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Stata VS Pandas

Compare Stata VS Pandas and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Stata logo Stata

Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Stata Landing page
    Landing page //
    2023-09-27
  • Pandas Landing page
    Landing page //
    2023-05-12

Stata features and specs

  • Comprehensive Statistical Tool
    Stata offers a wide array of built-in statistical procedures, making it ideal for complex data analysis and research.
  • User-Friendly Interface
    With a graphical user interface and command syntax, Stata caters to both novice and experienced users, improving ease of use and flexibility.
  • Extensive Documentation
    Stata provides thorough documentation and a vast range of tutorials, which can help users quickly find solutions and learn new techniques.
  • Strong Community Support
    Stata has an active user community and mailing list, enabling users to share knowledge, scripts, and advice efficiently.
  • Cross-Platform Compatibility
    Stata is available for Windows, Mac, and Linux, allowing users to work on their preferred operating system without any compromise.
  • Reproducible Research
    Stata promotes reproducible research by providing tools for scripting and automation, ensuring that analyses can be easily replicated and verified.

Possible disadvantages of Stata

  • High Cost
    Compared to some other statistical software, Stata can be expensive, particularly for individual users or small organizations without access to institutional licenses.
  • Steep Learning Curve
    Despite its user-friendly interface, mastering Stata's full capabilities requires time and a considerable learning effort, which can be daunting for beginners.
  • Limited Graphical Capabilities
    While adequate for many purposes, Stata's graphical capabilities are not as advanced as some other software options like R or Python's visualization packages.
  • Less Flexible for Custom Development
    Compared to open-source languages like R or Python, Stata is less flexible for custom development and integration with other software, which might limit advanced users.
  • Resource Intensive
    Stata can be resource-heavy, requiring substantial computing power for large datasets or complex operations, potentially limiting its use on lower-end machines.

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Stata videos

+ Add

What's it like–Getting started in Stata

More videos:

  • Review - Stata's dyndoc review
  • Review - 【Stata小课堂】第2讲：界面介绍

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Category Popularity

0-100% (relative to Stata and Pandas)

Stata

Pandas

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
41 41%
Data Dashboard
59% 59
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Stata and Pandas

Stata Reviews

25 Best Statistical Analysis Software
Stata is a robust statistical software widely utilized by professionals across various fields for efficient data management, in-depth statistical analysis, and comprehensive data visualization.
Source: www.scijournal.org
9 Best Analysis Software for PC 2023
Stata is statistical software that provides almost all the tools you need in data analysis and visualization. The software is crucial in data manipulation, computing statistics queries, visualization, and generating analytical reports. The software is owned by the StataCorp company and has several applications in various fields like science, engineering, biomedicine,...
Source: pdf.wps.com

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas seems to be more popular. It has been mentiond 219 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Stata mentions (0)

We have not tracked any mentions of Stata yet. Tracking of Stata recommendations started around Mar 2021.

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 15 days ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 1 month ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / about 1 month ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Stata and Pandas, you can also consider the following products

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

NumPy - NumPy is the fundamental package for scientific computing with Python

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

GraphPad Prism - Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

OpenCV - OpenCV is the world's biggest computer vision library

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