> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-fbfa8bee.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# How to query Apache Arrow with chDB
> In this guide, we will learn how to query Apache Arrow tables with chDB
[Apache Arrow](https://arrow.apache.org/) is a standardized column-oriented memory format that's gained popularity in the data community.
In this guide, we will learn how to query Apache Arrow using the `Python` table function.
Setup
Let's first create a virtual environment:
```bash theme={null}
python -m venv .venv
source .venv/bin/activate
```
And now we'll install chDB.
Make sure you have version 2.0.2 or higher:
```bash theme={null}
pip install "chdb>=2.0.2"
```
And now we're going to install PyArrow, pandas, and ipython:
```bash theme={null}
pip install pyarrow pandas ipython
```
We're going to use `ipython` to run the commands in the rest of the guide, which you can launch by running:
```bash theme={null}
ipython
```
You can also use the code in a Python script or in your favorite notebook.
Creating an Apache Arrow table from a file
Let's first download one of the Parquet files for the [Ookla dataset](https://github.com/teamookla/ookla-open-data), using the [AWS CLI tool](https://aws.amazon.com/cli/):
```bash theme={null}
aws s3 cp \
--no-sign \
s3://ookla-open-data/parquet/performance/type=mobile/year=2023/quarter=2/2023-04-01_performance_mobile_tiles.parquet .
```
If you want to download more files, use `aws s3 ls` to get a list of all the files and then update the above command.
Next, we'll import the Parquet module from the `pyarrow` package:
```python theme={null}
import pyarrow.parquet as pq
```
And then we can read the Parquet file into an Apache Arrow table:
```python theme={null}
arrow_table = pq.read_table("./2023-04-01_performance_mobile_tiles.parquet")
```
The schema is shown below:
```python theme={null}
arrow_table.schema
```
```text theme={null}
quadkey: string
tile: string
tile_x: double
tile_y: double
avg_d_kbps: int64
avg_u_kbps: int64
avg_lat_ms: int64
avg_lat_down_ms: int32
avg_lat_up_ms: int32
tests: int64
devices: int64
```
And we can get the row and column count by calling the `shape` attribute:
```python theme={null}
arrow_table.shape
```
```text theme={null}
(3864546, 11)
```
Querying Apache Arrow
Now let's query the Arrow table from chDB.
First, let's import chDB:
```python theme={null}
import chdb
```
And then we can describe the table:
```python theme={null}
chdb.query("""
DESCRIBE Python(arrow_table)
SETTINGS describe_compact_output=1
""", "DataFrame")
```
```text theme={null}
name type
0 quadkey String
1 tile String
2 tile_x Float64
3 tile_y Float64
4 avg_d_kbps Int64
5 avg_u_kbps Int64
6 avg_lat_ms Int64
7 avg_lat_down_ms Int32
8 avg_lat_up_ms Int32
9 tests Int64
10 devices Int64
```
We can also count the number of rows:
```python theme={null}
chdb.query("SELECT count() FROM Python(arrow_table)", "DataFrame")
```
```text theme={null}
count()
0 3864546
```
Now, let's do something a bit more interesting.
The following query excludes the `quadkey` and `tile.*` columns and then computes the average and max values for all remaining column:
```python theme={null}
chdb.query("""
WITH numericColumns AS (
SELECT * EXCEPT ('tile.*') EXCEPT(quadkey)
FROM Python(arrow_table)
)
SELECT * APPLY(max), * APPLY(avg) APPLY(x -> round(x, 2))
FROM numericColumns
""", "Vertical")
```
```text theme={null}
Row 1:
──────
max(avg_d_kbps): 4155282
max(avg_u_kbps): 1036628
max(avg_lat_ms): 2911
max(avg_lat_down_ms): 2146959360
max(avg_lat_up_ms): 2146959360
max(tests): 111266
max(devices): 1226
round(avg(avg_d_kbps), 2): 84393.52
round(avg(avg_u_kbps), 2): 15540.4
round(avg(avg_lat_ms), 2): 41.25
round(avg(avg_lat_down_ms), 2): 554355225.76
round(avg(avg_lat_up_ms), 2): 552843178.3
round(avg(tests), 2): 6.31
round(avg(devices), 2): 2.88
```