First create a new directory within your class folder data-a-user-manual called pandas-1. Launch Jupyter Labs and create a new Jupyter Notebook in that folder with the filename: pandas-1.ipynb.

We’re going to be working with census data collected about Colorado counties from 1900-1950. Download this CSV file and make sure it is in your pandas-1 folder.

To replicate best coding practices, you’re going to use alternating Markdown and Code cells in your Jupyter Notebook. Copy and paste each of the following steps into a new Markdown cell that documents in your own words what you’re doing in the following code cell. Then insert a new code cell and write your Python code that completes the task for that step.

1. Import the pandas library (import pandas as pd)
2. Read in the contents of the CSV of Colorado census data and assign it to a variable co_df (ie. Colorado dataframe)
3. Show the first 10 rows of the co_df dataframe
4. Show a random sample of 5 rows from the co_df dataframe
5. Follow Walsh’s example to just filter/select census data from 1940 and assign it to a new variable called co_df_1940.
6. Apply the max() function to the population column of co_df_1940 to find the largest number people counted in a single Colorado county in the 1940 census.
7. What county had the largest number of people in 1940? Hint: use filter/select in conjunction with your steps from the previous step (max() and the population column).
8. Use filter/select to create a new dataframe named denver_df that only has records from Denver.
9. Use denver_df.plot('year', 'population') to create a line graph of Denver’s population growth between 1900-1950.

Bonus Practice:

• Save your line graph for Denver’s population growth as a .png file
• Export a new CSV file that just contains records for Denver (denver_df) - see Walsh’s example.