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Python - Remove the missing (NaN) values in the DataFrame
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
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