Pychemist 0.0.2

Transform raw data into insights.

Pychemist

Transform raw data into insights.

Pychemist is a lightweight Python library designed to simplify and enrich your data science workflow. Inspired by the ancient craft of alchemy, it helps you turn raw data into analytical gold through expressive, intuitive tools built on top of pandas, statsmodels, and other popular Python data science libraries.

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Features

• Create lagged or lead variables for time-series and panel data
• Run quick, readable t-tests on treatment groups
• Filter model summaries to hide fixed effects
• Conditional mutation of DataFrames (similar to dplyr::mutate in R)
• Pandas accessor (.pch) for fluent, chainable workflows

Installation

pip install pychemist

Usage

import pychemist as pch

Example 1: Lagging a variable

df = pch.time_shift(variables=["sales"], dataframe=data, id="company", time="year", shift=1)

Example 2: Conditional mutation

df = pch.mutate(df, query_str='division == 24 & managerID==15014', "bonus", 1000)

Example 3: Conditional mutation using the DataFrame accessor

df=df.pch.mutate('company_id == "8ga62sav" & year==2025', "total_assets", 82000000)

Example 4: T-test between treated and control groups

pch.ttest(df, variable="outcome", treatment="treated")

Example 5: Model summary without fixed effects

import statsmodels.formula.api as smf
model = smf.ols("y ~ x + C(firm)", data=df).fit()
print(pch.summary_no_fe(model))

MIT License Copyright (c) Jeroen van Raak (2025)