Replace Excel with Python: A Step‑by‑Step Pandas Tutorial

1. Import Excel File

Use pandas.read_excel to load .xls or .xlsx files into a DataFrame. The tutorial demonstrates the most common parameters and suggests the Iris dataset for practice.

<code>pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, parse_cols=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skip_footer=0, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds)</code>

2. Important read_excel Options

Key arguments include sheet_name to select a worksheet, index_col to set the index column, and usecols to import specific columns.

3. Importing Sheets and Columns

By default the first sheet is loaded; you can specify a sheet by name or index. Use index_col to control the DataFrame index and usecols to limit columns.

4. Skipping Rows and Columns

Parameters like skiprows and header let you adjust how rows are interpreted during import.

5. Viewing DataFrame Attributes

Use head() , tail() , column selection, columns , info() , and describe() to inspect data.

6. Filtering Data

Apply filters with dot notation, bracket notation, or iloc . Combine multiple conditions using logical operators to mimic Excel’s advanced filter.

7. Statistical Functions

Perform descriptive statistics, sum, count, mean, max, min, and groupby operations directly on DataFrames.

8. Pivot Tables

Use pivot_table to create Excel‑like pivot tables, specifying values, index, columns, and aggregation functions; replace missing values with fill_value .

9. Emulating VLOOKUP

Since Pandas lacks a VLOOKUP function, use merge with left, right, inner, or outer joins to achieve similar results.

The tutorial includes numerous screenshots illustrating each step and provides a GitHub link for the full code repository.