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How to remove url's from string

Currently I have many rows in one column similar to the string below. On python I have run the code to remove , <a, href=, and the url itself using this code
df["text"] = df["text"].str.replace(r'\s*https?://\S+(\s+|$)', ' ').str.strip()
df["text"] = df["text"].str.replace(r'\s*href=//\S+(\s+|$)', ' ').str.strip()
However, the output continues to remain the same. Please advise.
<p>On 4 May 2019, The Financial Times (FT) reported that Huawei is planning to build a '400-person chip research and development factory' outside Cambridge. Planned to be operational by 2021, the factory will include an R&D centre and will be built on a 550-acre site reportedly purchased by Huawei in 2018 for £37.5 million. A Huawei spokesperson quoted in the FT article cited Huawei's long-term collaboration with Cambridge University, which includes a five-year, £25 million research partnership with BT, which launched a joint research group at the University of Cambridge. Read more about that partnership on this map.</p>
<p>In 2020 it was reported that the Huawei research and development center received approval by a local council despite the nation’s ongoing security concerns around the Chinese company.</p>
<p>Chinese state media later reported that Huawei's expansion in Cambridge 'is part of a five-year, £3 billion investment plan for the UK that [Huawei] announced alongside [then] British Prime Minister Theresa May' in February 2018.</p>

IIUC you want to replace the following html tags:
<p>, <a, href=, and the url
Code
df['text'] = df.text.replace(regex = {r'<p>': ' ', r'</p>': '', r'<a.*?\/a>': '+'})
Explanation
Regex dictionary does the following substitutions
<p> replaced by ' '
<a href = .../a> replaced by '+'
</p> replaced by ''
Example
Create Data
s = '''<p>On 4 May 2019, The Financial Times (FT) reported that Huawei is planning to build a '400-person chip research and development factory' outside Cambridge. Planned to be operational by 2021, the factory will include an R&D centre and will be built on a 550-acre site reportedly purchased by Huawei in 2018 for £37.5 million. A Huawei spokesperson quoted in the FT article cited Huawei's long-term collaboration with Cambridge University, which includes a five-year, £25 million research partnership with BT, which launched a joint research group at the University of Cambridge. Read more about that partnership on this map.</p>
<p>In 2020 it was reported that the Huawei research and development center received approval by a local council despite the nation’s ongoing security concerns around the Chinese company.</p>
<p>Chinese state media later reported that Huawei's expansion in Cambridge 'is part of a five-year, £3 billion investment plan for the UK that [Huawei] announced alongside [then] British Prime Minister Theresa May' in February 2018.</p>'''
data = {'text':s.split('\n')}
df = pd.DataFrame(data)
print(df.text[0]) # show first row pre-replacement
# Perform replacements
df['text'] = df.text.replace(regex = {r'<p>': ' ', r'</p>': '', r'<a.*?\/a>': '+'})
print(df.text[0]) # show first row post replacement
Output
The first row only
Before replacement
On 4 May 2019, The Financial Times (FT) reported that Huawei is planning to build a
'400-person chip research and development factory' outside Cambridge.
Planned to be operational by 2021, the factory will include an R&D
centre and will be built on a 550-acre site reportedly purchased by
Huawei in 2018 for £37.5 million. A Huawei spokesperson quoted
in the FT article cited Huawei's long-term collaboration with
Cambridge University, which includes a five-year, £25 million
research partnership with BT, which launched a joint research group at
the University of Cambridge. Read more about that partnership on this
map.
Post Replacement
On 4 May 2019, + (FT) reported that Huawei is planning to build a
'400-person chip research and development factory' outside Cambridge.
Planned to be operational by 2021, the factory will include an R&D
centre and will be built on a 550-acre site reportedly purchased by
Huawei in 2018 for £37.5 million. A Huawei spokesperson quoted
in the FT article cited Huawei's long-term collaboration with
Cambridge University, which includes a five-year, £25 million
research partnership with BT, which launched a joint research group at
the University of Cambridge. Read more about that partnership on this + ​

You can use the following regex pattern instead:
<a href=(.*?)">
I successfully tested this using your test string on regex101.
Full code:
import re
df["text"] = df["text"].str.replace(r'<a href=(.*?)">', "").str.strip()

I don't think your regex is quite right. Try:
df["text"] = df["text"].str.replace(r'<a href=\".*?\">', ' ').str.strip()
df["text"] = df["text"].str.replace(r'</a>', ' ').str.strip()

can't commit data to the database due to unknown characters in python

I am scraping some websites and storing the data in my database. Sometimes I get character maps to error, which I think is due to non-ASCII characters. Since I am scraping many websites with texts in different languages, I could not solve my issue in a general and efficient way.
an error example
Message: 'commit exception GRANTS.GOV'
Arguments: (UnicodeEncodeError('charmap', 'The Embassy of the United States in Nur-Sultan and the Consulate General of the United States in Almaty announces an open competition for past participants (“alumni”) of U.S. government-funded and U.S. government-sponsored exchange programs to submit applications to the 2021 Alumni Engagement Innovation Fund (AEIF) 2021.\xa0\xa0We seek proposals from teams of at least two alumni that meet all program eligibility requirements below. Exchange alumni interested in participating in AEIF 2021 should submit proposals to KazakhstanAlumni#state.gov\xa0by March 31, 2021, 18:00 Nur-Sultan time.\xa0\nAEIF provides alumni of U.S. sponsored and facilitated exchange programs with funding to expand on skills gained during their exchange experience to design and implement innovative solutions to global challenges facing their community. Since its inception in 2011, AEIF has funded nearly 500 alumni-led projects around the world through a competitive global competition.\n\nThis year, the U.S. Mission to Kazakhstan will accept proposals managed by teams of at least two (2) alumni that support the following theme:\n\u25cf\xa0\xa0\xa0\xa0\xa0\xa0Mental health awareness, promotion of mental wellbeing and resiliency.\nGoals. Projects may support one or more of the following goals:\nGoal 1: Increase in public understanding of mental health issues,\xa0its signs and strategies for providing timely help;\nGoal 2: Increase in public understanding of resources, methods, and tools that promote mental health and resiliency, especially among at-risk audiences; American best practices to promote mental health.\nGoal 3: Combatting stigma around mental health issues and dispelling common myths.\n\nFor full package of required forms please Related Documents section.', 1098, 1099, 'character maps to <undefined>'),)
my code :
title ='..............'
description ='......'
op = Op(
website='',
op_link='',
title='it might be a long text coming form websites,
description= it might be a long text coming from websites.,
organization_id=org_id,
close_date=',
checksum=singleData['checksum'],
published_date='',
language_id=lang_id,
is_open=1)
try:
session.add(op)
session.commit()
session.flush()
....
....
Please note: it should work on a Linux system; my database (Mysql) is in a Linux system.
I mostly face the issue with title and description, which can be in many languages and any length. How can I make encode it correctly so that I don't get any error while committing to the database?
Thank you

How to convert gzip.GzipFile to dictionary?

I have a gz format file. The file is very big and the first line is as follow:
{"originaltitle":"Leasing Specialist - WPM Real Estate Management","workexperiences":[{"company":"Home Properties","country":"US","customizeddaterange":"","daterange":{"displaydaterange":"","startdate":null,"enddate":null},"description":"Responsibilities: Inspect tour routes, models and show apartments daily to ensure cleanliness. Greeting prospective residents; determining the needs and preferences of the prospect and professionally present specific apartments while providing information regarding features and benefits. Answering incoming calls in a cheerful and professional manner. Handle each call accordingly whether it is a prospect call or an irate resident that just moved in. Develop and maintain Resident relations through the courtesy of on-site personnel, promptness of maintenance calls, and knowledge of community policies. Learn to develop professional sales and closing techniques. Accompany prospects to model apartments and discusses size and layout of rooms, available facilities, such as swimming pool and saunas, location of shopping centers, services available, and terms of lease. Demonstrate thorough knowledge and use of lead tracking system. Make follow-up calls to prospective Residents who did not fill out an application. Compile and update listings of available rental units.","location":"Baltimore, MD","normalizedtitle":"leasing specialist","title":"Leasing Specialist"},{"company":"WPM Real Estate Management","country":"US","customizeddaterange":"1 year, 3 months","daterange":{"displaydaterange":"July 2017 to October 2018","startdate":{"displaydate":"July 2017","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"October 2018","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: Inspect tour routes, models and show apartments daily to ensure cleanliness. Greeting prospective residents; determining the needs and preferences of the prospect and professionally present specific apartments while providing information regarding features and benefits. Answering incoming calls in a cheerful and professional manner. Handle each call accordingly whether it is a prospect call or an irate resident that just moved in. Develop and maintain Resident relations through the courtesy of on-site personnel, promptness of maintenance calls, and knowledge of community policies. Learn to develop professional sales and closing techniques. Accompany prospects to model apartments and discusses size and layout of rooms, available facilities, such as swimming pool and saunas, location of shopping centers, services available, and terms of lease. Demonstrate thorough knowledge and use of lead tracking system. Make follow-up calls to prospective Residents who did not fill out an application. Compile and update listings of available rental units.","location":"Baltimore, MD","normalizedtitle":"leasing specialist","title":"Leasing Specialist"},{"company":"Westminster Management","country":"US","customizeddaterange":"1 year","daterange":{"displaydaterange":"June 2016 to June 2017","startdate":{"displaydate":"June 2016","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"June 2017","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: Tour vacant units and model with future prospects.Process applications. Answer emails and incoming phone calls. Prepare lease agreement for signing. Collect all monies that is due on dateof move-in. Enter resident repair orders for resident. Walk vacant units to ensure that the unit is ready for show. Complete residency and employment verifications. Income qualify all applicants.","location":"Baltimore, MD","normalizedtitle":"leasing consultant","title":"Leasing Consultant"},{"company":"MARYLAND MANAGEMENT COMPANY","country":"US","customizeddaterange":"1 year, 1 month","daterange":{"displaydaterange":"April 2015 to May 2016","startdate":{"displaydate":"April 2015","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"May 2016","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: Lease apartments, sign lease agreements, complete residence maintenance repairrequest, answer phones, customer service, processed prospects applications, opened and closedinventory, responded to Level One emails Accomplishments: I was able to successfully finish FairHousing requirements. The first month I was able to properly and accurately process a application and move-in documents. Skills Used: The skills I used while at Americana were strong team work, strongcommunication, interpersonal, and leadership.","location":"Glen Burnie, MD","normalizedtitle":"leasing agent","title":"Leasing Agent"},{"company":"Amazon.com","country":"US","customizeddaterange":"1 year, 5 months","daterange":{"displaydaterange":"September 2014 to February 2016","startdate":{"displaydate":"September 2014","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"February 2016","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: I assure customers are receiving the correct merchandise in a timely fashion.And evaluate inventoryAccomplishments:I exceeded Amazon expectations of receiving 2800 items per hour, which allowed me to train otherassociates, building confidence and skills.Skills Used:The skills i used while performing my task were strong leadership, strong communications, and beingdetailed orientated.","location":"Baltimore, MD","normalizedtitle":"customer service representative","title":"Customer Service Representative"},{"company":"Carmax Superstore","country":"US","customizeddaterange":"1 year, 2 months","daterange":{"displaydaterange":"February 2014 to April 2015","startdate":{"displaydate":"February 2014","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"April 2015","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities:Greet customersSearch for the right vehicle that best suits the customers needs and wantsSubmit financial applicationsAssist customer with the purchasing process and document signingEnter customers information for appraisal offerAssist customer with purchasing Car Max extended warrantiesConducted follow- up on a daily, weekly, and monthy basisAccomplishments:I was acknowledged by the district for having 100% in Car Max extended warranties. Also I wasacknowledged by the district for having one of the highest Voice Of Customer survey scores. I passedthe 6 week training, obtaining my sales licenseSkills Used:I demonstrate strong communication, interpersonal and listening skills. I also have strongorganizational skills.","location":"Nottingham, MD","normalizedtitle":"sales consultant","title":"Certified Sales Consultant"},{"company":"rue21","country":"US","customizeddaterange":"1 year, 8 months","daterange":{"displaydaterange":"June 2011 to February 2013","startdate":{"displaydate":"June 2011","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"February 2013","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: Managed profit goals on a daily basisCustomer ServiceReceived Incoming shipmentDelivered daily bank depsoitsMaintained store appearanceOverlooked sales associates performanceCreated daily goals for each sales associateAccomplishments:The impact that I was able to have during my time at Rue21, I was able to build a strong team of individuals who were scored top in the region for Customer Service.Skills Used:I demonstrated strong leadership and verbal communication.","location":"Dundalk, MD","normalizedtitle":"assistant store manager","title":"Assistant Store Manager"},{"company":"Shaws Jewelers","country":"US","customizeddaterange":"1 year, 5 months","daterange":{"displaydaterange":"November 2009 to April 2011","startdate":{"displaydate":"November 2009","granularity":"MONTH","isodate":{"date":null}},"enddate":{"displaydate":"April 2011","granularity":"MONTH","isodate":{"date":null}}},"description":"Responsibilities: Customer serviceGeneral office( typing, faxing, )Made outgoing calls to valued customersCleaned and maintained show cases and lunch roomPrepared jewlery repair tickets for outgoing shipmentAccomplishments:During my time at Shaws Jewelers I was able to demonstrate excellent customer service.Also I wasable to achieve personal profit goals and credit application goals on a daily basis. I was acknowledged and rewarded by my DM for excellent team participation and over achieving the 6 standards on a dailybasis.Skills Used:I demonstrated strong verbal and listening skills. Also I have excellent interpersonal skills.","location":"Dundalk, MD","normalizedtitle":"sales associate","title":"Sales Associate"}],"skillslist":[{"monthsofexperience":0,"text":"yardi"},{"monthsofexperience":0,"text":"marketing"},{"monthsofexperience":0,"text":"outlook"},{"monthsofexperience":0,"text":"receptionist"},{"monthsofexperience":0,"text":"management"}],"url":"/r/Lashannon-Felton/1062d3b8cbb13886","additionalinfo":""}\n'
I am not familiar with gzip.GzipFile format.
Is there a way to make it a dictionary?

You will want to make use of the json module and the gzip module in Python, both of which are part of the Python Standard Library.
The gzip module provides the GzipFile class, as well as the open(),
compress() and decompress() convenience functions. The GzipFile class
reads and writes gzip-format files, automatically compressing or
decompressing the data so that it looks like an ordinary file object.
To read the compressed file, you can call gzip.open().
Opening the file with the default rb mode, will return a gzip.GzipFile object, from which you can obtain a bytes-like object by calling read().
Then, using json.loads(), you can convert the raw data into a usable Python object -- a dictionary.
The snippet below is a simple demonstration of this in action:
import gzip
import json
with gzip.open('gzipped_file.json.gz', 'rb') as f:
raw_json = f.read()
data = json.loads(raw_json)
print(type(data))
# Prints <class 'dict'>
print(data)
# Prints {'originaltitle': 'Leasing Specialist - WPM Real Estate Management', 'workexperience ...
print(data['workexperiences'][0]['company'])
# Prints Home Properties

Financial data Python API

I'm developing a financial app, and I'm in need for a financial data API which can provide me historical end of day prices for the stocks, both American and European stocks, news, dividends history, sector and industry information.
I'm having a hard time finding such an API or a data provider. Due to I'm just starting out I wish to find a data provider who is either free or reasonable priced, not interested in paying thousands of dollars for the data.
Does anyone here have any experience with such an API and any recommendations?

Its worth to check out:
https://iexcloud.io/docs/api/
https://finnhub.io/
Both available free

I like these packages:
https://github.com/alvarobartt/investpy – for anything on stock exchanges.
https://github.com/man-c/pycoingecko – for anything crypto.
Consider using https://github.com/ymyke/tessa, it unifies and simplifies access to investpy and pycoingecko. It also takes care of caching and rate limiting. (Disclosure: I'm the author or tessa.)

PyPDF2 won't extract all text from PDF

I'm trying to extract text from a PDF (https://www.sec.gov/litigation/admin/2015/34-76574.pdf) using PyPDF2, and the only result I'm getting is the following string:
b''
Here is my code:
import PyPDF2
import urllib.request
import io
url = 'https://www.sec.gov/litigation/admin/2015/34-76574.pdf'
remote_file = urllib.request.urlopen(url).read()
memory_file = io.BytesIO(remote_file)
read_pdf = PyPDF2.PdfFileReader(memory_file)
number_of_pages = read_pdf.getNumPages()
page = read_pdf.getPage(1)
page_content = page.extractText()
print(page_content.encode('utf-8'))
This code worked correctly on a few of the PDFs I'm working with (e.g. https://www.sec.gov/litigation/admin/2016/34-76837-proposed-amended-distribution-plan.pdf), but the others like the file above didn't work. Any idea what's wrong?

I don't know why pypdf2 can't extract the information from that PDF, but the package pdftotext can:
import pdftotext
from six.moves.urllib.request import urlopen
import io
url = 'https://www.sec.gov/litigation/admin/2015/34-76574.pdf'
remote_file = urlopen(url).read()
memory_file = io.BytesIO(remote_file)
pdf = pdftotext.PDF(memory_file)
# Iterate over all the pages
for page in pdf:
print(page)
Extracted
UNITED STATES OF AMERICA
Before the
SECURITIES AND EXCHANGE COMMISSION
SECURITIES EXCHANGE ACT OF 1934
Release No. 76574 / December 7, 2015
ADMINISTRATIVE PROCEEDING
File No. 3-16987
ORDER INSTITUTING CEASE-AND-DESIST
In the Matter of PROCEEDINGS, PURSUANT TO SECTION
21C OF THE SECURITIES EXCHANGE ACT
KEFEI WANG OF 1934, MAKING FINDINGS, AND
IMPOSING REMEDIAL SANCTIONS AND A
Respondent. CEASE-AND-DESIST ORDER
I.
The Securities and Exchange Commission (“Commission”) deems it appropriate and in the
public interest that cease-and-desist proceedings be, and hereby are, instituted pursuant to 21C of
the Securities Exchange Act of 1934 (“Exchange Act”) against Kefei Wang (“Respondent”).
II.
In anticipation of the institution of these proceedings, Respondent has submitted an Offer
of Settlement (the “Offer”) which the Commission has determined to accept. Solely for the
purpose of these proceedings and any other proceedings brought by or on behalf of the
Commission, or to which the Commission is a party, and without admitting or denying the findings
herein, except as to the Commission’s jurisdiction over him and the subject matter of these
proceedings, which are admitted, and except as provided herein in Section V, Respondent consents
to the entry of this Order Instituting Cease-and-Desist Proceedings, Pursuant to Section 21C of the
Securities Exchange Act of 1934, Making Findings, and Imposing Remedial Sanctions and a
Cease-and-Desist Order (“Order”), as set forth below.
III.
On the basis of this Order and Respondent’s Offer, the Commission finds1 that:
Summary
1. Respondent violated Section 15(a)(1) of the Exchange Act by acting as an
unregistered broker-dealer in connection with his representation of clients who were seeking U.S.
residency through the Immigrant Investor Program. Respondent helped effect certain individuals’
securities purchases in an EB-5 Regional Center. Respondent received a commission from that
Regional Center for each investment he facilitated.
Respondent
2. Kefei Wang, age 39, is a resident of China. During the relevant time period, he was
a U.S. resident and an owner of Nautilus Global Capital, LLC , a now defunct entity that was based
in Fremont, California.
Background
3. The United States Congress created the Immigrant Investor Program, also known as
“EB-5,” in 1990 to stimulate the U.S. economy through job creation and capital investment by
foreign investors. The Program offers EB-5 visas to individuals who invest $1 million in a new
commercial enterprise that creates or preserves at least 10 full-time jobs for qualifying U.S.
workers (or $500,000 in an enterprise located in a rural area or an area of high unemployment). A
certain number of EB-5 visas are set aside for investors in approved Regional Centers. A Regional
Center is defined as “any economic unit, public or private, which is involved with the promotion of
economic growth, including increased export sales, improved regional productivity, job creation,
and increased domestic capital investment.” 8 C.F.R. § 204.6(e) (2015).
4. Typical Regional Center investment vehicles are offered as limited partnership
interests. The partnership interests are securities, usually offered pursuant to one or more
exemptions from the registration requirements of the U.S. securities laws. The Regional Centers
are often managed by a person or entity which acts as a general partner of the limited partnership.
The Regional Centers, the investment vehicles, and the managers are collectively referred to herein
as “EB-5 Investment Offerers.”
5. Various EB-5 Investment Offerers paid commissions to anyone who successfully
sold limited partnership interests to new investors.
1
The findings herein are made pursuant to Respondent’s Offer of Settlement and are not
binding on any other person or entity in this or any other proceeding.
2
Respondent Received Commissions for His Clients’ EB-5 Investments
6. From at least January 2010 through May 2014, Respondent received a portion of
commissions from one EB-5 Investment Offerer totaling $40,000. The commissions constituted
his portion of the commissions that were paid pursuant to a written Agency Agreement between
Nautilus Global Capital and the EB-5 Investment Offerer. On one or more occasions the
commission was paid to a foreign bank account identified by the Respondent despite the fact that
the Respondent was U.S.-based during the relevant time period.
7. Respondent performed activities necessary to effectuate the transaction, including
recommending the specific EB-5 Investment Offerer referenced in paragraph 6 to his clients;
acting as a liaison between the EB-5 Investment Offerer and the investors; and facilitating the
transfer and/or documentation of investment funds to the EB-5 Investment Offerer. Respondent
received his portion of transaction-based commissions due to Nautilus Global Capital for its
services from that EB-5 Investment Offerer.
8. As a result of the conduct described above, Respondent violated Section 15(a)(1) of
the Exchange Act which makes it unlawful for any broker or dealer which is either a person other
than a natural person or a natural person not associated with a broker or dealer to make use of the
mails or any means or instrumentality of interstate commerce “to effect any transactions in, or to
induce or attempt to induce the purchase or sale of, any security” unless such broker or dealer is
registered in accordance with Section 15(b) of the Exchange Act.
IV.
In view of the foregoing, the Commission deems it appropriate to impose the sanctions
agreed to in Respondent Kefei Wang’s Offer.
Accordingly, pursuant to Section 21C of the Exchange Act, it is hereby ORDERED that:
A. Respondent shall cease and desist from committing or causing any violations and
any future violations of Section 15(a)(1) of the Exchange Act.
B. Respondent shall, within ten (10) days of the entry of this Order, pay disgorgement
of $40,000, prejudgment interest of $1,590, and a civil money penalty of $25,000 to the Securities
and Exchange Commission for transfer to the general fund of the United States Treasury in
accordance with Exchange Act Section 21F(g)(3). If timely payment of disgorgement and
prejudgment interest is not made, additional interest shall accrue pursuant to SEC Rule of Practice
600 [17 C.F.R. § 201.600]. If timely payment of the civil money penalty is not made, additional
interest shall accrue pursuant to 31 U.S.C. § 3717. Payment must be made in one of the following
ways:
(1) Respondent may transmit payment electronically to the Commission, which will
provide detailed ACH transfer/Fedwire instructions upon request;
3
(2) Respondent may make direct payment from a bank account via Pay.gov through the
SEC website at http://www.sec.gov/about/offices/ofm.htm; or
(3) Respondent may pay by certified check, bank cashier’s check, or United States
postal money order, made payable to the Securities and Exchange Commission and
hand-delivered or mailed to:
Enterprise Services Center
Accounts Receivable Branch
HQ Bldg., Room 181, AMZ-341
6500 South MacArthur Boulevard
Oklahoma City, OK 73169
Payments by check or money order must be accompanied by a cover letter identifying
Kefei Wang as a Respondent in these proceedings, and the file number of these proceedings; a
copy of the cover letter and check or money order must be sent to Stephen L. Cohen, Associate
Director, Division of Enforcement, Securities and Exchange Commission, 100 F St., NE,
Washington, DC 20549-5553.
V.
It is further Ordered that, solely for purposes of exceptions to discharge set forth in Section
523 of the Bankruptcy Code, 11 U.S.C. § 523, the findings in this Order are true and admitted by
Respondent, and further, any debt for disgorgement, prejudgment interest, civil penalty or other
amounts due by Respondent under this Order or any other judgment, order, consent order, decree
or settlement agreement entered in connection with this proceeding, is a debt for the violation by
Respondent of the federal securities laws or any regulation or order issued under such laws, as set
forth in Section 523(a)(19) of the Bankruptcy Code, 11 U.S.C. § 523(a)(19).
By the Commission.
Brent J. Fields
Secretary
4
[Finished in 0.5s]

I think that there might be an issue with how you are extracting the pages try making a loop and calling each page separately like so
for i in range(0 , number_of_pages ):
pageObj = pdfReader.getPage(i)
page = pageObj.extractText()