🚖 Uber Rides Data Analysis – Exploratory Data Analysis (EDA)¶
This project analyzes a dataset of Uber rides to uncover patterns in ride behavior. The dataset contains information such as ride start/end times, purposes, ride categories, and distances traveled. Our goal is to perform a detailed EDA to understand user preferences, peak activity times, and travel trends.
Libraries used include pandas, numpy, matplotlib, and seaborn for data manipulation and visualization.
❓ Problem Statement / Business Questions¶
In this EDA, we aim to answer the following key business questions:
- 🛺 In which category do people book the most Uber rides?
- 🎯 For which purpose do people book rides the most?
- ⏰ At what time of day are most rides booked?
- 📆 In which months are Uber rides least frequent?
- 📅 On which days of the week do people book rides most?
- 🚗 How many miles do users typically travel?
Answering these questions can help Uber improve their service offerings and marketing strategies.
In [24]:
# Importing essential libraries for data manipulation and visualization
import pandas as pd # For handling tabular data
import numpy as np # For numerical operations
import matplotlib.pyplot as plt # For data visualization
import seaborn as sns # For advanced statistical visualizations
📂 Load Dataset¶
In [26]:
# Reading the dataset from a CSV file
df = pd.read_csv('UberData_Analysis.csv') # Replace this path if the file is located elsewhere
df # Display the first few rows of the dataframe to get an overview of the dataset
Out[26]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | |
|---|---|---|---|---|---|---|---|
| 0 | 01-01-2016 21:11 | 01-01-2016 21:17 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain |
| 1 | 01-02-2016 01:25 | 01-02-2016 01:37 | Business | Fort Pierce | Fort Pierce | 5.0 | NaN |
| 2 | 01-02-2016 20:25 | 01-02-2016 20:38 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies |
| 3 | 01-05-2016 17:31 | 01-05-2016 17:45 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting |
| 4 | 01-06-2016 14:42 | 01-06-2016 15:49 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1151 | 12/31/2016 13:24 | 12/31/2016 13:42 | Business | Kar?chi | Unknown Location | 3.9 | Temporary Site |
| 1152 | 12/31/2016 15:03 | 12/31/2016 15:38 | Business | Unknown Location | Unknown Location | 16.2 | Meeting |
| 1153 | 12/31/2016 21:32 | 12/31/2016 21:50 | Business | Katunayake | Gampaha | 6.4 | Temporary Site |
| 1154 | 12/31/2016 22:08 | 12/31/2016 23:51 | Business | Gampaha | Ilukwatta | 48.2 | Temporary Site |
| 1155 | Totals | NaN | NaN | NaN | NaN | 12204.7 | NaN |
1156 rows × 7 columns
In [29]:
# Checking the structure of the dataset including data types and non-null counts
df.info() # Helps identify missing values and data types
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1156 entries, 0 to 1155 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 START_DATE 1156 non-null object 1 END_DATE 1155 non-null object 2 CATEGORY 1155 non-null object 3 START 1155 non-null object 4 STOP 1155 non-null object 5 MILES 1156 non-null float64 6 PURPOSE 653 non-null object dtypes: float64(1), object(6) memory usage: 63.3+ KB
🧹 Data Cleaning and Preprocessing¶
In [ ]:
# Fill missing values in PURPOSE column with 'Not Available'
df['PURPOSE'].fillna('Not Available',inplace = True)
In [34]:
# View the first 5 rows
df.head()
Out[34]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | |
|---|---|---|---|---|---|---|---|
| 0 | 01-01-2016 21:11 | 01-01-2016 21:17 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain |
| 1 | 01-02-2016 01:25 | 01-02-2016 01:37 | Business | Fort Pierce | Fort Pierce | 5.0 | Not Available |
| 2 | 01-02-2016 20:25 | 01-02-2016 20:38 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies |
| 3 | 01-05-2016 17:31 | 01-05-2016 17:45 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting |
| 4 | 01-06-2016 14:42 | 01-06-2016 15:49 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit |
In [36]:
# Convert START_DATE and END_DATE to datetime format
df['START_DATE'] = pd.to_datetime(df['START_DATE'],errors = 'coerce')
df['END_DATE'] = pd.to_datetime(df['END_DATE'],errors = 'coerce')
In [38]:
# Confirm changes
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1156 entries, 0 to 1155 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 START_DATE 421 non-null datetime64[ns] 1 END_DATE 420 non-null datetime64[ns] 2 CATEGORY 1155 non-null object 3 START 1155 non-null object 4 STOP 1155 non-null object 5 MILES 1156 non-null float64 6 PURPOSE 1156 non-null object dtypes: datetime64[ns](2), float64(1), object(4) memory usage: 63.3+ KB
🕒 Extract Date and Time Features¶
In [41]:
#CREATING NEW COLUMN TO SAVE THE TIME IN DIFFRENT COLUMN
In [43]:
from datetime import datetime
df['DATE'] = pd.DatetimeIndex(df['START_DATE']).date
df['TIME'] = pd.DatetimeIndex(df['START_DATE']).hour
df
Out[43]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | DATE | TIME | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 21:11:00 | 2016-01-01 21:17:00 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain | 2016-01-01 | 21.0 |
| 1 | 2016-01-02 01:25:00 | 2016-01-02 01:37:00 | Business | Fort Pierce | Fort Pierce | 5.0 | Not Available | 2016-01-02 | 1.0 |
| 2 | 2016-01-02 20:25:00 | 2016-01-02 20:38:00 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies | 2016-01-02 | 20.0 |
| 3 | 2016-01-05 17:31:00 | 2016-01-05 17:45:00 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting | 2016-01-05 | 17.0 |
| 4 | 2016-01-06 14:42:00 | 2016-01-06 15:49:00 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit | 2016-01-06 | 14.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1151 | NaT | NaT | Business | Kar?chi | Unknown Location | 3.9 | Temporary Site | NaT | NaN |
| 1152 | NaT | NaT | Business | Unknown Location | Unknown Location | 16.2 | Meeting | NaT | NaN |
| 1153 | NaT | NaT | Business | Katunayake | Gampaha | 6.4 | Temporary Site | NaT | NaN |
| 1154 | NaT | NaT | Business | Gampaha | Ilukwatta | 48.2 | Temporary Site | NaT | NaN |
| 1155 | NaT | NaT | NaN | NaN | NaN | 12204.7 | Not Available | NaT | NaN |
1156 rows × 9 columns
In [45]:
# Categorize time of day
df['DAY-NIGNT'] = pd.cut(df['TIME'],bins = [0,10,15,19,24], labels = ['Morning','Afternoon','Evening','Night'])
df.head()
Out[45]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | DATE | TIME | DAY-NIGNT | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 21:11:00 | 2016-01-01 21:17:00 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain | 2016-01-01 | 21.0 | Night |
| 1 | 2016-01-02 01:25:00 | 2016-01-02 01:37:00 | Business | Fort Pierce | Fort Pierce | 5.0 | Not Available | 2016-01-02 | 1.0 | Morning |
| 2 | 2016-01-02 20:25:00 | 2016-01-02 20:38:00 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies | 2016-01-02 | 20.0 | Night |
| 3 | 2016-01-05 17:31:00 | 2016-01-05 17:45:00 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting | 2016-01-05 | 17.0 | Evening |
| 4 | 2016-01-06 14:42:00 | 2016-01-06 15:49:00 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit | 2016-01-06 | 14.0 | Afternoon |
In [47]:
# Drop any remaining null values
df.dropna(inplace = True)
In [49]:
#Size of dataset
df.shape
Out[49]:
(413, 10)
📊 EDA Process Description¶
- Data Cleaning: Missing values were handled, and datetime columns were converted properly.
- Feature Engineering: New columns like
DATE,TIME,DAY,MONTH, andDAY-NIGHTwere created for deeper analysis. - Data Visualization: Bar plots and box plots were used to identify trends, peaks, and outliers in various ride metrics.
Q1. In which category do people book the most Uber rides?¶
Q2. For which purpose do people book Uber rides the most?¶
In [54]:
plt.figure(figsize = (20,5))
plt.subplot(1,2,1)
sns.countplot(df['CATEGORY']) #for finding Category
plt.subplot(1,2,2)
sns.countplot(df['PURPOSE'])#for finding the purpose
Out[54]:
<Axes: xlabel='count', ylabel='PURPOSE'>
Q3. At what time do people book cabs the most from Uber?¶
In [57]:
sns.countplot(df['DAY-NIGNT'])
plt.grid(True,alpha =0.3)
Q4. In which months do people book Uber rides less frequently?¶
In [79]:
# Extract month number
df['MONTH']=pd.DatetimeIndex(df['START_DATE']).month
# Map month number to month name
month_label = {
1.0: 'jan',
2.0: 'feb',
3.0: 'mar',
4.0: 'apr',
5.0: 'may',
6.0: 'jun',
7.0: 'jul',
8.0: 'aug',
9.0: 'sep',
10.0: 'oct',
11.0: 'nov',
12.0: 'dec'
}
df['MONTH']= df['MONTH'].map(month_label)
# Count rides by month
mon = df['MONTH'].value_counts(sort = False)
df.head()
Out[79]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | DATE | TIME | DAY-NIGNT | DAY | MONTH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 21:11:00 | 2016-01-01 21:17:00 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain | 2016-01-01 | 21.0 | Night | thursday | jan |
| 1 | 2016-01-02 01:25:00 | 2016-01-02 01:37:00 | Business | Fort Pierce | Fort Pierce | 5.0 | Not Available | 2016-01-02 | 1.0 | Morning | fri | jan |
| 2 | 2016-01-02 20:25:00 | 2016-01-02 20:38:00 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies | 2016-01-02 | 20.0 | Night | fri | jan |
| 3 | 2016-01-05 17:31:00 | 2016-01-05 17:45:00 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting | 2016-01-05 | 17.0 | Evening | mon | jan |
| 4 | 2016-01-06 14:42:00 | 2016-01-06 15:49:00 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit | 2016-01-06 | 14.0 | Afternoon | tue | jan |
In [92]:
# Create a DataFrame with total ride count and max MILES per month
df2 = pd.DataFrame({
"MONTHS": mon.values, # Total rides per month
"VALUE COUNT": df.groupby("MONTH", sort=False)["MILES"].max().values # Max miles per month
})
# Plot lineplot
plt.figure(figsize=(10, 5))
p = sns.lineplot(data=df2)
p.set(xlabel="MONTHS", ylabel="VALUE COUNT")
plt.title("Monthly Ride Count vs Max Miles Traveled")
plt.grid(True, alpha=0.3)
plt.show()
Q5. On which days of the week do people book Uber rides the most?¶
In [65]:
df['DAY']= df.START_DATE.dt.weekday # Monday = 0, Sunday = 6
# Map weekday to name
day_label = {
1: 'mon',
2: 'tue',
3: 'wed',
4: 'thursday',
5: 'fri',
6: 'sat',
7: 'sun',
}
df['DAY']= df['DAY'].map(day_label)
df.head()
Out[65]:
| START_DATE | END_DATE | CATEGORY | START | STOP | MILES | PURPOSE | DATE | TIME | DAY-NIGNT | DAY | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 21:11:00 | 2016-01-01 21:17:00 | Business | Fort Pierce | Fort Pierce | 5.1 | Meal/Entertain | 2016-01-01 | 21.0 | Night | thursday |
| 1 | 2016-01-02 01:25:00 | 2016-01-02 01:37:00 | Business | Fort Pierce | Fort Pierce | 5.0 | Not Available | 2016-01-02 | 1.0 | Morning | fri |
| 2 | 2016-01-02 20:25:00 | 2016-01-02 20:38:00 | Business | Fort Pierce | Fort Pierce | 4.8 | Errand/Supplies | 2016-01-02 | 20.0 | Night | fri |
| 3 | 2016-01-05 17:31:00 | 2016-01-05 17:45:00 | Business | Fort Pierce | Fort Pierce | 4.7 | Meeting | 2016-01-05 | 17.0 | Evening | mon |
| 4 | 2016-01-06 14:42:00 | 2016-01-06 15:49:00 | Business | Fort Pierce | West Palm Beach | 63.7 | Customer Visit | 2016-01-06 | 14.0 | Afternoon | tue |
In [67]:
# Plot
day_label = df.DAY.value_counts()
sns.barplot(x=day_label.index,y=day_label)
plt.xlabel('DAY')
plt.ylabel ('count')
plt.grid(True,alpha = 0.4)
Q6. How many miles do people usually book a cab for through Uber?¶
In [70]:
# Distance (MILES) Distribution
sns.boxplot(df['MILES'])
Out[70]:
<Axes: ylabel='MILES'>
In [72]:
# Filtered to < 100 miles
sns.boxplot(df[df['MILES']<100]['MILES'])
Out[72]:
<Axes: ylabel='MILES'>
In [74]:
# Further filtered to < 40 miles
sns.boxplot(df[df['MILES']<40]['MILES'])
Out[74]:
<Axes: ylabel='MILES'>
In [96]:
sns.distplot(df[df['MILES']<40]['MILES'])
C:\Users\Asus\AppData\Local\Temp\ipykernel_13736\1171915261.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(df[df['MILES']<40]['MILES'])
Out[96]:
<Axes: xlabel='MILES', ylabel='Density'>
✅ Key Insights & Conclusion¶
- 🛺 The most booked ride category is Business.
- 🎯 The top purpose for rides is Meetings.
- ⏰ Users mostly book rides during the Evening and Afternoon.
- 📅 The highest number of rides occur on weekdays, especially Monday and Friday.
- 📆 Rides are booked less frequently in June and July.
- 🚗 Most users travel under 20 miles, with a few long-distance outliers.
This EDA helps Uber understand user behavior and peak demand times, which can inform pricing, driver availability, and app experience improvements.
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