Python Virtual Training For Arcesium - Module III

Oct 11-15, 2021 Vikrant Patil

These notes are available online at https://notes.pipal.in/2021/arcesium_finop_batch2/

© Pipal Academy LLP

Day 1 | Day 2 | Day 3 | Day 4 | Day 5

We will be using jupyter hub from https://lab2.pipal.in for this training.

create a notebook with name module3-day3

Downloading data from internet

Http protocols 4 methods

• get
• post
• put
• delete

Webapi will usually have documentation about whetehr the url is got get/post/put/delete

import requests

API_KEY = "UKVFE0JLE0TBPDEF"
alphavantageurl = "https://www.alphavantage.co/query"
params = {
    "function":"TIME_SERIES_INTRADAY",
    "symbol":"AAPL",
    "interval":"15min",
    "apikey": API_KEY
}

resp = requests.get(alphavantageurl, params=params)

resp.status_code

200

data = resp.json()

type(data)

dict

data.keys()

dict_keys(['Meta Data', 'Time Series (15min)'])

data['Meta Data']

{'1. Information': 'Intraday (15min) open, high, low, close prices and volume',
 '2. Symbol': 'AAPL',
 '3. Last Refreshed': '2021-10-27 20:00:00',
 '4. Interval': '15min',
 '5. Output Size': 'Compact',
 '6. Time Zone': 'US/Eastern'}

type(data['Time Series (15min)'])

dict

len(data['Time Series (15min)'])

100

data['Time Series (15min)'].keys()

dict_keys(['2021-10-27 20:00:00', '2021-10-27 19:45:00', '2021-10-27 19:30:00', '2021-10-27 19:15:00', '2021-10-27 19:00:00', '2021-10-27 18:45:00', '2021-10-27 18:30:00', '2021-10-27 18:15:00', '2021-10-27 18:00:00', '2021-10-27 17:45:00', '2021-10-27 17:30:00', '2021-10-27 17:15:00', '2021-10-27 17:00:00', '2021-10-27 16:45:00', '2021-10-27 16:30:00', '2021-10-27 16:15:00', '2021-10-27 16:00:00', '2021-10-27 15:45:00', '2021-10-27 15:30:00', '2021-10-27 15:15:00', '2021-10-27 15:00:00', '2021-10-27 14:45:00', '2021-10-27 14:30:00', '2021-10-27 14:15:00', '2021-10-27 14:00:00', '2021-10-27 13:45:00', '2021-10-27 13:30:00', '2021-10-27 13:15:00', '2021-10-27 13:00:00', '2021-10-27 12:45:00', '2021-10-27 12:30:00', '2021-10-27 12:15:00', '2021-10-27 12:00:00', '2021-10-27 11:45:00', '2021-10-27 11:30:00', '2021-10-27 11:15:00', '2021-10-27 11:00:00', '2021-10-27 10:45:00', '2021-10-27 10:30:00', '2021-10-27 10:15:00', '2021-10-27 10:00:00', '2021-10-27 09:45:00', '2021-10-27 09:30:00', '2021-10-27 09:15:00', '2021-10-27 09:00:00', '2021-10-27 08:45:00', '2021-10-27 08:30:00', '2021-10-27 08:15:00', '2021-10-27 08:00:00', '2021-10-27 07:45:00', '2021-10-27 07:30:00', '2021-10-27 07:15:00', '2021-10-27 07:00:00', '2021-10-27 06:45:00', '2021-10-27 06:30:00', '2021-10-27 06:15:00', '2021-10-27 06:00:00', '2021-10-27 05:45:00', '2021-10-27 05:30:00', '2021-10-27 05:15:00', '2021-10-27 05:00:00', '2021-10-27 04:45:00', '2021-10-27 04:30:00', '2021-10-27 04:15:00', '2021-10-26 20:00:00', '2021-10-26 19:45:00', '2021-10-26 19:30:00', '2021-10-26 19:15:00', '2021-10-26 19:00:00', '2021-10-26 18:45:00', '2021-10-26 18:30:00', '2021-10-26 18:15:00', '2021-10-26 18:00:00', '2021-10-26 17:45:00', '2021-10-26 17:30:00', '2021-10-26 17:15:00', '2021-10-26 17:00:00', '2021-10-26 16:45:00', '2021-10-26 16:30:00', '2021-10-26 16:15:00', '2021-10-26 16:00:00', '2021-10-26 15:45:00', '2021-10-26 15:30:00', '2021-10-26 15:15:00', '2021-10-26 15:00:00', '2021-10-26 14:45:00', '2021-10-26 14:30:00', '2021-10-26 14:15:00', '2021-10-26 14:00:00', '2021-10-26 13:45:00', '2021-10-26 13:30:00', '2021-10-26 13:15:00', '2021-10-26 13:00:00', '2021-10-26 12:45:00', '2021-10-26 12:30:00', '2021-10-26 12:15:00', '2021-10-26 12:00:00', '2021-10-26 11:45:00', '2021-10-26 11:30:00', '2021-10-26 11:15:00'])

data['Time Series (15min)']['2021-10-27 20:00:00']

{'1. open': '149.1000',
 '2. high': '149.1400',
 '3. low': '149.0000',
 '4. close': '149.0700',
 '5. volume': '36823'}

import pandas as pd

pd.DataFrame(data['Time Series (15min)'])

	2021-10-27 20:00:00	2021-10-27 19:45:00	2021-10-27 19:30:00	2021-10-27 19:15:00	2021-10-27 19:00:00	2021-10-27 18:45:00	2021-10-27 18:30:00	2021-10-27 18:15:00	2021-10-27 18:00:00	2021-10-27 17:45:00	...	2021-10-26 13:30:00	2021-10-26 13:15:00	2021-10-26 13:00:00	2021-10-26 12:45:00	2021-10-26 12:30:00	2021-10-26 12:15:00	2021-10-26 12:00:00	2021-10-26 11:45:00	2021-10-26 11:30:00	2021-10-26 11:15:00
1. open	149.1000	149.1500	149.1400	149.1700	149.1700	149.1000	149.1400	149.0900	148.9800	149.0000	...	149.5300	149.4600	149.4900	149.6550	149.7200	149.6100	149.8600	149.9400	149.8500	149.3900
2. high	149.1400	149.1699	149.1700	149.1700	149.1700	149.1400	149.1700	149.1501	149.1100	149.0000	...	149.6450	149.5874	149.5300	149.7450	149.8590	149.7500	149.9100	149.9800	150.0499	149.9700
3. low	149.0000	149.0200	149.0300	149.1100	149.1200	149.0900	149.0700	149.0900	148.9700	148.9300	...	149.3800	149.2900	149.2000	149.3330	149.6300	149.3750	149.4601	149.4300	149.8300	149.3800
4. close	149.0700	149.1400	149.1700	149.1400	149.1400	149.1400	149.1000	149.1400	149.0800	148.9400	...	149.5800	149.5200	149.4604	149.4955	149.6562	149.7150	149.6050	149.8597	149.9400	149.8478
5. volume	36823	29874	21690	14188	13809	10752	13468	26187	19196	9171	...	1348407	1206561	1791171	1384721	1359349	2046436	2116729	2233785	1874156	2101928

5 rows × 100 columns

timeseries = pd.DataFrame(data['Time Series (15min)']).transpose()

timeseries

	1. open	2. high	3. low	4. close	5. volume
2021-10-27 20:00:00	149.1000	149.1400	149.0000	149.0700	36823
2021-10-27 19:45:00	149.1500	149.1699	149.0200	149.1400	29874
2021-10-27 19:30:00	149.1400	149.1700	149.0300	149.1700	21690
2021-10-27 19:15:00	149.1700	149.1700	149.1100	149.1400	14188
2021-10-27 19:00:00	149.1700	149.1700	149.1200	149.1400	13809
...	...	...	...	...	...
2021-10-26 12:15:00	149.6100	149.7500	149.3750	149.7150	2046436
2021-10-26 12:00:00	149.8600	149.9100	149.4601	149.6050	2116729
2021-10-26 11:45:00	149.9400	149.9800	149.4300	149.8597	2233785
2021-10-26 11:30:00	149.8500	150.0499	149.8300	149.9400	1874156
2021-10-26 11:15:00	149.3900	149.9700	149.3800	149.8478	2101928

100 rows × 5 columns

API_KEY = "UKVFE0JLE0TBPDEF"
alphavantageurl = "https://www.alphavantage.co/query"
params = {
    "function":"TIME_SERIES_INTRADAY",
    "symbol":"AAPL",
    "interval":"15min",
    "apikey": API_KEY,
    "datatype": "csv"
}

resp = requests.get(alphavantageurl, params=params)

with open("timeseries.csv", "w") as f:
    f.write(resp.text)
    

df = pd.read_csv("timeseries.csv")

df

	timestamp	open	high	low	close	volume
0	2021-10-27 20:00:00	149.10	149.1400	149.0000	149.0700	36823
1	2021-10-27 19:45:00	149.15	149.1699	149.0200	149.1400	29874
2	2021-10-27 19:30:00	149.14	149.1700	149.0300	149.1700	21690
3	2021-10-27 19:15:00	149.17	149.1700	149.1100	149.1400	14188
4	2021-10-27 19:00:00	149.17	149.1700	149.1200	149.1400	13809
...	...	...	...	...	...	...
95	2021-10-26 12:15:00	149.61	149.7500	149.3750	149.7150	2046436
96	2021-10-26 12:00:00	149.86	149.9100	149.4601	149.6050	2116729
97	2021-10-26 11:45:00	149.94	149.9800	149.4300	149.8597	2233785
98	2021-10-26 11:30:00	149.85	150.0499	149.8300	149.9400	1874156
99	2021-10-26 11:15:00	149.39	149.9700	149.3800	149.8478	2101928

100 rows × 6 columns

df.columns

Index(['timestamp', 'open', 'high', 'low', 'close', 'volume'], dtype='object')

df.timestamp

0     2021-10-27 20:00:00
1     2021-10-27 19:45:00
2     2021-10-27 19:30:00
3     2021-10-27 19:15:00
4     2021-10-27 19:00:00
             ...         
95    2021-10-26 12:15:00
96    2021-10-26 12:00:00
97    2021-10-26 11:45:00
98    2021-10-26 11:30:00
99    2021-10-26 11:15:00
Name: timestamp, Length: 100, dtype: object

df['timestamp'] = pd.to_datetime(df['timestamp'])

df

	timestamp	open	high	low	close	volume
0	2021-10-27 20:00:00	149.10	149.1400	149.0000	149.0700	36823
1	2021-10-27 19:45:00	149.15	149.1699	149.0200	149.1400	29874
2	2021-10-27 19:30:00	149.14	149.1700	149.0300	149.1700	21690
3	2021-10-27 19:15:00	149.17	149.1700	149.1100	149.1400	14188
4	2021-10-27 19:00:00	149.17	149.1700	149.1200	149.1400	13809
...	...	...	...	...	...	...
95	2021-10-26 12:15:00	149.61	149.7500	149.3750	149.7150	2046436
96	2021-10-26 12:00:00	149.86	149.9100	149.4601	149.6050	2116729
97	2021-10-26 11:45:00	149.94	149.9800	149.4300	149.8597	2233785
98	2021-10-26 11:30:00	149.85	150.0499	149.8300	149.9400	1874156
99	2021-10-26 11:15:00	149.39	149.9700	149.3800	149.8478	2101928

100 rows × 6 columns

df.info() # info method tells about basic information about columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype         
---  ------     --------------  -----         
 0   timestamp  100 non-null    datetime64[ns]
 1   open       100 non-null    float64       
 2   high       100 non-null    float64       
 3   low        100 non-null    float64       
 4   close      100 non-null    float64       
 5   volume     100 non-null    int64         
dtypes: datetime64[ns](1), float64(4), int64(1)
memory usage: 4.8 KB

Post request

resp = requests.post("https://httpbin.org/post", data={"input1":"x", "input2":"y"})

resp.status_code

200

resp.json()

{'args': {},
 'data': '',
 'files': {},
 'form': {'input1': 'x', 'input2': 'y'},
 'headers': {'Accept': '*/*',
  'Accept-Encoding': 'gzip, deflate',
  'Content-Length': '17',
  'Content-Type': 'application/x-www-form-urlencoded',
  'Host': 'httpbin.org',
  'User-Agent': 'python-requests/2.24.0',
  'X-Amzn-Trace-Id': 'Root=1-617a3601-24633c5a5cd717666f560652'},
 'json': None,
 'origin': '157.33.57.222',
 'url': 'https://httpbin.org/post'}

Authentication

user = "vikipedia"
pass_ = open("/tmp/pass.txt").read().strip()
resp = requests.get("http://api.github.com/user", auth=(user, pass_)) # simple user/password authentication

resp.status_code

401

resp.text

'{"message":"Requires authentication","documentation_url":"https://docs.github.com/rest/reference/users#get-the-authenticated-user"}'

There are different kinds of authentications, kerberos is one of those

pip install requests requests-kerberos

kerberos_auth=HTTPKerberosAuth(mutual_authentication=OPTIONAL)
response=requests.get(request_url, auth=kerberos_auth, params=params).json()

General Scraping

url = "http://www.thehindu.com"
respose = requests.get(url, params={"service":"rss"})
xmltext = respose.text

print(xmltext[:1500])

<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
    <channel>
        <title>The Hindu - Home</title>
        <link>https://www.thehindu.com/</link>
        <description>Default RSS Feed</description>
        <language>en-us</language>
        <copyright>Copyright 2021 The Hindu</copyright>
        <item>
            <title><![CDATA[5 subs in football matches to be allowed on a permanent basis]]></title>
            <author><![CDATA[AP]]></author>
            <category><![CDATA[Football]]></category>
            <link>https://www.thehindu.com/sport/football/5-subs-in-football-matches-to-be-allowed-on-a-permanent-basis/article37205294.ece</link>
            <description><![CDATA[
                The measure was introduced in May 2020 so teams could cope better with the congested schedule when football resumed following a suspension due to the coronavirus
            ]]></description>
            <pubDate><![CDATA[Thu, 28 Oct 2021 10:53:19 +0530]]></pubDate>
        </item>
        <item>
            <title><![CDATA[Facebook asks employees to preserve internal documents for legal inquiries]]></title>
            <author><![CDATA[Reuters]]></author>
            <category><![CDATA[Internet]]></category>
            <link>https://www.thehindu.com/sci-tech/technology/internet/facebook-asks-employees-to-preserve-internal-documents-for-legal-inquiries/article37205072.ece</link>
            <description><![CDATA[
                The increased scrutin

<tag1>
  <tag2>
      <item>
      <leaf>
      text
      </leaf>
      </item>
  </tag2>
</tag1>

from xml.etree import ElementTree as et
root = et.fromstring(xmltext)
items = root.findall(".//item")
items[0]

<Element 'item' at 0x7f3b44ecc3b0>

print(et.tostring(items[0]).decode())

<item>
            <title>5 subs in football matches to be allowed on a permanent basis</title>
            <author>AP</author>
            <category>Football</category>
            <link>https://www.thehindu.com/sport/football/5-subs-in-football-matches-to-be-allowed-on-a-permanent-basis/article37205294.ece</link>
            <description>
                The measure was introduced in May 2020 so teams could cope better with the congested schedule when football resumed following a suspension due to the coronavirus
            </description>
            <pubDate>Thu, 28 Oct 2021 10:53:19 +0530</pubDate>
        </item>
        

for item in items[:10]:
    print(item.findtext("title"))
    print(item.findtext("author"))
    print(item.findtext("link"))
    print("="*30)

5 subs in football matches to be allowed on a permanent basis
AP
https://www.thehindu.com/sport/football/5-subs-in-football-matches-to-be-allowed-on-a-permanent-basis/article37205294.ece
==============================
Facebook asks employees to preserve internal documents for legal inquiries
Reuters
https://www.thehindu.com/sci-tech/technology/internet/facebook-asks-employees-to-preserve-internal-documents-for-legal-inquiries/article37205072.ece
==============================
Intel teams with Google Cloud to develop new class of data centre chip 
Reuters
https://www.thehindu.com/sci-tech/technology/intel-teams-with-google-cloud-to-develop-new-class-of-data-centre-chip/article37205117.ece
==============================
Manipur rights panel to begin inquiry into death of social activist
Iboyaima Laithangbam
https://www.thehindu.com/news/national/other-states/manipur-rights-panel-to-begin-inquiry-into-death-of-social-activist/article37205188.ece
==============================
LaLiga | Barcelona sacks Koeman; 4 teams tied in Spain after Madrid draws
AP
https://www.thehindu.com/sport/football/laliga-barcelona-sacks-koeman-4-teams-tied-in-spain-after-madrid-draws/article37205161.ece
==============================
Business live | Nykaa to launch IPO today

https://www.thehindu.com/business/business-news-live-update-oct-28-2021/article37205119.ece
==============================
Home Minister, DGP interact with COVID victims’ families
Staff Reporter
https://www.thehindu.com/news/national/andhra-pradesh/home-minister-dgp-interact-with-covid-victims-families/article37205034.ece
==============================
Amazon seen triumphing over Apple privacy changes in digital ad business
Reuters
https://www.thehindu.com/sci-tech/technology/internet/amazon-seen-triumphing-over-apple-privacy-changes-in-digital-ad-business/article37204836.ece
==============================
Samsung says component shortages to affect some chip demand, profit hits 3-year high
Reuters
https://www.thehindu.com/sci-tech/technology/samsung-says-component-shortages-to-affect-some-chip-demand-profit-hits-3-year-high/article37204744.ece
==============================
TDP leaders confined to homes ahead of protest at Rushikonda
Special Correspondent
https://www.thehindu.com/news/national/andhra-pradesh/tdp-leaders-confined-to-homes-ahead-of-protest-at-rushikonda/article37205015.ece
==============================

Downloading file

def download(url, filename):
    resp = requests.get(url)
    with open(filename, "wb") as f:
        f.write(resp.content) # resp.content is always binary

url = "https://raw.githubusercontent.com/vikipedia/python-trainings/master/online_course/source/module2/wallet.csv"
download(url, "csvdata.csv")

!python3 head.py csvdata.csv

,date,category,description,debit
0,2021-03-07 14:53:28.377359,Music,Amazon,421.2073272347991
1,2020-10-08 09:53:28.377359,Food,Swiggy,328.4400802428426
2,2021-02-23 09:53:28.377359,Books,Amazon,244.67943701511354
3,2020-11-01 14:53:28.377359,Utility,Phone,222.7563175805277

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

download(excelurl, "exceldata.xlsx")

!file exceldata.xlsx

exceldata.xlsx: Microsoft Excel 2007+

def download_big(url, filename, chunksize=1024):
    resp = requests.get(url)
    with open(filename, "wb") as f:
        for chunk in resp.iter_content(chunk_size=chunksize):
            f.write(chunk)
            print(".",end="")

download_big(excelurl, "exceldata.xlsx")

...........

Example

url = "https://www.nseindia.com/get-quotes/equity?symbol=RELIANCE"
headers = {'Accept': '*/*',
  'Accept-Language':'en-US,en;q=0.5',
  'Host': 'www.nseindia.com',
  'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:28.0) Gecko/20100101 Firefox/28.0',
  'X-Requested-With':'XMLHttpRequest'
  }

url = "https://www.nseindia.com/get-quotes/equity"

resp = requests.get(url, params={"symbol":"RELIANCE"},headers=headers)

resp.status_code

200

print(resp.text[:100])

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta http-equiv="Content-Type" content="text/html; ch

import bs4

soup = bs4.BeautifulSoup(resp.content)

soup.findAll(attrs={'id':''})[0].text

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-85-b91a5b1718b7> in <module>
----> 1 soup.findAll(attrs={'id':'responseDiv'})[0].text

IndexError: list index out of range

tables = pd.read_html(resp.text)

len(tables)

10

tables[0]

	Series	Symbol	ISIN	Status	Date of Listing	Industry	Symbol P/E	Sectoral Index P/E	Sectoral Index

tables[2]

	Prev. Close	Open	Close*	VWAP	Lower Band	Upper Band	Price Band	Adjusted Price *

tables[3]

	Qty	Bid (₹)	Ask (₹)	Qty.1

tables[4]

	Trade Information	Trade Information.1
0	Traded Volume (Shares)	-
1	Traded Value (₹ Lakhs)	-
2	Total Market Cap (₹ Lakhs)	-
3	Free Float Market Cap (₹ Lakhs)	-
4	Impact cost	-

Some useful packages

which you can try in your private virtual env

• nsetools

https://nsetools.readthedocs.io/en/latest/

pip install nsetools

from nsetool import Nse
nse = Nse()
nse.get_quote("RELIANCE")
nse.get_index_list()
nse.get_index_quote("NIFTY BANK")
nse.get_stock_codes()
nse.get_advances_declines()

Reading pdf files

we will make use of PyPDF2 library to read pdf files

!python3 -m pip install PyPDF2

import PyPDF2

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-93-69e9e6162b4c> in <module>
----> 1 import PyPDF2

ModuleNotFoundError: No module named 'PyPDF2'

!python3 -m pip install PyPDF2

Processing /home/vikrant/.cache/pip/wheels/b1/1a/8f/a4c34be976825a2f7948d0fa40907598d69834f8ab5889de11/PyPDF2-1.26.0-py3-none-any.whl
Installing collected packages: PyPDF2
Successfully installed PyPDF2-1.26.0

import PyPDF2

we will make use of this pdf to write our sample code https://notes.pipal.in/2021/arcesium_finop_batch2/upload.pdf

download_big("https://notes.pipal.in/2021/arcesium_finop_batch2/upload.pdf", "upload.pdf")

....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................

with open("upload.pdf", "rb") as f:
    pdfreader = PyPDF2.PdfFileReader(f)
    n = pdfreader.getNumPages()
    for p in range(n):
        page = pdfreader.getPage(p)
        print(page.extractText()[:50])

 
National Load Despatch Centre
 
 
POWER SYSTEM O
NR
WR
SR
ER
NER
TOTAL
59882
41115
34238
21526
2730
16-Jul-2020
Sl 
No
Voltage Level
Line Details
Circ

with open("upload.pdf", "rb") as f:
    pdfreader = PyPDF2.PdfFileReader(f)
    n = pdfreader.getNumPages()
    page = pdfreader.getPage(1)
    print(page.extractText()[:300])

NR
WR
SR
ER
NER
TOTAL
59882
41115
34238
21526
2730
159491
1114
0
0
0
6
1120
1398
998
807
447
48
3698
355
33
77
149
29
643
11
49
128
-
-
187
39.60
16.60
41.59
4.60
0.03
102
12.6
0.0
0.0
0.0
0.0
12.6
65470
43593
38117
21535
2827
160654
22:20
10:29
10:00
21:20
19:41
21:26
Region
FVI
< 49.7
49.7 - 49.8

def download_big(url, filename, chunksize=1024):
    resp = requests.get(url)
    with open(filename, "wb") as f:
        for chunk in resp.iter_content(chunk_size=chunksize):
            f.write(chunk)
            print(".",end="")

def get_rows(lines, n, rowcount):
    s = 0
    rows = []
    for i in range(rowcount):
        rows.append(lines[s:s+n])
        s = s+n
    return rows
    
def extract_tableA(pagetext):
    lines = pagetext.split("\n")
    headers = """NR
WR
SR
ER
NER
TOTAL""".split("\n")
    n = len(headers)
    rowdata = get_rows(lines[n:], n, 9) # 9 is counted manually
    return pd.DataFrame(rowdata, columns=headers)

page1 = pdfreader.getPage(1).extractText()
data = extract_tableA(page1)

data

	NR	WR	SR	ER	NER	TOTAL
0	59882	41115	34238	21526	2730	159491
1	1114	0	0	0	6	1120
2	1398	998	807	447	48	3698
3	355	33	77	149	29	643
4	11	49	128	-	-	187
5	39.60	16.60	41.59	4.60	0.03	102
6	12.6	0.0	0.0	0.0	0.0	12.6
7	65470	43593	38117	21535	2827	160654
8	22:20	10:29	10:00	21:20	19:41	21:26

def get_rows(lines, n, rowcount):
    s = 0
    rows = []
    for i in range(rowcount):
        rows.append(lines[s:s+n])
        s = s+n
    return rows
    
def extract_tableA(pagetext, numrows=9):
    lines = pagetext.split("\n")
    headers = """NR
WR
SR
ER
NER
TOTAL""".split("\n")
    n = len(headers)
    rowdata = get_rows(lines[n:], n, numrows) # 9 is counted manually
    return pd.DataFrame(rowdata, columns=headers)

page1 = pdfreader.getPage(1).extractText()
extract_tableA(page1, 9)

	NR	WR	SR	ER	NER	TOTAL
0	59882	41115	34238	21526	2730	159491
1	1114	0	0	0	6	1120
2	1398	998	807	447	48	3698
3	355	33	77	149	29	643
4	11	49	128	-	-	187
5	39.60	16.60	41.59	4.60	0.03	102
6	12.6	0.0	0.0	0.0	0.0	12.6
7	65470	43593	38117	21535	2827	160654
8	22:20	10:29	10:00	21:20	19:41	21:26

lines = page1.split("\n")

"Total : 565656.54645 INR"
"TOTAL : 44343.432432 INR"

line = "this some line which has Total in it but no INR"

"Total" in line and "INR" in line

True

%%file extract_tableA.py
import PyPDF2
import sys
import pandas as pd
import typer

app = typer.Typer()

def get_rows(lines, n, rowcount):
    s = 0
    rows = []
    for i in range(rowcount):
        rows.append(lines[s:s+n])
        s = s+n
    return rows
    
def extract_tableA(pagetext):
    lines = pagetext.split("\n")
    headers = """NR
WR
SR
ER
NER
TOTAL""".split("\n")
    n = len(headers)
    rowdata = get_rows(lines[n:], n, 9) # 9 is counted manually
    return pd.DataFrame(rowdata, columns=headers)


def get_pdfpreader(filename):
    with open(filename, "rb") as f:
        pdfreader = PyPDF2.PdfFileReader(f)
        return pdfreader
    
def extract_tableA_(filename):
    with open(filename, "rb") as f:
        pdfreader = PyPDF2.PdfFileReader(f)
        page1 = pdfreader.getPage(1).extractText()
        data = extract_tableA(page1)
        return data

@app.command()
def extract_to_csv(filename:str, outputfile:str="tableA.csv"):
    data = extract_tableA_(filename)
    data.to_csv(outputfile, index=False)
    

if __name__ == "__main__":
    app()

Overwriting extract_tableA.py

!python3 extract_tableA.py --help

Usage: extract_tableA.py [OPTIONS] FILENAME

Arguments:
  FILENAME  [required]

Options:
  --outputfile TEXT               [default: tableA.csv]
  --install-completion [bash|zsh|fish|powershell|pwsh]
                                  Install completion for the specified shell.
  --show-completion [bash|zsh|fish|powershell|pwsh]
                                  Show completion for the specified shell, to
                                  copy it or customize the installation.

  --help                          Show this message and exit.

!python3 extract_tableA.py upload.pdf --outputfile A.csv

!cat A.csv

NR,WR,SR,ER,NER,TOTAL
59882,41115,34238,21526,2730,159491
1114,0,0,0,6,1120
1398,998,807,447,48,3698
355,33,77,149,29,643
11,49,128,-,-,187
39.60,16.60,41.59,4.60,0.03,102
12.6,0.0,0.0,0.0,0.0,12.6
65470,43593,38117,21535,2827,160654
22:20,10:29,10:00,21:20,19:41,21:26

import csv

help(csv.writer)

Help on built-in function writer in module _csv:

writer(...)
    csv_writer = csv.writer(fileobj [, dialect='excel']
                                [optional keyword args])
        for row in sequence:
            csv_writer.writerow(row)
    
        [or]
    
        csv_writer = csv.writer(fileobj [, dialect='excel']
                                [optional keyword args])
        csv_writer.writerows(rows)
    
    The "fileobj" argument can be any object that supports the file API.