Pandas read_excel loads rows and columns from .xls, .xlsx, .xlsm, .xlsb, and OpenDocument spreadsheets into a DataFrame (or a dictionary of frames when you ask for more than one sheet). Reading Excel files in Python with pandas still depends on small helper libraries called engines, so you install the engine that matches each file format before you call read_excel.

• read_excel returns one DataFrame by default (sheet_name=0). Pass sheet_name=None when you need every sheet at once.
• Modern .xlsx files use the openpyxl engine by default. Legacy .xls binaries need xlrd>=2.0.1, which only reads .xls. Binary .xlsb work needs pyxlsb.
• Optional python-calamine unlocks the fast calamine engine for multiple binary and OpenDocument formats when you pass engine="calamine".
• Pair dtype, parse_dates, and na_values together so finance and operations data land with the correct types on the first read.
• Large workbooks belong in CSV or Parquet for repeated processing. Use nrows while you prototype, then switch formats for production throughput.

1. Python 3.10 or newer on your workstation or Droplet. If you need a fresh runtime, follow How To Install Python 3 and Set Up a Local Programming Environment on Ubuntu.

2. A virtual environment plus pip:

   python3 -m venv ~/.venv-pandas && source ~/.venv-pandas/bin/activate
   python -m pip install --upgrade pip
   
   

3. Install pandas plus the engines you expect:

   python -m pip install pandas openpyxl xlrd pyxlsb python-calamine
   
   

4. Optional background reading on tabular Python tools: Python Pandas Module Tutorial, How To Install the pandas Package and Work with Data Structures in Python 3.

Assume records.xlsx contains two sheets named Employees and Cars. Preview headers with ExcelFile:

import pandas as pd

with pd.ExcelFile("records.xlsx") as xls:
    print(xls.sheet_names)

import pandas as pd

employees = pd.read_excel("records.xlsx", sheet_name="Employees")
print(employees)

Sample output:

   EmpID    EmpName   EmpRole
0      1     Pankaj       CEO
1      2  David Lee    Editor
2      3   Lisa Ray    Author

cars = pd.read_excel(
    "records.xlsx",
    sheet_name="Cars",
    usecols=["Car Name", "Car Price"],
)
print(cars)

You supply Excel column letters when you prefer positional selection:

subset = pd.read_excel("records.xlsx", sheet_name="Cars", usecols="A,B")

raw = pd.read_excel("records.xlsx", sheet_name="Numbers", header=None)

Set header=3 when the real column names live on the fourth row (zero indexed row 3). Rows above the header are ignored automatically.

all_sheets = pd.read_excel("records.xlsx", sheet_name=None)
print(all_sheets.keys())
employees_df = all_sheets["Employees"]

sheet_name=None returns an OrderedDict of DataFrame objects keyed by sheet name. Passing a list such as sheet_name=["Employees", "Cars"] returns a dict with only those sheets.

import pandas as pd

typed = pd.read_excel(
    "records.xlsx",
    sheet_name="Employees",
    dtype={"EmpID": "Int64", "EmpName": "string", "EmpRole": "string"},
    na_values={"EmpRole": ["TBD", "NA"]},
)

When a workbook stores real dates, name the columns explicitly:

shipments = pd.read_excel("shipments.xlsx", parse_dates=["ShipDate", "ArrivalDate"])

When Excel stores a column as opaque serial numbers, read the column with dtype="float" (or object) and pass the values through pd.to_datetime with the origin argument documented for your workbook’s date system.

Force an engine when pandas guesses wrong:

df = pd.read_excel("records.xlsx", engine="calamine")

import pandas as pd

url = "https://example.com/path/to/public-sheet.xlsx"
remote = pd.read_excel(url)

The URL must return a workbook directly, not an HTML landing page.

excel_data_df = pd.read_excel(
    "records.xlsx", sheet_name="Cars", usecols=["Car Name", "Car Price"]
)

print("Excel Sheet to Dict:", excel_data_df.to_dict(orient="records"))
print("Excel Sheet to JSON:", excel_data_df.to_json(orient="records"))
print("Excel Sheet to CSV:\n", excel_data_df.to_csv(index=False))

orient="records" yields one dict per row, which matches most REST payloads.

• Use nrows=5000 while you iterate on cleaning logic, then drop the cap for the final run.
• When the same multi megabyte workbook feeds a nightly job, convert once to Parquet and read with pd.read_parquet for faster IO and stricter schemas.
• Set na_filter=False when the sheet contains no missing values to skip extra parsing passes (see the pandas docs for tradeoffs).

• openpyxl by itself gives low level control over merged cells, styling, and incremental writes. Use openpyxl when pandas cannot represent the layout.
• xlwings controls a live Excel desktop process on Windows or macOS. Use xlwings when macros or legacy COM automation are required.
• Polars or other dataframe stacks often prefer converting Excel to Parquet once, then loading with a columnar reader for stricter schemas and faster IO.

For more pandas workflows after ingest, read Data Analysis and Visualization with pandas and Jupyter Notebook in Python 3 and Exploratory Data Analysis: Using Python Functions.

• ImportError: Missing optional dependency 'openpyxl' means you skipped the engine install. Run python -m pip install openpyxl.
• XLRDError: Excel xlsx file not supported appears when xlrd 2.x opens an .xlsx file. Point engine="openpyxl" or install openpyxl instead of downgrading xlrd.
• ValueError: Worksheet named 'Foo' not found signals a typo in sheet_name. Print ExcelFile.sheet_names first.
• Unexpected object dtypes usually mean mixed types in a column. Set an explicit dtype, or clean the sheet in Excel, then re export.

1. How do openpyxl and xlrd differ inside pandas?

openpyxl reads modern Office Open XML workbooks (.xlsx, .xlsm, .xltx). xlrd 2.x reads legacy .xls only. Choose openpyxl for every new spreadsheet, and keep xlrd around for older binary files only.

2. How do I load every sheet at once?

Call pd.read_excel("file.xlsx", sheet_name=None) and iterate over the dict of frames.

3. Why do my dates arrive as strings?

Excel often stores dates as text or floats. Pass parse_dates=["Column"], or read the column as text and run pd.to_datetime with an explicit format argument.

4. Is there a faster engine than openpyxl?

Install python-calamine and pass engine="calamine" for supported formats. Benchmark on your own files before you switch production jobs.

5. How should I debug a slow workbook?

Profile with nrows, drop unused columns with usecols, then archive cleaned results to CSV or Parquet for downstream consumers. How to Parse CSV Files in Python and How to Read and Write CSV Files in Python show the follow on steps once data leaves Excel.

You now know how pandas read_excel loads Excel files in Python, which engines match each format, and how to shape types while you read instead of patching issues later.

• Practice grouping and plotting in Data Analysis and Visualization with pandas and Jupyter Notebook in Python 3.
• Solidify core types with Understanding Data Types in Python and Understanding Dictionaries in Python 3.
• Deploy notebooks or ETL workers on DigitalOcean Droplets or ship managed apps on App Platform when you move from local experiments to production.