Hi, in the exercise, I use *data.isna* and ‘max’ function to find which column has the most missing value, do you guys have any other easy way to do?

Well, in my case, I use *data.isnull().sum()* to find the number of **NaN** in each columns and find the most one by using *max()*, then I use *dropna()* to delete to the column that I want.

I defined a function to find the column with the max na:

def get_max_col_name(data): max = data.isnull().sum().max() for i in range(len(data.columns)): if data.iloc[:,i].isnull().sum() == max: return data.iloc[:,i].name

Then used the function in dataframe.drop:

data_dropna = data.drop(columns=[get_max_col_name(data)], axis=1)

```
inputs.isnull().sum().index[inputs.isnull().sum().values == inputs.isnull().sum().max()]
```

this might work