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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

Inspecting a DataFrame When you get a new DataFrame to work with, the first thing you need to do is explore it and see what it contains. There are several useful methods and attributes for this.

.head() returns the first few rows (the “head” of the DataFrame). .info() shows information on each of the columns, such as the data type and number of missing values. .shape returns the number of rows and columns of the DataFrame. .describe() calculates a few summary statistics for each column. homelessness is a DataFrame containing estimates of homelessness in each U.S. state in 2018. The individual column is the number of homeless individuals not part of a family with children. The family_members column is the number of homeless individuals part of a family with children. The state_pop column is the state's total population.

pandas is imported for you.

Print the head of the homelessness DataFrame.

# 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.