Complete Python Pandas Tutorial Notes

Introduction

• Excited for the updated tutorial on Python Pandas.
• Grateful for past support; many updates and new tools in the Pandas library.
• Suitable for beginners and experienced users alike.

Getting Started with Pandas

• Options to start with Pandas:
  • Use Google Colab to edit and run code in your browser.
  • Alternatively, set up locally with editors like Visual Studio Code, PyCharm, or Jupyter Lab.
• Clone the repository for the tutorial data files using the command:

  git clone <repository_link>  
  

• Create and activate a virtual environment:

  python -m venv tutorial_env  
  source tutorial_env/bin/activate  
  

• Install necessary libraries using:

  pip install -r requirements.txt  
  

DataFrames in Pandas

• DataFrame: Main data structure in Pandas, resembling a table with enhanced functionality.
• Example of creating a DataFrame:

  import pandas as pd  
  df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})  
  

• Methods to explore DataFrames:
  • df.head() to view first few rows.
  • df.tail() to view last rows.
  • df.columns to see column names.
  • df.index to see index values.

Loading Data

• Load CSV files with:

  df = pd.read_csv('path/to/file.csv')  
  

• CSV is common, but other formats (like Parquet, Feather) can be more efficient.
• Load data from different formats:
  • CSV: pd.read_csv()
  • Excel: pd.read_excel()
  • Parquet: pd.read_parquet()

Accessing Data

• View data using head(), tail(), sample().
• Access specific rows/columns with .loc[] and .iloc[]:
  • .loc[] uses labels.
  • .iloc[] uses index positions.
• Use .at[] and .iat[] for accessing single values efficiently.

Modifying Data

• To modify a value in DataFrame:

  df.loc[row_index, 'column_name'] = new_value  
  

• Add or drop columns:
  • Add: df['new_column'] = values
  • Drop: df.drop('column_name', axis=1, inplace=True)

Filtering Data

• Filter rows based on conditions:

  filtered_df = df[df['column_name'] > value]  
  

• Use logical operators to combine conditions (e.g., &, |).

Handling Missing Values

• Fill missing values with fillna():

  df.fillna(value)  
  

• Drop missing values with dropna().
• Use isna() to identify missing values.

Aggregating Data

• Use groupby() to aggregate data.

  grouped = df.groupby('column_name').sum()  
  

• Create pivot tables with pivot_table().

Advanced Functionality

• Utilize the new Pi Arrow backend for performance improvements.
• AI tools (like GitHub Copilot) can assist with code generation.

Resources & Recommendations

• Explore the Olympic dataset further to practice with Pandas.
• Check out tutorials on cleaning datasets and solving Pandas puzzles for additional practice.