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Python Libraries
Python is one of the easiest programming languages to learn. It’s also regarded as one of the most powerful languages, attributing to its wide range of applications. In a small duration of time, Python has established its monopoly in the Data Analytics domain leveraging its widespread wingspan that covers the vast area of Artificial Intelligence, Machine Learning, Deep Learning, Web Application Development, and Visual Programming.
The operational variety that Python offers is due to the fact that it possesses one of the best and most vast library collections to sustain complex Data Science algorithms and computations. Libraries serve as a great asset for any programming language, as one does not need to delve into any implementation details.
In 2020, Data Science was one of the most trending career choices and will further expand its charm as more people are getting familiar with the field and the opportunities it unfolds. To align data and extract meaningful insights , Python is one of the most popular programming languages decorated with some of the most epic library-sets designed specifically for Data Science.
Although libraries are largely dependent on the use-case a data scientist is trying to solve, we came up with the 10 must know libraries every aspiring data scientist should know. From performing experiments to visualizing datasets, these libraries would serve you great.
10 Must-Know Python Libraries For Data Science
NumPy
NumPy sits at the heart of almost everything that is being done on the Data Science and Machine Learning horizon. It is a Python library mainly used for complex mathematical and scientific computations inside data science. It specifically offers ground to implement and manipulate n-dimensional arrays and matrices. It’s one of the most basic data science libraries in Python. NumPy also backs Tensorflow and other Python libraries along with a large collection of high-level mathematical functions to deal with the n-dimensional matrices known as Tensors.
Pandas
Pandas is another Python library that is available as a free source and is best suited for manipulating and merging data. Pandas is mainly used for data manipulation and data visualization. The operations can range anywhere from simple to very complex data operations. It is used to create in-memory data frames (Objects in Python, quite similar to a database table) from a CSV/JSON/XML/SQL/EXCEL file. Now-a-days, Pandas can read from almost any file format and load it in the form of a dataframe. The functions it offers include:
- DataFrame (Python object) for data manipulation with integrated indexing.
- Tools for loading different data structures and file formats.
- Handling missing or messed up data.
- Transformation of data sets.
- Categorization of large data sets.
- To insert and delete columns in the Data structure
- Merging and splitting Data Sets.
- Data shifting and data filtration
- Update data values, apply and map functions on columns to change values
Matplotlib
An important task for every Data Scientist is being able to tell a story from his dataset. They can share their story using numbers, that works too, but what really separates a good Data Scientist from a great Data Scientist is being able to showcase those numbers using intuitive, easy to follow graphs for their audience.
Matplolib is another vital library in Python that helps in Data Visualization. Matplotlib has rich tools and mechanisms to visualize data effectively. It allows the user to easily represent data in the form of line graphs, pie charts, histograms, and other statistical diagrams and graphs. Using Matplotlib, you can deal efficiently with every aspect and coordinate of a statistical diagram. It has improved interaction by offering options like zooming, changing, and saving the graph in a different format.
Scikit-Learn
Scikit-Learn is one of the leading dynamic and widespread machine learning libraries for machine learning algorithms. It is the compounded and improved form of two fundamental Python libraries, which are, NumPy (extensively for expert-level linear algebra and array handling) and SciPy. It sustains most of the supervised and unsupervised machine learning algorithms. This library can also be used for the mining, analysis, and collection of data.
Scikit-learn is available as a free source machine learning library dedicated to Python. It’s primary purpose is to simplify complex algorithms of classification, regression, and clustering. Some famous operations and algorithms it supports include vector machines, k-means algorithms, random forests, etc.
Tensorflow
TensorFlow is a free and open-source python library developed by Google to implement deep and machine Learning solutions. It is considered as a library for data-flow and differentiable programming, which is used by a variety of array functions. The library is dedicated for machine learning operations such as deep learning, genetic algorithms, and fuzzy logic.
Tensorflow is one of the top-ranked Machine Learning libraries, attributing to its ease of use and simple syntax. It has experienced a great demand surge and almost surpassed all the existing libraries in the market.
Keras
Built on top of Tensorflow/Pytorch/Theano in Keras: an essential and intuitive Machine Learning library for Python. It is built specifically to deal with deep neural networks. Keras makes it effortless for Machine Learning beginners to design, and develop a Deep Neural Network. Simple and instant prototyping is the iconic identity of Keras.
Keras offers the implementations of neural-network by providing support for the building blocks of neural networks such as layers, objectives, activation functions. It simplifies the use of image and text data to deploy deep neural networks.
Scrapy
Data scientists are often faced with the problem of collecting data sets on their own or to amputate data from a pre-existing data set. Scrapy is a Python library that specifically serves this purpose. It involves the process of extracting, managing, storing, and processing a large amount of web data.
It’s easier to automate the process of fetching data from websites using scrapy rather than manually creating files or images.
Seaborn
Seaborn is a data visualization library that is built on top of Matplotlib. This library enables us to draw informative statistical diagrams along with illustrative graphs. Data visualization holds great importance in exploring and showcasing the data using visual elements like charts, graphs, and maps. This library provides support for examining relationships among multiple variables and maps the results in a visual format, making data visualization possible on the python platform.
Seaborn performs all the crucial mapping and statistical functions and draws informative output graphs. This data visualization library also has tools for choosing colors to differentiate data-sets in graphs.
SciPy
SciPy is a Python library that builds upon the NumPy array object (part of Numpy stack which possesses tools like Matplotlib, pandas, and SymPy, and a very dynamic set of libraries for complex scientific computations), that provides support for complex calculus operations such as differentiation and integration, linear algebra, optimization, and statistics.
This open-source Python library allows experts to implement Fourier transformation, ODE solutions, signal processing, image processing, etc. It is also backed by NumFOCUS, a community group for supporting reproducible and accessible science.
Plotly
The Plotly Python library is an open-source plotting library that supports over 40 unique chart types and provides ground for a variety of statistical, financial, geographic, scientific, and 3D plots and graphs.
Built on top of the Plotly JavaScript library (plotly.js), it provides support for creating eye-catching and interactive web-based visuals that can be opened in Jupyter notebooks, saved as simple HTML files, or used to add values to Python-based web applications using Dash.
Conclusion
These aforementioned Top 10 Data Science Libraries are gems if you are planning to start a career in the field of Data Analytics or Machine Learning. Today, Data is one of the most expensive entities in the world of the IT industry.
Edwards Deming, an American statistician once stated, “Without data, you are just another person with an opinion.” So data, if handled and observed properly, allows you to generate deep insights that can pave way for growth.