Python libraries for data analysis and modeling in Data science

Python has become the first choice of data scientists, data analysts, and those who work with billions of data for data analysis and data modeling. It offers to predict future outcomes, streamline processes, and business intelligence sights. There are many IDEs to use python like Python IDLE, jupyter notebook, spiders, Atom, and more. Python contains many libraries that make the tasks easier and faster. These libraries are used for many purposes like data analysis, data visualization, data modeling, ML modeling, data processing, data handling, and like many.

Here I am mentioning some most important libraries of python that are used for data analysis, data processing, and model building, and statistical analysis.

1 Data processing:

numpy:
It is an abbreviation of numerical python which is a perfect tool for scientific computing and to perform from basic to advanced array operations. numpy offers many features to perform operations on n-array and matrics(n — indicates dimensions) like scaler and vectorization. It also helps to store the values of the same data type to make mathematical operations. It provides features to increase the performance in less execution time.

scipy:

scipy is useful for linear algebra, integration, statistics, and optimization. Its most important functionality is that it is built upon numpy so it has similar features as numpy contains. It works for all types of scientific programmings such as science, mathematics, and engineering. All the array operations can be performed by using this library of n-array.

2 Data manipulation and analysis:

pandas:

It provides to access the n dimensions dataset with .csv, .html, .doc, .text, .json files. It offers to work with labeled and relational datasets.

It contains the two most important features that are

1. It works with data structures like series and lists.
2. It allows us to work with a big dataset like data frames with n-dimensions and in table format.

It offers to convert the data structures into the data frame and to provide some data analysis tasks to find out the missing values, plot the data with histogram, drop the null values columns, and more. It has all the data manipulation, optimization, visualization, and data wrangling features.

3 Data visualization:

matplotlib:

matplotlib is a standard library for data science that helps in generating data representation in 2D and 3D planes. The data is represented in graphs, histograms, boxplots, scatter plots, bar charts, pie charts, and many types of graphs. It also provides an object-oriented API to embed the plot into applications. It has a feature that it provides the scientific tools, unlike Matlab and Mathematica. It is used for a structured dataset for manipulation and data management and also used for data preparation before feeding the data to the ML model.

seaborn:

As scipy built upon numpy similarly seaborn built upon the matplotlib, so it has all the features as matplotlib offers with additional qualities. It is also used for data visualization for statistical models. Heatmaps and other various kinds of visualization are used for data visualizations. It is used for statistical data visualization and gives informative statistical graphics.

4 Data mining and Machine learning:

sklearn(scikit learn):

It is an industry-standard library of python which is mostly used for data science projects. Many machine learning algorithms and data mining problems like regression, classification, clustering, and dimensionality reduction solved with sklearn. It uses scipy internally with its many packages that allow many statistical operations for the ML model.

5 Statistical modeling and testing:

statsmodel:

It is another most important library of python that is used after model building to find out the statistical issues and their solutions. It is used to explore the data, estimating statistical models, and statistical testing. It allows us to perform predictive and prescriptive analytics with inferential and descriptive statistics. For example, if we have categorical data so we would use a classification model for that, but which classification model is suitable according to the business problem we will choose that model.

Conclusion:

This is only a small list of python libraries there are many that we can use free of cost with the open-source such as TensorFlow, Keras, pydot, ploty, Bokesh and many. This python ecosystem provides many tools that are used for most of the data scientists in many industries. Python offers high-performance ML models.

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