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KPI Dashboard in Excel VS Pandas

Compare KPI Dashboard in Excel VS Pandas and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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KPI Dashboard in Excel logo KPI Dashboard in Excel

Professional Management KPI Dashboard. Includes trend charts, past year/target comparisons, monthly & cumulative analysis in performance dashboard.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • KPI Dashboard in Excel Landing page
    Landing page //
    2023-08-02
  • Pandas Landing page
    Landing page //
    2023-05-12

KPI Dashboard in Excel features and specs

  • Cost-Effective
    Using Excel for a KPI dashboard is generally cost-effective as it often utilizes existing software without the need for additional purchases or subscriptions.
  • Familiar Interface
    Many users are familiar with Excel, which reduces the learning curve and training costs associated with using the tool for creating KPI dashboards.
  • Customizable
    Excel provides a high degree of customization, allowing users to tailor the dashboard to fit specific business requirements and visual preferences.
  • Data Connectivity
    Excel can connect to various data sources and integrates well with other tools in the Microsoft Office suite, facilitating efficient data management.
  • Flexibility
    Excel offers flexibility for a wide range of analytical tasks, enabling users to build complex models and calculations as needed.

Possible disadvantages of KPI Dashboard in Excel

  • Scalability Issues
    Excel can struggle with scalability, particularly when handling large datasets or when multiple users need to collaborate simultaneously.
  • Performance Limitations
    Large or complex spreadsheets may impact performance, leading to slow load times and potential crashes.
  • Manual Data Entry
    Dashboards in Excel often require manual data entry which can lead to errors and consume significant time, reducing efficiency.
  • Limited Automation
    Excel offers limited options for automation, which means repetitive tasks may still need manual handling unless additional programming (via VBA) is implemented.
  • Security Concerns
    Excel files can pose security risks if not properly protected, as sensitive data might be easily accessed or altered by unauthorized users.

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 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.

KPI Dashboard in Excel videos

+ Add

How To... Create a Basic KPI Dashboard in Excel 2010

More videos:

  • Review - Build a KPI Dashboard in Excel - Sales Scorecard Template

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 KPI Dashboard in Excel and Pandas)

KPI Dashboard in Excel

Pandas

Data Dashboard
46 46%
Data Dashboard
54% 54
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Office & Productivity
100 100%
Office & Productivity
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using KPI Dashboard in Excel and Pandas. For example, how are they different and which one is better?
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Reviews

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

KPI Dashboard in Excel Reviews

We have no reviews of KPI Dashboard in Excel 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.

KPI Dashboard in Excel mentions (0)

We have not tracked any mentions of KPI Dashboard in Excel yet. Tracking of KPI Dashboard in Excel 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 KPI Dashboard in Excel and Pandas, you can also consider the following products

Kutools for Excel - A handy Microsoft Excel add-ins collection to free you from time-consuming operations.

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

Excel Dashboard School - Free Excel add-ins and tools on Excel Dashboard School. Boost your work productivity and save your time! No trials, 100% power!

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

UpSlide - UpSlide helps you produce high-quality reports and presentations faster in PowerPoint, Excel and Word. Save up to 12h each month with just a few clicks!

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

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