Uber Ride Data Analysis

Goal

Given Uber ride/trip records:

• Find peak hours and weekdays
• Visualize trip volume over time
• Identify hotspots (if location data exists)

Analysis pipeline

diagram Uber demand analysis pipeline mermaid

  flowchart LR
  A["Raw trips
(timestamps)"] --> B["Extract
(hour, weekday)"]
  B --> C["Count
(trips per hour/day)"]
  C --> D["Visualize
(demand curve)"]
  D --> E["Conclude
(peak hours/days)"]

From raw trip timestamps to peak-hour demand patterns.

From the book

McKinney’s Bitly example counts raw frequencies with Counter(time_zones).most_common(10) — a plain tally of how often each value shows up. Counting trips per hour is the exact same idea: df["hour"].value_counts() (or a countplot) turns thousands of timestamps into one simple frequency table you can read at a glance.

Step 1: Load and parse timestamps

Load trips

import pandas as pd
 
df = pd.read_csv("data/uber.csv")
 
df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
print(df.head())

Step 2: Extract time features

Time features

df["hour"] = df["timestamp"].dt.hour
df["weekday"] = df["timestamp"].dt.day_name()

Step 3: Plot trips by hour

Trips by hour

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(8, 4))
sns.countplot(data=df, x="hour")
plt.title("Trips by hour")
plt.tight_layout()
plt.show()

Step 4: Trend over dates

Trips by day

import matplotlib.pyplot as plt
 
daily = df.dropna(subset=["timestamp"]).groupby(df["timestamp"].dt.date).size()
 
plt.figure(figsize=(10, 4))
plt.plot(daily.index, daily.values)
plt.title("Trips over time")
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

Visualize it

sketch Trips by hour of day p5.js Pause Restart

Tall bars around commute hours reveal the demand peaks.

Deliverable

• Peak hours
• Peak weekdays
• Trend changes (seasonality/events)

🧪 Try It Yourself

Exercise 1 – Extract the hour from a timestamp

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Exercise 2 – Count trips per hour

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Exercise 3 – Weekday name from a date

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