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Pandas VS QlikSense

Compare Pandas VS QlikSense and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

QlikSense logo QlikSense

A business discovery platform that delivers self-service business intelligence capabilities
  • Pandas Landing page
    Landing page //
    2023-05-12
  • QlikSense Landing page
    Landing page //
    2022-08-02

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.

QlikSense features and specs

  • Data integration
    QlikSense offers robust data integration capabilities, allowing users to easily combine data from multiple sources, leveraging an associative data engine for more comprehensive analysis.
  • User-friendly interface
    It features an intuitive and user-friendly interface that makes it easy for users with varying levels of technical expertise to create and interpret visualizations and reports.
  • Self-service BI
    QlikSense supports self-service business intelligence, enabling users to build their own dashboards and reports without heavy reliance on IT or data experts.
  • Advanced analytics
    It integrates advanced analytics capabilities, including predictive analytics and AI-driven insights, helping users gain deeper and more actionable insights from their data.
  • Scalability
    QlikSense is highly scalable, suitable for individual users, small teams, and large enterprises, providing flexibility in deployment and usage.

Possible disadvantages of QlikSense

  • Cost
    Compared to some other BI tools, QlikSense can be relatively expensive, especially for smaller organizations or users with limited budgets.
  • Learning curve
    While the interface is user-friendly, there can be a steep learning curve for new users to understand the full functionality and to use advanced features effectively.
  • Performance issues
    Some users may experience performance issues, particularly when working with very large datasets or complex calculations without proper optimization.
  • Customization limitations
    Although QlikSense offers a range of customization options, there might be limitations in terms of highly specific or niche custom requirements.
  • Partial offline capabilities
    QlikSense's offline capabilities can be limited, which could be a drawback for users who need full offline access and functionality.

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

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

QlikSense videos

No QlikSense videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Pandas and QlikSense)
Data Science And Machine Learning
Data Dashboard
23 23%
77% 77
Data Science Tools
100 100%
0% 0
Business Intelligence
0 0%
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 Pandas and QlikSense

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

QlikSense Reviews

We have no reviews of QlikSense yet.
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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.

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 / 6 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 / 22 days 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 / 26 days 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 / 8 months ago
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QlikSense mentions (0)

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

What are some alternatives?

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

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

Qlik - Qlik offers an Active Intelligence platform, delivering end-to-end, real-time data integration and analytics cloud solutions to close the gaps between data, insights, and action.

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

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.