Best way to add pandas DataFrame column to row [duplicate]

Question

I have to find the best way to create a new Dataframe using existing DataFrame.

Look at this link to have full code : jdoodle.com/a/xKP

I have this kind of DataFrame :

df = pd.DataFrame({'length': [112, 214, 52,88], 'views': [10000, 50000, 25000,5000], 'click': [55, 64, 85,9]},
     index = ['id1', 'id2', 'id3','id4'])

     click  length  views
id1     55     112  10000
id2     64     214  50000
id3     85      52  25000
id4      9      88   5000

And need to have this result :

    type_stat   stat
id1     click     55
id2     click     64
id3     click     85
id4     click      9
id1    length    112
id2    length    214
id3    length     52
id4    length     88
id1     views  10000
id2     views  50000
id3     views  25000
id4     views   5000

Currently, I create a function who return DataFrame with one stat :

def df_by_stat(current_df,stat):
    current_df['type_stat'] = stat
    current_df['stat'] = current_df[stat].astype(int)

    return current_df[['type_stat','stat']]

After I make an .append with the function like this :

def final():
    return df_by_stat(df,'click').append(
    df_by_stat(df,'length')).append(
    df_by_stat(df,'views'))

print(final())

This way is working but its complexity depends on rows and columns cardinalities witch is too expensive. That's why I need your help to find a best method.

Answer 1

Using pandas.melt after elevating your index to a series:

res = pd.melt(df.assign(index=df.index), id_vars='index',
              value_name='stat', var_name='type_stat')\
        .set_index('index')

print(res)

      type_stat   stat
index                 
id1       click     55
id2       click     64
id3       click     85
id4       click      9
id1      length    112
id2      length    214
id3      length     52
id4      length     88
id1       views  10000
id2       views  50000
id3       views  25000
id4       views   5000