python async programming

Python async example1

Python recursion,memorization,decorator memorization

Python recursion example1

Python recursion with memorization

Python recursion with memorization with python decorator

Python data structures list,dic,set,counter etc.. explained in malayalam

I tried to explain some of the python built in data structures in Malayalam here.

Python example for list

Python example for dictionary

Python example for Set

Python example for Counter

Python example for defaultdictonary

Traverse through 2 dimensional list

A list is a data structure that holds an ordered collection of items i.e. you can store a sequence of items in a list
Slicing the list and manipulating the list is easy in python. Since its a sequence is iterable, we can use for loop on list. "Sequence in Python, sequence is the generic term for an ordered set.Lists are the most versatile sequence type. The elements of a list can be any object, and lists are mutable - they can be changed. Elements can be reassigned or removed, and new elements can be inserted" python two dimensional list

Python TRIE

Python implementation for TRIE


TRIE is very interesting data structure and its very much used in word suggestion algorithms. Python implementation for this would easiest way to learn it.



Python Trie example 1

Python Generator

Have you ever  think through how to build an function that generate infinite series ?
You might be  thinking why can't we use an infinite loop! the problem is you can't come of the loop and again resume from the position/state where you stopped .

But generators helps in this case, they are just like functions but the difference is they have yield keyword in the definition which is like a return wrapped with generator-iterator object

"When you call either a function or a generator, a stackframe is created. It has the local variables
(including the arguments passed into the function),a code pointer to the active opcode, and a stack for pending try-blocks,with-blocks, or loops.

In a regular function, execution begins immediately.When return is encountered, the final result is kept and the stackframe is freed along with everything it referenced.

In a generator function, the stackframe is wrapped in a generator-iterator object and returned immediately. The code in the generator function only runs when called by next(g) or g.send(v). Execution is suspended when yield is encountered. "



Python generator infinite sequence example

Python generator things to be known ( exhausted generator )

Python inheritance - Examples

Python inheritance explained with Visualization

Python inheritance example 1

Python inheritance ( with child methods) example 2

Python inheritance and overriding example3

How to deploy python programs to Docker

Running python programs in Docker

It is quite easy to run your python programs in docker. For getting started with python in docker, we need to install docker in our system.

I am using Ubuntu and followed the instruction details beginners commands to start with Docker.

Once you have installed docker, we can start building our docker. We can create a separate folder/directory for this in our system.

FROM tells Docker which image you base your image on (in the example, Python 3).

RUN tells Docker which additional commands to execute.

CMD tells Docker to execute the command when the image loads.

Dockerfile:

#base image
FROM python:3

#adding our first program/application to docker image
ADD app.py /
ADD app2.py /

#this time we are using a script to run our applications.
ADD script.sh /
#make the script executable
RUN ["chmod", "+x", "./script.sh"]
#changed the command to run the script
CMD ./script.sh 
#you can read more about commands in docker at https://docs.docker.com
#add the command instruction
#CMD ["python","./app.py"]

script file:

You can specify your python programs in the script.
Note: I have explicitly used python3 to run the programs.

#!bin/bash

#first process
python3 app.py

#second process
python3 app2.py

Python applications

print("this is my first pythong docker program")

python function arguments

Python function arguments 

You might have seen *, ** (asterisks ) in functions definition -arguments in python. Don't confuse them with C pointers.They are actually totally different.

Below examples take you through  defaults ,keyed and VarArgs parameter passing in python.


Reference: https://python.swaroopch.com/functions.html






python function arguments Example 1

python function arguments Example 2

Python function arguments Example 3



Python arguments with tuple and dictionary

6 ways to run python

Python language interpreter

Python interpreter can be used many ways from command line.

Beginners guide: https://wiki.python.org/moin/BeginnersGuide

Developers guide: https://devguide.python.org/

•  Call a Python interactive shell (REPL):

           python

•   Execute script in a given Python file:

           python script.py

• Execute script as part of an interactive shell:

          python -i script.py

•  Execute a Python expression:

          python -c "expression"

•  Run library module as a script (terminates option list):

          python -m module arguments

•  Interactively debug a Python script:

         python -m pdb script.py

     

8 beginners commands to start with Docker


Installation guides:


windows - https://docs.docker.com/docker-for-windows/

linux - https://docs.docker.com/engine/install/

mac - https://docs.docker.com/docker-for-mac/install/

1. List currently running docker containers:
   docker ps
   
2. List all docker containers (running and stopped):
   docker ps -a
   
3. Start a container from an image, with a custom name:
   docker run --name container_name image
   
4. Start or stop an existing container:
   docker start|stop container_name
   
5. Pull an image from a docker registry:
   docker pull image
   
6. Open a shell inside of an already running container:
   docker exec -it container_name sh
   
7. Remove a stopped container:
   docker rm container_name
   
8. Fetch and follow the logs of a container:
   docker logs -f container_name
   

gdb debugging - PART 2

gdb debugging techniques continutaion

This is a continuation from gdb part1 post -
http://naveendavisv.blogspot.com/2020/02/gdb-tips-part1.html

 - Debug an executable:
   gdb executable

 - Attach a process to gdb:
   gdb -p procID

 - Debug with a core file:
   gdb -c core executable

 - Execute given GDB commands upon start:
   gdb -ex "commands" executable

 - Start gdb and pass arguments:
   gdb --args executable argument1 argument2

Python data science libraries that you should know

8 python libraries for data science

1. NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

In python interpreter

>>> import numpy as np
>>> a = np.arange(15).reshape(3,5)
>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

2.scikit-learn - is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.

>>> clf = RandomForestClassifier(random_state=0)
>>> x=[[1,2,3],
... [11,12,13]]
>>> y = [0,1]
>>> clf.fit(x,y)
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=0, verbose=0,
                       warm_start=False)
>>> clf.predict(x)
array([0, 1])
>>> clf.predict([[4,5,6],[13,14,15]])
array([0, 1])

3.pandas
When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you to explore, clean and process your data. In Pandas, a data table is called a DataFrame.

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

The primary two components of pandas are the Series and DataFrame

>>> import pandas as pd
>>> data = {
...     'naveen':[50,40,30,20],
...     'John':[23,50,34,22]
... }
>>> marks = pd.DataFrame(data)
>>> marks
   naveen  John
0      50    23
1      40    50
2      30    34
3      20    22
>>> marks = pd.DataFrame(data,index=['English','Maths','Science','History'])
>>> marks
         naveen  John
English      50    23
Maths        40    50
Science      30    34
History      20    22

4.Sympy -
SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible

>>> from sympy import solve,Eq,symbols
>>> x, y, z, d = symbols('x y z d') 
>>> eq1 = Eq(x+y,8) 
>>> eq2 = Eq(x+z,13)  
>>> eq3 = Eq(z+d,6)  
>>> eq3 = Eq(z-d,6)  
>>> eq4 = Eq(y+d,8)
>>> solve(eq1,eq2,eq3,eq4,(x,y,z,d))

5.mathplotlib -Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy

>>> import matplotlib.pyplot as plt
>>> import numpy as nu
>>> x = nu.linspace(0,10,100)
>>> plt.plot(x,x,label='linear')
[<matplotlib.lines.Line2D object at 0x7fe300680910>]
>>> plt.legend()
<matplotlib.legend.Legend object at 0x7fe2f38f6450>
>>> plt.show()

6. Tensorflow
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

https://www.tensorflow.org/api_docs/python

https://machinelearningmastery.com/introduction-python-deep-learning-library-tensorflow/

7. Keras: The Python Deep Learning library
Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
There are two main types of models available in Keras: the Sequential model, and the Model class used with the functional API.

https://keras.io/

8.Scipy -

https://www.scipy.org/getting-started.html

The SciPy ecosystem

Scientific computing in Python builds upon a small core of packages:

• Python, a general purpose programming language. It is interpreted and dynamically typed and is very well suited for interactive work and quick prototyping, while being powerful enough to write large applications in.

• NumPy, the fundamental package for numerical computation. It defines the numerical array and matrix types and basic operations on them.

• The SciPy library, a collection of numerical algorithms and domain-specific toolboxes, including signal processing, optimization, statistics, and much more.

• Matplotlib, a mature and popular plotting package that provides publication-quality 2-D plotting, as well as rudimentary 3-D plotting.