Missing first column when converting value_count series to data frame in pandas

Question

I am new to Python and programming in general. I have some experience with R and finding pandas a little difficult.

I am trying to conduct a frequency count and then return this as a DataFrame object.

df = pd.read_csv('gender_data.csv')

x = df['Gender'].value_counts()

This returns the following series object:

       Gender
Male    200
Female  100

I want to convert this into a DataFrame object so I can plot and specify the axes information. This is where I am having a problem because .to_frame() cuts off the category label from the series.

x.to_frame()

returns

Gender
200
100

Is there a way to convert the series into a DataFrame and keep the category column? In R I think I could achieve this by using as.data.frame(x)

Hm, I cannot reproduce this. What version of pandas are you on? — mechanical_meat
– mechanical_meat, Commented Mar 8, 2017 at 23:25
Pandas version: 0.19.2, I'm using the Rodeo IDE which is a bit like RStudio but for Python if that makes a difference? — 0mm3
– 0mm3, Commented Mar 8, 2017 at 23:33
What exactly would you like the final plot to look like? It seems like the final plot should be a frequency histogram with two bins, male and female? The reason I ask is because I'm wondering why you need to convert the series to a data frame in the first place. — Filip Kilibarda
– Filip Kilibarda, Commented Mar 9, 2017 at 1:56

swebbo · Accepted Answer · 2017-03-09 07:42:47Z

1

I would suggest the following:

import pandas as pd
df = pd.DataFrame({"gender": ["male", "female", "male", "male", "female"]})
df = df["gender"].value_counts().reset_index()
df

This returns:

    index   gender
0   male    3
1   female  2

Afterwards you can clean up by redefining the column names:

df.columns = ["gender", "counts"]
df

With the result:

    gender  counts
0   male    3
1   female  2

edited Mar 9, 2017 at 7:42

answered Mar 9, 2017 at 7:36

swebbo

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