Chicago Uber/Lyft Trips Before/After COVID-19

🏆 80 points available
🤠 Author: Park (ypark32@illinois.edu)
✏️ Last updated on 3/20/2025

💎 Case Overview¶

Four Transportation Network Providers (TNPs), often called rideshare companies 🚗, have been licensed to operate in Chicago. These TNPs are required to routinely report information on vehicles, drivers, and trips to the City of Chicago, which is published on the Chicago Data Portal. The latest trips dataset can be downloaded from this page.

🐢 Your Dataset¶

The original dataset contains information about 170 million ridesharing trips between 2018 and 2021 translating to over 40GB of raw data. 🙀☠️🙀
Approximately 1% of the original data has been randomly sampled, preprocessed, and compressed.
Only 2019 and 2020 data have been selected.

⚔️ Your Goal¶

Analyze the dataset and answer the following questions:

👉 How has the COVID-19 affected the total number of trips in 2019 and 2020?
👉 How has the COVID-19 affected the number of monthly trips?
👉 Have passengers become more generous or frugal when tipping during the pandemic?
👉 What were the trip dimensions (trip duration, distance, and fare) of rides taken right before the July 4th fireworks in 2019 and 2020?
👉 Were passengers pooling rides during the pandemic?
👉 What are the top 20 pickup areas (by volume)?
👉 What is the average trip total for each of the top 20 pickup areas?
👉 Is there any area that has an unusually large trip total?

▶️ Run the code cell below to import unittest, a module used for 🧭 Check Your Work sections and the autograder.

# DO NOT MODIFY THE CODE IN THIS CELL
import base64
import unittest

tc = unittest.TestCase()

🔨 Import Packages and Dataset¶

▶️ Run the code cell below to import packages used in the case.

import pandas as pd
import numpy as np
import plotly
import plotly.express as px
import plotly.graph_objects as go

# plotly.io is a low-level interface for interacting with figures/
# plotly.io.templates lists available plotly templates
# https://plotly.com/python-api-reference/plotly.io.html
import plotly.io as pio

pd.set_option("display.max_columns", 50)

🧭 Check Plotly Version¶

Run the code below to ensure that your notebook uses the same Plotly version as the autograder.

# DO NOT CHANGE THE CODE IN THIS CELL
print(f"The current plotly version is {plotly.__version__}")
plotly_major_version = int(plotly.__version__.split(".")[0])

tc.assertGreaterEqual(
    plotly_major_version, 5, "Your plotly version should be greater than or equal to 5"
)

▶️ Run the code below to import and process the trips dataset.

df = pd.read_csv(
    "https://github.com/bdi475/datasets/raw/main/case-studies/chicago-ridesharing/chicago-ridesharing-trips-2019-2020.csv.gz",
    compression="gzip",
    parse_dates=["start"],
)
df_community_areas = pd.read_csv(
    "https://github.com/bdi475/datasets/raw/main/case-studies/chicago-ridesharing/chicago-community-area-numbers.csv"
)

# replace community area numbers with area names
df = df.merge(
    df_community_areas, left_on="pickup_area", right_on="area_number", how="left"
)
df["pickup_area"] = df["community"].copy()
df.drop(columns=["community", "area_number"], inplace=True)

df = df.merge(
    df_community_areas, left_on="dropoff_area", right_on="area_number", how="left"
)
df["dropoff_area"] = df["community"].copy()
df.drop(columns=["community", "area_number"], inplace=True)

# Create a copy for 🧭 Check Your Work section
df_backup = df.copy()

📐 Part 1: Data Overview¶

🎯 Deliverable 1: First 5 Rows¶

👇 Tasks¶

✔️ Display the first 5 rows of df using .head().

🚀 Hint¶

my_dataframe.head()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-01"
_points = 2

# manually graded cell
pass

🎯 Deliverable 2: Summary of DataFrame¶

👇 Tasks¶

✔️ Print a concise summary of df using .info().

🚀 Hint¶

my_dataframe.info()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-02"
_points = 2

# manually graded cell
pass

🎯 Deliverable 3: Number of Rows and Columns in the Dataset¶

👇 Tasks¶

✔️ Store the number of rows in df to a new variable named num_rows.
✔️ Store the number of columns in df to a new variable named num_cols.

# YOUR CODE BEGINS

# YOUR CODE ENDS

print(f"There are {num_rows} rows and {num_cols} columns in the dataset.")

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-03"
_points = 2

tc.assertEqual(
    num_rows, len(df_backup.index), f"Number of rows should be {len(df_backup.index)}"
)
tc.assertEqual(
    num_cols,
    len(df_backup.columns),
    f"Number of columns should be {len(df_backup.columns)}",
)

🗓️ Part 2: Extract Datetime Values into Separate Columns¶

The start column contains trip start timestamps. In this part of the case study, you will extract year, month, day of the month, day of the week, hour, and weekday/weekend information into separate columns.

🎯 Deliverable 4: Extract Year into a New Column¶

▶️ Run the code below to print the first 3 values of the start column and the data types.

display(df["start"].head(3))
print("=================================================")
print(f"df['start'] column's data type is {df['start'].dtype}.")

👇 Tasks¶

✔️ From df, extract the year (e.g., 2019, 2020) from the start column and store it in a new column named year.

🔑 Expected Output¶

	start	year
400000	2019-05-28 19:00:00	2019
600000	2019-08-12 19:15:00	2019
800000	2019-10-29 08:00:00	2019
1000000	2020-01-15 12:45:00	2020
1200000	2020-06-09 17:00:00	2020

# YOUR CODE BEGINS

# YOUR CODE ENDS

# display 5 rows at indices 400000, 600000, 800000, 1000000, 1200000
df_sample = df[df.index.isin([400000, 600000, 800000, 1000000, 1200000])]
display(df_sample[["start", "year"]])

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-04"
_points = 2
_obfuscate = True

df_backup["year"] = df_backup["start"].dt.year

tc.assertEqual(df.shape, df_backup.shape, "Incorrect number of rows and/or columns")
pd.testing.assert_frame_equal(
    df[["start", "year"]].sort_values("start").reset_index(drop=True),
    df_backup[["start", "year"]].sort_values("start").reset_index(drop=True),
)

🎯 Deliverable 5: Extract Month, Day of Month, Day of Week, and Hour into Columns¶

👇 Tasks¶

✔️ Similar to the previous deliverable, extract the month, day of month, day of week, and hour of each start time in df to new columns.
✔️ Use the following column names. Sample values are also given.
- month: 1, 2, ..., 11, 12
- day: 1, 2, ..., 28, 29, 30, 31
- dayofweek: 0 for Monday, 6 for Sunday
- hour: 0, 1, ..., 22, 23

🔑 Expected Output¶

	start	month	day	dayofweek	hour
400000	2019-05-28 19:00:00	5	28	1	19
600000	2019-08-12 19:15:00	8	12	0	19
800000	2019-10-29 08:00:00	10	29	1	8
1000000	2020-01-15 12:45:00	1	15	2	12
1200000	2020-06-09 17:00:00	6	9	1	17

# YOUR CODE BEGINS

# YOUR CODE ENDS

# display 5 rows at indices 400000, 600000, 800000, 1000000, 1200000
df_sample = df[df.index.isin([400000, 600000, 800000, 1000000, 1200000])]
display(df_sample[["start", "month", "day", "dayofweek", "hour"]])

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-05"
_points = 4

decoded_code = base64.b64decode(
    b"ZGZfYmFja3VwWydtb250aCddID0gZGZfYmFja3VwWydzd\
GFydCddLmR0Lm1vbnRoCmRmX2JhY2t1cFsnZGF5J10gPSBkZl9iYWNrdXBbJ3N0YXJ0J10uZHQuZG\
F5CmRmX2JhY2t1cFsnZGF5b2Z3ZWVrJ10gPSBkZl9iYWNrdXBbJ3N0YXJ0J10uZHQuZGF5b2Z3ZWV\
rCmRmX2JhY2t1cFsnaG91ciddID0gZGZfYmFja3VwWydzdGFydCddLmR0LmhvdXI="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(df.shape, df_backup.shape, "Incorrect number of rows and/or columns")
pd.testing.assert_frame_equal(
    df[["start", "month", "day", "dayofweek", "hour"]]
    .sort_values("start")
    .reset_index(drop=True),
    df_backup[["start", "month", "day", "dayofweek", "hour"]]
    .sort_values("start")
    .reset_index(drop=True),
)

🎯 Deliverable 6: Create `weekday_weekend` Column¶

👇 Tasks¶

✔️ Assume Mondays-Thursdays are weekdays and Fridays-Sundays are weekends.
✔️ Create a new column named weekday_weekend in df.
✔️ For each row, the value of weekday_weekend will either be string 'weekday' or string 'weekend' (case-sensitive) based on the value of the dayofweek column.
- 'weekday' if dayofweek is less than or equal to 3 (0 == Monday, 1 == Tuesday, 2 == Wednesday, 3 == Thursday)
- 'weekend' if otherwise (4 == Friday, 5 == Saturday, 6 == Sunday)

🚀 Hint¶

There are many ways to achieve this task.

The code below creates a new column named cheap_expensive where the value will be string 'cheap' if the price is less than or equal to 10 and 'expensive' if otherwise.

my_dataframe['cheap_expensive'] = np.where(my_dataframe['price'] <= 10, 'cheap', 'expensive')

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df[["dayofweek", "weekday_weekend"]].sample(5))

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-06"
_points = 3

decoded_code = base64.b64decode(
    b"ZGZfYmFja3VwWyd3ZWVrZGF5X3dlZWtlbmQnXSA9IG5wL\
ndoZXJlKGRmX2JhY2t1cFsnZGF5b2Z3ZWVrJ10gPD0gMywgJ3dlZWtkYXknLCAnd2Vla2VuZCcp"
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(df.shape, df_backup.shape, "Incorrect number of rows and/or columns")
pd.testing.assert_frame_equal(
    df[["start", "dayofweek", "weekday_weekend"]]
    .sort_values("start")
    .reset_index(drop=True),
    df_backup[["start", "dayofweek", "weekday_weekend"]]
    .sort_values("start")
    .reset_index(drop=True),
)

😷 Part 3: Visualize the Effects of COVID-19 on the Volume of Ridesharing Trips¶

Although the first case of COVID-19 was reported in January 2020 in the United States, people started to take it seriously in March 2020.

How did COVID-19 affect the volume of ridesharing trips? 💨 Let’s visualize and compare the monthly number of trips for both 2019 and 2020.

🎯 Deliverable 7: Total Number of Trips in 2019 and 2020¶

👇 Tasks¶

✔️ Using df, count the number of trips made in 2019 and store the number in a new variable named num_2019_trips.
✔️ Using df, count the number of trips made in 2020 and store the number in a new variable named num_2020_trips.

🚀 Hint¶

For num_2019_trips, retrieve the number of rows where df['year'] is 2019.

# YOUR CODE BEGINS

# YOUR CODE ENDS

print(f"There were {num_2019_trips} trips in 2019.")
print(f"There were {num_2020_trips} trips in 2020.")
print(
    f"The number of trips decreased by {(num_2019_trips - num_2020_trips) / num_2019_trips * 100:.1f}%."
)

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-07"
_points = 3

decoded_code = base64.b64decode(
    b"bnVtXzIwMTlfdHJpcHNfY2hlY2sgPSBkZ\
l9iYWNrdXBbJ3llYXInXS52YWx1ZV9jb3VudHMoKS5sb2NbMjAxOV0KbnVtXzIwMjBf\
dHJpcHNfY2hlY2sgPSBkZl9iYWNrdXBbJ3llYXInXS52YWx1ZV9jb3VudHMoKS5sb2NbMjAyMF0="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    num_2019_trips,
    num_2019_trips_check,
    f"Incorrect number of 2019 trips, should be {num_2019_trips_check}",
)
tc.assertEqual(
    num_2020_trips,
    num_2020_trips_check,
    f"Incorrect number of 2020 trips, should be {num_2020_trips_check}",
)

🎯 Deliverable 8: Monthly Number of Trips and Tip Percentage¶

👇 Tasks¶

✔️ Using df, calculate the number of monthly trips and average tip percentages.
✔️ Use the code given below.

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df_monthly.head(5))

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-08"
_points = 2
_obfuscate = True

df_monthly_check = (
    df_backup.groupby(["year", "month"], as_index=False)
    .agg(
        {
            "start": "count",
            "tip": "sum",
            "fare": "sum",
        }
    )
    .rename(columns={"start": "num_trips"})
)

df_monthly_check["tip_pct"] = df_monthly_check["tip"] / df_monthly_check["fare"]
df_monthly_check.drop(columns=["tip", "fare"], inplace=True)

tc.assertEqual(
    df_monthly.shape, df_monthly_check.shape, "Incorrect number of rows and/or columns"
)
pd.testing.assert_frame_equal(
    df_monthly.sort_values(["year", "month"]).reset_index(drop=True),
    df_monthly_check.sort_values(["year", "month"]).reset_index(drop=True),
    check_like=True,
)

🎯 Deliverable 9: Sunburst Chart of Monthly Trips¶

👇 Tasks¶

✔️ Using df_monthly, create a sunburst chart that shows the proportion of monthly number of trips for both 2019 and 2020.
✔️ Set an appropriate title.
✔️ Set both the width and height to 600.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.sunburst(
    my_dataframe,
    path=['column1', 'column2'],
    values='column3',
    title='Your Title Here',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-09"
_points = 4
_obfuscate = True

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(fig.data[0].type, "sunburst", "Must be a sunburst chart")
tc.assertEqual(fig.layout.width, 600, "Incorrect width")
tc.assertEqual(fig.layout.height, 600, "Incorrect height")

fig_check = px.sunburst(
    df_monthly_check,
    path=["year", "month"],
    values="num_trips",
    title="Trips Breakdown by Year and Month",
    width=600,
    height=600,
)

np.testing.assert_array_equal(
    fig.data[0].labels, fig_check.data[0].labels, "Label(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].parents, fig_check.data[0].parents, "Parent(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].values, fig_check.data[0].values, "Value(s) mismatch"
)

👇 Tasks¶

✔️ Using df_monthly, create two bar charts side-by-side within the same figure, showing the number of monthly trips for 2019 and 2020.
- You will have to use a facet grid that we haven’t covered in lectures.
- A facet grid is a group of plots within one figure categorized by 1 or 2 criteria.
- This deliverable expects you to do some research on your own, although the hint will provide the majority of the code required to complete this task.
✔️ Set an appropriate title.
✔️ Use the 'simple_white' theme (template).
✔️ Set the width to 1000 and height to 500.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    facet_col='column3',
    width=...,
    height=...,
    template='simple_white',
)
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-10"
_points = 4

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data), 2, "Must use a facet grid to display two bar charts side-by-side"
)
tc.assertEqual(fig.layout.width, 1000, "Incorrect width")
tc.assertEqual(fig.layout.height, 500, "Incorrect height")
tc.assertEqual(
    fig.layout.template, pio.templates["simple_white"], "Incorrect plotly template"
)

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHguYmFyKAogICAgZGZfbW9udGhseV9jaGV\
jaywKICAgIHRpdGxlPSdNb250aGx5IE51bWJlciBvZiBUcmlwcyBpbiAyMDE5IGFuZCAyMDIwJywKICAgIHg9\
J21vbnRoJywKICAgIHk9J251bV90cmlwcycsCiAgICBmYWNldF9jb2w9J3llYXInLAogICAgd2lkdGg9MTAwM\
CwKICAgIGhlaWdodD01MDAsCiAgICB0ZW1wbGF0ZT0ncGxvdGx5X2RhcmsnLAogICAgY29sb3I9J251bV90cm\
lwcycsCiAgICBjb2xvcl9jb250aW51b3VzX3NjYWxlPVsnV2hpdGUnLCAnWWVsbG93J10KKQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

for i in range(len(fig_check.data)):
    tc.assertEqual(fig.data[i].type, "bar", "Must be a bar chart")
    tc.assertEqual(fig.data[i].orientation, "v", "Must be a vertical bar chart")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

🎯 Deliverable 11: Monthly Number of Trips in 2019 and 2020 (Line Plot)¶

👇 Tasks¶

✔️ Using df_monthly, create a line plot of the monthly number of trips.
- Use year to create two separate lines (encoded by different colors) for 2019 and 2020.
✔️ Set an appropriate title.
✔️ Use the 'simple_white' theme (template).
✔️ Set the width to 800 and height to 500.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.line(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    color='column3',
    template='simple_white',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-11"
_points = 4

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data),
    df_monthly_check["year"].nunique(),
    "Must encode each year with different colors",
)
tc.assertEqual(fig.layout.width, 800, "Incorrect width")
tc.assertEqual(fig.layout.height, 500, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHgubGluZSgKICAgIG\
RmX21vbnRobHlfY2hlY2ssCiAgICB0aXRsZT0nTW9udGhseSBOdW1iZXIgb2YgVHJpcH\
MgQ29tcGFyaXNvbiBiZXR3ZWVuIDIwMTkgYW5kIDIwMjAnLAogICAgeD0nbW9udGgnLA\
ogICAgeT0nbnVtX3RyaXBzJywKICAgIGNvbG9yPSd5ZWFyJywKICAgIHRlbXBsYXRlPS\
dwbG90bHlfZGFyaycsCiAgICB3aWR0aD04MDAsCiAgICBoZWlnaHQ9NTAwCik="
)
eval(compile(decoded_code, "<string>", "exec"))

for i in range(len(fig_check.data)):
    # In plotly, a line plot is a scatter plot with lines connecting the dots
    tc.assertEqual(fig.data[i].type, "scatter", "Must be a line plot")
    tc.assertIsNotNone(fig_check.data[i].line.color, "Must be a line plot")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

📌 Interpreting the Pre-COVID vs. Post-COVID Monthly Number of Trips¶

🔍 In March 2020, the number of trips plummeted due to the widespread outbreak of COVID-19.
🔍 April 2020 was the worst month for ridesharing drivers.
🔍 The number of trips slowly increased from April to October.
🔍 In November 2020, a continued surge in the number of COVID-19 cases has caused the number of trips to go down again.

💵 Part 4: Visualize the Effect of COVID-19 on Tips¶

Did passengers tip more on average during the pandemic since they appreciated the drivers providing services during risky times?

Or did the passengers tip less on average after the pandemic devastated the nation’s economy in 2020?

👇 Tasks¶

✔️ Using df_monthly, create two bar charts side-by-side within the same figure, showing the monthly average tip percentages in 2019 and 2020.
- tip_pct column in df_monthly contains the monthly average tip percentages.
✔️ Set an appropriate title.
✔️ Use the 'simple_white' theme (template).
✔️ Set the width to 1000 and height to 500.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    facet_col='column3',
    width=...,
    height=...,
    template='simple_white',
)
fig.update_layout(yaxis_tickformat=',.1%')
fig.update_layout(yaxis2_tickformat=',.1%')
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-12"
_points = 4
_obfuscate = True

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data), 2, "Must use a facet grid to display two bar charts side-by-side"
)
tc.assertEqual(fig.layout.width, 1000, "Incorrect width")
tc.assertEqual(fig.layout.height, 500, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

fig_check = px.bar(
    df_monthly_check,
    title="Monthly Average Tip % Based on Fares in 2019 and 2020",
    x="month",
    y="tip_pct",
    facet_col="year",
    width=1000,
    height=500,
    template="plotly_dark",
    color="tip_pct",
    color_continuous_scale=["White", "GreenYellow"],
)

for i in range(len(fig_check.data)):
    tc.assertEqual(fig.data[i].type, "bar", "Must be a bar chart")
    tc.assertEqual(fig.data[i].orientation, "v", "Must be a vertical bar chart")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

🎯 Deliverable 13: Monthly Average Tip Percentages in 2019 and 2020¶

👇 Tasks¶

✔️ Using df_monthly, create a line plot of monthly average tip percentages.
- Use year to create two separate lines (encoded by different colors) for 2019 and 2020.
- tip_pct column in df_monthly contains the monthly average tip percentages.
✔️ Set an appropriate title.
✔️ Use the 'simple_white' theme (template).
✔️ Set the width to 800 and height to 500.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', and ...s with your own values from the code below.

fig = px.line(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    color='column3',
    template='simple_white',
    width=...,
    height=...
)
fig.update_layout(yaxis_tickformat=',.1%')
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-13"
_points = 4
_obfuscate = True

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data),
    df_monthly_check["year"].nunique(),
    "Must encode each year with different colors",
)
tc.assertEqual(fig.layout.width, 800, "Incorrect width")
tc.assertEqual(fig.layout.height, 500, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

fig_check = px.line(
    df_monthly_check,
    title="Monthly Average Tip % Comparison between 2019 and 2020",
    x="month",
    y="tip_pct",
    color="year",
    template="plotly_dark",
    width=800,
    height=500,
)

for i in range(len(fig_check.data)):
    # In plotly, a line plot is a scatter plot with lines connecting the dots
    tc.assertEqual(fig.data[i].type, "scatter", "Must be a line plot")
    tc.assertIsNotNone(fig_check.data[i].line.color, "Must be a line plot")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

📌 Interpreting the Pre-COVID vs. Post-COVID Average Percentage of Tips¶

🔍 Passengers tipped less after the COVID-19 outbreak.
🔍 Possible explanations:
- COVID-19 has caused an economic downturn.
- Many people have also lost jobs during this period.
- COVID-19 may have prevented the driver and the passenger(s) from having a conversation. Less conversation could result in less tips.

✨ Part 5: Trips Right Before July 4th Fireworks¶

Every July 4th, massive crowds gather around places (or boats) to view the fireworks. How did July 4th become the “national fireworks day”? July 4, 1776 is considered to be the birth of the United States of America as an independent nation. The Continental Congress approved the final wording of the Declaration of Independence on July 4, 1776. The first-ever recorded July 4th fireworks celebration was held in Philadelphia on July 4, 1777. Since then, there hasn’t been an Independence Day without a firework. 💥💥

[Source 1] [Source 2]

In this part, you will find trips started on July 4th between 5-6 PM and create different scatter plots based on those trips.

🎯 Deliverable 14: Filter July 4th 5-6 PM Trips¶

👇 Tasks¶

✔️ Using df, filter rows where:
- month is 7 (July) and,
- day is 4 (4th) and,
- hour is 17 (All trips starting between 5-6 pm)
✔️ Store the filtered DataFrame in a new variable named df_july_fourth.
✔️ df should remain unaltered after running your code.

🚀 Hint¶

The code below filters rows where column is 10, column2 is 20, and column3 is 30. The filtered DataFrame is stored in a new variable named my_filtered.

my_filtered_df = my_df[(my_df['column1'] == 10) & (my_df['column2'] == 20) & (my_df['column3'] == 30)]

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df_july_fourth.sample(3))

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-14"
_points = 3

decoded_code = base64.b64decode(
    b"ZGZfanVseV9mb3VydGhfY2hlY2sgPSBkZl9iYWNrdXBbKGRmX2JhY2t1cFs\
nbW9udGgnXSA9PSA3KSAmIChkZl9iYWNrdXBbJ2RheSddID09IDQpICYgKGRmX2JhY2t1cFsnaG91ciddID09IDE3KV0="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    df_july_fourth.shape,
    df_july_fourth_check.shape,
    "Incorrect number of rows and/or columns",
)
pd.testing.assert_frame_equal(
    df_july_fourth.sort_values(df_july_fourth.columns.tolist()).reset_index(drop=True),
    df_july_fourth_check.sort_values(df_july_fourth_check.columns.tolist()).reset_index(
        drop=True
    ),
    check_like=True,
)

🎯 Deliverable 15: July 4th 5-6 PM Trips Scatter Plots¶

👇 Tasks¶

✔️ Using df_july_fourth, create two scatter plots for each year side-by-side within the same figure with the following axes.
- x: trip_seconds,
- y: trip_miles,
✔️ Use the trip_total column to differentiate the sizes of points.
✔️ Use the shared_trip_authorized column to differentiate the colors of points.
✔️ Set an appropriate title.
✔️ Use the 'simple_white' theme (template).
✔️ Set the width to 1200 and height to 500.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', 'column3', 'column4', 'column5', and ...s with your own values from the code below.

fig = px.scatter(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    size='column3',
    facet_col='column4',
    color='column5',
    template='simple_white',
    width=...,
    height=...,
)
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-15"
_points = 3

# DO NOT CHANGE THE CODE IN THIS CELL
fig_check = px.scatter(
    df_july_fourth_check,
    title="Trip Seconds vs Trip Miles with July 4th 5-6 PM Trips",
    x="trip_seconds",
    y="trip_miles",
    size="trip_total",
    facet_col="year",
    color="shared_trip_authorized",
    template="plotly_dark",
    width=1200,
    height=500,
)


tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data),
    len(fig_check.data),
    "Must use a facet grid to display two scatter plots side-by-side with color-encoding",
)
tc.assertEqual(fig.layout.width, 1200, "Incorrect width")
tc.assertEqual(fig.layout.height, 500, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

for i in range(len(fig_check.data)):
    tc.assertEqual(fig.data[i].type, "scatter", "Must be a scatter plot")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

📌 Interpreting the Pre-COVID vs. Post-COVID Scatter Plots¶

🔍 There is a positive linear relationship between trip_seconds and trip_miles.
🔍 The number of trips made on July 4th has decreased significantly in 2020 due to COVID-19.
🔍 There are no pooled trips (shared_trips_authorized == True) in 2020.
- Why? Uber and Lyft both suspended pooled rides to limit the spread of coronavirus (Reuters article here) beginning in March 2020.

🎯 Deliverable 16: July 4th 5-6 PM Trips 3D Scatter Plots (Pre-COVID)¶

👇 Tasks¶

✔️ Using df_july_fourth, create a 3D scatter plot of trips in 2019 using the following axes.
- x: trip_seconds,
- y: trip_miles,
- z: trip_total
✔️ Use the shared_trip_authorized column to differentiate the colors of points.
✔️ Set an appropriate title.
✔️ Use the 'plotly_dark' theme (template).
✔️ Set both the width and height to 800.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace 'column1', 'column2', 'column3', 'column4', and ...s with your own values from the code below.

fig = px.scatter_3d(
    df_july_fourth[df_july_fourth['year'] == 2019],
    title='Your Title Here',
    x='column1',
    y='column2',
    z='column3',
    color='column4',
    template='plotly_dark',
    width=...,
    height=...
)
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-16"
_points = 3

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHguc2NhdHRl\
cl8zZCgKICAgIGRmX2p1bHlfZm91cnRoX2NoZWNrW2RmX2p1bHlfZm91cnRoX2\
NoZWNrWyd5ZWFyJ10gPT0gMjAxOV0sCiAgICB0aXRsZT0nVHJpcCBTZWNvbmRz\
IHZzIFRyaXAgTWlsZXMgdnMgVHJpcCBUb3RhbCB3aXRoIEp1bHkgNHRoLCAyMD\
E5IDUtNiBQTSBUcmlwcycsCiAgICB4PSd0cmlwX3NlY29uZHMnLAogICAgeT0n\
dHJpcF9taWxlcycsCiAgICB6PSd0cmlwX3RvdGFsJywKICAgIGNvbG9yPSdzaG\
FyZWRfdHJpcF9hdXRob3JpemVkJywKICAgIHRlbXBsYXRlPSdwbG90bHlfZGFy\
aycsCiAgICB3aWR0aD04MDAsCiAgICBoZWlnaHQ9ODAwCik="
)
eval(compile(decoded_code, "<string>", "exec"))


# DO NOT CHANGE THE CODE IN THIS CELL
tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(
    len(fig.data),
    len(fig_check.data),
    "Check whether you've supplied the color parameter to px.scatter_3d()",
)
tc.assertEqual(fig.layout.width, 800, "Incorrect width")
tc.assertEqual(fig.layout.height, 800, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["plotly_dark"],
    "Incorrect plotly theme (template)",
)

for i in range(len(fig_check.data)):
    tc.assertEqual(fig.data[i].type, "scatter3d", "Must be a 3D scatter plot")

    np.testing.assert_array_equal(
        fig.data[i].x, fig_check.data[i].x, "x-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].y, fig_check.data[i].y, "y-axis value(s) mismatch"
    )

    np.testing.assert_array_equal(
        fig.data[i].z, fig_check.data[i].z, "z-axis value(s) mismatch"
    )

▶️ Run the code cell below to repeat the previous deliverable with trips in 2020.

# DO NOT CHANGE THE CODE BELOW
fig = px.scatter_3d(
    df_july_fourth[df_july_fourth["year"] == 2020],
    title="Trip Seconds vs Trip Miles vs Trip Total with July 4th, 2020 5-6 PM Trips",
    x="trip_seconds",
    y="trip_miles",
    z="trip_total",
    color="shared_trip_authorized",
    template="plotly_dark",
    width=800,
    height=800,
)
fig.show()

📌 Interpreting the Pre-COVID vs. Post-COVID 3D Scatter Plots¶

🔍 There is a positive linear relationship between trip_seconds, trip_miles, and trip_total.
🔍 You may have to rotate the view of your 3D scatter plots to witness the linear relationship.

🏡 Part 6: Pickup/Dropoff Areas Analysis¶

In this part, you will find the top 20 pickup areas and analyze the trips originating from those areas.

🎯 Deliverable 17: Find Top 20 Pickup Areas by Number of Trips¶

👇 Tasks¶

✔️ Find the top 20 pickup areas in df_listings by number of listings.
✔️ Store the result in a new variable named top_20_pickup_areas.
✔️ top_20_pickup_areas should be a Python list type.
✔️ We’ll give you the fully-working code below.

🔥 Solution¶

# YOUR CODE BEGINS

# YOUR CODE ENDS

print(top_20_pickup_areas)

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-17"
_points = 2

decoded_code = base64.b64decode(
    b"dG9wXzIwX3BpY2t1cF9hcmVhc19jaGVjayA9IGRmX2JhY\
2t1cFsncGlja3VwX2FyZWEnXS52YWx1ZV9jb3VudHMoKS5oZWFkKDIwKS5pbmRleC50b2xpc3QoKQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    set(top_20_pickup_areas), set(top_20_pickup_areas_check), "Incorrect pickup areas"
)

🎯 Deliverable 18: Filter Trips from Top 20 Pickup Areas¶

👇 Tasks¶

✔️ Using df, filter only the rows where the pickup_area is in top_20_pickup_areas.
✔️ Store the filtered result in a new variable named df_filtered.

🚀 Hint¶

filtered_dataframe = my_dataframe[my_dataframe['my_column'].isin(my_list)]

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df_filtered.head(3))
print(
    f"There are {df_filtered.shape[0]} rows and {df_filtered.shape[1]} columns in the filtered DataFrame"
)

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-18"
_points = 2

decoded_code = base64.b64decode(
    b"ZGZfZmlsdGVyZWRfY2hlY2sgPSBkZl9iYWNrdXBbKGR\
mX2JhY2t1cFsncGlja3VwX2FyZWEnXS5pc2luKHRvcF8yMF9waWNrdXBfYXJlYXNfY2hlY2spKV0="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    df_filtered.shape,
    df_filtered_check.shape,
    "Incorrect number of rows and/or columns",
)

🎯 Deliverable 19: Number of Trips and Average Trip Total by Pickup_Area¶

👇 Tasks¶

✔️ Using df_filtered, calculate the following aggregated values by pickup_area.
- num_trips: Number of trips
- trip_total: Average trip_total
✔️ Store the resulting DataFrame in a new variable named df_by_pickup_area.
✔️ df_by_pickup_area should have three columns - pickup_area, num_trips, and trip_total.
✔️ print(df_by_pickup_area.columns.tolist()) should print out ['pickup_area', 'num_trips', 'trip_total'].

🔑 Expected Output¶

	pickup_area	num_trips	trip_total
0	Austin	16851	13.2463
1	Avondale	16425	12.6238
2	Edgewater	21689	13.8675
3	Hyde Park	21303	14.524
4	Irving Park	14570	13.3267

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df_by_pickup_area.head(5))

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-19"
_points = 4

decoded_code = base64.b64decode(
    b"ZGZfYnlfcGlja3VwX2FyZWFfY2hlY2sgPSBkZl9maWx0Z\
XJlZF9jaGVjay5ncm91cGJ5KAogICAgJ3BpY2t1cF9hcmVhJywgYXNfaW5kZXg9RmFsc2UKKS5hZ2co\
ewogICAgJ3N0YXJ0JzogJ2NvdW50JywKICAgICd0cmlwX3RvdGFsJzogJ21lYW4nLAp9KS5yZW5hbWU\
oY29sdW1ucz17CiAgICAnc3RhcnQnOiAnbnVtX3RyaXBzJwp9KQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    df_by_pickup_area.columns.tolist(),
    df_by_pickup_area_check.columns.tolist(),
    "Column(s) mismatch",
)

tc.assertEqual(
    df_by_pickup_area.shape,
    df_by_pickup_area_check.shape,
    "Incorrect number of rows and/or columns",
)

pd.testing.assert_frame_equal(
    df_by_pickup_area.sort_values(df_by_pickup_area.columns.tolist()).reset_index(
        drop=True
    ),
    df_by_pickup_area_check.sort_values(
        df_by_pickup_area_check.columns.tolist()
    ).reset_index(drop=True),
    check_like=True,
)

🎯 Deliverable 20: Number of Trips by Pickup Area (Bar Chart)¶

👇 Tasks¶

✔️ Using df_by_pickup_area, create a horizontal bar chart displaying the number of trips for each pickup area.
✔️ Set an appropriate title.
✔️ Set the height to 800 and do not supply a width (by default, a Plotly figure will expand to fit the window if the width parameter is omitted).
✔️ Use the simple_white (template).
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    text='column1',
    template='simple_white',
    height=...
)
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-20"
_points = 3

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(fig.layout.height, 800, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHguYmFyKAog\
ICAgZGZfYnlfcGlja3VwX2FyZWFfY2hlY2ssCiAgICB0aXRsZT0nTnVtYmVyIG\
9mIFRyaXBzIGJ5IFBpY2t1cCBBcmVhJywKICAgIHg9J251bV90cmlwcycsCiAg\
ICB5PSdwaWNrdXBfYXJlYScsCiAgICBjb2xvcj0nbnVtX3RyaXBzJywKICAgIG\
NvbG9yX2NvbnRpbnVvdXNfc2NhbGU9J2VtcmxkJywKICAgIHRleHQ9J251bV90\
cmlwcycsCiAgICB0ZW1wbGF0ZT0ncGxvdGx5X3doaXRlJywKICAgIGhlaWdodD04MDAKKQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(fig.data[0].type, "bar", "Must be a line plot")

np.testing.assert_array_equal(
    fig.data[0].x, fig_check.data[0].x, "x-axis value(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].y, fig_check.data[0].y, "y-axis value(s) mismatch"
)

🎯 Deliverable 21: Average Trip Total by Pickup Area (Bar Chart)¶

👇 Tasks¶

✔️ Using df_by_pickup_area, create a horizontal bar chart displaying the average trip total by each pickup area.
✔️ Set an appropriate title.
✔️ Set the height to 800 and do not supply a width (by default, a Plotly figure will expand to fit the window if the width parameter is omitted).
✔️ Use the simple_white theme (template).
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

🚀 Hint¶

Replace my_dataframe, 'column1', 'column2', and ...s with your own values from the code below.

fig = px.bar(
    my_dataframe,
    title='Your Title Here',
    x='column1',
    y='column2',
    text='column1',
    template='simple_white',
    height=...
)
fig.update_traces(texttemplate='$%{text:.1f}', textposition='outside')
fig.update_yaxes(categoryorder='total ascending')
fig.show()

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-21"
_points = 3

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(fig.layout.height, 800, "Incorrect height")
tc.assertEqual(
    fig.layout.template,
    pio.templates["simple_white"],
    "Incorrect plotly theme (template)",
)

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHguYmFyKAogICAg\
ZGZfYnlfcGlja3VwX2FyZWFfY2hlY2ssCiAgICB0aXRsZT0nQXZlcmFnZSBUcmlwIF\
RvdGFsICgkKSBieSBQaWNrdXAgQXJlYScsCiAgICB4PSd0cmlwX3RvdGFsJywKICAg\
IHk9J3BpY2t1cF9hcmVhJywKICAgIHRleHQ9J3RyaXBfdG90YWwnLAogICAgdGVtcG\
xhdGU9J3Bsb3RseV93aGl0ZScsCiAgICBoZWlnaHQ9ODAwCik="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    fig.data[0].type,
    fig_check.data[0].type,
    f"Must be a {fig_check.data[0].type} chart",
)

np.testing.assert_array_equal(
    fig.data[0].x, fig_check.data[0].x, "x-axis value(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].y, fig_check.data[0].y, "y-axis value(s) mismatch"
)

🎯 Deliverable 22: Pickup Area Breakdown (Treemap)¶

👇 Tasks¶

✔️ Using df_by_pickup_area, create a treemap of pickup areas.
✔️ Set an appropriate title.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-22"
_points = 3

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHgudHJlZW1hcCg\
KICAgIGRmX2J5X3BpY2t1cF9hcmVhX2NoZWNrLAogICAgdGl0bGU9J1BpY2t1cCBB\
cmVhIEJyZWFrZG93bicsCiAgICBwYXRoPVsncGlja3VwX2FyZWEnXSwKICAgIHZhb\
HVlcz0nbnVtX3RyaXBzJywKICAgIGhlaWdodD02MDAKKQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(fig.data[0].type, "treemap", "Must be a treemap")

np.testing.assert_array_equal(
    fig.data[0].labels, fig_check.data[0].labels, "Label value(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].parents, fig_check.data[0].parents, "Parent value(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].values, fig_check.data[0].values, "Size value(s) mismatch"
)

📌 Interpreting the Pickup Area Visualizations¶

🔍 “Near North Side” is the most popular pickup area, followed by “Loop” and “Near West Side”.
🔍 The average trip total for rides beginning in O’Hare is relatively much higher compared to any other pickup area.

🦄 Part 7: Pickup Area + Weekday/Weekend¶

In this part, you will add a new dimension (weekday/weekend) to the top 20 pickup areas.

🎯 Deliverable 23: Number of Trips and Average Trip Total by Pickup Area + Weekday/Weekend¶

👇 Tasks¶

✔️ Using df_filtered, calculate the following aggregated values by pickup_area and weekday_weekend.
- num_trips: Number of trips
- trip_total: Average trip_total
✔️ Store the resulting DataFrame in a new variable named df_by_pickup_area_weekday_weekend.
✔️ print(df_by_pickup_area_weekday_weekend.columns.tolist()) should print out ['pickup_area', 'weekday_weekend', 'num_trips', 'trip_total'].
✔️ df_filtered should remain unaltered.

🔑 Expected Output¶

	pickup_area	weekday_weekend	num_trips	trip_total
0	Austin	weekday	9064	13.2139
1	Austin	weekend	7787	13.284
2	Avondale	weekday	7942	12.6175
3	Avondale	weekend	8483	12.6297
4	Edgewater	weekday	10745	14.1318
5	Edgewater	weekend	10944	13.608
6	Hyde Park	weekday	11852	14.1627
7	Hyde Park	weekend	9451	14.977
8	Irving Park	weekday	7307	13.4092
9	Irving Park	weekend	7263	13.2436

# YOUR CODE BEGINS

# YOUR CODE ENDS

display(df_by_pickup_area_weekday_weekend.head(10))

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-23"
_points = 6

decoded_code = base64.b64decode(
    b"CmRmX2J5X3BpY2t1cF9hcmVhX3dlZWtkYXlfd2Vla2VuZF9jaGVj\
ayA9IGRmX2ZpbHRlcmVkX2NoZWNrLmdyb3VwYnkoCiAgICBbJ3BpY2t1cF9hcmVhJywgJ3dlZWtkYXlfd2Vla2\
VuZCddLCBhc19pbmRleD1GYWxzZQopLmFnZyh7CiAgICAnc3RhcnQnOiAnY291bnQnLAogICAgJ3RyaXBfdG90\
YWwnOiAnbWVhbicsCn0pLnJlbmFtZShjb2x1bW5zPXsKICAgICdzdGFydCc6ICdudW1fdHJpcHMnCn0pCg=="
)
eval(compile(decoded_code, "<string>", "exec"))

tc.assertEqual(
    df_by_pickup_area_weekday_weekend.columns.tolist(),
    df_by_pickup_area_weekday_weekend_check.columns.tolist(),
    "Column(s) mismatch",
)

tc.assertEqual(
    df_by_pickup_area_weekday_weekend.shape,
    df_by_pickup_area_weekday_weekend_check.shape,
    "Incorrect number of rows and/or columns",
)

pd.testing.assert_frame_equal(
    df_by_pickup_area_weekday_weekend.sort_values(
        df_by_pickup_area.columns.tolist()
    ).reset_index(drop=True),
    df_by_pickup_area_weekday_weekend_check.sort_values(
        df_by_pickup_area_check.columns.tolist()
    ).reset_index(drop=True),
    check_like=True,
)

🎯 Deliverable 24: Pickup Area + Weekday/Weekend Breakdown (Sunburst)¶

👇 Tasks¶

✔️ Using df_by_pickup_area_weekday_weekend, create a sunburst chart describing the breakdown of number of trips by pickup area and weekday/weekend classification.
✔️ Set an appropriate title.
✔️ Set both the width and height to 800.
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-24"
_points = 4

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(fig.data[0].type, "sunburst", "Must be a sunburst chart")
tc.assertEqual(fig.layout.width, 800, "Incorrect width")
tc.assertEqual(fig.layout.height, 800, "Incorrect height")

decoded_code = base64.b64decode(
    b"ZmlnX2NoZWNrID0gcHguc3VuYnVyc3\
QoCiAgICBkZl9ieV9waWNrdXBfYXJlYV93ZWVrZGF5X3dlZWtlbmQsCiAgICBwYX\
RoPVsncGlja3VwX2FyZWEnLCAnd2Vla2RheV93ZWVrZW5kJ10sCiAgICB2YWx1ZX\
M9J251bV90cmlwcycsCiAgICB0aXRsZT0nUGlja3VwIEFyZWEgQnJlYWtkb3duIC\
hXZWVrZGF5L1dlZWtlbmQpJywKICAgIHdpZHRoPTgwMCwKICAgIGhlaWdodD04MDAKKQ=="
)
eval(compile(decoded_code, "<string>", "exec"))

np.testing.assert_array_equal(
    fig.data[0].labels, fig_check.data[0].labels, "Label(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].parents, fig_check.data[0].parents, "Parent(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].values, fig_check.data[0].values, "Value(s) mismatch"
)

🎯 Deliverable 25: Pickup Area + Weekday/Weekend Breakdown (Treemap)¶

👇 Tasks¶

✔️ Using df_by_pickup_area_weekday_weekend, create a treemap chart describing the breakdown of number of trips by pickup area and weekday/weekend classification.
✔️ Set an appropriate title.
✔️ Set the height to 600 (do not set the width).
✔️ Store your figure in a variable named fig.
✔️ Display the figure using fig.show()

🔑 Sample Output¶

# YOUR CODE BEGINS

# YOUR CODE ENDS

🧭 Check Your Work¶

Run the code cell below to test your solution.

✔️ If the code cell runs without errors, you’re good to move on.
❌ If the code cell produces an error, review your code and fix any mistakes.

_test_case = "deliverable-25"
_points = 4

tc.assertIsNotNone(fig.layout.title.text, "Missing figure title")
tc.assertEqual(fig.data[0].type, "treemap", "Must be a treemap chart")
tc.assertEqual(fig.layout.height, 600, "Incorrect height")

decoded_code = base64.b64decode(
    b"ZmlnID0gcHgudHJlZW1hcCgKICAgIGRm\
X2J5X3BpY2t1cF9hcmVhX3dlZWtkYXlfd2Vla2VuZCwKICAgIHBhdGg9WydwaWNrdX\
BfYXJlYScsICd3ZWVrZGF5X3dlZWtlbmQnXSwKICAgIHZhbHVlcz0nbnVtX3RyaXBz\
JywKICAgIHRpdGxlPSdQaWNrdXAgQXJlYSBCcmVha2Rvd24gKFdlZWtkYXkvV2Vla2\
VuZCknLAogICAgaGVpZ2h0PTYwMAop"
)
eval(compile(decoded_code, "<string>", "exec"))

np.testing.assert_array_equal(
    fig.data[0].labels, fig_check.data[0].labels, "Label(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].parents, fig_check.data[0].parents, "Parent(s) mismatch"
)

np.testing.assert_array_equal(
    fig.data[0].values, fig_check.data[0].values, "Value(s) mismatch"
)

Assignments

Exercise 9 - Line, Scatter, Bar Plots

Chicago Uber/Lyft Trips Before/After COVID-19

💎 Case Overview¶

🐢 Your Dataset¶

⚔️ Your Goal¶

🔨 Import Packages and Dataset¶

🧭 Check Plotly Version¶

📐 Part 1: Data Overview¶

🎯 Deliverable 1: First 5 Rows¶

👇 Tasks¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 2: Summary of DataFrame¶

👇 Tasks¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 3: Number of Rows and Columns in the Dataset¶

👇 Tasks¶

🧭 Check Your Work¶

🗓️ Part 2: Extract Datetime Values into Separate Columns¶

🎯 Deliverable 4: Extract Year into a New Column¶

👇 Tasks¶

🔑 Expected Output¶

🧭 Check Your Work¶

🎯 Deliverable 5: Extract Month, Day of Month, Day of Week, and Hour into Columns¶

👇 Tasks¶

🔑 Expected Output¶

🧭 Check Your Work¶

🎯 Deliverable 6: Create weekday_weekend Column¶

👇 Tasks¶

🚀 Hint¶

🧭 Check Your Work¶

😷 Part 3: Visualize the Effects of COVID-19 on the Volume of Ridesharing Trips¶

🎯 Deliverable 7: Total Number of Trips in 2019 and 2020¶

👇 Tasks¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 8: Monthly Number of Trips and Tip Percentage¶

👇 Tasks¶

🧭 Check Your Work¶

🎯 Deliverable 9: Sunburst Chart of Monthly Trips¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 10: Monthly Number of Trips in 2019 and 2020 (Facet Grid Bar Chart)¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 11: Monthly Number of Trips in 2019 and 2020 (Line Plot)¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

📌 Interpreting the Pre-COVID vs. Post-COVID Monthly Number of Trips¶

💵 Part 4: Visualize the Effect of COVID-19 on Tips¶

🎯 Deliverable 12: Monthly Average Tip Percentages in 2019 and 2020 (Facet Grid Bar Chart)¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 13: Monthly Average Tip Percentages in 2019 and 2020¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

📌 Interpreting the Pre-COVID vs. Post-COVID Average Percentage of Tips¶

✨ Part 5: Trips Right Before July 4th Fireworks¶

🎯 Deliverable 14: Filter July 4th 5-6 PM Trips¶

👇 Tasks¶

🚀 Hint¶

🧭 Check Your Work¶

🎯 Deliverable 15: July 4th 5-6 PM Trips Scatter Plots¶

👇 Tasks¶

🔑 Sample Output¶

🚀 Hint¶

🧭 Check Your Work¶

📌 Interpreting the Pre-COVID vs. Post-COVID Scatter Plots¶

🎯 Deliverable 16: July 4th 5-6 PM Trips 3D Scatter Plots (Pre-COVID)¶

👇 Tasks¶

🎯 Deliverable 6: Create `weekday_weekend` Column¶