Unlock Vehicle Insights: Python Data Analysis Techniques for Driving Metrics

1. Data Import and Initial Exploration

Data import is the foundation of analysis. Using pandas' read_excel function, you can easily load Excel files. After loading, inspect the first rows with df.head() and basic info with df.info() to assess data quality, missing values, and column types.

import pandas as pd
# Import data
df = pd.read_excel(r'../../数据层/数据集合/车辆行驶记录表单 2.xlsx')

2. Data Calculations: From Basic Metrics to Business Requirements

(a) Time Difference and Trip Calculations

# Time difference calculation
df['行驶时长'] = (df['停止时间'] - df['启动时间']).dt.seconds
df['行驶时长sh'] = df['行驶时长'] / 3600
# Calculate driving distance
df['行驶里程'] = (df['平均速度'] * df['行驶时长sh']).round(1)

This computes driving duration in seconds, converts it to hours, and then derives distance by multiplying average speed with the duration, rounding to one decimal place.

(b) Energy Consumption and Unit SOC Mileage

# Calculate energy consumption
df['耗电量（SOC）'] = df['启动时剩余电量'] - df['停止时剩余电量']
# Calculate unit SOC mileage
df['单次SOC行驶里程'] = (df['行驶里程'] / df['耗电量（SOC）']).round(3)

These columns show how much battery (SOC) is used per trip and how many kilometers can be driven per unit of SOC, providing insight into vehicle energy efficiency.

3. Data Binning: Making Groups Clearer

# Bin average speed
bins = [0, 20, 30, 40, 50, 60, 70, 80, 100]
labels = [f'({bins[i]},{bins[i+1]})' for i in range(len(bins)-1)]
df['平均速度区间'] = pd.cut(df['平均速度'], bins=bins, labels=labels)

By examining df['平均速度'].describe(), appropriate intervals are chosen, and pd.cut discretizes the continuous speed values, facilitating analysis by speed range.

4. Group Aggregation: Mining Cohort Characteristics

(a) Count Vehicles per Speed Interval

# Count vehicles in each speed interval
vehicle_counts = df.groupby('平均速度区间').size()

(b) Average Unit SOC Mileage per Speed Interval

# Average SOC mileage per speed interval
avg_soc_mileage = df.groupby('平均速度区间')['单次SOC行驶里程'].mean()

These aggregations reveal the distribution of samples across speed ranges and uncover potential relationships between speed and energy efficiency.

5. Challenges Encountered

Common issues include syntax errors due to Python's strict indentation, logical errors that produce unexpected results, and dependency problems with third‑party libraries. Solutions involve careful code formatting, using debuggers like pdb, reading official documentation, and managing environments with venv or conda.

6. Summary and Takeaways

Through this vehicle‑driving‑data case study, the power of Python and pandas for end‑to‑end data analysis becomes evident: from importing data, performing calculations, binning, to group aggregation, all steps are streamlined. The experience also highlights the importance of aligning business logic with code, handling edge cases, and maintaining readable, robust scripts.