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A Beginner’s Guide To Python For Data Science
Python has been around since grime music hit the thought and dominated the airways. Over the years, several programming languages (like Perl) have returned and gone, however, Python has been growing from strength to strength. Data science with Python training in Bangalore
WHAT IS PYTHON?
The foundation for Python was laid in the late 1980s, but the code was only published in 1991. The primary aim here was to automate repetitive tasks, to rapidly prototype applications, and to implement them in other languages. Data Science training institute in Bangalore
AN INTRODUCTION TO PYTHON FOR DATA SCIENCE
Python has been around since grunge music hit the mainstream and dominated the airways. Over the years, many programming languages (like Perl) have come and gone, but Python has been growing from strength to strength.
Data Science with Python is the fastest-growing programming language in the world. As a high-level programming language, Python is widely used in mobile app development, web development, software development, and in the analysis and computing of numeric and scientific data.
OVERVIEW OF PYTHON LIBRARIES
There are plenty of active data science and ML libraries that can be leveraged for data science. Below, let's go over some of the leading Python libraries in the field.
PyTorch, based on Torch, is an open-source ML library that was primarily built for Face book’s artificial intelligence research group. While it’s a great tool for natural language processing and deep learning, it can also be leveraged effectively for data science.
Matplotlib can be described as a Python module that's useful for data visualization. For example, you can quickly generate line graphs, histograms, pie charts, and much more with Matplotlib. Further, you can also customize every aspect of a figure.
NumPy, short for “Numerical Python,” is an extension module that offers fast, precompiled functions for numerical routines. As a result, it becomes much easier to work with large multi-dimensional arrays and matrices.
SciPy is a Python module for linear algebra, integration, optimization, statistics, and other frequently used tasks in data science. SciPy’s main functionality is built upon NumPy, so its arrays heavily depend on NumPy. With the help of its specific submodules, it also provides efficient numerical routines like numerical integration and optimization.
Pandas is a Python package that contains high-level data structures and tools that are perfect for data wrangling and data munging. They are designed to enable fast and seamless data analysis, data manipulation, aggregation, and visualization.
Seaborn is highly focused on the visualization of statistical models and essentially treats Matplotlib as a core library (like Pandas with NumPy). Whether you’re trying to create heat maps, statistically meaningful plots or aesthetically pleasing plots, Seaborn does it all by default.
If you’re going to use dataflow programming across a range of tasks, TensorFlow is the open-source library to work with. It’s a symbolic math library that’s popular in ML applications like neural networks. More often than not, it’s considered an efficient replacement for disbelief.
PySpark enables data scientists to leverage Apache Spark (which comes with an interactive shell for Python and Scala) and Python to interface with Resilient Distributed Datasets. A popular library integrated within PySpark is Py4J, which allows Python to interface dynamically with JVM objects (RDDs).
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