Module 3 - Day 2

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Virtual Environment

Virtual Environment

Many times it happens that different projects have requirements of python packages such that they conflict each other. In such cases how do you work on two different project on same machine? If we install python packages for one project, those packages will confict with other projects. Virtual environment is there to help us. Virtual environment allows us to have set of python packages seperately for each project. Also added advantage is, it won’t affect system python’s packages. The way to handle this is with help of venv module we create virtual environment for each project. All requirements for the proejct are installed in the virtual environment and not in system python’s packages. Let’s take some examples.

Conflicting Requirements

Suppose we have two projects, datascraping and analytics. For datascraping project requirements are following packages::

requests==2.24.0 openpyxl==2.4.8

and analytics project needs following packages::

pandas==1.1.2 openpyxl==3.0.5 requests==2.24.0

Now here is confiliting requirement, one project needs openpyxl verson 2.4.8 and other needs 3.0.5.

creating evenv

To create virutal environment on your system, what you need is python version > 3.5. Python comes with a package called venv (virtual environment). For older python, virtualenv was seperate application. We are going to work with virtual environment that comes with python 3. Easy steps to work with it are as given below. Open up terminal on linux/mac or cmd terminal on windows. on the prompt type following command to create virtual environment with name env1::

  python -m venv env1

This will create a folder with name env1 in the current directory. On linux it will have following contents::

  +-env1
    |
    +-bin
    +-include
    +-lib
    +-lib64
    +-pyenv.cfg

on windows system it will have following contents::

  +-env1
    |
    +-Include
    +-Lib
    +-Scripts
    +-pyenv.cfg

To activate virtual environment on linux run following command on terminal.::

  bash$ source env1/bin/activate
  (env1) bash$ # you can see the env1 environment activated as change in prompt

To activate virtual environment on windows run following command on windows cmd terminal::

  C:\Users\vik> env1\bin\activate.bat
  (env1) C:\Users\vik>

Installing packages in virtual environment

Once the virtul environment is created and activated, we are ready to use it. To install packages in this active virtual environment use pip install::

  pip install typer
  Collecting typer
    Using cached https://files.pythonhosted.org/packages/90/34/d138832f6945432c638f32137e6c79a3b682f06a63c488dcfaca6b166c64/typer-0.3.2-py3-none-any.whl
  Collecting click<7.2.0,>=7.1.1 (from typer)
    Using cached https://files.pythonhosted.org/packages/d2/3d/fa76db83bf75c4f8d338c2fd15c8d33fdd7ad23a9b5e57eb6c5de26b430e/click-7.1.2-py2.py3-none-any.whl
  Installing collected packages: click, typer
  Successfully installed click-7.1.2 typer-0.3.2

to check packages installed ::

  pip list
  Package    Version
  ---------- -------
  click      7.1.2
  pip        19.2.3
  setuptools 41.2.0
  typer      0.3.2

requirements.txt

If we want to replicate exact same virtual environment on other machine we need list of packages that pip can understand. The format is called as requirements file. it can be generated using::

  pip freeze
  click==7.1.2
  typer==0.3.2

The output can be saved to a file with name requirements.txt. This file can be used in other virtual env to recreate the same environment. For example, lets make use of above requirements to recreate another environment with name env1copy::

  bash$ python -m venv env1copy
  bash$ source env1copy/bin/activate
  (env1copy) bash$ pip install -r requirements.txt
  Collecting click==7.1.2 (from -r env1/requirements.txt (line 1))
    Using cached https://files.pythonhosted.org/packages/d2/3d/fa76db83bf75c4f8d338c2fd15c8d33fdd7ad23a9b5e57eb6c5de26b430e/click-7.1.2-py2.py3-none-any.whl
  Collecting typer==0.3.2 (from -r env1/requirements.txt (line 2))
    Using cached https://files.pythonhosted.org/packages/90/34/d138832f6945432c638f32137e6c79a3b682f06a63c488dcfaca6b166c64/typer-0.3.2-py3-none-any.whl
  Installing collected packages: click, typer
  Successfully installed click-7.1.2 typer-0.3.2

you can check the packages installed::

  pip freeze
  click==7.1.2
  typer==0.3.2

Summary

1. Virtual environment can be created by any user. No admin privileges required.
2. Every virtual environment is stored in seperate folder.
3. Packages installed in a virtual environment are only in that particular virtual environment.
4. With requirements.txt it is very easy to recreate the same replica of a particular virtul environment.

Json

text format of list and dictionaries. At first site you might confused that does it look like python lists and dictionary

"[1, 2, 3, 4, 5]"

l = [1, 2, 3, 4, 5, 6]

l

[1, 2, 3, 4, 5, 6]

print(l)

[1, 2, 3, 4, 5, 6]

%%file words.json
["one", "two", "three", "four"]

Overwriting words.json

import json 

with open("words.json") as f:
    jsondata = f.read().strip()

jsondata

'["one", "two", "three", "four"]'

for word in jsondata: # this will treat it as seq char!
    print(word, sep=",", end=",")

[,",o,n,e,",,, ,",t,w,o,",,, ,",t,h,r,e,e,",,, ,",f,o,u,r,",],

pythondata = json.loads(jsondata)

data = ["one", "two", "three", "four", "five"]

json.dumps(data)

'["one", "two", "three", "four", "five"]'

json.loads(json.dumps(data))

['one', 'two', 'three', 'four', 'five']

nested json data

%%file menu.json
{
    "id": "0001",
    "type": "donut",
    "name": "Cake",
    "ppu": 0.55,
    "batters":
        {
            "batter":
                [
                    { "id": "1001", "type": "Regular" },
                    { "id": "1002", "type": "Chocolate" },
                    { "id": "1003", "type": "Blueberry" },
                    { "id": "1004", "type": "Devil's Food" }
                ]
        },
    "topping":
        [
            { "id": "5001", "type": "None" },
            { "id": "5002", "type": "Glazed" },
            { "id": "5005", "type": "Sugar" },
            { "id": "5007", "type": "Powdered Sugar" },
            { "id": "5006", "type": "Chocolate with Sprinkles" },
            { "id": "5003", "type": "Chocolate" },
            { "id": "5004", "type": "Maple" }
        ]
}

Writing menu.json

with open("menu.json") as f:
    menu_jsondata = f.read()

menu_jsondata

'{\n\t"id": "0001",\n\t"type": "donut",\n\t"name": "Cake",\n\t"ppu": 0.55,\n\t"batters":\n\t\t{\n\t\t\t"batter":\n\t\t\t\t[\n\t\t\t\t\t{ "id": "1001", "type": "Regular" },\n\t\t\t\t\t{ "id": "1002", "type": "Chocolate" },\n\t\t\t\t\t{ "id": "1003", "type": "Blueberry" },\n\t\t\t\t\t{ "id": "1004", "type": "Devil\'s Food" }\n\t\t\t\t]\n\t\t},\n\t"topping":\n\t\t[\n\t\t\t{ "id": "5001", "type": "None" },\n\t\t\t{ "id": "5002", "type": "Glazed" },\n\t\t\t{ "id": "5005", "type": "Sugar" },\n\t\t\t{ "id": "5007", "type": "Powdered Sugar" },\n\t\t\t{ "id": "5006", "type": "Chocolate with Sprinkles" },\n\t\t\t{ "id": "5003", "type": "Chocolate" },\n\t\t\t{ "id": "5004", "type": "Maple" }\n\t\t]\n}\n'

menu = json.loads(menu_jsondata)

type(menu)

dict

for key, value in menu.items():
    print(key)

id
type
name
ppu
batters
topping

menu['name']

'Cake'

Data from stocks website

%%file ibm.json
{
    "Meta Data": {
        "1. Information": "Intraday (5min) open, high, low, close prices and volume",
        "2. Symbol": "IBM",
        "3. Last Refreshed": "2024-08-20 19:55:00",
        "4. Interval": "5min",
        "5. Output Size": "Compact",
        "6. Time Zone": "US/Eastern"
    },
    "Time Series (5min)": {
        "2024-08-20 19:55:00": {
            "1. open": "195.9800",
            "2. high": "195.9900",
            "3. low": "195.9800",
            "4. close": "195.9900",
            "5. volume": "35"
        },
        "2024-08-20 19:50:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "299"
        },
        "2024-08-20 19:35:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "25"
        },
        "2024-08-20 19:30:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "1"
        },
        "2024-08-20 19:20:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "106"
        },
        "2024-08-20 19:15:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "4"
        },
        "2024-08-20 19:10:00": {
            "1. open": "195.9900",
            "2. high": "195.9900",
            "3. low": "195.9900",
            "4. close": "195.9900",
            "5. volume": "7"
        },
        "2024-08-20 19:05:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "2"
        },
        "2024-08-20 19:00:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "195.9900",
            "4. close": "195.9900",
            "5. volume": "343873"
        },
        "2024-08-20 18:50:00": {
            "1. open": "195.9900",
            "2. high": "195.9900",
            "3. low": "195.9900",
            "4. close": "195.9900",
            "5. volume": "1"
        },
        "2024-08-20 18:45:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "5"
        },
        "2024-08-20 18:40:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "100"
        },
        "2024-08-20 18:30:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "196.0300",
            "4. close": "196.0300",
            "5. volume": "343867"
        },
        "2024-08-20 18:25:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "6"
        },
        "2024-08-20 18:20:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "1"
        },
        "2024-08-20 18:15:00": {
            "1. open": "195.9900",
            "2. high": "195.9900",
            "3. low": "195.9900",
            "4. close": "195.9900",
            "5. volume": "1"
        },
        "2024-08-20 18:10:00": {
            "1. open": "195.9900",
            "2. high": "195.9900",
            "3. low": "195.9900",
            "4. close": "195.9900",
            "5. volume": "7"
        },
        "2024-08-20 18:05:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "25"
        },
        "2024-08-20 18:00:00": {
            "1. open": "196.0050",
            "2. high": "196.0050",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "50"
        },
        "2024-08-20 17:50:00": {
            "1. open": "196.0290",
            "2. high": "196.0300",
            "3. low": "196.0290",
            "4. close": "196.0300",
            "5. volume": "21"
        },
        "2024-08-20 17:45:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "196.0200",
            "4. close": "196.0200",
            "5. volume": "2"
        },
        "2024-08-20 17:35:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "180"
        },
        "2024-08-20 17:25:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "1"
        },
        "2024-08-20 17:20:00": {
            "1. open": "196.0000",
            "2. high": "196.0000",
            "3. low": "196.0000",
            "4. close": "196.0000",
            "5. volume": "6"
        },
        "2024-08-20 17:10:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "10206"
        },
        "2024-08-20 17:05:00": {
            "1. open": "195.9800",
            "2. high": "195.9800",
            "3. low": "195.9800",
            "4. close": "195.9800",
            "5. volume": "4"
        },
        "2024-08-20 16:55:00": {
            "1. open": "196.2320",
            "2. high": "196.2320",
            "3. low": "196.2320",
            "4. close": "196.2320",
            "5. volume": "35"
        },
        "2024-08-20 16:50:00": {
            "1. open": "195.9500",
            "2. high": "195.9500",
            "3. low": "195.9500",
            "4. close": "195.9500",
            "5. volume": "1"
        },
        "2024-08-20 16:35:00": {
            "1. open": "195.7800",
            "2. high": "195.7800",
            "3. low": "195.7800",
            "4. close": "195.7800",
            "5. volume": "15"
        },
        "2024-08-20 16:30:00": {
            "1. open": "196.2500",
            "2. high": "196.2500",
            "3. low": "195.7800",
            "4. close": "195.7800",
            "5. volume": "3"
        },
        "2024-08-20 16:25:00": {
            "1. open": "196.0300",
            "2. high": "196.2370",
            "3. low": "195.9500",
            "4. close": "196.2370",
            "5. volume": "684"
        },
        "2024-08-20 16:20:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "196.0300",
            "4. close": "196.0300",
            "5. volume": "1032"
        },
        "2024-08-20 16:15:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "195.9700",
            "4. close": "195.9700",
            "5. volume": "3"
        },
        "2024-08-20 16:10:00": {
            "1. open": "196.0300",
            "2. high": "196.0300",
            "3. low": "194.9700",
            "4. close": "195.9700",
            "5. volume": "344465"
        },
        "2024-08-20 16:05:00": {
            "1. open": "196.0300",
            "2. high": "196.2400",
            "3. low": "195.6500",
            "4. close": "196.0300",
            "5. volume": "3728"
        },
        "2024-08-20 16:00:00": {
            "1. open": "196.0500",
            "2. high": "196.1800",
            "3. low": "194.0200",
            "4. close": "196.1700",
            "5. volume": "768002"
        },
        "2024-08-20 15:55:00": {
            "1. open": "195.4900",
            "2. high": "196.2100",
            "3. low": "195.4700",
            "4. close": "196.0500",
            "5. volume": "261688"
        },
        "2024-08-20 15:50:00": {
            "1. open": "195.5200",
            "2. high": "195.6400",
            "3. low": "195.2900",
            "4. close": "195.5290",
            "5. volume": "52582"
        },
        "2024-08-20 15:45:00": {
            "1. open": "195.7300",
            "2. high": "195.7500",
            "3. low": "195.4510",
            "4. close": "195.4950",
            "5. volume": "52354"
        },
        "2024-08-20 15:40:00": {
            "1. open": "195.7900",
            "2. high": "195.8400",
            "3. low": "195.7100",
            "4. close": "195.7490",
            "5. volume": "23192"
        },
        "2024-08-20 15:35:00": {
            "1. open": "195.8800",
            "2. high": "195.8800",
            "3. low": "195.7600",
            "4. close": "195.8100",
            "5. volume": "25890"
        },
        "2024-08-20 15:30:00": {
            "1. open": "195.8300",
            "2. high": "195.8790",
            "3. low": "195.7300",
            "4. close": "195.8600",
            "5. volume": "21745"
        },
        "2024-08-20 15:25:00": {
            "1. open": "195.7900",
            "2. high": "195.8500",
            "3. low": "195.7490",
            "4. close": "195.8200",
            "5. volume": "13682"
        },
        "2024-08-20 15:20:00": {
            "1. open": "195.8770",
            "2. high": "195.9000",
            "3. low": "195.7100",
            "4. close": "195.7900",
            "5. volume": "12833"
        },
        "2024-08-20 15:15:00": {
            "1. open": "195.8700",
            "2. high": "195.8900",
            "3. low": "195.7400",
            "4. close": "195.8650",
            "5. volume": "17838"
        },
        "2024-08-20 15:10:00": {
            "1. open": "195.7900",
            "2. high": "195.9200",
            "3. low": "195.7800",
            "4. close": "195.8680",
            "5. volume": "17747"
        },
        "2024-08-20 15:05:00": {
            "1. open": "195.7100",
            "2. high": "195.8000",
            "3. low": "195.7100",
            "4. close": "195.8000",
            "5. volume": "14138"
        },
        "2024-08-20 15:00:00": {
            "1. open": "195.7000",
            "2. high": "195.7700",
            "3. low": "195.6900",
            "4. close": "195.7150",
            "5. volume": "9247"
        },
        "2024-08-20 14:55:00": {
            "1. open": "195.7800",
            "2. high": "195.8100",
            "3. low": "195.7300",
            "4. close": "195.7300",
            "5. volume": "11581"
        },
        "2024-08-20 14:50:00": {
            "1. open": "195.6850",
            "2. high": "195.8000",
            "3. low": "195.6750",
            "4. close": "195.7600",
            "5. volume": "16633"
        },
        "2024-08-20 14:45:00": {
            "1. open": "195.5900",
            "2. high": "195.7100",
            "3. low": "195.5900",
            "4. close": "195.6750",
            "5. volume": "11283"
        },
        "2024-08-20 14:40:00": {
            "1. open": "195.7000",
            "2. high": "195.7000",
            "3. low": "195.5800",
            "4. close": "195.5800",
            "5. volume": "14355"
        },
        "2024-08-20 14:35:00": {
            "1. open": "195.6700",
            "2. high": "195.7400",
            "3. low": "195.6700",
            "4. close": "195.7000",
            "5. volume": "10847"
        },
        "2024-08-20 14:30:00": {
            "1. open": "195.6700",
            "2. high": "195.7100",
            "3. low": "195.6000",
            "4. close": "195.6700",
            "5. volume": "12551"
        },
        "2024-08-20 14:25:00": {
            "1. open": "195.5750",
            "2. high": "195.7240",
            "3. low": "195.5100",
            "4. close": "195.6700",
            "5. volume": "12659"
        },
        "2024-08-20 14:20:00": {
            "1. open": "195.5200",
            "2. high": "195.6200",
            "3. low": "195.5000",
            "4. close": "195.6000",
            "5. volume": "8475"
        },
        "2024-08-20 14:15:00": {
            "1. open": "195.5800",
            "2. high": "195.6100",
            "3. low": "195.5100",
            "4. close": "195.5200",
            "5. volume": "6932"
        },
        "2024-08-20 14:10:00": {
            "1. open": "195.4000",
            "2. high": "195.5900",
            "3. low": "195.4000",
            "4. close": "195.5200",
            "5. volume": "8562"
        },
        "2024-08-20 14:05:00": {
            "1. open": "195.2950",
            "2. high": "195.4300",
            "3. low": "195.2300",
            "4. close": "195.4200",
            "5. volume": "7865"
        },
        "2024-08-20 14:00:00": {
            "1. open": "195.1900",
            "2. high": "195.3500",
            "3. low": "195.1300",
            "4. close": "195.2800",
            "5. volume": "14069"
        },
        "2024-08-20 13:55:00": {
            "1. open": "195.1200",
            "2. high": "195.1700",
            "3. low": "195.1100",
            "4. close": "195.1700",
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        "2024-08-20 11:25:00": {
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        "2024-08-20 10:55:00": {
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        "2024-08-20 10:50:00": {
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    }
}

Writing ibm.json

with open("ibm.json") as f:
    ibmdata = json.loads(f.read())

ibmdata

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import pandas as pd

with open("ibm.json") as f:
    df = pd.read_json(f.read())

/tmp/ipykernel_1343637/148234076.py:2: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.
  df = pd.read_json(f.read())

df.transpose

<bound method DataFrame.transpose of                                                              Meta Data  \
1. Information       Intraday (5min) open, high, low, close prices ...   
2. Symbol                                                          IBM   
3. Last Refreshed                                  2024-08-20 19:55:00   
4. Interval                                                       5min   
5. Output Size                                                 Compact   
...                                                                ...   
2024-08-20 11:00:00                                                NaN   
2024-08-20 10:55:00                                                NaN   
2024-08-20 10:50:00                                                NaN   
2024-08-20 10:45:00                                                NaN   
2024-08-20 10:40:00                                                NaN   

                                                    Time Series (5min)  
1. Information                                                     NaN  
2. Symbol                                                          NaN  
3. Last Refreshed                                                  NaN  
4. Interval                                                        NaN  
5. Output Size                                                     NaN  
...                                                                ...  
2024-08-20 11:00:00  {'1. open': '194.8400', '2. high': '194.8400',...  
2024-08-20 10:55:00  {'1. open': '194.9600', '2. high': '195.0000',...  
2024-08-20 10:50:00  {'1. open': '194.9350', '2. high': '194.9900',...  
2024-08-20 10:45:00  {'1. open': '194.9600', '2. high': '195.1500',...  
2024-08-20 10:40:00  {'1. open': '195.0500', '2. high': '195.0650',...  

[106 rows x 2 columns]>

ibmdata

{'Meta Data': {'1. Information': 'Intraday (5min) open, high, low, close prices and volume',
  '2. Symbol': 'IBM',
  '3. Last Refreshed': '2024-08-20 19:55:00',
  '4. Interval': '5min',
  '5. Output Size': 'Compact',
  '6. Time Zone': 'US/Eastern'},
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  '2024-08-20 14:45:00': {'1. open': '195.5900',
   '2. high': '195.7100',
   '3. low': '195.5900',
   '4. close': '195.6750',
   '5. volume': '11283'},
  '2024-08-20 14:40:00': {'1. open': '195.7000',
   '2. high': '195.7000',
   '3. low': '195.5800',
   '4. close': '195.5800',
   '5. volume': '14355'},
  '2024-08-20 14:35:00': {'1. open': '195.6700',
   '2. high': '195.7400',
   '3. low': '195.6700',
   '4. close': '195.7000',
   '5. volume': '10847'},
  '2024-08-20 14:30:00': {'1. open': '195.6700',
   '2. high': '195.7100',
   '3. low': '195.6000',
   '4. close': '195.6700',
   '5. volume': '12551'},
  '2024-08-20 14:25:00': {'1. open': '195.5750',
   '2. high': '195.7240',
   '3. low': '195.5100',
   '4. close': '195.6700',
   '5. volume': '12659'},
  '2024-08-20 14:20:00': {'1. open': '195.5200',
   '2. high': '195.6200',
   '3. low': '195.5000',
   '4. close': '195.6000',
   '5. volume': '8475'},
  '2024-08-20 14:15:00': {'1. open': '195.5800',
   '2. high': '195.6100',
   '3. low': '195.5100',
   '4. close': '195.5200',
   '5. volume': '6932'},
  '2024-08-20 14:10:00': {'1. open': '195.4000',
   '2. high': '195.5900',
   '3. low': '195.4000',
   '4. close': '195.5200',
   '5. volume': '8562'},
  '2024-08-20 14:05:00': {'1. open': '195.2950',
   '2. high': '195.4300',
   '3. low': '195.2300',
   '4. close': '195.4200',
   '5. volume': '7865'},
  '2024-08-20 14:00:00': {'1. open': '195.1900',
   '2. high': '195.3500',
   '3. low': '195.1300',
   '4. close': '195.2800',
   '5. volume': '14069'},
  '2024-08-20 13:55:00': {'1. open': '195.1200',
   '2. high': '195.1700',
   '3. low': '195.1100',
   '4. close': '195.1700',
   '5. volume': '3865'},
  '2024-08-20 13:50:00': {'1. open': '195.1000',
   '2. high': '195.1500',
   '3. low': '195.0500',
   '4. close': '195.1200',
   '5. volume': '14232'},
  '2024-08-20 13:45:00': {'1. open': '195.3100',
   '2. high': '195.3300',
   '3. low': '195.1500',
   '4. close': '195.1750',
   '5. volume': '11634'},
  '2024-08-20 13:40:00': {'1. open': '195.3450',
   '2. high': '195.3900',
   '3. low': '195.3100',
   '4. close': '195.3400',
   '5. volume': '11062'},
  '2024-08-20 13:35:00': {'1. open': '195.4800',
   '2. high': '195.4800',
   '3. low': '195.2900',
   '4. close': '195.3350',
   '5. volume': '10018'},
  '2024-08-20 13:30:00': {'1. open': '195.5900',
   '2. high': '195.6100',
   '3. low': '195.4900',
   '4. close': '195.5050',
   '5. volume': '8854'},
  '2024-08-20 13:25:00': {'1. open': '195.5750',
   '2. high': '195.6400',
   '3. low': '195.4800',
   '4. close': '195.5600',
   '5. volume': '12037'},
  '2024-08-20 13:20:00': {'1. open': '195.6000',
   '2. high': '195.6500',
   '3. low': '195.5500',
   '4. close': '195.6000',
   '5. volume': '16260'},
  '2024-08-20 13:15:00': {'1. open': '195.5000',
   '2. high': '195.5800',
   '3. low': '195.4680',
   '4. close': '195.5700',
   '5. volume': '16144'},
  '2024-08-20 13:10:00': {'1. open': '195.3750',
   '2. high': '195.5500',
   '3. low': '195.3650',
   '4. close': '195.5000',
   '5. volume': '18888'},
  '2024-08-20 13:05:00': {'1. open': '195.2350',
   '2. high': '195.3800',
   '3. low': '195.2050',
   '4. close': '195.3800',
   '5. volume': '5488'},
  '2024-08-20 13:00:00': {'1. open': '195.3700',
   '2. high': '195.3700',
   '3. low': '195.1600',
   '4. close': '195.2300',
   '5. volume': '8255'},
  '2024-08-20 12:55:00': {'1. open': '195.4790',
   '2. high': '195.4930',
   '3. low': '195.3600',
   '4. close': '195.3800',
   '5. volume': '9441'},
  '2024-08-20 12:50:00': {'1. open': '195.4550',
   '2. high': '195.4800',
   '3. low': '195.4140',
   '4. close': '195.4140',
   '5. volume': '7033'},
  '2024-08-20 12:45:00': {'1. open': '195.4250',
   '2. high': '195.4600',
   '3. low': '195.3850',
   '4. close': '195.4550',
   '5. volume': '6749'},
  '2024-08-20 12:40:00': {'1. open': '195.5480',
   '2. high': '195.5600',
   '3. low': '195.3610',
   '4. close': '195.4400',
   '5. volume': '9811'},
  '2024-08-20 12:35:00': {'1. open': '195.4200',
   '2. high': '195.5200',
   '3. low': '195.3400',
   '4. close': '195.5100',
   '5. volume': '8085'},
  '2024-08-20 12:30:00': {'1. open': '195.4050',
   '2. high': '195.4380',
   '3. low': '195.3300',
   '4. close': '195.3900',
   '5. volume': '9071'},
  '2024-08-20 12:25:00': {'1. open': '195.3400',
   '2. high': '195.4100',
   '3. low': '195.3150',
   '4. close': '195.3900',
   '5. volume': '5532'},
  '2024-08-20 12:20:00': {'1. open': '195.2750',
   '2. high': '195.3400',
   '3. low': '195.1500',
   '4. close': '195.3400',
   '5. volume': '12053'},
  '2024-08-20 12:15:00': {'1. open': '195.3500',
   '2. high': '195.3500',
   '3. low': '195.2300',
   '4. close': '195.2700',
   '5. volume': '10863'},
  '2024-08-20 12:10:00': {'1. open': '195.3400',
   '2. high': '195.3800',
   '3. low': '195.2300',
   '4. close': '195.3000',
   '5. volume': '17031'},
  '2024-08-20 12:05:00': {'1. open': '195.4370',
   '2. high': '195.4370',
   '3. low': '195.2500',
   '4. close': '195.3410',
   '5. volume': '6835'},
  '2024-08-20 12:00:00': {'1. open': '195.4880',
   '2. high': '195.6600',
   '3. low': '195.3700',
   '4. close': '195.3990',
   '5. volume': '21588'},
  '2024-08-20 11:55:00': {'1. open': '195.1900',
   '2. high': '195.4700',
   '3. low': '195.1900',
   '4. close': '195.4440',
   '5. volume': '16191'},
  '2024-08-20 11:50:00': {'1. open': '195.0480',
   '2. high': '195.1800',
   '3. low': '194.9700',
   '4. close': '195.1800',
   '5. volume': '15736'},
  '2024-08-20 11:45:00': {'1. open': '194.9300',
   '2. high': '195.0600',
   '3. low': '194.8900',
   '4. close': '195.0050',
   '5. volume': '9557'},
  '2024-08-20 11:40:00': {'1. open': '194.9450',
   '2. high': '195.0150',
   '3. low': '194.9100',
   '4. close': '194.9500',
   '5. volume': '7813'},
  '2024-08-20 11:35:00': {'1. open': '194.8060',
   '2. high': '194.9300',
   '3. low': '194.7250',
   '4. close': '194.9300',
   '5. volume': '7621'},
  '2024-08-20 11:30:00': {'1. open': '194.8000',
   '2. high': '194.8300',
   '3. low': '194.7200',
   '4. close': '194.8000',
   '5. volume': '7981'},
  '2024-08-20 11:25:00': {'1. open': '194.8250',
   '2. high': '194.9000',
   '3. low': '194.7700',
   '4. close': '194.8000',
   '5. volume': '11947'},
  '2024-08-20 11:20:00': {'1. open': '194.7900',
   '2. high': '194.9170',
   '3. low': '194.7900',
   '4. close': '194.8650',
   '5. volume': '5188'},
  '2024-08-20 11:15:00': {'1. open': '194.8000',
   '2. high': '194.9100',
   '3. low': '194.6800',
   '4. close': '194.8100',
   '5. volume': '13280'},
  '2024-08-20 11:10:00': {'1. open': '194.6900',
   '2. high': '194.9350',
   '3. low': '194.5650',
   '4. close': '194.8250',
   '5. volume': '20746'},
  '2024-08-20 11:05:00': {'1. open': '194.6400',
   '2. high': '194.7150',
   '3. low': '194.5300',
   '4. close': '194.5900',
   '5. volume': '6254'},
  '2024-08-20 11:00:00': {'1. open': '194.8400',
   '2. high': '194.8400',
   '3. low': '194.5900',
   '4. close': '194.6300',
   '5. volume': '9074'},
  '2024-08-20 10:55:00': {'1. open': '194.9600',
   '2. high': '195.0000',
   '3. low': '194.8900',
   '4. close': '194.8950',
   '5. volume': '12864'},
  '2024-08-20 10:50:00': {'1. open': '194.9350',
   '2. high': '194.9900',
   '3. low': '194.8800',
   '4. close': '194.9250',
   '5. volume': '17711'},
  '2024-08-20 10:45:00': {'1. open': '194.9600',
   '2. high': '195.1500',
   '3. low': '194.8200',
   '4. close': '194.9700',
   '5. volume': '24543'},
  '2024-08-20 10:40:00': {'1. open': '195.0500',
   '2. high': '195.0650',
   '3. low': '194.9700',
   '4. close': '194.9700',
   '5. volume': '14711'}}}

df

	Meta Data	Time Series (5min)
1. Information	Intraday (5min) open, high, low, close prices ...	NaN
2. Symbol	IBM	NaN
3. Last Refreshed	2024-08-20 19:55:00	NaN
4. Interval	5min	NaN
5. Output Size	Compact	NaN
...	...	...
2024-08-20 11:00:00	NaN	{'1. open': '194.8400', '2. high': '194.8400',...
2024-08-20 10:55:00	NaN	{'1. open': '194.9600', '2. high': '195.0000',...
2024-08-20 10:50:00	NaN	{'1. open': '194.9350', '2. high': '194.9900',...
2024-08-20 10:45:00	NaN	{'1. open': '194.9600', '2. high': '195.1500',...
2024-08-20 10:40:00	NaN	{'1. open': '195.0500', '2. high': '195.0650',...

106 rows × 2 columns

pd.read_json(json.dumps(ibmdata['Time Series (5min)'])).transpose()

/tmp/ipykernel_1343637/3780874574.py:1: FutureWarning: Passing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.
  pd.read_json(json.dumps(ibmdata['Time Series (5min)'])).transpose()

	1. open	2. high	3. low	4. close	5. volume
2024-08-20 19:55:00	195.980	195.990	195.98	195.990	35.0
2024-08-20 19:50:00	195.980	195.980	195.98	195.980	299.0
2024-08-20 19:35:00	196.000	196.000	196.00	196.000	25.0
2024-08-20 19:30:00	196.000	196.000	196.00	196.000	1.0
2024-08-20 19:20:00	195.980	195.980	195.98	195.980	106.0
...	...	...	...	...	...
2024-08-20 11:00:00	194.840	194.840	194.59	194.630	9074.0
2024-08-20 10:55:00	194.960	195.000	194.89	194.895	12864.0
2024-08-20 10:50:00	194.935	194.990	194.88	194.925	17711.0
2024-08-20 10:45:00	194.960	195.150	194.82	194.970	24543.0
2024-08-20 10:40:00	195.050	195.065	194.97	194.970	14711.0

100 rows × 5 columns

• json
• json.dumps (converts python data into json string)
• json.loads (converts json string into python data)

problem

json data is given for color values in color.json file . write a function to find value of given color.

%%file color.json
[
    {
        "color": "red",
        "value": "#f00"
    },
    {
        "color": "black",
        "value": "#000"
    }
]

Overwriting color.json

import json

def colorvalue(colorjsonfile, color):
    with open(colorjsonfile) as f:
        colordata = json.loads(f.read())

    for color_ in colordata:
        if color_['color'] == color:
            return color_['value']

colorvalue("color.json", "red")

'#f00'

pd.read_json("color.json") # even filename works !

	color	value
0	red	#f00
1	black	#000

import json

def find_color_value(json_data, color_name):
    # Parse the JSON data
    data = json.loads(json_data)
    
    # Iterate through the list to find the color
    for item in data:
        if item['color'] == color_name:
            return item['value']
    
    # If color is not found, return None
    return None

with open("color.json") as f:
    print(find_color_value(f.read(), "red"))

#f00

Scraping data from internet

lets do a simple search on two search engines and observe the url

• https://www.google.com/search?q=json&sca_esv=fea2c737c8eb45dd&sca_upv=1&source=hp&ei=_I_FZrLjF86MnesPzvbK2Qg&iflsig=AL9hbdgAAAAAZsWeDBTTXHiT3Rvamc2LK9v18m793lt7&ved=0ahUKEwiy0bOCwYWIAxVORmcHHU67MosQ4dUDCA8&uact=5&oq=json&gs_lp=Egdnd3Mtd2l6IgRqc29uMgsQABiABBixAxiDATILEAAYgAQYsQMYgwEyCxAAGIAEGLEDGIMBMgsQABiABBixAxiDATILEAAYgAQYsQMYgwEyCxAAGIAEGLEDGIMBMgsQABiABBixAxiDATILEAAYgAQYsQMYgwEyCBAAGIAEGLEDMgsQABiABBixAxiDAUj0EFDlCFiADHABeACQAQCYAfMBoAH7BqoBBTAuMS4zuAEDyAEA-AEBmAIFoALJB6gCCsICEBAAGAMY5QIY6gIYjAMYjwHCAhAQLhgDGOUCGOoCGIwDGI8BwgIOEAAYgAQYsQMYgwEYigXCAhEQLhiABBixAxjRAxiDARjHAcICExAuGIAEGLEDGNEDGMcBGIoFGArCAgUQABiABMICDhAuGIAEGLEDGIMBGIoFwgIREC4YgAQYsQMYgwEYxwEYrwHCAhQQLhiABBixAxiDARjHARiKBRivAZgDLJIHBTEuMC40oAerJg&sclient=gws-wiz

• https://duckduckgo.com/?t=ffab&q=json&ia=web

• https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey=demo

HTTP protocol

• get (data that you need given to server is empbed in url)
• post (form)
• put
• delete