{"id":53299,"date":"2019-11-28T00:00:00","date_gmt":"2019-11-27T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/56-proektov-na-python-s-otkrytym-ishodnym-kodom"},"modified":"2020-02-18T14:01:11","modified_gmt":"2020-02-18T11:01:11","slug":"56-proektov-na-python-s-otkrytym-ishodnym-kodom","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom","title":{"rendered":"56 projekt\u016f na Pythonu s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"56 projekt\u016f na Pythonu s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem\" src=\"\/wp-content\/uploads\/2019\/11\/424ea06c178ea7e7823676a1119763e9.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>1. Flask<\/h3>\n<p>\nToto je mikroframework napsan\u00fd v Pythonu. Nem\u00e1 validaci pro formul\u00e1\u0159e a \u00farove\u0148 abstrakce datab\u00e1ze, ale umo\u017e\u0148uje v\u00e1m pou\u017e\u00edvat extern\u00ed knihovny pro b\u011b\u017en\u00e9 funkce. A p\u0159esn\u011b proto je to mikroframework. Flask je navr\u017een pro snadn\u00e9 a rychl\u00e9 vytv\u00e1\u0159en\u00ed aplikac\u00ed a je tak\u00e9 \u0161k\u00e1lovateln\u00fd a lehk\u00fd. Je zalo\u017een na projektech Werkzeug a Jinja2. M\u016f\u017eete se o n\u011bm dozv\u011bd\u011bt v\u00edce v posledn\u00edm \u010dl\u00e1nku DataFlair o <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-flask-tutorial\/\">Python Flask<\/a><\/noindex>.<\/p>\n<h3>2. Keras<\/h3>\n<p>\nKeras je knihovna pro neuronov\u00e9 s\u00edt\u011b s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem, napsan\u00e1 v Pythonu. Je u\u017eivatelsky p\u0159\u00edv\u011btiv\u00e1, modul\u00e1rn\u00ed a roz\u0161i\u0159iteln\u00e1, a tak\u00e9 m\u016f\u017ee b\u011b\u017eet na TensorFlow, Theano, PlaidML nebo Microsoft Cognitive Toolkit (CNTK). Keras obsahuje v\u0161echno: \u0161ablony, c\u00edlov\u00e9 a p\u0159enosov\u00e9 funkce, optimaliz\u00e1tory a mnoho dal\u0161\u00edho. Tak\u00e9 podporuje konvolu\u010dn\u00ed a rekurentn\u00ed neuronov\u00e9 s\u00edt\u011b.<\/p>\n<p>Pr\u00e1ce na posledn\u00edm projektu s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem zalo\u017een\u00e9m na Keras je <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/project-in-python-breast-cancer-classification\/\">Klasifikace rakoviny prsu<\/a><\/noindex>.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<blockquote><p><noindex><a rel=\"nofollow\" href=\"https:\/\/www.edsd.ru\/\" title=\"EDISON Software - v\u00fdvoj webov\u00fdch aplikac\u00ed\"><img decoding=\"async\" alt=\"56 projekt\u016f na Pythonu s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem\" src=\"\/wp-content\/uploads\/2019\/11\/4b13d579a890b450683f1a60ba814766.jpeg\" style=\"display:block;margin: 0 auto;\" \/><\/a><\/noindex><br clear=\"right\"><br \/>\n\u010cl\u00e1nek byl p\u0159elo\u017een s podporou spole\u010dnosti EDISON Software, kter\u00e1 <noindex><a rel=\"nofollow\" href=\"https:\/\/www.edsd.ru\/diagnostika-hranilishha-dokumentov-vivaldi\">vyv\u00edj\u00ed diagnostick\u00fd syst\u00e9m pro spr\u00e1vu dokument\u016f Vivaldi<\/a><\/noindex>, a tak\u00e9 <noindex><a rel=\"nofollow\" href=\"https:\/\/www.edsd.ru\/ru\/princypy\/investiruem-v-produkty\">investuje do startup\u016f<\/a><\/noindex>.<\/p><\/blockquote>\n<h3>3. SpaCy<\/h3>\n<p>\nToto je knihovna s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem, kter\u00e1 se zab\u00fdv\u00e1 <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/nlp-natural-language-processing\/\">zpracov\u00e1n\u00edm p\u0159irozen\u00e9ho jazyka (NLP)<\/a><\/noindex> a je naps\u00e1na v Pythonu a Cythonu. Zat\u00edmco NLTK je vhodn\u011bj\u0161\u00ed pro v\u00fduku a v\u00fdzkumn\u00e9 \u00fa\u010dely, c\u00edlem spaCy je poskytov\u00e1n\u00ed softwaru pro produkci. D\u00e1le je Thinc knihovna pro strojov\u00e9 u\u010den\u00ed spaCy, kter\u00e1 obsahuje modely CNN pro tagy \u010d\u00e1sti \u0159e\u010di, anal\u00fdzu z\u00e1vislost\u00ed a rozpozn\u00e1v\u00e1n\u00ed pojmenovan\u00fdch entit.<\/p>\n<h3>4. Sentry<\/h3>\n<p>\nSentry nab\u00edz\u00ed hosting pro sledov\u00e1n\u00ed chyb s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem, abyste mohli v re\u00e1ln\u00e9m \u010dase odhalovat a t\u0159\u00eddit chyby. Sta\u010d\u00ed nainstalovat SDK pro va\u0161e jazyky nebo frameworky a za\u010d\u00edt pracovat. Umo\u017e\u0148uje zaznamen\u00e1vat neo\u010dek\u00e1van\u00e9 v\u00fdjimky, prozkoum\u00e1vat trasov\u00e1n\u00ed z\u00e1sobn\u00edku, analyzovat dopad ka\u017ed\u00e9ho probl\u00e9mu, sledovat chyby v r\u016fzn\u00fdch projektech, p\u0159id\u011blovat probl\u00e9my a mnoho dal\u0161\u00edho. Pou\u017eit\u00ed Sentry znamen\u00e1 m\u00e9n\u011b chyb a v\u00edce odes\u00edlan\u00e9ho k\u00f3du.<\/p>\n<h3>5. OpenCV<\/h3>\n<p>\nOpenCV je knihovna po\u010d\u00edta\u010dov\u00e9ho vid\u011bn\u00ed a strojov\u00e9ho u\u010den\u00ed s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem. Knihovna obsahuje v\u00edce ne\u017e 2500 optimalizovan\u00fdch algoritm\u016f pro \u00falohy po\u010d\u00edta\u010dov\u00e9ho vid\u011bn\u00ed, jako je detekce a rozpozn\u00e1v\u00e1n\u00ed objekt\u016f, klasifikace r\u016fzn\u00fdch typ\u016f lidsk\u00fdch aktivit, sledov\u00e1n\u00ed pohyb\u016f pomoc\u00ed kamery, vytv\u00e1\u0159en\u00ed trojrozm\u011brn\u00fdch model\u016f objekt\u016f, stitching obr\u00e1zk\u016f pro dosa\u017een\u00ed vysok\u00e9ho rozli\u0161en\u00ed a mnoho dal\u0161\u00edch \u00faloh. Knihovna je dostupn\u00e1 pro mnoho jazyk\u016f, jako jsou Python, C ++, Java atd.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na GitHubu: 39585<\/p>\n<p>Pracovali jste ji\u017e na n\u011bjak\u00e9m projektu OpenCV? Tady je jeden \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-project-gender-age-detection\/\">Projekt pro ur\u010den\u00ed pohlav\u00ed a v\u011bku<\/a><\/noindex><\/p>\n<h3>6. Nilearn<\/h3>\n<p>\nToto je modul pro rychlou a jednoduchou implementaci statistick\u00e9ho u\u010den\u00ed na datech NeuroImaging. Umo\u017e\u0148uje vyu\u017e\u00edvat scikit-learn pro v\u00edcerozm\u011brnou statistiku pro prediktivn\u00ed modelov\u00e1n\u00ed, klasifikaci, dek\u00f3dov\u00e1n\u00ed a anal\u00fdzu konektivity. Nilearn je sou\u010d\u00e1st\u00ed ekosyst\u00e9mu NiPy, co\u017e je komunita v\u011bnuj\u00edc\u00ed se pou\u017e\u00edv\u00e1n\u00ed Pythonu pro anal\u00fdzu dat neurozobrazen\u00ed.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/nilearn\/nilearn\">Github<\/a><\/noindex>: 549<\/p>\n<h3>7. scikit-Learn<\/h3>\n<p>\nScikit-learn je dal\u0161\u00ed open source projekt v Pythonu. Je to velmi zn\u00e1m\u00e1 knihovna strojov\u00e9ho u\u010den\u00ed pro Python. \u010casto se pou\u017e\u00edv\u00e1 s NumPy a SciPy, SciPy nab\u00edz\u00ed klasifikaci, regresi a shlukov\u00e1n\u00ed \u2014 podporuje <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/svm-support-vector-machine-tutorial\/\">SVM (Support Vector Machines)<\/a><\/noindex>, n\u00e1hodn\u00e9 lesy, gradientn\u00ed boosting, k-pr\u016fm\u011bry a DBSCAN. Tato knihovna je napsan\u00e1 v jazyc\u00edch Python a Cython.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na GitHubu: 37,144<\/p>\n<h3>8. PyTorch<\/h3>\n<p>\nPyTorch je dal\u0161\u00ed otev\u0159en\u00e1 knihovna strojov\u00e9ho u\u010den\u00ed, napsan\u00e1 v Pythonu a pro Python. Je zalo\u017eena na knihovn\u011b Torch a je v\u00fdborn\u00e1 pro oblasti jako je po\u010d\u00edta\u010dov\u00e9 vid\u011bn\u00ed a zpracov\u00e1n\u00ed p\u0159irozen\u00e9ho jazyka (NLP). M\u00e1 tak\u00e9 frontend v C++. <\/p>\n<p>Mezi mnoha dal\u0161\u00edmi funkcemi PyTorch nab\u00edz\u00ed dv\u011b vysoce \u00farov\u0148ov\u00e9:<\/p>\n<ul>\n<li>Tenzorov\u00e9 v\u00fdpo\u010dty s v\u00fdrazn\u00fdm zrychlen\u00edm pomoc\u00ed GPU<\/li>\n<li>Hloubkov\u00e9 neuronov\u00e9 s\u00edt\u011b<\/li>\n<\/ul>\n<p>Po\u010det hv\u011bzdi\u010dek na GitHubu: 31 779<\/p>\n<h3>9. Librosa<\/h3>\n<p>\nLibrosa je jedna z nejlep\u0161\u00edch python knihoven pro anal\u00fdzu hudby a audia. Obsahuje nezbytn\u00e9 komponenty, kter\u00e9 se pou\u017e\u00edvaj\u00ed pro z\u00edsk\u00e1v\u00e1n\u00ed informac\u00ed z hudby. Knihovna je dob\u0159e zdokumentov\u00e1na a obsahuje n\u011bkolik pr\u016fvodc\u016f a p\u0159\u00edklad\u016f, kter\u00e9 usnadn\u00ed spln\u011bn\u00ed va\u0161eho \u00fakolu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 3107<\/p>\n<p>Implementace projektu Python s open-source a Librosa \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-mini-project-speech-emotion-recognition\/\">rozpozn\u00e1v\u00e1n\u00ed emoc\u00ed v \u0159e\u010di<\/a><\/noindex>. <\/p>\n<h3>10. Gensim<\/h3>\n<p>\nGensim je Python knihovna pro modelov\u00e1n\u00ed t\u00e9mat, indexaci dokument\u016f a vyhled\u00e1v\u00e1n\u00ed shod s velk\u00fdmi korporacemi. Zam\u011b\u0159uje se na NLP a informa\u010dn\u00ed vyhled\u00e1vac\u00ed komunity. Gensim je zkr\u00e1tka pro \u201egenerov\u00e1n\u00ed podobn\u00e9ho\u201c. D\u0159\u00edve vytv\u00e1\u0159el kr\u00e1tk\u00fd seznam \u010dl\u00e1nk\u016f podobn\u00fdch tomuto \u010dl\u00e1nku. Gensim je srozumiteln\u00fd, efektivn\u00ed a \u0161k\u00e1lovateln\u00fd. Gensim implementuje efektivn\u00ed a jednoduchou realizaci ne\u0159\u00edzen\u00e9ho s\u00e9mantick\u00e9ho modelov\u00e1n\u00ed z jednoduch\u00e9ho textu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 9 870<\/p>\n<h3>11. Django<\/h3>\n<p>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/django-tutorials-home\/\">Django<\/a><\/noindex> \u2014 vysokofrekven\u010dn\u00ed Python framework, kter\u00fd podporuje rychl\u00fd v\u00fdvoj a v\u011b\u0159\u00ed v princip DRY (neopakuj se). Je to velmi siln\u00fd a nejv\u00edce pou\u017e\u00edvan\u00fd framework pro Python. Je zalo\u017een na vzoru MTV (Model-Template-View).<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 44 214<\/p>\n<h3>12. Rozpozn\u00e1v\u00e1n\u00ed obli\u010dej\u016f<\/h3>\n<p>\nRozpozn\u00e1v\u00e1n\u00ed obli\u010dej\u016f je popul\u00e1rn\u00ed projekt na GitHubu. Snadno rozpozn\u00e1v\u00e1 obli\u010deje a manipuluje s nimi pomoc\u00ed Pythonu \/ p\u0159\u00edkazov\u00e9ho \u0159\u00e1dku a pou\u017e\u00edv\u00e1 k tomu nejjednodu\u0161\u0161\u00ed knihovnu pro rozpozn\u00e1v\u00e1n\u00ed obli\u010dej\u016f na sv\u011bt\u011b. K tomu se pou\u017e\u00edv\u00e1 dlib s hlubok\u00fdm u\u010den\u00edm pro detekci obli\u010dej\u016f s p\u0159esnost\u00ed 99,38 % v testu Wild benchmark.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 28 267<\/p>\n<h3>13. Cookiecutter<\/h3>\n<p>\nCookiecutter je p\u0159\u00edkazov\u00e1 utility, kterou lze pou\u017e\u00edt k vytv\u00e1\u0159en\u00ed projekt\u016f z \u0161ablon (cookiecutters). Jedn\u00edm z p\u0159\u00edklad\u016f m\u016f\u017ee b\u00fdt vytvo\u0159en\u00ed projektov\u00e9ho bal\u00ed\u010dku ze \u0161ablony projektov\u00e9ho bal\u00ed\u010dku. Jsou to multiplatformn\u00ed \u0161ablony, a \u0161ablony projekt\u016f mohou b\u00fdt v jak\u00e9mkoli jazyce nebo form\u00e1tu zna\u010dkov\u00e1n\u00ed, jako je Python, JavaScript, HTML, Ruby, CoffeeScript, RST a Markdown. Umo\u017e\u0148uje tak\u00e9 pou\u017e\u00edvat n\u011bkolik jazyk\u016f ve stejn\u00e9 \u0161ablon\u011b projektu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 10 291<\/p>\n<h3>14. Pandas<\/h3>\n<p>\nPandas je knihovna pro anal\u00fdzu dat a manipulaci s nimi pro Python, kter\u00e1 nab\u00edz\u00ed ozna\u010den\u00e9 datov\u00e9 struktury a statistick\u00e9 funkce.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 21 404<\/p>\n<p>Python projekt s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem na vyzkou\u0161en\u00ed Pandas \u2014 <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-machine-learning-project-detecting-parkinson-disease\/\">detekce Parkinsonovy choroby<\/a><\/noindex><\/p>\n<h3>15. Pipenv<\/h3>\n<p>\nPipenv slibuje b\u00fdt n\u00e1strojem p\u0159ipraven\u00fdm na produkci, zam\u011b\u0159en\u00fdm na to, aby p\u0159inesl to nejlep\u0161\u00ed ze v\u0161ech bal\u00ed\u010dkov\u00fdch sv\u011bt\u016f do sv\u011bta Pythonu. Jeho termin\u00e1l m\u00e1 kr\u00e1sn\u00e9 barvy a spojuje Pipfile, pip a virtualenv do jednoho p\u0159\u00edkazu. Automaticky vytv\u00e1\u0159\u00ed a spravuje virtu\u00e1ln\u00ed prost\u0159ed\u00ed pro va\u0161e projekty a poskytuje u\u017eivatel\u016fm snadn\u00fd zp\u016fsob, jak nastavit pracovn\u00ed prost\u0159ed\u00ed.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 18 322<\/p>\n<h3>16. SimpleCoin<\/h3>\n<p>\nJedn\u00e1 se o implementaci Blockchainu pro kryptom\u011bnu, vytvo\u0159enou v Pythonu, ale je jednoduch\u00e1, nezabezpe\u010den\u00e1 a ne\u00fapln\u00e1. SimpleCoin nen\u00ed ur\u010den pro pou\u017eit\u00ed v produkci. Nen\u00ed ur\u010den pro produk\u010dn\u00ed pou\u017eit\u00ed, SimpleCoin je navr\u017een pro vzd\u011bl\u00e1vac\u00ed \u00fa\u010dely a sna\u017e\u00ed se zp\u0159\u00edstupnit pracovn\u00ed blockchain a zjednodu\u0161it ho. Umo\u017e\u0148uje ukl\u00e1dat t\u011b\u017een\u00e9 hash a sm\u011b\u0148ovat je za jakoukoli podporovanou m\u011bnu.<br \/>\nPo\u010det hv\u011bzd na Githubu: 1 343<\/p>\n<h3>17. Pyray<\/h3>\n<p>\nJe to knihovna 3D renderov\u00e1n\u00ed napsan\u00e1 v \u010dist\u00e9m Pythonu. Vizu\u00e1ln\u011b 2D, 3D objekty a vy\u0161\u0161\u00ed sc\u00e9ny v Pythonu a animace. Pom\u00e1h\u00e1 n\u00e1m v oblasti vytv\u00e1\u0159en\u00fdch vide\u00ed, videoher, fyzik\u00e1ln\u00edch simulac\u00ed a dokonce i p\u011bkn\u00fdch obr\u00e1zk\u016f. Po\u017eadavky na to jsou: PIL, numpy a scipy.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 451<\/p>\n<h3>18. MicroPython<\/h3>\n<p>\nMicroPython je Python pro mikrokontrol\u00e9ry. Je to efektivn\u00ed implementace Python3, kter\u00e1 obsahuje mnoho bal\u00ed\u010dk\u016f ze standardn\u00ed knihovny Python a je optimalizov\u00e1na pro pr\u00e1ci na mikrokontrol\u00e9rech a v omezen\u00fdch podm\u00ednk\u00e1ch. Pyboard je mal\u00e1 elektronick\u00e1 deska, na kter\u00e9 MicroPython b\u011b\u017e\u00ed na hol\u00e9m kovu, tak\u017ee m\u016f\u017ee ovl\u00e1dat v\u0161echny druhy elektronick\u00fdch projekt\u016f.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/micropython\/micropython\">Github<\/a><\/noindex>: 9,197<\/p>\n<h3>19. Kivy<\/h3>\n<p>\nKivy je knihovna Python pro v\u00fdvoj mobiln\u00edch aplikac\u00ed a dal\u0161\u00edch aplikac\u00ed s v\u00edcetrak\u010dn\u00edm u\u017eivatelsk\u00fdm rozhran\u00edm (NUI). M\u00e1 grafickou knihovnu, n\u011bkolik mo\u017enost\u00ed widget\u016f, meziproduktov\u00fd jazyk Kv pro vytv\u00e1\u0159en\u00ed vlastn\u00edch widget\u016f, podporu my\u0161i, kl\u00e1vesnice, TUIO a ud\u00e1losti v\u00edcetrak\u010dn\u00edho vstupu. Je to open-source knihovna pro rychl\u00fd v\u00fdvoj aplikac\u00ed s inovativn\u00edmi u\u017eivatelsk\u00fdmi rozhran\u00edmi. Je multiplatformn\u00ed, podnikatelsky p\u0159\u00edv\u011btiv\u00e1 a m\u00e1 akceleraci GPU.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 9 930<\/p>\n<h3>20. Dash<\/h3>\n<p>\nDash by Plotly je r\u00e1mec pro webov\u00e9 aplikace. Postaven na Flask, Plotly.js, React a React.js, umo\u017e\u0148uje pou\u017e\u00edvat Python k vytv\u00e1\u0159en\u00ed informa\u010dn\u00edch panel\u016f. Zaji\u0161\u0165uje provoz model\u016f Python a R v m\u011b\u0159\u00edtku. Dash umo\u017e\u0148uje vytv\u00e1\u0159et, testovat, nasazovat a sestavovat zpr\u00e1vy bez pou\u017eit\u00ed DevOps, JavaScriptu, CSS nebo CronJobs. Dash je v\u00fdkonn\u00fd, p\u0159izp\u016fsobiteln\u00fd, lehk\u00fd a snadno spravovateln\u00fd. Tak\u00e9 m\u00e1 otev\u0159en\u00fd zdrojov\u00fd k\u00f3d.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 9 883<\/p>\n<h3>21. Magenta<\/h3>\n<p>\nMagenta je v\u00fdzkumn\u00fd projekt s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem, kter\u00fd se zam\u011b\u0159uje na strojov\u00e9 u\u010den\u00ed jako n\u00e1stroj v kreativn\u00edm procesu. Umo\u017e\u0148uje vytv\u00e1\u0159et hudbu a um\u011bn\u00ed pomoc\u00ed strojov\u00e9ho u\u010den\u00ed. Magenta je knihovna Python zalo\u017een\u00e1 na TensorFlow, s utilitami pro pr\u00e1ci se vstupn\u00edmi daty, jejich pou\u017eit\u00ed pro tr\u00e9nink strojov\u00fdch model\u016f a vytv\u00e1\u0159en\u00ed nov\u00e9ho obsahu.<\/p>\n<h3>22. Mask R-CNN<\/h3>\n<p>\nToto je implementace Mask R-CNN na Pythonu 3, TensorFlow a Keras. Model bere ka\u017edou instanci objektu na rastru a vytv\u00e1\u0159\u00ed pro n\u011bj ohrani\u010duj\u00edc\u00ed r\u00e1my a masky segmentace. Pou\u017e\u00edv\u00e1 s\u00ed\u0165 Feature Pyramid Network (FPN) a z\u00e1klad ResNet101. K\u00f3d je snadno roz\u0161i\u0159iteln\u00fd. Tento projekt tak\u00e9 nab\u00edz\u00ed dataset Matterport3D o rekonstruovan\u00fdch 3D prostorech, zachycen\u00fdch z\u00e1kazn\u00edky...<br \/>\nPo\u010det hv\u011bzd na Githubu: 14 055<\/p>\n<h3>23. Modely TensorFlow<\/h3>\n<p>\nToto je repozit\u00e1\u0159 s r\u016fzn\u00fdmi modely implementovan\u00fdmi v TensorFlow \u2013 ofici\u00e1ln\u00edmi i v\u00fdzkumn\u00fdmi modely. M\u00e1 tak\u00e9 p\u0159\u00edklady a tutori\u00e1ly. Ofici\u00e1ln\u00ed modely pou\u017e\u00edvaj\u00ed vysoce \u00farov\u0148ov\u00e9 API TensorFlow. V\u00fdzkumn\u00e9 modely jsou modely implementovan\u00e9 v TensorFlow v\u00fdzkumn\u00edky pro jejich podporu nebo \u0159e\u0161en\u00ed po\u017eadavk\u016f.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 57 745<\/p>\n<h3>24. Snallygaster<\/h3>\n<p>\nSnallygaster je zp\u016fsob organizace probl\u00e9m\u016f s projektov\u00fdmi tabulemi. D\u00edky tomu m\u016f\u017eete p\u0159izp\u016fsobit panel s projekty na GitHubu, optimalizovat a automatizovat pracovn\u00ed tok. Umo\u017e\u0148uje t\u0159\u00eddit \u00fakoly, pl\u00e1novat projekty, automatizovat pracovn\u00ed tok, sledovat pokrok, sd\u00edlet stav a nakonec dokon\u010dovat. Snallygaster m\u016f\u017ee prohled\u00e1vat tajn\u00e9 soubory na HTTP serverech \u2013 hled\u00e1 soubory dostupn\u00e9 na webov\u00fdch serverech, kter\u00e9 by nem\u011bly b\u00fdt ve\u0159ejn\u011b p\u0159\u00edstupn\u00e9 a mohou p\u0159edstavovat bezpe\u010dnostn\u00ed hrozbu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 1 477<\/p>\n<h3>25. Statsmodels<\/h3>\n<p>\nTo je <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-packages\/\">Python bal\u00ed\u010dek<\/a><\/noindex>, kter\u00fd dopl\u0148uje scipy pro statistick\u00e9 v\u00fdpo\u010dty, v\u010detn\u011b popisn\u00e9 statistiky, a tak\u00e9 odhady a z\u00e1v\u011bry pro statistick\u00e9 modely. Pro to m\u00e1 t\u0159\u00eddy a funkce. Umo\u017e\u0148uje n\u00e1m tak\u00e9 prov\u00e1d\u011bt statistick\u00e9 testy a zkoum\u00e1n\u00ed statistick\u00fdch dat.<br \/>\nPo\u010det hv\u011bzdi\u010dek na Githubu: 4 246<\/p>\n<h3>26. WhatWaf<\/h3>\n<p>\nJe to pokro\u010dil\u00fd n\u00e1stroj pro detekci firewallu, kter\u00fd m\u016f\u017eeme pou\u017e\u00edt k pochopen\u00ed, zda je p\u0159\u00edtomen firewall pro webov\u00e9 aplikace. Detekuje firewall v webov\u00e9 aplikaci a pokou\u0161\u00ed se objevit jednu nebo v\u00edce obch\u00e1zen\u00ed na uveden\u00e9m c\u00edli.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 1 300<\/p>\n<h3>27. Chainer<\/h3>\n<p>\nChainer je <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/deep-learning-with-python-libraries\/\">prost\u0159ed\u00ed pro hlubok\u00e9 u\u010den\u00ed<\/a><\/noindex>, zam\u011b\u0159en\u00e9 na flexibilitu. Z\u00e1kladem je Python a nab\u00edz\u00ed diferencovan\u00e9 API, zalo\u017een\u00e9 na p\u0159\u00edstupu define-by-run. Chainer tak\u00e9 nab\u00edz\u00ed objektov\u011b orientovan\u00e1 API vysok\u00e9 \u00farovn\u011b pro budov\u00e1n\u00ed a tr\u00e9nov\u00e1n\u00ed neuronov\u00fdch s\u00edt\u00ed. Je to mocn\u00e1, flexibiln\u00ed a intuitivn\u00ed struktura pro neuronov\u00e9 s\u00edt\u011b.<br \/>\nPo\u010det hv\u011bzdi\u010dek na Githubu: 5 054<\/p>\n<h3>28. Rebound<\/h3>\n<p>\nRebound je n\u00e1stroj p\u0159\u00edkazov\u00e9ho \u0159\u00e1dku. Kdy\u017e obdr\u017e\u00edte chybovou zpr\u00e1vu od p\u0159eklada\u010de, okam\u017eit\u011b z\u00edsk\u00e1 v\u00fdsledky z p\u0159ete\u010den\u00e9ho z\u00e1sobn\u00edku. K tomu m\u016f\u017eete pou\u017e\u00edt p\u0159\u00edkaz rebound pro proveden\u00ed va\u0161eho souboru. Je to jeden z 50 nejobl\u00edben\u011bj\u0161\u00edch projekt\u016f s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem Pythonu roku 2018. Nav\u00edc vy\u017eaduje Python 3.0 nebo nov\u011bj\u0161\u00ed. Podporovan\u00e9 typy soubor\u016f: Python, Node.js, Ruby, Golang a Java.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 2 913<\/p>\n<h3>29. Detectron<\/h3>\n<p>\nDetectron prov\u00e1d\u00ed modern\u00ed detekci objekt\u016f (tak\u00e9 implementuje masku R-CNN). Tento software je od Facebook AI Research (FAIR), napsan\u00fd v Pythonu a b\u011b\u017e\u00edc\u00ed na platform\u011b Caffe2 Deep Learning. C\u00edlem Detectron je poskytnout vysoce kvalitn\u00ed a vysoce v\u00fdkonnou k\u00f3dovou z\u00e1kladnu pro v\u00fdzkum detekce objekt\u016f. Je flexibiln\u00ed a implementuje n\u00e1sleduj\u00edc\u00ed algoritmy - maska R-CNN, RetinaNet, rychlej\u0161\u00ed R-CNN, RPN, rychl\u00fd R-CNN, R-FCN.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 21 873<\/p>\n<h3>30. Python-fire<\/h3>\n<p>\nToto je knihovna pro automatickou generaci CLI (p\u0159\u00edkazov\u00fdch \u0159\u00e1dk\u016f) z (jak\u00e9hokoli) objektu Python. Umo\u017e\u0148uje v\u00e1m tak\u00e9 vyv\u00edjet a ladit k\u00f3d, zkoumat existuj\u00edc\u00ed k\u00f3d nebo p\u0159ev\u00e1d\u011bt ciz\u00ed k\u00f3d na CLI. Python Fire usnad\u0148uje p\u0159echod mezi Bash a Pythonem a usnad\u0148uje pou\u017eit\u00ed REPL.<br \/>\nPo\u010det hv\u011bzd na Githubu: 15 299<\/p>\n<h3>31. Pylearn2<\/h3>\n<p>\nPylearn2 je knihovna strojov\u00e9ho u\u010den\u00ed, kter\u00e1 je postavena p\u0159edev\u0161\u00edm na Theano. Jej\u00edm c\u00edlem je usnadnit v\u00fdzkum ML. Umo\u017e\u0148uje ps\u00e1t nov\u00e9 algoritmy a modely.<br \/>\nPo\u010det hv\u011bzd na Githubu: 2681<\/p>\n<h3>32. Matplotlib<\/h3>\n<p>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-matplotlib-tutorial\/\">Matplotlib<\/a><\/noindex> je 2D knihovna pro vykreslov\u00e1n\u00ed v Pythonu \u2013 generuje kvalitn\u00ed publikace v r\u016fzn\u00fdch form\u00e1tech.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 10 072<\/p>\n<h3>33. Theano<\/h3>\n<p>\nTheano je knihovna pro manipulaci s matematick\u00fdmi a maticov\u00fdmi v\u00fdrazy. Je to tak\u00e9 optimaliza\u010dn\u00ed kompil\u00e1tor. Theano pou\u017e\u00edv\u00e1 <noindex><a rel=\"nofollow\" href=\"https:\/\/data-flair.training\/blogs\/python-numpy-tutorial\/\">NumPy<\/a><\/noindex>- podobn\u00fd syntaktick\u00fd pro vyj\u00e1d\u0159en\u00ed v\u00fdpo\u010dt\u016f a kompiluje je pro b\u011bh na architektur\u00e1ch CPU nebo GPU. Je to knihovna strojov\u00e9ho u\u010den\u00ed Python s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem, napsan\u00e1 v Pythonu a CUDA, funguj\u00edc\u00ed na Linuxu, macOS a Windows.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Theano\/Theano\">Github<\/a><\/noindex>: 8,922<\/p>\n<h3>34. Multidiff<\/h3>\n<p>\nMultidiff byl navr\u017een tak, aby usnadnil porozum\u011bn\u00ed strojov\u011b orientovan\u00fdm dat\u016fm. Pom\u00e1h\u00e1 prohl\u00ed\u017eet rozd\u00edly mezi velk\u00fdm mno\u017estv\u00edm objekt\u016f, prov\u00e1d\u011bn\u00edm rozd\u00edl\u016f mezi odpov\u00eddaj\u00edc\u00edmi objekty a n\u00e1sledn\u00fdm jejich zobrazen\u00edm. Tato vizualizace n\u00e1m umo\u017e\u0148uje hledat vzory v na\u0161ich protokolech nebo neobvykl\u00fdch form\u00e1tech soubor\u016f. Tak\u00e9 se p\u0159ev\u00e1\u017en\u011b pou\u017e\u00edv\u00e1 pro reverzn\u00ed in\u017een\u00fdrstv\u00ed a anal\u00fdzu bin\u00e1rn\u00edch dat.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 262<\/p>\n<h3>35. Som-tsp<\/h3>\n<p>\nTento projekt se v\u011bnuje pou\u017eit\u00ed samoorganizuj\u00edc\u00edch se map pro \u0159e\u0161en\u00ed probl\u00e9mu obchodn\u00edho cestuj\u00edc\u00edho. Pou\u017eit\u00edm SOM nach\u00e1z\u00edme suboptim\u00e1ln\u00ed \u0159e\u0161en\u00ed pro probl\u00e9m TSP a pou\u017e\u00edv\u00e1me k tomu form\u00e1t .tsp. TSP je NP-\u00fapln\u00fd probl\u00e9m a s rostouc\u00edm po\u010dtem m\u011bst je st\u00e1le obt\u00ed\u017en\u011bj\u0161\u00ed jej vy\u0159e\u0161it.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 950<\/p>\n<h3>36. Photon<\/h3>\n<p>\nPhoton je extr\u00e9mn\u011b rychl\u00fd webov\u00fd skener ur\u010den\u00fd pro OSINT. M\u016f\u017ee z\u00edsk\u00e1vat URL adresy, URL adresy s parametry, informace o intelu, soubory, tajn\u00e9 kl\u00ed\u010de, soubory JavaScript, shody s regul\u00e1rn\u00edmi v\u00fdrazy a subdom\u00e9ny. Extrahovan\u00e9 informace lze pot\u00e9 ulo\u017eit a exportovat ve form\u00e1tu json. Photon je flexibiln\u00ed a geni\u00e1ln\u00ed. M\u016f\u017eete k n\u011bmu tak\u00e9 p\u0159idat n\u011bkter\u00e9 pluginy.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 5714<\/p>\n<h3>37. Social Mapper<\/h3>\n<p>\nSocial Mapper je n\u00e1stroj pro mapov\u00e1n\u00ed na soci\u00e1ln\u00edch s\u00edt\u00edch, kter\u00fd koreluje profily pomoc\u00ed rozpozn\u00e1v\u00e1n\u00ed obli\u010deje. D\u011bl\u00e1 to na r\u016fzn\u00fdch webech ve velk\u00e9m m\u011b\u0159\u00edtku. Social Mapper automatizuje hled\u00e1n\u00ed jmen a fotografi\u00ed na soci\u00e1ln\u00edch s\u00edt\u00edch a pot\u00e9 se pokou\u0161\u00ed p\u0159esn\u011b ur\u010dit a seskupit n\u011b\u010d\u00ed p\u0159\u00edtomnost. Pot\u00e9 vytv\u00e1\u0159\u00ed zpr\u00e1vu k posouzen\u00ed \u010dlov\u011bkem. Je u\u017eite\u010dn\u00fd v oblasti bezpe\u010dnosti (nap\u0159\u00edklad pro phishing). Podporuje platformy LinkedIn, Facebook, Twitter, Google Plus, Instagram, VKontakte, Weibo a Douban.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 2,396<\/p>\n<h3>38. Camelot<\/h3>\n<p>\nCamelot je Python knihovna, kter\u00e1 pom\u00e1h\u00e1 extrahovat tabulky z PDF soubor\u016f. Funguje s textov\u00fdmi PDF soubory, ale ne s naskenovan\u00fdmi dokumenty. Ka\u017ed\u00e1 tabulka je pandas DataFrame. D\u00e1le m\u016f\u017eete exportovat tabulky do .json, .xls, .html nebo .sqlite.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 2415<\/p>\n<h3>39. Lector<\/h3>\n<p>\nJe to Qt \u010dte\u010dka pro \u010dten\u00ed elektronick\u00fdch knih. Podporuje form\u00e1ty soubor\u016f .pdf, .epub, .djvu, .fb2, .mobi, .azw \/.azw3 \/.azw4, .cbr \/.cbz a .md. V Lectoru je hlavn\u00ed okno, prohl\u00ed\u017een\u00ed tabulky, prohl\u00ed\u017een\u00ed knih, prohl\u00ed\u017een\u00ed bez rozpt\u00fdlen\u00ed, podpora anotac\u00ed, prohl\u00ed\u017een\u00ed komiks\u016f a okno nastaven\u00ed. Tak\u00e9 podporuje z\u00e1lo\u017eky, prohl\u00ed\u017een\u00ed profil\u016f, editor metadat a zabudovan\u00fd slovn\u00edk.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 835<\/p>\n<h3>40. m00dbot<\/h3>\n<p>\nJe to Telegram bot pro sebehodnocen\u00ed deprese a \u00fazkosti.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 145<\/p>\n<h3>41. Manim<\/h3>\n<p>\nJe to anima\u010dn\u00ed engine pro vytv\u00e1\u0159en\u00ed matematiky ve vide\u00edch, kter\u00fd lze pou\u017e\u00edt k programov\u00e9mu vytv\u00e1\u0159en\u00ed p\u0159esn\u00e9 animace. K tomu pou\u017e\u00edv\u00e1 Python.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 13 491<\/p>\n<h3>42. Douyin-Bot<\/h3>\n<p>\nBot napsan\u00fd v Pythonu pro aplikaci podobnou Tinderu. V\u00fdvoj\u00e1\u0159i z \u010c\u00edny.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 5,959<\/p>\n<h3>43. XSStrike<\/h3>\n<p>\nToto je bal\u00ed\u010dek pro detekci mezis\u00edtov\u00fdch skript\u016f se \u010dty\u0159mi ru\u010dn\u011b psan\u00fdmi analyz\u00e1tory. Je tak\u00e9 vybaven inteligentn\u00edm gener\u00e1torem u\u017eite\u010dn\u00fdch dat, v\u00fdkonn\u00fdm mechanismem fuzzingu a neuv\u011b\u0159iteln\u011b rychl\u00fdm vyhled\u00e1vac\u00edm modulem. Na rozd\u00edl od ostatn\u00edch n\u00e1stroj\u016f, kter\u00e9 zav\u00e1d\u011bj\u00ed u\u017eite\u010dn\u00e9 informace a testuj\u00ed jejich funk\u010dnost, XSStrike rozpozn\u00e1v\u00e1 odpov\u011b\u010f pomoc\u00ed n\u011bkolika analyz\u00e1tor\u016f a pot\u00e9 zpracov\u00e1v\u00e1 u\u017eite\u010dn\u00e9 \u00fadaje, kter\u00e9 zaru\u010den\u011b budou fungovat pomoc\u00ed kontextov\u00e9 anal\u00fdzy integrovan\u00e9 do mechanismu fuzzingu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 7050<\/p>\n<h3>44. PythonRobotics<\/h3>\n<p>\nTento projekt je sb\u00edrkou k\u00f3du pro algoritmy robotiky v Pythonu, stejn\u011b jako algoritmy autonomn\u00ed navigace.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 6,746<\/p>\n<h3>45. Google Images Download<\/h3>\n<p>\nGoogle Images Download je program v Pythonu pro p\u0159\u00edkazov\u00fd \u0159\u00e1dek, kter\u00fd hled\u00e1 kl\u00ed\u010dov\u00e1 slova v obr\u00e1zc\u00edch Google a z\u00edsk\u00e1v\u00e1 obr\u00e1zky pro v\u00e1s. Je to mal\u00fd program bez z\u00e1vislost\u00ed, pokud pot\u0159ebujete pouze st\u00e1hnout a\u017e 100 obr\u00e1zk\u016f pro ka\u017ed\u00e9 kl\u00ed\u010dov\u00e9 slovo.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 5749<\/p>\n<h3>46. Trape<\/h3>\n<p>\nUmo\u017e\u0148uje sledovat a prov\u00e1d\u011bt inteligentn\u00ed \u00fatoky soci\u00e1ln\u00edho in\u017een\u00fdrstv\u00ed v re\u00e1ln\u00e9m \u010dase. Pom\u00e1h\u00e1 zjistit, jak velk\u00e9 internetov\u00e9 spole\u010dnosti mohou z\u00edsk\u00e1vat d\u016fv\u011brn\u00e9 informace a ovl\u00e1dat u\u017eivatele bez jejich v\u011bdom\u00ed. Trape tak\u00e9 m\u016f\u017ee pomoci sledovat kyberzlo\u010dince.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 4256<\/p>\n<h3>47. Xonsh<\/h3>\n<p>\nXonsh je multiplatformn\u00ed Unix-gazing jazyk p\u0159\u00edkazov\u00e9ho \u0159\u00e1dku a shellu zalo\u017een\u00fd na Pythonu. Je to supermno\u017eina Pythonu 3.5+ s dal\u0161\u00edmi primitivy shellu, jako v Bash a IPython. Xonsh b\u011b\u017e\u00ed na Linuxu, Mac OS X, Windows a dal\u0161\u00edch hlavn\u00edch syst\u00e9mech.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 3426<\/p>\n<h3>48. GIF pro CLI<\/h3>\n<p>\nTo vy\u017eaduje GIF nebo kr\u00e1tk\u00e9 video \u010di dotaz, a s pomoc\u00ed API rozhran\u00ed Tenor GIF se p\u0159evede na animovanou grafiku ASCII. Pou\u017e\u00edv\u00e1 escape sekvence ANSI pro animaci a barvu.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 2,547<\/p>\n<h3>49. Cartoonify<\/h3>\n<p>\nDraw This \u2014 polaroidov\u00e1 kamera, kter\u00e1 um\u00ed vykreslovat karikatury. Pou\u017e\u00edv\u00e1 neuronovou s\u00ed\u0165 pro rozpozn\u00e1v\u00e1n\u00ed objekt\u016f, datov\u00fd soubor Google Quickdraw, termotisk\u00e1rnu a Raspberry Pi. Quick, Draw! je hra od Google, kde maj\u00ed hr\u00e1\u010di za \u00fakol rychle nakreslit obraz objektu\/my\u0161lenky, a pot\u00e9 se sna\u017e\u00ed uh\u00e1dnout, co p\u0159edstavuje, za m\u00e9n\u011b ne\u017e 20 sekund.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 1760<\/p>\n<h3>50. Zulip<\/h3>\n<p>\nZulip je aplikace pro skupinov\u00fd chat, kter\u00e1 funguje v re\u00e1ln\u00e9m \u010dase a je produktivn\u00ed d\u00edky vl\u00e1knov\u00fdm konverzac\u00edm. Mnoho firem ze seznamu Fortune 500 a projekty s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem ji pou\u017e\u00edvaj\u00ed k real-time chatu, kter\u00fd dok\u00e1\u017ee zpracov\u00e1vat tis\u00edce zpr\u00e1v denn\u011b.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 10 432<\/p>\n<h3>51. YouTube-dl<\/h3>\n<p>\nJe to p\u0159\u00edkazov\u00fd \u0159\u00e1dek, kter\u00fd umo\u017e\u0148uje stahovat videa z YouTube a n\u011bkter\u00fdch dal\u0161\u00edch webov\u00fdch str\u00e1nek. Nen\u00ed v\u00e1z\u00e1n na konkr\u00e9tn\u00ed platformu.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 55 868<\/p>\n<h3>52. Ansible<\/h3>\n<p>\nJe to jednoduch\u00fd syst\u00e9m automatizace IT, kter\u00fd m\u016f\u017ee vykon\u00e1vat n\u00e1sleduj\u00edc\u00ed funkce: spr\u00e1va konfigurace, nasazen\u00ed aplikac\u00ed, inicializace cloudu, prov\u00e1d\u011bn\u00ed specializovan\u00fdch \u00faloh, automatizace s\u00edt\u011b a orchestrace v\u00edce uzl\u016f.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 39 443<\/p>\n<h3>53. HTTPie<\/h3>\n<p>\nHTTPie je HTTP klient pro p\u0159\u00edkazov\u00fd \u0159\u00e1dek. Usnad\u0148uje interakci CLI s webov\u00fdmi slu\u017ebami. Pro p\u0159\u00edkaz http n\u00e1m umo\u017e\u0148uje odes\u00edlat libovoln\u00e9 HTTP po\u017eadavky se jednoduchou syntax\u00ed a p\u0159ij\u00edmat barevn\u00e9 v\u00fdstupy. M\u016f\u017eeme ho pou\u017e\u00edt k testov\u00e1n\u00ed, lad\u011bn\u00ed a interakci s HTTP servery.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 43 199<\/p>\n<h3>54. Tornado Web Server<\/h3>\n<p>\nJe to webov\u00fd framework, asynchronn\u00ed s\u00ed\u0165ov\u00e1 knihovna pro Python. Pou\u017e\u00edv\u00e1 neblokuj\u00edc\u00ed s\u00ed\u0165ov\u00e1 vstupy\/v\u00fdstupy pro \u0161k\u00e1lov\u00e1n\u00ed na v\u00edce ne\u017e tis\u00edc otev\u0159en\u00fdch spojen\u00ed. To z n\u011bj d\u011bl\u00e1 dobrou volbu pro dlouh\u00e9 po\u017eadavky a WebSockets.<\/p>\n<p>Po\u010det hv\u011bzdi\u010dek na Githubu: 18 306<\/p>\n<h3>55. Requests<\/h3>\n<p>\nRequests je knihovna, kter\u00e1 usnad\u0148uje odes\u00edl\u00e1n\u00ed HTTP\/1.1 po\u017eadavk\u016f. Nen\u00ed nutn\u00e9 ru\u010dn\u011b p\u0159id\u00e1vat parametry k URL adres\u00e1m nebo k\u00f3dovat data PUT a POST.<br \/>\nPo\u010det hv\u011bzdi\u010dek na Githubu: 40 294<\/p>\n<h3>56. Scrapy<\/h3>\n<p>\nScrapy je rychl\u00fd, vysoce \u00farov\u0148ov\u00fd r\u00e1mec pro proch\u00e1zen\u00ed webov\u00fdch str\u00e1nek \u2014 m\u016f\u017eete ho pou\u017e\u00edt k proch\u00e1zen\u00ed webov\u00fdch str\u00e1nek za \u00fa\u010delem extrakce strukturovan\u00fdch dat. M\u016f\u017eete ho tak\u00e9 vyu\u017e\u00edt pro anal\u00fdzu dat, monitoring a automatizovan\u00e9 testov\u00e1n\u00ed.<\/p>\n<p>Po\u010det hv\u011bzd na Githubu: 34 493<br \/>\n<br \/>Zdroj: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/edison\/blog\/477442\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>1. Flask \u042d\u0442\u043e \u043c\u0438\u043a\u0440\u043e-\u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0439 \u043d\u0430 Python. \u041e\u043d \u043d\u0435 \u0438\u043c\u0435\u0435\u0442 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u0439 \u0434\u043b\u044f \u0444\u043e\u0440\u043c \u0438 \u0443\u0440\u043e\u0432\u043d\u044f \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0446\u0438\u0438 \u0431\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u0432\u0430\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u0442\u043e\u0440\u043e\u043d\u043d\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0434\u043b\u044f \u043e\u0431\u0449\u0438\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439. \u0418 \u0438\u043c\u0435\u043d\u043d\u043e \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u044d\u0442\u043e \u043c\u0438\u043a\u0440\u043e-\u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a. Flask \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d \u0434\u043b\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0433\u043e \u0438 \u0431\u044b\u0441\u0442\u0440\u043e\u0433\u043e \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043f\u0440\u0438\u043b\u043e\u0436\u0435\u043d\u0438\u0439, \u0430 \u0442\u0430\u043a\u0436\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u0443\u0435\u043c\u044b\u043c \u0438 \u043b\u0435\u0433\u043a\u0438\u043c. \u041e\u043d \u043e\u0441\u043d\u043e\u0432\u0430\u043d \u043d\u0430 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u0445 Werkzeug \u0438 Jinja2. \u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-53299","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"1.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/prohoster.info\/cs\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom\" \/>\n\t\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.3\" \/>\n\t\t<meta property=\"og:locale\" content=\"cs_CZ\" \/>\n\t\t<meta property=\"og:site_name\" content=\"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"\ud83e\udd4756 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 Python \u0441 \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u043c \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u043c \u043a\u043e\u0434\u043e\u043c | ProHoster\" \/>\n\t\t<meta property=\"og:description\" content=\"1.\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/cs\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2019-11-27T21:00:00+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2020-02-18T11:01:11+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One SEO -->\n\n","aioseo_head_json":{"title":"\ud83e\udd4756 projekt\u016f na Pythonu s otev\u0159en\u00fdm zdrojov\u00fdm k\u00f3dem | ProHoster","description":"1.","canonical_url":"https:\/\/prohoster.info\/cs\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"cs_CZ","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd4756 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 Python \u0441 \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u043c \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u043c \u043a\u043e\u0434\u043e\u043c | ProHoster","og:description":"1.","og:url":"https:\/\/prohoster.info\/cs\/blog\/news\/56-proektov-na-python-s-otkrytym-ishodnym-kodom","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2019-11-27T21:00:00+00:00","article:modified_time":"2020-02-18T11:01:11+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":{"post_id":"53299","title":null,"description":null,"keywords":null,"keyphrases":null,"primary_term":null,"canonical_url":null,"og_title":null,"og_description":null,"og_object_type":"default","og_image_type":"default","og_image_url":null,"og_image_width":null,"og_image_height":null,"og_image_custom_url":null,"og_image_custom_fields":null,"og_video":null,"og_custom_url":null,"og_article_section":null,"og_article_tags":null,"twitter_use_og":false,"twitter_card":"default","twitter_image_type":"default","twitter_image_url":null,"twitter_image_custom_url":null,"twitter_image_custom_fields":null,"twitter_title":null,"twitter_description":null,"schema":{"blockGraphs":[],"customGraphs":[],"default":{"data":{"Article":[],"Course":[],"Dataset":[],"FAQPage":[],"Movie":[],"Person":[],"Product":[],"ProductReview":[],"Car":[],"Recipe":[],"Service":[],"SoftwareApplication":[],"WebPage":[]},"graphName":"","isEnabled":true},"graphs":[]},"schema_type":null,"schema_type_options":null,"pillar_content":false,"robots_default":true,"robots_noindex":false,"robots_noarchive":false,"robots_nosnippet":false,"robots_nofollow":false,"robots_noimageindex":false,"robots_noodp":false,"robots_notranslate":false,"robots_max_snippet":null,"robots_max_videopreview":null,"robots_max_imagepreview":"large","priority":null,"frequency":null,"local_seo":null,"seo_analyzer_scan_date":"2026-01-24 06:51:21","breadcrumb_settings":null,"limit_modified_date":false,"reviewed_by":null,"ai":null,"created":"2021-02-28 20:27:39","updated":"2026-01-24 06:51:21","focus_keyword":null,"additional_keywords":null,"truseo_locale":null},"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/posts\/53299","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/comments?post=53299"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/posts\/53299\/revisions"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/media?parent=53299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/categories?post=53299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/cs\/wp-json\/wp\/v2\/tags?post=53299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}