w3resource

Pandas: Indexes of missing values in a given DataFrame

Pandas Handling Missing Values: Exercise-18 with Solution

Write a Pandas program to find the Indexes of missing values in a given DataFrame.

Test Data:

     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id
0   70001.0     150.50     10.50  2012-10-05         3002       5002.0
1       NaN        NaN     20.65  2012-09-10         3001       5003.0
2   70002.0      65.26       NaN         NaN         3001       5001.0
3   70004.0     110.50     11.50  2012-08-17         3003          NaN
4       NaN     948.50     98.50  2012-09-10         3002       5002.0
5   70005.0        NaN       NaN  2012-07-27         3001       5001.0
6       NaN    5760.00     57.00  2012-09-10         3001       5001.0
7   70010.0    1983.43     19.43  2012-10-10         3004          NaN
8   70003.0        NaN       NaN  2012-10-10         3003       5003.0
9   70012.0     250.45     25.45  2012-06-27         3002       5002.0
10      NaN      75.29     75.29  2012-08-17         3001       5003.0
11  70013.0    3045.60     35.60  2012-04-25         3001          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':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],
'purch_amt':[150.5,np.nan,65.26,110.5,948.5,np.nan,5760,1983.43,np.nan,250.45, 75.29,3045.6],
'sale_amt':[10.5,20.65,np.nan,11.5,98.5,np.nan,57,19.43,np.nan,25.45, 75.29,35.6],
'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],
'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],
'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})
print("Original Orders DataFrame:")
print(df) 
print("\nMissing values in purch_amt column:")
result = df['ord_no'].isnull().to_numpy().nonzero()
print(result)

Sample Output:

Original Orders DataFrame:
     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id
0   70001.0     150.50     10.50  2012-10-05         3002       5002.0
1       NaN        NaN     20.65  2012-09-10         3001       5003.0
2   70002.0      65.26       NaN         NaN         3001       5001.0
3   70004.0     110.50     11.50  2012-08-17         3003          NaN
4       NaN     948.50     98.50  2012-09-10         3002       5002.0
5   70005.0        NaN       NaN  2012-07-27         3001       5001.0
6       NaN    5760.00     57.00  2012-09-10         3001       5001.0
7   70010.0    1983.43     19.43  2012-10-10         3004          NaN
8   70003.0        NaN       NaN  2012-10-10         3003       5003.0
9   70012.0     250.45     25.45  2012-06-27         3002       5002.0
10      NaN      75.29     75.29  2012-08-17         3001       5003.0
11  70013.0    3045.60     35.60  2012-04-25         3001          NaN

Missing values in purch_amt column:
(array([ 1,  4,  6, 10]),)

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to count the missing values in a given DataFrame.
Next: Write a Pandas program to replace the missing values with the most frequent values present in each column of a given dataframe.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.



Become a Patron!

Follow us on Facebook and Twitter for latest update.

It will be nice if you may share this link in any developer community or anywhere else, from where other developers may find this content. Thanks.

https://www.w3resource.com/python-exercises/pandas/missing-values/python-pandas-missing-values-exercise-18.php