seekwellpandas (SQL-pandas) is a pandas extension that provides SQL-inspired methods to manipulate DataFrames in a more intuitive way, closely resembling SQL syntax.
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.
You can install seekwellpandas via pip:
pip install seekwellpandasHere 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')Contributions are welcome! Feel free to open an issue or submit a pull request on my GitHub repository.
This project is licensed under the GPLv3 License. See the LICENSE file for details.