Los Angeles, California 😎. The City of Angels. Tinseltown. The Entertainment Capital of the World!
Known for its warm weather, palm trees, sprawling coastline, and Hollywood, along with producing some of the most iconic films and songs. However, as with any highly populated city, it isn't always glamorous and there can be a large volume of crime. That's where you can help!
You have been asked to support the Los Angeles Police Department (LAPD) by analyzing crime data to identify patterns in criminal behavior. They plan to use your insights to allocate resources effectively to tackle various crimes in different areas.
The Data
They have provided you with a single dataset to use. A summary and preview are provided below.
It is a modified version of the original data, which is publicly available from Los Angeles Open Data.
crimes.csv
| Column | Description |
|---|---|
'DR_NO' | Division of Records Number: Official file number made up of a 2-digit year, area ID, and 5 digits. |
'Date Rptd' | Date reported - MM/DD/YYYY. |
'DATE OCC' | Date of occurrence - MM/DD/YYYY. |
'TIME OCC' | In 24-hour military time. |
'AREA NAME' | The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example, the 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles. |
'Crm Cd Desc' | Indicates the crime committed. |
'Vict Age' | Victim's age in years. |
'Vict Sex' | Victim's sex: F: Female, M: Male, X: Unknown. |
'Vict Descent' | Victim's descent:
|
'Weapon Desc' | Description of the weapon used (if applicable). |
'Status Desc' | Crime status. |
'LOCATION' | Street address of the crime. |
# Re-run this cell
# Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
crimes = pd.read_csv("crimes.csv", dtype={"TIME OCC": str})
crimes.head()Data overview
crimes.info()crimes.isna().sum()crimes.nunique()Important
According to the column's description, the victim's sex should only have three labels: F for female, M for male, and X for unknown. After reviewing the unique values it appears that in that column there is an additional label ("H").
While this is not relevant for the three tasks at hand, I found 41 unknown values in the Victim's Sex column.For future analysis, the "H" and "NaN" will be changed to "X" as it is currently unknown.
Likewise, a similar scenario happened on the Victim's Descent column although there are two additional labels ("NaN" and "-""). This will be changed to "X" which stands for "Unknown".
It is suggested the LAPD be consulted on what "H" on the victim's sex column stands for.
print(crimes["Vict Sex"].value_counts(dropna=False))print(crimes["Vict Descent"].value_counts(dropna=False))Data cleaning and preparation
# Renaming all column names to standardize format
crimes.rename(columns={
"DR NO":"DR No",
"DATE OCC":"Date Occ",
"TIME OCC":"Time Occ",
"AREA NAME":"Area Name",
"LOCATION":"Location"},
inplace=True)# Updating data types
crimes["Date Rptd"]=pd.to_datetime(crimes["Date Rptd"])
crimes["Date Occ"]=pd.to_datetime(crimes["Date Occ"])
crimes["Hour Occ"]=crimes["Time Occ"].str[:2].astype(int)# Adding additional time-related columns
crimes["Month Occ"]=crimes["Date Occ"].dt.month
crimes["Year Occ"]=crimes["Date Occ"].dt.year
crimes["Time Reported"]=crimes["Date Occ"]-crimes["Date Rptd"]# I'll calculate the missing values threshold for the crimes df. If a column is below or equal to the threshold, I'll proceed to drop the rows.
threshold=len(crimes)*0.05
print("Threshold equals to:", threshold)# As weapon description is above the threshold, I'll impute the missing values for this column.
# First I will identify if there exists a value similar to "Unknown weapon" to fill the missing values with this.
crimes["Weapon Desc"].value_counts()