PythonLabTools — Python Lab Exam Toolkit

Version 2.0.0

This package turns common Python lab-exam programs into reusable functions.

Covered areas

• NumPy arrays and slicing
• Pandas DataFrames, filtering, grouping, joining and merging
• Missing values and dirty-data preprocessing
• Categorical encoding
• Feature scaling and train/test splitting
• Line, bar, scatter, histogram and multiple plots
• Correlation heatmaps
• Regression: Linear Regression, Decision Tree, Random Forest
• Classification: Logistic Regression, Decision Tree, Random Forest
• Classification metrics and confusion matrix
• K-Means clustering and Elbow method
• PCA
• Association-rule calculations: support, confidence and lift
• Simple SQLite CRUD helpers
• Exam templates

Install locally

Extract the ZIP, open a terminal in the extracted folder:

pip install .

Then:

from pythonlabtools import *

Google Colab

Upload the ZIP, extract it, and install:

!unzip -q /content/pythonlabtools-exam-toolkit-v2.zip -d /content/
!pip install /content/pythonlabtools_exam_toolkit

Or after extracting:

%cd /content/pythonlabtools_exam_toolkit
!pip install .

Example

import pandas as pd
from pythonlabtools import show_missing, fill_missing_mean
from pythonlabtools import line_plot

df = pd.DataFrame({
    "Day": ["Mon", "Tue", "Wed"],
    "Temperature": [30, None, 32]
})

show_missing(df)
df = fill_missing_mean(df, "Temperature")

line_plot(
    df["Day"],
    df["Temperature"],
    title="Temperature vs Day",
    xlabel="Day",
    ylabel="Temperature"
)

Important exam principle

Use the toolkit to save typing, but understand what each function does. In a viva, you should be able to explain:

• groupby
• concat
• merge
• fillna
• get_dummies
• StandardScaler
• train_test_split
• fit
• predict
• R2 / MAE / MSE / RMSE
• accuracy / recall / confusion matrix
• K-Means / inertia / PCA
• support / confidence / lift