python - How do I convert upgrouped Data to Grouped Data in Pandas -


so have dataset customers in store , sales of store on each day.

which looks - 

store id     sales      customers 1           250        500 2           276        786 3           124        256 5           164        925

how convert grouped data, this

sales           customers 0-100           0 100-200         1181 200-300         1286

i have searched while , found pandas site - http://pandas.pydata.org/pandas-docs/version/0.15.2/groupby.html

df2.groupby(['x'], sort=true).sum()

but unable understand how apply same example.

use cut bins , groupby , aggregate sum:

df = df.groupby(pd.cut(df['sales'], [0,100,200,300]))['customers'].sum().fillna(0) print (df) sales (0, 100]         0.0 (100, 200]    1181.0 (200, 300]    1286.0 name: customers, dtype: float64

also possible define labels:

l =['0-100','100-200','200-300'] b = [0,100,200,300] df = df.groupby(pd.cut(df['sales'], bins=b, labels=l))['customers'].sum()        .fillna(0)            .reset_index() print (df)      sales  customers 0    0-100        0.0 1  100-200     1181.0 2  200-300     1286.0

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