Python Data Analysis with NumPy, Pandas and Matplotlib
A Chinese study pack for building a small data-analysis workflow from NumPy arrays and Pandas tables to Matplotlib charts and evidence-based conclusions.
A reproducible analysis chain
The material follows arrays, table cleaning, grouping and chart explanation. A useful analysis states the data source, transformations, assumptions and conclusion boundary instead of presenting a chart without context.
Clean before drawing
Check types, missing values, duplicate rows and unusual ranges before calculating or plotting. Choose a chart that fits the question: bars for category comparison, lines for time change and scatter plots for relationships between numeric values.
File scope and licenses
Examples, dependencies, images and source projects may have different licenses or versions. Use sanitized sample data, record the environment and verify the relevant license before publishing a derived work.
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Python data-analysis workflow
Extract the archive, create a reproducible environment, process a small table in stages and explain every chart with units and data boundaries.
Before you start
- Prepare a Python environment, code editor and a writable practice directory.
- Know variables, lists, functions and basic loops.
- Prepare a CSV or table sample without sensitive information.
Quick start
- 01
Inspect the archive
Extract the ZIP into an isolated directory, read the notes and identify code, data and image-output locations.
- 02
Record the environment
Note Python, NumPy, Pandas and Matplotlib versions, then run one example in the isolated environment.
- 03
Complete the data chain
Read the table, inspect types, missing values and duplicates, then filter, group and calculate derived columns.
- 04
Plot and explain
Select a chart that matches the question, label units and ranges and write only the conclusions supported by the data.
Usage tips
- Check types, missing values and outliers before statistics or plotting.
- Titles, axes and legends should state the metric and unit without hiding differences in decoration.
- Verify upstream versions, resource sources and licenses before publishing examples.
Troubleshooting and uninstall
What if Pandas or Matplotlib cannot be imported?
Check the active terminal environment and installed versions, record the complete error and reinstall matching dependencies in an isolated environment.
Why do Chinese labels render as boxes?
Confirm a usable Chinese font exists and configure that font explicitly before rendering the chart again.
Frequently asked questions
Which library should a beginner learn first?
Start with NumPy arrays and basic operations, use Pandas for tables and then use Matplotlib to communicate the result.
Why clean data before plotting?
Types, missing values, duplicates and unusual ranges affect statistics directly and can make a chart misleading.
How should a chart be selected?
Use bars for categories, lines for time change and scatter plots for relationships, always labeling units.
Can the examples be used in a commercial project?
Check the license for each upstream project and example, including attribution, non-commercial and share-alike requirements where applicable.