Replace all NaN values with 0's in a column of Pandas dataframe

import modules

import pandas as pd
import numpy as np

create dummy dataframe

raw_data = {'name': ['Willard Morris', 'Al Jennings', 'Omar Mullins', 'Spencer McDaniel'],
'age': [20, 19, 22, 21],
'favorite_color': ['blue', 'red', 'yellow', "green"],
'grade': [88, 92, 95, 70]}
df = pd.DataFrame(raw_data, index = ['Willard Morris', 'Al Jennings', 'Omar Mullins', 'Spencer McDaniel'])
df

	age	favorite_color	grade	name
Willard Morris	20	blue	88	Willard Morris
Al Jennings	19	red	92	Al Jennings
Omar Mullins	22	yellow	95	Omar Mullins
Spencer McDaniel	21	green	70	Spencer McDaniel

Create some NaN values in the dataframe

#First, we have to create the NaN values
df = df.replace(20,np.NaN)
df = df.replace(70,np.NaN)
df

	age	favorite_color	grade	name
Willard Morris	NaN	blue	88.0	Willard Morris
Al Jennings	19.0	red	92.0	Al Jennings
Omar Mullins	22.0	yellow	95.0	Omar Mullins
Spencer McDaniel	21.0	green	NaN	Spencer McDaniel

Replace the NaN values in the dataframe (with a 0 in this case)

#Now, we can replace them
df = df.fillna(0)
df

	age	favorite_color	grade	name
Willard Morris	0.0	blue	88.0	Willard Morris
Al Jennings	19.0	red	92.0	Al Jennings
Omar Mullins	22.0	yellow	95.0	Omar Mullins
Spencer McDaniel	21.0	green	0.0	Spencer McDaniel