pandas-liteql 0.5.3

A simple pandas extension that enables users to execute SQL statements against DataFrames using in-memory SQLite.

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What is pandas-liteql?

pandas-liteql is a simple pandas extension that enables users to execute SQL statements against DataFrames using in-memory SQLite. It is meant to streamline data manipulation and analysis tasks. For more detailed information and examples on pandas-liteql, visit the documentation pages.

What pandas-liteql is not

pandas-liteql is not a competitor to libraries such as PySpark or DuckDB 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.

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.

import pandas as pd
from src import pandas_liteql as lql

# Data set creation
person_data = {
    'name': ['Bill', 'Ted', 'Abraham', 'Genghis', 'Napoleon'],
    'age': [25, 24, 56, 64, 51],
    'email': ['[e-mail]', '[e-mail]',
              '[e-mail]', '[e-mail]',
              '[e-mail]']
}

# 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.

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  [e-mail]
1      1   Ted   24   [e-mail]

Dropping

If finished with a table within the flow of a script, you can simply drop it with the drop function to preserve memory.

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.

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': ['[e-mail]', '[e-mail]',
              '[e-mail]', '[e-mail]',
              '[e-mail]']
}

# 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  [e-mail]
1      1   Ted   24   [e-mail]

Contributing

Currently, pandas-liteql will not be receiving any additional updates. Contributions will not be accepted here, but feel free to fork this project if you desire.