Last Updated on: August 25, 2022
Python is a general-purpose programming language that is widely used to build websites and software and automate tasks. It is also a popular language for conducting data analysis and data visualisation. The language has a simple syntax and code readability, which makes it simple and flexible to use to create a variety of programs. Python can be used by developers and non-developers alike. The language has no compilation step and follows an edit-test-debug process, which makes coding quick and easy. Python learning for data science is highly popular among programmers and web developers.
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Python – A Brief Introduction
Python, they say, is a high level interpreted general-purpose programming language.
creator: Guido Van Rossum
Python, I say, is a language which makes sense without semicolon “;”
I can see a lot of them already in envy. The groom finding ceremony and Python won the hands of Data science again. Yes, pair of worthiness together, and they have complemented their way out of language barriers.
When they ask you, “Do you want to be a Data Scientist?” the Python coder in you smiles behind the veils? Binary of Python gets complimented with Data Science. If you are planning to take a Data science course, it is subjuga-tory to know Python, and if in any way it’s not in your checklist yet, it is advisable to take Data Science and Python certification in a stack.
So what makes Python stand out of all to create a place in the data science world. “Easy makes things breezy.” Python is a lot easier to comprehend than the counterparts due to its easy readability and language friendliness. IEEE Spectrum has acknowledged Python to be on the top of the frame list.
Let us see what it exactly mean:
shout out “Holla people” in different languages:
std::cout << “Holla, people!\n”;
print “Holla, people!”
Competing on the basis of number of lines of codes, Indeed R and Python are the clear winners. But at the end of the day, Python takes away the trophy with a minimal margin.
Python is suitable for new programmers to rejump to new programs when they need help.
Python for Data Science
In addition to its applications in web development, report generation and simulation, Python is widely used in the area of data science. The language allows data analysts and other professionals to conduct complex statistical calculations, build machine learning algorithms, analyse data and create data visualisations like histograms, pie charts and bar graphs. Python’s vast number of libraries allows coders to quickly write programs for data analysis and machine learning. Here are some common applications of Python and data science:
- Gathering Data – Python libraries Scrapy and BeautifulSoup make it easy to extract data from the internet.
- Cleaning and Pre-processing of Data – Generally the data collected from the web requires to be cleaned because of the presence of noise, missing values, invalid values and other issues. Several Python libraries and methods are used to carry out exploratory data analysis.
- Data Visualisation – When dealing with large amounts of data, there is a need to identify trends and other patterns. Python libraries like Matplotlib and Seaborn have a variety of tools that help in data visualisation and exploratory data analysis.
- Building a Model – Python is highly suitable for building and implementing machine learning models for classification, regression, clustering and image recognition.
Advantage of Python over other languages.
1.Object-oriented and user-friendly data structure:
Python provides three main types of environments.
- text editors
- Full IDES
- Notebook environments.
Python data structure is built-in and possesses faster run time. Other languages follow static-typing but Python uses dynamic typing, and we can reuse variables (reassign variables) to different data types. This makes Python flexible in assigning data types. Objects in Python have built-in methods, and these methods themselves are essential functions.
2.Easily learnable and flexible support dictionaries:
Python has easy to learn the syntax and convenient segregation. For the mapping of strong objects, a dictionary uses a key-value pair. This key-value pair allows the user to grab objects without needing to know the index location quickly.
3.Availability of third-party module:
“Python package index” PYPI. Few modules come with standard modules pre-installed. But they are not universal providers. You may need to develop a program at some point in time, which will be beyond regular python usage. There comes the need for the third module. You can outsource a few modules which have been created earlier to solve a problem you are encountering now. You have to be careful about the authenticity though.
4.Open source and community development:
Python is an OSI approved open source licensed programing language. It is viable for free use and distribution and can be used commercially.
5.Enhanced productivity and efficient speed:
Python compiles faster. PyPy speeds up Python as a whole. The language omits unwanted loops, and multiple coding approaches to gain productivity and speed.
Python and Data Science Compatibility:
Data Science workflow has python libraries at its rescue. Be it Data Engineering, Data Analytics, Data statistics, Data visualisation, Data execution, Data evaluation. Python libraries present accumulated formats to run down every aspect of data.
Commonly Used Python Libraries for Data Science
NumPy stands for Numerical Python. It contains an n-dimensional array object which is used for scientific computing. It is also used in linear algebra and random number capability.NumPy arrays can be initialised by nested python lists to access its element. NumPy is used instead of the conventional list because it is fast, convenient, and occupies less space.
SciPy is a scientific computing library which contains a highly manipulating command to maintain and visualise data. It helps in mathematical module optimization like integration, linear algebra, signal processing, image processing, and fast Fourier transforms. The availability of this software is free and is open source.
Pandas have the function to make and access data analytics easily. Pandas python library contains two essential data structures.
- One-dimensional arrays: It stores useful information in terms of strings, integers, and float. Its ability to index all the elements together is what makes it different from standard languages.
- Two-dimensional array: It indexes rows and columns. It is the preferable choice when it comes to map excel sheets or extract SQL data to Python.
Panda libraries provide a lot of functions to operate on mathematical modules like series, average, sum, concatenate, and order by. Panda libraries are the most suitable for database executions.
4. Scikits learn:
It leverages support for machine learning algorithms. It is forever compatible with other python modules like pandas, NumPy, and SciPy. Many machine learning modules can be implemented with its functionalities like Regressions, SVM (Support vector machines), and clustering. It builds functionality to curate data accuracy to the maximum.
StatsModel handles all the data statistics. It helps a data engineer to execute data exploration, analyse statistical models, and build test upon the statistical analysis. An extensive length of statistic models, plotting functions are available on the statsModels.
Data Visualisation and Python:
Python makes use of libraries for data visualisation like matplotlib and Seabourn.
The exploration of data and the following statistical analysis demands for a data interpretation and visualisation. How do you see what has happened? There matplotlib comes to picture which maps and reflects data statistics into pictures in the form of 2d and 3d graphs. matplotlib library is used to create figures like bar graphs, Histograms, bar charts, pie charts, scatter plots, and box plots. matplotlib gets integrated with pandas to execute data visualisation more quickly.
Seabourn is a tributary of matplotlib, which is built above it to create some superlative plot types. It adds to the edginess and sharp features to already built matplotlib plots.
Heatmap is a kind of visualisation that can be created with Seabourn with just one line of code.
The Power of IDE (Integrated development editor) for Data Scientists:
IDE is a magical organiser for Data Scientists. It has changed the way python coders run codes in the documentation and with live output. Code can be written in multiple languages such as R and Scala and which makes the workflow efficiently. Jupyter app creates a notebook environment that contains both code and rich texts like paragram, equations, and links.
TensorFlow and Theano have helped Data Scientist to build an artificial neural network. Did you see it? Python has nuggets for every hierarchy of Data Science. The feasibility, efficiency, performance and accessibility of Python has made it outweighs other algorithms to catch an eye.