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Data Manipulation with pandas
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Data Manipulation with pandas

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

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Import the four datasets

### 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 code snippets here``
• 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.