Python remains the most popular language when it comes to data analysis, due to its ability to support vast libraries that aid data scientists, analysts, and engineers. Python offers tools for simple data manipulation and complex statistical modeling, which makes this language helpful for analysis. This article will review the 10 most popular and essential Python libraries for data analysis that may be useful in various industries for computational purposes, model building, etc. Regardless of the level of expertise, these libraries will greatly assist you in optimizing data analysis and gaining maximum value from your data.
What is Data Analysis?
Data analysis is a systematic process of assessing data to gain meaningful information and insights to support decision-making. It is a complex process that includes several methods and tools to analyse data collected from multiple sources and in diverse formats – quantitative and qualitative data.
Data analysis is not simply a concept; in fact, it can be seen as an instrument that enables organisations to solve problems, forecast future situations, and facilitate organizational learning. It is a necessity in the formulation of strategic management plans for businesses, governments, and other institutions.
Let’s take an example of a leading e-commerce industry. Customers’ buying behavior, preferences, and patterns can be analyzed from such data, to make sound business decisions.
Companies can leverage this data to tailor interactions with customers, predict and plan sales, and even improve their marketing efforts to ensure business success and customer loyalty.
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What are Python and Python Libraries?
Python is a general-purpose, high-level, and object-oriented programming language that supports a clear and understandable structure. One of the many advantages of Python is the fact that it derives from the English syntax so learning how to code in Python is relatively easy.
This universal language of programming is suitable for almost any process that involves data, lines of code, or even mathematical calculations. It enables users to carry out complex manipulations of data and numerical computations on data frames.
Of all the programming languages being used today, Python is among those with the most rapid development. This can be used in small projects like the Reddit moderation bot or in complex tasks like handling large datasets of hedge fund financial information.
Because this software is free and open-source, many people use it all over the world. Python has many uses in professional settings when it comes to big data and there are various Python libraries for data analysis available that are also helpful to those who are dealing with data management and data visualization.
In computer programming, a library means an assembly of code that has dozens and sometimes even hundreds of modules containing many types of functionality. Each library has a set of codes combined in advance which helps to minimize the time for coding.
Libraries come in handy as they provide pre-developed codes that are commonly used over and over again, hence avoiding cases where the user has to write them over and over from scratch.
There are more than 137,000 libraries available in Python. The Python Standard Library is a collection of hundreds of modules intended for performing elementary operations such as reading JSON files or sending emails.
The Standard Library is included with a Python installation and its modules can be used without downloading them. In general, within Python, all the libraries or modules serve different purposes.
A number of these modules perform significant functions in areas such as data science, data wrangling, data analysis, and machine intelligence.
Advantages of using Python Libraries for Data Analysis
Python is now widely used for data analysis and there are good reasons for that. There are numerous advantages of using Python in the context of data science.
Firstly, Python supports plenty of rich libraries and frameworks that help to cover vast functionality for data manipulation, analysis, and modeling, including NumPy, Pandas, SciPy, and others.
It is easy to read and write compared to other programming languages, thus ideal for persons who are new to data analysis while at the same time, it offers the advanced functionality that experienced data scientists can use to design advanced algorithms and lineage.
Furthermore, Python is an open-source language that has a large and engaged community that works on the development of resources, guidelines, and forums.
It can easily connect with other languages, tools, various structures, and platforms, and is quite scalable, flexible, and compatible, which are some of the reasons why Python is beneficial for data analytics programs.
In summary,
Python enhances the capabilities of data analysts by providing all the tools and resources that are essential for handling and analyzing big and heterogeneous datasets. Now that we know the benefits, let us look at the top 10 Python libraries for data analysis.
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Top 10 Python Libraries for Data Analysis:
1. NumPy
NumPy is one of the most globally-used open-source libraries in Python and is primarily employed in scientific computing. The built-in mathematical functions allow calculations to be made at lightning speed and it can handle multidimensional data and large matrices.
It is also used in Linear Algebra. NumPy Array is preferred over lists because it is memory efficient and more convenient than lists for operations. NumPy’s official website indicates that it is an open-source project whose main goal is to support numerical computations with Python.
It was created in 2005 and is based on the innovations of the Numeric and Numarray libraries. One of the most significant strengths of NumPy is the fact that its creators have issued it under the modified BSD license.
So, it will always remain free to use for everyone. NumPy is developed collaboratively on GitHub with everyone in the NumPy community and the broader scientific Python ecosystem.
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2. Pandas
Panda is an open-source library that is widely used in data science. It is mainly used in data analysis, data manipulation, and data cleansing. Pandas enable efficient data modeling as well as data analysis tasks without writing many lines of code.
According to the information on the official website, Pandas is a tool for data analysis and manipulation that is fast, powerful, flexible, easy to use, and open-source.
Some key features of these Python libraries for data analysis include:
1. DataFrames as they enable fast and efficient data manipulation and come equipped with built-in indexing.
2. Best used when applied to critical code paths written in C or Cythoning.
3. Smart and practical labeling of slices, flexibility of indexing, and sub-setting of big data.
4. Integration and combination of high volumes and large datasets.
5. A powerful group by an engine that can be used for data aggregation or transformation where users can use split-apply-combine transformations on DataFrames.
3. MatPlotLib
Matplotlib is a very large library of the Python programming language for creating fixed, interactive, and animated graphics. Numerous third-party packages exist that extend and/or rely on Matplotlib, many of which include higher-level plot interfaces, such as Seaborn, HoloViews, ggplot, etc.
Matplotlib is functional like MATLAB but is more powerful since one can use Python with it. It also has the advantage of being free as well as being an open-source tool.
It enables the user to plot data into different forms of graphs such as scatter, histogram, bar, error, and box plots among others. Moreover, each of the visualizations described above can be built in a matter of a few lines of code.
4. Seaborn
Seaborn is an additional Matplotlib-based Python data visualization library that allows for creating more valuable and beautiful statistical graphics, which are necessary for data analysis.
It works closely with both NumPy and pandas data structures within the Python programming language. In Seaborn, the concept of plotting is integrated into the concept of data analysis and specifically data exploration. Therefore, Seaborn’s plotting algorithms are based on special data frames called DataFrames, which include entire data sets.
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5. Plotly
Plotly is a well-known open-source graphing library often used with web and mobile applications to develop data visualizations. Plotly uses the Plotly JavaScript library and can be used to create web-based visualizations that can be saved in HTML format or published to Jupyter notebooks/Dash applications.
It offers over 40 different chart types including scatter, histograms, line, bar, pie, error, box, dual axis, sparklines, tree maps, and 3D charts. They also provide contour plots which are not so frequently available in other data visualization tools.
For line and bar graphs, scatter and bubble plots, and if you need interactivity with figures, plots, and maps like in a dashboard, you can go with Plotly instead. It is now usable under the terms given by the MIT license.
6. Scikit-Learn
The terms machine learning and scikit-learn are practically synonymous since scikit-learn is a machine learning library. As an extension of NumPy, SciPy is an open-source Python library that can be used in commercial applications under the terms of the BSD license and built on the backs of SciPy and Matplotlib.
In the use of the tool for predictive data analysis tasks, it is proved to be quite easy to use and effective.
The first version of scikit-learn was developed in 2007 as part of Google Summer of Code and thus is a community-led initiative while institutional and private grants are used to sustain the project. One of the most advantageous features of scikit-learn is that it is extremely convenient to work with.
7. XGBoost
Another very popular distributed gradient boosting library is XGBoost which is also designed to be portable, flexible, and efficient. It allows the building of machine learning algorithms inside the gradient boosting framework.
XGBoost has gradient-boosted decision trees (GBDT), another parallel tree-boosting model that provides solutions to many data science challenges efficiently and effectively. The given code works on significant distributed environments including Hadoop, SGE, and MPI, and can solve numerous problems.
XGBoost updated over recent years as it helped all kinds of people and teams become victorious in practically every Kaggle structured data competition.
8. Auto-sklearn
Auto-sklearn is a machine learning toolkit that can be used as a substitute for a scikit-learn model. In this aspect, it is an effective method that can take much time to determine the optimal numeric settings and the best algorithm for machine learning computations.
It is built based on recent developments in meta-learning, ensemble creation, and using Bayesian optimization.
Auto-sklearn was envisioned as an extension of the scikit-learn project and behaves as an FTH that employs a Bayesian Optimization search strategy to determine the best model pipeline for a specific dataset. Auto-sklearn is quite simple to use and can be used in not only classification but also regression problems.
9. TensorFlow
TensorFlow is an open-source library that was developed by the Google Brain team at Google for high-performance numerical computation and is widely used in deep learning research.
As mentioned on the official website of TensorFlow, it is an end-to-end open-source platform for machine learning. It provides a diverse, general set of tools, libraries, and support resources for machine learning theorists and practitioners.
Features include:
1. It is easy to build models.
2. Large-number calculations can also be performed with ease.
3. TensorFlow contains abundant APIs and offers both low-level and high-level stable APIs in Python and C.
4. It is easy to deploy and compute using the Central Processing Unit (CPU) and Graphics Processing Unit (GPU).
5. Includes pre-trained models and data sets.
6. Models for mobiles, embedded devices, and production ready.
7. Keras – a high-level neural networks API compatible with TensorFlow.
10. Keras
Keras is an open-source neural network library also developed with human interaction in mind. Keras follows best practices for reducing cognitive load: it gives predictable and straightforward interfaces, requires fewer steps for typical operations, and gives descriptive and actionable error messages.
Keras is very easy to use, TensorFlow decided to use Keras as their default interface in the new version of their framework, the TF 2. 0 release.
Keras has a less complicated way of defining neural networks, and it also comes with some of the most effective tools for building models, data set management, graph representation, and the rest.
Its features include:
1. It can support almost any type of neural network model such as convolutional, embedding, pooling, recurrent, and so on, which can also be stacked to create even more complex models.
2. Keras, due to its modularity, is very versatile and perfect for prototype development and new ideas testing. It is very easy and convenient to debug and also to inspect.
3. These were some of the best Python libraries for data analysis. Now let’s see how to select the right one.
How to Choose the Right Python Libraries for Data Analysis?
Choosing the right Python library for a project that involves data science, machine learning, or natural language processing, for instance, is a very decisive move that determines the success of a task.
Since there are many libraries to choose from, different factors have to be taken into consideration to make the right decision. Here are key points to remember when choosing the right Python libraries for data analysis:
Project Requirements
It is good to provide clear, measurable, realistic, and achievable objectives for your project. Determine the particular type of operations including data analysis, data wrangling, data visualization, machine learning, or natural language processing required for your task.
Think about possible areas of your work. This means that depending on the strengths that a given library has in terms of its ability to meet the needs of clients, it is important that one’s needs are aligned with the library’s strengths.
Usability and Degree of Difficulty
Evaluate the extent to which each of the Python libraries for data analysis is user-friendly. Some factors that would ease the development process include friendly APIs, libraries that are well-documented, and libraries with good community backing.
Ensure that you look at the tutorials, courses, and community forums of each library available in the current market. Academic libraries which possess vast learning facilities can enhance the learning process.
A nice and active community suggests that the library is not only still relevant but also well-cared and maintained. Reflect on how many people have written for the blog, how often posts are created; and if the bloggers actively address inquiries posed by their followers.
Look at the repositories of projects created on GitHub to see if there has been any recent commit, issue, or discussion. Maintaining an active and vibrant GitHub repository implies an active development process and the presence of a community around the project.
Performance and Scalability
Look at the number of objects that can be handled by the library at the same time or how it performs complex calculations. There are some libraries designed specifically for performance, so scalability with larger data sets is manageable.
Make sure that the selected library works well with the rest of the current technologies being employed. Ensure that the library of choice is compatible with other libraries or frameworks/filters that you propose to use as a part of your work.
License and Legal Considerations
It is also important to familiarize oneself with the licensing conditions of each library. Make sure that the license fits your project’s needs and any legal concerns that your organization will have.
Community Feedback and Reputation
Search for ratings and recommendations from other developers and data scientists who worked with the libraries. Such firsthand feedback may offer valuable information about the libraries’ actual application.
Ongoing maintenance and Updates
Search the page to know when was the last time the library page was updated. This is an indication that the required maintenance and improvements are being conducted systematically with updates in order. Do not use libraries that are obsolete and which are not supported anymore.
Performance Benchmarks
Consider searching for the performance comparison indices for the libraries, if performance is a crucial aspect in your work. This feature allows you to look into how fast and efficient various Python libraries for data analysis are, using benchmarks.
Consideration of Future Developments
Find out whether there is a roadmap or any future development plans set for each library. It is important to select a library that has pre-established plans for future improvements so that your projects are supported in the long term.
Frequently Asked Questions
1. Which is the best Python library for data analysis?
Depending on the needs of the individual, one has to determine which library is the best for one to use while working with Python. For general data handling Pandas is used while for numerical computation NumPy is one of the best. Matplotlib and Seaborn are the most commonly used libraries for visualization. If you’re working with machine learning, there is nothing better than Scikit-learn out there.
2. Is it possible to use more than one Python library in a single project?
Yes, it is possible to use several Python libraries in one project and recommended from time to time. For example, while working on a project, you might use pandas to manipulate datasets, use matplotlib for visualization, and sci-kit-learn for machine learning algorithms.
3. Are these Python libraries easy for beginners?
Pandas and Matplotlib are particularly easy to use and there is much information that one can easily find online about these types of libraries. Other libraries like SciPy or Statsmodels are easier to use but might be more complex than NumPy.
4. Do I need to download each of the Python libraries individually, or can I download a batch of them?
Yes, many of the Python libraries require having the packages installed individually using pip or other similar tools. For instance, the process of installing Pandas entails pip install pandas or conda install pandas. NumPy and Pandas are some of the libraries that may be installed automatically as part of a data science distribution such as Anaconda.
5. Are these Python libraries for data analysis efficient in handling large datasets?
Yes, Python works well with big data, with the help of the PySpark library, developed specifically for working with big data. However, the efficiency also depends on your system’s hardware and the specific library required in a certain program.
Conclusion
In conclusion, the availability of numerous Python libraries for data analysis makes it one of the most flexible and effective toolkits data individuals can use. Starting from data handling with Pandas and NumPy, which are optimized for operation on big data, to data analysis with Scikit-learn, Python offers solid tools for each phase of data analysis. Every library has a unique function, which facilitates the possibility of incorporating the various libraries as necessary depending on the project.
From a person who is starting their journey with data to an experienced data scientist who is working with big data, the Python ecosystem provides tools for processing data quickly, effectively, and comprehensively. Using these Python libraries for data analysis, data analysts and scientists can extract more insights from raw data, which can be applied to solve problems across various industries.
Vanthana Baburao
Currently serving as Vice President of the Data Analytics Department at IIM SKILLS......
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