6. Creating DataFrames

Dictionaries

• A dictionary is a way of storing data in Python
• It holds a set of key-value pairs
• Create a dictionary using curly braces (Each key-value pair is written as “key colon value”)

my_dict = {

“key1” : value1,

“key2” : value2,

“key1” : value3

}

• Access values of a dictionary via their keys in square brackets

my_dict[“title”]

Creating DataFrame

• From a list of dictionaries
  • DataFrame is constructed row by row
• From a dictionary of lists
  • DataFrame is constructed column by column

List of dictionaries – by row

• First has to create a new list using square brackets to hold the dictionaries
• Example:

import pandas as pd

avocados_list = [ # Create a list of dictionaries

{"date": "2019-11-03", "small_sold": 10376832, "large_sold": 7835071},

{"date": "2019-11-10", "small_sold": 10717154, "large_sold": 8561348},

]

avocados_2019 = pd.DataFrame(avocados_list) # Convert list into DataFrame

print(avocados_2019)

Dictionary of lists – by column

• Go through the data by column by column
• Keys are to the left of a colon, and values are to the right
• Key = column name & Value = list of column values
• Has to create a dictionary using curly braces
• Example:

import pandas as pd

avocados_dict = { # Create a dictionary of lists

"date": ["2019-11-17","2019-12-01"],

"small_sold": [10859987,9291631],

"large_sold": [7674135,6238096]

}

avocados_2019 = pd.DataFrame(avocados_dict) # Convert dictionary into DataFrame

print(avocados_2019)

Store the following tables of data into three DataFrames, add a column to store the month of the sales,

and concatenate the three DataFrames into a single DataFrame.

import pandas as pd

import numpy as np

df1 = {"Quantity Sold": [500, 600],

       "Product": ["Apples", "Orange"],

       "Unit Price": [1.0, 2.0],

       "Month": ["Jan", "Jan"]}

df2 = {"Quantity Sold": [900, 300],

       "Product": ["Apples", "Bananas"],

       "Unit Price": [0.80, 0.50],

       "Month": ["Feb", "Feb"]}

df3 = {"Quantity Sold": [200, 100],

       "Product": ["Apples", "Bananas"],

       "Unit Price": [0.80, 1.0],

       "Month": ["Mar", "Mar"]}

jan = pd.DataFrame(df1)

feb = pd.DataFrame(df2)

mar = pd.DataFrame(df3)

jan.index = [0,1]

feb.index = [2,3]

mar.index = [4,5]

table = pd.concat([jan,feb,mar])

print(table)

Quantity Sold Product Unit Price Month

0 500 Apples 1.0 Jan

1 600 Orange 2.0 Jan

2 900 Apples 0.8 Feb

3 300 Bananas 0.5 Feb

4 200 Apples 0.8 Mar

5 100 Bananas 1.0 Mar