How to Move Pandas Data from Index to Column After Multiple Groupby

How to move pandas data from index to column after multiple groupby

Method #1: reset_index()

>>> g
              uses  books
               sum    sum
token   year             
xanthos 1830     3      3
        1840     3      3
        1868     2      2
        1875     1      1

[4 rows x 2 columns]
>>> g = g.reset_index()
>>> g
     token  year  uses  books
                   sum    sum
0  xanthos  1830     3      3
1  xanthos  1840     3      3
2  xanthos  1868     2      2
3  xanthos  1875     1      1

[4 rows x 4 columns]

Method #2: don't make the index in the first place, using as_index=False

>>> g = dfalph[['token', 'year', 'uses', 'books']].groupby(['token', 'year'], as_index=False).sum()
>>> g
     token  year  uses  books
0  xanthos  1830     3      3
1  xanthos  1840     3      3
2  xanthos  1868     2      2
3  xanthos  1875     1      1

[4 rows x 4 columns]

pandas groupby multiple columns gives weird index behavior

When we try to groupby is we do not want the groupby key as index, we can pass to the as_index=False

key1_sum = df1.groupby([0,1,2,3,4,5],as_index=False).sum()

How to convert index of a pandas dataframe into a column

either:

df['index1'] = df.index

or, .reset_index:

df = df.reset_index(level=0)

---

so, if you have a multi-index frame with 3 levels of index, like:

>>> df
                       val
tick       tag obs        
2016-02-26 C   2    0.0139
2016-02-27 A   2    0.5577
2016-02-28 C   6    0.0303

and you want to convert the 1st (tick) and 3rd (obs) levels in the index into columns, you would do:

>>> df.reset_index(level=['tick', 'obs'])
          tick  obs     val
tag                        
C   2016-02-26    2  0.0139
A   2016-02-27    2  0.5577
C   2016-02-28    6  0.0303

pandas groupby without turning grouped by column into index

df.groupby(['col2','col3'], as_index=False).sum()

Pandas dataframe columns after groupby

Specify parameter as_index=False while grouping:

df.groupby('Names', as_index=False).agg(
    {'Column1':'sum', 'Column2':'sum','Column3':'min'})

  Names  Column1  Column2  Column3
0   Bob        3        3     2011
1  John        4        5     2005
2  Pier        1        1     2003

Turn Pandas Multi-Index into column

The reset_index() is a pandas DataFrame method that will transfer index values into the DataFrame as columns. The default setting for the parameter is drop=False (which will keep the index values as columns).

All you have to do call .reset_index() after the name of the DataFrame:

df = df.reset_index()  

How to keep original index of a DataFrame after groupby 2 columns?

I think you are are looking for transform in this situation:

df['count'] = df.groupby(['col1', 'col2'])['col3'].transform('count')

Pandas: How to remove the index column after groupby and unstack?

An alternative to your solution, but the key is just to add a rename_axis(columns = None), as the date is the name for the columns axis:

(example[["date", "customer", "sales"]]
.groupby(["date", "customer"])
.sum()
.unstack("date")
.droplevel(0, axis="columns")
.rename_axis(columns=None)
.reset_index())

    customer    2016-12 2017-01 2017-02
0   123          10.5   6.8     29.5
1   456          25.2   23.4    33.9

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

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