Delete columns with extremely unequally distributed values from pandas dataframe

Question

Given the following code:

import numpy as np
import pandas as pd

arr = np.array([
    [1,2,9,1,1,1],
    [2,3,3,1,0,1],
    [1,4,2,1,2,1],
    [2,3,1,1,2,1],
    [1,2,3,1,8,1],
    [2,2,5,1,1,1],
    [1,3,8,7,4,1],
    [2,4,7,8,3,3]
    ])
#    1,2,3,4,5,6 <- Number of the columns.
df = pd.DataFrame(arr)

for _ in df.columns.values:
    print {x: list(df[_]).count(x) for x in set(df[_])}

I want to delete from the dataframe all the columns in which one value occurs more often than all the other values of the column together. In this case I would like to drop the columns 4 and 6 (see comment) since the number 1 occurs more often than all the other numbers in these columns together (6 > 2 in column 4 and 7 > 1 in column 6). I don't want to drop the first column (4 = 4). How would I do that?

Answer 1

Another option is to do a value counts on each column and if the maximum of the count is smaller or equal to half of the number of rows of the data frame, then select it:

df.loc[:, df.apply(lambda col: max(col.value_counts()) <= df.shape[0]/2)]

#   0   1   2   4
#0  1   2   9   1
#1  2   3   3   0
#2  1   4   2   2
#3  2   3   1   2
#4  1   2   3   8
#5  2   2   5   1
#6  1   3   8   4
#7  2   4   7   3