What are the Python libraries that are used by Data-Scientists?

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Python has a large number of libraries that have been specially developed for the purpose of data science and analysis. It provides a lot of useful libraries that help you in manipulating data, exploratory data analysis, and building models.
“Python has been a charmer for data scientists”

1. Pandas:
Pandas stand for “Python Data Analysis Library". Pandas is one of the most powerful libraries for data manipulation. In the pandas' library, there are various numbers of import and export functions. It also includes a method for data structures
What can you do with Pandas?
  • Indexing, manipulating, renaming, sorting
  • Update, Add, Delete columns
  • handle missing data or NANs
    • 2. NumPy:
    Numpy is the most important package in Python, it is a general-purpose array-processing package. NumPy contains generic multi-dimensional data.
    What can you do with NumPy?
    • Basic array operations
    • Advanced array operations
    • Work with DateTime or Linear Algebra
    • Slicing and Advanced Indexing in NumPy Python
    3. SciPy:
    SciPy library contains modules for efficient mathematical routines as linear algebra, optimization, and statistics. SciPy library is built upon NumPy and its arrays. SciPy makes significant use of NumPy.
    4. Matplotlib:
    This is one of my favourite and a quintessential Python library. You can create visualized stories with the use of Matplotlib. When to use? Matplotlib provides an object-oriented API.
    5. Seaborn:
    Seaborn is an extended version of Matplotlib with advanced features. Matplotlib is used for creating bars, pies, lines, whereas, seaborn provides several visualization patterns with less complex and fewer syntax.
    What can you do with Seaborn?
    • Relationships between multiple variables
    • Observe categorical variables for aggregate statistics
    • Analyze uni-variate or bi-variate distributions
    • Plot linear regression models
    • Provide high-level abstractions
    6. Scikit Learn
    Scikit learn contains various features of data analysis. Basically, scikit learn is for machine learning. This library contains a lot of tools for machine learning and statistical modelling.
    What can you do with Scikit Learn?
    • Classification
    • Clustering
    • Regression
    • Dimensionality reduction
    • Model selection
    • Pre-processing
    I hope you may find my answer to python libraries helpful.
    All the best for your future as a data scientist.
    Thank You!
    Keep learning…..

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