Module III - Day I

Python Foundation

Nov-Jan 2025 batch, Vikrant Patil

Date: 10 Feb 2026

Click here for All Notes

login to https://traininghub.vikrant.dev/ with your username and create a notebook with name module1-day1.ipynb

© Vikrant Patil

Classes - Recap

class Point:

    def __init__(self, x, y): # contructor of the class
        self.x = x
        self.y = y

    def get_x(self):
        return self.x

    def get_y(self):
        return self.x

        

+---------+
|         |------> get_x
|  x =    |
|  y =    |------> get_y
+---------+

nums = [1, 2, 3, 4, 5]

nums.index(5)

4

type(nums)

list

Point

__main__.Point

p = Point(10, 20) # pass the argument that are defined in the constructor

type(p)

__main__.Point

nums

[1, 2, 3, 4, 5]

p

<__main__.Point at 0x7f4ea445ae90>

class Point:

    def __init__(self, x, y): # contructor of the class
        self.x = x
        self.y = y

    def get_x(self):
        return self.x

    def get_y(self):
        return self.x

    def __repr__(self):
        """magic method that will show reprensentation of the object inside interpreter
        """
        return f"Pair({self.x}, {self.y})" # string formatting

   
    def __rmul__(self, scale):
        return Point(scale*self.x, scale*self.y)

    def __add__(self, p):
        return Point(self.x + p.x, 
                     self.y + p.y)

    def __getitem__(self, name):
        if name == "x":
            return self.x
        elif name == "y":
            return self.y
        else:
            raise NameError(f"Unknown attribute {name}")

p1 = Point(0, 0)

p1

Pair(0, 0)

p2 = Point(1, 2)

p2

Pair(1, 2)

3*p2

Pair(3, 6)

"hello"*3

'hellohellohello'

3*"hello"

'hellohellohello'

p1 + p2

Pair(1, 2)

p3 = Point(10, 20)

p2

Pair(1, 2)

p3

Pair(10, 20)

p2 + p3

Pair(11, 22)

p = p2 + p3

p['x'] # like dictionary we can access data

11

p1

Pair(0, 0)

print(p1)

Pair(0, 0)

class Point:

    def __init__(self, x, y): # contructor of the class
        self.x = x
        self.y = y

    def get_x(self):
        return self.x

    def get_y(self):
        return self.x

    def __repr__(self):
        """magic method that will show reprensentation of the object inside interpreter
        """
        return f"Pair({self.x}, {self.y})" # string formatting

    def __rmul__(self, scale):
        """ overloas the right side multiplication
        3*point
        """
        return Point(scale*self.x, scale*self.y)

    def __lmul__(self, scale):
        """ overloas the left side multiplication
        point*3
        """
        return Point(scale*self.x, scale*self.y)

    
    def __add__(self, p):
        """overrides + operator allowing us to add two points
        """
        return Point(self.x + p.x, 
                     self.y + p.y)

    def __getitem__(self, name):
        """creates a dictionary like access
        p['x'] -> will call this method
        """
        if name == "x":
            return self.x
        elif name == "y":
            return self.y
        else:
            raise NameError(f"Unknown attribute {name}")
    
    def __str__(self):
        """will be called when object is printed or converted to str
        """
        return f"({self.x},{self.x})"
    

p = Point(45, 42)

p

Pair(45, 42)

print(p)

(45,45)

f"My robot will start at location {p}" # here __str__ will be called

'My robot will start at location (45,45)'

str(p)

'(45,45)'

print(p.x)

45

Convetions for public/private - normal attribute name is public - attribute which is private we start it with _

class Pair:

    def __init__(self, x, y):
        self._x = x
        self._y = y 

    def get_x(self): # public attribute
        return self._x

    

p = Pair(45, 34)

p.get_x()

45

Pair

__main__.Pair

Pair.get_x

<function __main__.Pair.get_x(self)>

Pair.get_x()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[74], line 1
----> 1 Pair.get_x()

TypeError: Pair.get_x() missing 1 required positional argument: 'self'

p = Pair(0, 0)

p.get_x()  # actually calls the function Pair.get_x(p)

0

Pair.get_x(p)

0

Pair(3, 4) # it will create empty instance..and that will be passed as self

Downloading from internet

pip install requests

import requests

Understanding HTTP protocol

https://vikrant.dev/python-foundation-professionals/module3-day1.html

https:// protocal name vikrant.dev is name of the machine on which webserver is running there is folder on server python-foundation-professionals inside that folder there is file module3-day1.html

• get - get the resourse pointed by url
• post - in addition to hidden parameters and url
• put - put a resource at the url location
• delete - delete a resource from given url location

url = "https://vikrant.dev/python-foundation-professionals/module3-day1.html"
r = requests.get(url)

r.status_code # status 200 means it worked!

200

print(r.text[:1000])

<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>

<meta charset="utf-8">
<meta name="generator" content="quarto-1.8.26">

<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">


<title>Module III - Day I – Python Foundation</title>
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
div.column{flex: auto; overflow-x: auto;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
ul.task-list li input[type="checkbox"] {
  width: 0.8em;
  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
  vertical-align: middle;
}
/* CSS for syntax highlighting */
html { -webkit-text-size-adjust: 100%; }
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
pre > code.sour

def download_textfile(url, filename):
    print(f"Downloading ..{url}")
    r = requests.get(url)
    if r.status_code == 200:
        print(f"Saving it to {filename}")
        with open(filename, "w") as f:
            f.write(r.text)

download_textfile(url, "sample.html")

Downloading ..https://vikrant.dev/python-foundation-professionals/module3-day1.html
Saving it to sample.html

def download(url, filename):
    print(f"Downloading {url}")
    r = requests.get(url)
    if r.status_code == 200:
        with open(filename, "wb") as f:
            print(f"Saving it to {filename}")
            f.write(r.content) # content is binary data

download(url, "sample1.html")

Downloading https://vikrant.dev/python-foundation-professionals/module3-day1.html
Saving it to sample1.html

excelurl = "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.xlsx"

download(excelurl, "wallet.xlsx")

Downloading https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.xlsx
Saving it to wallet.xlsx

API call

url = "https://www.alphavantage.co/query"
API_KEY = "demo"  # "UKVFE0JLE0TBPDEF"
p = {"function": "TIME_SERIES_INTRADAY",
     "symbol": "IBM",
     "interval": "5min",
     "apikey": API_KEY}
r = requests.get(url, params=p)

r.status_code

200

r.json()

{'Meta Data': {'1. Information': 'Intraday (5min) open, high, low, close prices and volume',
  '2. Symbol': 'IBM',
  '3. Last Refreshed': '2026-02-09 19:55:00',
  '4. Interval': '5min',
  '5. Output Size': 'Compact',
  '6. Time Zone': 'US/Eastern'},
 'Time Series (5min)': {'2026-02-09 19:55:00': {'1. open': '296.6000',
   '2. high': '296.6000',
   '3. low': '296.3604',
   '4. close': '296.3604',
   '5. volume': '54'},
  '2026-02-09 19:50:00': {'1. open': '296.6000',
   '2. high': '296.6000',
   '3. low': '296.5900',
   '4. close': '296.5900',
   '5. volume': '21'},
  '2026-02-09 19:45:00': {'1. open': '296.5300',
   '2. high': '296.6900',
   '3. low': '296.3400',
   '4. close': '296.3400',
   '5. volume': '424'},
  '2026-02-09 19:40:00': {'1. open': '296.3400',
   '2. high': '296.5300',
   '3. low': '296.3400',
   '4. close': '296.5300',
   '5. volume': '46'},
  '2026-02-09 19:30:00': {'1. open': '296.1800',
   '2. high': '296.2590',
   '3. low': '296.1800',
   '4. close': '296.2590',
   '5. volume': '10'},
  '2026-02-09 19:25:00': {'1. open': '296.3400',
   '2. high': '296.3400',
   '3. low': '296.3399',
   '4. close': '296.3400',
   '5. volume': '297'},
  '2026-02-09 19:20:00': {'1. open': '296.3300',
   '2. high': '296.3400',
   '3. low': '296.3300',
   '4. close': '296.3400',
   '5. volume': '4'},
  '2026-02-09 19:15:00': {'1. open': '296.2800',
   '2. high': '296.3000',
   '3. low': '296.2200',
   '4. close': '296.2200',
   '5. volume': '21'},
  '2026-02-09 19:10:00': {'1. open': '296.3400',
   '2. high': '296.3400',
   '3. low': '296.3200',
   '4. close': '296.3200',
   '5. volume': '124'},
  '2026-02-09 19:05:00': {'1. open': '296.3500',
   '2. high': '296.3500',
   '3. low': '296.3500',
   '4. close': '296.3500',
   '5. volume': '14'},
  '2026-02-09 19:00:00': {'1. open': '296.3400',
   '2. high': '296.5900',
   '3. low': '296.3400',
   '4. close': '296.3544',
   '5. volume': '702177'},
  '2026-02-09 18:55:00': {'1. open': '296.3500',
   '2. high': '296.5500',
   '3. low': '296.3500',
   '4. close': '296.4100',
   '5. volume': '11'},
  '2026-02-09 18:50:00': {'1. open': '296.3700',
   '2. high': '296.6311',
   '3. low': '296.3400',
   '4. close': '296.3400',
   '5. volume': '259'},
  '2026-02-09 18:45:00': {'1. open': '296.6400',
   '2. high': '296.6900',
   '3. low': '296.3800',
   '4. close': '296.3800',
   '5. volume': '27'},
  '2026-02-09 18:40:00': {'1. open': '296.6389',
   '2. high': '296.6400',
   '3. low': '296.6389',
   '4. close': '296.6400',
   '5. volume': '26'},
  '2026-02-09 18:35:00': {'1. open': '296.6900',
   '2. high': '296.6900',
   '3. low': '296.6900',
   '4. close': '296.6900',
   '5. volume': '20'},
  '2026-02-09 18:30:00': {'1. open': '296.3400',
   '2. high': '296.6365',
   '3. low': '296.3400',
   '4. close': '296.3824',
   '5. volume': '702072'},
  '2026-02-09 18:25:00': {'1. open': '296.3800',
   '2. high': '296.3942',
   '3. low': '296.3400',
   '4. close': '296.3942',
   '5. volume': '8'},
  '2026-02-09 18:20:00': {'1. open': '296.2500',
   '2. high': '296.6800',
   '3. low': '296.2500',
   '4. close': '296.6800',
   '5. volume': '243'},
  '2026-02-09 18:15:00': {'1. open': '296.3400',
   '2. high': '296.5000',
   '3. low': '296.2500',
   '4. close': '296.2500',
   '5. volume': '20'},
  '2026-02-09 18:10:00': {'1. open': '296.6000',
   '2. high': '296.6400',
   '3. low': '296.4600',
   '4. close': '296.4900',
   '5. volume': '411'},
  '2026-02-09 18:05:00': {'1. open': '296.6400',
   '2. high': '296.6900',
   '3. low': '296.6400',
   '4. close': '296.6900',
   '5. volume': '104'},
  '2026-02-09 18:00:00': {'1. open': '296.5700',
   '2. high': '296.5700',
   '3. low': '296.5700',
   '4. close': '296.5700',
   '5. volume': '14'},
  '2026-02-09 17:55:00': {'1. open': '296.6000',
   '2. high': '296.6500',
   '3. low': '296.5012',
   '4. close': '296.6494',
   '5. volume': '20'},
  '2026-02-09 17:50:00': {'1. open': '296.5600',
   '2. high': '296.6000',
   '3. low': '296.5600',
   '4. close': '296.6000',
   '5. volume': '19'},
  '2026-02-09 17:45:00': {'1. open': '296.5232',
   '2. high': '296.5232',
   '3. low': '296.5232',
   '4. close': '296.5232',
   '5. volume': '5'},
  '2026-02-09 17:40:00': {'1. open': '296.6200',
   '2. high': '296.6200',
   '3. low': '296.5500',
   '4. close': '296.5500',
   '5. volume': '10'},
  '2026-02-09 17:35:00': {'1. open': '296.6201',
   '2. high': '296.6500',
   '3. low': '296.6201',
   '4. close': '296.6500',
   '5. volume': '30'},
  '2026-02-09 17:30:00': {'1. open': '296.5000',
   '2. high': '296.6495',
   '3. low': '296.5000',
   '4. close': '296.6100',
   '5. volume': '16'},
  '2026-02-09 17:25:00': {'1. open': '296.6494',
   '2. high': '296.6500',
   '3. low': '296.5000',
   '4. close': '296.6500',
   '5. volume': '20'},
  '2026-02-09 17:20:00': {'1. open': '296.6800',
   '2. high': '296.6800',
   '3. low': '296.5000',
   '4. close': '296.5000',
   '5. volume': '80'},
  '2026-02-09 17:15:00': {'1. open': '296.5000',
   '2. high': '296.6895',
   '3. low': '296.5000',
   '4. close': '296.6600',
   '5. volume': '61'},
  '2026-02-09 17:10:00': {'1. open': '296.5700',
   '2. high': '296.6300',
   '3. low': '296.3400',
   '4. close': '296.3400',
   '5. volume': '967'},
  '2026-02-09 17:05:00': {'1. open': '296.6690',
   '2. high': '296.7000',
   '3. low': '296.5700',
   '4. close': '296.5700',
   '5. volume': '12'},
  '2026-02-09 17:00:00': {'1. open': '296.4900',
   '2. high': '296.5800',
   '3. low': '296.4600',
   '4. close': '296.5500',
   '5. volume': '136'},
  '2026-02-09 16:55:00': {'1. open': '296.4400',
   '2. high': '296.4900',
   '3. low': '296.3000',
   '4. close': '296.4900',
   '5. volume': '18156'},
  '2026-02-09 16:50:00': {'1. open': '296.4300',
   '2. high': '296.4300',
   '3. low': '296.4300',
   '4. close': '296.4300',
   '5. volume': '1'},
  '2026-02-09 16:45:00': {'1. open': '296.3790',
   '2. high': '296.4800',
   '3. low': '296.3100',
   '4. close': '296.4800',
   '5. volume': '68'},
  '2026-02-09 16:40:00': {'1. open': '296.4700',
   '2. high': '296.5000',
   '3. low': '296.3400',
   '4. close': '296.4000',
   '5. volume': '8584'},
  '2026-02-09 16:35:00': {'1. open': '296.5000',
   '2. high': '296.5100',
   '3. low': '296.4900',
   '4. close': '296.4900',
   '5. volume': '5'},
  '2026-02-09 16:30:00': {'1. open': '296.3324',
   '2. high': '296.5000',
   '3. low': '296.3324',
   '4. close': '296.5000',
   '5. volume': '25'},
  '2026-02-09 16:25:00': {'1. open': '296.2100',
   '2. high': '296.3300',
   '3. low': '296.2100',
   '4. close': '296.2100',
   '5. volume': '34'},
  '2026-02-09 16:20:00': {'1. open': '296.3000',
   '2. high': '297.0000',
   '3. low': '296.3000',
   '4. close': '296.3000',
   '5. volume': '255'},
  '2026-02-09 16:15:00': {'1. open': '296.3400',
   '2. high': '298.9300',
   '3. low': '296.2100',
   '4. close': '296.3200',
   '5. volume': '2141'},
  '2026-02-09 16:10:00': {'1. open': '296.3400',
   '2. high': '298.9300',
   '3. low': '296.2100',
   '4. close': '298.9300',
   '5. volume': '702348'},
  '2026-02-09 16:05:00': {'1. open': '296.3400',
   '2. high': '296.5800',
   '3. low': '295.9209',
   '4. close': '296.5800',
   '5. volume': '1134'},
  '2026-02-09 16:00:00': {'1. open': '296.3600',
   '2. high': '296.6000',
   '3. low': '295.6400',
   '4. close': '296.3400',
   '5. volume': '1500937'},
  '2026-02-09 15:55:00': {'1. open': '296.4100',
   '2. high': '296.8900',
   '3. low': '296.2500',
   '4. close': '296.3200',
   '5. volume': '257505'},
  '2026-02-09 15:50:00': {'1. open': '296.9000',
   '2. high': '297.0300',
   '3. low': '296.2800',
   '4. close': '296.4400',
   '5. volume': '135844'},
  '2026-02-09 15:45:00': {'1. open': '296.7850',
   '2. high': '297.0600',
   '3. low': '296.7500',
   '4. close': '296.9100',
   '5. volume': '61653'},
  '2026-02-09 15:40:00': {'1. open': '296.7550',
   '2. high': '296.9600',
   '3. low': '296.6400',
   '4. close': '296.7600',
   '5. volume': '45662'},
  '2026-02-09 15:35:00': {'1. open': '296.6593',
   '2. high': '296.7900',
   '3. low': '296.2950',
   '4. close': '296.7550',
   '5. volume': '46918'},
  '2026-02-09 15:30:00': {'1. open': '297.0100',
   '2. high': '297.0100',
   '3. low': '296.5750',
   '4. close': '296.5900',
   '5. volume': '60736'},
  '2026-02-09 15:25:00': {'1. open': '297.4200',
   '2. high': '297.5500',
   '3. low': '296.9400',
   '4. close': '296.9750',
   '5. volume': '36754'},
  '2026-02-09 15:20:00': {'1. open': '297.1700',
   '2. high': '297.4700',
   '3. low': '297.0950',
   '4. close': '297.4200',
   '5. volume': '46148'},
  '2026-02-09 15:15:00': {'1. open': '297.5600',
   '2. high': '297.5600',
   '3. low': '297.0725',
   '4. close': '297.1700',
   '5. volume': '32640'},
  '2026-02-09 15:10:00': {'1. open': '297.4750',
   '2. high': '297.7200',
   '3. low': '297.4000',
   '4. close': '297.5480',
   '5. volume': '28309'},
  '2026-02-09 15:05:00': {'1. open': '297.0100',
   '2. high': '297.5900',
   '3. low': '296.9750',
   '4. close': '297.5000',
   '5. volume': '237402'},
  '2026-02-09 15:00:00': {'1. open': '297.1700',
   '2. high': '297.2400',
   '3. low': '296.8800',
   '4. close': '296.9750',
   '5. volume': '31310'},
  '2026-02-09 14:55:00': {'1. open': '297.1000',
   '2. high': '297.2300',
   '3. low': '296.9900',
   '4. close': '297.1350',
   '5. volume': '29220'},
  '2026-02-09 14:50:00': {'1. open': '296.8801',
   '2. high': '297.1800',
   '3. low': '296.8801',
   '4. close': '297.1000',
   '5. volume': '30047'},
  '2026-02-09 14:45:00': {'1. open': '296.9915',
   '2. high': '297.0000',
   '3. low': '296.7500',
   '4. close': '296.9400',
   '5. volume': '31926'},
  '2026-02-09 14:40:00': {'1. open': '297.3800',
   '2. high': '297.5300',
   '3. low': '296.8900',
   '4. close': '296.9750',
   '5. volume': '38999'},
  '2026-02-09 14:35:00': {'1. open': '297.5400',
   '2. high': '297.7000',
   '3. low': '297.3200',
   '4. close': '297.3800',
   '5. volume': '38613'},
  '2026-02-09 14:30:00': {'1. open': '297.3200',
   '2. high': '297.6000',
   '3. low': '297.2200',
   '4. close': '297.6000',
   '5. volume': '51778'},
  '2026-02-09 14:25:00': {'1. open': '297.3100',
   '2. high': '297.3750',
   '3. low': '297.0900',
   '4. close': '297.2700',
   '5. volume': '38721'},
  '2026-02-09 14:20:00': {'1. open': '296.5400',
   '2. high': '297.3400',
   '3. low': '296.5201',
   '4. close': '297.3400',
   '5. volume': '48695'},
  '2026-02-09 14:15:00': {'1. open': '296.2200',
   '2. high': '296.5700',
   '3. low': '296.1222',
   '4. close': '296.5700',
   '5. volume': '48274'},
  '2026-02-09 14:10:00': {'1. open': '295.9200',
   '2. high': '296.3600',
   '3. low': '295.9000',
   '4. close': '296.2200',
   '5. volume': '29424'},
  '2026-02-09 14:05:00': {'1. open': '295.5450',
   '2. high': '295.9899',
   '3. low': '295.5000',
   '4. close': '295.8700',
   '5. volume': '33886'},
  '2026-02-09 14:00:00': {'1. open': '295.1000',
   '2. high': '295.5997',
   '3. low': '295.0600',
   '4. close': '295.5900',
   '5. volume': '26693'},
  '2026-02-09 13:55:00': {'1. open': '294.9600',
   '2. high': '295.2000',
   '3. low': '294.9600',
   '4. close': '295.1150',
   '5. volume': '30817'},
  '2026-02-09 13:50:00': {'1. open': '294.7100',
   '2. high': '294.9600',
   '3. low': '294.6800',
   '4. close': '294.9300',
   '5. volume': '19793'},
  '2026-02-09 13:45:00': {'1. open': '295.0300',
   '2. high': '295.1100',
   '3. low': '294.6800',
   '4. close': '294.6800',
   '5. volume': '30177'},
  '2026-02-09 13:40:00': {'1. open': '294.5100',
   '2. high': '295.0200',
   '3. low': '294.2800',
   '4. close': '295.0000',
   '5. volume': '37941'},
  '2026-02-09 13:35:00': {'1. open': '294.6100',
   '2. high': '294.6899',
   '3. low': '294.4200',
   '4. close': '294.4750',
   '5. volume': '16500'},
  '2026-02-09 13:30:00': {'1. open': '294.1500',
   '2. high': '294.5900',
   '3. low': '294.0900',
   '4. close': '294.5900',
   '5. volume': '42024'},
  '2026-02-09 13:25:00': {'1. open': '293.9300',
   '2. high': '294.2900',
   '3. low': '293.8600',
   '4. close': '294.1600',
   '5. volume': '29760'},
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   '2. high': '294.0600',
   '3. low': '293.3000',
   '4. close': '293.9300',
   '5. volume': '47663'},
  '2026-02-09 13:15:00': {'1. open': '294.2800',
   '2. high': '294.3300',
   '3. low': '293.5100',
   '4. close': '293.7075',
   '5. volume': '116778'},
  '2026-02-09 13:10:00': {'1. open': '294.3300',
   '2. high': '294.4000',
   '3. low': '294.0750',
   '4. close': '294.2200',
   '5. volume': '15912'},
  '2026-02-09 13:05:00': {'1. open': '293.4800',
   '2. high': '294.3800',
   '3. low': '293.3000',
   '4. close': '294.2600',
   '5. volume': '35817'},
  '2026-02-09 13:00:00': {'1. open': '293.5700',
   '2. high': '293.6200',
   '3. low': '293.2500',
   '4. close': '293.4600',
   '5. volume': '35708'},
  '2026-02-09 12:55:00': {'1. open': '293.7150',
   '2. high': '293.8199',
   '3. low': '293.5200',
   '4. close': '293.5400',
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  '2026-02-09 12:50:00': {'1. open': '294.0300',
   '2. high': '294.0350',
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  '2026-02-09 12:45:00': {'1. open': '293.7100',
   '2. high': '294.0300',
   '3. low': '293.5900',
   '4. close': '294.0300',
   '5. volume': '37694'},
  '2026-02-09 12:40:00': {'1. open': '293.5750',
   '2. high': '293.7750',
   '3. low': '293.4501',
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  '2026-02-09 12:15:00': {'1. open': '294.5750',
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   '5. volume': '30493'},
  '2026-02-09 12:10:00': {'1. open': '294.5200',
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   '3. low': '294.4200',
   '4. close': '294.5300',
   '5. volume': '29345'},
  '2026-02-09 12:05:00': {'1. open': '294.3880',
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   '4. close': '294.5300',
   '5. volume': '27059'},
  '2026-02-09 12:00:00': {'1. open': '294.3850',
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   '4. close': '294.3750',
   '5. volume': '33297'},
  '2026-02-09 11:55:00': {'1. open': '293.8600',
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   '4. close': '294.3150',
   '5. volume': '67469'},
  '2026-02-09 11:50:00': {'1. open': '293.9800',
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  '2026-02-09 11:45:00': {'1. open': '293.4200',
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   '2. high': '293.4600',
   '3. low': '292.8000',
   '4. close': '292.8700',
   '5. volume': '41140'}}}

data = r.json()

type(data)

dict

data.keys()

dict_keys(['Meta Data', 'Time Series (5min)'])

data['Meta Data']

{'1. Information': 'Intraday (5min) open, high, low, close prices and volume',
 '2. Symbol': 'IBM',
 '3. Last Refreshed': '2026-02-09 19:55:00',
 '4. Interval': '5min',
 '5. Output Size': 'Compact',
 '6. Time Zone': 'US/Eastern'}

type(data['Time Series (5min)'])

dict

data['Time Series (5min)'].keys()

dict_keys(['2026-02-09 19:55:00', '2026-02-09 19:50:00', '2026-02-09 19:45:00', '2026-02-09 19:40:00', '2026-02-09 19:30:00', '2026-02-09 19:25:00', '2026-02-09 19:20:00', '2026-02-09 19:15:00', '2026-02-09 19:10:00', '2026-02-09 19:05:00', '2026-02-09 19:00:00', '2026-02-09 18:55:00', '2026-02-09 18:50:00', '2026-02-09 18:45:00', '2026-02-09 18:40:00', '2026-02-09 18:35:00', '2026-02-09 18:30:00', '2026-02-09 18:25:00', '2026-02-09 18:20:00', '2026-02-09 18:15:00', '2026-02-09 18:10:00', '2026-02-09 18:05:00', '2026-02-09 18:00:00', '2026-02-09 17:55:00', '2026-02-09 17:50:00', '2026-02-09 17:45:00', '2026-02-09 17:40:00', '2026-02-09 17:35:00', '2026-02-09 17:30:00', '2026-02-09 17:25:00', '2026-02-09 17:20:00', '2026-02-09 17:15:00', '2026-02-09 17:10:00', '2026-02-09 17:05:00', '2026-02-09 17:00:00', '2026-02-09 16:55:00', '2026-02-09 16:50:00', '2026-02-09 16:45:00', '2026-02-09 16:40:00', '2026-02-09 16:35:00', '2026-02-09 16:30:00', '2026-02-09 16:25:00', '2026-02-09 16:20:00', '2026-02-09 16:15:00', '2026-02-09 16:10:00', '2026-02-09 16:05:00', '2026-02-09 16:00:00', '2026-02-09 15:55:00', '2026-02-09 15:50:00', '2026-02-09 15:45:00', '2026-02-09 15:40:00', '2026-02-09 15:35:00', '2026-02-09 15:30:00', '2026-02-09 15:25:00', '2026-02-09 15:20:00', '2026-02-09 15:15:00', '2026-02-09 15:10:00', '2026-02-09 15:05:00', '2026-02-09 15:00:00', '2026-02-09 14:55:00', '2026-02-09 14:50:00', '2026-02-09 14:45:00', '2026-02-09 14:40:00', '2026-02-09 14:35:00', '2026-02-09 14:30:00', '2026-02-09 14:25:00', '2026-02-09 14:20:00', '2026-02-09 14:15:00', '2026-02-09 14:10:00', '2026-02-09 14:05:00', '2026-02-09 14:00:00', '2026-02-09 13:55:00', '2026-02-09 13:50:00', '2026-02-09 13:45:00', '2026-02-09 13:40:00', '2026-02-09 13:35:00', '2026-02-09 13:30:00', '2026-02-09 13:25:00', '2026-02-09 13:20:00', '2026-02-09 13:15:00', '2026-02-09 13:10:00', '2026-02-09 13:05:00', '2026-02-09 13:00:00', '2026-02-09 12:55:00', '2026-02-09 12:50:00', '2026-02-09 12:45:00', '2026-02-09 12:40:00', '2026-02-09 12:35:00', '2026-02-09 12:30:00', '2026-02-09 12:25:00', '2026-02-09 12:20:00', '2026-02-09 12:15:00', '2026-02-09 12:10:00', '2026-02-09 12:05:00', '2026-02-09 12:00:00', '2026-02-09 11:55:00', '2026-02-09 11:50:00', '2026-02-09 11:45:00', '2026-02-09 11:40:00', '2026-02-09 11:35:00'])

import pandas as pd

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[107], line 1
----> 1 import pandas as pd

ModuleNotFoundError: No module named 'pandas'

!pip install pandas

Collecting pandas

  Downloading pandas-3.0.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (79 kB)

Collecting numpy>=1.26.0 (from pandas)

  Using cached numpy-2.4.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)

Requirement already satisfied: python-dateutil>=2.8.2 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from pandas) (2.9.0.post0)

Requirement already satisfied: six>=1.5 in /home/vikrant/usr/local/default/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)

Downloading pandas-3.0.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (10.9 MB)

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.9/10.9 MB 6.6 MB/s eta 0:00:00m eta 0:00:0136m0:00:01m

Using cached numpy-2.4.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.6 MB)

Installing collected packages: numpy, pandas

   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/2 [pandas]━━━━ 1/2 [pandas]

Successfully installed numpy-2.4.2 pandas-3.0.0

import pandas as pd

pd.DataFrame(data['Time Series (5min)']).T

	1. open	2. high	3. low	4. close	5. volume
2026-02-09 19:55:00	296.6000	296.6000	296.3604	296.3604	54
2026-02-09 19:50:00	296.6000	296.6000	296.5900	296.5900	21
2026-02-09 19:45:00	296.5300	296.6900	296.3400	296.3400	424
2026-02-09 19:40:00	296.3400	296.5300	296.3400	296.5300	46
2026-02-09 19:30:00	296.1800	296.2590	296.1800	296.2590	10
...	...	...	...	...	...
2026-02-09 11:55:00	293.8600	294.6600	293.7839	294.3150	67469
2026-02-09 11:50:00	293.9800	294.0000	293.7701	293.8600	33729
2026-02-09 11:45:00	293.4200	294.0200	293.4200	293.9700	34428
2026-02-09 11:40:00	292.8700	293.6500	292.8700	293.4800	49376
2026-02-09 11:35:00	293.3300	293.4600	292.8000	292.8700	41140

100 rows × 5 columns

Authentication

%%file passkey.txt
tyt%^^%@#

Writing passkey.txt

user = "vikrant"
with open("passkey.txt") as f:
    password = f.read()
url = "https://httpbin.org/get" 
r = requests.get(url, auth=(user, password))

r.status_code

200

r.json()

{'args': {},
 'headers': {'Accept': '*/*',
  'Accept-Encoding': 'gzip, deflate',
  'Authorization': 'Basic dmlrcmFudDp0eXQlXl4lQCMK',
  'Host': 'httpbin.org',
  'User-Agent': 'python-requests/2.32.5',
  'X-Amzn-Trace-Id': 'Root=1-698a9ed5-77f74bef566e41387a822408'},
 'origin': '103.215.167.18',
 'url': 'https://httpbin.org/get'}

homework

Write a function download_notes which will download all notes from our training website - It should create a folder first - then download notebook html for each day for each module in that folder - can you also create a index page through python function so that you can access the notes by opening index.html?

Observe the pattern of urls for example there is module number and day for each notebook.

• https://vikrant.dev/python-foundation-professionals/module1-day1.html
• https://vikrant.dev/python-foundation-professionals/module1-day2.html
• https://vikrant.dev/python-foundation-professionals/module2-day1.html