> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-parallel-read-in-order-multi-part.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Documentation for the Npy format

# Npy

| Input | Output | Alias |
| - | - | - |
| ✔ | ✔ | |

<h2 id="description">
  Description
</h2>

The `Npy` format is designed to load a NumPy array from a `.npy` file into ClickHouse.
The NumPy file format is a binary format used for efficiently storing arrays of numerical data.
During import, ClickHouse treats the top level dimension as an array of rows with a single column.

The table below gives the supported Npy data types and their corresponding type in ClickHouse:

<h2 id="data-types-matching">
  Data types matching
</h2>

| Npy data type (`INSERT`) | ClickHouse data type | Npy data type (`SELECT`) |
| - | - | - |
| `i1` | [Int8](/reference/data-types/int-uint) | `i1` |
| `i2` | [Int16](/reference/data-types/int-uint) | `i2` |
| `i4` | [Int32](/reference/data-types/int-uint) | `i4` |
| `i8` | [Int64](/reference/data-types/int-uint) | `i8` |
| `u1`, `b1` | [UInt8](/reference/data-types/int-uint) | `u1` |
| `u2` | [UInt16](/reference/data-types/int-uint) | `u2` |
| `u4` | [UInt32](/reference/data-types/int-uint) | `u4` |
| `u8` | [UInt64](/reference/data-types/int-uint) | `u8` |
| `f2`, `f4` | [Float32](/reference/data-types/float) | `f4` |
| `f8` | [Float64](/reference/data-types/float) | `f8` |
| `S`, `U` | [String](/reference/data-types/string) | `S` |
| | [FixedString](/reference/data-types/fixedstring) | `S` |

<h2 id="example-usage">
  Example usage
</h2>

<h3 id="saving-an-array-in-npy-format-using-python">
  Saving an array in .npy format using Python
</h3>

```Python theme={null}
import numpy as np
arr = np.array([[[1],[2],[3]],[[4],[5],[6]]])
np.save('example_array.npy', arr)
```

<h3 id="reading-a-numpy-file-in-clickhouse">
  Reading a NumPy file in ClickHouse
</h3>

```sql title="Query" theme={null}
SELECT *
FROM file('example_array.npy', Npy)
```

```response title="Response" theme={null}
┌─array─────────┐
│ [[1],[2],[3]] │
│ [[4],[5],[6]] │
└───────────────┘
```

<h3 id="selecting-data">
  Selecting data
</h3>

You can select data from a ClickHouse table and save it into a file in the Npy format using the following command with clickhouse-client:

```bash theme={null}
$ clickhouse-client --query="SELECT {column} FROM {some_table} FORMAT Npy" > {filename.npy}
```

<h2 id="format-settings">
  Format settings
</h2>