Converting a Pandas Groupby Output from Series to Dataframe

Converting a Pandas GroupBy output from Series to DataFrame

g1 here is a DataFrame. It has a hierarchical index, though:

In [19]: type(g1)
Out[19]: pandas.core.frame.DataFrame

In [20]: g1.index
Out[20]: 
MultiIndex([('Alice', 'Seattle'), ('Bob', 'Seattle'), ('Mallory', 'Portland'),
       ('Mallory', 'Seattle')], dtype=object)

Perhaps you want something like this?

In [21]: g1.add_suffix('_Count').reset_index()
Out[21]: 
      Name      City  City_Count  Name_Count
0    Alice   Seattle           1           1
1      Bob   Seattle           2           2
2  Mallory  Portland           2           2
3  Mallory   Seattle           1           1

Or something like:

In [36]: DataFrame({'count' : df1.groupby( [ "Name", "City"] ).size()}).reset_index()
Out[36]: 
      Name      City  count
0    Alice   Seattle      1
1      Bob   Seattle      2
2  Mallory  Portland      2
3  Mallory   Seattle      1

Groupby a DataFrame into a new DataFrame with arange as index

You have a parameter as_index in groupby for that

df_a.groupby('id', as_index = False)['amount'].sum()

You get

    id  amount
0   1001    1710.34
1   1003    18.95
2   1004    321.20

Convert DataFrameGroupBy object to DataFrame pandas

The result of kl.aggregate(np.sum) is a normal DataFrame, you just have to assign it to a variable to further use it. With some random data:

>>> df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
>>>                         'foo', 'bar', 'foo', 'foo'],
...                  'B' : ['one', 'one', 'two', 'three',
...                         'two', 'two', 'one', 'three'],
...                  'C' : randn(8), 'D' : randn(8)})
>>> grouped = df.groupby('A')
>>> grouped
<pandas.core.groupby.DataFrameGroupBy object at 0x04E2F630>
>>> test = grouped.aggregate(np.sum)
>>> test
            C         D
A                      
bar -1.852376  2.204224
foo -3.398196 -0.045082

Using pandas groupby to write new information into the original DataFrame?

Check your output with reindex

df['new'] = grouped.agg(lambda x: idx(x)).reindex(pd.MultiIndex.from_frame(df)).values

Pandas groupby Increment datetime to make it unique for the group

You can use groupby().cumcount() with pd.to_timedelta:

df['ts'] += pd.to_timedelta(df.groupby('State').cumcount(), unit='s')

Output:

     State  a   b                         ts
0    Texas  4   6 2022-09-06 15:40:46.429416
1    Texas  5  10 2022-09-06 15:40:47.429416
2  Florida  1   3 2022-09-06 15:40:46.429416
3  Florida  3  11 2022-09-06 15:40:47.429416

python pandas - how to transform ds into dataframe

When grouping the DataFrame using more columns you get a MultiIndex.

You can use the reset_index method (see docs) to transform the MultiIndex into columns of a DataFrame.

For your example it would give something like:

> ds_perg1_2_merged.reset_index()

           DescricaoProblema                        strRazaoSocial    
0         Cobrança indevida.               CAIXA ECONOMICA FEDERAL  66
1         Cobrança indevida.                     CAIXA SEGUROS S.A  45
2         Cobrança indevida.                        BANCO BMG S.A.  38
3  Cobrança indevida/abusiva                             CLARO S/A  50
4  Cobrança indevida/abusiva                       TIM CELULAR S/A  47
5  Cobrança indevida/abusiva  COMPANHIA PIRATININGA DE FORÇA E LUZ  34
6          Produto com vício                        VIA VAREJO S/A  46
7          Produto com vício   SAMSUNG ELETRONICA DA AMAZONIA LTDA  27
8          Produto com vício                         WHIRLPOOL S/A  23

How converting a pandas groupby/describe to standard dataframe?

You can specify column after groupby:

label = dataframe.groupby('label')['area'].describe()

In your solution remove first level of MultiIndex:

label = dataframe[['label', 'area']].groupby(['label']).describe().droplevel(0, axis=1)

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