Pandas: Detect missing values of a given DataFrame
1. Detect Missing Values
Write a Pandas program to detect missing values of a given DataFrame. Display True or False.
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("\nMissing values of the said dataframe:")
print(df.isna())
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
Missing values of the said dataframe:
ord_no purch_amt ord_date customer_id salesman_id
0 False False False False False
1 True False False False False
2 False False True False False
3 False False False False True
4 True False False False False
5 False False False False False
6 True False False False False
7 False False False False True
8 False False False False False
9 False False False False False
10 True False False False False
11 False False False False True
For more Practice: Solve these Related Problems:
- Write a Pandas program to check for missing values in a DataFrame and output a boolean DataFrame indicating their positions.
- Write a Pandas program to verify the presence of NaNs by using the isnull() function and then display the resulting True/False matrix.
- Write a Pandas program to test if each element in the DataFrame is missing and print a summary of True values.
- Write a Pandas program to apply a lambda function to each cell in the DataFrame to determine if it is null, returning True or False.
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Python Code Editor:
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