# Write a program in Python to find which column has the minimum number of missing values in a given dataframe

Assume, you have a dataframe and the minimum number of missing value column is,

DataFrame is:
Id    Salary     Age
0 1.0    20000.0   22.0
1 2.0    NaN       23.0
2 3.0    50000.0   NaN
3 NaN    40000.0   25.0
4 5.0    80000.0   NaN
5 6.0    NaN       25.0
6 7.0    350000.0  26.0
7 8.0    55000.0   27.0
8 9.0    60000.0   NaN
9 10.0   70000.0   24.0
lowest missing value column is: Id

To solve this, we will follow the steps given below −

## Solution

• Define a dataframe with three columns Id,Salary and Age

• Set df.apply() inside lambda function to check the sum of null values from all rows

df = df.apply(lambda x: x.isnull().sum(),axis=0)
• Finally, print the lowest value from the df using df.idxmin()

df.idxmin()

### Example

Let’s see the below code to get a better understanding −

import pandas as pd
import numpy as np
df = pd.DataFrame({'Id':[1,2,3,np.nan,5,6,7,8,9,10],
'Salary':[20000,np.nan,50000,40000,80000,np.nan,350000,55000,60000,70000],
'Age': [22,23,np.nan,25,np.nan,25,26,27,np.nan,24]
})
print("DataFrame is:\n",df)
df = df.apply(lambda x: x.isnull().sum(),axis=0)
print("lowest missing value column is:",df.idxmin())

### Output

DataFrame is:
Id    Salary     Age
0 1.0    20000.0   22.0
1 2.0    NaN       23.0
2 3.0    50000.0   NaN
3 NaN    40000.0   25.0
4 5.0    80000.0   NaN
5 6.0    NaN       25.0
6 7.0    350000.0  26.0
7 8.0    55000.0   27.0
8 9.0    60000.0   NaN
9 10.0   70000.0   24.0
lowest missing value column is: Id