Python Libraries

Python Libraries:

There are over 137,000 python libraries present today. Python libraries play a vital role in developing machine learning, data science, data visualization, image and data manipulation applications and more.

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components.

Many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development.

Few are,

1. Pandas: 

Pandas is primarily used for data analysis, and it is one of the most commonly used Python libraries. It provides you with some of the most useful set of tools to explore, clean, and analyze your data. With Pandas, you can load, prepare, manipulate, and analyze all kinds of structured data. Machine learning libraries also revolve around Pandas DataFrames as an input.

2. NumPy (Numerical Python):

NumPy is mainly used for its support for N-dimensional arrays. These multi-dimensional arrays are 50 times more robust compared to Python lists, making NumPy a favorite for data scientists.

It is the core library for scientific computing, which contains a powerful n-dimensional array object.

We use python "NumPy" array instead of a "List" because of the below three reasons:

• Less Memory
• Fast
• Convenient.

ndim:

You can find the dimension of the array, whether it is a two-dimensional array or a single dimensional array. 

In the below code, with the help of ‘ndim’ function, We can find whether the array is of single dimension or multi dimension.

import numpy as np

a = np.array([(1,2,3),(4,5,6)])

print(a.ndim)

Output – 2

itemsize:

We can calculate the byte size of each element. In the below code, We have defined a single dimensional array and with the help of ‘itemsize’ function, we can find the size of each element.

import numpy as np

a = np.array([(1,2,3)])

print(a.itemsize)

Output – 4

So every element occupies 4 byte in the above numpy array.

dtype:

We can find the data type of the elements that are stored in an array. So, if we want to know the data type of a particular element, we can use ‘dtype’ function which will print the datatype along with the size. 

In the below code, We have defined an array where We have used the same function.

 

import numpy as np

a = np.array([(1,2,3)])

print(a.dtype)

Output – int32

We can find the size and shape of the array using ‘size’ and ‘shape’ function respectively.

import numpy as np

a = np.array([(1,2,3,4,5,6)])

print(a.size)

print(a.shape)

Output – 6 (1,6)

Other function that we can use, reshape, slicing, linspace, max/ min, Square Root & Standard Deviation, etc

3. Scikit-learn:

Scikit-learn is arguably the most important library in Python for machine learning. After cleaning and manipulating your data with Pandas or NumPy, scikit-learn is used to build machine learning models as it has tons of tools used for predictive modelling and analysis.

4. Gradio:

Gradio lets you build and deploy web apps for your machine learning models in as little as three lines of code. It serves the same purpose as Streamlit or Flask, but I found it much faster and easier to get a model deployed.

5. TensorFlow:

TensorFlow is one of the most popular libraries of Python for implementing neural networks. It uses multi-dimensional arrays, also known as tensors, which allows it to perform several operations on a particular input.

6. Keras:

Keras is mainly used for creating deep learning models, specifically neural networks. It’s built on top of TensorFlow and Theano and allows you to build neural networks very simply. Since Keras generates a computational graph using back-end infrastructure, it is relatively slow compared to other libraries.

7. SciPy:

SciPy is mainly used for its scientific functions and mathematical functions derived from NumPy. Some useful functions which this library provides are stats functions, optimization functions, and signal processing functions. To solve differential equations and provide optimization, it includes functions for computing integrals numerically. Some of the applications which make SciPy important are:

Multi-dimensional image processing

Ability to solve Fourier transforms, and differential equations

Due to its optimized algorithms, it can do linear algebra computations very robustly and efficiently

8. Statsmodels:

Statsmodels is a great library for doing hardcore statistics. This multi-functional library is a blend of different Python libraries, taking its graphical features and functions from Matplotlib, for data handling, it uses Pandas, for handling R-like formulas, it uses Pasty, and is built on NumPy and SciPy.

Specifically, it’s useful for creating statistical models, like OLS (Ordinary Least Squares), and also for performing statistical tests.

9. Plotly:

Plotly is definitely a must-know tool for building visualizations since it is extremely powerful, easy to use, and has a big benefit of being able to interact with the visualizations.

Along with Plotly is Dash, which is a tool that allows you to build dynamic dashboards using Plotly visualizations. Dash is a web-based python interface that removes the need for JavaScript in these types of analytical web applications and allows you to run these plots online and offline.

10. Seaborn:

Built on the top of Matplotlib, Seaborn is an effective library for creating different visualizations.

One of the most important features of Seaborn is the creation of amplified data visuals. Some of the correlations that are not obvious initially can be displayed in a visual context, allowing Data Scientists to understand the models more properly.

Due to its customizable themes and high-level interfaces, it provides well-designed and extraordinary data visualizations, hence making the plots very attractive, which can, later on, be shown to stakeholders.