NumPy, Pandas, Matplotlib, and Seaborn Explained for Beginners.
As we know that NumPy, Pandas, Matplotlib, and Seaborn are essential libraries for Data Science and Machine Learning. So, Here is a small Introduction to these Data Libraries.
NumPy stands for Numerical Python. It is a Python Package for mathematical and logical operations on arrays in Python. It is used in most Python Projects Involving managing data sets. It is different from Lists.
- NumPy can contain only one type of data, hence not flexible with data types.
- It is widely used for Arithmetic operations.
- It Cannot be directly Initialize. It can be operated with the NumPy package only.
- In NumPy functions like concatenation, appending, etc, are not trivially possible with arrays.
- Arrays take less memory space.
Pandas is a library in Python for Data Manipulation and analysis. It offers data structures and operations for manipulating tables and time-series data. Pandas is built on top of NumPy and can be integrated with third parties libraries.
Pandas is well suited for many different kinds of data:
- Tabular data such as SQL table or Excel
- Time series data
- Any form of data sets - labeled or unlabelled
The two data structures of Pandas are:
- Series (1-D)
- DataFrame (2-D)
Few use cases of Pandas include:
- Handling of missing data or NaN
- Columns can be inserted and deleted from the data frame
- Slicing, indexing, and subsetting of large data sets
- Managing data sets - joining and pivoting
Matplotlib is a visualization library built on NumPy in Python for 2-D plots of arrays. It is useful to visualize and interpret large data sets. Matplotlib comes with a variety of inbuilt plots and offers lots of flexibility.
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
I hope you understand well, this is a short introduction to NumPy, Matplotlib, Pandas, and Seaborn.
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