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: 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, window functions, filter, date-time values, and joins.
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: CSV files, grouping, strings, formatting, dates and times, and plotting
Get better at: CSV files, grouping, strings, formatting, dates and times, and plotting
Get better at: CSV files, working with dates and times, grouping, plotting, and handling multiple files.
