In this post we will be creating a Python script that will replace any NaN values present with zeros within a Pandas data frame. Pandas is a Python library which is an amazing tool for data analysis and manipulation.
In this example our data will be a CSV file that we’ll be reading from in Python, a sample of the data can be seen in the image below.
To load the CSV data in to a Pandas data frame we would use the snippet of code below.
import pandas as pd
df = pd.read_csv('Sample.csv')
print(df)The snippet of code above would return the following as output where we can see the NaN values present.
Rank NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales
0 1 NaN 29.02 3.77 8.46 82.74
1 2 29.08 3.58 NaN NaN 40.24
2 3 NaN NaN 3.79 NaN 35.82
3 4 15.75 NaN NaN 2.96 33.00
4 5 NaN 8.89 NaN 1.00 NaN
5 6 NaN NaN NaN NaN 30.26
6 7 11.38 NaN NaN 2.90 30.01
7 8 NaN 9.20 2.93 2.85 NaN
8 9 14.59 7.06 4.70 2.26 28.62
9 10 26.93 NaN 0.28 0.47 28.31To replace the NaN values as seen above with zeros instead, we would use the following.
df = df.fillna(0)
print(df)
As a result our data frame would now be seen as.
Rank NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales
0 1 0.00 29.02 3.77 8.46 82.74
1 2 29.08 3.58 0.00 0.00 40.24
2 3 0.00 0.00 3.79 0.00 35.82
3 4 15.75 0.00 0.00 2.96 33.00
4 5 0.00 8.89 0.00 1.00 0.00
5 6 0.00 0.00 0.00 0.00 30.26
6 7 11.38 0.00 0.00 2.90 30.01
7 8 0.00 9.20 2.93 2.85 0.00
8 9 14.59 7.06 4.70 2.26 28.62
9 10 26.93 0.00 0.28 0.47 28.31