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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.

Add your notes here

When slicing on a date index, can use partial dates to define the slice iloc is the version to use the numerical locations, working more like a list; last value is excluded

# Add your code snippets here
# slicing in 2 directions syntax using loc:
df.loc[("outer_start", "inner_start"):("outer_end","inner_end"), "col_start": "col_end"]

# Filtering for max value syntax:
df[df == df.max()]

# Plots - the type of plot is defined by 'kind', not 'type'

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.