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Pandas: Keep only the rows with at least 2 non-NA values

Pandas Handling Missing Values: Exercise-8 with Solution

Write a Pandas program to keep the rows with at least 2 NaN values 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       NaN        NaN         NaN          NaN
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      NaN        NaN         NaN          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':[np.nan,np.nan,70002,np.nan,np.nan,70005,np.nan,70010,70003,70012,np.nan,np.nan],
'purch_amt':[np.nan,270.65,65.26,np.nan,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,np.nan],
'ord_date': [np.nan,'2012-09-10',np.nan,np.nan,'2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17',np.nan],
'customer_id':[np.nan,3001,3001,np.nan,3002,3001,3001,3004,3003,3002,3001,np.nan]})
print("Original Orders DataFrame:")
print(df)
print("\nKeep the rows with at least 2 NaN values of the said DataFrame:")
result = df.dropna(thresh=2)
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       NaN        NaN         NaN          NaN
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      NaN        NaN         NaN          NaN

Keep the rows with at least 2 NaN values of the said DataFrame:
     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
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

Python Code Editor:

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Previous: Write a Pandas program to drop the rows where all elements are missing in a given DataFrame.
Next: Write a Pandas program to drop those rows from a given DataFrame in which specific columns have missing values.

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Python: Tips of the Day

Creates a list of elements, grouped based on the position in the original lists:

Example:

def tips_zip(*args, fill_value=None):
  	max_length = max([len(lst) for lst in args])
 	 result = []
  	for i in range(max_length):
   	 result.append([
      args[k][i] if i < len(args[k]) else fillvalue for k in range(len(args))
   	 ])
 	 return result
print(tips_zip(['a', 'b'], [1, 2], [True, False]))
print(tips_zip(['a'], [1, 2], [True, False]))
print(tips_zip(['a'], [1, 2], [True, False], fill_value = '1'))

Output:

[['a', 1, True], ['b', 2, False]]
[['a', 1, True], [None, 2, False]]
[['a', 1, True], ['1', 2, False]]