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", parse_dates=["Date Rptd", "DATE OCC"], dtype={"TIME OCC": str})
crimes.head()# Start coding here
# Use as many cells as you need#Frecuencia de Delitos Según la Hora de Ocurrencia Puede observarse que la hora donde se cometen delitos con mayor frecuencia en esta ciudad es a las 12 del medio día.
# Extracción y conversión de los dos primeros caracteres de la columna 'TIME OCC' a entero
crimes['HOUR_OCC'] = crimes['TIME OCC'].apply(lambda x: int(x[:2]))
crimes.head()
# Frecuencia de Crímenes por hora
plt.figure(figsize=(10, 6))
ax = sns.countplot(data=crimes, x='HOUR_OCC')
# Hora de mayor criminalidad.
peak_crime_hour = crimes['HOUR_OCC'].value_counts().idxmax()
print(peak_crime_hour)
# Colorear en rojo la barra de mayor frecuencia
for p in ax.patches:
if int(p.get_x() + 0.5) == peak_crime_hour:
p.set_color('red')
plt.title('Frecuencia de Delitos Según la Hora de Ocurrencia')
plt.xlabel('Hora de Ocurrencia')
plt.ylabel('Frecuencia')
plt.show()#Frecuencia de Delitos Nocturnos por Zona La mayor parte de los crímenes nocturnos ocurren en el área del centro de la ciudad.
# Lista de horas que queremos conservar en el nuevo DataFrame
horas_a_conservar = [22, 23, 0, 1, 2, 3, 4, 5]
# Crear un nuevo DataFrame utilizando la indexación booleana y el método .isin()
crimes_nocturnos = crimes[crimes['HOUR_OCC'].isin(horas_a_conservar)]
# Contar los delitos por área
crimes_por_area = crimes_nocturnos.groupby('AREA NAME').size().reset_index(name='TOTAL_CRIMES')
# Ordenar los resultados en orden descendente
crimes_por_area = crimes_por_area.sort_values(by='TOTAL_CRIMES', ascending=False)
# Aislar la primera fila y extraer el valor de la columna "AREA NAME"
peak_night_crime_location = crimes_por_area.iloc[0]['AREA NAME']
# Mostrar las primeras filas del nuevo DataFrame
print(peak_night_crime_location)#Victimas por Grupo de Edad
# Definir los bins y labels para la columna 'AGE_GROUP' y 'Age Bracket'
age_bins = [0, 17, 25, 34, 44, 54, 64, np.inf]
age_labels = ["0-17", "18-25", "26-34", "35-44", "45-54", "55-64", "65+"]
# Asignar una nueva columna 'Age Bracket' en el DataFrame 'crimes' usando pd.cut()
crimes["Age Bracket"] = pd.cut(crimes["Vict Age"],
bins=age_bins,
labels=age_labels)
# Crear victim_ages usando el método .value_counts() en la Serie 'Age Bracket'
victim_ages = crimes["Age Bracket"].value_counts()
print(victim_ages)