. This can be done by using the fillna() method and passing in the dictionary as an argument. The dictionary should contain the value that needs to be replaced as the key, and the value to replace it with as the value. This process can be repeated until all NaN values have been replaced with the desired values.
You can use the fillna() function with a dictionary to replace NaN values in one column of a pandas DataFrame based on values in another column.
You can use the following basic syntax to do so:
#define dictionary dict = {'A':5, 'B':10, 'C':15, 'D':20} #replace values in col2 based on dictionary values in col1 df['col2'] = df['col2'].fillna(df['col1'].map(dict))
The following example shows how to use this syntax in practice.
Example: Fill NaN Values in Pandas Using a Dictionary
Suppose we have the following pandas DataFrame that contains information about the sales made at various retail stores:
import pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'store': ['A', 'A', 'B', 'C', 'D', 'C', 'B', 'D'], 'sales': [12, np.nan, 30, np.nan, 24, np.nan, np.nan, 13]}) #view DataFrame print(df) store sales 0 A 12.0 1 A NaN 2 B 30.0 3 C NaN 4 D 24.0 5 C NaN 6 B NaN 7 D 13.0
Notice that there are several NaN values in the sales column.
Suppose we would like to fill these NaNs in the sales column using values that correspond to specific values in the store column.
We can use the following syntax to do so:
#define dictionary dict = {'A':5, 'B':10, 'C':15, 'D':20} #replace values in sales column based on dictionary values in store column df['sales'] = df['sales'].fillna(df['store'].map(dict)) #view updated DataFrame print(df) store sales 0 A 12.0 1 A 5.0 2 B 30.0 3 C 15.0 4 D 24.0 5 C 15.0 6 B 10.0 7 D 13.0
We used a dictionary to make the following replacements in the sales column:
- If store is A, replace NaN in sales with the value 5.
- If store is B, replace NaN in sales with the value 10.
- If store is C, replace NaN in sales with the value 15.
- If store is D, replace NaN in sales with the value 20.
You can find the complete online documentation for the fillna() function .