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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.

Efficient summaries While pandas and NumPy have tons of functions, sometimes, you may need a different function to summarize your data.

The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super-efficient. For example,

df['column'].agg(function)

Add your notes here

# Add your code snippets here

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.