How does df.groupby('A').agg('min') translate to featuretools?

Question

Say I have this simple snippet of code. I will group, aggregate, and merge the dataframe:

Using Pandas:

Data

df = pd.DataFrame({'A': [1, 1, 2, 2],
               'B': [1, 2, 3, 4],
               'C': [0.3, 0.2, 1.2, -0.5]})

df:

    A   B   C
0   1   1   0.3
1   1   2   0.2
2   2   3   1.2
3   2   4   -0.5

Group and Aggregate

df_result = df.groupby('A').agg('min')
df_result.columns =  ['groupby_A(min_'+x+')' for x in df_result.columns]

df_result:

    groupby_A(min_B)    groupby_A(min_C)
A       
1   1                   0.2
2   3                   -0.5

Merge

df_new = pd.merge(df,df_result,on='A')
df_new

df_new:

    A   B   C       groupby_A(min_B)    groupby_A(min_C)
0   1   1   0.3     1                   0.2
1   1   2   0.2     1                   0.2
2   2   3   1.2     3                  -0.5
3   2   4   -0.5    3                  -0.5

An Attempt using featuretools:

# ---- Import the Module ----
import featuretools as ft

# ---- Make the Entity Set (the set of all tables) ----
es = ft.EntitySet()

# ---- Make the Entity (the table) ----
es.entity_from_dataframe(entity_id = 'df', 
                         dataframe = df)


# ---- Do the Deep Feature Synthesis (group, aggregate, and merge the features) ----
feature_matrix, feature_names = ft.dfs(entityset = es, 
                                       target_entity = 'df',
                                       trans_primitives = ['cum_min'])

feature_matrix

feature_matrix:

        A       B       C       CUM_MIN(A)  CUM_MIN(B)  CUM_MIN(C)
index                       
0       1       1       0.3     1           1           0.3
1       1       2       0.2     1           1           0.2
2       2       3       1.2     1           1           0.2
3       2       4       -0.5    1           1           -0.5

How does the operation with Pandas translate into featuretools (preferably without adding another table)?

My attempt with featuretools does not give the right output, but I believe the process that I used is somewhat correct.

kindly add code in question not as picture ,code from pic cannot be copied — tawab_shakeel, Jun 28 '19 at 17:12
Welcome to StackOverflow. Please paste the code you've tried using `code-formatting` rather than in an image. Could you also explain what you are trying to do and what you have tried? Pasting starting data and expected output helps. — Ian Thompson, Jun 28 '19 at 17:13

score 2 · Accepted Answer · answered Jun 28 '19 at 17:55

Here is the recommended way to do it in Featuretools. You do need to create another table to make it work exactly as you want.

import featuretools as ft
import pandas as pd

df = pd.DataFrame({'A': [1, 1, 2, 2],
                   'B': [1, 2, 3, 4],
                   'C': [0.3, 0.2, 1.2, -0.5]})

es = ft.EntitySet()

es.entity_from_dataframe(entity_id="example",
                          index="id",
                          make_index=True,
                          dataframe=df)

es.normalize_entity(new_entity_id="a_entity",
                    base_entity_id="example",
                    index="A")

fm, fl = ft.dfs(target_entity="example",
                entityset=es,
                agg_primitives=["min"])

fm

this returns

    A  B    C  a_entity.MIN(example.B)  a_entity.MIN(example.C)
id                                                             
0   1  1  0.3                        1                      0.2
1   1  2  0.2                        1                      0.2
2   2  3  1.2                        3                     -0.5
3   2  4 -0.5                        3                     -0.5

If you don't want to create an extra table you could try using the cum_min primitive which calculate the cumulative after grouping by A

df = pd.DataFrame({'A': [1, 1, 2, 2],
                   'B': [1, 2, 3, 4],
                   'C': [0.3, 0.2, 1.2, -0.5]})

es = ft.EntitySet()

es.entity_from_dataframe(entity_id="example",
                          index="id",
                          make_index=True,
                          variable_types={
                              "A": ft.variable_types.Id
                          },
                          dataframe=df,)

fm, fl = ft.dfs(target_entity="example",
                entityset=es,
                groupby_trans_primitives=["cum_min"])

fm