Excel to DataFrame in Python — pandas Tutorial

About this lesson

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:

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