How to get the number of the most frequent value in a column?

Question

I have a data frame and I would like to know how many times a given column has the most frequent value.

I try to do it in the following way:

items_counts = df['item'].value_counts()
max_item = items_counts.max()

As a result I get:

ValueError: cannot convert float NaN to integer

As far as I understand, with the first line I get series in which the values from a column are used as key and frequency of these values are used as values. So, I just need to find the largest value in the series and, because of some reason, it does not work. Does anybody know how this problem can be solved?

Answer 1

It looks like you may have some nulls in the column. You can drop them with df = df.dropna(subset=['item']). Then df['item'].value_counts().max() should give you the max counts, and df['item'].value_counts().idxmax() should give you the most frequent value.

Answer 2

To continue to @jonathanrocher answer you could use mode in pandas DataFrame. It'll give a most frequent values (one or two) across the rows or columns:

import pandas as pd
import numpy as np
df = pd.DataFrame({"a": [1,2,2,4,2], "b": [np.nan, np.nan, np.nan, 3, 3]})

In [2]: df.mode()
Out[2]: 
   a    b
0  2  3.0