Delve into foundational Python programming, explore libraries like pandas and Matplotlib, and gain insights into data management, analysis, and visualization with practical projects.

jup.tar.gz

pythonCodeRun

jupyter

lesson1

lesson2

Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting.

This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries.

By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.

Introduction to Data Science with Python

Please follow the instructions while solving the exercises. If you are new to matplotlib, the tasks may be tricky. You can always refer to the solutions if you get stuck.

# Creating a DataFrame
The code segment below is used to create our DataFrame and print the `head()` of it.


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
df = pd.DataFrame(np.random.rand(1000,4),columns=['a', 'b', 'c','d'])
print(df.head())

# Task 1: Plot DataFrame as a scatter plot

We can see in the code above that we have four columns—`a`, `b`, `c`, and `d`. Plot `c` against `d` as a scatter plot to recreate the plot given below. Try to match the color and size of the data points. You should be able to check the size of the figure as well. The following points may be useful as you solve this exercise.

- The initial things related to DataFrame creation are already added in the code.
- You can use `df` to make a scatter plot of the data from columns `c` and `d`.
- Refer to the code segment above for variable names and DataFrame.
- You don’t need to save the figure.
- Use `df.plot` to plot the data.

> The values used for the figure are as follows:
> - Figure size: `10,4`. 
> - Size of data points: `s=40`.

Practice the topics we have covered so far related to Pandas for Data Visualization.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Pandas: Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Challenge: Data Visualization in pandas

Creating a DataFrame

Task 1: Plot DataFrame as a scatter plot