# Lesson 6

Lets take a look at the groupby function.

In [1]:
# Import libraries
import pandas as pd
import sys

In [2]:
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)

Python version 3.5.1 |Anaconda custom (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]
Pandas version 0.23.4

In [3]:
# Our small data set
d = {'one':[1,1,1,1,1],
'two':[2,2,2,2,2],
'letter':['a','a','b','b','c']}

# Create dataframe
df = pd.DataFrame(d)
df

Out[3]:
letter one two
0 a 1 2
1 a 1 2
2 b 1 2
3 b 1 2
4 c 1 2
In [4]:
# Create group object
one = df.groupby('letter')

# Apply sum function
one.sum()

Out[4]:
one two
letter
a 2 4
b 2 4
c 1 2
In [5]:
letterone = df.groupby(['letter','one']).sum()
letterone

Out[5]:
two
letter one
a 1 4
b 1 4
c 1 2
In [6]:
letterone.index

Out[6]:
MultiIndex(levels=[['a', 'b', 'c'], [1]],
labels=[[0, 1, 2], [0, 0, 0]],
names=['letter', 'one'])

You may want to not have the columns you are grouping by become your index, this can be easily achieved as shown below.

In [7]:
letterone = df.groupby(['letter','one'], as_index=False).sum()
letterone

Out[7]:
letter one two
0 a 1 4
1 b 1 4
2 c 1 2
In [8]:
letterone.index

Out[8]:
Int64Index([0, 1, 2], dtype='int64')

This tutorial was created by HEDARO