Python Packages for Applied Economists

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

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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

   • Description: Essential for data manipulation and analysis.
   • Installation: pip install pandas
   • Link: https://pandas.pydata.org/

3. SciPy

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

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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/

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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

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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

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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

   • Description: Toolkit for matching company names.
   • Capabilities:
     • Standardizes company names for accurate matching
     • Handles common abbreviations and variations
   • Installation: pip install company-matching
   • Link: https://github.com/IntelligentSoftwareSystems/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

   • Description: Debugging tool for printing informative tracebacks.
   • Installation: pip install stackprinter
   • Link: https://github.com/cknd/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
   • Description: Interface to call R functions and use R packages directly from Python.
   • Use Case: When specific R packages have no Python equivalent, especially for advanced econometric methods not yet available in Python.
   • Example R Packages Accessible via RPy2:
     • did: Implements the Callaway and Sant'Anna (2020) DiD estimator.
       • Link: https://github.com/iamnaanm/did
     • bacondecomp: For the Goodman-Bacon decomposition in DiD settings.
       • Link: https://github.com/evanjflack/bacondecomp
     • fixest: Used for estimation with multiple fixed effects.
       • Link: https://github.com/lrberge/fixest
   • Installation: pip install rpy2
   • Link: https://rpy2.github.io/

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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

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