# Python Packages for Data Science

# Python Packages for Data Science

### In this article, we have covered the top Python Data Science Libraries which you should know if you want to master Data Science.

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Join For FreePython is one of the most widely used programming languages. Although standard Python does not offer too much, its insane number of open-source and third-party libraries holding its popularity amongst the developers. You just name the domain and Python will provide you with its best packages and libraries. Data Science and Machine Learning are two demanding technologies of this era, and Python is doing better than excellent in these two fields.

Apart from Python, R is another programming language that often used in Data Science projects. R is faster and contains more computational and statistical libraries; however, in this article, we have only covered the top Python Data Science Libraries which you should know if you want to master Data Science.

Before Jumping to the meat of this article, let’s discuss what is data Science and Why should we use Python for it?

**Data Science Introduction**

Right now, business data has become as valuable as money. Currently, we are in the era of big data and generating a considerable amount of data every second. And big businesses are leveraging this data for their growth in the market.

Using Data Science and other technologies, we extract informative detail from the data to solve complex real-world problems and to build predictive models. Data Science is not a tool or technique; it is a skill that you build and nourish by mastering some tools and libraries present in the market.

**Why Use Python for Data Science?**

Python is considered as one of the top programming languages to implement data science and machine learning models.

Now Let’s discuss some primary reasons why developers and data scientists prefer to use Python over other programming languages for their data science projects.

**Easy to Learn**

It’s a clear and straightforward reason to choose Python over any other programming language. Python uses a straightforward and clean syntax for code writing, writing code in Python is very easy, and it feels like you are writing direct instructions in English.

**Less coding**

Data Science and machine algorithms are very complicated so there we require such programming language that can implement those algorithms with ease and less code. Here comes Python with its smooth and indented syntax, which help developers to build a program within less number of code.

**Libraries**

Open source and third-party libraries are the main assets of Python. Python has many libraries for Data Science which comes with pre-built complex algorithms, so we do not have to write the code from scratch.

**Platform Independent**

Python is available for various platforms which include window, mac, Linux, and Unix, so code written in once platform can be run on another without any changes.

**Huge Community Support**

Python has one of the vast communities supports there are various active forums present on the dev op community where python developers post their error and community try to help them.

**Various Python libraries for Data Science**

By far we have covered what data science is and why do we use Python for it, now let’s discuss the various python libraries we can use for Data Science.

- Numpy
- SciPy
- Pandas
- Statsmodels
- Matplotlib
- Seaborn
- Plotly
- Bokeh
- Scikit Learn
- Keras

**1. NumPy**

It is one of the most commonly used python libraries. NumPy stands for Numerical Python, and it comes with many features and built-in data-structure which include Single and multi-dimensional arrays. The standard Python does not support the concept of arrays; however, it provides an alternative called list, but lists are not that efficient with mathematical computation. The array structure provided by NumPy is specially designed for mathematical and numerical calculations.

**Features of Numpy**

- It can be used to perform simple as well as complex scientific computation.
- It supports multi-dimensional arrays, which are missing in standard Python.
- It comes with various built-in methods that can perform different numerical calculations on the multi-dimensional array.
- Data manipulation, which includes linear regression algorithms, can also be performed using NumPy.
- It also supports Date time and Linear algebra.

**2. SciPy**

SciPy is built using NumPy and some other numerical sub-packages. It is widely used when there is a need for statistical calculation. All the elements that are defined using the NumPy library can be solved using SciPy, so it’s often used to solve those mathematical calculations that NumPy could not. SciPy all modules are more efficient as compared to NumPy, which makes it a perfect library for Data Science.

**Features of SciPy**

- SciPy work along with NumPy.
- It supports numerical integration and calculation using the NumPy array.
- Apart from NumPy, it includes many other numerical sub-packages.
- Its sub-packages are capable of dealing with vector quantization, integration, interpolation, Fourier transformation, and many more other complex mathematical computations.
- It also supports advance linear algebra methods.

**3. Pandas**

Apart from Python's NumPy library, Pandas is the second most known library that heavily using in python Data Science projects. It is used in various domains which include statistics, finance, economic and data analysis. It is built on NumPy which means it uses NumPy arrays for processing Pandas objects. Pandas often use when we have to process a massive chunk of data, and it cannot perform all the processing alone, so it uses NumPy to structure the data and SciPy for statistical methods. When you are working on a Data Science model, you need to use all three tools for an effective model

**Pandas Features**

- It comes with pre-defined and customizes indexing objects for fast and effective Data Frame.
- It is the best library for data wrangling or munging.
- It can be used to manipulate large data sets which include data subsetting, data slicing, data manipulation, and data visualization.
- It can deal with different data formats which include CSV, TSV, and SQL Database.

**4. StatsModels**

StatsModel is built at the top of NumPy and SciPy, and it is widely used for data handling and modification. It is very popular for its statistical, computational modules and apart from NumPy and SciPy, it can also be integrated with Pandas for data handling. Other statistical libraries like SciPy make it complex to work with statistical models, but Statsmodels make it easy.

**StatsModels:**

- Many Data Scientist uses this library for Statistical Test.
- It also includes some similar statistical methods present in the R programming language.
- It also uses to implement generalized linear models, Uni-variate, bi-variate analysis, and Hypothesis Testing.

**5. Matplotlib**

It is the most famous python Data visualization library; you can also say it is the most basic library that you need to master if you are into Python and Data Science. It comes with a wide range of intuitive graphs such as histograms, bar charts, power charts, error charts, and many more.

It can work along with other Data Science libraries such as NumPy, and SciPy and plot very precise 2-D graphs. It also comes with in-built object-oriented APIs which can embed charts into applications.

**Features of Matplotlib:**

- It makes it easy to plot various carts using various pre-defined methods.
- The color and font of the chart can also be customized using various functions.
- It also provides an object-oriented API to integrate with different applications.

**6. Seaborn**

Seaborn is an extension of the Matplotlib library that used to plot more discrete and appropriate graphs. It also supports a built-in data science API that is used for studying the relationship between different variables. Like Matplotlib, Seaborn supports various charts, but it can plot all those with better visualization and less complexity.

**Seaborn Features**

- With it, we can analysis uni as well as bi-variate data points.
- It supports various data formats.
- It can plot graphs for linear regression models.
- It is highly used to plot complex visualization with n number of points.
- It also supports various themes for its visualizations.

**7. Plotly**

It is another well-known Python Data Science visualization library. It provided us with interactive graphs to visualize the relationship between the result variable and the predicted variable. Apart from statistical graph visualization plotly also used in finance, economic and scientific data. 3-D charts are one of the significant features of Plotly that you miss in matplotlib.

**Plotly features**

- It supports all the necessary charts (line, pie, scatter, bubble, dot, filled area, treemap, etc.).
- It also supports the statistical and scientific charts.
- It also supports 3D charts.
- It forms charts in JSON format that can be sent to servers and web-applications.

**8. Bokeh**

Bokeh is generally used to plot graphs on web-applications. It can be easily integrated with various python frameworks such as Flask and Django. Using bokeh, we can plot multiple accurate complex statistical and scientific graphs. It is one of the straightforward and easy libraries; within fewer lines of code, you can plot interactive graphs.

**Bokeh Features**

- It supports data visualization for statistical and scientific data sets.
- It supports different formats which include HTML, Notebook, and server output.
- This library is available for different programming languages.
- It is easily integrated with Django and Flask.

**9. Scikit-Learn**

Scikit-Learn is a machine learning library, and it includes mostly all the features and tools required for Data Science. It was introduced as a Google Summer code project for machine learning. It comes with various built-in modules that provide all the popular pre-written ML algorithms such as random forests, spectral clustering, cross-validation, k-means clustering and many more. Scikit-Learn can be used for both supervised as well as unsupervised machine learning algorithms.

**Features of Scikit-Learn**

- It supports spam detection and image recognition feature.
- Support various regression algorithms.
- It has modules for supervised as well as unsupervised learning.
- It supports cross-validation for model evaluation.

**10. Keras**

Keras is a Deep Learning python library that widely used for the neural network. It is one of the most powerful Python open-source libraries which can work with different data sets such as statistical models, images, and text data. There are many other robust Deep learning libraries in Python, but Keras makes it easy to work with complex deep learning models.

**Features of Keras**

- It supports all types of neural networks.
- It comes with various built-in data structures for image processing.
- It comes with popular pre-processed machine learning models.
- It is a very scalable library which means you can add additional functions to learn and practice in-depth deep learning.

**Conclusion**

With this, we have reached the end of our top python libraries for Data Science. All the libraries we have mentioned here are the popular ones, apart from these there are many other libraries you can use for Data Science and machine learning. If you want to set career as a Data Scientist with Python, then you need to learn most of these libraries.

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