Pandas & Python for Data Analysis by Example – Full Course for Beginners

Introduction

This tutorial will guide you through using Pandas and Python for data analysis, focusing on various real-life projects. You will learn essential skills such as data cleaning, wrangling, filtering, and analysis, which are crucial for anyone pursuing a career in data science.

Step 1: Understanding DataFrames with English Words

• Familiarize yourself with Pandas DataFrames, which are two-dimensional data structures similar to tables.
• Load the dataset containing a dictionary of English words.
• Practice the following:
  • Creating a DataFrame from the dataset.
  • Modifying the DataFrame (e.g., adding or removing columns).
  • Accessing and manipulating data within the DataFrame.

Practical Tip

Start with simple operations like displaying the first few rows of the DataFrame using:

import pandas as pd

df = pd.read_csv('path_to_your_file.csv')
print(df.head())

Step 2: Filtering and Sorting Pokemon Data

• Use a dataset containing information about various Pokémon.
• Focus on:
  • Filtering data based on specific criteria (e.g., type, strength).
  • Sorting the data in ascending or descending order based on attributes (e.g., HP or attack).

Common Pitfall

Ensure your filtering conditions are correct to avoid empty results. Use:

filtered_data = df[df['type'] == 'Water']

Step 3: Exploring the Birthday Paradox in the NBA

• Understand the concept of the Birthday Paradox.
• Analyze NBA player data to determine how many players share birthdays.
• Steps to take:
  • Calculate the probability of shared birthdays among players.
  • Identify teams with players who share birthdays.

Real-World Application

This analysis can be useful for creating engaging content for sports fans or for statistical modeling in sports analytics.

Step 4: Matching Strings by Similarity Using Levenshtein Distance

• Learn how to handle string data by using the Levenshtein distance to measure similarity.
• Key tasks include:
  • Cleaning company names by identifying and correcting irregularities.
  • Using libraries like fuzzywuzzy for string matching.

Code Snippet

To calculate the Levenshtein distance:

from Levenshtein import distance

dist = distance('company_name_1', 'company_name_2')

Step 5: Data Cleaning with Google Playstore Dataset

• Work with a dataset scraped from the Google Play Store.
• Focus on data cleaning tasks:
  • Identify and handle null values.
  • Remove duplicate entries and outliers.

Practical Steps

Use Pandas functions like:

df.dropna(inplace=True)  # Remove null values
df.drop_duplicates(inplace=True)  # Remove duplicates

Step 6: Analyzing Premier League Matches

• Combine data cleaning with analysis by examining match data from the Premier League.
• Tasks include:
  • Grouping data by team or match outcome.
  • Performing statistical analysis on match results.

Key Consideration

Ensure data is clean before analysis to avoid misleading results.

Step 7: NBA 2017 Season Analysis with Joining and Groupby

• Test your skills by merging multiple dataframes related to the 2017 NBA season.
• Key actions:
  • Perform joins on different datasets.
  • Use the groupby function to aggregate data and answer specific questions.

Example Code

To merge two DataFrames:

merged_df = pd.merge(df1, df2, on='common_column')

Conclusion

By completing these steps, you will have gained practical experience with Pandas and Python for data analysis. Each project builds upon the previous one, enhancing your skills in data cleaning, manipulation, and analysis. As a next step, consider exploring additional datasets or challenges to further hone your abilities in data science.