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COURSE TOPIC

Python, NumPy, and Pandas for Data Science

Use Python arrays and Pandas tables to inspect, clean, transform, and summarize data with documented choices.

Part of course Data Science & Machine Learning Course
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Difficulty Level
Beginner to intermediate
Estimated Learning Time
90 minutes
OVERVIEW

About Python, NumPy, and Pandas for Data Science

Python provides a flexible environment for data work. NumPy supports numerical arrays, while Pandas provides labeled Series and DataFrames for common table operations.

LEARNING OUTCOMES

What You Will Learn

  • Load and inspect a tabular dataset.
  • Use Pandas filters, groupings, and transformations.
  • Check how cleaning choices change counts and summaries.
TOPIC GUIDE

Learn Python, NumPy, and Pandas for Data Science

Start with an inspection pass

Check shape, column names, data types, missing values, and a few representative records. Verify what a row represents before calculating totals or averages.

Make cleaning decisions explicit

Missing values can mean different things; dropping them without analysis may bias the result. Check duplicates and category labels, document the rule used, and compare row counts before and after a transformation.

Use groupby for focused summaries

Pandas grouping can summarize records by time, category, or another meaningful field. Confirm the aggregation matches the question and include a denominator when comparing rates.

Keep notebooks readable

Use descriptive variable names, small functions for repeated steps, and markdown notes for assumptions. A notebook should make it clear how raw inputs became the reported result.

PRACTICAL EXAMPLE

Example / Code

Example 
import pandas as pd

data = pd.read_csv('service_records.csv')
print(data.shape)
print(data.dtypes)
print(data.isna().sum())

monthly = (
    data.assign(month=pd.to_datetime(data['date']).dt.to_period('M'))
        .groupby(['month', 'category'], dropna=False)
        .size()
        .rename('records')
        .reset_index()
)
print(monthly.head())
TRY IT YOURSELF

Practice Task

Apply what you learned with a practical activity.

Use a synthetic CSV to report monthly totals by category. Inspect types and missing values first, write down a cleaning choice, and verify that your grouped totals reconcile with the number of included rows.

TOOLKIT

Tools & Technologies

  • Python
  • NumPy
  • Pandas
  • Jupyter
CONTINUE LEARNING

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COURSE TOPICS

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Follow a reproducible data science process from a clear question and data checks to analysis, evaluation, and communication.

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Statistics and Visualization for Machine Learning

Use descriptive statistics and charts to understand distributions, sampling, uncertainty, and relationships before modeling.

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Supervised Machine Learning: Regression and Classification

Understand labeled data, features and targets, simple baselines, and the difference between regression and classification.

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Evaluate Machine Learning Models Without Data Leakage

Choose metrics that match the task and avoid leakage, overfitting, and misleading evaluation splits.

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Data Science Capstone: A Reproducible Project from Start to Finish

Plan a compact data science project with documented sources, repeatable preparation, a baseline, evaluation, and clear limitations.

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