Classwork examples and exercises for the Data Analysis with Python I module of the Degree Apprenticeship in Data Analytics and Visualisation.
If you spot any mistakes or problems, or have any suggestions, please raise an issue.
- Read csv
- Datetime format
- Ordering ordinal data
- Ordinal categories
- Read tab separated values
- Deal with the thousands comma
- Convert a column to integer
- Drop a single row of data
- Setting an index
- Sorting by the index
- Accessing columns
- Basic sorting and filtering
- Groupby
- Filter with 'like'
- loc and iloc
- Filter with 'regex'
- The query function
- The (numpy) where function
- Categorising data with cut
- Crosstabulation
- Select by data type
- Get the index back as a column
- Make new columns from existing columns
- Make a new, constant, column
- Combining by concatenation
- Combining by joining on a common column
- When the shared column has a different name
- Overview of visualisation libraries
- Scatter plots with pandas and seaborn
- Time series
- Also time series
- Scatter plot with seaborn with markers and colours
- Seaborn distribution plot
- Grouped box plots
- Seaborn catplot for grouped count plots
- Adding data labels in pyplot/seaborn
- Interactivity in notebooks
- Chartify from Spotify
- Holoviews
- Grouped plots in seaborn - relplot
- Multiple scatter plots - pairplot
- Maps with geopandas