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# Import data from CSV 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 |
Records |
Use case |
| customers.csv |
10,000 |
Add AI-generated summaries |
| products.xlsx |
500 |
Generate embeddings for search |
| logs.csv |
1M |
Filter and aggregate |
`, `
| cca3 |
country |
continent |
pop_2023 |
pop_2022 |
pop_2000 |
area__km__ |
| IND |
India |
Asia |
1428627663 |
1417173173 |
1059633675 |
3287590. |
| CHN |
China |
Asia |
1425671352 |
1425887337 |
1264099069 |
9706961. |
| USA |
United States |
North America |
339996563 |
338289857 |
282398554 |
9372610. |
| IDN |
Indonesia |
Asia |
277534122 |
275501339 |
214072421 |
1904569. |
| PAK |
Pakistan |
Asia |
240485658 |
235824862 |
154369924 |
881912. |
`, `
| name |
age |
city |
| Alice |
25 |
NYC |
| Bob |
30 |
LA |
| Charlie |
35 |
Chicago |
`, `
| Source |
Example |
| CSV file path |
source='/path/to/data.csv' |
| CSV URL |
source='https://example.com/data.csv' |
| Excel file |
source='/path/to/data.xlsx' |
| Pandas DataFrame |
source=df |
`];
Load data from CSV and Excel files into Pixeltable tables for processing
and analysis.
## Problem
You have data in CSV or Excel files that you want to process with AI
models, add computed columns to, or combine with other data sources.
## Solution
**What’s in this recipe:**
* Import CSV files directly into tables
* Import from Pandas DataFrames
* Handle different data types
You use `pxt.create_table()` with a `source` parameter to create a table
from a CSV file, or insert DataFrame rows into an existing table.
### Setup
```python theme={null}
%pip install -qU pixeltable pandas
```
```python theme={null}
import pandas as pd
import pixeltable as pxt
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('import_demo', force=True)
pxt.create_dir('import_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory 'import\_demo'.
\
### Import CSV directly
Use `create_table` with `source` to create a table from a CSV file:
```python theme={null}
# Import CSV from URL
csv_url = 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/world-population-data.csv'
population = pxt.create_table('import_demo/population', source=csv_url)
```
Created table 'population'.
Inserting rows into \`population\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`population\`: 234 rows \[00:00, 9032.63 rows/s]
Inserted 234 rows with 0 errors.
```python theme={null}
# View the imported data
population.head(5)
```
### Import from Pandas DataFrame
You can also create a DataFrame first and insert it:
```python theme={null}
# Create a DataFrame
df = pd.DataFrame(
{
'name': ['Alice', 'Bob', 'Charlie'],
'age': [25, 30, 35],
'city': ['NYC', 'LA', 'Chicago'],
}
)
# Create table and insert DataFrame
users = pxt.create_table(
'import_demo/users',
{'name': pxt.String, 'age': pxt.Int, 'city': pxt.String},
)
users.insert(df)
```
Created table 'users'.
Inserting rows into \`users\`: 0 rows \[00:00, ? rows/s]
Inserting rows into \`users\`: 3 rows \[00:00, 923.31 rows/s]
Inserted 3 rows with 0 errors.
3 rows inserted, 6 values computed.
```python theme={null}
# View the data
users.collect()
```
## Explanation
**Source types supported:**
**Type inference:**
Pixeltable automatically infers column types from CSV data. You can
override types using `schema_overrides`.
**Large files:**
For very large CSV files, consider:
* Using `create_table(source=...)` which streams data
* Importing in batches if memory is limited
## See also
* [Tables
documentation](/tutorials/tables-and-data-operations)
* [Bringing data
guide](/howto/cookbooks/data/data-import-csv)
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