Let's start by importing Pandas. Click on a cell to activate it. Press Shift + Enter to execute. Cells should be executed in order.

In [1]:

import pandas as pd

Load / Create Data Frame¶

We can read CSV or excel files to import data. We can also create dataframes programatically like this:

In [2]:

df = pd.DataFrame({'Artist':['Billie Holiday','Jimi Hendrix', 'Miles Davis', 'SIA'],
              'Genre': ['Jazz', 'Rock', 'Jazz', 'Pop'],
              'Listeners': [1300000, 2700000, 1500000, 2000000],
              'Plays': [27000000, 70000000, 48000000, 74000000]})

The variable df now contains a dataframe. Executing this cell shows the contents of the dataframe

In [3]:

df

Out[3]:

	Artist	Genre	Listeners	Plays
0	Billie Holiday	Jazz	1300000	27000000
1	Jimi Hendrix	Rock	2700000	70000000
2	Miles Davis	Jazz	1500000	48000000
3	SIA	Pop	2000000	74000000

Selection¶

We can select any column using its label:

In [4]:

df['Artist']

Out[4]:

0    Billie Holiday
1      Jimi Hendrix
2       Miles Davis
3               SIA
Name: Artist, dtype: object

We can select one or multiple rows using their numbers (inclusive of both bounding row numbers):

In [5]:

df.loc[1:3]

Out[5]:

	Artist	Genre	Listeners	Plays
1	Jimi Hendrix	Rock	2700000	70000000
2	Miles Davis	Jazz	1500000	48000000
3	SIA	Pop	2000000	74000000

In [6]:

# Exercise: Select only row #0

We can select any slice of the table using a both column label and row numbers using loc:

In [7]:

df.loc[1:3,['Artist']]

Out[7]:

	Artist
1	Jimi Hendrix
2	Miles Davis
3	SIA

In [8]:

# Exercise: Select 'Artist' and 'Plays' for rows #1 and #2

Filtering¶

Now it gets more interesting. We can easily filter rows using the values of a specific row. For example, here are our jazz musicians:

In [9]:

df[df['Genre'] == "Jazz" ]

Out[9]:

	Artist	Genre	Listeners	Plays
0	Billie Holiday	Jazz	1300000	27000000
2	Miles Davis	Jazz	1500000	48000000

In [10]:

# Exercise: Select the row where the Genre is 'Rock'

Here are the artists who have more than 1,800,000 listeners:

In [11]:

df[df['Listeners'] > 1800000 ]

Out[11]:

	Artist	Genre	Listeners	Plays
1	Jimi Hendrix	Rock	2700000	70000000
3	SIA	Pop	2000000	74000000

In [12]:

# Exercise: Select the rows where 'Plays' is less than 50,000,000

Grouping¶

In [13]:

df.groupby('Genre').sum()

Out[13]:

	Listeners	Plays
Genre
Jazz	2800000	75000000
Pop	2000000	74000000
Rock	2700000	70000000

In [14]:

# Exercise: Group by Genre, use mean() as the aggregation function

In [15]:

# Exercise: Group by Genre, use max() as the aggregation function

Martin ZugnoniMartin Zugnoni

Pandas Intro

Load / Create Data Frame¶

Selection¶

Filtering¶

Grouping¶