Pandas: Drop the rows where all elements are missing
Pandas Handling Missing Values: Exercise-7 with Solution
Write a Pandas program to drop the rows where all elements are missing in a given 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 70004.0 110.50 2012-08-17 3003.0 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 70013.0 3045.60 2012-04-25 3001.0
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,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],
'purch_amt':[np.nan,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'ord_date': [np.nan,'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':[np.nan,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001]})
print("Original Orders DataFrame:")
print(df)
print("\nDrop the rows where all elements are missing:")
result = df.dropna(how='all')
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 70004.0 110.50 2012-08-17 3003.0 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 70013.0 3045.60 2012-04-25 3001.0 Drop the rows where all elements are missing: ord_no purch_amt ord_date customer_id 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 70004.0 110.50 2012-08-17 3003.0 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 70013.0 3045.60 2012-04-25 3001.0
Python Code Editor:
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