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Data Manipulation with pandas
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    • Data Manipulation with pandas

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

    Take Notes

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

    table.shape geeft de dimensies van een tabel.

    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.
    #Slicing a time series:
# Use Boolean conditions to subset temperatures for rows in 2010 and 2011
temperatures_bool =temperatures[(temperatures["date"] >= "2010-01-01") & (temperatures["date"] <= "2011-12-31")]
print(temperatures_bool)

# Set date as the index and sort the index
temperatures_ind = temperatures.set_index("date").sort_index()

# Use .loc[] to subset temperatures_ind for rows in 2010 and 2011
print(temperatures_ind.loc["2010-01-01":"2011-12-31"])

# Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011
print(temperatures_ind.loc["2010-08-01":"2011-02-01"])

# Subset for Egypt to India
temp_by_country_city_vs_year.loc["Egypt":"India"]

# Subset for Egypt, Cairo to India, Delhi
temp_by_country_city_vs_year.loc[("Egypt", "Cairo"):("India", "Delhi")]

# Subset for Egypt, Cairo to India, Delhi, and 2005 to 2010
temp_by_country_city_vs_year.loc[("Egypt", "Cairo"):("India", "Delhi"), "2005":"2010"]