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ATLAS.ti VS Pandas

Compare ATLAS.ti VS Pandas and see what are their differences

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  3. » Reviews
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ATLAS.ti logo ATLAS.ti

ATLAS.ti is a powerful workbench for the qualitative analysis of large bodies of textual, graphical, audio and video data. It offers a variety of sophisticated tools for accomplishing the tasks associated with any systematic approach to "soft" data.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • ATLAS.ti Landing page
    Landing page //
    2022-11-04
  • Pandas Landing page
    Landing page //
    2023-05-12

ATLAS.ti features and specs

  • Comprehensive Analysis Tools
    ATLAS.ti provides a wide variety of tools for qualitative data analysis, including coding, annotating, and data visualization, making it suitable for handling extensive qualitative datasets.
  • User-Friendly Interface
    The software boasts an intuitive and modern user interface, making it accessible for both novice and experienced researchers.
  • Cross-Platform Compatibility
    ATLAS.ti is available on multiple platforms, including Windows, macOS, and mobile devices, allowing for flexible use and data handling.
  • Collaborative Features
    The software includes options for team collaboration, enabling multiple users to work on the same project simultaneously and streamline the research process.
  • Customer Support and Training
    ATLAS.ti offers extensive customer support, including online resources, tutorials, and training workshops to help users make the most of the software.

Possible disadvantages of ATLAS.ti

  • High Cost
    The software is relatively expensive, which may not be feasible for all researchers or small organizations, especially those on a tight budget.
  • Steep Learning Curve
    Despite its user-friendly interface, the depth of features and tools can be overwhelming for new users, requiring a significant time investment to master.
  • System Resource Intensive
    ATLAS.ti can be demanding on computer resources, which might limit its performance on older or less powerful machines.
  • Limited Quantitative Capabilities
    While excellent for qualitative analysis, ATLAS.ti has limited quantitative analysis tools, potentially necessitating additional software for mixed-methods research.
  • Periodic Updates Required
    Frequent software updates may be necessary to address bugs or add new features, which can be a hassle for users requiring consistent, uninterrupted access.

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.

Analysis of ATLAS.ti

Overall verdict

  • ATLAS.ti is considered a highly effective tool for qualitative data analysis, especially favored by academic researchers, social scientists, and market researchers. However, its complexity might require some users to invest time in learning and training to fully leverage its capabilities.

Why this product is good

  • ATLAS.ti is a powerful qualitative data analysis software that is widely recognized for its extensive range of analytical tools and features. It enables researchers to streamline their data organization, coding, and analysis processes. The software supports various forms of qualitative data, including text, videos, images, and audio. Its user-friendly interface, combined with functionalities like data visualization and powerful text search, makes it an attractive option for individual researchers and teams alike.

Recommended for

  • Academic researchers conducting qualitative research
  • Social scientists analyzing interview or survey data
  • Market researchers seeking insights from consumer feedback
  • Students engaged in thesis or dissertation work involving qualitative analysis
  • Teams collaborating on comprehensive qualitative research projects

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

ATLAS.ti videos

+ Add

Literature Review with ATLAS.ti 8 Windows and Mac (Jan. 25th, 2018)

More videos:

  • Review - Overview of ATLAS.ti 8 Windows April 10th, 2018
  • Review - Literature Review and Qualitative Data Analysis Using Atlas.ti by Muhammad Farooq Buzdar

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 ATLAS.ti and Pandas)

ATLAS.ti

Pandas

Market Research
100 100%
Market Research
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Text Analytics
100 100%
Text Analytics
0% 0
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 ATLAS.ti and Pandas

ATLAS.ti Reviews

We have no reviews of ATLAS.ti yet.
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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.

ATLAS.ti mentions (0)

We have not tracked any mentions of ATLAS.ti yet. Tracking of ATLAS.ti 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 / about 2 months 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 / 2 months 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 / 2 months 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 / 4 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 / 10 months ago
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What are some alternatives?

When comparing ATLAS.ti and Pandas, you can also consider the following products

NVivo - Buy NVivo now for flexible solutions to meet your specific research and data analysis needs. 

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

MAXQDA - a professional software for qualitative and mixed methods data analysis

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

QualCoder - A very complete Free and Open Source Software (FOSS) Computer-Assisted Qualitative Data Analysis Software (CAQDAS) written in Python. It works with text, images, and multimedia such as audios and videos.

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

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