Pandas: Count the number of missing values in each column of a given DataFrame
3. Count Missing Values in Each Column
Write a Pandas program to count the number of missing values in each column of a given DataFrame.
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaN
Sample Solution:
Python Code :
Sample Output:
Original Orders DataFrame: ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaN Number of missing values of the said dataframe: ord_no 4 purch_amt 0 ord_date 1 customer_id 0 salesman_id 3 dtype: int64
For more Practice: Solve these Related Problems:
- Write a Pandas program to compute the count of NaN values for each column in a DataFrame using isnull() and sum().
- Write a Pandas program to generate a summary that lists each column and its respective number of missing entries.
- Write a Pandas program to apply the sum() function on the boolean DataFrame from isna() to count NaNs per column.
- Write a Pandas program to calculate and display the number of missing values in every column using the agg() function.
Go to:
Previous: Write a Pandas program to identify the column(s) of a given DataFrame which have at least one missing value.
Next: Write a Pandas program to find and replace the missing values in a given DataFrame which do not have any valuable information.
Python Code Editor:
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