Find the column with the highest percentage of missing information in demographics

Hi, Although I've finished the practice question. I was hoping if anyone could share their input if there's a simpler/easier way to solve this problem. My solution is as follows:

valid_entries = demo.count()
total_rows = len(demo.index)
missing_data = total_rows - valid_entries
missing_data.head()
missing_percentage = missing_data / total_rows * 100
missing_percentage.head()

missing_percentage_array = np.array(list(missing_percentage[:,]))
max_missing_perc_index = np.where(missing_percentage_array == 
missing_percentage.max())
np.array(list(missing_percentage.index))[max_missing_perc_index]

I'm quite certain there's an easier method to solve this and would love to know! For instance i was able to find the maximum missing percentage value directly from the dataframe (missing_percentage) but I couldn't find it's corresponding row label. So instead converted the list of values to a np.array, found the index of the largest percentage value, and used that as an index to find the corresponding row label, which was separately converted to a np.array.

Thanks and greatly appreciated!