Pandas offers a wide set of functionality having to do with dates and times, from extracting values to calculating the differences between dates, to time series, to resampling.
Get better at: Working with Excel, grouping, cleaning, regular expressions, and multi-indexes.
Get better at: Working with Excel, grouping, cleaning, regular expressions, and multi-indexes.
Get better at: Working with CSV, working with dates and times, cleaning, grouping, and plotting.
Get better at: Working with CSV, working with dates and times, cleaning, grouping, and plotting.
Get better at: Working with multiple CSV files, grouping, pivot tables, dates and times, speed optimization, and memory optimization.
Get better at: Working with multiple CSV files, grouping, pivot tables, dates and times, speed optimization, and memory optimization.
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
