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Problem Set 5

See {doc}Optimization <../scientific/optimization>, {doc}Introduction <../pandas/intro>, and {doc}Basic Functionality <../pandas/basics>

import pandas as pd
import numpy as np

%matplotlib inline

Setup for Question 1-5

Load data from the {doc}Basic Functionality <../pandas/basics> lecture.

url = "https://datascience.quantecon.org/assets/data/state_unemployment.csv"
unemp_raw = pd.read_csv(url, parse_dates=["Date"])

And do the same manipulation as in the pandas basics lecture.

states = [
    "Arizona", "California", "Florida", "Illinois",
    "Michigan", "New York", "Texas"
]

unemp = (
    unemp_raw
    .reset_index()
    .pivot_table(index="Date", columns="state", values="UnemploymentRate")
    [states]
)

Question 1

At each date, what is the minimum unemployment rate across all states in our sample?

# Your code here

What was the median unemployment rate in each state?

# Your code here

What was the maximum unemployment rate across the states in our sample? In what state did it happen? In what month/year was this achieved?

  • Hint 1: What Python type (not dtype) is returned by a reduction?
  • Hint 2: Read documentation for the method idxmax.
# Your code here

Classify each state as high or low volatility based on whether the variance of their unemployment is above or below 4.

# Your code here

Question 2

Imagine that we want to determine whether unemployment was high (> 6.5), medium (4.5 < x <= 6.5), or low (<= 4.5) for each state and each month.

Write a Python function that takes a single number as an input and outputs a single string which notes whether that number is high, medium, or low.

# Your code here

Pass your function to either apply or applymap and save the result in a new DataFrame called unemp_bins.

# Your code here

Question 3

This exercise has multiple parts:

Use another transformation on unemp_bins to count how many times each state had each of the three classifications.

  • Hint 1: Will you need to use apply or applymap for transformation?
  • Hint 2: Try googling "pandas count unique value" or something similar to find the proper transformation.
# Your code here

Construct a horizontal bar chart to detail the occurrences of each level. Use one bar per state and classification for 21 total bars.

# Your code here

Question 4

Repeat Question 3, but count how many states had each classification in each month. Which month had the most states with high unemployment? What about medium and low?

Part 1: Write a Python function to classify unemployment levels

# Your code here

Part 2: Decide whether you should use .apply or .applymap.

Part 3: Pass your function from part 1 to the method you determined in Part 2.

unemp_bins = unemp#replace this comment with your code!!

Part 4: Count the number of times each state had each classification.

## then make a horizontal bar chart here

Part 5: Apply the same transformation from Part 4 to each date instead of to each state.

# Your code here

Question 5

For a single state of your choice, determine the mean unemployment during "Low", "Medium", and "High" unemployment times. (recall your unemp_bins DataFrame from the exercise above)

# Your code here

Which states in our sample performs the best during "bad times?" To determine this, compute each state's mean unemployment in months where the mean unemployment rate is greater than 7.

# Your code here