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Intermediate Python

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

# Import the course packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Import the two datasets
gapminder = pd.read_csv("datasets/gapminder.csv")
brics = pd.read_csv("datasets/brics.csv")

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



note on data manipulation
# Add total col as sum of individuals and family_members
#total = homelessness["individuals"] 
homelessness = homelessness.assign(total=homelessness['individuals'] + homelessness['family_members'])

# Add p_individuals col as proportion of total that are individuals
#____
homelessness = homelessness.assign(p_individuals=homelessness['individuals'] / (homelessness['individuals'] + homelessness['family_members']))

# See the result
print(homelessness)


#combo attack

# Create indiv_per_10k col as homeless individuals per 10k state pop
homelessness["indiv_per_10k"] = 10000 * homelessness["individuals"] / homelessness["state_pop"]

# Subset rows for indiv_per_10k greater than 20
high_homelessness = homelessness[homelessness["indiv_per_10k"] > 20]

# Sort high_homelessness by descending indiv_per_10k
high_homelessness_srt = high_homelessness.sort_values("indiv_per_10k", ascending = False)

# From high_homelessness_srt, select the state and indiv_per_10k cols
result = high_homelessness_srt[["state", "indiv_per_10k"]]

# See the result
print(result)
# Filter the DataFrame for object columns
non_numeric = planes.select_dtypes("object")

# Loop through columns
for i in non_numeric.columns:
  
  # Print the number of unique values
  print(f"Number of unique values in {i} column: ", non_numeric[i].nunique())

Explore Datasets

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

  • Create a loop that iterates through the brics DataFrame and prints "The population of {country} is {population} million!".
  • Create a histogram of the life expectancies for countries in Africa in the gapminder DataFrame. Make sure your plot has a title, axis labels, and has an appropriate number of bins.
  • Simulate 10 rolls of two six-sided dice. If the two dice add up to 7 or 11, print "A win!". If the two dice add up to 2, 3, or 12, print "A loss!". If the two dice add up to any other number, print "Roll again!".