Need help with a python project on jupyter notebook platform.
Budget: $30 – $250 AUD
This project is basically supposed to explore data provided in a 'raw CSV' format which needs to be cleaned beforehand. It contains some of the calculation work as well.
Everything should be included in a single Jupyter notebook. The Python code included should follow best practices as outline in the lectures, including using well chosen identifier names, writing clear simple code, and not repeating yourself.
All data processing should be done using the Pandas library and should make use of the following Pandas features:
• Reading input data files.
• Parsing dates.
• Selecting appropriate column(s) of a Data Frame to act as the index.
• Computing new columns.
• Using Python functions to compute new columns.
• Filtering rows by condition.
• At least two different kinds of Matplotlib plots.
• Group By to explore relationships between variables.
• Error bars in plots to show variation (e.g., standard deviation).
• All plots should be appropriately titled, and axis appropriately labelled.
• User friendly axis labels, e.g., Mon, Tue, Wed or Jan, Feb, Mar.
• Appropriately sized figures that are large enough to easy read.
• Binning of continuous values so as to investigate relationships between continuous variables.
• Using Python functions to compute aggregate information for groups of rows.
Everything should be included in a single Jupyter notebook. The Python code included should follow best practices as outline in the lectures, including using well chosen identifier names, writing clear simple code, and not repeating yourself.
All data processing should be done using the Pandas library and should make use of the following Pandas features:
• Reading input data files.
• Parsing dates.
• Selecting appropriate column(s) of a Data Frame to act as the index.
• Computing new columns.
• Using Python functions to compute new columns.
• Filtering rows by condition.
• At least two different kinds of Matplotlib plots.
• Group By to explore relationships between variables.
• Error bars in plots to show variation (e.g., standard deviation).
• All plots should be appropriately titled, and axis appropriately labelled.
• User friendly axis labels, e.g., Mon, Tue, Wed or Jan, Feb, Mar.
• Appropriately sized figures that are large enough to easy read.
• Binning of continuous values so as to investigate relationships between continuous variables.
• Using Python functions to compute aggregate information for groups of rows.