My notes day 4


Python

Characteristics of Python:

  • Easy to code and debug 
  • Easy in readability, various available resources
  • Open source, std. libraries. IDE easily available
  • R or Python. Only tool for communication. Output important
  • Data Analysis both R and Python easy
  • OOP-> Python
  • Easily installable from the Python website
  • More easier version is Anaconda, best for all the areas
  • Jupyter note book
We use python and its standard libraries. 
NumPy -> Helps in multidimensional array handling
Scipy -> Statistical model and analysis
Matplotlib -> Visualization packages
Pandas -> Array handling and dataframes handling

*NumPy basic operation:
NumPy: Fundamental package for scientific computing with python. Powerful N dimensional array object. An array can either be a vector or matrix. By NumPy we can create both matrix or vector arrays
Arbitrary datatypes can be defined. This allows NumPy to handle and integrate wide databases.
The package should always call the python kernel when session is rebooted or restarted.

import numpy as np

some of the operations














 

    

 NumPy vs Pandas

Both are used for data analytics. A matrix in numpy and data frame in pandas. Matrix is only single datatype. Data frame can keep all the different datatypes(like array vs structure in C). Python actual datatypes include dictionary, tuple and lists.

Vector-> Starts from zero

Series->custom index values


https://pandas.pydata.org/docs/getting_started/index.html




Please refer the DataCamp app for Python related stuffs and the Git repo:

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