Learning Scientific Programming with Python (2nd edition)

Chapter 9: Data Analysis with pandas

• Examples
  • E9.1: Replacing NaN and infinite values in pandas
  • E9.2: loc and iloc in pandas
  • E9.3: Data analysis: female literacy in India
  • E9.4: Retrieving ionization energies from a pandas DataFrame
  • E9.5: Using converter functions to read data files to pandas DataFrames
  • E9.6: Writing a comma-separated file from a pandas DataFrame
  • E9.7: Reading an Excel sheet into a pandas DataFrame
  • E9.8: Writing a pandas DataFrame to an Excel file
  • E9.9: A simple webscraping script with pandas
  • E9.10: Resampling a DataFrame to plot statistics of a river gauge
  • E9.11: Millikan's oil-drop experiment
  • E9.12: Split–Apply–Combine
  • E9.13: Analysing the history of nuclear explosions with pandas
  • E9.14: Analysing the history of volcanic eruptions with pandas
• Problems
  • P9.2.1: Antarctic ozone depletion
  • P9.2.2: The Oddo–Harkins rule
  • P9.2.3: The Hertzsprung–Russell diagram
  • P9.2.4: London Underground usage statistics
  • P9.2.5: The line spectrum of carbon dioxide
  • P9.3.1: Tuberculosis rates by state in the USA
  • P9.3.2: Tuberculosis rates per head by state in the USA
  • P9.4.1: Coloured Hertzsprung-Russell diagram
  • P9.4.2: Millikan's oil-drop experiment
  • P9.4.3: Analysing the weather in Cambridge, UK
  • P9.4.4: Investments over 10 years
  • P9.5.1: Analysing PISA data
  • P9.5.2: Analysing Formula One data