Pandas: Total number of missing values in a DataFrame
11. Total Number of Missing Values in DataFrame
Write a Pandas program to calculate the total number of missing values in a DataFrame.
Test Data:
ord_no purch_amt ord_date customer_id
0 NaN NaN NaN NaN
1 NaN 270.65 2012-09-10 3001.0
2 70002.0 65.26 NaN 3001.0
3 NaN NaN NaN NaN
4 NaN 948.50 2012-09-10 3002.0
5 70005.0 2400.60 2012-07-27 3001.0
6 NaN 5760.00 2012-09-10 3001.0
7 70010.0 1983.43 2012-10-10 3004.0
8 70003.0 2480.40 2012-10-10 3003.0
9 70012.0 250.45 2012-06-27 3002.0
10 NaN 75.29 2012-08-17 3001.0
11 NaN NaN NaN NaN
Sample Solution:
Python Code :
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[np.nan,np.nan,70002,np.nan,np.nan,70005,np.nan,70010,70003,70012,np.nan,np.nan],
'purch_amt':[np.nan,270.65,65.26,np.nan,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,np.nan],
'ord_date': [np.nan,'2012-09-10',np.nan,np.nan,'2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17',np.nan],
'customer_id':[np.nan,3001,3001,np.nan,3002,3001,3001,3004,3003,3002,3001,np.nan]})
print("Original Orders DataFrame:")
print(df)
print("\nTotal number of missing values of the said DataFrame:")
result = df.isna().sum().sum()
print(result)
Sample Output:
Original Orders DataFrame:
ord_no purch_amt ord_date customer_id
0 NaN NaN NaN NaN
1 NaN 270.65 2012-09-10 3001.0
2 70002.0 65.26 NaN 3001.0
3 NaN NaN NaN NaN
4 NaN 948.50 2012-09-10 3002.0
5 70005.0 2400.60 2012-07-27 3001.0
6 NaN 5760.00 2012-09-10 3001.0
7 70010.0 1983.43 2012-10-10 3004.0
8 70003.0 2480.40 2012-10-10 3003.0
9 70012.0 250.45 2012-06-27 3002.0
10 NaN 75.29 2012-08-17 3001.0
11 NaN NaN NaN NaN
Total number of missing values of the said DataFrame:
17
For more Practice: Solve these Related Problems:
- Write a Pandas program to calculate the overall count of missing values in a DataFrame.
- Write a Pandas program to sum the missing values across all columns and output the total number.
- Write a Pandas program to use isnull() and sum() twice (over rows and columns) to compute the total NaN count.
- Write a Pandas program to aggregate the number of missing entries in the entire DataFrame and display the result.
Go to:
PREV : Keep Only Valid Entries.
NEXT : Replace NaNs with a Constant in Specified Columns.
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.