Python for Data Science: overview
Python is a strong language that is widely used in many industries today. Python is particularly well suited for data science and scientific computing, and has become one of the most popular languages for these applications in recent years.
As a data scientist, you will need to clean and analyze data, build models and algorithms, and interpret your results. Python is a versatile language that can be used for all of these tasks. In this guide, we will cover the basics of Python for data science.
By the end of this guide, you will be able to use Python to perform all of the common tasks in data science. Let’s get started!
Basic Syntax: In this section, we will cover the basic syntax of Python. This will include how to write variables, operators, and control flow statements.
1.Variables
In Python, variables are used to store values. They can be either numbers, strings, or objects. To create a variable, you just need to specify the name of the variable and its value. For example:
- x = 5
- y = “Hello world”
- z = { “key1” : “value1”, “key2” : “value2” }
The value of a variable can be find out by using its name. For example:
- print(x)
- print(y)
- print(z[“key1”])
You can also change the value of a variable by reassigning it. For example:
- x = 10
- y = “Goodbye world”
- z[“key1”] = “value3”
2.Operators
In Python, they are used to perform different operations.There are many different types of operators, but we will focus on the most common ones.
Arithmetic Operators
These are known for performing arithmetic operations on variables. The commonly used operators are Add(+),Sub(-),Mul(x),Div(/).
For example:
- a = 15
- b = 2
- print(a + b)
- print(a – b)
- print(a * b)
- print(a / b)
Assignment Operators
These operators are used for assigning values to variables. The most common assignment operator is = (equal). For example:
- x = 5
Comparison Operators
These operators are used to compare two values/terms. The most common comparison operators are
- ==(known as Equal to)
- != (known as not equal to)
- > (known as greater than)
- < (known as less than)
- >= (known as greater than or equal to)
- <= (known as less than or equal to)
For example:
- a = 2
- b = 3
- print(a == b)
- print(a != b)
- print(a > b)
- print(a < b)
- print(a >= b)
- print(a <= b)
Logical Operators
These are used to combine multiple comparisons. The most common logical operators are and, or, and not. For example:
- a = 1
- b = 7
- print(a > 0 and b > 0)
- print(a > 0 or b > 0)
- print(not a > 0)
Control Flow Statements
In Python, control flow statements are used to control the execution of code. The most common control flow statements are if, elif, and else. For example:
- a = 2
- if a > 0:
- print(“a is positive”)
- elif a < 0:
- print(“x is negative”)
- else: print(“x is zero”)
Python also has a while loop, which can be used to execute code multiple times.
E.g:
- a= 5
- while a > 0:
- print(a)
- a = a- 1
Data Structures and Algorithms
In this section, we will cover the basics of data structures and algorithms. Data structures are known for storing the data, and algorithms are used to handle the t data that’s involved.
Data Structures:
Data structures are the building blocks of any programming language. In Python, there are four main data structures:
- Lists
- Tuples
- Dictionary
- Sets
Lists
A list is a collection of values. To create a list, you just need to specify the values, A list is simply a pile of items, that can be of any data type (integer, float, string, etc.). These are enclosed in square brackets [ ] whereas the items and elements inside are separated/distinguished by comma’s (,).
- my_list = [“value1”, “value2”, “value3”]
You can access the values in a list by using their index. For example:
- print(my_list)
Tuples
A tuple is alike a list, whereas the values in a tuple are fixed.To create a tuple, you just need to specify the values separated by commas.
To create a tuple, you use parentheses () instead of brackets [] like you would for a list, Its a sequence of immutable and unchangeable python objects/elements. Tuples are usually used in situations where a list might be used, but the items in the list need to remain unchanged.
For example:
- my_tuple = (“value1”, “value2”, “value3”)
You can access the values in a tuple by using their index. For example:
- print(my_tuple)
Dictionaries
A dictionary is a collection of core-value sets. To create a dictionary, you just need to specify the keys and values separated by commas. For example:
my_dict = { “key1” : “value1”, “key2” : “value2”, “key3” : “value3” }
The values in a dictionary can be accessed by using their key. For example:
print(my_dict[“key1”])
Libraries:
The Python standard library provides a wide range of functionality. Libraries for scientific computing, data mining and machine learning are some of the most popular.
Following are the most popular python libraries for data science:
- NumPy: Provides support for huge multivariate arrays and matrices, it also involves a big collection of effective mathematical, statistical and arithematic functions to execute these arrays.
- SciPy: Provides accessible and structured numerical routines such as routines for numerical and statistical integration and development.
- Matplotlib: Known as plotting library that provides publication quality figures/elements in a diversity of manuscription formats and immersive environments.
- Pandas: Provides high-efficiency, easy-to-use and practical, data structures and data analysis, visualization tools.
Sets:
Sets are an important part of python data science. They are used to store data in a way that is easy to manipulate and access. Sets can be created from any iterable, such as a list or a tuple.
There are two ways to create a set: by using the set() function or by using curly braces.
The set() function takes an iterable as an argument and returns a new set. If you want to create an empty set, you can use the set() function without any arguments.
Curly braces can be used to create a set from a list/tuple. The elements involved in a list or tuple must be separated by commas.
Algorithms, Techniques
Development, data wrangling, data exploration, and visualization are some of the very important roles that a data scientist has to play. Python is considered as one of the most popular and adaptable programming languages that is widely used for these purposes. It is easy to learn and has a wide range of applications.
Python is a free- open source language and thus has a large community including users who contribute to its development. This makes it a very powerful tool for data science.