Last Updated on May 15, 2023 by mishou

Work in progress.

1. Basics 1 Self-taught learning
2. Basics 2 Making your cheat sheets
3. Basics 3 Google Colaboratory
4. Basics 4 Mathematical operators
5. Basics 5 Autofill and List Comprehensions
6. Basics 6 Tables
7. Basics 7 Classes and Objects
8. Basics 8 Pivot Tables
9. Basics 9 Sample Data Sets
10. Basics 10 Flash Fill
11. Basics 11 Charts
12. Basics 12 Loops
13. Basics 13 Funcitons
14. Basics 14 Macros

I. The tables

These are the sample datasets for the lectures.

1.HTML tables

These are sample datasets for learning Python with Cheat Sheets.

Table 1

Table 2

Table 3

2.CSV raw data

These are the links to the CSV raw data pages. The data are the same as the data above.

Table 1

https://pastebin.com/raw/cSZ8pYWh

Table 2

https://pastebin.com/raw/19gskrJK

Table 3

https://pastebin.com/raw/DiqmYyz3

II. Retrieving data using Google Sheets

Reading the first table of this page with Google Sheets:

=IMPORTHTML("http://www.mishou.be/2023/03/27/python-basics-8-sample-data-sets/","table",1)

Reading the raw data at https://pastebin.com/raw/cSZ8pYWh:

=IMPORTDATA("https://pastebin.com/raw/cSZ8pYWh")

You can see all the tables here:

https://docs.google.com/spreadsheets/d/1gm-tvvbq8QZwgXBHVFwBuEAQRmNpzH1lPD4Eja0cx0E/edit?usp=sharing

III. How to retrieve CSV data as DataFrame using Polars in Python

Polars utilizes a columnar data structure, where data is organized and stored by columns rather than rows. So Polars doesn’t have any index.

You can see some sample code in Google Colaboratory:

https://colab.research.google.com/drive/1jNj4GpUfR2mYJjffN2KSZFRZzv5xfwID?usp=sharing

IV. How to retrieve CSV data as DataFrame using Pandas in Python

1. Reading HTML tables with Pandas

You can read the tables with Pandas and show the first table with the following codes:

import panda as pd
df = pd.read_html('http://www.mishou.be/2021/10/04/pythonr-sample-data-for-data-analysis/')
df[0]

You can see the scripts here:

https://colab.research.google.com/drive/1etXZ-2-RVrCuc3fD6L-8HS6HeeUVQrzS?usp=sharing

2. Reading CSV raw data with Pandas

You can read CSV raw data with the following codes:

import pandas as pd
df = pd.read_csv('https://pastebin.com/raw/cSZ8pYWh')
df.head()

You can see the scripts here:

https://colab.research.google.com/drive/1nzk_YaALD52ASJe0FyOKFuEj-2zHNhbb?usp=sharing

V. How to retrieve tables using Classes and Objects in Python

You can also use Classes and Objects to handle CSV data.

Loading the data:

# load data from url
import csv
import requests

CSV_URL = 'https://pastebin.com/raw/cSZ8pYWh'

with requests.Session() as s:
    download = s.get(CSV_URL)

    decoded_content = download.content.decode('utf-8')

    cr = csv.reader(decoded_content.splitlines(), delimiter=',')
    
    my_list = list(cr)
    for row in my_list:
        print(row)
# remove the header
my_list.pop(0)

Creating a class:

# create a class
class Student:
    
    def __init__(self, number, english, japanese, nationality, department, classes, gender):
      self.number = number
      self.english = english
      self.japanese = japanese
      self.nationality = nationality
      self.department = department
      self.classes = classes
      self.gender = gender

    def average(self):
        return (round((self.english + self.japanese)/2, 2))

    def report(self):
        print(f"Your number: {self.number}, English: {self.english}, Japanese: {self.japanese}, Average: {self.average()}")

Creating a list of objects:

# create a list of objecst
student_list = []
for id, english, japanese, nationality, department, classes, gender in my_list:
   # covert types
   id = int(id)
   english = float(english)
   japanese = float(japanese)
   nationality = str(nationality)
   department = str(department)
   classes = str(classes)
   gender = str(gender)
   # create Student instances and append them to a list
   student_list.append(Student(id, english, japanese, nationality, department, classes, gender))

You can see all the scripts here:

https://colab.research.google.com/drive/1B84Ykq1FfuNZjl_znLgoXzjz4QRUkfPr?usp=sharing