`DataFrame` Indexing and Loading

Introduction to CSV Files

• CSV (Comma Separated Values): A lightweight, ubiquitous format for data storage.
  • Commonly used by spreadsheet software like Excel and Google Sheets.
  • Format is flexible but lacks a strict specification, leading to possible variations in structure.

Viewing CSV File Contents

• Viewing CSV Files with Shell Commands:
  • Jupyter notebooks can integrate shell commands using an exclamation mark (!).
  • Example: Using !cat to display the contents of a CSV file:

    !cat datasets/Admission_Predict.csv

Loading CSV Data into a DataFrame

• Import pandas:

  import pandas as pd

• Loading Data:

  df = pd.read_csv('datasets/Admission_Predict.csv')

• Viewing Data:

  df.head()

Setting a Specific Column as the Index

• Using index_col parameter:

  df = pd.read_csv('datasets/Admission_Predict.csv', index_col=0)
  df.head()

Renaming Columns

• Basic Renaming Using a Dictionary:

  new_df = df.rename(columns={
      'GRE Score': 'GRE Score', 
      'TOEFL Score': 'TOEFL Score',
      'University Rating': 'University Rating', 
      'SOP': 'Statement of Purpose',
      'LOR': 'Letter of Recommendation',
      'CGPA': 'CGPA', 
      'Research': 'Research',
      'Chance of Admit': 'Chance of Admit'
  })
  new_df.head()

• Identifying Issues with Column Names:

  new_df.columns

• Addressing Issues with Spaces:

  new_df = new_df.rename(columns={'LOR ': 'Letter of Recommendation'})
  new_df.head()

• Using str.strip() for Robustness:

  new_df = new_df.rename(mapper=str.strip, axis='columns')
  new_df.head()

Modifying Column Names Directly

• Using df.columns Attribute:

  cols = list(df.columns)
  cols = [x.lower().strip() for x in cols]
  df.columns = cols
  df.head()

Summary

• Loading CSV into pandas DataFrame: Utilized pd.read_csv().
• Basic Data Cleaning: Performed column renaming and handled issues with extra spaces.
• Direct Column Modification: Demonstrated using lists and list comprehensions for efficient renaming.