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Posts Tagged ‘programming’

Python: SQlite Project – P2

January 23, 2020 Leave a comment


Learning : Python and Sqlite3
Subject: Sqlite3, Database functions ” Employee App” P2

In Part1 of this project (Click to Read) we create the database and set the connection, also we create an Employee table with very basic fields and also we wrote a dummy_data() function to Insert some records into the table. And to make the application usable we wrote the Main-Men function and we test it with selecting to display the records that we have.

Today we will write other functions from our Menu. INSERT NEW EMPLOYEE: To Insert new employee we will ask the user to input or to fill the fields we have in our table such as First Name, Last Name and the Salary. .Let’s see the code ..

 # Insert Function

def insert_emp ():
   
    os.system("clear")
    print("\n\n ======== INSERT NEW RECORD ========")
    if input("\n Do you want to enter new employee data press. (y,n) ") in ["Y","y"] :
        f_name = input("    Enter the first name: ")
        l_name = input("    Enter the last name: ")
        p_pay = input("    Enter the salary: ")

        c.execute ("INSERT INTO emp (fname,lname,pay) VALUES(:fname,:lname, :pay)",{"fname":f_name,"lname":l_name, "pay":p_pay})
        db_conn.commit()
        print(input ("\n  One record has been Inserted. .. Press any key .. ."))
    else :
        print(input ("\n  Ok .. you don't want to enter any data. .. Press any key .. .")) 


So, if we select to Insert a new Employee and we Enter First name as : Jacob Last Name as: Noha also we we set the salary to 3200 and press Enter, as in this page ..

Then if we select to show all data we have in the database, we can see the new record added..



DELETE AN EMPLOYEE: To Delete or remove an employee from the database, First we will print-out all the records on the screen and ask the user to enter the ID_ number of the employee he want to delete. As shown here ..

In the above example we select ID number 3 to be deleted and press enter, the system will show the record and ask to confirm the deletion and wait for ‘Y’ to be pressed, then the record will be deleted.
..Here is the code..


In this post we cover the INSERT AND DELETE of the records from the database, in the next post we will cover the SEARCH AND EDIT Functions also some search conditions like salary range and group-by command.



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By: Ali Radwani




Python: My Fake Data Generator P-7

January 19, 2020 Leave a comment


Learning : Python: Functions, Procedures and documentation
Subject: About fake data P-7: (Fake File Name)

In this post we will write a function to generate a file name. Each file name consist of two part, first one is the name which present (or should present) a meaning part, then there is a dot (.) then mostly three characters showing the file type or extension.

Scope of work: Our files names will have 4 syllables all to gather will be one file name. Each syllables will be loaded in a variable as shown ..
1. fext: for Files extensions such as: (doc, jpeg, pdf, bmp …. and so on)
2. name_p1: Is a noun such as (customers, visitors, players .. . and so on )
3. name_p2: will be an nouns, adjective or characteristics such as (name, index, table .. .. and so on)
4. Then we will add a random integer number between (1,30) to give the file a version, or a number.

All parts or syllables will be as one file name.

Let’s Work: First we need to load the File name syllables from the json file called : file_dict.json

 # Loading the json from a url 

import json , requests, random

fname = "https://raw.githubusercontent.com/Ali-QT/Ideas-and-Plan/master/file_dict.json"

def call_json_url(fname):
    """
    Function to load the json file from URL.
    Argument: str :fname
    
    Return : dict: data
    """
    req = requests.get(fname)
    cont = req.content
    data = json.loads(cont)
    return data

fdict = call_json_url(fname)

# Save each syllables into variable. 
f_ext = fdict["fext"]
f_p1_name = fdict["name_p1"]
f_p2_name = fdict["name_p2"]


Now we will write the function that will generate the file name:

 # Function to generate the file name 

def generate_fname():
    """
    Function to generate a Fake files name.
    
    File Name consist of four syllables, Two names, a random number and an extension.
    First two syllables of the file name will be selected randomly from a dictuenary stored in a json       file.
    
    Return : str : f_file_name

    To read the information key in the json file use this code.
    ------ CODE TO READ DATA-SET INFORMATION --------------
     	for each in fdict["information"]:
          print(each,":",fdict["information"])

    ---END OF CODE ------------------------------------------
    """
    fp1 = (random.choice (f_p1_name)["n_p1"])
    fp2 = (random.choice (f_p2_name)["n_p2"])
    fp3 = (random.choice (f_ext)["ext"])

    f_file_name = (fp1 + "_" + fp2 + "_" + str(random.randint(1,30)) + "." + fp3)

    return f_file_name


Last thing we just will call the function for X numbers of files Name we want.

 # Generate 15 file Name. 

for x in range (15):
    generate_fname()

[Output]:

 
kids_name_15.ico
speakers_list_1.asp
cars_photos_27.csv
students_database_26.xml
kids_details_27.html
animals_index_10.mov
speakers_parameters_17.csv
drivers_name_8.doc
males_attributes_16.mov
players_sketches_11.py
animals_sketches_3.wav
cars_details_12.css
animals_list_17.txt
flowers_parameters_4.doc
players_database_28.log





:: Fake Function List ::

Function Name Description
Color To return a random color code in RGB or Hex.
Date To return a random date.
Mobile To return a mobile number.
Country To return a random country name.
City To return a random City name.
ID To return X random dig as ID.
Time To return random time.
Car’s Brand
file_name file name: list for fake file names.
Creatures Random animal names of a certain type: Mammals, Birds, Insect, Reptiles
Foods To return a random list of foods



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By: Ali Radwani




Python: My Fake Data Generator P-4

December 19, 2019 3 comments


Learning : Python: Functions, Procedures and documentation
Subject: About fake data P-4: (Fake Dates)

The fourth function of our Fake Data Generator will be the date function, from it’s name this one will generate a FAKE Date in yyyy/mm/dd format. The function will have one argument (go_back) for range, the max-limit is current date (today) and mini-limit will be (1/1/1900), if the user did’t pass any thing for (go_back) then the range is (from current to 1/1/1900) and if the user pass (X) then the range will be ((current date) to current – X_YEARS). Also in dates we need to take care of Leap Years, In leap year, the month of February has 29 days instead of 28. To solve this in our function we can use two ways, first one (the easy way) we know that we are generating a random numbers for months and days; so we can say if the month is February, then days can’t be more than 28. But if we want this thing to be more realistic we need to add more conditions such as :
1. The year can be evenly divided by 4.
2. If the year can be evenly divided by 100, it is NOT a leap year, unless the year is also evenly divisible by 400. Then it is a leap year (February has 29 days).



'''
10/12/2019
By: Ali Radwani
To get Fake Date.

'''

import random, datetime

def fdate(go_back = 0): 
    """      
        ###   Fake Date Generator V.01  ###
        Date: 10.12.2019, By: Ali Radwani

        This function will generate and return a fake date in string format.
        The function accept one int argument go_pback.
        If go_past = X, and current year - X is less than 1900 then
        the range of FAKE time will be (current year to current year - X).
    
        If NO argument passed to the function, then default limit set to 1900. 

        Default limits:  Date are from current (today) and back to 1900.

        Import: random, datetime
    
        Argument: int : go_back to set the upper limit of the date
        
        Return: str: dd/mm/yyyy  

    """

    # Get current year. 
    c_year = datetime.datetime.today().year

    # set the maximum year limit.
    if go_back > 0 :
        max_y_limit = c_year - go_back
    else :
        max_y_limit = 1900

    if max_y_limit < 1900 :
        max_y_limit = 1900

    yy = random.randint(max_y_limit, c_year)

    mm = random.randint(1,12)

    if mm in [1,3,5,7,8,10,12] :
        dd = random.randint(1,31)
    elif mm in [4,6,9,11]:
        dd = random.randint(4,30)

    else :
        # IF the month is February (2) 
        if (yy % 4 == 0 ) or ((yy % 100 == 0)and (yy % 400 == 0)):
            # It is a leap year February has 29 days.
            dd = random.randint(1,29)

        else : # it is NOT a leap year February has 28 days.
            dd = random.randint(1,28)

    d = (str(dd) +'/'+ str(mm)+'/' + str(yy))
    
    return (str(dd) +'/'+ str(mm)+'/' + str(yy))


# To check the output.
for x in range (30):
    print(fdate())


<
Here is a screenshot of the code, also available on the Download Page . . .



:: Fake Function List ::

Function Name Description
Color To return a random color code in RGB or Hex.
Date To return a random date.
Mobile To return a mobile number.
Country To return a random country name.
City To return a random City name.
ID To return X random dig as ID.
Time To return random time.
Car’s Brand
file_name

Done



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By: Ali Radwani




Python: My Fake Data Generator P-2

December 15, 2019 3 comments


Learning : Python: Functions, Procedures and documentation
Subject: About fake data P-2: (Fake ID)

Before we start i’d like to mention that with our last fcolor() function we write some comments in the first part of the function between three double quote(“””), and if we load the function and call help() as help(fcolor()) we will get that information on the python console as a help as in screen shot.


In this post we will write a function to generate a fake ID number, for ID’s there could be several styles, sometime we just want a random number without any meaning; just X number of random digits. Most of the time we need this number to be mean-full based on certain rules. For example, in Banks they may use some digits that indicate the branch. In sport club, they may include the date … and so-on.

Here we will write a function called key_generator(), the function will take two arguments (dig, s) dig is your key digits number, s is the style, if s = d then the first 6 digits of the key will be the date as ddmmyy + random digits, and if s = anything else or s not passed then the key will be as default (just x-digits). Let’s see the code.

First the summary or say information about the function:

def key_generator(dig, s = 'n'):
    """
       ### Date: 8/12/2019, By: Ali Radwani ###
       Summary:
            This function will generate x-digit key randomly.
            If the argument s = 'd' or 'D' then the key is two part, first (6) digits
            are date as ddmmyy then x-digit random numbers.

            If the argument s anything else than ['d','D'] or no argument passes, then the key
            is random numbers without any meaning.

            The numbers will randomly be selected in range of (10 to 99).

            import: random, datetime

            Argument: int: dig: The number of digits for the key.
                 str: s  : The key style (with date or just random numbers)

            return: int: the_key
    """


Now, if the user pass s=’d’ then part of the key will be the current date, to do this we will call the datetime function in python and split it into dd,mm,yy. Here is the key_generator() function.

def key_generator(dig, s = 'n'):
    """
       ### Date: 8/12/2019, By: Ali Radwani ###
       Summary:
            This function will generate x-digit key randomly.
            If the argument s = 'd' or 'D' then the key is two part, first (6) digits
            are date as ddmmyy then x-digit random numbers.

            If the argument s anything else than ['d','D'] or no argument passes, then the key
            is random numbers without any meaning.

            The numbers will randomly be selected in range of (10 to 99).

            import: random, datetime

            Argument: int: dig: The number of digits for the key.
                 str: s  : The key style (with date or just random numbers)

            return: int: the_key
    """
    the_key=''
    if s in ['d','D'] :
        d = str(datetime.date.today())
        dd = d[8:10]
        mm = d[5:7]
        yy = d[2:4]
        the_key = dd + mm + yy
        for x in range (dig):
            the_key = the_key + str( random.randint(10,99))
        return int(the_key[:(dig + 6)])
        
    else :
        for x in range (dig):
            the_key = the_key + str( random.randint(10,99))

        return int(the_key[:dig])


In next Fake Data function we will try to write one to generate the date. It will be published on next Sunday.



:: Fake Function List ::

Function Name Description
Color To return a random color code in RGB or Hex.
Date To return a random date.
Mobile To return a mobile number.
Country To return a random country name.
City To return a random City name.
ID To return X random dig as ID.
Time To return random time.

Done



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By: Ali Radwani




python: Fake Data-set

December 9, 2019 1 comment


Learning : Python to generate fake data-set
Subject: About Fake data library

Most of the time when we working on a project, we need to test our procedures and functions with some data. In most cases we need just dummy data such as dates, names, address .. and so-on.

Last week, I was reading on the net and i fond an article about generating fake data using a library in PHP (PHP is a Computer Programming Language) so I start to find if we have one in Python! and the answer is YES there is a library that we can import called ‘Fake’. I start to work on it and discover it. This post is about the Fake Data-set Library.

The library called ‘Faker’ and we need to install it in our python environment, i use : pip install Faker to install it. In it’s documentation we can use some properties like : name, city, date, job .. and others. So if we want to generate a fake name we write this:

# Using lib:fake to generate fake name

print(fake.name()) 
[Output]: Victoria Campbell

Here is a screen-shot from Jupyter notbook screen.


To generate more than one name we can use for loop as:

# Using lib:fake to generate (X) fake name

for x in range (10) :
    print(fake.name())
[Output]: Jared Hawkins
Michael Reid
Ricky Brown
Mary Tyler
Kristy Dudley
Karen Cain
Jennifer Underwood
Desiree Jensen
Carla Rivera
Brandon Cooper


Other properties that we can use are :address, company, job, country, date_time and many other, and with all this we can create a data-set full of fake data.

So if we want to create a fake data-set contain:
Name, Date-of-birth, Company, Job, Country as one person data we will use it like this:

# Using lib:fake to generate (X) person fake data
# Data-set contain: Name, Date-of-birth, Company, Job, Country
p_count = 1
for x in range (p_count):
    print('Name:',fake.name())
    print('DOB:',fake.date())
    print('Company:',fake.company())
    print('Job:',fake.job())
    print('country:',fake.country())


[Output]: 
Name: Crystal Mcconnell
DOB: 2002-09-30
Company: Bailey LLC
Job: Insurance underwriter
country: Pakistan


Now if we want to store the person data in a dictionary type variable and use it later, we can do this as following:

# Using lib:fake to generate (X) person fake data and store it in a dictionary 
people_d ={}
p_count = 5
for x in range (p_count):
    ID = x
    people_d[ID]={'name':fake.name(),'date':fake.date(),'company':fake.company(),'job':fake.job(),'country':fake.country()}

# To print-out the people_d data-set.
for x in people_d :
    print(people_d[x])


Just in case we want a complicated ID we can use a random function (8-dig) integer, or combining two fake numbers such as (fake.zipcode() and fake.postcode()) just to make sure that we will not have a duplicate ID.

Using fake library will help a lot, and it has many attributes and properties that can be inserted in a data-set. For more information on this document you may read it here: Fake Library



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By: Ali Radwani




Python: Machine Learning – Part 1

November 27, 2019 1 comment


Learning :Python and Machine Learning
Subject: Requirements, Sample and Implementation

Machine Learning: I will not go through definitions and uses of ML, I think there is a lot of other posts that may be more informative than whatever i will write. In this post I will write about my experience and learning carve to learn and implement ML model and test my own data.

The Story: Two, three days ago I start to read and watch videos about Machine Learning, I fond the “scklearn” site, from there I create the first ML to test an Iris data-set and then I wrote a function to generate data (my own random data) and test it with sklearn ML model.

Let’s start ..

Requirements:

1. Library to Import: To work with sklearn models and other functions that we will use, we need to import coming libraries:

import os # I will use it to clear the terminal.

import random # I will use it to generate my data-set.

import numpy as np

import bunch # To create data-set as object

from sklearn import datasets

from sklearn import svm

from sklearn import tree

from sklearn.model_selection import train_test_split as tts

2. Data-set: In my learning steps I use one of sklearn data-set named ” Iris” it store information about a flower called ‘Iris’. To use sklear ML Model on other data-sets, I create several functions to generate random data that can be passed into the ML, I will cover this part later in another post.
First we will see what is the Iris dataset, this part of information is copied from sklearn site.

::Iris dataset description ::
dataset type: Classification
contain: 3 classes, 50 Samples per class (Total of 150 sample)
4 Dimensionality
Features: real, positive

The data is Dictionary-like object, the interesting attributes are:
‘data’: the data to learn.
‘target’: the classification labels.
‘target_names’: the meaning of the labels.
‘feature_names’: the meaning of the features.
‘DESCR’: the full description of the dataset.
‘filename’: the physical location of iris csv.

Note: This part helps me to write me data-set generating function, that’s why we import the Bunch library to add lists to a data-set so it will appear as an object data-set, so the same code we use for Iris data-set will work fine with our data-set. In another post I will cover I will load the data from csv file and discover how to create a such file..

Start Writing the code parts: After I wrote the code and toned it, I create several functions to be called with other data-set and not hard-code any names in iris data-set. This way we can load other data-set in easy way.


The Code

 # import libraries 

import numpy as np
from sklearn import datasets
#from sklearn import svm
from sklearn import tree
from sklearn.model_selection import train_test_split as tts
import random, bunch


Next step we will load the iris dataset into a variable called “the_data”

 # loading the iris dataset. 

the_data = datasets.load_iris() 


From the above section “Iris dataset description” we fond that the data is stored in data, and the classification labels stored in target, so now we will store the data and the target in another two variables.

 # load the data into all_data, and target in all_labels. 
all_data= the_data.data 
all_labels = the_data.target   


We will create an object called ‘clf’ and will use the Decision Tree Classifier from sklearn.

 #  create Decision Tree Classifier 

clf = tree.DecisionTreeClassifier()


In Machine Learning programs, we need some data for training and another set of data for testing before we pass the original data or before we deploy our code for real data. The sklearn providing a way or say function to split a given data into two parts test and train. To do this part and to split the dataset into training and test I create a function that we will call and pass data and label set to it and it will return the following : train_data, test_data, train_labels, test_labels.

 #  Function to split a data-set into training and testing data. 

def get_test_train_data(data,labels):

  train_data, test_data, train_labels, test_labels = tts(data,labels,test_size = 0.1)
  return train_feats, test_feats, train_labels, test_labels


After splitting the data we will have four list or say data-sets, we will pass the train_data and the train_labels to the train_me() function, I create this function so we can pass the train_data, train_labels and it will call the (clf.fit) from sklearn. By finishing this part we have trained our ML Model and is ready to test a sample data. But first let’s see the train_me() function.

 #  Function train_me() will pass the train_data to sklearn Model. 

def train_me(train_data1,train_labels1):
  clf.fit(train_data1,train_labels1)
  print('\n The Model been trained. ')


As we just say, now we have a trained Model and ready for testing. To test the data set we will use the clf.predict function in sklearn, this should return a prediction labels list as the ML Model think that is right. To check if the predictions of the Model is correct or not also to have the percentage of correct answers we will count and compare the prediction labels with the actual labels in the test_data that we have. Here is the code for get_prediction()

 #  get_prediction() to predict the data labels. 

def get_prediction(new_data_set,test_labels2,accu):

  print('\n This is the prediction labels of the data.\n')

  # calling prediction function clf.predict
  prediction = clf.predict(new_data_set)
  print('\n prediction labels are : ',prediction,len(prediction))
  
  # print the Accuracy
  if accu == 't' :
    cot = 0
    for i in range (len(prediction)) :
      print(prediction[i] , new_data_set[i],test_labels2[i])
      if [prediction[i]] == test_labels2[i]:
        cot = cot + 1
    print('\ncount :',cot)
    print('\n The Accuracy:',(cot/len(prediction))*100,'%')


The accuracy value determine if we can use the model in a real life or tray to use other model. In the real data scenario, we need to pass ‘False’ flag for accu, because we can’t cross check the predicted result with any data, we can try to check manually for some result.

End of part 1: by now, we have all functions that we can use with our data-set, in coming images of the code and run-time screen we can see that we have a very high accuracy level so we can use our own data-set, and this will be in the coming post.

Result screen shot after running the Iris dataset showing high accuracy level.



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Python and Lindenmayer System – P3

November 5, 2019 Leave a comment


Learning : Lindenmayer System P3
Subject: Drawing Fractal Tree using Python L-System

In the first two parts of the L-System posts (Read Here: P1, P2) we talk and draw some geometric shapes and patterns. Here in this part 3 we will cover only the Fractal Tree and looking for other functions that we may write to add leaves and flowers on the tree.

Assuming that we have the Pattern generating function and l-system drawing function from part one, I will write the rules and attributes to draw the tree and see what we may get.
So, first tree will have:


# L-System Rule to draw ‘Fractal Tree’
# Rule: F: F[+F]F[-F]F
# Angle: 25
# Start With: F
# Iteration : 4


and the output will be this:


If we need to add some flowers on the tree, then we need to do two things, first one is to write a function to draw a flower, then we need to add a variable to our rule that will generate a flower position in the pattern. First let’s write a flower function. We will assume that we may want just to draw a small circles on the tree, or we may want to draw a full open flower, a simple flower will consist of 4 Petals and a Stamen, so our flower drawing function will draw 4 circles to present the Petals and one middle circle as the Stamen. We will give the function a variable to determine if we want to draw a full flower or just a circle, also the size and color of the flowers.

Here is the code ..

font = 516E92
commint = #8C8C8C

Header here
# Functin to draw Flower
def d_flower () :

if random.randint (1,1) == 1 :

# if full_flower = ‘y’ the function will draw a full flower,

# if full_flower = ‘n’ the function will draw only a circle

full_flower = ‘y’

t.penup()

x1 = t.xcor()

y1 = t.ycor()

f_size = 2

offset = 3

deg = 90

if full_flower == ‘y’ :

t.color(‘#FAB0F4’)

t.fillcolor(‘#FAB0F4’)

t.goto(x1,y1)

t.setheading(15)

for x in range (0,4) : # To draw a 4-Petals

t.pendown()

t.begin_fill()

t.circle(f_size)

t.end_fill()

t.penup()

t.right(deg)

t.forward(offset)

t.setheading(15)

t.goto(x1,y1 – offset * 2 + 2)

t.pendown() # To draw a white Stamen

t.color(‘#FFFFF’)

t.fillcolor(‘#FFFFFF’)

t.begin_fill()

t.circle(f_size)

t.end_fill()

t.penup()

else: # To draw a circle as close flower

t.pendown()

t.color(‘#FB392C’)

t.end_fill()

t.circle(f_size)

t.end_fill()

t.penup()

t.color(‘black’)

Then we need to add some code to our rule and we will use variable ‘o’ to draw the flowers, also I will add a random number selecting to generate the flowers density. Here is the code for it ..

In the code the random function will pick a number between (1-5) if it is a 3 then the flower will be drawn. More density make it (1-2), less density (1-20)

And here is the output if we run the l-System using this rule: Rule: F: F[+F]F[-F]Fo

Using the concepts, here is some samples with another Fractal Tree and flowers.

Another Fractal Tree without any Flowers.


Fractal Tree with closed Pink Flowers.


Fractal Tree with closed Red Flowers.


Fractal Tree with open pink Flowers.




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