List the visualization libraries in python.
1)Matplotlib
2)Seaborn
3)Plotly
4)Bokeh
5)Pyplot
What is the purpose of data visualization?
Data visualization is used to represent data graphically, making complex patterns and trends more understandable. Its purposes include:
Communicating insights effectively
Identifying patterns and outliers
Supporting decision-making
Presenting data in a visually appealing manner.
-Write details notes on basic data visualization tools?
Matplotlib: A popular 2D plotting library for Python.
Seaborn: Built on Matplotlib, it provides a high-level interface for attractive and informative statistical graphics.
Tableau: A powerful and interactive data visualization tool.
-Write the tools used for geospatial data.
ArcGIS: A geographic information system for working with maps and geographic information.
QGIS (Quantum GIS): An open-source alternative for geospatial data analysis
Word clouds visually represent the frequency of words in a text, with the size of each word indicating its frequency. They are often used for textual data exploration and visualization.
Data visualization is the presentation of data in graphical or visual formats, making complex patterns and trends easily understandable. It conveys insights, patterns, and relationships within the data.
- Explain data visualization libraries in Python.
Matplotlib:
- A versatile 2D plotting library that provides a wide range of charts and plots.
Seaborn:
- Built on top of Matplotlib, it simplifies the creation of attractive statistical graphics.
Pandas Plotting:
- Integrated with the Pandas library, it offers a simple interface for creating basic visualizations directly from DataFrames.
Plotly:
- Enables the creation of interactive, web-based visualizations and supports various chart types.
Bokeh:
- Another library for interactive visualizations, with a focus on modern web browsers and dynamic plots.