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Book (2nd ed.)
Chapter 1: Introduction
Chapter 2: The Core Python Language I
Examples
Questions
Problems
Additional Problems
Chapter 3: Simple Plots and Charts
Examples
Problems
Chapter 4: The core Python language II
Examples
Questions
Problems
Additional Problems
Chapter 5: IPython and Jupyter Notebook
Examples
Problems
Chapter 6: NumPy
Examples
Questions
Problems
Additional Examples
Additional Problems
Chapter 7: Matplotlib
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Questions
Problems
Chapter 8: SciPy
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Additional Examples
Additional Problems
Chapter 9: Data Analysis with pandas
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Chapter 10: General Scientific Programming
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Book (2nd ed.)
Chapter 9: Data Analysis with pandas
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
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
P9.3.1 Tuberculosis rates by state in the USA
P9.3.2 Tuberculosis rates per head by state in the USA
P9.4
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
P9.5.1 Analysing PISA data
P9.5.2 Analysing Formula One data