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codebasics
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regex
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‎Advanced/regex/regex_tutorial_exercise_answer.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import re"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**1. Extract all twitter handles from following text. Twitter handle is the text that appears after https://twitter.com/ and is a single word. Also it contains only alpha numeric characters i.e. A-Z a-z , o to 9 and underscore _**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['elonmusk', 'teslarati', 'dummy_tesla', 'dummy_2_tesla']"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text = '''\n",
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"Follow our leader Elon musk on twitter here: https://twitter.com/elonmusk, more information \n",
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"on Tesla's products can be found at https://www.tesla.com/. Also here are leading influencers \n",
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"for tesla related news,\n",
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"https://twitter.com/teslarati\n",
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"https://twitter.com/dummy_tesla\n",
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"https://twitter.com/dummy_2_tesla\n",
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"'''\n",
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"pattern = 'https://twitter\\.com/([a-zA-Z0-9_]+)'\n",
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"\n",
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"re.findall(pattern, text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**2. Extract Concentration Risk Types. It will be a text that appears after \"Concentration Risk:\", In below example, your regex should extract these two strings**\n",
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"\n",
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"(1) Credit Risk\n",
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"\n",
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"(2) Supply Rish"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Credit Risk', 'Credit Risk']"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text = '''\n",
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"Concentration of Risk: Credit Risk\n",
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"Financial instruments that potentially subject us to a concentration of credit risk consist of cash, cash equivalents, marketable securities,\n",
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"restricted cash, accounts receivable, convertible note hedges, and interest rate swaps. Our cash balances are primarily invested in money market funds\n",
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"or on deposit at high credit quality financial institutions in the U.S. These deposits are typically in excess of insured limits. As of September 30, 2021\n",
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"and December 31, 2020, no entity represented 10% or more of our total accounts receivable balance. The risk of concentration for our convertible note\n",
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"hedges and interest rate swaps is mitigated by transacting with several highly-rated multinational banks.\n",
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"Concentration of Risk: Supply Risk\n",
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"We are dependent on our suppliers, including single source suppliers, and the inability of these suppliers to deliver necessary components of our\n",
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"products in a timely manner at prices, quality levels and volumes acceptable to us, or our inability to efficiently manage these components from these\n",
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"suppliers, could have a material adverse effect on our business, prospects, financial condition and operating results.\n",
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"'''\n",
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"pattern = 'Concentration of Risk: ([^\\n]*)'\n",
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"\n",
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"re.findall(pattern, text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**3. Companies in europe reports their financial numbers of semi annual basis and you can have a document like this. To exatract quarterly and semin annual period you can use a regex as shown below**\n",
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"\n",
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"Hint: you need to use (?:) here to match everything enclosed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['2021 Q1', '2021 S1']"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text = '''\n",
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"Tesla's gross cost of operating lease vehicles in FY2021 Q1 was $4.85 billion.\n",
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"BMW's gross cost of operating vehicles in FY2021 S1 was $8 billion.\n",
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"'''\n",
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"\n",
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"pattern = 'FY(\\d{4} (?:Q[1-4]|S[1-2]))'\n",
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"matches = re.findall(pattern, text)\n",
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"matches"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎Advanced/regex/regex_tutorial_exercise_questions.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h1 align='center'>Python Regular Expression Tutorial Exericse</h1>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import re"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**1. Extract all twitter handles from following text. Twitter handle is the text that appears after https://twitter.com/ and is a single word. Also it contains only alpha numeric characters i.e. A-Z a-z , o to 9 and underscore _**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"text = '''\n",
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"Follow our leader Elon musk on twitter here: https://twitter.com/elonmusk, more information \n",
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"on Tesla's products can be found at https://www.tesla.com/. Also here are leading influencers \n",
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"for tesla related news,\n",
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"https://twitter.com/teslarati\n",
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"https://twitter.com/dummy_tesla\n",
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"https://twitter.com/dummy_2_tesla\n",
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"'''\n",
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"pattern = '' # todo: type your regex here\n",
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"\n",
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"re.findall(pattern, text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**2. Extract Concentration Risk Types. It will be a text that appears after \"Concentration Risk:\", In below example, your regex should extract these two strings**\n",
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"\n",
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"(1) Credit Risk\n",
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"\n",
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"(2) Supply Rish"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"text = '''\n",
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"Concentration of Risk: Credit Risk\n",
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"Financial instruments that potentially subject us to a concentration of credit risk consist of cash, cash equivalents, marketable securities,\n",
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"restricted cash, accounts receivable, convertible note hedges, and interest rate swaps. Our cash balances are primarily invested in money market funds\n",
68+
"or on deposit at high credit quality financial institutions in the U.S. These deposits are typically in excess of insured limits. As of September 30, 2021\n",
69+
"and December 31, 2020, no entity represented 10% or more of our total accounts receivable balance. The risk of concentration for our convertible note\n",
70+
"hedges and interest rate swaps is mitigated by transacting with several highly-rated multinational banks.\n",
71+
"Concentration of Risk: Supply Risk\n",
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"We are dependent on our suppliers, including single source suppliers, and the inability of these suppliers to deliver necessary components of our\n",
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"products in a timely manner at prices, quality levels and volumes acceptable to us, or our inability to efficiently manage these components from these\n",
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"suppliers, could have a material adverse effect on our business, prospects, financial condition and operating results.\n",
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"'''\n",
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"pattern = '' # todo: type your regex here\n",
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"\n",
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"re.findall(pattern, text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**3. Companies in europe reports their financial numbers of semi annual basis and you can have a document like this. To exatract quarterly and semin annual period you can use a regex as shown below**\n",
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"\n",
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"Hint: you need to use (?:) here to match everything enclosed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"text = '''\n",
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"Tesla's gross cost of operating lease vehicles in FY2021 Q1 was $4.85 billion.\n",
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"BMW's gross cost of operating vehicles in FY2021 S1 was $8 billion.\n",
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"'''\n",
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"\n",
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"pattern = '' # todo: type your regex here\n",
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"matches = re.findall(pattern, text)\n",
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"matches"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"__[Solution](http://ndtv.com)__"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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