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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

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Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

About

Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

About

Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

About

Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

Resources

Stars

0 stars

Watchers

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Forks

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Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

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Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

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Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

About

Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Python weekend entry task

Write a python script/module/package, that for a given flight data in a form of csv file (check the examples), prints out a structured list of all flight combinations for a selected route between airports A -> B, sorted by the final price for the trip.

Description

You've been provided with some semi-randomly generated example csv datasets you can use to test your solution. The datasets have following columns:

  • flight_no: Flight number.
  • origin, destination: Airport codes.
  • departure, arrival: Dates and times of the departures/arrivals.
  • base_price, bag_price: Prices of the ticket and one piece of baggage.
  • bags_allowed: Number of allowed pieces of baggage for the flight.

In addition to the dataset, your script will take some additional arguments as input:

Argument nametypeDescriptionNotes
originstringOrigin airport code
destinationstringDestination airport code

Search restrictions

  • By default you're performing search on ALL available combinations, according to search parameters.
  • In case of a combination of A -> B -> C, the layover time in B should not be less than 1 hour and more than 6 hours.
  • No repeating airports in the same trip!
    • A -> B -> A -> C is not a valid combination for search A -> C.
  • Output is sorted by the final price of the trip.

Optional arguments

You may add any number of additional search parameters to boost your chances to attend. Here are 2 recommended ones:

Argument nametypeDescriptionNotes
bagsintegerNumber of requested bagsOptional (defaults to 0)
returnbooleanIs it a return flight?Optional (defaults to false)
Performing return trip search

Example input (assuming solution.py is the main module):

python -m solution example/example0.csv RFZ WIW --bags=1 --return

will perform a search RFZ -> WIW -> RFZ for flights which allow at least 1 piece of baggage.

  • NOTE: Since WIW is in this case the final destination for one part of the trip, the layover rule does not apply.

Output

The output will be a json-compatible structured list of trips sorted by price. The trip has the following schema:

FieldDescription
flightsA list of flights in the trip according to the input dataset.
originOrigin airport of the trip.
destinationThe final destination of the trip.
bags_allowedThe number of allowed bags for the trip.
bags_countThe searched number of bags.
total_priceThe total price for the trip.
travel_timeThe total travel time.

For more information, check the example section.

Points of interest

Assuming your solution is working, we'll be additionally judging based on following skills:

  • input, output - what if we input garbage?
  • modules, packages & code structure (hint: it's easy to overdo it)
  • usage of standard library and built-in data structures
  • code readability, clarity, used conventions, documentation and comments

Requirements and restrictions

  • Your solution needs to contain a README file describing what it does and how to run it.
  • Only the standard library is allowed, no 3rd party packages, notebooks, specialized distros (Conda) etc.
  • The code should run as is, no environment setup should be required.

Submissions

Follow the instructions you received in the email.

Example behaviour

Let's imagine we wrote our solution into one file, solution.py and our datatset is in data.csv. We want to test the script by performing a flight search on route BTW -> REJ (we know the airports are present in the dataset) with one bag. We run the thing:

python -m solution data.csv BTW REJ --bags=1

and get the following result:

[
{
"flights": [
{
"flight_no": "XC233",
"origin": "BTW",
"destination": "WTF",
"departure": "2021-09-02T05:50:00",
"arrival": "2021-09-02T8:20:00",
"base_price": 67.0,
"bag_price": 7.0,
"bags_allowed": 2
},
{
"flight_no": "VJ832",
"origin": "WTF",
"destination": "REJ",
"departure": "2021-09-02T11:05:00",
"arrival": "2021-09-02T12:45:00",
"base_price": 31.0,
"bag_price": 5.0,
"bags_allowed": 1
}
],
"bags_allowed": 1,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 110.0,
"travel_time": "6:55:00"
},
{
"flights": [
{
"flight_no": "JV042",
"origin": "BTW",
"destination": "REJ",
"departure": "2021-09-01T17:35:00",
"arrival": "2021-09-01T21:05:00",
"base_price": 216.0,
"bag_price": 11.0,
"bags_allowed": 2
}
],
"bags_allowed": 2,
"bags_count": 1,
"destination": "REJ",
"origin": "BTW",
"total_price": 227.0,
"travel_time": "3:30:00"
}
]

About

Entry task assignment for python weekend in Budapest 4.3.2022 | https://pythonweekend.cz/

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