如何使用 chDB 查询 Pandas DataFrames
Pandas 是一个流行的用于数据操作和分析的 Python 库。
在 chDB 的 2 版本中，我们改进了查询 Pandas DataFrames 的性能，并引入了 Python 表函数。
在本指南中，我们将学习如何使用 Python 表函数查询 Pandas。
设置
首先，让我们创建一个虚拟环境：
python -m venv .venv
source .venv/bin/activate
现在我们将安装 chDB。 确保你拥有 2.0.2 版本或更高版本：
pip install "chdb>=2.0.2"
接下来，我们将安装 Pandas 和其他一些库：
pip install pandas requests ipython
我们将使用 ipython 执行本指南中剩余的命令，可以通过运行以下命令启动：
ipython
你也可以在 Python 脚本或你喜欢的笔记本中使用这些代码。
从 URL 创建 Pandas DataFrame
我们将从 StatsBomb GitHub 代码库 查询一些数据。 首先，让我们导入 requests 和 pandas：
import requests
import pandas as pd
然后，我们将其中一场比赛的 JSON 文件加载到 DataFrame 中：
response = requests.get(
"https://raw.githubusercontent.com/statsbomb/open-data/master/data/matches/223/282.json"
)
matches_df = pd.json_normalize(response.json(), sep='_')
让我们看看我们将要处理的数据：
matches_df.iloc[0]
match_id 3943077
match_date 2024-07-15
kick_off 04:15:00.000
home_score 1
away_score 0
match_status available
match_status_360 unscheduled
last_updated 2024-07-15T15:50:08.671355
last_updated_360 None
match_week 6
competition_competition_id 223
competition_country_name South America
competition_competition_name Copa America
season_season_id 282
season_season_name 2024
home_team_home_team_id 779
home_team_home_team_name Argentina
home_team_home_team_gender male
home_team_home_team_group None
home_team_country_id 11
home_team_country_name Argentina
home_team_managers [{'id': 5677, 'name': 'Lionel Sebastián Scalon...
away_team_away_team_id 769
away_team_away_team_name Colombia
away_team_away_team_gender male
away_team_away_team_group None
away_team_country_id 49
away_team_country_name Colombia
away_team_managers [{'id': 5905, 'name': 'Néstor Gabriel Lorenzo'...
metadata_data_version 1.1.0
metadata_shot_fidelity_version 2
metadata_xy_fidelity_version 2
competition_stage_id 26
competition_stage_name Final
stadium_id 5337
stadium_name Hard Rock Stadium
stadium_country_id 241
stadium_country_name United States of America
referee_id 2638
referee_name Raphael Claus
referee_country_id 31
referee_country_name Brazil
Name: 0, dtype: object
接下来，我们将加载一个事件 JSON 文件，并向该 DataFrame 添加一个名为 match_id 的列：
response = requests.get(
"https://raw.githubusercontent.com/statsbomb/open-data/master/data/events/3943077.json"
)
events_df = pd.json_normalize(response.json(), sep='_')
events_df["match_id"] = 3943077
再一次，让我们看看第一行的数据：
with pd.option_context("display.max_rows", None):
first_row = events_df.iloc[0]
non_nan_columns = first_row[first_row.notna()].T
display(non_nan_columns)
id 279b7d66-92b5-4daa-8ff6-cba8fce271d9
index 1
period 1
timestamp 00:00:00.000
minute 0
second 0
possession 1
duration 0.0
type_id 35
type_name Starting XI
possession_team_id 779
possession_team_name Argentina
play_pattern_id 1
play_pattern_name Regular Play
team_id 779
team_name Argentina
tactics_formation 442.0
tactics_lineup [{'player': {'id': 6909, 'name': 'Damián Emili...
match_id 3943077
Name: 0, dtype: object
查询 Pandas DataFrames
接下来，让我们看看如何使用 chDB 查询这些 DataFrames。 我们将导入库：
import chdb
我们可以使用 Python 表函数查询 Pandas DataFrames：
SELECT *
FROM Python(<name-of-variable>)
所以，如果我们想列出 matches_df 中的列，我们可以编写如下内容：
chdb.query("""
DESCRIBE Python(matches_df)
SETTINGS describe_compact_output=1
""", "DataFrame")
name type
0 match_id Int64
1 match_date String
2 kick_off String
3 home_score Int64
4 away_score Int64
5 match_status String
6 match_status_360 String
7 last_updated String
8 last_updated_360 String
9 match_week Int64
10 competition_competition_id Int64
11 competition_country_name String
12 competition_competition_name String
13 season_season_id Int64
14 season_season_name String
15 home_team_home_team_id Int64
16 home_team_home_team_name String
17 home_team_home_team_gender String
18 home_team_home_team_group String
19 home_team_country_id Int64
20 home_team_country_name String
21 home_team_managers String
22 away_team_away_team_id Int64
23 away_team_away_team_name String
24 away_team_away_team_gender String
25 away_team_away_team_group String
26 away_team_country_id Int64
27 away_team_country_name String
28 away_team_managers String
29 metadata_data_version String
30 metadata_shot_fidelity_version String
31 metadata_xy_fidelity_version String
32 competition_stage_id Int64
33 competition_stage_name String
34 stadium_id Int64
35 stadium_name String
36 stadium_country_id Int64
37 stadium_country_name String
38 referee_id Int64
39 referee_name String
40 referee_country_id Int64
41 referee_country_name String
然后，我们可以通过编写以下查询找出裁判执法超过一场比赛的情况：
chdb.query("""
SELECT referee_name, count() AS count
FROM Python(matches_df)
GROUP BY ALL
HAVING count > 1
ORDER BY count DESC
""", "DataFrame")
referee_name count
0 César Arturo Ramos Palazuelos 3
1 Maurizio Mariani 3
2 Piero Maza Gomez 3
3 Mario Alberto Escobar Toca 2
4 Wilmar Alexander Roldán Pérez 2
5 Jesús Valenzuela Sáez 2
6 Wilton Pereira Sampaio 2
7 Darío Herrera 2
8 Andrés Matonte 2
9 Raphael Claus 2
现在，让我们探索 events_df。
chdb.query("""
SELECT pass_recipient_name, count()
FROM Python(events_df)
WHERE type_name = 'Pass' AND pass_recipient_name <> ''
GROUP BY ALL
ORDER BY count() DESC
LIMIT 10
""", "DataFrame")
pass_recipient_name count()
0 Davinson Sánchez Mina 76
1 Ángel Fabián Di María Hernández 64
2 Alexis Mac Allister 62
3 Enzo Fernandez 57
4 James David Rodríguez Rubio 56
5 Johan Andrés Mojica Palacio 55
6 Rodrigo Javier De Paul 54
7 Jefferson Andrés Lerma Solís 53
8 Jhon Adolfo Arias Andrade 52
9 Carlos Eccehomo Cuesta Figueroa 50
联接 Pandas DataFrames
我们还可以在查询中连接 DataFrames。 例如，要获取比赛概览，我们可以编写如下查询：
chdb.query("""
SELECT home_team_home_team_name, away_team_away_team_name, home_score, away_score,
countIf(type_name = 'Pass' AND possession_team_id=home_team_home_team_id) AS home_passes,
countIf(type_name = 'Pass' AND possession_team_id=away_team_away_team_id) AS away_passes,
countIf(type_name = 'Shot' AND possession_team_id=home_team_home_team_id) AS home_shots,
countIf(type_name = 'Shot' AND possession_team_id=away_team_away_team_id) AS away_shots
FROM Python(matches_df) AS matches
JOIN Python(events_df) AS events ON events.match_id = matches.match_id
GROUP BY ALL
LIMIT 5
""", "DataFrame").iloc[0]
home_team_home_team_name Argentina
away_team_away_team_name Colombia
home_score 1
away_score 0
home_passes 527
away_passes 669
home_shots 11
away_shots 19
Name: 0, dtype: object
从 DataFrame 填充表格
我们还可以从 DataFrames 创建并填充 ClickHouse 表。 如果我们想在 chDB 中创建一个表，我们需要使用有状态会话 API。
让我们导入会话模块：
from chdb import session as chs
初始化一个会话：
sess = chs.Session()
接下来，我们将创建一个数据库：
sess.query("CREATE DATABASE statsbomb")
然后，基于 events_df 创建一个 events 表：
sess.query("""
CREATE TABLE statsbomb.events ORDER BY id AS
SELECT *
FROM Python(events_df)
""")
然后我们可以运行查询，以返回接球最多的球员：
sess.query("""
SELECT pass_recipient_name, count()
FROM statsbomb.events
WHERE type_name = 'Pass' AND pass_recipient_name <> ''
GROUP BY ALL
ORDER BY count() DESC
LIMIT 10
""", "DataFrame")
pass_recipient_name count()
0 Davinson Sánchez Mina 76
1 Ángel Fabián Di María Hernández 64
2 Alexis Mac Allister 62
3 Enzo Fernandez 57
4 James David Rodríguez Rubio 56
5 Johan Andrés Mojica Palacio 55
6 Rodrigo Javier De Paul 54
7 Jefferson Andrés Lerma Solís 53
8 Jhon Adolfo Arias Andrade 52
9 Carlos Eccehomo Cuesta Figueroa 50
联接 Pandas DataFrame 和表
最后，我们还可以更新我们的联接查询，将 matches_df DataFrame 与 statsbomb.events 表连接：
sess.query("""
SELECT home_team_home_team_name, away_team_away_team_name, home_score, away_score,
countIf(type_name = 'Pass' AND possession_team_id=home_team_home_team_id) AS home_passes,
countIf(type_name = 'Pass' AND possession_team_id=away_team_away_team_id) AS away_passes,
countIf(type_name = 'Shot' AND possession_team_id=home_team_home_team_id) AS home_shots,
countIf(type_name = 'Shot' AND possession_team_id=away_team_away_team_id) AS away_shots
FROM Python(matches_df) AS matches
JOIN statsbomb.events AS events ON events.match_id = matches.match_id
GROUP BY ALL
LIMIT 5
""", "DataFrame").iloc[0]
home_team_home_team_name Argentina
away_team_away_team_name Colombia
home_score 1
away_score 0
home_passes 527
away_passes 669
home_shots 11
away_shots 19
Name: 0, dtype: object
