Every data scientist Must know about the python libraries

 Python is a general-purpose programming language that is used to build software, websites, and analyze data. It is also used for automation, scientific computing, and machine learning. 

It is simple, easy to learn, and used for different disciplines like Data Science, Mathematics, Visualization, and Automation. Following some reasons that make Python rich:

• Multi-purpose programming language
• Large eco-system
• High-level programming
• Simple and easy to learn
• Cross-platform development

Why Python for data science

• Using Python and its open-source ecosystem, data scientists can more efficiently uncover new patterns, relationships, and trends in big data using techniques such as statistical analysis, data visualization, and machine learning algorithms. Data scientists rely on Python for AI and ML application development.
• Every data scientist must know the below mentioned libraries

        NumPy:

• NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents due to the absence of compiler optimization. NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays; using these requires rewriting some code, mostly inner loops, using NumPy.

  Using NumPy in Python gives functionality comparable to MATLAB since they are both interpreted,^[18] and they both allow the user to write fast programs as long as most operations work on arrays or matrices instead of scalars. In comparison, MATLAB boasts a large number of additional toolboxes, notably Simulink, whereas NumPy is intrinsically integrated with Python, a more modern and complete programming language. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Although matlab can perform sparse matrix operations, numpy alone cannot perform such operations and requires the use of the scipy.sparse library. Internally, both MATLAB and NumPy rely on BLAS and LAPACK for efficient linear algebra computations.

• Pandas
  
  
  
  

• Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.^[8]

• There are some features in python are

• Data Frames, which allow for quick, efficient data manipulation and include integrated indexing;

• Several tools which enable users to write and read data between in-memory data structures and diverse formats, including Excel files, text and CSV files, Microsoft, HDF5 formats, and SQL databases;
• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
• High-performance merging and joining of data sets;
• A powerful group by engine which enables data aggregation or transformation, allowing users to perform split-apply-combine operations on data sets;  
• Time series-functionality which enables date range generation and frequency conversion, moving window statistics, date shifting, and lagging. You’ll even be able to join time series and create domain-specific time offsets without worrying you’ll lose data;
• Ideal when working with critical code paths written in C or Cython

• Matplotlib

  

  
  

  Matplotlib is an extensive library for creating fixed, interactive, and animated Python visualizations.  A large number of third-party packages extend and build on Matplotlib’s functionality, including several higher-level plotting interfaces (Seaborn, HoloViews, ggplot, etc.)

  Matplotlib is designed to be as functional as MATLAB, with the additional benefit of being able to use Python. It also has the advantage of being free and open source. It allows the user to visualize data using a variety of different types of plots, including but not limited to scatterplots, histograms, bar charts, error charts, and boxplots. What's more, all visualizations can be implemented with just a few lines of code.

• Seaborn

•  Another popular Matplotlib-based Python data visualization framework, Seaborn is a high-level interface for creating aesthetically appealing and valuable statistical visuals which are crucial for studying and comprehending data. This Python library is closely connected with both NumPy and pandas data structures. The driving principle behind Seaborn is to make visualization an essential component of data analysis and exploration; thus, its plotting algorithms use data frames that encompass entire datasets.