0.2.3

adxpandas

Execute Kusto Query Language (KQL) queries directly over one or more pandas DataFrames — no database required.

Installation

pip install adxpandas

For Jupyter notebook support (magic commands and chart rendering):

pip install adxpandas[notebook]

Quick Start

import pandas as pd
from adxpandas import AdxPandasClient

df = pd.DataFrame({
    "name": ["Ada", "Alan", "Grace"],
    "city": ["London", "London", "Arlington"],
    "score": [10, 20, 30],
})

client = AdxPandasClient({"Users": df})
result = client.query('Users | where city == "London" | project name, score')
print(result)

Wrap: Quick Single-DataFrame Queries

from adxpandas import Wrap

w = Wrap(df)
result = w.execute('self | where city == "London" | project name, score')
print(result.df)

# Method chaining
w.where('city == "London"').project("name", "score").take(5).df

Jupyter Magic

import adxpandas.magic  # registers %kql magic

# Line magic
%kql df | where city == "London" | take 5

# Cell magic
%%kql
df
| where score > 10
| summarize count() by city

Render Charts

w = Wrap(df)
w.execute('self | summarize avg(score) by city | render barchart')

Features

• Pure pandas execution — no SQLite or other database dependencies
• Full KQL parser with support for common operators
• Operators: where, project, project-away, extend, summarize, sort, top, take, distinct, count, parse, join, union, render
• Wrap class for quick single-DataFrame queries with method chaining
• Jupyter %kql / %%kql magic for interactive notebooks
• Chart rendering: timechart, barchart, columnchart, piechart, linechart
• Scalar functions: string, math, datetime operations
• Aggregate functions: count, sum, avg, min, max, dcount, countif, sumif, avgif
• let statements (scalar and tabular)
• Union source form queries

Documentation

See the full documentation for comprehensive guides covering both adxlite and adxpandas.

Acknowledgments

Some functionality in adxpandas was inspired by and references KustoPandas. We thank the KustoPandas authors for their pioneering work on KQL-over-pandas execution.

Links

• GitHub Repository
• Documentation
• Issue Tracker

License

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