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Data Manipulation with pandas

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

1 hidden cell

Take Notes

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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.

when using .agg() , we create the function that performs the math formula ,then pass this func to agg

A custom IQR function

def iqr(column): return column.quantile(0.75) - column.quantile(0.25)

Print IQR of the temperature_c column

print(sales["temperature_c"].agg(iqr))

when working with .agg and more than argument function pass them as a list with out () .agg([func1,func2])

Make a list of cities to subset on

cities = ["Moscow", "Saint Petersburg"]

Subset temperatures using square brackets

print(temperatures[temperatures["city"].isin(cities)])

Subset temperatures_ind using .loc[]

print(temperatures_ind.loc[["Moscow", "Saint Petersburg"]])

Get the worldwide mean temp by year

mean_temp_by_year = temp_by_country_city_vs_year.mean(axis="index")

Filter for the year that had the highest mean temp

print(mean_temp_by_year[mean_temp_by_year==mean_temp_by_year.max()])

Get the mean temp by city

mean_temp_by_city = temp_by_country_city_vs_year.mean(axis="columns")

Filter for the city that had the lowest mean temp

print(mean_temp_by_city[mean_temp_by_city==mean_temp_by_city.min()])

Two ways to summary the dataset ***1- .groupby()[].agg([]) ***2- subset in a variable data[data[""]==""] then, send the subset[""] as an argument to the np.mean (subset["column"]) "column" contains the numbers we want to summarize