Part 1: data frames (extra powerful dictionaries) - SOLVED

This workbook does not require you to load any datasets.

We will need to import both pandas and numpy by running the cell below:

import pandas as pd
import numpy as np

Before we analyse this spreadsheet let us dig in a bit more into Data Frames.

Let us create a simple dictionary first:

gradebook = {}

gradebook["Student id"] = ["UP123", "UP124", "UP125", "UP126"]
gradebook["Marks (out of 10)"] = ["10", "5", "7", "6"]

#display the gradebook

gradebook

{'Student id': ['UP123', 'UP124', 'UP125', 'UP126'],
 'Marks (out of 10)': ['10', '5', '7', '6']}

Now, let’s create a Data Frame that holds the same information, but it is much more flexible than a dictionary in terms of functionality:

df_gradebook = pd.DataFrame()

df_gradebook["Student id"] = ["UP123", "UP124", "UP125", "UP126"]
df_gradebook["Marks (out of 10)"] = ["10", "5", "7", "6"]

# Display the gradebook

df_gradebook

	Student id	Marks (out of 10)
0	UP123	10
1	UP124	5
2	UP125	7
3	UP126	6

Even though we have used lists to construct the Data Frame columns they are flexible to use. The columns were converted to pandas Series: the pandas Series uses numpy arrays, but adds extra functionality!

type(df_gradebook["Student id"])

pandas.core.series.Series

To print a single column of the data frame we use:

print(df_gradebook["Marks (out of 10)"])

0    10
1     5
2     7
3     6
Name: Marks (out of 10), dtype: object

Exercise 1.1

Create a simple Data Frame that represents the following spreadsheet:

#make the empty data frame
my_data_frame = pd.DataFrame()

#add the data
my_data_frame["Item"] = ["Shoes", "Boots", "Bike", "Laptop", "Fridge"]
my_data_frame["Colour"] = ["Blue", "Yellow", "Copper", "Black", "White"]
my_data_frame["Price"] = [120,20,400,1000,600]

my_data_frame

	Item	Colour	Price
0	Shoes	Blue	120
1	Boots	Yellow	20
2	Bike	Copper	400
3	Laptop	Black	1000
4	Fridge	White	600

Print each of the columns of this Data Frame.

my_data_frame["Item"]

0     Shoes
1     Boots
2      Bike
3    Laptop
4    Fridge
Name: Item, dtype: object

my_data_frame["Colour"]

0      Blue
1    Yellow
2    Copper
3     Black
4     White
Name: Colour, dtype: object

my_data_frame["Price"]

0     120
1      20
2     400
3    1000
4     600
Name: Price, dtype: int64