Monday 11 October 2021

Saturday 9 October 2021

Introduction to Python

Evolution of Python.

 -Python was developed by Guido Van Rossum in the late eighties at the ' National Research Institute       for Mathematics and Computer Science, at Netherlands.

 Python Editions 

  - Python 1.0
  - Python 2.0
  - Python 3.0

Advantages of  using Python

- Python has several features that make it well suited for data science

- Open source and community development 

- Developed under Open Source Intiative approved license making it free to use and      distribute even commercially

- Syntax used is simple to understand and code 

- Libraries designed for specific data science tasks 

- Combines well with majority of the cloud platform service providers

Integrated development enviroment (IDE)

- Software application consisiting of a cohesive unit of tools required for development

- Designed to simplify software development

- Utilities Provided by IDEs include tools for managing, compiling, deploying and debugging software

Feature of IDE 

- IDE should centralize three key tools nthat form the crux of software development 

- Syntax and error highlighting 

-  Code completion 

- Version control

Commonly used IDEs

-Jupyter Notebook

But in this course we are going to be looking at jupyter notebook; and that is primarily because it is a very good software that has been developed only for data science and python; and it as an interface that is very very appealing and easy to use for beginners.

Jupyter Notebook

-Web application that allows creation and manipulation of notebook documents called 'notebook',

-Supported across Linux, Mac Os X and windows platforms.

-Available as open source version.

-Bundled with Anaconda distribution or can be installed seperately.

-Supports Julia, Python, R and Scala.
-Consists of ordered collection of input and output cells that contain code,text,plots etc.

-Allows sharing of code and narrative text through output format likePDF,HTML etc.

Wednesday 6 October 2021

Control structures [if elif]

Control structures 

if elif

  •    Whenever you want to execute certain commands only when the certain condition is satisfied. 
  •    So, in that case you can go for if else statements, the condition can also be single or you can also give multiple condition, in that case you will have multiple else statements.
  • In the image below you can see this, more clearly.


  • So, first we will look into the if else family of constructs, if else and If-elif-else are a family of constructs, where a condition is first checked, if it is satisfied only then the operations will be performed. 
  • If, the condition is not satisfied the code exits the construct or moves on to the other options. So, whenever we use just an if statement or with an else statement or with using multiple if's and multiple else clause. 
  • The first check would be the condition, whenever the condition is satisfied only then the code will be executed or the statement will be executed, otherwise the code exits the construct itself and moves to the other options. So, that is how the if else family of the constructs works. 

  • Let us see different task for each construct. So, first we will look into if construct, the command would be if expression colon and statements in the next line.
  • If is a key word, if the condition is satisfied whatever condition you have given it under the expression, then the statements will get executed. Otherwise, the code exit the construct itself. 
  • Next, we will move ahead and see what is the syntax would be for If-else construct
  • It forms a basis from the if construct, wherever we have given the first statement, using the if keyword and followed by if keyword you have to give the expression to be checked, that is where the condition to be specified.

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Tuesday 5 October 2021

Introduction to Python for Data Science

What is Data Science?

  • Data Science is the art of analyzing using statistics and machine learning techniques raw data with a perspective of drawing valuable insight from it.
  • Data Science is used in many industries to allow them to make better business and decisions, and in the sciences to test models or theories.
  • This requires process of inspecting, cleaning, modelling, analyzing and interpreting raw data.

Why Using Python?

  • Python libraries provide basic key features sets which are essential for data science.
  • Data Manipulation and Pre-Processing
Python's pandas library offers a variety of functions and data wrangling process

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Operations On Dataframe in Python [Part II]

Concise summary of Dataframe.

  • So, next we are going to see about how to get the concise summary of DataFrame.

  • So, there is a command called info that returns a concise summary of a DataFrame, the concise summary includes the data type of index; index being the row labels, the data type of row labels is what the output gives as well as it gives the data type of columns, it also gives the count of non-null values.

  • Basically, how many filled values are there in your DataFrame. 

  • Also, it gives the memory usage of the DataFrame and the syntax would be you use the info command along with the DataFrame name.

Syntax =

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Operations On Dataframe in Python

Checking data types of each Column in a Data Frame.

  • If you want to check the data type of each column, because whenever you have been given a data, you want to really check what is the structure of the data; that means, which variable has which data type?
  • In, that case you can use dtypes, because that returns a series with the data type of each column and the syntax would be you use dtypes along with the Data Frame name.
  • So, Data Frame.dtypes will give you a series with the data type of each column

Here is the syntax.

Syntax = DataFrame.dtypes


Count of unique data types

  • So, now we have an overall idea about what are the data types that we are going to work with using the cars_data. 
  • There is also an option where you can get the count of unique data types available in your Data Frame.
  • So, in that case get_dtype_counts, returns the counts of unique data types in the data frame.

Here is the syntax.

Syntax = get_dtype_counts()

Selecting data based on data types

  • So, now we also have an overall idea about the count of unique data types that we are going to handle with. 

  • So, now, we know about how to get the data type of each variables. So, there might be cases where you want to perform the operations only on a numerical data type.

  • Similarly, there can be cases where you are going to work with only categorical data type.

Here is the syntax.

Syntax =

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Wifi Password Generator in Python



  • a simple python script that tells you the password of the wifi you're connected with


  • just need to install python in your system.

Source Code:- 

import subprocess

data = (
    subprocess.check_output(["netsh", "wlan", "show", "profiles"])
profiles = [i.split(":")[1][1:-1] for i in data if "All User Profile" in i]
for i in profiles:
    results = (
        .check_output(["netsh", "wlan", "show", "profile", i, "key=clear"])
    results = [b.split(":")[1][1:-1] for b in results if "Key Content" in b]
        print("{:<30}|  {:<}".format(i, results[0]))
    except IndexError:
        print("{:<30}|  {:<}".format(i, ""))


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Monday 4 October 2021

Python Project [ Age_Calculator]

Calculate Your Age!

This script prints your age in three different ways :

  1. Years
  2. Months
  3. Days


You only need Python to run this script. You can visit here to download Python.


import time
from calendar import isleap

# judge the leap year
def judge_leap_year(year):
    if isleap(year):
        return True
        return False

# returns the number of days in each month
def month_days(month, leap_year):
    if month in [1, 3, 5, 7, 8, 10, 12]:
        return 31
    elif month in [4, 6, 9, 11]:
        return 30
    elif month == 2 and leap_year:
        return 29
    elif month == 2 and (not leap_year):
        return 28

name = input("input your name: ")
age = input("input your age: ")
localtime = time.localtime(time.time())

year = int(age)
month = year * 12 + localtime.tm_mon
day = 0

begin_year = int(localtime.tm_year) - year
end_year = begin_year + year

# calculate the days
for y in range(begin_year, end_year):
    if (judge_leap_year(y)):
        day = day + 366
        day = day + 365

leap_year = judge_leap_year(localtime.tm_year)
for m in range(1, localtime.tm_mon):
    day = day + month_days(m, leap_year)

day = day + localtime.tm_mday
print("%s's age is %d years or " % (name, year), end="")
print("%d months or %d days" % (month, day))

Output :

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Python Project [Whatsapp Bot]

Whatsapp Bot

Perform Operation like

  1. Put your details
  2. connect with internet
  3. Pass your message


import pywhatkit
from datetime import datetime

now =

chour = now.strftime("%H")
mobile = input('Enter Mobile No of Receiver : ')
message = input('Enter Message you wanna send : ')
hour = int(input('Enter hour : '))
minute = int(input('Enter minute : '))


Output :

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Python Project [ Digital Clock]

This script create a digital clock as per the system's current time.


import tkinter as tk
from time import strftime
def light_theme():
frame = tk.Frame(root, bg="white"), rely=0.1, relwidth=0.8, relheight=0.8)
lbl_1 = tk.Label(frame, font=('calibri', 40, 'bold'),
background='White', foreground='black')
def time():
string = strftime('%I:%M:%S %p')
lbl_1.after(1000, time)
def dark_theme():
frame = tk.Frame(root, bg="#22478a"), rely=0.1, relwidth=0.8, relheight=0.8)
lbl_2 = tk.Label(frame, font=('calibri', 40, 'bold'),
background='#22478a', foreground='black')
def time():
string = strftime('%I:%M:%S %p')
lbl_2.after(1000, time)
root = tk.Tk()
canvas = tk.Canvas(root, height=140, width=400)
frame = tk.Frame(root, bg='#22478a'), rely=0.1, relwidth=0.8, relheight=0.8)
lbl = tk.Label(frame, font=('calibri', 40, 'bold'),
background='#22478a', foreground='black')
def time():
string = strftime('%I:%M:%S %p')
lbl.after(1000, time)
menubar = tk.Menu(root)
theme_menu = tk.Menu(menubar, tearoff=0)
theme_menu.add_command(label="Light", command=light_theme)
theme_menu.add_command(label="Dark", command=dark_theme)
menubar.add_cascade(label="Theme", menu=theme_menu)

Output :

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