How to Import Excel to MySQL (4 Methods)

MySQL doesn't read Excel files natively. You either convert to CSV first or use a programming language to parse the spreadsheet. Here are four approaches.

Method 1: Convert to CSV, Then LOAD DATA INFILE

Export your Excel file as CSV (UTF-8), create the table, and load:

CREATE TABLE customers (
  id INT,
  name VARCHAR(200),
  email VARCHAR(200),
  signup_date DATE
);
 
LOAD DATA INFILE '/var/lib/mysql-files/customers.csv'
INTO TABLE customers
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS
(id, name, email, @signup_date)
SET signup_date = STR_TO_DATE(@signup_date, '%m/%d/%Y');

The IGNORE 1 ROWS skips the header. The @signup_date variable trick lets you transform dates during import.

Secure File Path Restriction

MySQL only reads files from the directory specified by secure_file_priv. Check it with:

SHOW VARIABLES LIKE 'secure_file_priv';

Place your CSV in that directory, or use LOAD DATA LOCAL INFILE to read from the client machine (requires local_infile=ON in both server and client config).

Method 2: Python with pandas

pandas reads Excel directly and writes to MySQL without CSV conversion:

import pandas as pd
from sqlalchemy import create_engine
 
engine = create_engine("mysql+pymysql://user:pass@localhost/mydb?charset=utf8mb4")
 
df = pd.read_excel("report.xlsx", sheet_name="Sales")
df.columns = [c.strip().lower().replace(" ", "_") for c in df.columns]
 
df.to_sql("sales", engine, if_exists="replace", index=False)

Install dependencies: pip install pandas openpyxl pymysql sqlalchemy.

For large files, use chunksize and method='multi' for faster batch inserts:

df.to_sql("big_table", engine, if_exists="append", index=False,
          chunksize=5000, method="multi")

To control column types explicitly:

from sqlalchemy import types
 
df.to_sql("orders", engine, if_exists="replace", index=False, dtype={
    "id": types.INTEGER(),
    "total": types.DECIMAL(10, 2),
    "notes": types.TEXT()
})

Method 3: MySQL Workbench Import Wizard

MySQL Workbench has a built-in Table Data Import Wizard that accepts both CSV and JSON files (not Excel directly). The workflow:

1. Export your Excel file as CSV
2. In MySQL Workbench, right-click a table and select Table Data Import Wizard
3. Select the CSV file
4. Map columns and configure types
5. Run the import

The wizard works for smaller datasets (under ~1 million rows). For larger imports, LOAD DATA INFILE is significantly faster because the wizard uses individual INSERT statements.

Method 4: Mako

Mako imports Excel files directly without conversion:

1. Connect to your MySQL instance in Mako
2. Click Import and select your .xlsx file
3. Choose the sheet to import
4. Review the column preview and type mappings
5. Select the target table and run

For quick, one-off imports without writing SQL or converting files, this is the simplest option.

Type Mapping: Excel to MySQL

Common Gotchas

Character set mismatch. Excel CSV exports from Windows may use Windows-1252. MySQL defaults to utf8mb4 on modern versions. If you see garbled characters, specify the character set:

LOAD DATA INFILE '/path/to/file.csv'
INTO TABLE t
CHARACTER SET 'latin1'
FIELDS TERMINATED BY ',';

Date formatting. Excel dates are locale-dependent. US exports use MM/DD/YYYY, European exports use DD/MM/YYYY. Use STR_TO_DATE with the matching format string during import.

NULL values. Excel exports empty cells as empty strings, not NULL. To convert:

LOAD DATA INFILE '/path/file.csv'
INTO TABLE t
FIELDS TERMINATED BY ','
(id, name, @amount)
SET amount = NULLIF(@amount, '');

Row count limits. Excel .xlsx supports up to 1,048,576 rows per sheet. If your data exceeds this, it was already truncated before you exported. Check the source data.

LOAD DATA speed. LOAD DATA INFILE is 10-20x faster than individual INSERTs. For millions of rows, always prefer it over the Workbench wizard or programmatic row-by-row inserts. Disable indexes before loading and rebuild after for even faster throughput:

ALTER TABLE big_table DISABLE KEYS;
-- LOAD DATA here
ALTER TABLE big_table ENABLE KEYS;

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