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Data Manipulation with pandas
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    • Data Manipulation with pandas

    Run the hidden code cell below to import the data used in this course.

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here

    Explore Datasets

    Use the DataFrames imported in the first cell to explore the data and practice your skills!

    • Print the highest weekly sales for each department in the walmart DataFrame. Limit your results to the top five departments, in descending order. If you're stuck, try reviewing this video.
    • What was the total nb_sold of organic avocados in 2017 in the avocado DataFrame? If you're stuck, try reviewing this video.
    • Create a bar plot of the total number of homeless people by region in the homelessness DataFrame. Order the bars in descending order. Bonus: create a horizontal bar chart. If you're stuck, try reviewing this video.
    • Create a line plot with two lines representing the temperatures in Toronto and Rome. Make sure to properly label your plot. Bonus: add a legend for the two lines. If you're stuck, try reviewing this video.

    pivot_table()

    # Pivot for mean weekly_sales by store type and holiday 
mean_sales_by_type_holiday = sales.pivot_table(values = 'weekly_sales', index = 'type', columns= 'is_holiday')

# Print mean_sales_by_type_holiday
print(mean_sales_by_type_holiday)
    Hidden output

    Output:

    is_holiday      False     True
type                          
A           23768.584  590.045
B           25751.981  810.705
    Hidden output

    Removing 'columns' and passing 'type' and 'is_holiday' as list values to 'index'

    mean_sales_by_type_holiday = sales.pivot_table(values = 'weekly_sales', index = ['type', 'is_holiday'])

    Output:

                     weekly_sales
type is_holiday              
A    False          23768.584
     True             590.045
B    False          25751.981
     True             810.705