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Python Tutorial
- What is Python?
- How to Install Python?
- Python Variables and Operators
- Python Loops
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- Python Multithreading and Synchronization
- Python Modules
- What is Python bytecode?
- Python Regular Expressions
Python Panda Tutorial
- Python Pandas Tutorial
- Python Pandas Features
- Advantages and Disadvantages of Python Pandas
- Pandas Library In Python
- Pandas Series To Frame
- Python Dataframeaggregate and Assign
- Pandas Dataframe Describe
- Pandas Dataframe Mean
- Pandas Hist
- Pandas Dataframe Sum
- How to convert Pandas DataFrame to Numpy array
- Pandas Concatenation
- Pandas Shift
- Pandas Rolling
- Data Analysis With Pandas and Python
- How Python Panda Are Helpful For With Data Science
Python Selenium
- Selenium Basics
- Selenium with Python Introduction and Installation
- Navigating links using get method Selenium Python
- Locating Single Elements in Selenium Python
- Locating Multiple elements in Selenium Python
Python Flask Tutorial
Python Django
- How to Install Django and Set Up a Virtual Environment in 6 Steps
- Django MTV Architecture
- Django Models
- Django Views
- Django Templates
- Django Template Language
- Django Project Layout
- Django Admin Interface
- Django Database
- Django URLs and URLConf
- Django Redirects
- Django Cookies and Cookies Handling
- Django Caching
- Types of Caching in Django
- Django Sessions
- Django Forms Handling & Validation
- Django Exceptions & Error-handling
- Django Forms Validation
- Django Redirects
- Django Admin Interface
- Django Bootstrap
- Ajax in Django
- Django Migrations and Database Connectivity
- Django Web Hosting and IDE
- Django Admin Customization
- What is CRUD?
- Django ORM
- Django Request-Response Cycle
- Django ORM
- Making a basic_api with DRF
- Django Logging
- Django Applications
- Difference between Flask vs Django
- Difference between Django vs PHP
Numpy
- Numpy Introduction
- NumPy– Environment Setup
- NumPy - Data Types
- NumPy–Functions
- NumPy Histogram
- numPy.where
- numpy.sort
- NumPyfloor
- Matrix in NumPy
- NumPy Arrays
- NumPy Array Functions
- Matrix Multiplication in NumPy
- NumPy Matrix Transpose
- NumPy Array Append
- NumPy empty array
- NumPy Linear Algebra
- numpy.diff()
- numpy.unique()
- numpy.dot()
- numpy.mean()
- Numpy.argsort()
- numpy.pad()
- NumPyvstack
- NumPy sum
- NumPy Normal Distribution
- NumPylogspace()
- NumPy correlation
- Why we learn and use Numpy?
Tensorflow
- Introduction To Tensorflow
- INTRODUCTION TO DEEP LEARNING
- EXPLAIN NEURAL NETWORK?
- CONVOLUTIONAL AND RECURRENT NEURAL NETWORK
- INTRODUCTION TO TENSORFLOW
- INSTALLATION OF TENSORFLOW
- TENSORBOARD VISUALIZATION
- Linear regression in tensorflow
- Word Embedding
- Difference between CNN And RNN
- Explain Keras
- Program elements in tensorflow
- Recurrent Neural Network
- Tensorflow Object Detection
- EXPLAIN MULTILAYER PERCEPTRON
- GRADIENT DESCENT OPTIMIZATION
Interview Questions & Answers
NumPy– Environment Setup
Standard Python distribution doesn’t come bundled with NumPy module. A lightweight alternative is to install NumPy using popular Python package installer, pip.
pip install numpy
The best way to enable NumPy is to use an installable binary package specific to your operating system. These binaries contain full SciPy stack (inclusive of NumPy, SciPy, matplotlib, IPython, SymPy and nose packages along with core Python).
Windows
Anaconda (from https://www.continuum.io) is a free Python distribution for SciPy stack. It is also available for Linux and Mac.
Canopy (https://www.enthought.com/products/canopy/) is available as free as well as commercial distribution with full SciPy stack for Windows, Linux and Mac.
Python (x,y): It is a free Python distribution with SciPy stack and Spyder IDE for Windows OS. (Downloadable from https://www.python-xy.github.io/)
Linux
Package managers of respective Linux distributions are used to install one or more packages in SciPy stack.
For Ubuntu
sudo apt-get install python-numpy
python-scipy python-matplotlibipythonipythonnotebook python-pandas
python-sympy python-nose
For Fedora
sudo yum install numpyscipy python-matplotlibipython
python-pandassympy python-nose atlas-devel
Building from Source
Core Python (2.6.x, 2.7.x and 3.2.x onwards) must be installed with distutils and zlib module should be enabled.
GNU gcc (4.2 and above) C compiler must be available.
To install NumPy, run the following command.
Python setup.py install
To test whether NumPy module is properly installed, try to import it from Python prompt.
importnumpy
If it is not installed, the following error message will be displayed.
Traceback (most recent call last):
File “< pyshell#0>“, line 1, in < module>
importnumpy
ImportError: No module named ‘numpy’
Alternatively, NumPy package is imported using the following syntax −
importnumpy as np