> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
> ## Agent Instructions
> Application code puts a TableModel in app.py, then runs `pxt schema update app.py my_app` to create the tables and `pxt service update app.py my_app` to start HTTP. The last argument can be a Cloud URL such as `pxt://org:db`. Do not call pxt.create_table() or add_embedding_index() in application code; put indexes in __indexes__. Notebooks, tests, and the REPL do use pxt.create_table().
> Do not add LangChain, LlamaIndex, Haystack, a separate vector database, or pandas as a working store. Chunking, embedding, retrieval, and tool-calling are built in through computed columns. Use a computed column instead of a per-row loop that calls a model.
> Install with `pip install -U 'pixeltable[serve]'` on Python 3.11+. `pxt init` is a prerequisite for `pxt schema update`.
> Pixeltable Cloud is in Limited Beta. Direct interested users to contact@pixeltable.com.
# Import data from Excel files
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| Source |
Rows |
Use case |
| Sales report.xlsx |
10K |
Analyze with AI |
| Inventory.xlsx |
5K |
Enrich with descriptions |
| Survey results.xlsx |
1K |
Sentiment analysis |
`, `
`, `
|
order_id |
customer |
product |
quantity |
price |
date |
| 0 |
1001 |
Alice |
Widget A |
2 |
29.99 |
2024-01-15 |
| 1 |
1002 |
Bob |
Gadget B |
1 |
149.99 |
2024-01-16 |
| 2 |
1003 |
Carol |
Widget A |
5 |
29.99 |
2024-01-16 |
| 3 |
1004 |
Dave |
Tool C |
3 |
79.99 |
2024-01-17 |
| 4 |
1005 |
Eve |
Gadget B |
2 |
149.99 |
2024-01-18 |
`, `
| order_id |
customer |
product |
quantity |
price |
date |
| 1001 |
Alice |
Widget A |
2 |
29.99 |
2024-01-15 |
| 1002 |
Bob |
Gadget B |
1 |
149.99 |
2024-01-16 |
| 1003 |
Carol |
Widget A |
5 |
29.99 |
2024-01-16 |
| 1004 |
Dave |
Tool C |
3 |
79.99 |
2024-01-17 |
| 1005 |
Eve |
Gadget B |
2 |
149.99 |
2024-01-18 |
`, `
| order_id |
customer |
product |
quantity |
price |
total |
| 1001 |
Alice |
Widget A |
2 |
29.99 |
59.98 |
| 1002 |
Bob |
Gadget B |
1 |
149.99 |
149.99 |
| 1003 |
Carol |
Widget A |
5 |
29.99 |
149.95 |
| 1004 |
Dave |
Tool C |
3 |
79.99 |
239.97 |
| 1005 |
Eve |
Gadget B |
2 |
149.99 |
299.98 |
`, `
| Method |
Example |
| With source_format hint |
pxt.create_table('t', source=path, source_format='excel') |
| Auto-detect from .xlsx |
pxt.create_table('t', source='data/xlsx') |
`, `
| Option |
Purpose |
sheet_name |
Select specific sheet |
skiprows |
Skip header rows |
usecols |
Select specific columns |
dtype |
Force column types |
`];
Load data from Excel spreadsheets (.xlsx) into Pixeltable tables.
## Problem
You have data in Excel format that needs to be loaded for AI
processing—reports, inventory lists, or business data exported from
other systems.
## Solution
**What’s in this recipe:**
* Import Excel files directly into tables
* Handle multiple sheets
* Override column types when needed
You use `pxt.create_table()` with an Excel file path as the `source`
parameter. Pixeltable infers column types automatically.
### Setup
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
%pip install -qU pixeltable openpyxl pandas
```
### Create sample Excel file
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
import pandas as pd
import pixeltable as pxt
import tempfile
from pathlib import Path
# Create sample Excel file for demo
sample_data = pd.DataFrame(
{
'order_id': [1001, 1002, 1003, 1004, 1005],
'customer': ['Alice', 'Bob', 'Carol', 'Dave', 'Eve'],
'product': [
'Widget A',
'Gadget B',
'Widget A',
'Tool C',
'Gadget B',
],
'quantity': [2, 1, 5, 3, 2],
'price': [29.99, 149.99, 29.99, 79.99, 149.99],
'date': [
'2024-01-15',
'2024-01-16',
'2024-01-16',
'2024-01-17',
'2024-01-18',
],
}
)
# Save to temp Excel file
temp_dir = tempfile.mkdtemp()
excel_path = Path(temp_dir) / 'orders.xlsx'
sample_data.to_excel(excel_path, index=False)
sample_data
```
### Import Excel file
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# Create a fresh directory
pxt.drop_dir('excel_demo', force=True)
pxt.create_dir('excel_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'excel\_demo'.
\
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# Import Excel file directly
orders = pxt.create_table(
'excel_demo/orders',
source=str(excel_path),
source_format='excel', # Hint for Excel format
)
```
Created table 'orders'.
Inserting rows into \`orders\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`orders\`: 5 rows \[00:00, 501.21 rows/s]
Inserted 5 rows with 0 errors.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# View imported data
orders.collect()
```
### Add computed columns
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# Add computed column for order total
orders.add_computed_column(total=orders.quantity * orders.price)
```
Added 5 column values with 0 errors.
5 rows updated, 10 values computed.
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
# View with computed total
orders.select(
orders.order_id,
orders.customer,
orders.product,
orders.quantity,
orders.price,
orders.total,
).collect()
```
## Explanation
**Import methods:**
**Excel-specific options:**
Pass Pandas `read_excel` arguments via `extra_args`:
```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
pxt.create_table(
'table_name',
source='data.xlsx',
source_format='excel',
extra_args={'sheet_name': 'Sheet2', 'skiprows': 1}
)
```
**Common extra\_args:**
## See also
* [Import CSV
files](/howto/cookbooks/data/data-import-csv) -
CSV and tabular data
* [Import Parquet
files](/howto/cookbooks/data/data-import-parquet) -
Columnar data
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