Beta
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.
Add your notes here
# 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 thewalmart
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 theavocado
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.