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A curated collection of Python packages for applied economists, organized by functionality to support econometric analysis, data management, visualization, and specialized tasks.

Python Packages for Applied Economists

License: CC BY 4.0 Python Version

A comprehensive guide to Python packages for applied economists, organized by functionality to support econometric analysis, data management, visualization, and specialized tasks.

Table of Contents

  • Core Libraries
  • Econometric Methods and Research Designs
    • General Statistical Methods
    • Instrumental Variables
    • Panel Data Methods
    • Regression Discontinuity Designs
    • Difference-in-Differences and Synthetic Control Methods
  • Treatment Effect Estimation Tools
    • Sensitivity Analysis
  • Machine Learning
  • Time Series Tools
  • Bayesian Analysis Tools
  • Data Management and Processing
    • DataFrame Libraries
    • Record Linkage and Data Matching
    • Distance Metrics and String Matching
  • Visualization and Reporting
    • Static Visualization
    • Interactive Visualization
    • Publication-Ready Outputs
      • Table Export and Formatting
  • Specialized Tools
    • Geospatial Analysis
    • Text Analysis
    • PDF Processing and Document Analysis
    • Web Scraping
  • Development Tools
    • Debugging and Testing
    • Cross-Language Integration
  • Installation Summary

Core Libraries

Before diving into specialized packages, ensure you have the foundational libraries installed:

  1. NumPy

    • Description: Fundamental package for numerical computations.
    • Installation: pip install numpy
    • Link: https://numpy.org/

  2. Pandas

  3. SciPy

    • Description: Provides additional statistical functions and tools.
    • Installation: pip install scipy
    • Link: https://www.scipy.org/

Econometric Methods and Research Designs

General Statistical Methods

  1. Statsmodels

    • Description: Provides classes and functions for estimating various statistical models, performing statistical tests, and data exploration.
    • Capabilities:
      • Linear Regression: Ordinary Least Squares (OLS)
      • Generalized Linear Models (GLM)
      • Discrete Choice Models: Logit, Probit
      • Time Series Analysis: ARIMA, VAR, and state-space models
      • Instrumental Variable Estimation: IV regression
    • Installation: pip install statsmodels
    • Stata Equivalent: regress, logit, probit, arima, var, ivregress
    • Link: https://www.statsmodels.org/

  2. Pingouin

    • Description: Statistical package offering statistical tests and plotting functions.
    • Capabilities:
      • ANOVAs, t-tests, correlations
      • Effect sizes, power analyses
    • Installation: pip install pingouin
    • Link: https://pingouin-stats.org/

Instrumental Variables

  1. Linearmodels
    • Description: Specialized for panel data econometrics, including fixed effects, random effects, and instrumental variable models.
    • Capabilities:
      • Panel Data Analysis: Fixed effects, random effects, between estimators
      • Instrumental Variables: IV estimators, Generalized Method of Moments (GMM)
      • Seemingly Unrelated Regressions: System estimation
    • Installation: pip install linearmodels
    • Stata Equivalent: xtreg, ivregress, sureg
    • Link: https://bashtage.github.io/linearmodels/

Panel Data Methods

  1. PyFixest
    • Description: Allows for fast estimation of linear models with multiple fixed effects, inspired by the R package fixest.
    • Capabilities:
      • High-dimensional fixed effects models
      • Clustered and robust standard errors
      • Support for instrumental variables and interaction terms
    • Installation: pip install pyfixest
    • Stata Equivalent: reghdfe, areg
    • Link: https://github.com/py-econometrics/pyfixest

Regression Discontinuity Designs

  1. rdrobust

    • Description: Implements local polynomial RD point estimators with robust bias-corrected confidence intervals and inference procedures.
    • Capabilities:
      • RD estimation and inference
      • Automatic bandwidth selection
    • Installation: pip install rdrobust
    • Stata Equivalent: rdrobust
    • Link: https://pypi.org/project/rdrobust/

  2. rdlocrand

    • Description: Provides tools for local randomization methods in RD designs.
    • Capabilities:
      • Inference in RD designs using local randomization
    • Installation: pip install rdlocrand
    • Stata Equivalent: rdlocrand
    • Link: https://pypi.org/project/rdlocrand/

  3. rddensity

    • Description: Provides manipulation testing based on density discontinuity.
    • Capabilities:
      • Density discontinuity tests at cutoff
    • Installation: pip install rddensity
    • Stata Equivalent: rddensity
    • Link: https://pypi.org/project/rddensity/

  4. rdmulti

    • Description: Analysis of RD designs with multiple cutoffs or scores.
    • Capabilities:
      • Multivariate RD analysis
    • Installation: pip install rdmulti
    • Stata Equivalent: rdmulti
    • Link: https://pypi.org/project/rdmulti/

  5. rdpower

    • Description: Power calculations for RD designs.
    • Capabilities:
      • Computes power and sample size for RD designs
    • Installation: pip install rdpower
    • Stata Equivalent: rdpower
    • Link: https://pypi.org/project/rdpower/

  6. lpdensity

    • Description: Implements local polynomial point estimation with robust bias-corrected confidence intervals.
    • Capabilities:
      • Kernel density estimation
      • Local polynomial estimation
    • Installation: pip install lpdensity
    • Stata Equivalent: Part of the RD analysis toolkit
    • Link: https://pypi.org/project/lpdensity/

Difference-in-Differences and Synthetic Control Methods

  1. CSDID

    • Description: Implements the Callaway and Sant'Anna (2020) Difference-in-Differences estimator for staggered adoption designs with treatment effect heterogeneity.
    • Capabilities:
      • Estimation of group-time average treatment effects
      • Handles multiple time periods and variation in treatment timing
      • Allows for treatment effect heterogeneity
    • Installation: 
      git clone https://github.com/d2cml-ai/csdid.git
cd csdid
pip install .
    • Stata Equivalent: csdid (user-contributed command)
    • Link: https://github.com/d2cml-ai/csdid

  2. synthdid

    • Description: Implements synthetic difference-in-differences estimation with inference and graphing procedures.
    • Capabilities:
      • Synthetic DiD estimation
      • Multiple inference methods (placebo, bootstrap, jackknife)
      • Plotting tools for outcomes and weights
      • Support for covariates
      • Handles staggered adoption over multiple treatment periods
    • Installation: pip install synthdid
    • Stata Equivalent: sdid
    • Link: https://pypi.org/project/synthdid/

  3. SyntheticControlMethods

    • Description: A Python package for causal inference using various Synthetic Control Methods.
    • Capabilities:
      • Synthetic Control estimation
      • Placebo tests
      • Support for panel data
    • Installation: pip install SyntheticControlMethods
    • Stata Equivalent: synth
    • Link: https://pypi.org/project/SyntheticControlMethods/

Treatment Effect Estimation Tools

  1. MarginalEffects

    • Description: Provides methods for computing and interpreting marginal effects in statistical models.
    • Capabilities:
      • Calculates marginal effects for various models
      • Supports models from scikit-learn, statsmodels, and others
    • Installation: pip install marginaleffects
    • Link: https://pypi.org/project/marginaleffects/

  2. EconML

    • Description: Developed by Microsoft, EconML provides methods for estimating causal effects with machine learning techniques.
    • Capabilities:
      • Double Machine Learning (DML)
      • Treatment Effect Estimation: Heterogeneous effects, policy evaluation
      • Support for Machine Learning Models: Integration with scikit-learn, LightGBM, and more
    • Installation: pip install econml
    • Stata Equivalent: teffects, ddml
    • Link: https://econml.azurewebsites.net/

  3. DoubleML

    • Description: Implements the Double Machine Learning framework for causal inference in high-dimensional settings.
    • Capabilities:
      • Treatment effect estimation using DML
      • Support for various machine learning algorithms
    • Installation: pip install doubleml
    • Stata Equivalent: ddml
    • Link: https://docs.doubleml.org/stable/index.html

Sensitivity Analysis

  1. PySensemakr
    • Description: Sensitivity analysis toolkit for regression models.
    • Capabilities:
      • Quantify robustness of regression coefficients to unobserved confounding
      • Implements methods similar to the sensemakr R package
    • Installation: pip install PySensemakr
    • Link: https://github.com/Carloscinelli/PySensemakr

Machine Learning

  1. scikit-learn

    • Description: A comprehensive library for machine learning algorithms.
    • Capabilities:
      • Supervised Learning: Regression, classification
      • Unsupervised Learning: Clustering, dimensionality reduction
      • Model Selection and Evaluation: Cross-validation, grid search
    • Installation: pip install scikit-learn
    • Stata Equivalent: Machine learning methods for predictive modeling
    • Link: https://scikit-learn.org/

  2. XGBoost

    • Description: An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.
    • Capabilities:
      • High-performance gradient boosting algorithms
      • Support for regression, classification, and ranking problems
    • Installation: pip install xgboost
    • Stata Equivalent: Advanced machine learning methods
    • Link: https://xgboost.readthedocs.io/

  3. LightGBM

    • Description: A fast, distributed, high-performance gradient boosting framework.
    • Capabilities:
      • Efficient gradient boosting algorithms
      • Support for large-scale data
    • Installation: pip install lightgbm
    • Link: https://github.com/microsoft/LightGBM

Time Series Tools

  1. Statsmodels Time Series

    • Description: Provides extensive time series analysis capabilities.
    • Capabilities:
      • ARIMA Models: Autoregressive Integrated Moving Average
      • SARIMAX Models: Seasonal components and exogenous variables
      • Vector Autoregression (VAR): Multivariate time series
      • State Space Models: Flexible modeling of time series
    • Installation: Part of statsmodels
    • Stata Equivalent: arima, var, dfuller, kpSS
    • Link: https://www.statsmodels.org/stable/tsa.html

  2. ARCH

    • Description: Tools for analyzing financial time series, including volatility modeling.
    • Capabilities:
      • ARCH and GARCH models
      • Volatility forecasting
    • Installation: pip install arch
    • Link: https://arch.readthedocs.io/en/latest/

  3. Ruptures

    • Description: A Python library for offline change point detection.
    • Capabilities:
      • Multiple change point detection methods
      • Handling univariate and multivariate signals
    • Installation: pip install ruptures
    • Link: https://centre-borelli.github.io/ruptures-docs/

  4. xarray

    • Description: N-D labeled arrays and datasets in Python.
    • Capabilities:
      • Work with multi-dimensional arrays (similar to netCDF data)
      • Convenient data structures for time series data
    • Installation: pip install xarray
    • Link: https://xarray.pydata.org/en/stable/

  5. StatsForecast

    • Description: A collection of statistical models for time series forecasting.
    • Capabilities:
      • Efficient implementation of forecasting models
      • Support for large-scale time series data
    • Installation: pip install statsforecast
    • Link: https://github.com/Nixtla/statsforecast

  6. NeuralForecast

    • Description: Deep learning models for time series forecasting.
    • Capabilities:
      • State-of-the-art neural network architectures
      • Handling of complex seasonality and trends
    • Installation: pip install neuralforecast
    • Link: https://github.com/Nixtla/neuralforecast

Bayesian Analysis Tools

  1. PyMC

    • Description: Probabilistic programming library for Bayesian modeling and inference.
    • Capabilities:
      • Bayesian statistical models
      • Markov Chain Monte Carlo (MCMC)
      • Variational inference
    • Installation: pip install pymc
    • Link: https://docs.pymc.io/

  2. PyStan

    • Description: Python interface to the Stan language for statistical modeling and high-performance statistical computation.
    • Capabilities:
      • Bayesian inference
      • Customizable statistical models
    • Installation: pip install pystan
    • Link: https://pystan.readthedocs.io/en/latest/

  3. Bambi

    • Description: High-level Bayesian model-building interface in Python.
    • Capabilities:
      • Simplifies specification of Bayesian models using formulas
      • Built on top of PyMC
    • Installation: pip install bambi
    • Link: https://bambinos.org/

Data Management and Processing

DataFrame Libraries

  1. Polars

    • Description: Modern, high-performance DataFrame library optimized for performance and memory efficiency.
    • Capabilities:
      • Fast parallel execution of data operations
      • Memory-efficient processing
      • Syntax familiar to pandas and R's tidyverse users
      • Strong integration with Apache Arrow
    • Installation: pip install polars
    • Link: https://pola.rs/

  2. Datatable

    • Description: High-performance library for processing large datasets (up to 100GB) on a single machine.
    • Capabilities:
      • Superior performance in sorting and grouping operations
      • Efficient memory usage
      • Seamless interoperability with pandas/NumPy
      • Optimized for single-node processing
    • Installation: pip install datatable
    • Link: https://github.com/h2oai/datatable

  3. Vaex

    • Description: Out-of-core DataFrame library for large datasets with lazy evaluation.
    • Capabilities:
      • Memory-efficient handling of large datasets
      • Lazy evaluation for optimized performance
      • Built-in visualization capabilities
      • Good for datasets that don't fit in memory
    • Installation: pip install vaex
    • Link: https://vaex.io/

  4. DuckDB

    • Description: SQL database engine with DataFrame-like functionality and exceptional performance for analytical queries.
    • Capabilities:
      • Top-tier performance for large-scale data operations
      • SQL interface for data manipulation
      • Efficient handling of large datasets (50GB+)
      • Strong integration with pandas and Arrow
    • Installation: pip install duckdb
    • Link: https://duckdb.org/

Record Linkage and Data Matching

  1. Recordlinkage

    • Description: Python toolkit for linking and deduplicating records.
    • Capabilities:
      • Preprocessing and data cleaning
      • Index/blocking methods to reduce comparisons
      • Various comparison methods
      • Classification of record pairs
      • Evaluation metrics
    • Installation: pip install recordlinkage
    • Stata Equivalent: merge, reclink
    • Link: https://recordlinkage.readthedocs.io/en/latest/

  2. Dedupe

    • Description: Machine learning powered deduplication and entity resolution.
    • Capabilities:
      • Active learning approach to training
      • Scalable blocking methods
      • Automated matching decisions
    • Installation: pip install dedupe
    • Link: https://github.com/dedupeio/dedupe

  3. Python-Levenshtein

    • Description: Fast implementation of Levenshtein distance and string similarity metrics.
    • Capabilities:
      • Compute edit distances for fuzzy matching
    • Installation: pip install python-Levenshtein
    • Link: https://pypi.org/project/python-Levenshtein/

  4. Jellyfish

    • Description: Library for approximate and phonetic matching of strings.
    • Capabilities:
      • Soundex, Metaphone, and other phonetic algorithms
      • Damerau-Levenshtein distance
    • Installation: pip install jellyfish
    • Link: https://pypi.org/project/jellyfish/

  5. PyStemmer

    • Description: Snowball stemming algorithms for various languages.
    • Capabilities:
      • Stemming words to their root forms for better matching
    • Installation: pip install PyStemmer
    • Link: https://pypi.org/project/PyStemmer/

  6. NameParser

    • Description: Parser for human names.
    • Capabilities:
      • Splits names into components (first name, last name, etc.)
      • Useful for matching records based on names
    • Installation: pip install nameparser
    • Link: https://pypi.org/project/nameparser/

  7. Company-Matching

Distance Metrics and String Matching

  1. py_stringmatching

    • Description: Comprehensive toolkit for string matching.
    • Capabilities:
      • Multiple string similarity measures
      • Phonetic encoding
      • Token-based similarities
    • Installation: pip install py_stringmatching
    • Link: https://github.com/J535D165/py_stringmatching

  2. pyjarowinkler

    • Description: Implementation of Jaro-Winkler distance.
    • Capabilities:
      • Jaro similarity
      • Jaro-Winkler similarity
    • Installation: pip install pyjarowinkler
    • Link: https://pypi.org/project/pyjarowinkler/

  3. RapidFuzz

    • Description: Fast string matching library.
    • Capabilities:
      • Quick fuzzy string matching
      • Multiple distance metrics
      • Optimized for performance
    • Installation: pip install rapidfuzz
    • Link: https://github.com/rapidfuzz/RapidFuzz

  4. FuzzyWuzzy

    • Description: Fuzzy string matching in Python.
    • Capabilities:
      • String similarity matching
      • Partial and token-based ratios
    • Installation: pip install fuzzywuzzy
    • Link: https://pypi.org/project/fuzzywuzzy/

Visualization and Reporting

Static Visualization

  1. Matplotlib

    • Description: The foundational plotting library in Python.
    • Capabilities:
      • Line plots, scatter plots, histograms, bar charts
      • Highly customizable visualizations
      • Support for LaTeX formatting in labels
    • Installation: pip install matplotlib
    • Stata Equivalent: Basic plotting functions
    • Link: https://matplotlib.org/

  2. Seaborn

    • Description: A statistical data visualization library built on top of Matplotlib.
    • Capabilities:
      • Enhanced statistical graphics
      • Regression plots, distribution plots, heatmaps
      • Integration with pandas data structures
    • Installation: pip install seaborn
    • Stata Equivalent: Enhanced plotting functions
    • Link: https://seaborn.pydata.org/

  3. Plotnine

    • Description: A grammar of graphics for Python, based on ggplot2 in R.
    • Capabilities:
      • Declarative syntax for creating complex plots
      • Supports layering, scaling, and theming
      • Ideal for creating publication-quality visualizations
    • Installation: pip install plotnine
    • Link: https://plotnine.readthedocs.io/

  4. Binsreg

    • Description: Provides binned regression methods for RD designs and data visualization.
    • Capabilities:
      • Binned scatter plots
      • Regression discontinuity analysis
      • Data-driven bin selection
    • Installation: pip install binsreg
    • Stata Equivalent: binsreg, binscatter
    • Link: https://pypi.org/project/binsreg/

Interactive Visualization

  1. Plotly

    • Description: An interactive, open-source plotting library.
    • Capabilities:
      • Interactive plots
      • Support for web-based applications
      • Wide range of chart types
    • Installation: pip install plotly
    • Link: https://plotly.com/python/

  2. Altair

    • Description: Declarative statistical visualization library for Python.
    • Capabilities:
      • Grammar of graphics approach
      • Interactive visualizations
    • Installation: pip install altair
    • Link: https://altair-viz.github.io/

  3. Bokeh

    • Description: Interactive visualization library for modern web browsers.
    • Capabilities:
      • Interactive plots and dashboards
      • Real-time streaming and data updates
    • Installation: pip install bokeh
    • Link: https://bokeh.org/

Publication-Ready Outputs

Table Export and Formatting

  1. Stargazer

    • Description: A Python package that emulates the R package stargazer, generating LaTeX code for regression tables.
    • Capabilities:
      • Formats regression results into LaTeX tables
      • Supports models from statsmodels and linearmodels
    • Installation: pip install stargazer
    • Link: https://pypi.org/project/stargazer/

  2. PyTableWriter

    • Description: A library to write tabular data in various formats.
    • Capabilities:
      • Export data to formats like LaTeX, Markdown, Excel, CSV
      • Supports styling and formatting options
    • Installation: pip install pytablewriter
    • Link: https://pypi.org/project/pytablewriter/

  3. pystout

    • Description: A package to create publication-quality LaTeX tables from Python regression output.
    • Capabilities:
      • Generates LaTeX tables from regression models
      • Supports models from statsmodels and linearmodels
      • Customizable table appearance and statistics
    • Installation: pip install pystout
    • Link: https://pypi.org/project/pystout/

  4. tableone

    • Description: Produces summary statistics for research papers.
    • Capabilities:
      • Generates descriptive statistics tables
      • Supports grouping variables and statistical tests
      • Exports tables to LaTeX and other formats
    • Installation: pip install tableone
    • Link: https://pypi.org/project/tableone/

  5. GreatTables

    • Description: A package for creating beautiful and complex tables in Python.
    • Capabilities:
      • Compose tables with headers, footers, stubs, and spanners
      • Format cell values in various ways
      • Integrates with pandas DataFrames
    • Installation: pip install great_tables
    • Link: https://pypi.org/project/great-tables/

  6. tabulate

    • Description: Formats tabular data in plain-text tables and can output in formats like LaTeX.
    • Capabilities:
      • Convert arrays or DataFrames into formatted tables
      • Multiple output formats: plain text, GitHub-flavored Markdown, LaTeX, HTML, and more
    • Installation: pip install tabulate
    • Link: https://pypi.org/project/tabulate/

Specialized Tools

Geospatial Analysis

  1. GeoPandas

    • Description: Extends pandas to allow spatial operations on geometric types.
    • Capabilities:
      • Reading and writing spatial data
      • Spatial joins and operations
      • Handling geospatial data formats like Shapefiles and GeoJSON
    • Installation: pip install geopandas
    • Stata Equivalent: Limited geospatial capabilities
    • Link: https://geopandas.org/

  2. Geoplot

    • Description: A high-level geospatial plotting library.
    • Capabilities:
      • Geospatial visualizations
      • Choropleth maps, cartograms, kernel density plots
    • Installation: pip install geoplot
    • Stata Equivalent: Basic mapping (with limited functionality)
    • Link: https://github.com/ResidentMario/geoplot

  3. Geopy

    • Description: A Python client for several popular geocoding web services.
    • Capabilities:
      • Geocoding addresses (converting addresses to coordinates)
      • Reverse geocoding
      • Calculating distances between points
    • Installation: pip install geopy
    • Stata Equivalent: Not directly available
    • Link: https://geopy.readthedocs.io/

  4. Geocoder

    • Description: Geocoding library supporting multiple services.
    • Capabilities:
      • Address standardization
      • Geographic entity matching
      • Multiple provider support
    • Installation: pip install geocoder
    • Link: https://geocoder.readthedocs.io/

  5. libpysal

    • Description: Core components of PySAL (Python Spatial Analysis Library).
    • Capabilities:
      • Spatial weights matrices
      • Spatial graph analysis
      • Computational geometry
    • Installation: pip install libpysal
    • Stata Equivalent: spreg, spatial econometrics tools
    • Link: https://pysal.org/libpysal/

Text Analysis

  1. NLTK

    • Description: Natural Language Toolkit, a leading platform for building Python programs to work with human language data.
    • Capabilities:
      • Tokenization, stemming, tagging, parsing
      • Corpora and lexical resources
    • Installation: pip install nltk
    • Link: https://www.nltk.org/install.html

  2. LangDetect

    • Description: Port of Google's language-detection library.
    • Capabilities:
      • Detects language of a text
    • Installation: pip install langdetect
    • Link: https://pypi.org/project/langdetect/

PDF Processing and Document Analysis

  1. LayoutParser

    • Description: A unified toolkit for Deep Learning-based Document Image Analysis.
    • Capabilities:
      • Deep Learning Models: Perform layout detection in a few lines of code
      • Layout Data Structures: Optimized APIs for document image analysis tasks
      • OCR Integration: Perform OCR for each detected layout region
      • Visualization Tools: Flexible APIs for visualizing the detected layouts
      • Data Loading: Load layout data stored in JSON, CSV, and even PDFs
    • Installation: 
      pip install layoutparser
# For deep learning layout models
pip install "layoutparser[layoutmodels]"
# For OCR toolkit
pip install "layoutparser[ocr]"
    • Link: https://github.com/Layout-Parser/layout-parser

  2. PyTesseract

    • Description: Python wrapper for Google's Tesseract-OCR Engine.
    • Capabilities:
      • Optical Character Recognition (OCR)
      • Extract text from images and PDFs
    • Installation: pip install pytesseract
    • Link: https://pypi.org/project/pytesseract/

  3. Tabula-py

    • Description: Simple wrapper of tabula-java, which can read tables in PDF and convert them into pandas DataFrames.
    • Capabilities:
      • Extract tables from PDFs
    • Installation: pip install tabula-py
    • Link: https://pypi.org/project/tabula-py/

  4. Python-PDFBox

    • Description: Python interface to Apache PDFBox.
    • Capabilities:
      • PDF manipulation (extract text, merge, split)
    • Installation: pip install python-pdfbox
    • Link: https://pypi.org/project/python-pdfbox/

  5. PDFMiner

    • Description: Tool for extracting information from PDF documents.
    • Capabilities:
      • Text extraction
      • Layout analysis
    • Installation: pip install pdfminer.six
    • Link: https://pypi.org/project/pdfminer/

Web Scraping

  1. BeautifulSoup

    • Description: Library for pulling data out of HTML and XML files.
    • Capabilities:
      • Parse and navigate HTML/XML documents
    • Installation: pip install beautifulsoup4
    • Link: https://pypi.org/project/beautifulsoup4/

  2. Requests

    • Description: HTTP library for Python.
    • Capabilities:
      • Send HTTP requests
      • Handle HTTP sessions and cookies
    • Installation: pip install requests
    • Link: https://pypi.org/project/requests/

  3. Requests-HTML

    • Description: HTML Parsing for Humans.
    • Capabilities:
      • Parse HTML with JavaScript support
      • Simplify web scraping tasks
    • Installation: pip install requests-html
    • Link: https://github.com/psf/requests-html

Development Tools

Debugging and Testing

  1. StackPrinter

  2. Pdb++

    • Description: Drop-in replacement for pdb (Python debugger), with additional features.
    • Installation: pip install pdbpp
    • Link: https://github.com/pdbpp/pdbpp

  3. tqdm

    • Description: Fast, extensible progress bar for Python.
    • Installation: pip install tqdm
    • Link: https://tqdm.github.io/

Cross-Language Integration

  1. RPy2

Installation Summary

You can install most of these packages using pip:

pip install numpy pandas scipy statsmodels pingouin pymc pystan bambi linearmodels pyfixest econml doubleml marginaleffects pysensemakr scikit-learn xgboost lightgbm matplotlib seaborn plotnine rpy2 rdrobust rdlocrand rddensity rdmulti rdpower lpdensity synthdid SyntheticControlMethods arch ruptures xarray statsforecast neuralforecast recordlinkage dedupe py_stringmatching pyjarowinkler rapidfuzz fuzzywuzzy nameparser company-matching python-Levenshtein jellyfish PyStemmer nltk langdetect beautifulsoup4 requests requests-html pytesseract tabula-py python-pdfbox pdfminer.six plotly altair bokeh prettytable tabulate stackprinter pdbpp tqdm geopandas geoplot geopy geocoder libpysal binsreg prophet layoutparser stargazer pytablewriter xtable pystout tableone great_tables

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.

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Metadata

A curated collection of Python packages for applied economists, organized by functionality to support econometric analysis, data management, visualization, and specialized tasks.

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