Creating a dataframe in a for loop based on another dataframe

Question

I have a data frame, df, and I'd like to get all the columns in it and the count of unique values in it and save it as another data frame. I can't seem to find a way to do that. I can, however, print what I want on the console. Here's what I mean:

def counting_unique_values_in_df(df):
    for evry_colm in df:
        print (evry_colm, "-", df[evry_colm].value_counts().count())

Now that prints what I want just fine. Instead of printing, if I do something like newdf = pd.DataFrame(evry_colm, df[evry_colm].value_counts().count(), columns = ('a', 'b')), it throws an error that reads "TypeError: object of type 'numpy.int32' has no len()". Obviously, that isn't right.

Soo, how can I make a data frame like columnName and UniqueCounts?

Konrad · Accepted Answer · 2019-03-07 18:45:57Z

1

To count unique values per column you can use apply and nunique function on data frame. Something like:

import pandas as pd

df = pd.DataFrame([
       {'a': 1, 'b': 2}, 
       {'a': 2, 'b': 2}
     ])

count_series = df.apply(lambda col: col.nunique())

#   returned object is pandas Series 
#   a    2
#   b    1
#   to map it to DataFrame try

pd.DataFrame(count_series).T

answered Mar 7, 2019 at 18:45

Konrad

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Skyy2010 · Accepted Answer · 2019-03-07 18:42:13Z

0

import pandas as pd
df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})
print(df)
print()
df = pd.DataFrame({col: [df[col].nunique()] for col in df})
print(df)

Output:

answered Mar 7, 2019 at 18:42

Skyy2010

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