Pandas: Replace the missing values with the most frequent values present in each column
19. Replace Missing Values with Most Frequent Value
Write a Pandas program to replace the missing values with the most frequent values present in each column of 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("\nReplace the missing values with the most frequent values present in each column:")
result = df.fillna(df.mode().iloc[0])
print(result)
Sample Output:
Original Orders DataFrame:
ord_no purch_amt ... customer_id salesman_id
0 70001.0 150.50 ... 3002 5002.0
1 NaN NaN ... 3001 5003.0
2 70002.0 65.26 ... 3001 5001.0
3 70004.0 110.50 ... 3003 NaN
4 NaN 948.50 ... 3002 5002.0
5 70005.0 NaN ... 3001 5001.0
6 NaN 5760.00 ... 3001 5001.0
7 70010.0 1983.43 ... 3004 NaN
8 70003.0 NaN ... 3003 5003.0
9 70012.0 250.45 ... 3002 5002.0
10 NaN 75.29 ... 3001 5003.0
11 70013.0 3045.60 ... 3001 NaN
[12 rows x 6 columns]
Replace the missing values with the most frequent values present in each column:
ord_no purch_amt ... customer_id salesman_id
0 70001.0 150.50 ... 3002 5002.0
1 70001.0 65.26 ... 3001 5003.0
2 70002.0 65.26 ... 3001 5001.0
3 70004.0 110.50 ... 3003 5001.0
4 70001.0 948.50 ... 3002 5002.0
5 70005.0 65.26 ... 3001 5001.0
6 70001.0 5760.00 ... 3001 5001.0
7 70010.0 1983.43 ... 3004 5001.0
8 70003.0 65.26 ... 3003 5003.0
9 70012.0 250.45 ... 3002 5002.0
10 70001.0 75.29 ... 3001 5003.0
11 70013.0 3045.60 ... 3001 5001.0
[12 rows x 6 columns]
For more Practice: Solve these Related Problems:
- Write a Pandas program to impute missing values in each column with its mode value.
- Write a Pandas program to replace NaNs with the most frequently occurring value in each specified column.
- Write a Pandas program to compute the mode of a column and then use fillna() to replace missing entries with this value.
- Write a Pandas program to iterate over DataFrame columns, determine the mode, and substitute NaNs with the most common value.
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Python Code Editor:
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