<div align="center">
<img src="https://forgineer.pythonanywhere.com/static/pandas_liteql/pandas-liteql-logo.png" alt="pandas-liteql-logo.png"><br>
</div>
---
# What is pandas-liteql?
**pandas-liteql** is a simple [pandas](https://pandas.pydata.org/) extension that enables users to execute SQL statements against DataFrames using in-memory [SQLite](https://www.sqlite.org/index.html). It is meant to streamline data manipulation and analysis tasks. For more detailed information and examples on **pandas-liteql**, visit the [documentation pages](https://forgineer.pythonanywhere.com/pandas-liteql).
# What pandas-liteql is not
**pandas-liteql** is not a competitor to libraries such as [PySpark](https://spark.apache.org/docs/latest/api/python/index.html) or [DuckDB](https://duckdb.org/) that can perform SQL queries on larger data sets and perform more advanced data science use-cases. Rather, it is inspired by those projects and similar libraries that have performed the same function, but have since been abandoned or were not as user-friendly.
# Installing pandas-liteql
**pandas-liteql** requires a minimum of Python 3.7 and the following libraries:
| Library | Version |
|------------|--------------|
| Pandas | `>= 1.3.5` |
| SQLAlchemy | `>= 1.4.36` |
Assuming these prerequisites are already installed, adding **pandas-liteql** is as simple as...
```
pip install pandas-liteql
```
# Examples
Below are some usage examples to load, query, and drop data from the in-memory SQLite sessions established with **pandas-liteql** and pandas DataFrame integration. For more in-depth information and examples visit the [documentation pages](https://forgineer.pythonanywhere.com/pandas-liteql).
## Loading
Start by loading your DataFrame with the `load` function. When **pandas-liteql** is imported, an in-memory SQLite session is created where data can be loaded to.
```python
import pandas as pd
import pandas_liteql as lql
# Data set creation
person_data = {
'name': ['Bill', 'Ted', 'Abraham', 'Genghis', 'Napoleon'],
'age': [25, 24, 56, 64, 51],
'email': ['bill@excellent.com', 'ted@excellent.com',
'lincoln@excellent.com', 'khan@excellent.com',
'bonaparte@excellent.com']
}
# DataFrame creation
person_df = pd.DataFrame(data=person_data)
# Loading the DataFrame to in-memory SQLite as the 'person' table
# The 'person' variable is also a LiteQL class containing the table name and schema information
person = lql.load(df=person_df, table_name='person')
print(f'Table name: {person.name}')
print(person.schema)
```
Output:
```
Table name: person
name type nullable default autoincrement primary_key
0 index BIGINT True None auto 0
1 name TEXT True None auto 0
2 age BIGINT True None auto 0
3 email TEXT True None auto 0
```
## Querying
Next, query the table using the `query` function. Using SQL syntax, the loaded table can be queried and the results will be returned as a pandas DataFrame.
```python
bill_and_ted = lql.query(sql='SELECT * FROM person WHERE age < 30')
print(bill_and_ted)
```
Output:
```
index name age email
0 0 Bill 25 bill@excellent.com
1 1 Ted 24 ted@excellent.com
```
## Dropping
If finished with a table within the flow of a script, you can simply drop it with the `drop` function to preserve memory.
```python
lql.drop(table_name='person')
```
## The DataFrame SQL Accessor
Lastly, for a more simplistic approach, you can use the `liteql.sql` accessor to perform the same functions above in one line and return the result as a pandas DataFrame. This approach requires that you query from the `liteql` table that is loaded from the DataFrame, queried, and then dropped.
```python
import pandas as pd
import pandas_liteql as lql
# Data set creation
person_data = {
'name': ['Bill', 'Ted', 'Abraham', 'Genghis', 'Napoleon'],
'age': [25, 24, 56, 64, 51],
'email': ['bill@excellent.com', 'ted@excellent.com',
'lincoln@excellent.com', 'khan@excellent.com',
'bonaparte@excellent.com']
}
# DataFrame creation
person_df = pd.DataFrame(data=person_data)
bill_and_ted = person_df.liteql.sql('SELECT * FROM liteql WHERE age < 30')
print(bill_and_ted)
```
Output:
```
index name age email
0 0 Bill 25 bill@excellent.com
1 1 Ted 24 ted@excellent.com
```
# Contributing
Contributions are welcome via pull request from a fork. Log issues if any bugs are found or if you have some good ideas of how this project could be expanded on. Depending on interest, other contributors could be added to meet the demand in features.
Raw data
{
"_id": null,
"home_page": "https://github.com/forgineer/pandas-liteql",
"name": "pandas-liteql",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "dataframe,pandas,sql,sqlite",
"author": "forgineer",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/83/8d/2c0f887c44e9d38878567db2422a97320dcde9e50f88e4b44bb12e0d5657/pandas-liteql-0.5.1.tar.gz",
"platform": null,
"description": "<div align=\"center\">\r\n <img src=\"https://forgineer.pythonanywhere.com/static/pandas_liteql/pandas-liteql-logo.png\" alt=\"pandas-liteql-logo.png\"><br>\r\n</div>\r\n\r\n---\r\n\r\n# What is pandas-liteql?\r\n**pandas-liteql** is a simple [pandas](https://pandas.pydata.org/) extension that enables users to execute SQL statements against DataFrames using in-memory [SQLite](https://www.sqlite.org/index.html). It is meant to streamline data manipulation and analysis tasks. For more detailed information and examples on **pandas-liteql**, visit the [documentation pages](https://forgineer.pythonanywhere.com/pandas-liteql).\r\n\r\n# What pandas-liteql is not\r\n**pandas-liteql** is not a competitor to libraries such as [PySpark](https://spark.apache.org/docs/latest/api/python/index.html) or [DuckDB](https://duckdb.org/) that can perform SQL queries on larger data sets and perform more advanced data science use-cases. Rather, it is inspired by those projects and similar libraries that have performed the same function, but have since been abandoned or were not as user-friendly.\r\n\r\n# Installing pandas-liteql\r\n**pandas-liteql** requires a minimum of Python 3.7 and the following libraries:\r\n\r\n| Library | Version |\r\n|------------|--------------|\r\n| Pandas | `>= 1.3.5` |\r\n| SQLAlchemy | `>= 1.4.36` |\r\n\r\nAssuming these prerequisites are already installed, adding **pandas-liteql** is as simple as...\r\n\r\n```\r\npip install pandas-liteql\r\n```\r\n\r\n# Examples\r\nBelow are some usage examples to load, query, and drop data from the in-memory SQLite sessions established with **pandas-liteql** and pandas DataFrame integration. For more in-depth information and examples visit the [documentation pages](https://forgineer.pythonanywhere.com/pandas-liteql).\r\n\r\n## Loading\r\nStart by loading your DataFrame with the `load` function. When **pandas-liteql** is imported, an in-memory SQLite session is created where data can be loaded to.\r\n\r\n```python\r\nimport pandas as pd\r\nimport pandas_liteql as lql\r\n\r\n# Data set creation\r\nperson_data = {\r\n 'name': ['Bill', 'Ted', 'Abraham', 'Genghis', 'Napoleon'],\r\n 'age': [25, 24, 56, 64, 51],\r\n 'email': ['bill@excellent.com', 'ted@excellent.com',\r\n 'lincoln@excellent.com', 'khan@excellent.com',\r\n 'bonaparte@excellent.com']\r\n}\r\n\r\n# DataFrame creation\r\nperson_df = pd.DataFrame(data=person_data)\r\n\r\n# Loading the DataFrame to in-memory SQLite as the 'person' table\r\n# The 'person' variable is also a LiteQL class containing the table name and schema information\r\nperson = lql.load(df=person_df, table_name='person')\r\n\r\nprint(f'Table name: {person.name}')\r\nprint(person.schema)\r\n```\r\n\r\nOutput:\r\n```\r\nTable name: person\r\n name type nullable default autoincrement primary_key\r\n0 index BIGINT True None auto 0\r\n1 name TEXT True None auto 0\r\n2 age BIGINT True None auto 0\r\n3 email TEXT True None auto 0\r\n```\r\n\r\n## Querying\r\nNext, query the table using the `query` function. Using SQL syntax, the loaded table can be queried and the results will be returned as a pandas DataFrame.\r\n\r\n```python\r\nbill_and_ted = lql.query(sql='SELECT * FROM person WHERE age < 30')\r\n\r\nprint(bill_and_ted)\r\n```\r\n\r\nOutput:\r\n```\r\n index name age email\r\n0 0 Bill 25 bill@excellent.com\r\n1 1 Ted 24 ted@excellent.com\r\n```\r\n\r\n## Dropping\r\nIf finished with a table within the flow of a script, you can simply drop it with the `drop` function to preserve memory.\r\n\r\n```python\r\nlql.drop(table_name='person')\r\n```\r\n\r\n## The DataFrame SQL Accessor\r\nLastly, for a more simplistic approach, you can use the `liteql.sql` accessor to perform the same functions above in one line and return the result as a pandas DataFrame. This approach requires that you query from the `liteql` table that is loaded from the DataFrame, queried, and then dropped.\r\n\r\n```python\r\nimport pandas as pd\r\nimport pandas_liteql as lql\r\n\r\n# Data set creation\r\nperson_data = {\r\n 'name': ['Bill', 'Ted', 'Abraham', 'Genghis', 'Napoleon'],\r\n 'age': [25, 24, 56, 64, 51],\r\n 'email': ['bill@excellent.com', 'ted@excellent.com',\r\n 'lincoln@excellent.com', 'khan@excellent.com',\r\n 'bonaparte@excellent.com']\r\n}\r\n\r\n# DataFrame creation\r\nperson_df = pd.DataFrame(data=person_data)\r\n\r\nbill_and_ted = person_df.liteql.sql('SELECT * FROM liteql WHERE age < 30')\r\n\r\nprint(bill_and_ted)\r\n```\r\n\r\nOutput:\r\n```\r\n index name age email\r\n0 0 Bill 25 bill@excellent.com\r\n1 1 Ted 24 ted@excellent.com\r\n```\r\n\r\n\r\n# Contributing\r\nContributions are welcome via pull request from a fork. Log issues if any bugs are found or if you have some good ideas of how this project could be expanded on. Depending on interest, other contributors could be added to meet the demand in features.\r\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A simple pandas extension that enables users to execute SQL statements against DataFrames using",
"version": "0.5.1",
"project_urls": {
"Homepage": "https://github.com/forgineer/pandas-liteql"
},
"split_keywords": [
"dataframe",
"pandas",
"sql",
"sqlite"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "438d506cec1e2851c2d3c854ed3a3bbc26cc7ca14681efb1499ba865438ee60e",
"md5": "12ff16afdb688c14ff224a871ba1c487",
"sha256": "a48189cb07d6dc2e5ed9968a30bd16bb58f13c7f917da00d7b0fb4b69ef16ede"
},
"downloads": -1,
"filename": "pandas_liteql-0.5.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "12ff16afdb688c14ff224a871ba1c487",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 5595,
"upload_time": "2023-10-16T04:06:53",
"upload_time_iso_8601": "2023-10-16T04:06:53.064330Z",
"url": "https://files.pythonhosted.org/packages/43/8d/506cec1e2851c2d3c854ed3a3bbc26cc7ca14681efb1499ba865438ee60e/pandas_liteql-0.5.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "838d2c0f887c44e9d38878567db2422a97320dcde9e50f88e4b44bb12e0d5657",
"md5": "f03712eaa1b0f41d8b7eccab066f6fdf",
"sha256": "6ea178277d7441469b00e98ac412b797e513f1d0e61657f682af374b78fe0564"
},
"downloads": -1,
"filename": "pandas-liteql-0.5.1.tar.gz",
"has_sig": false,
"md5_digest": "f03712eaa1b0f41d8b7eccab066f6fdf",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 5181,
"upload_time": "2023-10-16T04:06:54",
"upload_time_iso_8601": "2023-10-16T04:06:54.619896Z",
"url": "https://files.pythonhosted.org/packages/83/8d/2c0f887c44e9d38878567db2422a97320dcde9e50f88e4b44bb12e0d5657/pandas-liteql-0.5.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-16 04:06:54",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "forgineer",
"github_project": "pandas-liteql",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "pandas-liteql"
}
JSON
