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Data Manipulation with pandas

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

1 hidden cell

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