Step-by-Step Guide to Data Analysis with Python: Import, Clean, Visualize, and Merge Using Pandas

The article begins by emphasizing Python's versatility for data analysts and recommends installing Anaconda and using Jupyter Notebook as a visual coding interface.

It then shows how to import data from various sources—SQL databases, Excel files, CSV, HTML tables—using Pandas' read_* functions, and highlights the advantage of fetching web data with requests and BeautifulSoup .

Next, it demonstrates cleaning steps: renaming columns with rename , deleting unwanted columns with del , and converting data types (e.g., removing commas via re.sub and casting to numeric).

The guide proceeds to filtering data using boolean indexing, combining conditions with & (AND) and | (OR), and shows how to view subsets with head() .

It then covers basic calculations such as computing column means and sums, and introduces data visualization using seaborn and matplotlib to create histograms of per‑capita GDP.

Further, the tutorial explains grouping and aggregation with groupby , merging datasets (SQL‑style joins) using merge , and cleaning the merged result with drop to remove unnecessary columns.

Finally, it reflects on the shallow analysis performed, suggests extending calculations (e.g., weighted averages) and encourages readers to apply their SQL/Excel knowledge within the Python notebook environment.