Create a pandas dataframe of counts

Question

I want to create a pandas dataframe with two columns, the first being the unique values of one of my columns and the second being the count of unique values.

I have seen many posts (such here) as that describe how to get the counts, but the issue I'm running into is when I try to create a dataframe the column values become my index.

Sample data: df = pd.DataFrame({'Color': ['Red', 'Red', 'Blue'], 'State': ['MA', 'PA', 'PA']}). I want to end up with a dataframe like:

   Color Count
0   Red  2
1  Blue  1

I have tried the following, but in all cases the index ends up as Color and the Count is the only column in the dataframe.

Attempt 1:

df2 = pd.DataFrame(data=df['Color'].value_counts())
# And resetting the index just gets rid of Color, which I want to keep
df2 = df2.reset_index(drop=True)

Attempt 2:

df3 = df['Color'].value_counts()
df3 = pd.DataFrame(data=df3, index=range(df3.shape[0]))

Attempt 3:

df4 = df.groupby('Color')
df4 = pd.DataFrame(df4['Color'].count())

Answer 1

Another way to do this, using value_counts:

In [10]: df = pd.DataFrame({'Color': ['Red', 'Red', 'Blue'], 'State': ['MA', 'PA', 'PA']})

In [11]: df.Color.value_counts().reset_index().rename(
           columns={'index': 'Color', 0: 'count'})
Out[11]:
  Color  count
0   Red      2
1  Blue      1

Answer 2

Essentially equivalent to setting the column names, but using the rename method instead:

df.groupby('Color').count().reset_index().rename(columns={'State': 'Count'})

Answer 3

One readable solution is to use to_frame and rename_axis methods:

res = df['Color'].value_counts()\
                 .to_frame('count').rename_axis('Color')\
                 .reset_index()

print(res)

  Color  count
0   Red      2
1  Blue      1