Python for Data Analysts

🐍 Python for Data Analysts

Python is the second most important skill for data analysts after SQL. It's what separates analysts who can clean a spreadsheet from analysts who can build a full automated reporting pipeline.

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Why Python for Data Analysis?

  • Automation — stop doing the same report manually every Monday
  • Scale — analyse millions of rows that would crash Excel
  • Flexibility — join, reshape, and clean data in ways no spreadsheet can
  • Visualisation — Matplotlib and Seaborn produce publication-quality charts
  • Career value — Python adds an average 12–22% salary premium for analysts

Python Learning Path for Analysts

  1. Week 1: Python Basics — Variables, loops, functions, lists, dictionaries
  2. Week 2: Pandas Fundamentals — Loading data, filtering, selecting, cleaning nulls
  3. Week 3: Pandas Advanced — GroupBy, merge, pivot tables, apply()
  4. Week 4: Visualisation — Matplotlib line charts, Seaborn bar charts, heatmaps
  5. Week 5: NumPy + Stats — Arrays, mean, median, std, percentiles
  6. Week 6: Real Projects — Sales analysis, RFM segmentation, weekly report automation

Essential Pandas Code Reference

Task Code
Load CSV df = pd.read_csv('file.csv')
Filter rows df[df['salary'] > 60000]
Group and aggregate df.groupby('dept')['salary'].mean()
Handle missing values df.fillna(df.mean())
Sort values df.sort_values('revenue', ascending=False)
Merge dataframes pd.merge(df1, df2, on='id', how='left')
Export to Excel df.to_excel('output.xlsx', index=False)

Free Python Learning Resources


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