[Folder 2 - Lesson 7 - notebook-covidspread] fix incorrect indexing

[wangamber8-collab]

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Update the slicing of the series from [2:] to [3:] because indexes 0-2 contains non-date metadata (province, latitude, longitude). The label "longitude" is included in the dataframe and appears in the plot. Additionally, drop the column "province/state" column before the function is called. This is because "province/state" contains non-date data that will cause an error when the function is called.

[Copilot]

Pull request overview

This pull request fixes incorrect data slicing in a COVID-19 spread analysis notebook that was causing non-date metadata to appear in plots. The fix adjusts the slice indexing from [2:] to [3:] to properly exclude Province/State, Latitude, and Longitude columns, and adds Province/State to the list of columns to be dropped.

Changes:

• Updated data slicing from [2:] to [3:] to exclude three metadata columns instead of two
• Added 'Province/State' column to the drop list alongside 'Lat' and 'Long' for all three dataframes
• Removed trailing whitespace in the mkframe function

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[Copilot]

Pull request overview

Copilot reviewed 1 out of 2 changed files in this pull request and generated 1 comment.

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[wangamber8-collab]

@leestott Hi! I’m a freshman and fairly new to contributing to open-source projects, so I really appreciate the feedback you left earlier. I’ve gone through and addressed all the comments and made the requested changes.

When you have a chance, could you take another look and let me know if this is ready to be merged, or if there’s anything else I should update?

Thanks so much for your time!

[Copilot]

Pull request overview

Copilot reviewed 1 out of 2 changed files in this pull request and generated 1 comment.

Comments suppressed due to low confidence (2)

2-Working-With-Data/07-python/notebook-covidspread.ipynb:1611

• This note claims the first three elements are non-date metadata including Province/State, but after the earlier groupby('Country/Region').sum() the example output shows only Lat/Long as non-date columns. Update the note to match the actual post-groupby dataframe shape (or adjust preprocessing so Province/State is still present if that’s intended).

    "> **Note** how we use `[3:]` to remove the first three elements of a sequence that contain non-date metadata (the Province/State and geolocation columns). We can also drop those three columns altogether:"

2-Working-With-Data/07-python/notebook-covidspread.ipynb:1622

• infected.groupby('Country/Region').sum() earlier produces a dataframe that (per the shown infected.head() output) does not include a Province/State column. Dropping ['Lat','Long','Province/State'] will therefore raise a KeyError on Province/State in the current notebook. Either drop Province/State before the groupby step, or add errors='ignore' / conditional logic so this cell is robust across dataset versions.

    "infected.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
    "recovered.drop(columns=['Lat','Long','Province/State'],inplace=True)\n",
    "deaths.drop(columns=['Lat','Long','Province/State'],inplace=True)"

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[Copilot]

infected/recovered/deaths are grouped by Country/Region earlier; the displayed infected.head() after that step shows columns starting with Lat, Long, then date columns (no Province/State). With that shape, slicing with [3:] will skip the first date column (e.g., 1/22/20). Consider keeping [2:] (to drop only Lat/Long) or explicitly dropping the non-date columns before plotting and then plotting the remaining date-indexed series.

This issue also appears in the following locations of the same file:

• line 1611
• line 1620

[wangamber8-collab]

Even after grouping by Country/Region, the Province/State column is still in the dataframe (see attachment), so I think we should keep [3:] in order to remove this column. Additionally, keeping [2:] results in an error in the plot, causing "long" to appear on the x-axis (see attachment).