# The Python Libraries that you should Learn for Analyzing IoT Data

Data Science offers a number of techniques that help you analyze IoT data. Basically, there are a number of programming languages that you can use for applying data science techniques including Python, R, and JavaScript. However, Python remains the most widely used language among others. This because the language contains many libraries that make our life easier. In this post, I will discuss the python libraries that you should learn for analyzing IoT data.

A list of the python libraries that you should learn for analyzing IoT data is given below. Although there are many more libraries available in Python for Data Science, the following libraries are really essential for performing any kind of data analysis. If you are a beginner in the field of data science, then these libraries you need to learn first.

**Essential Python Libraries for Data Science**

**NumPy**

NumPy library makes it easier for ease to perform **Num**erical operations in **Py**thon. Basically, this library contains several functions that we can apply on arrays, especially the multi-dimensional arrays. NumPy is a very powerful library that greatly simplifies the operations. Moreover, the other Python libraries such as Pandas and scikit-learn also use NumPy extensively.

**Pandas**

The Pandas library is used for manipulating data. Basically, it is a powerful tool for quickly managing the data. The library contains functions that help you to handle data in an efficient way. In particular, the library contains functions for indexing, cleanup data, and handling missing values. You can group, merge, and join datasets and perform time series analysis and do various mathematical operations as well. Moreover, the Pandas library also provides you the facility to visualize the data.

**Matplotlib**

Matplotlib library in Python is essential for the visualization of data analysis results. Basically, this library contains a comprehensive set of functions that enable you to plot a number of charts. Moreover, you can control every minute detail of the plots. Matplotlib allows you to create both two-dimensional as well as three-dimensional plots. The 2D plots that the Matplotlib library supports are the Bar plot, Box plot, Contour plot, Scatter plot, Quiver plot, Violin plot, Pie chart, and the Histogram.

The 3D plots that we can create using Matplotlob are the Surface plot, Wireframe plot, and the Contour plot. Besides these plots, the Matplotlib library also contains classes for Axis and formatting. Moreover, using this library we can embed the plots in GUI applications also.

**Scikit-Learn**

This library is essential when you want to use machine learning for predicting the outcome. Basically, this library contains a comprehensive list of functions for supervised and unsupervised learning. Unlike NumPy and Pandas, the Scikit-Learn library is used to build models. For supporting supervised learning, this library contains a number of classifiers including Naive-Bayes classifier, decision tree classifier, support vector machines classifier, and several other classifiers.

Additionally, it also implements algorithms for clustering, feature selection, feature extraction, ensemble methods, dimensionality reduction, parameter tuning, and cross-validation.

### Summary

The Python Libraries that you should Learn for Analyzing IoT Data include NumPy, Pandas, Matplotlib, and sci-kit learn. These libraries are very powerful and make many complex and tedious mathematical operations easier. Although, these libraries are essential to start with Data Science, yet there are many more libraries that exist for Python language.