Excel to DataFrame in Python — pandas Tutorial

Most finance data lives in spreadsheets. Pricing sheets, contract terms, fund holdings, board decks. Excel is still the lingua franca of business data, and getting it into Python is the most common handoff every analyst has to solve. Pandas reads xlsx in one line. The output is a DataFrame — the same rows, the same columns, ready for everything that comes next. Source code: https://github.com/GoCelesteAI/excel-to-dataframe-in-python This tutorial covers the three patterns every Python data person should know — all with pandas. The pd.read_excel one-liner — the canonical single-sheet read, the path of least resistance. The sheet_name equals None trick — one call returns every sheet as a dictionary, keyed by sheet name, no loop, no glob, no filenames. The pd.ExcelFile context manager — open the workbook once, parse multiple sheets without re-opening the file. Three patterns, one library, one workbook, the same shape every time. What You'll Build: - excel_to_dataframe.py — read a 420-row Prices sheet from a two-sheet workbook three different ways. Single sheet, all sheets at once, ExcelFile context manager. All arrive at the same shape. - The pd.read_excel idiom — pass the path and a sheet name. Returns a DataFrame. Works with any modern xlsx through the openpyxl engine that ships with pandas. - The sheet_name equals None pattern — returns a dictionary keyed by sheet name. The whole workbook in a single call. Skip every for-loop-over-sheets pattern you've ever written. - The pd.ExcelFile context manager — open the workbook once with a with statement, parse each sheet by name. The file handle stays open across reads — faster than calling read_excel three separate times. - The two-sheet workbook structure — Prices with OHLCV rows, Tickers with a sector lookup. The standard layout for analyst-shared finance data. - The openpyxl engine — pandas default, no extra install needed for read-only use cases. Handles every modern xlsx without any configuration. Timestamps: 0:00 - Intro — Excel to DataFrame in one line 0:22 - Preview — three methods, one workbook 1:07 - Open excel_to_dataframe.py in nvim 1:30 - Method 1 — pd.read_excel single sheet 1:48 - Method 2 — sheet_name equals None returns a dict 2:24 - Method 3 — pd.ExcelFile context manager 3:02 - Save and run 3:13 - Three reads, same shapes 3:48 - End screen — recap Key Takeaways: 1. pd.read_excel is the canonical Excel-to-DataFrame in Python. One line per sheet. Works with any modern xlsx. The openpyxl engine ships with pandas — no extra install for read-only use cases. 2. Pass sheet_name equals None and pandas returns a dictionary. Keys are sheet names, values are DataFrames. No looping over sheets, no filename glob, no per-sheet code. The whole workbook in a single read. 3. pd.ExcelFile is the context manager for repeated reads. Open the workbook once, parse multiple sheets without re-opening the file. The win is invisible on a small workbook. On a multi-megabyte book with twenty sheets, you feel it. The context manager also guarantees the file closes cleanly. 4. The engine choice rarely matters for you. Pandas defaults to openpyxl for xlsx — no extra install for reads. You only specify the engine if you need read-write support or you are reading legacy xls files via xlrd. 5. Excel is not your final layer. Read once, work in DataFrame. Spreadsheets are great for capture and review. They are not great for joins, aggregations, rolling windows, or large data. The point of this tutorial is the bridge — get out of Excel, get into pandas, do the work there. This channel is run by Claude AI. Tutorials AI-produced; reviewed and published by Codegiz. Source code at codegiz.com. #Python #Excel #Pandas #DataAnalytics #xlsx #DataEngineering #PythonTutorial #LearnPython #DataFrame #openpyxl --- Generated by Claude AI · part of the Common Questions in Python series