GGR274 Lab 4: Introduction to Data Wrangling, Part 1

GGR274 Lab 4: Introduction to Data Wrangling, Part 1#

Logistics#

Like last week, our lab grade will be based on attendance and submission of a few small tasks to MarkUs during the lab session (or by 23:59 on Thursday).

Complete the tasks in this Jupyter notebook and submit your completed file to MarkUs. Here are the instructions for submitting to MarkUs (same as last week):

  1. Download this file (Lab_4.ipynb) from JupyterHub. (See our JupyterHub Guide for detailed instructions.)

  2. Submit this file to MarkUs under the lab4 assignment. (See our MarkUs Guide for detailed instructions.)

Note: You can use autotests with this week’s lab to see if you are on the right track. Your TA and instructors can look in more detail to see if you answered all questions. The submission is a part of your Lab attendance grade.

Task 1: Read the csv file into a DataFrame#

a. Read the file#

Read the csv file ArrestsStripSearches.csv into a pandas Dataframe called police_df. The file contains records related to Toronto Police Race and Identity Based Data - Arrests and Strip Searches. Inspect the data with .head().

The file is located in the same folder as the notebook.

import pandas as pd

police_df = pd.read_csv("ArrestsStripSearches.csv")

police_df.head()

b. Check the size#

  • Use .shape to extract the the size of the table, (number of rows, number of columns).

  • Store the size to a variable named police_df_size.

police_df_size = police_df.shape

police_df_size

Task 2: Create a DataFrame of arrests in 2021#

a. Subset the tabular data by rows#

  • Create a boolean variable named Arrests_2021 computed from police_df that is True if Arrest_Year is 2021 and False otherwise.

  • Use Arrests_2021 to select rows of police_df that correspond to arrests in 2021. Save this new DataFrame in a variable called police_2021_df. Examine the head() of this DataFrame.

Arrests_2021 = police_df["Arrest_Year"] == 2021

police_2021_df = police_df[Arrests_2021]

police_2021_df.head()

b. Check the size#

  • Use .shape[0] to extract the number of rows of the subset, police_2021_df.

  • Store the arrest coutns to a variable named arrest_counts_2021.

arrest_counts_2021 = police_2021_df.shape[0]
arrest_counts_2021

c. Inspect columns of the tabular data#

  • Create a variable called Arrest_column_names that stores a list of the column names of police_2021_df and print the list.

Arrest_column_names = list(police_2021_df)

Arrest_column_names

Task 3: Select columns from police_2021_df and examine the distributions of each column#

  • Create a new DataFrame from police_2021_df with the following columns: _id, Sex, Perceived_Race, SearchReason_PossessWeapons and assign it to the variable police_2021_raceweapons. Examine the head() of this DataFrame.

  • Use .value_counts() to compute the distributions of Sex, Perceived_Race, SearchReason_PossessWeapons. You do not need to save these values in variables, though you may do so if you want.

police_2021_raceweapons = police_2021_df[["_id", "Sex", "Perceived_Race", "SearchReason_PossessWeapons"]]

police_2021_raceweapons.head()

police_2021_df["Sex"].value_counts()

police_2021_df["Perceived_Race"].value_counts()

police_2021_df["SearchReason_PossessWeapons"].value_counts()
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