How to Import Excel to PostgreSQL (4 Methods)

PostgreSQL doesn't read .xlsx files directly. Every method involves either converting to CSV first or using a programming language to parse the spreadsheet. Here are four practical approaches.

Method 1: Convert to CSV, Then COPY

The simplest path. Save the Excel file as CSV, then use PostgreSQL's fast COPY command.

In Excel or LibreOffice Calc, export as CSV (UTF-8). Then create the target table and import:

CREATE TABLE sales (
  id INTEGER,
  product TEXT,
  amount NUMERIC(10,2),
  sale_date DATE
);

# From psql
\copy sales FROM 'sales.csv' WITH (FORMAT csv, HEADER true, ENCODING 'UTF8')

This is the fastest approach for single-sheet files with clean data. COPY is optimized for bulk loading and handles millions of rows efficiently.

When This Breaks Down

• Multi-sheet workbooks: you'll need to export each sheet separately
• Formulas: only the computed values export, not the formulas themselves (usually what you want)
• Merged cells: CSV export flattens them, often leaving blank values where you don't expect them
• Rich formatting: dates may export in locale-specific formats that PostgreSQL can't parse without a custom DATESTYLE setting

Method 2: Python with pandas

For anything beyond a simple single-sheet file, Python gives you the most control.

import pandas as pd
from sqlalchemy import create_engine
 
engine = create_engine("postgresql://user:pass@localhost/mydb")
 
# Read specific sheet
df = pd.read_excel("report.xlsx", sheet_name="Q1 Sales")
 
# Clean up column names (spaces, special chars)
df.columns = [c.strip().lower().replace(" ", "_") for c in df.columns]
 
# Write to PostgreSQL
df.to_sql("q1_sales", engine, if_exists="replace", index=False)

pandas uses openpyxl under the hood for .xlsx files (install it with pip install openpyxl). For older .xls files, it uses xlrd.

For large files, load in chunks to avoid memory issues:

# Read in chunks of 10,000 rows
for chunk in pd.read_excel("large_file.xlsx", chunksize=10000):
    chunk.to_sql("big_table", engine, if_exists="append", index=False)

pandas handles type inference reasonably well -- integers, floats, dates, and strings are mapped to appropriate PostgreSQL types. You can override with explicit dtypes if needed:

df = pd.read_excel("data.xlsx", dtype={"zip_code": str, "amount": float})

Method 3: pgAdmin Import

pgAdmin 4 has a built-in import tool, but it only accepts CSV and text files -- not Excel directly. The workflow is:

1. Save your Excel file as CSV
2. In pgAdmin, right-click the target table and select Import/Export Data
3. Select the CSV file, set delimiter to comma, enable Header
4. Map columns if names don't match exactly
5. Click OK to import

For small to medium datasets where you're already using pgAdmin, this is convenient. For anything larger or repeatable, use COPY or Python.

Method 4: Mako

Mako's import accepts Excel files directly without CSV conversion.

1. Connect to your PostgreSQL instance in Mako
2. Click Import and select your .xlsx file
3. Pick the sheet you want to import (if the workbook has multiple sheets)
4. Mako shows a preview with detected column types
5. Adjust mappings and types as needed
6. Choose the target table (create new or append to existing) and run

Mako handles the .xlsx parsing internally and maps Excel types to PostgreSQL types. For one-off imports where you don't want to write code or convert files, this is the fastest path.

Type Mapping: Excel to PostgreSQL

Excel stores data differently than you might expect. Numbers are all IEEE 754 doubles internally (even integers). Dates are serial numbers (days since January 1, 1900). Here's how they typically map:

Common Gotchas

Encoding issues. Excel files saved on Windows often use Windows-1252 encoding when exported to CSV. PostgreSQL expects UTF-8 by default. Convert before importing:

iconv -f WINDOWS-1252 -t UTF-8 data.csv > data_utf8.csv

Or specify the encoding in the COPY command: WITH (ENCODING 'WIN1252').

Date format mismatches. Excel dates exported as text may appear as MM/DD/YYYY or DD.MM.YYYY depending on locale. Set PostgreSQL's DATESTYLE before importing:

SET DATESTYLE = 'ISO, MDY';  -- for MM/DD/YYYY

Leading zeros stripped. Excel removes leading zeros from numbers. ZIP codes like 01234 become 1234. If your data has these, format the column as text in Excel before exporting, or use dtype={"zip": str} in pandas.

Empty rows at the bottom. Excel files often have invisible empty rows below the data. pandas and CSV export usually handle this, but check your row count after import.

Multi-sheet workbooks. The CSV and pgAdmin methods only handle one sheet at a time. Python with pandas is the only approach here that lets you loop through sheets programmatically:

xls = pd.ExcelFile("workbook.xlsx")
for sheet in xls.sheet_names:
    df = pd.read_excel(xls, sheet_name=sheet)
    df.to_sql(sheet.lower().replace(" ", "_"), engine, if_exists="replace", index=False)

Mako connects to PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake, and ClickHouse with AI-powered autocomplete. Try it free at mako.ai.