Pandas allows us to create a number of different plot types, using an API that wraps around Matplotlib. We can create line, bar, pie, scatter, and box plots, among others.
Get better at: CSV files, PyArrow, reducing memory usage, working with dates and times, pivot tables, and plotting.
Get better at: CSV files, PyArrow, reducing memory usage, working with dates and times, pivot tables, and plotting.
Get better at: Working with Excel files, plotting with Plotly, applying functions, handling datetime data, and invoking window functions
Get better at: Working with Excel files, plotting with Plotly, applying functions, handling datetime data, and invoking window functions
Get better at: Using PyArrow, pivot tables, plotting, optimizing query speed, datetime, multi-indexes, and using xarray
Get better at: Using PyArrow, pivot tables, plotting, optimizing query speed, datetime, multi-indexes, and using xarray
Get better at: Working with Excel files, plotting with Seaborn, mult-indexes, and window functions
Get better at: Working with Excel files, plotting with Seaborn, mult-indexes, and window functions
Get better at: Working with CSV files, dates and times, filtering, correlations, grouping, and plotting
Get better at: Working with CSV files, dates and times, filtering, correlations, grouping, and plotting
Get better at: Working with Excel files, regular expressions, cleaning data, window functions, and plotting.
Get better at: Working with Excel files, regular expressions, cleaning data, window functions, and plotting.
