Data Science with Python
This notebook contains the data science applications possible in python and starts with basic introduction to python language from data science utilization perspective to dealing with data manipulation. Its divided intro 4 parts shown below
- Python Basics (Data types, storing values taking user input, string manipulation, mathematical operations) - Python Data Structures ( String, Arrays, Lists, Tuple, Dictionary) - Python Condition Programming ( OOPS, conditional statements, exception handling) - Python Data Manipulation (Read & Write files, data manipulations, statistical analysis, sampling, etc.)
Python Basics
Let's start by taking input from the user
Now taking multiple inputs from the user
Variables in python along with some basic operations on them
Operators
Python Data Structures
Discussing about various data structures present in python and how to use and manipulate them#
List data structure in python are like dynamically typed arrays.
Tuples in python are collections just like above list data structure, any data type can be stored in tuple and are indexed by integers
Set in python is unordered collection data structure of data type that are iterative.
Dictionary in Python is the data structure that is collection of keys values used for data values like map and holds single value as an element
Array data structure in Python is a collection of same data type values at contiguous memory locations
Python Condition Programming
Discussing about various conditional programming concepts present in python such as OOPS, conditional statements and exception handling
Exception handling in python is managed using three keywords try, except and finally. Exceptions are instances that are raised when some internal event occurs which changes the flow of program.
Python Data Manipulation
Data manipulation techniques and ways present in Python for Data Science work namely data preprocessing, data transformation and data visualization.
Data preprocessing is the task of converting data from a given form to a much useful and consumable form.
Data Transformation is process of converting data from one format or structure into another format or structure
Data wrangling is the process of gathering, collecting, and transforming Raw data into another format for better understanding, decision-making, accessing, and analysis in less time.
It deals with issues such as data exploration, dealing with mining values, reshaping data, filtering data. All of the them are discussed above.
Data Visualization is the process of presenting data in the form of graphs or charts. It helps to understand large and complex amounts of data very easily. It allows the decision-makers to make decisions very efficiently and also allows them in identifying new trends and patterns very easily.
Data visualization using seaborn also as it offer more graphs and more attractive visuals
Statistics with Python