adxpandas 0.2.0

Execute Kusto Query Language (KQL) queries over pandas DataFrames

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.

Links

• GitHub Repository
• Documentation
• Issue Tracker