Most popular Python libraries for data visualization:
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#python
Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#python
❤4
Mastering pandas%22.pdf
1.6 MB
🌟 A new and comprehensive book "Mastering pandas"
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
❤9
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The #Python library #PandasAI has been released for simplified data analysis using AI.
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link 😍
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link 😍