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Pandas VS Qlik

Compare Pandas VS Qlik and see what are their differences

Cyclr
Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Pandas logo Pandas

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

Qlik logo 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.
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Qlik Landing page
    Landing page //
    2023-06-28

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.

Qlik features and specs

  • Data Integration
    Qlik offers powerful data integration capabilities, allowing users to pull in data from multiple sources, clean it, and prepare it for analysis. This is particularly useful for organizations dealing with diverse datasets.
  • Associative Data Engine
    Qlik's unique associative data engine enables users to explore data freely, without the limitations of traditional hierarchical or query-based models. This feature ensures that all data relationships are maintained and accessible.
  • Interactive Visualizations
    Qlik provides highly interactive and customizable visualizations, making it easier for users to derive insights and share findings. The visualizations are intuitive and can be tailored to meet specific business needs.
  • AI Capabilities
    The platform includes AI-driven features like Insight Advisor, which helps users uncover insights and generate analytics automatically. This reduces the learning curve and makes advanced analytics more accessible.
  • Scalability
    Qlik is designed to scale from small teams to large enterprises. It supports both on-premises and cloud deployments, making it flexible to meet various business sizes and infrastructure preferences.

Possible disadvantages of Qlik

  • Complexity in Initial Setup
    The initial setup and configuration of Qlik can be complex and time-consuming, often requiring specialized knowledge or professional services to get started effectively.
  • Cost
    Qlik can be expensive, especially for smaller businesses. The cost includes not just licensing fees but also potential expenditures on training, deployment, and maintenance.
  • Learning Curve
    Although Qlik offers a powerful feature set, there is a steep learning curve for new users. Mastering the platform's full capabilities can take significant time and effort.
  • Performance Issues
    In some instances, users have reported performance issues, particularly when dealing with extremely large datasets or complex queries, which can hinder real-time analysis.
  • Limited Third-Party Integration
    While Qlik does support integration with various third-party tools, it may not be as extensive as some other analytics platforms. This can limit its usefulness in a highly diversified technology stack.

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

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

Qlik videos

+ Add

A Day in the life of a Qlik Cloud User

More videos:

  • Demo - Qlik Sense Product Tour

Category Popularity

0-100% (relative to Pandas and Qlik)

Pandas

Qlik

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Dashboard
17 17%
Data Dashboard
83% 83
Data Science Tools
100 100%
Data Science Tools
0% 0
Business Intelligence
0 0%
Business Intelligence
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 Qlik

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

Qlik Reviews

10 Best Alternatives to Looker in 2024
Qlik: Qlik sets itself apart with its associative analytics engine, enabling users to uncover trends and patterns through intuitive exploration without predefined queries. This offers a more flexible and dynamic analytical process.
Source: www.sigmacomputing.com
Top 11 Fivetran Alternatives for 2024
Qlik provides three data integration products - Stitch (covered under Stitch) Talend Data Fabric (covered under Talend) and Qlik Replicate, which was originally Attunity. Qlik Replicate has both on-premises and cloud replication deployment options for streaming real-time data.
Source: estuary.dev
The 7 Best Embedded iPaaS Solutions to Consider for 2024
Description: Qlik offers a range of integration capabilities that span four product lines. The flagship product is Qlik Replicate, a tool that replicates, synchronizes, distributes, consolidates, and ingests data across major databases, data warehouses, and Hadoop. The portfolio is buoyed by Qlik Compose for data lake and data warehouse automation and Qlik Catalog for...
Source: solutionsreview.com
25 Best Reporting Tools for 2022
QlikView is a classic Reporting Tool that provides analytical solutions and allows you to develop appealing visualization from the data. It is an Enterprise Tool that converts raw data into a meaningful format. Some features of QlikView are as follows:
Source: hevodata.com
Top 10 Visual Analytics Provider For 2021
With some of the most sophisticated array of visualisations, Qlik is a pioneer in visualisation analytics software. With Qlik Sense and QlikView, it helps with a wide range and unorthodox ways of presenting data. Its ‘associative analytics engine’ in Qlik Sense moves away from a query-based approach and lets you explore data without any limitations. The engine lets you...
Source: analyticsindiamag.com

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than Qlik. While we know about 219 links to Pandas, we've tracked only 1 mention of Qlik. 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 / 7 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 / 23 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
View more

Qlik mentions (1)

  • GME FTD - Moving Daily Avg.
    All files was pulled into a program called : QLIK, qlik.com is the company and my company uses it for our reporting and our customer's reporting needs. Source: about 4 years ago

What are some alternatives?

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

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

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.

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

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

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