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. |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# import and investigate data
crimes = pd.read_csv("crimes.csv", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()## data cleaning# standardize column names to be titlecase
new_columns = []
for column in crimes.columns:
new_columns.append(column.title())
crimes.columns = new_columns
# rename the Dr_No columns
crimes = crimes.rename(columns={"Dr_No": "DR Num"})
print(crimes.columns)print(crimes.dtypes)# get the size/shape of the data
print(crimes.shape)# get amount of missing/NA values
print(crimes.isna().sum())# fill the missing values for Vict Sex and Vict Descent with 'X' (unknown)
crimes[["Vict Sex", "Vict Descent"]] = crimes[["Vict Sex", "Vict Descent"]].fillna("X")
# fill the missing values for Weapon Desc with 'Not Recorded'
crimes["Weapon Desc"] = crimes["Weapon Desc"].fillna("Not Recorded")print(crimes.isna().sum())## Which hour has the highest frequency of crimes? Store as an integer variable called peak_crime_hour.
# get the hour from the Time Occ column (first two chars)
crimes["Hour Occ"] = crimes["Time Occ"].str[:2].astype(int)
# sort by the value counts of each hour
print(crimes["Hour Occ"].value_counts().head())sns.countplot(data=crimes, x="Hour Occ")
plt.xlabel("Hour of Occurrence (Military Time)")
plt.ylabel("Number of crimes reported")
plt.show();peak_crime_hour = crimes["Hour Occ"].value_counts().index[0]
print(peak_crime_hour)## Which area has the largest frequency of night crimes (crimes committed between 10pm and 3:59am)? Save as a string variable called peak_night_crime_location.
# filter by times between 2200 and 0400
night_times = crimes[(crimes["Hour Occ"] >= 22) | (crimes["Hour Occ"] < 4)]
# get the counts by area and show the top results
night_times["Area Name"].value_counts().head()sns.countplot(data=night_times, x="Area Name")
plt.xticks(rotation=45)
plt.ylabel("Number of Crimes Reported")
plt.show();peak_night_crime_location = str(night_times["Area Name"].value_counts().index[0])
print(peak_night_crime_location)