Remove Missing NaN Values in DataFrame using Python

To remove the missing values i.e. the NaN values, use the dropna() method. At first, let us import the required library −

import pandas as pd

Read the CSV and create a DataFrame −

dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")

Use the dropna() to remove the missing values. NaN will get displayed for missing values after dropna() is used −

dataFrame.dropna()

Example

Following is the complete code

import pandas as pd

# reading csv file
dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")
print("DataFrame with some NaN (missing) values...\n",dataFrame)

# count the rows and columns in a DataFrame
print("\nNumber of rows and column in our DataFrame = ",dataFrame.shape)

# drop the missing values
print("\nDataFrame after removing NaN values...\n",dataFrame.dropna())

Output

This will produce the following output −

DataFrame with some NaN (missing) values...
          Car        Place   UnitsSold
0        Audi    Bangalore        80.0
1     Porsche       Mumbai         NaN
2  RollsRoyce         Pune       100.0
3         BMW        Delhi         NaN
4     Mercedes   Hyderabad        80.0
5  Lamborghini  Chandigarh        80.0
6         Audi      Mumbai         NaN
7     Mercedes        Pune       120.0
8  Lamborghini       Delhi       100.0

Number of rows and colums in our DataFrame = (9, 3)

DataFrame after removing NaN values ...
           Car       Place   UnitsSold
0         Audi   Bangalore        80.0
2   RollsRoyce        Pune       100.0
4     Mercedes   Hyderabad        80.0
5  Lamborghini  Chandigarh        80.0
7     Mercedes        Pune       120.0
8  Lamborghini       Delhi       100.0