Essential Python libraries for Data Science

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“Data is the new science. Big Data holds the answers.” — By Pat Gelsinger

Data Science is one of the most popular fields of the 21st century. It uses various tools and algorithms to extract knowledge and insights from the raw data. Then companies use these insights and knowledge for making predictions and taking decisions.

The rising popularity of Python in recent years made it one of the musts for Data Science. Its ease, flexibility and open-source nature are the reasons for its quick adoption in the industry.

There are various open-source Python libraries that makes it much easier and effective to use. So here, I will be highlighting the most important ones with their uses and resources.

1. Pandas

Pandas is a Python package that is mainly used for data analysis and manipulation. It provides different data structures and operations for the manipulation of numerical tables and time series.

The various data structures in Pandas are as discussed below:

1. Series (1D): The series is a one-dimensional data structure. It can be considered as a 1D labeled array that is capable of holding data of any type. For example, a column of a table.
2. Dataframe (2D): It is a two-dimensional data structure. It can be considered as a 2D labeled array. For example, a table with both rows and columns.

It is used for tasks like importing the data from various file formats, data wrangling, data cleaning and data manipulation, etc.

Resources:

1. Pandas documentation: Click here
2. Pandas cheat sheet: Click here

2. NumPy

NumPy stands for Numerical Python. It is one of the most fundamental packages of Python which is required for scientific computing. It provides a multi-dimensional array object and various derived objects like the masked arrays and matrices.

It is used for performing array operations like mathematical, logical and shapes manipulation, statistical operations, linear algebra, discrete Fourier transform and random simulations, etc.

Resources:

1. Numpy documentation: Click here
2. Numpy cheat sheet: Click here

3. SciPy

The SciPy library is a part of the core SciPy packages that together form a SciPy stack. There is a difference between the SciPy stack and the SciPy library. The SciPy stack is a combination of tools like NumPy, Pandas, SciPy, Matplotlib, IPython, SymPy, etc. Whereas the SciPy library is a combination of modules for linear algebra, statistics, optimization, integration and interpolation. Its main functionality is built upon NumPy and its arrays and hence make significant use of NumPy.

It is used for linear algebra, statistics, optimization, integration, interpolation, Fourier transforms and signal processing, etc.

Resources:

1. SciPy documentation: Click here
2. SciPy cheat sheet: Click here

4. Matplotlib

It is a data visualization library for Python. It is also a part of the SciPy stack. Matlplotlib provides static, animated and interactive visualization and object-oriented API for embedding the plots into the applications.

It is used for the various plots such as:

1. Line plots
2. Bar charts and histograms
3. Scatter plots
4. Area plots
5. Pie charts
6. Contour plots
7. Stem plots
8. Quiver plots
9. Spectrograms
10. Stream Plots

All these plots can be customized by adding labels, grids, legends, markers and other formatting features.

Resources:

1. Matplotlib documentation: Click here
2. Matplotlib cheat sheet: Click here

5. Seaborn

Seaborn is also a data visualization library for Python which is based on Matplotlib. In other words, it is an advanced version of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics like heat maps.

Resources:

1. Seaborn documentation: Click here
2. Seaborn cheat sheet: Click here

6. Plotly

Plotly is an open-source browser-based interactive graphing library for Python. It can be used for creating different types of charts like scientific charts, 3D graphs, statistical charts, SVG maps, and financial charts, etc. Plotly also provides the feature of sending data directly to the cloud servers.

Resources:

1. Plotly documentation: Click here
2. Plotly cheat sheet: Click here

7. Scikit Learn

Scikit Learn is a Machine Learning library for Python which was developed as a Google Summer of Code project. It contains various tools for Machine Learning and statistical modeling. The benefit of using Scikit learn is that the code for algorithms need not be written from scratch hence it is more effective, time-saving and reliable.

It provides a wide range of Machine Learning algorithms and features such as:

1. Classification
2. Regression
3. Clustering
4. Dimensionality Reduction
5. Model Selection
6. Data Preprocessing

Resources:

1. Scikit Learn documentation: Click here
2. Scikit Learn cheat sheet: Click here

8. TensorFlow

It is an open-source platform for Machine Learning. TensorFlow helps in building the Deep Learning models and helps the researchers push the state-of-the-art in ML and the developers can easily build and deploy ML-powered applications. Using Tensorflow the developers can also easily create large-scale neural networks with numerous layers using the data flow graphs.

It has vast applications such as face recognition, time series, sentiment analysis, voice and sound recognition, text-based application, video detection and object detection, etc.

Resources:

1. TensorFlow documentation: Click here

9. Keras

Keras is a Deep Learning API of Tensorflow written in Python. It is used to provide a Python interface for artificial neural networks. It makes statistical modeling and working with text and images a lot easier due to its simplicity and ease to learn and use. It increases productivity and effectiveness because it allows trying more ideas quickly.

Resources:

1. Keras documentation: Click here
2. Keras cheat sheet: Click here

10. Statsmodels

Statsmodels is a Python library that is used to estimate the statistical models, conducting statistical tests and statistical data exploration using its classes and functions. It also provides the plotting functions that are used for statistical analysis.

Resources:

1. Statsmodels documentation: Click here

These were some of the most essential libraries for Data Science. There are more such libraries like Bokeh, Theano, NLTK, Gensim and Scrapy, etc. that are equally important and have various functions which makes it easier to perform the Data Science tasks easily and effectively.

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Happy Learning!😊