Skip to main content

SQL queries on Pandas data frames

Project description

Seek well, pandas

seekwellpandas (SQL-pandas) is a pandas extension that provides SQL-inspired methods to manipulate DataFrames in a more intuitive way, closely resembling SQL syntax.

Features

seekwellpandas adds the following methods to your pandas DataFrames:

  • select(): Select specific columns, including negative selection.
  • where_(): Filter rows based on a condition.
  • group_by(): Group data by one or more columns.
  • having(): Filter groups based on a condition.
  • order_by(): Sort data by one or more columns.
  • limit(): Limit the number of returned rows.
  • join_(): Join two DataFrames.
  • union(): Union two DataFrames.
  • distinct(): Remove duplicates.
  • intersect(): Find the intersection between two DataFrames.
  • difference(): Find the difference between two DataFrames.
  • with_column(): Add a new column based on an expression.
  • rename_column(): Rename a column.
  • cast(): Change the data type of a column.
  • drop_column(): Remove one or more columns.
  • unpivot(): Transform columns into rows (melt).
  • group_having(): Combine grouping and group filtering.

Installation

You can install seekwellpandas via pip:

pip install seekwellpandas

Usage

Here are some examples of how to use SeekwellPandas:

import pandas as pd
import seekwellpandas

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': ['a', 'b', 'a', 'b'],
    'C': [10, 20, 30, 40]
})

# Select columns
result = df.select('A', 'B')

# Negative selection
result = df.select('-A')

# Filter rows redirecting to .query() (the _ avoids overlapping with pandas.DataFrame.where)
result = df.where_('A > 2')

# Group and aggregate
result = df.group_by('B').agg({'A': 'mean', 'C': 'sum'})

# Sort data
result = df.order_by('C', ascending=False)

# Add a new column
result = df.with_column('D', 'A * C')

# Join two DataFrames (the _ avoids overlapping with pandas.DataFrame.join)
df2 = pd.DataFrame({'B': ['a', 'b'], 'D': [100, 200]})
result = df.join_(df2, on='B')

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request on my GitHub repository.

License

This project is licensed under the GPLv3 License. See the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

seekwellpandas-0.2.1.tar.gz (17.4 kB view details)

Uploaded Source

Built Distribution

seekwellpandas-0.2.1-py3-none-any.whl (17.7 kB view details)

Uploaded Python 3

File details

Details for the file seekwellpandas-0.2.1.tar.gz.

File metadata

  • Download URL: seekwellpandas-0.2.1.tar.gz
  • Upload date:
  • Size: 17.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for seekwellpandas-0.2.1.tar.gz
Algorithm Hash digest
SHA256 fb201622c25acccb3d587bd081c483b382736fdf4f7043247840adeb5c024112
MD5 e83304d025f3f492eae5e73bdd6cdae2
BLAKE2b-256 4f152b8d943591a88c0d9dd6e0d8074006b16b5f32c2abf0e5dc3ff124469aa0

See more details on using hashes here.

File details

Details for the file seekwellpandas-0.2.1-py3-none-any.whl.

File metadata

File hashes

Hashes for seekwellpandas-0.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b28c1fb0c1a02180db0ad0cb7c35ef50e88b98bf4e2f31eac6f45623cf48c1e4
MD5 e4f88e1e92485d5ec2a9371b7c138a30
BLAKE2b-256 b7ead218a7c633d37df100d00fb4f611f1155114c90c10866fc58bb6adc67696

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page