Tuesday notes

Building bins and data insights

df.groupby('IssueReported').size().sort_values(ascending=False).head(10)

df['Date'] = pd.to_datetime(df['Published Date'])
df['Month'] = df['Date'].dt.month
monthly_counts = df.groupby('Month').size()

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Cleaning up Longitude and Latitude

df = df[(df['Latitude'] != 0)]
df = df[(df['Longitude'] != 0)]
df = df[(df['Latitude'] <= 35)]

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Traffic 2021-2025

A sample Python notebook courtesy of Juan Carlos Barrante.

Python Notebook

Code

import pandas as pd 
import matplotlib.pyplot as plt
df= pd.read_csv("Real-Time_Traffic_Incident_Reports_20250818.csv", low_memory=False)

df['Published Date'] = pd.to_datetime(df['Published Date'], errors='coerce') # Convert to a datetime variable 

df['Month']= df['Published Date'].dt.month

# Filter
crash_df = df[df['Issue Reported'] == 'Crash Urgent'].copy()
crash_df["Year"] = crash_df["Published Date"].dt.year
crash_df["Month"] = crash_df["Published Date"].dt.month

crash_df = crash_df[crash_df["Year"].isin([2020,2021,2022])] # Years of study


# Grouping
monthly_crashes = crash_df.groupby(["Year", "Month"]).size().unstack(level=0) # Month and year series

# Graph
monthly_crashes.plot(kind="bar", figsize=(10,6), edgecolor="black")

plt.title("Crash Urgent Reports by Month and Year")
plt.xlabel("Month")
plt.ylabel("Number of Reports")
plt.xticks(rotation=0)
plt.grid(axis='y')
plt.legend(title="Year")
plt.show()