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Pandas: Identify the columns which have at least one missing value in a DataFrame

Pandas Handling Missing Values: Exercise-2 with Solution

Write a Pandas program to identify the column(s) of a given DataFrame which have at least one missing value.

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 :

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':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'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("\nIdentify the columns which have at least one missing value:")
print(df.isna().any())

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

Identify the columns which have at least one missing value:
ord_no          True
purch_amt      False
ord_date        True
customer_id    False
salesman_id     True
dtype: bool         

Python Code Editor:


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Previous: Write a Pandas program to detect missing values of a given DataFrame. Display True or False.
Next: Write a Pandas program to count the number of missing values in each column of a given DataFrame.

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Python: Tips of the Day

Python: Time library

Time library provides lots of time related functions and methods and is good to know whether you're developing a website or apps and games or working with data science or trading financial markets. Time is essential in most development pursuits and Python's standard time library comes very handy for that.

Let's check out a few simple examples:

moment=time.strftime("%Y-%b-%d__%H_%M_%S",time.localtime())

import time
time_now=time.strftime("%H:%M:%S",time.localtime())
print(time_now)
date_now=time.strftime("%Y-%b-%d",time.localtime())
print(date_now)

Output:

11:36:34
2020-Nov-30