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Lesson 1.5 Developing Backend 3rd Party APIs with analysis and cleaning Python, Flask, Pandas

Key Topics:

3rd Party APIs

Analysis and Cleaning using Python, Flask, and Pandas

3rd Party APIs

What are 3rd Party APIs?

3rd Party APIs are application programming interfaces that are created by third-party developers or companies

3rd Party API’s allow developers to save time and resources by not having to make the API’s by themselves

There are security risks with using less popular 3rd party APIs

Developers should ensure that they are secure and reliable before integrating them into their applications

Some popular API’s

Rapid API

Github Public APIs

Google Maps API

Twitter API

Facebook API

etc

Example 1 (Python)

Example 2 (Javascript)

Covid 19 3rd Party API

fetch('https://api.covid19api.com/summary')
    .then(response => response.json())
    .then(data => {
      const tbody = document.querySelector('#myTable tbody');
      data.Countries.forEach(country => {
        const row = document.createElement('tr');
        row.innerHTML = `
          <td>${country.Country}</td>
          <td>${country.TotalConfirmed}</td>
          <td>${country.TotalDeaths}</td>
        `;
        tbody.appendChild(row);
      });
    })
    .catch(error => console.error(error));

Country	Cases	Deaths

Analysis and Cleaning using Python, Flask, and Pandas

What is Analysis and Cleaning?

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset

Allows for accurate, reputable, organized data that generates a reliable representation of whatever it is that will be put through data analysis

Pandas

Pandas is a package, which contains functions mainly used for data analysis, cleaning, and manipulation

Pandas uses other Python libraries such as NumPy, Scikit-learn, and TensorFlow

Function	Purpose
head()	Returns the first n rows of a DataFrame
tail()	Returns the last n rows of a DataFrame
read_csv()	Reads a CSV file into a Pandas DataFrame
dropna()	Removes rows with null values
merge()	Merges two DataFrames based on a specified column
sort_values()	Sorts a DataFrame by a specified column
describe()	Generates descriptive statistics of a DataFrame
scatter()	Create a scatter plot of DataFrame data

Example 1

Example 2

Json file:

{
    "Player ID": {
        "0": 101,
        "1": 102,
        "2": 103,
        "3": 104,
        "4": 105,
        "5": 106,
        "6": 107,
        "7": 108,
        "8": 109,
        "9": 110
    },
    "Name": {
        "0": "Lionel Messi",
        "1": "Cristiano Ronaldo",
        "2": "Neymar Jr",
        "3": "Kylian Mbappé",
        "4": "Mohamed Salah",
        "5": "Kevin De Bruyne",
        "6": "Robert Lewandowski",
        "7": "Virgil van Dijk",
        "8": "Sadio Mané",
        "9": "Jan Oblak"
    },
}

Python code:

Flask

Flask is a back-end framework

This framework provides tools and libraries for building web applications, including URL routing, templating, request and response handling, and session management

It is designed to be flexible and extensible, allowing developers to add functionality when needed

Example

Hacks

Choose from 1 of 2 of these hacks:

Option 1:

Find a third party api and display it in a table

Option 2:

Take this python table and use a minimum of 3 pandas functions to analyze the data

import pandas as pd
data = {
    'Name': ['Dillon', 'Noor', 'Steven', 'Lucas', 'Harsha', 'Varalu', 'Ryan', 'Emaad'],
    'Age': [24, 31, 42, 27, 29, 26, 90, 15],
    'Gender': ['M', 'M', 'M', 'M', 'F', 'F', 'F', 'F'],
    'Grade': ['A', 'B', 'A', 'D', 'C', 'F', 'B', 'A']
}
df = pd.DataFrame(data)
print(df)