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Data Manipulation with pandas

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

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

# Import the four datasets
avocado = pd.read_csv("datasets/avocado.csv")
homelessness = pd.read_csv("datasets/homelessness.csv")
temperatures = pd.read_csv("datasets/temperatures.csv")
walmart = pd.read_csv("datasets/walmart.csv")

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

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# Sort the index of temperatures_ind
temperatures_srt = temperatures_ind.sort_index()
# Subset rows from Pakistan, Lahore to Russia, Moscow
print(temperatures_srt.loc[("Pakistan", "Lahore"):("Russia", "Moscow")])
# Subset in both directions at once
print(temperatures_srt.loc[("India", "Hyderabad"):("Iraq","Baghdad"),"date":"avg_temp_c"])
# Get the mean temp by city
mean_temp_by_city = temp_by_country_city_vs_year.mean(axis="columns")
# Filter for the year that had the highest mean temp
print(mean_temp_by_year[mean_temp_by_year==mean_temp_by_year.max()])

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.