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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.

    # 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

    # 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.

    Inspecting a DataFrame

    # Print the head of the homelessness data
print(homelessness.head())

# Print information about homelessness
print(homelessness.info()
      
# Print the shape of homelessness
print(homelessness.shape)
      
# Print a description of homelessness
print(homelessness.describe())

# Print the values of homelessness
print (homelessness.values)

# Print the column index of homelessness
print (homelessness.columns)

# Print the row index of homelessness
print (homelessness.index)

    Sorting rows

    # Sort homelessness by individuals
homelessness_ind = homelessness.sort_values("individuals")

# Print the top few rows
print(homelessness_ind.head())

# Sort homelessness by descending family members
homelessness_fam = homelessness.sort_values("family_members", ascending = False)

# Print the top few rows
print (homelessness_fam.head())

# Sort homelessness by region, then descending family members
homelessness_reg_fam = homelessness.sort_values(["region", "family_members"], ascending=[True, False])

# Print the top few rows
print (homelessness_reg_fam.head())

    Subsetting columns

    # Select the individuals column
individuals = homelessness["individuals"]

# Print the head of the result
print (individuals.head())

# Select the state and family_members columns
state_fam = homelessness[["state", "family_members"]]

# Print the head of the result
print (state_fam.head())

# Select only the individuals and state columns, in that order
ind_state = homelessness[["individuals", "state"]]

# Print the head of the result
print (ind_state.head())

    Subsetting rows

    # Filter for rows where individuals is greater than 10000
ind_gt_10k = homelessness[homelessness["individuals"]>10000]

# See the result
print(ind_gt_10k)

# Filter for rows where region is Mountain
mountain_reg = homelessness[homelessness["region"] == "Mountain"]

# See the result
print (mountain_reg)

# Filter for rows where family_members is less than 1000 
# and region is Pacific
fam_lt_1k_pac = homelessness[ (homelessness["family_members"] < 1000) & (homelessness["region"] == "Pacific") ]

# See the result
print(fam_lt_1k_pac)

    Subsetting rows by categorical variables

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