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pd.read_excel()

AI & DATA SCIENCE // pd-read-excel

pd.read_excel() reads data from an Excel spreadsheet file (.xlsx or .xls) into a DataFrame, optionally targeting a specific sheet.

Syntax

pd.read_excel(filepath, sheet_name=0, header=0)

Deep Dive Course

read_excel() requires an additional library installed under the hood, typically openpyxl for .xlsx files, to actually parse the Excel binary format, unlike read_csv(), which only needs to parse plain text. The sheet_name parameter selects which sheet to read — by index, 0 for the first sheet, the default, by name, a string, or, passing None, reads every sheet at once into a dict of DataFrames keyed by sheet name. Since Excel files can contain formatting, merged cells, and formulas, read_excel() reads the calculated values, not the underlying formulas themselves.

1Understanding pd.read_excel()

read_excel() requires an additional library installed under the hood, typically openpyxl for .xlsx files, to actually parse the Excel binary format, unlike read_csv(), which only needs to parse plain text. The sheet_name parameter selects which sheet to read — by index, 0 for the first sheet, the default, by name, a string, or, passing None, reads every sheet at once into a dict of DataFrames keyed by sheet name. Since Excel files can contain formatting, merged cells, and formulas, read_excel() reads the calculated values, not the underlying formulas themselves.

💡

Pass sheet_name=None to read_excel() to load every sheet in the workbook at once into a dict of DataFrames, keyed by sheet name, instead of calling read_excel() separately for each sheet you need.

editor.html
import pandas as pd

df = pd.read_excel("report.xlsx", sheet_name="Q1")
print(df.shape)
localhost:3000

2Practical Example

Here is a real-world application of pd.read_excel() showing how it is used in production Pandas code.

editor.html
import pandas as pd

all_sheets = pd.read_excel("report.xlsx", sheet_name=None)
print(list(all_sheets.keys()))
localhost:3000

3Best Practices

Follow these guidelines when working with pd.read_excel():

1. Specify sheet_name explicitly, by name or index, rather than relying on the default first-sheet behavior, for clarity and to avoid surprises if the sheet order changes

2. Install the appropriate optional engine, like openpyxl, ahead of time, since read_excel() depends on it and fails with an import error otherwise

3. Use sheet_name=None when you need to process every sheet in a workbook, to avoid a separate read_excel() call per sheet

⚠️

Tip: Pass sheet_name=None to read_excel() to load every sheet in the workbook at once into a dict of DataFrames, keyed by sheet name, instead of calling read_excel() separately for each sheet you need.

editor.html
import pandas as pd

df = pd.read_excel("report.xlsx", sheet_name="Q1")
print(df.shape)
localhost:3000

Examples

Example 01Basic Usage
import pandas as pd

df = pd.read_excel("report.xlsx", sheet_name="Q1")
print(df.shape)
Example 02Advanced Example
import pandas as pd

all_sheets = pd.read_excel("report.xlsx", sheet_name=None)
print(list(all_sheets.keys()))

Best Practices

  • Specify sheet_name explicitly, by name or index, rather than relying on the default first-sheet behavior, for clarity and to avoid surprises if the sheet order changes
  • Install the appropriate optional engine, like openpyxl, ahead of time, since read_excel() depends on it and fails with an import error otherwise
  • Use sheet_name=None when you need to process every sheet in a workbook, to avoid a separate read_excel() call per sheet

Interview Question

Why does pd.read_excel() typically require an extra package to be installed, unlike pd.read_csv()?

Hint: Think about the complexity of the file format each function needs to parse.

CSV is a simple, plain-text format that pandas can parse directly with its own built-in logic. Excel's .xlsx format is a much more complex binary/XML-based container that can include styling, formulas, multiple sheets, and other spreadsheet-specific features, so pandas relies on a separate specialized library, like openpyxl, to handle the actual low-level parsing of that format, keeping pandas' own core dependencies lighter for people who never need to work with Excel files.

Exercises

MediumPractice using pd.read_excel() in a real scenario.
View Solution
import pandas as pd

df = pd.read_excel("report.xlsx", sheet_name="Q1")
print(df.shape)

Frequently Asked Questions

Why does pd.read_excel() typically require an extra package to be installed, unlike pd.read_csv()?

CSV is a simple, plain-text format that pandas can parse directly with its own built-in logic. Excel's .xlsx format is a much more complex binary/XML-based container that can include styling, formulas, multiple sheets, and other spreadsheet-specific features, so pandas relies on a separate specialized library, like openpyxl, to handle the actual low-level parsing of that format, keeping pandas' own core dependencies lighter for people who never need to work with Excel files.

Related Functions

pd-read-csvdf-to-excelpd-read-html