Python Pandas: Convert ".value_counts" output to dataframe

Use rename_axis for name of column from index and reset_index:

df = df.value_counts().rename_axis('unique_values').reset_index(name='counts')
print (df)
   unique_values  counts
0              2       3
1              1       2

Or if need one column DataFrame use Series.to_frame:

df = df.value_counts().rename_axis('unique_values').to_frame('counts')
print (df)
               counts
unique_values        
2                   3
1                   2

I just run into the same problem, so I provide my thoughts here.

Warning

When you deal with the data structure of Pandas, you have to aware of the return type.

Another solution here

Like @jezrael mentioned before, Pandas do provide API pd.Series.to_frame.

Step 1

You can also wrap the pd.Series to pd.DataFrame by just doing

df_val_counts = pd.DataFrame(value_counts) # wrap pd.Series to pd.DataFrame

Then, you have a pd.DataFrame with column name 'a', and your first column become the index

Input:  print(df_value_counts.index.values)
Output: [2 1]

Input:  print(df_value_counts.columns)
Output: Index(['a'], dtype='object')

Step 2

What now?

If you want to add new column names here, as a pd.DataFrame, you can simply reset the index by the API of reset_index().

And then, change the column name by a list by API df.coloumns

df_value_counts = df_value_counts.reset_index()
df_value_counts.columns = ['unique_values', 'counts']

Then, you got what you need

Output:

       unique_values    counts
    0              2         3
    1              1         2

Full Answer here

import pandas as pd

df = pd.DataFrame({'a':[1, 1, 2, 2, 2]})
value_counts = df['a'].value_counts(dropna=True, sort=True)

# solution here
df_val_counts = pd.DataFrame(value_counts)
df_value_counts_reset = df_val_counts.reset_index()
df_value_counts_reset.columns = ['unique_values', 'counts'] # change column names

I'll throw in my hat as well, essentially the same as @wy-hsu solution, but in function format:

def value_counts_df(df, col):
    """
    Returns pd.value_counts() as a DataFrame

    Parameters
    ----------
    df : Pandas Dataframe
        Dataframe on which to run value_counts(), must have column `col`.
    col : str
        Name of column in `df` for which to generate counts

    Returns
    -------
    Pandas Dataframe
        Returned dataframe will have a single column named "count" which contains the count_values()
        for each unique value of df[col]. The index name of this dataframe is `col`.

    Example
    -------
    >>> value_counts_df(pd.DataFrame({'a':[1, 1, 2, 2, 2]}), 'a')
       count
    a
    2      3
    1      2
    """
    df = pd.DataFrame(df[col].value_counts())
    df.index.name = col
    df.columns = ['count']
    return df