Explore Transport Delay Data
Load the public transport delays dataset and perform exploratory data analysis to understand delay patterns, weather effects, and event impacts.
Explore Transport Delay Data
python • Step 1
Load the public transport delays dataset and perform exploratory data analysis to understand delay patterns, weather effects, and event impacts.
Learning Objectives
- →Load CSV data with pandas
- →Examine dataset structure and data types
- →Identify missing values and outliers
- →Create initial visualizations of delay patterns
Dataset
View on KaggleTransit departure / arrival delays with weather & event context (synthetic).
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/public_transport_delays.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named public_transport_delays.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "public_transport_delays.csv").
Dataset Columns
trip_iddatetransport_typeroute_idactual_departure_delay_minweather_conditiontemperature_Cevent_typepeak_hourdelayed- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Shape: Columns:
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