CODEWITHSUNDEEP
pythonpandas
Collective Action
Collective Action

Reputation: 8009

How to assign a value_count output to a dataframe

I am trying to assign the output from a value_count to a new df. My code follows.

import pandas as pd
import glob


df = pd.concat((pd.read_csv(f, names=['date','bill_id','sponsor_id']) for f in glob.glob('/home/jayaramdas/anaconda3/df/s11?_s_b')))


column_list = ['date', 'bill_id']

df = df.set_index(column_list, drop = True)
df = df['sponsor_id'].value_counts()

df.columns=['sponsor', 'num_bills']
print (df)

The value count is not being assigned the column headers specified 'sponsor', 'num_bills'. I'm getting the following output from print.head

1036    426
791     408
1332    401
1828    388
136     335
Name: sponsor_id, dtype: int64

Upvotes: 8

Views: 11948

Answers (2)

BSalita
BSalita

Reputation: 8931

Appending value_counts() to multi-column dataframe:

df = pd.DataFrame({'C1':['A','B','A'],'C2':['A','B','A']})
vc_df = df.value_counts().to_frame('Count').reset_index()
display(df, vc_df)

    C1  C2
0   A   A
1   B   B
2   A   A

    C1   C2 Count
0   A   A   2
1   B   B   1

Upvotes: 0

EdChum
EdChum

Reputation: 394031

your column length doesn't match, you read 3 columns from the csv and then set the index to 2 of them, you calculated value_counts which produces a Series with the column values as the index and the value_counts as the values, you need to reset_index and then overwrite the column names:

df = df.reset_index()
df.columns=['sponsor', 'num_bills']

Example:

In [276]:
df = pd.DataFrame({'col_name':['a','a','a','b','b']})
df

Out[276]:
  col_name
0        a
1        a
2        a
3        b
4        b

In [277]:
df['col_name'].value_counts()

Out[277]:
a    3
b    2
Name: col_name, dtype: int64

In [278]:    
type(df['col_name'].value_counts())

Out[278]:
pandas.core.series.Series

In [279]:
df = df['col_name'].value_counts().reset_index()
df.columns = ['col_name', 'count']
df

Out[279]:
  col_name  count
0        a      3
1        b      2

Upvotes: 12

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