Python Data Science for Beginners
Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.
Python is a popular high-level object-oriented programming language which is used widely by the huge number of software developers. Guido van Rossum designed this in 1991, and Python software foundation has developed this. But the question is there were already dozens of programming language available based on OOP concepts then why this new one? So, the main purpose to develop this language is to emphasize on code readability and science and mathematical computing (NumPy, SymPy, Orange).
Python’s syntax is very clean and short in length. Python is open-source and portable language which supports a large standard library.
You must heard about this but what do you understand by this term? Who can be a data scientist?
Data science is a collection of various tools, data interface and algorithms with machine learning principles to discover the hidden patterns from the raw data. The raw data is stored in enterprise data warehouses and used in creative ways by using Data Science to generate business value from it.
Use of Data Science can be understand by this below infographic.
A Data Analyst and Data Scientist are both the different as data analyst work on to only process the data history and explains what is going on, whereas Data Scientist needs various advanced algorithms of machine learning to identify the occurrence for a particular event by using the concept of analysis to discover the all about the data.
There are various programming languages that can be used for Data Science i.e. SQL, Java, Matlab, Sas, R and many more but Python is the most preferred choice by data scientists among all the other programming languages in this list.
Python has some extra ordinary features because of them it is preferred mostly. These are the listed features:
These are several reasons that why developers prefer Python over the other programming languages. Now there are other terms introduced which we need to clarify in detail. Move on to that start with Data Manipulation.
Data Manipulation is used to extract, filter and transform the data in quick and easily with the efficient result. There are two important libraries that are used to perform these tasks which is NumPy and Pandas.
NumPy is an open source library available in Python for free and stands for Numerical Python. It is the popular core library of Python which is useful in scientific calculations which provide array objects and also provides tools to integrate C and C++ as well. NumPy is an powerful N dimensional array which is in the form of rows and columns. You can initialize this from the Pythons list and access it. To use this, first you just need to install this library using the command prompt by typing: conda install numpy. After that you can go to simply in your IDE and type import numpy to use it.
First you need to import NumPy library. For that write
Similarly, Pandas is powerful library which is known for its ability to create Data Frames in Python and used for Data Manipulation and Data Analysis. Pandas is suitable for various data such as matrices, statistical, observational etc. To install Pandas you have to follow the same steps as NumPy, install this library from the command prompt by typing: conda install pandas. After that you can go to simply in your IDE and type import pandas to use it.
First you need to import Pandas library. For that write as:
Here in the output, 0, 1, 2 is the index. If you want to show the index value according to your reference, you can do as following:
Python has many frameworks for data analysis, data manipulation or data visualization. Python programming is an ideal choice for data science, for evaluating large datasets, for data visualization etc.
Data analysis and Python programming are complementary to each other.
