Learning Pandas (vi)

Original English: 06-lesson

Let's take a look at the groupby function.

# import library Import
pandas as PD
import sys

Print (' Python version ' + sys.version)
print (' Pandas version ' + pd.__version__)

    Python version 3.6.1 | Packaged by Conda-forge | (Default, Mar 2017, 21:57:00) 
    [GCC 4.2.1 compatible Apple LLVM 6.1.0 (clang-602.0.53)]
    Pandas version 0.19.2

# Our decimal aggregates
D = {' One ': [1,1,1,1,1],
     ' two ': [2,2,2,2,2], ' Letter
     ': [' a ', ' a ', ' B ', ' B ', ' C ']}

# Create a Dataframe
df = PD. Dataframe (d)
DF

Letter One

			two
0	A	1	2
1	A	1	2
2	B	1	2
3	B	1	2
4	C	1	2

# Create a GroupBy object one
= df.groupby (' letter ')

# Apply the sum () function on a group
one.sum ()

One

		two
Letter
A	2	4
B	2	4
C	1	2

LetterOne = Df.groupby ([' Letter ', ' one ']). SUM ()
LetterOne

Letter One

		Two
a	1	4
b	1	4
C	1	2

Letterone.index

    Multiindex (levels=[[' A ', ' B ', ' C '], [1]],
               labels=[[0, 1, 2], [0, 0, 0]],
               names=[' letter ', ' one ']

You may not want to index the column names used for grouping, as is easy to do in the following ways.

LetterOne = Df.groupby ([' Letter ', ' one '], as_index=false). SUM ()
LetterOne

Letter One

			two
0	A	1	4
1	B	1	4
2	C	1	2

Letterone.index

   Int64index ([0, 1, 2], dtype= ' Int64 ')