Python3 101: Introduction to python for Data Science

Python is a high-level, general-purpose programming language that was first released in 1991 by its creator Guido van Rossum. Guido van Rossum began work on Python in the late 1980s while he was working at the National Research Institute for Mathematics and Computer Science in the Netherlands. He wanted to create a language that was easy to read, write and understand, and that was also open-source and available to everyone.

Python was initially designed as a scripting language to automate system administration tasks and other small programs. Its design philosophy emphasizes code readability, and its syntax is meant to be simple and easy to understand. As a result, Python quickly gained popularity among developers and has since become one of the most widely used programming languages in the world.

Guido van Rossum continued to lead the development of Python until he stepped down as the project's lead in 2018. Today, Python is maintained by the Python Software Foundation, a non-profit organization that is dedicated to advancing and promoting the use of Python.
Python is a versatile programming language that can be used for a wide variety of applications. Here are some of the most common uses of Python:

1. Web development: Python is widely used in web development, with frameworks like Django and Flask being popular choices for building web applications.

2. Data analysis and visualization: Python's rich set of libraries like Pandas, NumPy, and Matplotlib, make it a popular choice for data analysis, machine learning, and data visualization.

3. Scientific computing: Python has a strong presence in the scientific computing field, with libraries like SciPy and BioPython providing powerful tools for scientific computing.

4. Artificial intelligence and machine learning: Python is used extensively in artificial intelligence and machine learning applications. Frameworks like TensorFlow, PyTorch, and Keras are popular choices for building machine-learning models.

5. Desktop GUI applications: Python can also be used to create desktop GUI applications with libraries like PyQt and wxPython.
   Game development: Python can be used for game development, with libraries like Pygame providing tools for game development.

6. Scripting and automation: Python's simplicity and ease of use make it a popular choice for scripting and automation tasks.

7. Education: Python's readability and easy-to-understand syntax make it an ideal language for beginners, and many educational institutions use Python as a teaching language.

This article will focus on data science and data analysis with python. As mentioned, python has rich libraries like pandas and numpy that come in handy when getting statistics from data.

Why python is preferred for data science and data analysis.

Python has certain features which make it preferable for data science, data analysis, machine learning, and Artificial intelligence. These features include:

1. Large and active community: Python has a large and active community of developers, which means that there is a wealth of resources, libraries, and tools available to help with data science and data analysis. This makes it easier for developers to get started and find solutions to problems they encounter.

2. Extensive libraries: Python has several powerful libraries for data science and data analysis, including NumPy, Pandas, SciPy, Scikit-learn, and Matplotlib, to name just a few. These libraries provide a broad range of functionality, such as data manipulation, statistical analysis, and visualization.

3. Interactivity: Python can be easily integrated with other programming languages, which makes it an ideal choice for data scientists and analysts who work in multi-language environments.

4. Open-source: Python is an open-source language, which means that it is free to use and can be customized to suit individual needs. This makes it an affordable and flexible option for data scientists and analysts.

Python in-built functions

Built-in functions are actions that are embedded in a system and are denoted by certain specific names or signs. Each time an inbuilt function is run, it is expected to perform a specific task.
Some of the python's built-in functions include:

1. print(): Prints the specified message to the console.

2. input(): Accepts input from the user through the console.

3. len(): Returns the length of an object, such as a string, list, or tuple.

4. range(): Returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default) and stopping before a specified number.

5. type(): Returns the type of an object, such as int, str, or list.

6. int(): Converts a string or float to an integer.

7. str(): Converts an object to a string.

8. float(): Converts a string or integer to a float.

9. list[]: Converts an object to a list.

10. tuple(): Converts an object to a tuple.

11.dictionaries{}: They include a key to which values are assigned.

Difference between a list, a tuple, and a set.

1. A list is used to show several items that exist in a variable. A list is denoted by [], square brackets, and can be altered in the future.
2. A tuple is similar to a list in that it shows items that exist in a given variable. A tuple is enclosed in (), brackets. Unlike a list, the components of a tuple cannot be altered in the future.
3. A set is also used to show items that exist in a certain variable. A set is denoted by {}, curly brackets. Sets are useful when filtering items in a list since they do not repeat items in them. If an item occurs in a frequency of more than one, it will be recorded once.

Operators in python

In Python, operators are special symbols or characters that perform operations on values or variables. Here are the different types of operators in Python:

Arithmetic operators: These operators are used to perform mathematical operations. Examples include + (addition), - (subtraction), * (multiplication), / (division), % (modulo or remainder), ** (exponentiation), and // (floor division).

Assignment operators: These operators are used to assign values to variables. Examples include = (simple assignment), += (addition and assignment), -= (subtraction and assignment), = (multiplication and assignment), /= (division and assignment), %= (modulo and assignment), *= (exponentiation and assignment), and //= (floor division and assignment).

Comparison operators: These operators are used to compare values or variables. Examples include == (equality), != (inequality), > (greater than), < (less than), >= (greater than or equal to), and <= (less than or equal to).

Logical operators: These operators are used to perform logical operations on Boolean values. Examples include and (logical AND), or (logical OR), and not (logical NOT).

Bitwise operators: These operators are used to perform operations on binary values. Examples include & (bitwise AND), | (bitwise OR), ^ (bitwise XOR), ~ (bitwise NOT), << (left shift), and >> (right shift).

Membership operators: These operators are used to test whether a value or variable is a member of a sequence or collection. Examples include in (value is in the sequence) and not in (value is not in the sequence).

Identity operators: These operators are used to test whether two variables or values refer to the same object in memory. Examples include is (variables refer to the same object) and is not (variables do not refer to the same object).

Pandas

We mentioned pandas as one of the extensive libraries that python uses to work with data and give useful output that could further be used to make informed decisions.
Pandas create data frames from items like lists, and tuples but the most common is dictionaries.

Pandas is a popular open-source Python library for data manipulation and analysis. It provides high-performance, easy-to-use data structures and data analysis tools for data processing, cleaning, and analysis.

Pandas provide two primary data structures: Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, while a data frame is a two-dimensional table-like data structure that consists of columns and rows.

Pandas also provide a rich set of tools for data manipulation, including merging and joining data sets, pivoting tables, and data reshaping. It also provides tools for handling missing data, time series analysis, and statistical analysis.
Some key features of pandas :

1. Data manipulation and cleaning: Pandas provides powerful tools for data cleaning and manipulation, such as removing duplicates, filling in missing data, and transforming data.

2. Data aggregation: Pandas provides functions for grouping and aggregating data, which allows users to perform complex data manipulations.

3. Easy data input and output: Pandas supports reading and writing data in a variety of formats, including CSV, Excel, SQL databases, and JSON.

4. Data visualization: Pandas integrates with other Python libraries such as Matplotlib and Seaborn to create high-quality data visualizations.

5. Fast and efficient: Pandas is designed to be fast and efficient, with functions that can handle large data sets without compromising performance.

Numpy

NumPy is a popular Python library for numerical computing that provides support for large, multi-dimensional arrays and matrices, along with a vast collection of high-level mathematical functions to operate on these arrays.
NumPy is a useful library in Python for several reasons, especially in the fields of data science, machine learning, and scientific computing.
Here are some of how NumPy is useful:

1. Efficient mathematical operations: NumPy provides an array object that is much more efficient than Python's built-in list data type for operations that involve mathematical operations on large datasets. This is because NumPy is implemented in C and uses vectorization to perform mathematical operations, which allows for faster and more efficient computation.

2. Multi-dimensional arrays: NumPy provides support for multi-dimensional arrays and matrices, which are essential in many areas of data science and scientific computing. These arrays make it easy to store and manipulate large amounts of data, such as images, audio, and time series data.

3. Mathematical functions: NumPy provides a range of mathematical functions such as trigonometric functions, logarithmic functions, and statistical functions. These functions are optimized for use with NumPy arrays, which allows for faster and more efficient computation.

4. Linear algebra: NumPy provides support for linear algebra operations such as matrix multiplication, inversion, and decomposition. These operations are critical in many areas of data science and scientific computing, such as regression analysis and image processing.

5. Fourier transforms: NumPy provides support for Fourier transforms, which are used in many areas of signal processing and image analysis.

6. Random number generation: NumPy provides support for random number generation, which is important in many areas of data science and scientific computing, such as simulation and modeling.

Numpy

NumPy is a fundamental package for scientific computing with Python, as many other libraries and tools depend on it for data processing, analysis, and visualization. Some examples of popular data science and machine learning libraries built on top of NumPy include pandas, sci-kit-learn, and TensorFlow.
NumPy provides efficient and convenient ways to perform mathematical operations on arrays, such as element-wise addition, subtraction, multiplication, and division, as well as more advanced linear algebra operations, like matrix multiplication, inversion, and eigenvalue decomposition. It also offers tools for statistical analysis, random number generation, and signal processing.

functions using Numpy

Numpy offers a variety of functions to manipulate data and get useful output. Some functions using the Numpy module include:
numpy. array(): creates an array from a list or tuple.

numpy. zeros(): creates an array of all zeros.

numpy. ones(): creates an array of all ones.

numpy. random.rand(): creates an array of random numbers between 0 and 1.
numpy. concatenate(): concatenates two or more arrays.

numpy. sum(): calculates the sum of array elements.

numpy. mean(): calculates the mean of array elements.

numpy. std(): calculates the standard deviation of array elements.

numpy. max(): returns the maximum element in an array.

numpy. min(): returns the minimum element in an array.

numpy. exp(): calculates the exponential of array elements.

numpy.log(): calculates the natural logarithm of array elements

Python is suitable for performing data analytics, data science, machine learning, and AI because of its extensive libraries and modules. MastPythonpython is relatively easy because of its simple syntax.