rozwiazanka
This commit is contained in:
Maksymilian Stachowiak
parent
c37b42a4f4
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,CustomerId,FirstName,LastName,Company,Address,City,State,Country,PostalCode,Phone,Fax,Email,SupportRepId
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0,1,Luís,Gonçalves,Embraer - Empresa Brasileira de Aeronáutica S.A.,"Av. Brigadeiro Faria Lima, 2170",São José dos Campos,SP,Brazil,12227-000,+55 (12) 3923-5555,+55 (12) 3923-5566,luisg@embraer.com.br,3
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1,2,Leonie,Köhler,,Theodor-Heuss-Straße 34,Stuttgart,,Germany,70174,+49 0711 2842222,,leonekohler@surfeu.de,5
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2,3,François,Tremblay,,1498 rue Bélanger,Montréal,QC,Canada,H2G 1A7,+1 (514) 721-4711,,ftremblay@gmail.com,3
|
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3,4,Bjørn,Hansen,,Ullevålsveien 14,Oslo,,Norway,0171,+47 22 44 22 22,,bjorn.hansen@yahoo.no,4
|
||||
4,5,František,Wichterlová,JetBrains s.r.o.,Klanova 9/506,Prague,,Czech Republic,14700,+420 2 4172 5555,+420 2 4172 5555,frantisekw@jetbrains.com,4
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||||
5,6,Helena,Holý,,Rilská 3174/6,Prague,,Czech Republic,14300,+420 2 4177 0449,,hholy@gmail.com,5
|
||||
6,7,Astrid,Gruber,,"Rotenturmstraße 4, 1010 Innere Stadt",Vienne,,Austria,1010,+43 01 5134505,,astrid.gruber@apple.at,5
|
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7,8,Daan,Peeters,,Grétrystraat 63,Brussels,,Belgium,1000,+32 02 219 03 03,,daan_peeters@apple.be,4
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8,9,Kara,Nielsen,,Sønder Boulevard 51,Copenhagen,,Denmark,1720,+453 3331 9991,,kara.nielsen@jubii.dk,4
|
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9,10,Eduardo,Martins,Woodstock Discos,"Rua Dr. Falcão Filho, 155",São Paulo,SP,Brazil,01007-010,+55 (11) 3033-5446,+55 (11) 3033-4564,eduardo@woodstock.com.br,4
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10,11,Alexandre,Rocha,Banco do Brasil S.A.,"Av. Paulista, 2022",São Paulo,SP,Brazil,01310-200,+55 (11) 3055-3278,+55 (11) 3055-8131,alero@uol.com.br,5
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11,12,Roberto,Almeida,Riotur,"Praça Pio X, 119",Rio de Janeiro,RJ,Brazil,20040-020,+55 (21) 2271-7000,+55 (21) 2271-7070,roberto.almeida@riotur.gov.br,3
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12,13,Fernanda,Ramos,,Qe 7 Bloco G,Brasília,DF,Brazil,71020-677,+55 (61) 3363-5547,+55 (61) 3363-7855,fernadaramos4@uol.com.br,4
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13,14,Mark,Philips,Telus,8210 111 ST NW,Edmonton,AB,Canada,T6G 2C7,+1 (780) 434-4554,+1 (780) 434-5565,mphilips12@shaw.ca,5
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14,15,Jennifer,Peterson,Rogers Canada,700 W Pender Street,Vancouver,BC,Canada,V6C 1G8,+1 (604) 688-2255,+1 (604) 688-8756,jenniferp@rogers.ca,3
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15,16,Frank,Harris,Google Inc.,1600 Amphitheatre Parkway,Mountain View,CA,USA,94043-1351,+1 (650) 253-0000,+1 (650) 253-0000,fharris@google.com,4
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16,17,Jack,Smith,Microsoft Corporation,1 Microsoft Way,Redmond,WA,USA,98052-8300,+1 (425) 882-8080,+1 (425) 882-8081,jacksmith@microsoft.com,5
|
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17,18,Michelle,Brooks,,627 Broadway,New York,NY,USA,10012-2612,+1 (212) 221-3546,+1 (212) 221-4679,michelleb@aol.com,3
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18,19,Tim,Goyer,Apple Inc.,1 Infinite Loop,Cupertino,CA,USA,95014,+1 (408) 996-1010,+1 (408) 996-1011,tgoyer@apple.com,3
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19,20,Dan,Miller,,541 Del Medio Avenue,Mountain View,CA,USA,94040-111,+1 (650) 644-3358,,dmiller@comcast.com,4
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20,21,Kathy,Chase,,801 W 4th Street,Reno,NV,USA,89503,+1 (775) 223-7665,,kachase@hotmail.com,5
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21,22,Heather,Leacock,,120 S Orange Ave,Orlando,FL,USA,32801,+1 (407) 999-7788,,hleacock@gmail.com,4
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22,23,John,Gordon,,69 Salem Street,Boston,MA,USA,2113,+1 (617) 522-1333,,johngordon22@yahoo.com,4
|
||||
23,24,Frank,Ralston,,162 E Superior Street,Chicago,IL,USA,60611,+1 (312) 332-3232,,fralston@gmail.com,3
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||||
24,25,Victor,Stevens,,319 N. Frances Street,Madison,WI,USA,53703,+1 (608) 257-0597,,vstevens@yahoo.com,5
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25,26,Richard,Cunningham,,2211 W Berry Street,Fort Worth,TX,USA,76110,+1 (817) 924-7272,,ricunningham@hotmail.com,4
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26,27,Patrick,Gray,,1033 N Park Ave,Tucson,AZ,USA,85719,+1 (520) 622-4200,,patrick.gray@aol.com,4
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27,28,Julia,Barnett,,302 S 700 E,Salt Lake City,UT,USA,84102,+1 (801) 531-7272,,jubarnett@gmail.com,5
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28,29,Robert,Brown,,796 Dundas Street West,Toronto,ON,Canada,M6J 1V1,+1 (416) 363-8888,,robbrown@shaw.ca,3
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29,30,Edward,Francis,,230 Elgin Street,Ottawa,ON,Canada,K2P 1L7,+1 (613) 234-3322,,edfrancis@yachoo.ca,3
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30,31,Martha,Silk,,194A Chain Lake Drive,Halifax,NS,Canada,B3S 1C5,+1 (902) 450-0450,,marthasilk@gmail.com,5
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31,32,Aaron,Mitchell,,696 Osborne Street,Winnipeg,MB,Canada,R3L 2B9,+1 (204) 452-6452,,aaronmitchell@yahoo.ca,4
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32,33,Ellie,Sullivan,,5112 48 Street,Yellowknife,NT,Canada,X1A 1N6,+1 (867) 920-2233,,ellie.sullivan@shaw.ca,3
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33,34,João,Fernandes,,Rua da Assunção 53,Lisbon,,Portugal,,+351 (213) 466-111,,jfernandes@yahoo.pt,4
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34,35,Madalena,Sampaio,,"Rua dos Campeões Europeus de Viena, 4350",Porto,,Portugal,,+351 (225) 022-448,,masampaio@sapo.pt,4
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35,36,Hannah,Schneider,,Tauentzienstraße 8,Berlin,,Germany,10789,+49 030 26550280,,hannah.schneider@yahoo.de,5
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36,37,Fynn,Zimmermann,,Berger Straße 10,Frankfurt,,Germany,60316,+49 069 40598889,,fzimmermann@yahoo.de,3
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37,38,Niklas,Schröder,,Barbarossastraße 19,Berlin,,Germany,10779,+49 030 2141444,,nschroder@surfeu.de,3
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38,39,Camille,Bernard,,"4, Rue Milton",Paris,,France,75009,+33 01 49 70 65 65,,camille.bernard@yahoo.fr,4
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39,40,Dominique,Lefebvre,,"8, Rue Hanovre",Paris,,France,75002,+33 01 47 42 71 71,,dominiquelefebvre@gmail.com,4
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40,41,Marc,Dubois,,"11, Place Bellecour",Lyon,,France,69002,+33 04 78 30 30 30,,marc.dubois@hotmail.com,5
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41,42,Wyatt,Girard,,"9, Place Louis Barthou",Bordeaux,,France,33000,+33 05 56 96 96 96,,wyatt.girard@yahoo.fr,3
|
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42,43,Isabelle,Mercier,,"68, Rue Jouvence",Dijon,,France,21000,+33 03 80 73 66 99,,isabelle_mercier@apple.fr,3
|
||||
43,44,Terhi,Hämäläinen,,Porthaninkatu 9,Helsinki,,Finland,00530,+358 09 870 2000,,terhi.hamalainen@apple.fi,3
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||||
44,45,Ladislav,Kovács,,Erzsébet krt. 58.,Budapest,,Hungary,H-1073,,,ladislav_kovacs@apple.hu,3
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||||
45,46,Hugh,O'Reilly,,3 Chatham Street,Dublin,Dublin,Ireland,,+353 01 6792424,,hughoreilly@apple.ie,3
|
||||
46,47,Lucas,Mancini,,"Via Degli Scipioni, 43",Rome,RM,Italy,00192,+39 06 39733434,,lucas.mancini@yahoo.it,5
|
||||
47,48,Johannes,Van der Berg,,Lijnbaansgracht 120bg,Amsterdam,VV,Netherlands,1016,+31 020 6223130,,johavanderberg@yahoo.nl,5
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48,49,Stanisław,Wójcik,,Ordynacka 10,Warsaw,,Poland,00-358,+48 22 828 37 39,,stanisław.wójcik@wp.pl,4
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||||
49,50,Enrique,Muñoz,,C/ San Bernardo 85,Madrid,,Spain,28015,+34 914 454 454,,enrique_munoz@yahoo.es,5
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||||
50,51,Joakim,Johansson,,Celsiusg. 9,Stockholm,,Sweden,11230,+46 08-651 52 52,,joakim.johansson@yahoo.se,5
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51,52,Emma,Jones,,202 Hoxton Street,London,,United Kingdom,N1 5LH,+44 020 7707 0707,,emma_jones@hotmail.com,3
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||||
52,53,Phil,Hughes,,113 Lupus St,London,,United Kingdom,SW1V 3EN,+44 020 7976 5722,,phil.hughes@gmail.com,3
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53,54,Steve,Murray,,110 Raeburn Pl,Edinburgh ,,United Kingdom,EH4 1HH,+44 0131 315 3300,,steve.murray@yahoo.uk,5
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54,55,Mark,Taylor,,421 Bourke Street,Sidney,NSW,Australia,2010,+61 (02) 9332 3633,,mark.taylor@yahoo.au,4
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55,56,Diego,Gutiérrez,,307 Macacha Güemes,Buenos Aires,,Argentina,1106,+54 (0)11 4311 4333,,diego.gutierrez@yahoo.ar,4
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56,57,Luis,Rojas,,"Calle Lira, 198",Santiago,,Chile,,+56 (0)2 635 4444,,luisrojas@yahoo.cl,5
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57,58,Manoj,Pareek,,"12,Community Centre",Delhi,,India,110017,+91 0124 39883988,,manoj.pareek@rediff.com,3
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58,59,Puja,Srivastava,,"3,Raj Bhavan Road",Bangalore,,India,560001,+91 080 22289999,,puja_srivastava@yahoo.in,3
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@ -478,14 +478,60 @@
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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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"execution_count": 13,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": []
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6 6\n",
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"7 7\n",
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"8 8\n",
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"9 9\n",
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"10 10\n",
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"dtype: int64 0 0\n",
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"1 1\n",
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"3 9\n",
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"4 16\n",
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"7 49\n",
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"dtype: float64\n"
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}
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],
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"source": [
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"n = pd.Series(range(0, 11))\n",
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"n2 = n * n\n",
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"trojkatne = n + n2 / 2\n",
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"print(n, n2, trojkatne)"
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]
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},
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{
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"cell_type": "markdown",
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@ -522,7 +568,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 14,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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@ -581,7 +627,7 @@
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"July 779511 194316"
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]
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},
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"execution_count": 9,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -607,7 +653,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 15,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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@ -666,7 +712,7 @@
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"2 779511 194316"
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]
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},
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"execution_count": 10,
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"execution_count": 15,
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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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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 16,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 17,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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"[175 rows x 8 columns]"
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]
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},
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"execution_count": 12,
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"execution_count": 17,
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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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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 18,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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"5 0 0 373450 8.0500 NaN S "
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]
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},
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"execution_count": 13,
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"execution_count": 18,
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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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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 19,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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@ -1175,103 +1221,27 @@
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},
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"outputs": [
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{
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"ename": "ImportError",
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|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\compat\\_optional.py:142\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 142\u001b[0m module \u001b[39m=\u001b[39m importlib\u001b[39m.\u001b[39;49mimport_module(name)\n\u001b[0;32m 143\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\importlib\\__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m 125\u001b[0m level \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m--> 126\u001b[0m \u001b[39mreturn\u001b[39;00m _bootstrap\u001b[39m.\u001b[39;49m_gcd_import(name[level:], package, level)\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1050\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1027\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
|
||||
"File \u001b[1;32m<frozen importlib._bootstrap>:1004\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'openpyxl'",
|
||||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32mj:\\Python\\2023-programowanie-w-pythonie\\zajecia2\\data_analysis.ipynb Cell 39\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X53sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_excel(\u001b[39m'\u001b[39;49m\u001b[39m./bikes.xlsx\u001b[39;49m\u001b[39m'\u001b[39;49m, engine\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mopenpyxl\u001b[39;49m\u001b[39m'\u001b[39;49m, nrows\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m)\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X53sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:478\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m 477\u001b[0m should_close \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m--> 478\u001b[0m io \u001b[39m=\u001b[39m ExcelFile(io, storage_options\u001b[39m=\u001b[39;49mstorage_options, engine\u001b[39m=\u001b[39;49mengine)\n\u001b[0;32m 479\u001b[0m \u001b[39melif\u001b[39;00m engine \u001b[39mand\u001b[39;00m engine \u001b[39m!=\u001b[39m io\u001b[39m.\u001b[39mengine:\n\u001b[0;32m 480\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 481\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mEngine should not be specified when passing \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 482\u001b[0m \u001b[39m\"\u001b[39m\u001b[39man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 483\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:1513\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[0;32m 1510\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine \u001b[39m=\u001b[39m engine\n\u001b[0;32m 1511\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstorage_options \u001b[39m=\u001b[39m storage_options\n\u001b[1;32m-> 1513\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reader \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engines[engine](\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_io, storage_options\u001b[39m=\u001b[39;49mstorage_options)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:548\u001b[0m, in \u001b[0;36mOpenpyxlReader.__init__\u001b[1;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[0;32m 533\u001b[0m \u001b[39m@doc\u001b[39m(storage_options\u001b[39m=\u001b[39m_shared_docs[\u001b[39m\"\u001b[39m\u001b[39mstorage_options\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m 534\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 535\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 536\u001b[0m filepath_or_buffer: FilePath \u001b[39m|\u001b[39m ReadBuffer[\u001b[39mbytes\u001b[39m],\n\u001b[0;32m 537\u001b[0m storage_options: StorageOptions \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 538\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 539\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 540\u001b[0m \u001b[39m Reader using openpyxl engine.\u001b[39;00m\n\u001b[0;32m 541\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[39m {storage_options}\u001b[39;00m\n\u001b[0;32m 547\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 548\u001b[0m import_optional_dependency(\u001b[39m\"\u001b[39;49m\u001b[39mopenpyxl\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 549\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(filepath_or_buffer, storage_options\u001b[39m=\u001b[39mstorage_options)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\compat\\_optional.py:145\u001b[0m, in \u001b[0;36mimport_optional_dependency\u001b[1;34m(name, extra, errors, min_version)\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m 144\u001b[0m \u001b[39mif\u001b[39;00m errors \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mraise\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> 145\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mImportError\u001b[39;00m(msg)\n\u001b[0;32m 146\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 148\u001b[0m \u001b[39m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n",
|
||||
"\u001b[1;31mImportError\u001b[0m: Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
|
@ -1293,7 +1263,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
|
|
@ -1409,7 +1379,7 @@
|
|||
"[347 rows x 2 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1422,23 +1392,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2023-11-17 15:53:28,542 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n",
|
||||
"2023-11-17 15:53:28,543 INFO sqlalchemy.engine.base.Engine ()\n",
|
||||
"2023-11-17 15:53:28,544 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n",
|
||||
"2023-11-17 15:53:28,545 INFO sqlalchemy.engine.base.Engine ()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
|
|
@ -1548,7 +1508,7 @@
|
|||
"[347 rows x 2 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -1602,7 +1562,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
|
|
@ -1618,14 +1578,30 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'openpyxl'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32mj:\\Python\\2023-programowanie-w-pythonie\\zajecia2\\data_analysis.ipynb Cell 47\u001b[0m line \u001b[0;36m4\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df\u001b[39m.\u001b[39mto_csv(\u001b[39m'\u001b[39m\u001b[39mtmp.csv\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# zapis do arkusza kalkulacyjnego \u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia2/data_analysis.ipynb#X64sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m df\u001b[39m.\u001b[39;49mto_excel(\u001b[39m'\u001b[39;49m\u001b[39mtmp.xlsx\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\core\\generic.py:2252\u001b[0m, in \u001b[0;36mNDFrame.to_excel\u001b[1;34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, inf_rep, freeze_panes, storage_options)\u001b[0m\n\u001b[0;32m 2239\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mformats\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mexcel\u001b[39;00m \u001b[39mimport\u001b[39;00m ExcelFormatter\n\u001b[0;32m 2241\u001b[0m formatter \u001b[39m=\u001b[39m ExcelFormatter(\n\u001b[0;32m 2242\u001b[0m df,\n\u001b[0;32m 2243\u001b[0m na_rep\u001b[39m=\u001b[39mna_rep,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2250\u001b[0m inf_rep\u001b[39m=\u001b[39minf_rep,\n\u001b[0;32m 2251\u001b[0m )\n\u001b[1;32m-> 2252\u001b[0m formatter\u001b[39m.\u001b[39;49mwrite(\n\u001b[0;32m 2253\u001b[0m excel_writer,\n\u001b[0;32m 2254\u001b[0m sheet_name\u001b[39m=\u001b[39;49msheet_name,\n\u001b[0;32m 2255\u001b[0m startrow\u001b[39m=\u001b[39;49mstartrow,\n\u001b[0;32m 2256\u001b[0m startcol\u001b[39m=\u001b[39;49mstartcol,\n\u001b[0;32m 2257\u001b[0m freeze_panes\u001b[39m=\u001b[39;49mfreeze_panes,\n\u001b[0;32m 2258\u001b[0m engine\u001b[39m=\u001b[39;49mengine,\n\u001b[0;32m 2259\u001b[0m storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[0;32m 2260\u001b[0m )\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\formats\\excel.py:934\u001b[0m, in \u001b[0;36mExcelFormatter.write\u001b[1;34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options)\u001b[0m\n\u001b[0;32m 930\u001b[0m need_save \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m 931\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 932\u001b[0m \u001b[39m# error: Cannot instantiate abstract class 'ExcelWriter' with abstract\u001b[39;00m\n\u001b[0;32m 933\u001b[0m \u001b[39m# attributes 'engine', 'save', 'supported_extensions' and 'write_cells'\u001b[39;00m\n\u001b[1;32m--> 934\u001b[0m writer \u001b[39m=\u001b[39m ExcelWriter( \u001b[39m# type: ignore[abstract]\u001b[39;49;00m\n\u001b[0;32m 935\u001b[0m writer, engine\u001b[39m=\u001b[39;49mengine, storage_options\u001b[39m=\u001b[39;49mstorage_options\n\u001b[0;32m 936\u001b[0m )\n\u001b[0;32m 937\u001b[0m need_save \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 939\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
|
||||
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:56\u001b[0m, in \u001b[0;36mOpenpyxlWriter.__init__\u001b[1;34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 44\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 45\u001b[0m path: FilePath \u001b[39m|\u001b[39m WriteExcelBuffer \u001b[39m|\u001b[39m ExcelWriter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 54\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 55\u001b[0m \u001b[39m# Use the openpyxl module as the Excel writer.\u001b[39;00m\n\u001b[1;32m---> 56\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mopenpyxl\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mworkbook\u001b[39;00m \u001b[39mimport\u001b[39;00m Workbook\n\u001b[0;32m 58\u001b[0m engine_kwargs \u001b[39m=\u001b[39m combine_kwargs(engine_kwargs, kwargs)\n\u001b[0;32m 60\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(\n\u001b[0;32m 61\u001b[0m path,\n\u001b[0;32m 62\u001b[0m mode\u001b[39m=\u001b[39mmode,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 65\u001b[0m engine_kwargs\u001b[39m=\u001b[39mengine_kwargs,\n\u001b[0;32m 66\u001b[0m )\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'openpyxl'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"# zapis do formatu CSV\n",
|
||||
"df.to_csv('tmp.csv')\n",
|
||||
"# zapis do arkusza kalkulacyjnego \n",
|
||||
|
|
@ -1645,7 +1621,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
|
|
@ -1666,7 +1642,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
|
|
@ -1698,7 +1674,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
|
|
@ -1739,14 +1715,450 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{0: 'Germany',\n",
|
||||
" 1: 'Norway',\n",
|
||||
" 2: 'Belgium',\n",
|
||||
" 3: 'Canada',\n",
|
||||
" 4: 'USA',\n",
|
||||
" 5: 'Germany',\n",
|
||||
" 6: 'Germany',\n",
|
||||
" 7: 'France',\n",
|
||||
" 8: 'France',\n",
|
||||
" 9: 'Ireland',\n",
|
||||
" 10: 'United Kingdom',\n",
|
||||
" 11: 'Germany',\n",
|
||||
" 12: 'USA',\n",
|
||||
" 13: 'USA',\n",
|
||||
" 14: 'USA',\n",
|
||||
" 15: 'USA',\n",
|
||||
" 16: 'USA',\n",
|
||||
" 17: 'Canada',\n",
|
||||
" 18: 'France',\n",
|
||||
" 19: 'United Kingdom',\n",
|
||||
" 20: 'Australia',\n",
|
||||
" 21: 'Chile',\n",
|
||||
" 22: 'India',\n",
|
||||
" 23: 'Norway',\n",
|
||||
" 24: 'Brazil',\n",
|
||||
" 25: 'USA',\n",
|
||||
" 26: 'Canada',\n",
|
||||
" 27: 'Portugal',\n",
|
||||
" 28: 'Germany',\n",
|
||||
" 29: 'Germany',\n",
|
||||
" 30: 'France',\n",
|
||||
" 31: 'Netherlands',\n",
|
||||
" 32: 'Chile',\n",
|
||||
" 33: 'Brazil',\n",
|
||||
" 34: 'Brazil',\n",
|
||||
" 35: 'Canada',\n",
|
||||
" 36: 'USA',\n",
|
||||
" 37: 'USA',\n",
|
||||
" 38: 'USA',\n",
|
||||
" 39: 'Germany',\n",
|
||||
" 40: 'Spain',\n",
|
||||
" 41: 'Sweden',\n",
|
||||
" 42: 'United Kingdom',\n",
|
||||
" 43: 'Australia',\n",
|
||||
" 44: 'India',\n",
|
||||
" 45: 'Czech Republic',\n",
|
||||
" 46: 'Canada',\n",
|
||||
" 47: 'Canada',\n",
|
||||
" 48: 'Canada',\n",
|
||||
" 49: 'Canada',\n",
|
||||
" 50: 'Portugal',\n",
|
||||
" 51: 'Germany',\n",
|
||||
" 52: 'Finland',\n",
|
||||
" 53: 'United Kingdom',\n",
|
||||
" 54: 'Belgium',\n",
|
||||
" 55: 'Denmark',\n",
|
||||
" 56: 'Brazil',\n",
|
||||
" 57: 'Brazil',\n",
|
||||
" 58: 'USA',\n",
|
||||
" 59: 'USA',\n",
|
||||
" 60: 'Canada',\n",
|
||||
" 61: 'Ireland',\n",
|
||||
" 62: 'Italy',\n",
|
||||
" 63: 'Poland',\n",
|
||||
" 64: 'Sweden',\n",
|
||||
" 65: 'Australia',\n",
|
||||
" 66: 'Germany',\n",
|
||||
" 67: 'Brazil',\n",
|
||||
" 68: 'USA',\n",
|
||||
" 69: 'USA',\n",
|
||||
" 70: 'USA',\n",
|
||||
" 71: 'Canada',\n",
|
||||
" 72: 'Portugal',\n",
|
||||
" 73: 'France',\n",
|
||||
" 74: 'Poland',\n",
|
||||
" 75: 'Norway',\n",
|
||||
" 76: 'Czech Republic',\n",
|
||||
" 77: 'Austria',\n",
|
||||
" 78: 'Denmark',\n",
|
||||
" 79: 'Brazil',\n",
|
||||
" 80: 'USA',\n",
|
||||
" 81: 'USA',\n",
|
||||
" 82: 'France',\n",
|
||||
" 83: 'France',\n",
|
||||
" 84: 'Hungary',\n",
|
||||
" 85: 'Italy',\n",
|
||||
" 86: 'Sweden',\n",
|
||||
" 87: 'Chile',\n",
|
||||
" 88: 'Austria',\n",
|
||||
" 89: 'USA',\n",
|
||||
" 90: 'USA',\n",
|
||||
" 91: 'USA',\n",
|
||||
" 92: 'USA',\n",
|
||||
" 93: 'Canada',\n",
|
||||
" 94: 'Germany',\n",
|
||||
" 95: 'Hungary',\n",
|
||||
" 96: 'India',\n",
|
||||
" 97: 'Brazil',\n",
|
||||
" 98: 'Canada',\n",
|
||||
" 99: 'Czech Republic',\n",
|
||||
" 100: 'Denmark',\n",
|
||||
" 101: 'Canada',\n",
|
||||
" 102: 'USA',\n",
|
||||
" 103: 'Germany',\n",
|
||||
" 104: 'France',\n",
|
||||
" 105: 'France',\n",
|
||||
" 106: 'France',\n",
|
||||
" 107: 'Italy',\n",
|
||||
" 108: 'United Kingdom',\n",
|
||||
" 109: 'Canada',\n",
|
||||
" 110: 'USA',\n",
|
||||
" 111: 'USA',\n",
|
||||
" 112: 'USA',\n",
|
||||
" 113: 'USA',\n",
|
||||
" 114: 'USA',\n",
|
||||
" 115: 'Canada',\n",
|
||||
" 116: 'France',\n",
|
||||
" 117: 'Australia',\n",
|
||||
" 118: 'Argentina',\n",
|
||||
" 119: 'India',\n",
|
||||
" 120: 'Brazil',\n",
|
||||
" 121: 'Czech Republic',\n",
|
||||
" 122: 'Brazil',\n",
|
||||
" 123: 'USA',\n",
|
||||
" 124: 'Portugal',\n",
|
||||
" 125: 'Portugal',\n",
|
||||
" 126: 'Germany',\n",
|
||||
" 127: 'France',\n",
|
||||
" 128: 'France',\n",
|
||||
" 129: 'Poland',\n",
|
||||
" 130: 'India',\n",
|
||||
" 131: 'Brazil',\n",
|
||||
" 132: 'Canada',\n",
|
||||
" 133: 'USA',\n",
|
||||
" 134: 'USA',\n",
|
||||
" 135: 'USA',\n",
|
||||
" 136: 'USA',\n",
|
||||
" 137: 'Germany',\n",
|
||||
" 138: 'Sweden',\n",
|
||||
" 139: 'United Kingdom',\n",
|
||||
" 140: 'United Kingdom',\n",
|
||||
" 141: 'Argentina',\n",
|
||||
" 142: 'Brazil',\n",
|
||||
" 143: 'Austria',\n",
|
||||
" 144: 'USA',\n",
|
||||
" 145: 'Canada',\n",
|
||||
" 146: 'Canada',\n",
|
||||
" 147: 'Canada',\n",
|
||||
" 148: 'Portugal',\n",
|
||||
" 149: 'France',\n",
|
||||
" 150: 'Hungary',\n",
|
||||
" 151: 'United Kingdom',\n",
|
||||
" 152: 'Denmark',\n",
|
||||
" 153: 'Brazil',\n",
|
||||
" 154: 'Brazil',\n",
|
||||
" 155: 'Canada',\n",
|
||||
" 156: 'USA',\n",
|
||||
" 157: 'USA',\n",
|
||||
" 158: 'Canada',\n",
|
||||
" 159: 'Italy',\n",
|
||||
" 160: 'Netherlands',\n",
|
||||
" 161: 'Spain',\n",
|
||||
" 162: 'United Kingdom',\n",
|
||||
" 163: 'Argentina',\n",
|
||||
" 164: 'Canada',\n",
|
||||
" 165: 'Brazil',\n",
|
||||
" 166: 'USA',\n",
|
||||
" 167: 'USA',\n",
|
||||
" 168: 'Canada',\n",
|
||||
" 169: 'Canada',\n",
|
||||
" 170: 'Portugal',\n",
|
||||
" 171: 'France',\n",
|
||||
" 172: 'Spain',\n",
|
||||
" 173: 'Czech Republic',\n",
|
||||
" 174: 'Czech Republic',\n",
|
||||
" 175: 'Belgium',\n",
|
||||
" 176: 'Brazil',\n",
|
||||
" 177: 'Canada',\n",
|
||||
" 178: 'USA',\n",
|
||||
" 179: 'Canada',\n",
|
||||
" 180: 'France',\n",
|
||||
" 181: 'Finland',\n",
|
||||
" 182: 'Ireland',\n",
|
||||
" 183: 'Netherlands',\n",
|
||||
" 184: 'United Kingdom',\n",
|
||||
" 185: 'India',\n",
|
||||
" 186: 'Belgium',\n",
|
||||
" 187: 'USA',\n",
|
||||
" 188: 'USA',\n",
|
||||
" 189: 'USA',\n",
|
||||
" 190: 'USA',\n",
|
||||
" 191: 'Canada',\n",
|
||||
" 192: 'Germany',\n",
|
||||
" 193: 'Ireland',\n",
|
||||
" 194: 'Brazil',\n",
|
||||
" 195: 'Germany',\n",
|
||||
" 196: 'Norway',\n",
|
||||
" 197: 'Czech Republic',\n",
|
||||
" 198: 'Brazil',\n",
|
||||
" 199: 'USA',\n",
|
||||
" 200: 'USA',\n",
|
||||
" 201: 'France',\n",
|
||||
" 202: 'France',\n",
|
||||
" 203: 'France',\n",
|
||||
" 204: 'Finland',\n",
|
||||
" 205: 'Netherlands',\n",
|
||||
" 206: 'United Kingdom',\n",
|
||||
" 207: 'Norway',\n",
|
||||
" 208: 'USA',\n",
|
||||
" 209: 'USA',\n",
|
||||
" 210: 'USA',\n",
|
||||
" 211: 'USA',\n",
|
||||
" 212: 'USA',\n",
|
||||
" 213: 'Canada',\n",
|
||||
" 214: 'France',\n",
|
||||
" 215: 'Argentina',\n",
|
||||
" 216: 'Chile',\n",
|
||||
" 217: 'India',\n",
|
||||
" 218: 'Germany',\n",
|
||||
" 219: 'Czech Republic',\n",
|
||||
" 220: 'Brazil',\n",
|
||||
" 221: 'USA',\n",
|
||||
" 222: 'Portugal',\n",
|
||||
" 223: 'Germany',\n",
|
||||
" 224: 'Germany',\n",
|
||||
" 225: 'France',\n",
|
||||
" 226: 'Finland',\n",
|
||||
" 227: 'Spain',\n",
|
||||
" 228: 'India',\n",
|
||||
" 229: 'Canada',\n",
|
||||
" 230: 'Canada',\n",
|
||||
" 231: 'USA',\n",
|
||||
" 232: 'USA',\n",
|
||||
" 233: 'USA',\n",
|
||||
" 234: 'Canada',\n",
|
||||
" 235: 'Germany',\n",
|
||||
" 236: 'United Kingdom',\n",
|
||||
" 237: 'United Kingdom',\n",
|
||||
" 238: 'Australia',\n",
|
||||
" 239: 'Chile',\n",
|
||||
" 240: 'Germany',\n",
|
||||
" 241: 'Belgium',\n",
|
||||
" 242: 'USA',\n",
|
||||
" 243: 'Canada',\n",
|
||||
" 244: 'Canada',\n",
|
||||
" 245: 'Portugal',\n",
|
||||
" 246: 'Germany',\n",
|
||||
" 247: 'France',\n",
|
||||
" 248: 'Ireland',\n",
|
||||
" 249: 'Australia',\n",
|
||||
" 250: 'Brazil',\n",
|
||||
" 251: 'Brazil',\n",
|
||||
" 252: 'Brazil',\n",
|
||||
" 253: 'Canada',\n",
|
||||
" 254: 'USA',\n",
|
||||
" 255: 'USA',\n",
|
||||
" 256: 'Portugal',\n",
|
||||
" 257: 'Netherlands',\n",
|
||||
" 258: 'Poland',\n",
|
||||
" 259: 'Sweden',\n",
|
||||
" 260: 'United Kingdom',\n",
|
||||
" 261: 'Chile',\n",
|
||||
" 262: 'Norway',\n",
|
||||
" 263: 'Brazil',\n",
|
||||
" 264: 'USA',\n",
|
||||
" 265: 'USA',\n",
|
||||
" 266: 'Canada',\n",
|
||||
" 267: 'Canada',\n",
|
||||
" 268: 'Germany',\n",
|
||||
" 269: 'France',\n",
|
||||
" 270: 'Sweden',\n",
|
||||
" 271: 'Czech Republic',\n",
|
||||
" 272: 'Austria',\n",
|
||||
" 273: 'Denmark',\n",
|
||||
" 274: 'Brazil',\n",
|
||||
" 275: 'Canada',\n",
|
||||
" 276: 'USA',\n",
|
||||
" 277: 'Canada',\n",
|
||||
" 278: 'Finland',\n",
|
||||
" 279: 'Hungary',\n",
|
||||
" 280: 'Italy',\n",
|
||||
" 281: 'Poland',\n",
|
||||
" 282: 'United Kingdom',\n",
|
||||
" 283: 'India',\n",
|
||||
" 284: 'Denmark',\n",
|
||||
" 285: 'USA',\n",
|
||||
" 286: 'USA',\n",
|
||||
" 287: 'USA',\n",
|
||||
" 288: 'USA',\n",
|
||||
" 289: 'Canada',\n",
|
||||
" 290: 'Germany',\n",
|
||||
" 291: 'Italy',\n",
|
||||
" 292: 'Germany',\n",
|
||||
" 293: 'Canada',\n",
|
||||
" 294: 'Czech Republic',\n",
|
||||
" 295: 'Austria',\n",
|
||||
" 296: 'Brazil',\n",
|
||||
" 297: 'USA',\n",
|
||||
" 298: 'USA',\n",
|
||||
" 299: 'France',\n",
|
||||
" 300: 'France',\n",
|
||||
" 301: 'France',\n",
|
||||
" 302: 'Hungary',\n",
|
||||
" 303: 'Poland',\n",
|
||||
" 304: 'Australia',\n",
|
||||
" 305: 'Czech Republic',\n",
|
||||
" 306: 'USA',\n",
|
||||
" 307: 'USA',\n",
|
||||
" 308: 'USA',\n",
|
||||
" 309: 'USA',\n",
|
||||
" 310: 'USA',\n",
|
||||
" 311: 'Portugal',\n",
|
||||
" 312: 'France',\n",
|
||||
" 313: 'Chile',\n",
|
||||
" 314: 'India',\n",
|
||||
" 315: 'Brazil',\n",
|
||||
" 316: 'Canada',\n",
|
||||
" 317: 'Austria',\n",
|
||||
" 318: 'Brazil',\n",
|
||||
" 319: 'USA',\n",
|
||||
" 320: 'Germany',\n",
|
||||
" 321: 'Germany',\n",
|
||||
" 322: 'France',\n",
|
||||
" 323: 'France',\n",
|
||||
" 324: 'Hungary',\n",
|
||||
" 325: 'Sweden',\n",
|
||||
" 326: 'Brazil',\n",
|
||||
" 327: 'Canada',\n",
|
||||
" 328: 'USA',\n",
|
||||
" 329: 'USA',\n",
|
||||
" 330: 'USA',\n",
|
||||
" 331: 'USA',\n",
|
||||
" 332: 'Canada',\n",
|
||||
" 333: 'France',\n",
|
||||
" 334: 'United Kingdom',\n",
|
||||
" 335: 'United Kingdom',\n",
|
||||
" 336: 'Argentina',\n",
|
||||
" 337: 'India',\n",
|
||||
" 338: 'Canada',\n",
|
||||
" 339: 'Denmark',\n",
|
||||
" 340: 'USA',\n",
|
||||
" 341: 'Canada',\n",
|
||||
" 342: 'Canada',\n",
|
||||
" 343: 'Portugal',\n",
|
||||
" 344: 'Germany',\n",
|
||||
" 345: 'France',\n",
|
||||
" 346: 'Italy',\n",
|
||||
" 347: 'Argentina',\n",
|
||||
" 348: 'Brazil',\n",
|
||||
" 349: 'Brazil',\n",
|
||||
" 350: 'Canada',\n",
|
||||
" 351: 'USA',\n",
|
||||
" 352: 'USA',\n",
|
||||
" 353: 'USA',\n",
|
||||
" 354: 'Portugal',\n",
|
||||
" 355: 'Poland',\n",
|
||||
" 356: 'Spain',\n",
|
||||
" 357: 'United Kingdom',\n",
|
||||
" 358: 'United Kingdom',\n",
|
||||
" 359: 'India',\n",
|
||||
" 360: 'Czech Republic',\n",
|
||||
" 361: 'Canada',\n",
|
||||
" 362: 'USA',\n",
|
||||
" 363: 'Canada',\n",
|
||||
" 364: 'Canada',\n",
|
||||
" 365: 'Canada',\n",
|
||||
" 366: 'Germany',\n",
|
||||
" 367: 'France',\n",
|
||||
" 368: 'United Kingdom',\n",
|
||||
" 369: 'Austria',\n",
|
||||
" 370: 'Belgium',\n",
|
||||
" 371: 'Brazil',\n",
|
||||
" 372: 'Brazil',\n",
|
||||
" 373: 'USA',\n",
|
||||
" 374: 'USA',\n",
|
||||
" 375: 'Canada',\n",
|
||||
" 376: 'Hungary',\n",
|
||||
" 377: 'Ireland',\n",
|
||||
" 378: 'Netherlands',\n",
|
||||
" 379: 'Spain',\n",
|
||||
" 380: 'United Kingdom',\n",
|
||||
" 381: 'Brazil',\n",
|
||||
" 382: 'Brazil',\n",
|
||||
" 383: 'USA',\n",
|
||||
" 384: 'USA',\n",
|
||||
" 385: 'USA',\n",
|
||||
" 386: 'Canada',\n",
|
||||
" 387: 'Canada',\n",
|
||||
" 388: 'France',\n",
|
||||
" 389: 'Netherlands',\n",
|
||||
" 390: 'Canada',\n",
|
||||
" 391: 'Norway',\n",
|
||||
" 392: 'Czech Republic',\n",
|
||||
" 393: 'Belgium',\n",
|
||||
" 394: 'Brazil',\n",
|
||||
" 395: 'USA',\n",
|
||||
" 396: 'USA',\n",
|
||||
" 397: 'France',\n",
|
||||
" 398: 'France',\n",
|
||||
" 399: 'Finland',\n",
|
||||
" 400: 'Ireland',\n",
|
||||
" 401: 'Spain',\n",
|
||||
" 402: 'Argentina',\n",
|
||||
" 403: 'Czech Republic',\n",
|
||||
" 404: 'USA',\n",
|
||||
" 405: 'USA',\n",
|
||||
" 406: 'USA',\n",
|
||||
" 407: 'USA',\n",
|
||||
" 408: 'Canada',\n",
|
||||
" 409: 'Portugal',\n",
|
||||
" 410: 'Finland',\n",
|
||||
" 411: 'India'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import sqlalchemy\n",
|
||||
"\n",
|
||||
"engine = sqlalchemy.create_engine('sqlite:///Chinook.sqlite', echo=True)\n",
|
||||
"connection = engine.raw_connection()\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Customer', con='sqlite:///Chinook.sqlite')\n",
|
||||
"df.to_csv('customers.csv')\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Employee', con='sqlite:///Chinook.sqlite')\n",
|
||||
"# df['City'].drop_duplicates()\n",
|
||||
"\n",
|
||||
"df = pd.read_sql('SELECT * FROM Invoice', con='sqlite:///Chinook.sqlite')\n",
|
||||
"df['BillingCountry'].drop_duplicates().to_dict()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -8095,7 +8507,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.3"
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
,members,occasionals
|
||||
May,682758,147898
|
||||
June,737011,171494
|
||||
July,779511,194316
|
||||
,members,occasionals
|
||||
May,682758,147898
|
||||
June,737011,171494
|
||||
July,779511,194316
|
||||
|
|
|
|||
|
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|
|
@ -39,6 +39,7 @@
|
|||
"source": [
|
||||
"Istnieje wieje miar (metryk), na podstawie których możemy ocenić jakość modelu. Podobnie jak w przypadku regresji liniowej potrzebne są dwie listy: lista poprawnych klas i lista predykcji z modelu. Najpopularniejszą z metryk jest trafność, którą definiuje się w następujący sposób:\n",
|
||||
" $$ACC = \\frac{k}{N}$$ \n",
|
||||
" $$SUM = \\sum_{x}^{y}{k}{n}{i}$$\n",
|
||||
" \n",
|
||||
" gdzie: \n",
|
||||
" * $k$ to liczba poprawnie zaklasyfikowanych przypadków,\n",
|
||||
|
|
@ -54,12 +55,28 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"ACC: 0.4\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def accuracy_measure(true, predicted):\n",
|
||||
" pass\n",
|
||||
" if len(true_label) != len(predicted):\n",
|
||||
" raise ValueError(\"Input lists can't have different sizes.\")\n",
|
||||
" \n",
|
||||
" correct_values = 0\n",
|
||||
" for i in range(len(true_label)):\n",
|
||||
" if true_label[i] == predicted[i]:\n",
|
||||
" correct_values += 1\n",
|
||||
"\n",
|
||||
" return correct_values / len(predicted)\n",
|
||||
"\n",
|
||||
"true_label = [1, 1, 1, 0, 0]\n",
|
||||
"predicted = [0, 1, 0, 1, 0]\n",
|
||||
|
|
@ -125,10 +142,164 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sepal length</th>\n",
|
||||
" <th>Sepal width</th>\n",
|
||||
" <th>Petal length</th>\n",
|
||||
" <th>Petal width</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Class</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-setosa</th>\n",
|
||||
" <td>5.1</td>\n",
|
||||
" <td>3.5</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-setosa</th>\n",
|
||||
" <td>4.9</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-setosa</th>\n",
|
||||
" <td>4.7</td>\n",
|
||||
" <td>3.2</td>\n",
|
||||
" <td>1.3</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-setosa</th>\n",
|
||||
" <td>4.6</td>\n",
|
||||
" <td>3.1</td>\n",
|
||||
" <td>1.5</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-setosa</th>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>3.6</td>\n",
|
||||
" <td>1.4</td>\n",
|
||||
" <td>0.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-virginica</th>\n",
|
||||
" <td>6.7</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.2</td>\n",
|
||||
" <td>2.3</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-virginica</th>\n",
|
||||
" <td>6.3</td>\n",
|
||||
" <td>2.5</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1.9</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-virginica</th>\n",
|
||||
" <td>6.5</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.2</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-virginica</th>\n",
|
||||
" <td>6.2</td>\n",
|
||||
" <td>3.4</td>\n",
|
||||
" <td>5.4</td>\n",
|
||||
" <td>2.3</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Iris-virginica</th>\n",
|
||||
" <td>5.9</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>5.1</td>\n",
|
||||
" <td>1.8</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>150 rows × 4 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sepal length Sepal width Petal length Petal width\n",
|
||||
"Class \n",
|
||||
"Iris-setosa 5.1 3.5 1.4 0.2\n",
|
||||
"Iris-setosa 4.9 3.0 1.4 0.2\n",
|
||||
"Iris-setosa 4.7 3.2 1.3 0.2\n",
|
||||
"Iris-setosa 4.6 3.1 1.5 0.2\n",
|
||||
"Iris-setosa 5.0 3.6 1.4 0.2\n",
|
||||
"... ... ... ... ...\n",
|
||||
"Iris-virginica 6.7 3.0 5.2 2.3\n",
|
||||
"Iris-virginica 6.3 2.5 5.0 1.9\n",
|
||||
"Iris-virginica 6.5 3.0 5.2 2.0\n",
|
||||
"Iris-virginica 6.2 3.4 5.4 2.3\n",
|
||||
"Iris-virginica 5.9 3.0 5.1 1.8\n",
|
||||
"\n",
|
||||
"[150 rows x 4 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(\n",
|
||||
" './iris.data', \n",
|
||||
" index_col=4, \n",
|
||||
" names=[\n",
|
||||
" 'Sepal length', \n",
|
||||
" 'Sepal width', \n",
|
||||
" 'Petal length', \n",
|
||||
" 'Petal width',\n",
|
||||
" 'Class'\n",
|
||||
" ])\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -560,7 +731,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
Loading…
Reference in New Issue