From 3289e62082fb86ec86418c1908f8a8d88003de4e Mon Sep 17 00:00:00 2001 From: Juanlacalle Date: Wed, 30 Oct 2019 18:04:50 +0000 Subject: [PATCH 1/4] [perception and crime]Juan --- your-project/Antisystem.ipynb | 71 +++++++++++++++++++++++++++++++++ your-project/Perception.csv | 26 ++++++++++++ your-project/crime 2018.cvs.csv | 41 +++++++++++++++++++ 3 files changed, 138 insertions(+) create mode 100644 your-project/Antisystem.ipynb create mode 100644 your-project/Perception.csv create mode 100644 your-project/crime 2018.cvs.csv diff --git a/your-project/Antisystem.ipynb b/your-project/Antisystem.ipynb new file mode 100644 index 0000000..2de7ef7 --- /dev/null +++ b/your-project/Antisystem.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] File b'Perception' does not exist: b'Perception'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Perception'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m 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"execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/Perception.csv b/your-project/Perception.csv new file mode 100644 index 0000000..3957c38 --- /dev/null +++ b/your-project/Perception.csv @@ -0,0 +1,26 @@ +;;;;;;;;;;;; +;;;;;;;;;;;; +5. Encuesta de victimización de Barcelona;;;;;;;;;;;; +;;;;;;;;;;;; +5.5. Percepción de la seguridad en la ciudad y el barrio por distritos. 2015-2019;;;;;;;;;;;; +;;;;;;;;;;;; +;;"Nivel de seguridad en la ciudad    +(puntuación de 0 a 10) +";;;;;;"Nivel de seguridad en el barrio    +(puntuación de 0 a 10) +";;;; +Distritos;;2015;2016 (1);2017;2018;2019 (1);;2015;2016 (1);2017;2018;2019 (1) +;;;;;;;;;;;; +;;;;;;;;;;;; +BARCELONA;;6,1;6,2;6,3;6,2;5,2;;6,4;6,3;6,5;6,4;5,9 +;;;;;;;;;;;; +1. Ciutat Vella;;6,3;-;6,6;6,2;-;;5,2;-;5,7;5,2;- +2. Eixample;;6,2;-;6,3;6,3;-;;6,8;-;7,1;6,9;- +3. Sants-Montjuïc;;6,1;-;6,1;6,1;-;;6,0;-;6,1;6,2;- +4. Les Corts;;6,2;-;6,2;6,4;-;;7,1;-;7,3;7,3;- +5. Sarrià-Sant Gervasi;;6,0;-;6,2;6,0;-;;7,0;-;7,0;6,8;- +6. Gràcia;;6,1;-;6,2;6,3;-;;6,8;-;6,9;7,0;- +7. Horta-Guinardó;;6,1;-;6,4;6,2;-;;6,4;-;6,5;6,3;- +8. Nou Barris;;6,2;-;6,3;6,2;-;;5,9;-;5,7;5,9;- +9. Sant Andreu;;5,8;-;6,5;6,2;-;;6,1;-;6,3;6,3;- +10. Sant Martí;;6,1;-;6,4;6,2;-;;6,2;-;6,4;6,1;- diff --git a/your-project/crime 2018.cvs.csv b/your-project/crime 2018.cvs.csv new file mode 100644 index 0000000..e8fc177 --- /dev/null +++ b/your-project/crime 2018.cvs.csv @@ -0,0 +1,41 @@ +;;;;;;;;;;;;;;; +;Catalonia Police [Policía de la Generalitat - Mossos d’Esquadra];;;;;;;;;;;;;; +;;;;;;;;;;;;;;; +;Type of facts known per district in Barcelona. 2014-2018;;;;;;;;;;;;;; +;;;;;;;;;;;;;;; +;;;;BARCELONA;"1.Ciutat   +Vella";2.Eixample;"3.Sants-   +Montjuïc";4.Les Corts;"5.Sarrià-   +Sant Gervasi";6.Gràcia;"7.Horta-   +Guinardó";"8.Nou   +Barris";"9.Sant   +Andreu";"10.Sant   +Martí";"Distrito   +desconocido" +;;;;;;;;;;;;;;; +;2018;;;219.521;54.056;56.231;24.065;7.794;10.231;10.300;8.852;8.865;11.406;25.650;2.071 +;;;;;;;;;;;;;;; +;Homicide;;;73;15;7;14;2;2;6;5;8;3;11;0 +;Omission of S.O.S. duty;;;1;0;0;0;0;0;0;0;0;0;1;0 +;Falsehoods;;;820;99;167;155;57;31;35;51;55;66;94;10 +;Lesions;;;5.699;1.090;1.035;821;159;267;226;374;512;445;747;23 +;Torture;;;162;18;25;20;6;5;12;14;22;17;22;1 +;Human traffic;;;10;2;1;0;3;0;1;0;1;0;2;0 +;Economics crimes;;;205.231;51.228;53.832;21.985;7.313;9.591;9.710;7.920;7.545;10.323;23.817;1.967 +;Against foreign;;;1;0;1;0;0;0;0;0;0;0;0;0 +;Against honor;;;24;0;5;4;2;5;2;2;3;0;0;1 +;Against Constitution;;;67;9;6;8;2;0;4;1;8;5;3;21 +;Against privacy;;;491;65;99;66;35;35;28;29;51;37;43;3 +;Against freedom;;;3.313;444;497;495;125;178;164;271;372;292;462;13 +;Against sexual freedom;;;743;128;131;113;18;36;31;45;64;61;102;14 +;Against Justice;;;647;96;95;114;19;16;13;30;78;65;118;3 +;Against public administration;;;8;2;1;3;0;0;0;0;0;1;1;0 +;Against familiar relationships;;;173;16;17;22;3;8;7;32;29;21;18;0 +;Crimes against public order;;;974;317;216;117;30;38;25;30;55;34;100;12 +;Crimes related to genetic manipulation;;;1;0;0;0;0;0;0;1;0;0;0;0 +;Workers' rights;;;8;0;2;4;0;0;0;1;1;0;0;0 +;Against the Public Treasury and against Social Security;;;2;0;0;0;0;1;0;0;0;1;0;0 +;Against collective security;;;1.044;523;92;119;19;18;34;46;55;34;102;2 +;Crimes related to urban planning, the protection of the historical heritage and the environment;;;29;4;2;5;1;0;2;0;6;1;7;1 +;Departament d'Estadística i Difusió de Dades. Ajuntament de Barcelona.;;;;;;;;;;;;;; +;Fuente: Generalitat de Catalunya. Departament d'Interior. Direcció General de la Policia.;;;;;;;;;;;;;; \ No newline at end of file From 069572868819e7707542c453c1b6392aeec63b91 Mon Sep 17 00:00:00 2001 From: Mattia Lobascio Date: Wed, 30 Oct 2019 18:09:29 +0000 Subject: [PATCH 2/4] density and income cleaned --- .../Untitled-checkpoint.ipynb | 6 + .../cleaner-checkpoint.ipynb | 1841 +++++++++++++++++ your-project/DataSet/2017_densitat.csv | 74 + .../DataSet/2017_territorial_income.csv | 74 + your-project/DataSet/clean-pop-density.csv | 7 + .../DataSet/clean-territorial-income.csv | 4 + your-project/cleaner.ipynb | 1841 +++++++++++++++++ 7 files changed, 3847 insertions(+) create mode 100644 your-project/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 your-project/.ipynb_checkpoints/cleaner-checkpoint.ipynb create mode 100644 your-project/DataSet/2017_densitat.csv create mode 100644 your-project/DataSet/2017_territorial_income.csv create mode 100644 your-project/DataSet/clean-pop-density.csv create mode 100644 your-project/DataSet/clean-territorial-income.csv create mode 100644 your-project/cleaner.ipynb diff --git a/your-project/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/your-project/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/your-project/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/.ipynb_checkpoints/cleaner-checkpoint.ipynb b/your-project/.ipynb_checkpoints/cleaner-checkpoint.ipynb new file mode 100644 index 0000000..c3c638d --- /dev/null +++ b/your-project/.ipynb_checkpoints/cleaner-checkpoint.ipynb @@ -0,0 +1,1841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "#NAME OF DATA-SET TO CLEAN\n", + "#Take into account FILE PATH as WELL\n", + "path = \"DataSet/2017_densitat.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AnyCodi_DistricteNom_DistricteCodi_BarriNom_BarriPoblacióSuperfície (ha)Superfície Residencial (ha)Densitat (hab/ha)Densitat neta (hab/ha)
020171Ciutat Vella1el Raval47608109.849.7433959
120171Ciutat Vella2el Barri Gòtic1606284.234.1191471
220171Ciutat Vella3la Barceloneta14996131.413.51141107
320171Ciutat Vella4Sant Pere, Santa Caterina i la Ribera22721111.432.5204700
420172Eixample5el Fort Pienc3201692.933.4345959
520172Eixample6la Sagrada Família51539105.151.24901006
620172Eixample7la Dreta de l'Eixample44052212.3113.9207387
720172Eixample8l'Antiga Esquerra de l'Eixample42284123.467.0343631
820172Eixample9la Nova Esquerra de l'Eixample58180133.865.3435891
920172Eixample10Sant Antoni3834580.141.3479928
1020173Sants-Montjuïc11el Poble Sec40228460.538.6871043
1120173Sants-Montjuïc12la Marina del Prat Vermell11491428.435.9132
1220173Sants-Montjuïc13la Marina de Port30584125.539.4244777
1320173Sants-Montjuïc14la Font de la Guatlla1040130.211.4344912
1420173Sants-Montjuïc15Hostafrancs1590441.022.4388710
1520173Sants-Montjuïc16la Bordeta1853057.720.8321891
1620173Sants-Montjuïc17Sants - Badal2398741.125.1584955
1720173Sants-Montjuïc18Sants41127109.855.6375740
1820174Les Corts19les Corts46009141.365.3326705
1920174Les Corts20la Maternitat i Sant Ramon23948190.332.6126734
2020174Les Corts21Pedralbes12076270.282.345147
2120175Sarrià-Sant Gervasi22Vallvidrera, el Tibidabo i les Planes46701152.289.5452
2220175Sarrià-Sant Gervasi23Sarrià25032304.296.282260
2320175Sarrià-Sant Gervasi24les Tres Torres1666778.842.6211391
2420175Sarrià-Sant Gervasi25Sant Gervasi - la Bonanova25774223.582.1115314
2520175Sarrià-Sant Gervasi26Sant Gervasi - Galvany47666165.995.5287499
2620175Sarrià-Sant Gervasi27el Putxet i el Farró2947084.653.1348555
2720176Gràcia28Vallcarca i els Penitents15759120.947.0130335
2820176Gràcia29el Coll741235.812.4207598
2920176Gràcia30la Salut1318564.319.4205680
3020176Gràcia31la Vila de Gràcia50662132.683.5382607
3120176Gràcia32el Camp d'en Grassot i Gràcia Nova3432965.038.5529891
3220177Horta-Guinardó33el Baix Guinardó2567256.023.74591082
3320177Horta-Guinardó34Can Baró899438.414.3234627
3420177Horta-Guinardó35el Guinardó36467130.852.1279699
3520177Horta-Guinardó36la Font d'en Fargues938365.739.4143238
3620177Horta-Guinardó37el Carmel3155194.239.4335802
3720177Horta-Guinardó38la Teixonera1161933.718.4345630
3820177Horta-Guinardó39Sant Genís dels Agudells6854171.618.140379
3920177Horta-Guinardó40Montbau5102204.711.425448
4020177Horta-Guinardó41la Vall d'Hebron578473.68.079724
4120177Horta-Guinardó42la Clota61017.86.134100
4220177Horta-Guinardó43Horta26715308.263.487422
4320178Nou Barris44Vilapicina i la Torre Llobeta2559156.729.4451870
4420178Nou Barris45Porta2500084.135.2297710
4520178Nou Barris46el Turó de la Peira1546735.413.44371152
4620178Nou Barris47Can Peguera227112.06.1190375
4720178Nou Barris48la Guineueta1523161.122.0249691
4820178Nou Barris49Canyelles685679.311.187618
4920178Nou Barris50les Roquetes1559064.218.2243856
5020178Nou Barris51Verdun1235323.714.3520867
5120178Nou Barris52la Prosperitat2638959.527.3444965
5220178Nou Barris53la Trinitat Nova726156.012.6130578
5320178Nou Barris54Torre Baró2856176.821.216135
5420178Nou Barris55Ciutat Meridiana1034235.515.1291683
5520178Nou Barris56Vallbona137259.86.823203
5620179Sant Andreu57la Trinitat Vella998381.012.7123786
5720179Sant Andreu58Baró de Viver253923.03.9110649
5820179Sant Andreu59el Bon Pastor12560188.217.967702
5920179Sant Andreu60Sant Andreu57183184.176.6311746
6020179Sant Andreu61la Sagrera2908497.239.5299736
6120179Sant Andreu62el Congrés i els Indians1411640.719.1347737
6220179Sant Andreu63Navas2212942.322.5523984
63201710Sant Martí64el Camp de l'Arpa del Clot3816874.243.0515888
64201710Sant Martí65el Clot2703969.623.63891145
65201710Sant Martí66el Parc i la Llacuna del Poblenou15134111.420.0136758
66201710Sant Martí67la Vila Olímpica del Poblenou936794.324.299387
67201710Sant Martí68el Poblenou33843154.542.6219795
68201710Sant Martí69Diagonal Mar i el Front Marítim del Poblenou13629123.723.0110592
69201710Sant Martí70el Besòs i el Maresme23009127.426.0181885
70201710Sant Martí71Provençals del Poblenou20487110.513.81851490
71201710Sant Martí72Sant Martí de Provençals2614674.523.43511116
72201710Sant Martí73la Verneda i la Pau28691112.340.1256716
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" + ], + "text/plain": [ + " Any Codi_Districte Nom_Districte Codi_Barri \\\n", + "0 2017 1 Ciutat Vella 1 \n", + "1 2017 1 Ciutat Vella 2 \n", + "2 2017 1 Ciutat Vella 3 \n", + "3 2017 1 Ciutat Vella 4 \n", + "4 2017 2 Eixample 5 \n", + "5 2017 2 Eixample 6 \n", + "6 2017 2 Eixample 7 \n", + "7 2017 2 Eixample 8 \n", + "8 2017 2 Eixample 9 \n", + "9 2017 2 Eixample 10 \n", + "10 2017 3 Sants-Montjuïc 11 \n", + "11 2017 3 Sants-Montjuïc 12 \n", + "12 2017 3 Sants-Montjuïc 13 \n", + "13 2017 3 Sants-Montjuïc 14 \n", + "14 2017 3 Sants-Montjuïc 15 \n", + "15 2017 3 Sants-Montjuïc 16 \n", + "16 2017 3 Sants-Montjuïc 17 \n", + "17 2017 3 Sants-Montjuïc 18 \n", + "18 2017 4 Les Corts 19 \n", + "19 2017 4 Les Corts 20 \n", + "20 2017 4 Les Corts 21 \n", + "21 2017 5 Sarrià-Sant Gervasi 22 \n", + "22 2017 5 Sarrià-Sant Gervasi 23 \n", + "23 2017 5 Sarrià-Sant Gervasi 24 \n", + "24 2017 5 Sarrià-Sant Gervasi 25 \n", + "25 2017 5 Sarrià-Sant Gervasi 26 \n", + "26 2017 5 Sarrià-Sant Gervasi 27 \n", + "27 2017 6 Gràcia 28 \n", + "28 2017 6 Gràcia 29 \n", + "29 2017 6 Gràcia 30 \n", + "30 2017 6 Gràcia 31 \n", + "31 2017 6 Gràcia 32 \n", + "32 2017 7 Horta-Guinardó 33 \n", + "33 2017 7 Horta-Guinardó 34 \n", + "34 2017 7 Horta-Guinardó 35 \n", + "35 2017 7 Horta-Guinardó 36 \n", + "36 2017 7 Horta-Guinardó 37 \n", + "37 2017 7 Horta-Guinardó 38 \n", + "38 2017 7 Horta-Guinardó 39 \n", + "39 2017 7 Horta-Guinardó 40 \n", + "40 2017 7 Horta-Guinardó 41 \n", + "41 2017 7 Horta-Guinardó 42 \n", + "42 2017 7 Horta-Guinardó 43 \n", + "43 2017 8 Nou Barris 44 \n", + "44 2017 8 Nou Barris 45 \n", + "45 2017 8 Nou Barris 46 \n", + "46 2017 8 Nou Barris 47 \n", + "47 2017 8 Nou Barris 48 \n", + "48 2017 8 Nou Barris 49 \n", + "49 2017 8 Nou Barris 50 \n", + "50 2017 8 Nou Barris 51 \n", + "51 2017 8 Nou Barris 52 \n", + "52 2017 8 Nou Barris 53 \n", + "53 2017 8 Nou Barris 54 \n", + "54 2017 8 Nou Barris 55 \n", + "55 2017 8 Nou Barris 56 \n", + "56 2017 9 Sant Andreu 57 \n", + "57 2017 9 Sant Andreu 58 \n", + "58 2017 9 Sant Andreu 59 \n", + "59 2017 9 Sant Andreu 60 \n", + "60 2017 9 Sant Andreu 61 \n", + "61 2017 9 Sant Andreu 62 \n", + "62 2017 9 Sant Andreu 63 \n", + "63 2017 10 Sant Martí 64 \n", + "64 2017 10 Sant Martí 65 \n", + "65 2017 10 Sant Martí 66 \n", + "66 2017 10 Sant Martí 67 \n", + "67 2017 10 Sant Martí 68 \n", + "68 2017 10 Sant Martí 69 \n", + "69 2017 10 Sant Martí 70 \n", + "70 2017 10 Sant Martí 71 \n", + "71 2017 10 Sant Martí 72 \n", + "72 2017 10 Sant Martí 73 \n", + "\n", + " Nom_Barri Població Superfície (ha) \\\n", + "0 el Raval 47608 109.8 \n", + "1 el Barri Gòtic 16062 84.2 \n", + "2 la Barceloneta 14996 131.4 \n", + "3 Sant Pere, Santa Caterina i la Ribera 22721 111.4 \n", + "4 el Fort Pienc 32016 92.9 \n", + "5 la Sagrada Família 51539 105.1 \n", + "6 la Dreta de l'Eixample 44052 212.3 \n", + "7 l'Antiga Esquerra de l'Eixample 42284 123.4 \n", + "8 la Nova Esquerra de l'Eixample 58180 133.8 \n", + "9 Sant Antoni 38345 80.1 \n", + "10 el Poble Sec 40228 460.5 \n", + "11 la Marina del Prat Vermell 1149 1428.4 \n", + "12 la Marina de Port 30584 125.5 \n", + "13 la Font de la Guatlla 10401 30.2 \n", + "14 Hostafrancs 15904 41.0 \n", + "15 la Bordeta 18530 57.7 \n", + "16 Sants - Badal 23987 41.1 \n", + "17 Sants 41127 109.8 \n", + "18 les Corts 46009 141.3 \n", + "19 la Maternitat i Sant Ramon 23948 190.3 \n", + "20 Pedralbes 12076 270.2 \n", + "21 Vallvidrera, el Tibidabo i les Planes 4670 1152.2 \n", + "22 Sarrià 25032 304.2 \n", + "23 les Tres Torres 16667 78.8 \n", + "24 Sant Gervasi - la Bonanova 25774 223.5 \n", + "25 Sant Gervasi - Galvany 47666 165.9 \n", + "26 el Putxet i el Farró 29470 84.6 \n", + "27 Vallcarca i els Penitents 15759 120.9 \n", + "28 el Coll 7412 35.8 \n", + "29 la Salut 13185 64.3 \n", + "30 la Vila de Gràcia 50662 132.6 \n", + "31 el Camp d'en Grassot i Gràcia Nova 34329 65.0 \n", + "32 el Baix Guinardó 25672 56.0 \n", + "33 Can Baró 8994 38.4 \n", + "34 el Guinardó 36467 130.8 \n", + "35 la Font d'en Fargues 9383 65.7 \n", + "36 el Carmel 31551 94.2 \n", + "37 la Teixonera 11619 33.7 \n", + "38 Sant Genís dels Agudells 6854 171.6 \n", + "39 Montbau 5102 204.7 \n", + "40 la Vall d'Hebron 5784 73.6 \n", + "41 la Clota 610 17.8 \n", + "42 Horta 26715 308.2 \n", + "43 Vilapicina i la Torre Llobeta 25591 56.7 \n", + "44 Porta 25000 84.1 \n", + "45 el Turó de la Peira 15467 35.4 \n", + "46 Can Peguera 2271 12.0 \n", + "47 la Guineueta 15231 61.1 \n", + "48 Canyelles 6856 79.3 \n", + "49 les Roquetes 15590 64.2 \n", + "50 Verdun 12353 23.7 \n", + "51 la Prosperitat 26389 59.5 \n", + "52 la Trinitat Nova 7261 56.0 \n", + "53 Torre Baró 2856 176.8 \n", + "54 Ciutat Meridiana 10342 35.5 \n", + "55 Vallbona 1372 59.8 \n", + "56 la Trinitat Vella 9983 81.0 \n", + "57 Baró de Viver 2539 23.0 \n", + "58 el Bon Pastor 12560 188.2 \n", + "59 Sant Andreu 57183 184.1 \n", + "60 la Sagrera 29084 97.2 \n", + "61 el Congrés i els Indians 14116 40.7 \n", + "62 Navas 22129 42.3 \n", + "63 el Camp de l'Arpa del Clot 38168 74.2 \n", + "64 el Clot 27039 69.6 \n", + "65 el Parc i la Llacuna del Poblenou 15134 111.4 \n", + "66 la Vila Olímpica del Poblenou 9367 94.3 \n", + "67 el Poblenou 33843 154.5 \n", + "68 Diagonal Mar i el Front Marítim del Poblenou 13629 123.7 \n", + "69 el Besòs i el Maresme 23009 127.4 \n", + "70 Provençals del Poblenou 20487 110.5 \n", + "71 Sant Martí de Provençals 26146 74.5 \n", + "72 la Verneda i la Pau 28691 112.3 \n", + "\n", + " Superfície Residencial (ha) Densitat (hab/ha) Densitat neta (hab/ha) \n", + "0 49.7 433 959 \n", + "1 34.1 191 471 \n", + "2 13.5 114 1107 \n", + "3 32.5 204 700 \n", + "4 33.4 345 959 \n", + "5 51.2 490 1006 \n", + "6 113.9 207 387 \n", + "7 67.0 343 631 \n", + "8 65.3 435 891 \n", + "9 41.3 479 928 \n", + "10 38.6 87 1043 \n", + "11 35.9 1 32 \n", + "12 39.4 244 777 \n", + "13 11.4 344 912 \n", + "14 22.4 388 710 \n", + "15 20.8 321 891 \n", + "16 25.1 584 955 \n", + "17 55.6 375 740 \n", + "18 65.3 326 705 \n", + "19 32.6 126 734 \n", + "20 82.3 45 147 \n", + "21 89.5 4 52 \n", + "22 96.2 82 260 \n", + "23 42.6 211 391 \n", + "24 82.1 115 314 \n", + "25 95.5 287 499 \n", + "26 53.1 348 555 \n", + "27 47.0 130 335 \n", + "28 12.4 207 598 \n", + "29 19.4 205 680 \n", + "30 83.5 382 607 \n", + "31 38.5 529 891 \n", + "32 23.7 459 1082 \n", + "33 14.3 234 627 \n", + "34 52.1 279 699 \n", + "35 39.4 143 238 \n", + "36 39.4 335 802 \n", + "37 18.4 345 630 \n", + "38 18.1 40 379 \n", + "39 11.4 25 448 \n", + "40 8.0 79 724 \n", + "41 6.1 34 100 \n", + "42 63.4 87 422 \n", + "43 29.4 451 870 \n", + "44 35.2 297 710 \n", + "45 13.4 437 1152 \n", + "46 6.1 190 375 \n", + "47 22.0 249 691 \n", + "48 11.1 87 618 \n", + "49 18.2 243 856 \n", + "50 14.3 520 867 \n", + "51 27.3 444 965 \n", + "52 12.6 130 578 \n", + "53 21.2 16 135 \n", + "54 15.1 291 683 \n", + "55 6.8 23 203 \n", + "56 12.7 123 786 \n", + "57 3.9 110 649 \n", + "58 17.9 67 702 \n", + "59 76.6 311 746 \n", + "60 39.5 299 736 \n", + "61 19.1 347 737 \n", + "62 22.5 523 984 \n", + "63 43.0 515 888 \n", + "64 23.6 389 1145 \n", + "65 20.0 136 758 \n", + "66 24.2 99 387 \n", + "67 42.6 219 795 \n", + "68 23.0 110 592 \n", + "69 26.0 181 885 \n", + "70 13.8 185 1490 \n", + "71 23.4 351 1116 \n", + "72 40.1 256 716 " + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Import file\n", + "data = pd.read_csv(path)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of duplicate records dropped: 0\n" + ] + } + ], + "source": [ + "#DROP DUPLICATES\n", + "before_data = len(data)\n", + "data_nodup = data.drop_duplicates()\n", + "after_data = len(data_nodup)\n", + "print('Number of duplicate records dropped: ', str(before_data - after_data))\n", + "\n", + "data = data.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "#CHANGE RELEVANT COLUMN NAMES (translate if not in English) IF NEEDED\n", + "col_names = [\"Year\",\"Distr-Code\",\"District-Name\",\"Neigh-Name\",\n", + " \"Neigh-Name\",\"Population\",\"Area (ha)\",\"Residential Area (ha)\",\"Density (hab/ha)\",\n", + " \"Net Density (hab/ha)\"]\n", + "data.columns = col_names" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationArea (ha)Residential Area (ha)Density (hab/ha)Net Density (hab/ha)
District-Name
Ciutat Vella101387436.8129.89423237
Eixample266416747.6372.122994802
Gràcia121347418.6200.814533111
Horta-Guinardó1687511194.7294.320606151
Les Corts82033601.8180.24971586
Nou Barris166579804.1232.733788703
Sant Andreu147594656.5192.217805340
Sant Martí2355131052.4279.724418772
Sants-Montjuïc1819102294.2249.223446060
Sarrià-Sant Gervasi1492792009.2459.010472071
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" + ], + "text/plain": [ + " Population Area (ha) Residential Area (ha) \\\n", + "District-Name \n", + "Ciutat Vella 101387 436.8 129.8 \n", + "Eixample 266416 747.6 372.1 \n", + "Gràcia 121347 418.6 200.8 \n", + "Horta-Guinardó 168751 1194.7 294.3 \n", + "Les Corts 82033 601.8 180.2 \n", + "Nou Barris 166579 804.1 232.7 \n", + "Sant Andreu 147594 656.5 192.2 \n", + "Sant Martí 235513 1052.4 279.7 \n", + "Sants-Montjuïc 181910 2294.2 249.2 \n", + "Sarrià-Sant Gervasi 149279 2009.2 459.0 \n", + "\n", + " Density (hab/ha) Net Density (hab/ha) \n", + "District-Name \n", + "Ciutat Vella 942 3237 \n", + "Eixample 2299 4802 \n", + "Gràcia 1453 3111 \n", + "Horta-Guinardó 2060 6151 \n", + "Les Corts 497 1586 \n", + "Nou Barris 3378 8703 \n", + "Sant Andreu 1780 5340 \n", + "Sant Martí 2441 8772 \n", + "Sants-Montjuïc 2344 6060 \n", + "Sarrià-Sant Gervasi 1047 2071 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#EXTRACT RELEVANT PARAMETERS by RELEVANT COLUMN\n", + "result = data.groupby('District-Name', as_index=True).agg({'Population':'sum','Area (ha)':'sum',\n", + " \"Residential Area (ha)\":\"sum\",\n", + " \"Density (hab/ha)\":\"sum\",\n", + " \"Net Density (hab/ha)\":\"sum\"})\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationArea (ha)Residential Area (ha)Density (hab/ha)Net Density (hab/ha)Year
District-Name
Ciutat Vella101387436.8129.894232372017
Eixample266416747.6372.1229948022017
Gràcia121347418.6200.8145331112017
Horta-Guinardó1687511194.7294.3206061512017
Les Corts82033601.8180.249715862017
Nou Barris166579804.1232.7337887032017
Sant Andreu147594656.5192.2178053402017
Sant Martí2355131052.4279.7244187722017
Sants-Montjuïc1819102294.2249.2234460602017
Sarrià-Sant Gervasi1492792009.2459.0104720712017
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" + ], + "text/plain": [ + " Population Area (ha) Residential Area (ha) \\\n", + "District-Name \n", + "Ciutat Vella 101387 436.8 129.8 \n", + "Eixample 266416 747.6 372.1 \n", + "Gràcia 121347 418.6 200.8 \n", + "Horta-Guinardó 168751 1194.7 294.3 \n", + "Les Corts 82033 601.8 180.2 \n", + "Nou Barris 166579 804.1 232.7 \n", + "Sant Andreu 147594 656.5 192.2 \n", + "Sant Martí 235513 1052.4 279.7 \n", + "Sants-Montjuïc 181910 2294.2 249.2 \n", + "Sarrià-Sant Gervasi 149279 2009.2 459.0 \n", + "\n", + " Density (hab/ha) Net Density (hab/ha) Year \n", + "District-Name \n", + "Ciutat Vella 942 3237 2017 \n", + "Eixample 2299 4802 2017 \n", + "Gràcia 1453 3111 2017 \n", + "Horta-Guinardó 2060 6151 2017 \n", + "Les Corts 497 1586 2017 \n", + "Nou Barris 3378 8703 2017 \n", + "Sant Andreu 1780 5340 2017 \n", + "Sant Martí 2441 8772 2017 \n", + "Sants-Montjuïc 2344 6060 2017 \n", + "Sarrià-Sant Gervasi 1047 2071 2017 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#ADD REFERENCE YEAR OF DATA\n", + "year = [2017]*len(result[\"Population\"])\n", + "result[\"Year\"] = year\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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District-NameCiutat VellaEixampleGràciaHorta-GuinardóLes CortsNou BarrisSant AndreuSant MartíSants-MontjuïcSarrià-Sant Gervasi
Population101387.0266416.0121347.0168751.082033.0166579.0147594.0235513.0181910.0149279.0
Area (ha)436.8747.6418.61194.7601.8804.1656.51052.42294.22009.2
Residential Area (ha)129.8372.1200.8294.3180.2232.7192.2279.7249.2459.0
Density (hab/ha)942.02299.01453.02060.0497.03378.01780.02441.02344.01047.0
Net Density (hab/ha)3237.04802.03111.06151.01586.08703.05340.08772.06060.02071.0
Year2017.02017.02017.02017.02017.02017.02017.02017.02017.02017.0
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" + ], + "text/plain": [ + "District-Name Ciutat Vella Eixample Gràcia Horta-Guinardó \\\n", + "Population 101387.0 266416.0 121347.0 168751.0 \n", + "Area (ha) 436.8 747.6 418.6 1194.7 \n", + "Residential Area (ha) 129.8 372.1 200.8 294.3 \n", + "Density (hab/ha) 942.0 2299.0 1453.0 2060.0 \n", + "Net Density (hab/ha) 3237.0 4802.0 3111.0 6151.0 \n", + "Year 2017.0 2017.0 2017.0 2017.0 \n", + "\n", + "District-Name Les Corts Nou Barris Sant Andreu Sant Martí \\\n", + "Population 82033.0 166579.0 147594.0 235513.0 \n", + "Area (ha) 601.8 804.1 656.5 1052.4 \n", + "Residential Area (ha) 180.2 232.7 192.2 279.7 \n", + "Density (hab/ha) 497.0 3378.0 1780.0 2441.0 \n", + "Net Density (hab/ha) 1586.0 8703.0 5340.0 8772.0 \n", + "Year 2017.0 2017.0 2017.0 2017.0 \n", + "\n", + "District-Name Sants-Montjuïc Sarrià-Sant Gervasi \n", + "Population 181910.0 149279.0 \n", + "Area (ha) 2294.2 2009.2 \n", + "Residential Area (ha) 249.2 459.0 \n", + "Density (hab/ha) 2344.0 1047.0 \n", + "Net Density (hab/ha) 6060.0 2071.0 \n", + "Year 2017.0 2017.0 " + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#TRANSPOSE DATA\n", + "result = result.transpose()\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "#EXPORT TO csv FILE WITH APPROPRIATE NAME and PATH\n", + "\n", + "result.to_csv('DataSet/clean-pop-density.csv', index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/DataSet/2017_densitat.csv b/your-project/DataSet/2017_densitat.csv new file mode 100644 index 0000000..3480e7f --- /dev/null +++ b/your-project/DataSet/2017_densitat.csv @@ -0,0 +1,74 @@ +"Any","Codi_Districte","Nom_Districte","Codi_Barri","Nom_Barri","Població","Superfície (ha)","Superfície Residencial (ha)","Densitat (hab/ha)","Densitat neta (hab/ha)" +2017,1,"Ciutat Vella",1,"el Raval",47608,"109.8","49.7","433","959" +2017,1,"Ciutat Vella",2,"el Barri Gòtic",16062,"84.2","34.1","191","471" +2017,1,"Ciutat Vella",3,"la Barceloneta",14996,"131.4","13.5","114","1107" +2017,1,"Ciutat Vella",4,"Sant Pere, Santa Caterina i la Ribera",22721,"111.4","32.5","204","700" +2017,2,"Eixample",5,"el Fort Pienc",32016,"92.9","33.4","345","959" +2017,2,"Eixample",6,"la Sagrada Família",51539,"105.1","51.2","490","1006" +2017,2,"Eixample",7,"la Dreta de l'Eixample",44052,"212.3","113.9","207","387" +2017,2,"Eixample",8,"l'Antiga Esquerra de l'Eixample",42284,"123.4","67.0","343","631" +2017,2,"Eixample",9,"la Nova Esquerra de l'Eixample",58180,"133.8","65.3","435","891" +2017,2,"Eixample",10,"Sant Antoni",38345,"80.1","41.3","479","928" +2017,3,"Sants-Montjuïc",11,"el Poble Sec",40228,"460.5","38.6","87","1043" +2017,3,"Sants-Montjuïc",12,"la Marina del Prat Vermell",1149,"1428.4","35.9","1","32" +2017,3,"Sants-Montjuïc",13,"la Marina de Port",30584,"125.5","39.4","244","777" +2017,3,"Sants-Montjuïc",14,"la Font de la Guatlla",10401,"30.2","11.4","344","912" +2017,3,"Sants-Montjuïc",15,"Hostafrancs",15904,"41.0","22.4","388","710" +2017,3,"Sants-Montjuïc",16,"la Bordeta",18530,"57.7","20.8","321","891" +2017,3,"Sants-Montjuïc",17,"Sants - Badal",23987,"41.1","25.1","584","955" +2017,3,"Sants-Montjuïc",18,"Sants",41127,"109.8","55.6","375","740" +2017,4,"Les Corts",19,"les Corts",46009,"141.3","65.3","326","705" +2017,4,"Les Corts",20,"la Maternitat i Sant Ramon",23948,"190.3","32.6","126","734" +2017,4,"Les Corts",21,"Pedralbes",12076,"270.2","82.3","45","147" +2017,5,"Sarrià-Sant Gervasi",22,"Vallvidrera, el Tibidabo i les Planes",4670,"1152.2","89.5","4","52" +2017,5,"Sarrià-Sant Gervasi",23,"Sarrià",25032,"304.2","96.2","82","260" +2017,5,"Sarrià-Sant Gervasi",24,"les Tres Torres",16667,"78.8","42.6","211","391" +2017,5,"Sarrià-Sant Gervasi",25,"Sant Gervasi - la Bonanova",25774,"223.5","82.1","115","314" +2017,5,"Sarrià-Sant Gervasi",26,"Sant Gervasi - Galvany",47666,"165.9","95.5","287","499" +2017,5,"Sarrià-Sant Gervasi",27,"el Putxet i el Farró",29470,"84.6","53.1","348","555" +2017,6,"Gràcia",28,"Vallcarca i els Penitents",15759,"120.9","47.0","130","335" +2017,6,"Gràcia",29,"el Coll",7412,"35.8","12.4","207","598" +2017,6,"Gràcia",30,"la Salut",13185,"64.3","19.4","205","680" +2017,6,"Gràcia",31,"la Vila de Gràcia",50662,"132.6","83.5","382","607" +2017,6,"Gràcia",32,"el Camp d'en Grassot i Gràcia Nova",34329,"65.0","38.5","529","891" +2017,7,"Horta-Guinardó",33,"el Baix Guinardó",25672,"56.0","23.7","459","1082" +2017,7,"Horta-Guinardó",34,"Can Baró",8994,"38.4","14.3","234","627" +2017,7,"Horta-Guinardó",35,"el Guinardó",36467,"130.8","52.1","279","699" +2017,7,"Horta-Guinardó",36,"la Font d'en Fargues",9383,"65.7","39.4","143","238" +2017,7,"Horta-Guinardó",37,"el Carmel",31551,"94.2","39.4","335","802" +2017,7,"Horta-Guinardó",38,"la Teixonera",11619,"33.7","18.4","345","630" +2017,7,"Horta-Guinardó",39,"Sant Genís dels Agudells",6854,"171.6","18.1","40","379" +2017,7,"Horta-Guinardó",40,"Montbau",5102,"204.7","11.4","25","448" +2017,7,"Horta-Guinardó",41,"la Vall d'Hebron",5784,"73.6","8.0","79","724" +2017,7,"Horta-Guinardó",42,"la Clota",610,"17.8","6.1","34","100" +2017,7,"Horta-Guinardó",43,"Horta",26715,"308.2","63.4","87","422" +2017,8,"Nou Barris",44,"Vilapicina i la Torre Llobeta",25591,"56.7","29.4","451","870" +2017,8,"Nou Barris",45,"Porta",25000,"84.1","35.2","297","710" +2017,8,"Nou Barris",46,"el Turó de la Peira",15467,"35.4","13.4","437","1152" +2017,8,"Nou Barris",47,"Can Peguera",2271,"12.0","6.1","190","375" +2017,8,"Nou Barris",48,"la Guineueta",15231,"61.1","22.0","249","691" +2017,8,"Nou Barris",49,"Canyelles",6856,"79.3","11.1","87","618" +2017,8,"Nou Barris",50,"les Roquetes",15590,"64.2","18.2","243","856" +2017,8,"Nou Barris",51,"Verdun",12353,"23.7","14.3","520","867" +2017,8,"Nou Barris",52,"la Prosperitat",26389,"59.5","27.3","444","965" +2017,8,"Nou Barris",53,"la Trinitat Nova",7261,"56.0","12.6","130","578" +2017,8,"Nou Barris",54,"Torre Baró",2856,"176.8","21.2","16","135" +2017,8,"Nou Barris",55,"Ciutat Meridiana",10342,"35.5","15.1","291","683" +2017,8,"Nou Barris",56,"Vallbona",1372,"59.8","6.8","23","203" +2017,9,"Sant Andreu",57,"la Trinitat Vella",9983,"81.0","12.7","123","786" +2017,9,"Sant Andreu",58,"Baró de Viver",2539,"23.0","3.9","110","649" +2017,9,"Sant Andreu",59,"el Bon Pastor",12560,"188.2","17.9","67","702" +2017,9,"Sant Andreu",60,"Sant Andreu",57183,"184.1","76.6","311","746" +2017,9,"Sant Andreu",61,"la Sagrera",29084,"97.2","39.5","299","736" +2017,9,"Sant Andreu",62,"el Congrés i els Indians",14116,"40.7","19.1","347","737" +2017,9,"Sant Andreu",63,"Navas",22129,"42.3","22.5","523","984" +2017,10,"Sant Martí",64,"el Camp de l'Arpa del Clot",38168,"74.2","43.0","515","888" +2017,10,"Sant Martí",65,"el Clot",27039,"69.6","23.6","389","1145" +2017,10,"Sant Martí",66,"el Parc i la Llacuna del Poblenou",15134,"111.4","20.0","136","758" +2017,10,"Sant Martí",67,"la Vila Olímpica del Poblenou",9367,"94.3","24.2","99","387" +2017,10,"Sant Martí",68,"el Poblenou",33843,"154.5","42.6","219","795" +2017,10,"Sant Martí",69,"Diagonal Mar i el Front Marítim del Poblenou",13629,"123.7","23.0","110","592" +2017,10,"Sant Martí",70,"el Besòs i el Maresme",23009,"127.4","26.0","181","885" +2017,10,"Sant Martí",71,"Provençals del Poblenou",20487,"110.5","13.8","185","1490" +2017,10,"Sant Martí",72,"Sant Martí de Provençals",26146,"74.5","23.4","351","1116" +2017,10,"Sant Martí",73,"la Verneda i la Pau",28691,"112.3","40.1","256","716" diff --git a/your-project/DataSet/2017_territorial_income.csv b/your-project/DataSet/2017_territorial_income.csv new file mode 100644 index 0000000..e0858a8 --- /dev/null +++ b/your-project/DataSet/2017_territorial_income.csv @@ -0,0 +1,74 @@ +"Any","Codi_Districte","Nom_Districte","Codi_Barri","Nom_Barri","Població","Índex RFD Barcelona = 100" +2017,1,"Ciutat Vella",1,"el Raval",47986,"71.2" +2017,1,"Ciutat Vella",2,"el Barri Gòtic",16240,"106.1" +2017,1,"Ciutat Vella",3,"la Barceloneta",15101,"79.6" +2017,1,"Ciutat Vella",4,"Sant Pere, Santa Caterina i la Ribera",22923,"99.4" +2017,2,"Eixample",5,"el Fort Pienc",32048,"106.5" +2017,2,"Eixample",6,"la Sagrada Família",51651,"101.8" +2017,2,"Eixample",7,"la Dreta de l'Eixample",44246,"175.9" +2017,2,"Eixample",8,"l'Antiga Esquerra de l'Eixample",42512,"137.2" +2017,2,"Eixample",9,"la Nova Esquerra de l'Eixample",58315,"110.2" +2017,2,"Eixample",10,"Sant Antoni",38412,"104.2" +2017,3,"Sants-Montjuïc",11,"el Poble Sec",40358,"82.2" +2017,3,"Sants-Montjuïc",12,"la Marina del Prat Vermell",1151,"40.0" +2017,3,"Sants-Montjuïc",13,"la Marina de Port",30622,"69.3" +2017,3,"Sants-Montjuïc",14,"la Font de la Guatlla",10422,"82.9" +2017,3,"Sants-Montjuïc",15,"Hostafrancs",15949,"99.0" +2017,3,"Sants-Montjuïc",16,"la Bordeta",18561,"79.0" +2017,3,"Sants-Montjuïc",17,"Sants - Badal",24047,"81.0" +2017,3,"Sants-Montjuïc",18,"Sants",41244,"99.0" +2017,4,"Les Corts",19,"les Corts",46104,"120.0" +2017,4,"Les Corts",20,"la Maternitat i Sant Ramon",23980,"114.2" +2017,4,"Les Corts",21,"Pedralbes",12117,"248.8" +2017,5,"Sarrià-Sant Gervasi",22,"Vallvidrera, el Tibidabo i les Planes",4689,"144.1" +2017,5,"Sarrià-Sant Gervasi",23,"Sarrià",25106,"193.6" +2017,5,"Sarrià-Sant Gervasi",24,"les Tres Torres",16660,"215.8" +2017,5,"Sarrià-Sant Gervasi",25,"Sant Gervasi - la Bonanova",25909,"184.6" +2017,5,"Sarrià-Sant Gervasi",26,"Sant Gervasi - Galvany",47753,"192.1" +2017,5,"Sarrià-Sant Gervasi",27,"el Putxet i el Farró",29617,"144.6" +2017,6,"Gràcia",28,"Vallcarca i els Penitents",15615,"112.5" +2017,6,"Gràcia",29,"el Coll",7428,"87.0" +2017,6,"Gràcia",30,"la Salut",13207,"109.9" +2017,6,"Gràcia",31,"la Vila de Gràcia",50885,"104.4" +2017,6,"Gràcia",32,"el Camp d'en Grassot i Gràcia Nova",34431,"105.7" +2017,7,"Horta-Guinardó",33,"el Baix Guinardó",25734,"92.0" +2017,7,"Horta-Guinardó",34,"Can Baró",9020,"83.3" +2017,7,"Horta-Guinardó",35,"el Guinardó",36538,"79.1" +2017,7,"Horta-Guinardó",36,"la Font d'en Fargues",9390,"92.5" +2017,7,"Horta-Guinardó",37,"el Carmel",31583,"54.2" +2017,7,"Horta-Guinardó",38,"la Teixonera",11634,"73.7" +2017,7,"Horta-Guinardó",39,"Sant Genís dels Agudells",6971,"84.1" +2017,7,"Horta-Guinardó",40,"Montbau",5171,"79.8" +2017,7,"Horta-Guinardó",41,"la Vall d'Hebron",5792,"95.8" +2017,7,"Horta-Guinardó",42,"la Clota",611,"93.5" +2017,7,"Horta-Guinardó",43,"Horta",26743,"79.8" +2017,8,"Nou Barris",44,"Vilapicina i la Torre Llobeta",25618,"63.8" +2017,8,"Nou Barris",45,"Porta",25046,"64.4" +2017,8,"Nou Barris",46,"el Turó de la Peira",15506,"51.9" +2017,8,"Nou Barris",47,"Can Peguera",2233,"51.5" +2017,8,"Nou Barris",48,"la Guineueta",15247,"53.8" +2017,8,"Nou Barris",49,"Canyelles",6863,"52.2" +2017,8,"Nou Barris",50,"les Roquetes",15648,"49.7" +2017,8,"Nou Barris",51,"Verdun",12368,"51.3" +2017,8,"Nou Barris",52,"la Prosperitat",26398,"56.0" +2017,8,"Nou Barris",53,"la Trinitat Nova",7271,"48.2" +2017,8,"Nou Barris",54,"Torre Baró",2859,"46.5" +2017,8,"Nou Barris",55,"Ciutat Meridiana",10369,"38.6" +2017,8,"Nou Barris",56,"Vallbona",1379,"40.9" +2017,9,"Sant Andreu",57,"la Trinitat Vella",10006,"47.1" +2017,9,"Sant Andreu",58,"Baró de Viver",2539,"68.9" +2017,9,"Sant Andreu",59,"el Bon Pastor",12582,"65.1" +2017,9,"Sant Andreu",60,"Sant Andreu",57223,"77.7" +2017,9,"Sant Andreu",61,"la Sagrera",29031,"77.1" +2017,9,"Sant Andreu",62,"el Congrés i els Indians",14141,"75.1" +2017,9,"Sant Andreu",63,"Navas",22171,"81.6" +2017,10,"Sant Martí",64,"el Camp de l'Arpa del Clot",38371,"81.7" +2017,10,"Sant Martí",65,"el Clot",27089,"83.6" +2017,10,"Sant Martí",66,"el Parc i la Llacuna del Poblenou",15204,"100.4" +2017,10,"Sant Martí",67,"la Vila Olímpica del Poblenou",9404,"164.2" +2017,10,"Sant Martí",68,"el Poblenou",33931,"99.9" +2017,10,"Sant Martí",69,"Diagonal Mar i el Front Marítim del Poblenou",13710,"150.1" +2017,10,"Sant Martí",70,"el Besòs i el Maresme",22893,"60.4" +2017,10,"Sant Martí",71,"Provençals del Poblenou",20649,"102.3" +2017,10,"Sant Martí",72,"Sant Martí de Provençals",26187,"67.4" +2017,10,"Sant Martí",73,"la Verneda i la Pau",28725,"57.0" diff --git a/your-project/DataSet/clean-pop-density.csv b/your-project/DataSet/clean-pop-density.csv new file mode 100644 index 0000000..382da88 --- /dev/null +++ b/your-project/DataSet/clean-pop-density.csv @@ -0,0 +1,7 @@ +,Ciutat Vella,Eixample,Gràcia,Horta-Guinardó,Les Corts,Nou Barris,Sant Andreu,Sant Martí,Sants-Montjuïc,Sarrià-Sant Gervasi +Population,101387.0,266416.0,121347.0,168751.0,82033.0,166579.0,147594.0,235513.0,181910.0,149279.0 +Area (ha),436.79999999999995,747.6,418.6,1194.6999999999998,601.8,804.0999999999999,656.5,1052.4,2294.2000000000003,2009.2 +Residential Area (ha),129.8,372.1,200.8,294.3,180.2,232.7,192.2,279.70000000000005,249.20000000000002,459.0 +Density (hab/ha),942.0,2299.0,1453.0,2060.0,497.0,3378.0,1780.0,2441.0,2344.0,1047.0 +Net Density (hab/ha),3237.0,4802.0,3111.0,6151.0,1586.0,8703.0,5340.0,8772.0,6060.0,2071.0 +Year,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0 diff --git a/your-project/DataSet/clean-territorial-income.csv b/your-project/DataSet/clean-territorial-income.csv new file mode 100644 index 0000000..cd89422 --- /dev/null +++ b/your-project/DataSet/clean-territorial-income.csv @@ -0,0 +1,4 @@ +,Ciutat Vella,Eixample,Gràcia,Horta-Guinardó,Les Corts,Nou Barris,Sant Andreu,Sant Martí,Sants-Montjuïc,Sarrià-Sant Gervasi +Population,102250.0,267184.0,121566.0,169187.0,82201.0,166805.0,147693.0,236163.0,182354.0,149734.0 +Avg Households Income Pc b=100,89.07499999999999,122.63333333333337,103.9,82.52727272727272,161.0,51.44615384615384,70.37142857142858,96.7,79.05,179.13333333333333 +Year,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0,2017.0 diff --git a/your-project/cleaner.ipynb b/your-project/cleaner.ipynb new file mode 100644 index 0000000..c3c638d --- /dev/null +++ b/your-project/cleaner.ipynb @@ -0,0 +1,1841 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "#NAME OF DATA-SET TO CLEAN\n", + "#Take into account FILE PATH as WELL\n", + "path = \"DataSet/2017_densitat.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AnyCodi_DistricteNom_DistricteCodi_BarriNom_BarriPoblacióSuperfície (ha)Superfície Residencial (ha)Densitat (hab/ha)Densitat neta (hab/ha)
020171Ciutat Vella1el Raval47608109.849.7433959
120171Ciutat Vella2el Barri Gòtic1606284.234.1191471
220171Ciutat Vella3la Barceloneta14996131.413.51141107
320171Ciutat Vella4Sant Pere, Santa Caterina i la Ribera22721111.432.5204700
420172Eixample5el Fort Pienc3201692.933.4345959
520172Eixample6la Sagrada Família51539105.151.24901006
620172Eixample7la Dreta de l'Eixample44052212.3113.9207387
720172Eixample8l'Antiga Esquerra de l'Eixample42284123.467.0343631
820172Eixample9la Nova Esquerra de l'Eixample58180133.865.3435891
920172Eixample10Sant Antoni3834580.141.3479928
1020173Sants-Montjuïc11el Poble Sec40228460.538.6871043
1120173Sants-Montjuïc12la Marina del Prat Vermell11491428.435.9132
1220173Sants-Montjuïc13la Marina de Port30584125.539.4244777
1320173Sants-Montjuïc14la Font de la Guatlla1040130.211.4344912
1420173Sants-Montjuïc15Hostafrancs1590441.022.4388710
1520173Sants-Montjuïc16la Bordeta1853057.720.8321891
1620173Sants-Montjuïc17Sants - Badal2398741.125.1584955
1720173Sants-Montjuïc18Sants41127109.855.6375740
1820174Les Corts19les Corts46009141.365.3326705
1920174Les Corts20la Maternitat i Sant Ramon23948190.332.6126734
2020174Les Corts21Pedralbes12076270.282.345147
2120175Sarrià-Sant Gervasi22Vallvidrera, el Tibidabo i les Planes46701152.289.5452
2220175Sarrià-Sant Gervasi23Sarrià25032304.296.282260
2320175Sarrià-Sant Gervasi24les Tres Torres1666778.842.6211391
2420175Sarrià-Sant Gervasi25Sant Gervasi - la Bonanova25774223.582.1115314
2520175Sarrià-Sant Gervasi26Sant Gervasi - Galvany47666165.995.5287499
2620175Sarrià-Sant Gervasi27el Putxet i el Farró2947084.653.1348555
2720176Gràcia28Vallcarca i els Penitents15759120.947.0130335
2820176Gràcia29el Coll741235.812.4207598
2920176Gràcia30la Salut1318564.319.4205680
3020176Gràcia31la Vila de Gràcia50662132.683.5382607
3120176Gràcia32el Camp d'en Grassot i Gràcia Nova3432965.038.5529891
3220177Horta-Guinardó33el Baix Guinardó2567256.023.74591082
3320177Horta-Guinardó34Can Baró899438.414.3234627
3420177Horta-Guinardó35el Guinardó36467130.852.1279699
3520177Horta-Guinardó36la Font d'en Fargues938365.739.4143238
3620177Horta-Guinardó37el Carmel3155194.239.4335802
3720177Horta-Guinardó38la Teixonera1161933.718.4345630
3820177Horta-Guinardó39Sant Genís dels Agudells6854171.618.140379
3920177Horta-Guinardó40Montbau5102204.711.425448
4020177Horta-Guinardó41la Vall d'Hebron578473.68.079724
4120177Horta-Guinardó42la Clota61017.86.134100
4220177Horta-Guinardó43Horta26715308.263.487422
4320178Nou Barris44Vilapicina i la Torre Llobeta2559156.729.4451870
4420178Nou Barris45Porta2500084.135.2297710
4520178Nou Barris46el Turó de la Peira1546735.413.44371152
4620178Nou Barris47Can Peguera227112.06.1190375
4720178Nou Barris48la Guineueta1523161.122.0249691
4820178Nou Barris49Canyelles685679.311.187618
4920178Nou Barris50les Roquetes1559064.218.2243856
5020178Nou Barris51Verdun1235323.714.3520867
5120178Nou Barris52la Prosperitat2638959.527.3444965
5220178Nou Barris53la Trinitat Nova726156.012.6130578
5320178Nou Barris54Torre Baró2856176.821.216135
5420178Nou Barris55Ciutat Meridiana1034235.515.1291683
5520178Nou Barris56Vallbona137259.86.823203
5620179Sant Andreu57la Trinitat Vella998381.012.7123786
5720179Sant Andreu58Baró de Viver253923.03.9110649
5820179Sant Andreu59el Bon Pastor12560188.217.967702
5920179Sant Andreu60Sant Andreu57183184.176.6311746
6020179Sant Andreu61la Sagrera2908497.239.5299736
6120179Sant Andreu62el Congrés i els Indians1411640.719.1347737
6220179Sant Andreu63Navas2212942.322.5523984
63201710Sant Martí64el Camp de l'Arpa del Clot3816874.243.0515888
64201710Sant Martí65el Clot2703969.623.63891145
65201710Sant Martí66el Parc i la Llacuna del Poblenou15134111.420.0136758
66201710Sant Martí67la Vila Olímpica del Poblenou936794.324.299387
67201710Sant Martí68el Poblenou33843154.542.6219795
68201710Sant Martí69Diagonal Mar i el Front Marítim del Poblenou13629123.723.0110592
69201710Sant Martí70el Besòs i el Maresme23009127.426.0181885
70201710Sant Martí71Provençals del Poblenou20487110.513.81851490
71201710Sant Martí72Sant Martí de Provençals2614674.523.43511116
72201710Sant Martí73la Verneda i la Pau28691112.340.1256716
\n", + "
" + ], + "text/plain": [ + " Any Codi_Districte Nom_Districte Codi_Barri \\\n", + "0 2017 1 Ciutat Vella 1 \n", + "1 2017 1 Ciutat Vella 2 \n", + "2 2017 1 Ciutat Vella 3 \n", + "3 2017 1 Ciutat Vella 4 \n", + "4 2017 2 Eixample 5 \n", + "5 2017 2 Eixample 6 \n", + "6 2017 2 Eixample 7 \n", + "7 2017 2 Eixample 8 \n", + "8 2017 2 Eixample 9 \n", + "9 2017 2 Eixample 10 \n", + "10 2017 3 Sants-Montjuïc 11 \n", + "11 2017 3 Sants-Montjuïc 12 \n", + "12 2017 3 Sants-Montjuïc 13 \n", + "13 2017 3 Sants-Montjuïc 14 \n", + "14 2017 3 Sants-Montjuïc 15 \n", + "15 2017 3 Sants-Montjuïc 16 \n", + "16 2017 3 Sants-Montjuïc 17 \n", + "17 2017 3 Sants-Montjuïc 18 \n", + "18 2017 4 Les Corts 19 \n", + "19 2017 4 Les Corts 20 \n", + "20 2017 4 Les Corts 21 \n", + "21 2017 5 Sarrià-Sant Gervasi 22 \n", + "22 2017 5 Sarrià-Sant Gervasi 23 \n", + "23 2017 5 Sarrià-Sant Gervasi 24 \n", + "24 2017 5 Sarrià-Sant Gervasi 25 \n", + "25 2017 5 Sarrià-Sant Gervasi 26 \n", + "26 2017 5 Sarrià-Sant Gervasi 27 \n", + "27 2017 6 Gràcia 28 \n", + "28 2017 6 Gràcia 29 \n", + "29 2017 6 Gràcia 30 \n", + "30 2017 6 Gràcia 31 \n", + "31 2017 6 Gràcia 32 \n", + "32 2017 7 Horta-Guinardó 33 \n", + "33 2017 7 Horta-Guinardó 34 \n", + "34 2017 7 Horta-Guinardó 35 \n", + "35 2017 7 Horta-Guinardó 36 \n", + "36 2017 7 Horta-Guinardó 37 \n", + "37 2017 7 Horta-Guinardó 38 \n", + "38 2017 7 Horta-Guinardó 39 \n", + "39 2017 7 Horta-Guinardó 40 \n", + "40 2017 7 Horta-Guinardó 41 \n", + "41 2017 7 Horta-Guinardó 42 \n", + "42 2017 7 Horta-Guinardó 43 \n", + "43 2017 8 Nou Barris 44 \n", + "44 2017 8 Nou Barris 45 \n", + "45 2017 8 Nou Barris 46 \n", + "46 2017 8 Nou Barris 47 \n", + "47 2017 8 Nou Barris 48 \n", + "48 2017 8 Nou Barris 49 \n", + "49 2017 8 Nou Barris 50 \n", + "50 2017 8 Nou Barris 51 \n", + "51 2017 8 Nou Barris 52 \n", + "52 2017 8 Nou Barris 53 \n", + "53 2017 8 Nou Barris 54 \n", + "54 2017 8 Nou Barris 55 \n", + "55 2017 8 Nou Barris 56 \n", + "56 2017 9 Sant Andreu 57 \n", + "57 2017 9 Sant Andreu 58 \n", + "58 2017 9 Sant Andreu 59 \n", + "59 2017 9 Sant Andreu 60 \n", + "60 2017 9 Sant Andreu 61 \n", + "61 2017 9 Sant Andreu 62 \n", + "62 2017 9 Sant Andreu 63 \n", + "63 2017 10 Sant Martí 64 \n", + "64 2017 10 Sant Martí 65 \n", + "65 2017 10 Sant Martí 66 \n", + "66 2017 10 Sant Martí 67 \n", + "67 2017 10 Sant Martí 68 \n", + "68 2017 10 Sant Martí 69 \n", + "69 2017 10 Sant Martí 70 \n", + "70 2017 10 Sant Martí 71 \n", + "71 2017 10 Sant Martí 72 \n", + "72 2017 10 Sant Martí 73 \n", + "\n", + " Nom_Barri Població Superfície (ha) \\\n", + "0 el Raval 47608 109.8 \n", + "1 el Barri Gòtic 16062 84.2 \n", + "2 la Barceloneta 14996 131.4 \n", + "3 Sant Pere, Santa Caterina i la Ribera 22721 111.4 \n", + "4 el Fort Pienc 32016 92.9 \n", + "5 la Sagrada Família 51539 105.1 \n", + "6 la Dreta de l'Eixample 44052 212.3 \n", + "7 l'Antiga Esquerra de l'Eixample 42284 123.4 \n", + "8 la Nova Esquerra de l'Eixample 58180 133.8 \n", + "9 Sant Antoni 38345 80.1 \n", + "10 el Poble Sec 40228 460.5 \n", + "11 la Marina del Prat Vermell 1149 1428.4 \n", + "12 la Marina de Port 30584 125.5 \n", + "13 la Font de la Guatlla 10401 30.2 \n", + "14 Hostafrancs 15904 41.0 \n", + "15 la Bordeta 18530 57.7 \n", + "16 Sants - Badal 23987 41.1 \n", + "17 Sants 41127 109.8 \n", + "18 les Corts 46009 141.3 \n", + "19 la Maternitat i Sant Ramon 23948 190.3 \n", + "20 Pedralbes 12076 270.2 \n", + "21 Vallvidrera, el Tibidabo i les Planes 4670 1152.2 \n", + "22 Sarrià 25032 304.2 \n", + "23 les Tres Torres 16667 78.8 \n", + "24 Sant Gervasi - la Bonanova 25774 223.5 \n", + "25 Sant Gervasi - Galvany 47666 165.9 \n", + "26 el Putxet i el Farró 29470 84.6 \n", + "27 Vallcarca i els Penitents 15759 120.9 \n", + "28 el Coll 7412 35.8 \n", + "29 la Salut 13185 64.3 \n", + "30 la Vila de Gràcia 50662 132.6 \n", + "31 el Camp d'en Grassot i Gràcia Nova 34329 65.0 \n", + "32 el Baix Guinardó 25672 56.0 \n", + "33 Can Baró 8994 38.4 \n", + "34 el Guinardó 36467 130.8 \n", + "35 la Font d'en Fargues 9383 65.7 \n", + "36 el Carmel 31551 94.2 \n", + "37 la Teixonera 11619 33.7 \n", + "38 Sant Genís dels Agudells 6854 171.6 \n", + "39 Montbau 5102 204.7 \n", + "40 la Vall d'Hebron 5784 73.6 \n", + "41 la Clota 610 17.8 \n", + "42 Horta 26715 308.2 \n", + "43 Vilapicina i la Torre Llobeta 25591 56.7 \n", + "44 Porta 25000 84.1 \n", + "45 el Turó de la Peira 15467 35.4 \n", + "46 Can Peguera 2271 12.0 \n", + "47 la Guineueta 15231 61.1 \n", + "48 Canyelles 6856 79.3 \n", + "49 les Roquetes 15590 64.2 \n", + "50 Verdun 12353 23.7 \n", + "51 la Prosperitat 26389 59.5 \n", + "52 la Trinitat Nova 7261 56.0 \n", + "53 Torre Baró 2856 176.8 \n", + "54 Ciutat Meridiana 10342 35.5 \n", + "55 Vallbona 1372 59.8 \n", + "56 la Trinitat Vella 9983 81.0 \n", + "57 Baró de Viver 2539 23.0 \n", + "58 el Bon Pastor 12560 188.2 \n", + "59 Sant Andreu 57183 184.1 \n", + "60 la Sagrera 29084 97.2 \n", + "61 el Congrés i els Indians 14116 40.7 \n", + "62 Navas 22129 42.3 \n", + "63 el Camp de l'Arpa del Clot 38168 74.2 \n", + "64 el Clot 27039 69.6 \n", + "65 el Parc i la Llacuna del Poblenou 15134 111.4 \n", + "66 la Vila Olímpica del Poblenou 9367 94.3 \n", + "67 el Poblenou 33843 154.5 \n", + "68 Diagonal Mar i el Front Marítim del Poblenou 13629 123.7 \n", + "69 el Besòs i el Maresme 23009 127.4 \n", + "70 Provençals del Poblenou 20487 110.5 \n", + "71 Sant Martí de Provençals 26146 74.5 \n", + "72 la Verneda i la Pau 28691 112.3 \n", + "\n", + " Superfície Residencial (ha) Densitat (hab/ha) Densitat neta (hab/ha) \n", + "0 49.7 433 959 \n", + "1 34.1 191 471 \n", + "2 13.5 114 1107 \n", + "3 32.5 204 700 \n", + "4 33.4 345 959 \n", + "5 51.2 490 1006 \n", + "6 113.9 207 387 \n", + "7 67.0 343 631 \n", + "8 65.3 435 891 \n", + "9 41.3 479 928 \n", + "10 38.6 87 1043 \n", + "11 35.9 1 32 \n", + "12 39.4 244 777 \n", + "13 11.4 344 912 \n", + "14 22.4 388 710 \n", + "15 20.8 321 891 \n", + "16 25.1 584 955 \n", + "17 55.6 375 740 \n", + "18 65.3 326 705 \n", + "19 32.6 126 734 \n", + "20 82.3 45 147 \n", + "21 89.5 4 52 \n", + "22 96.2 82 260 \n", + "23 42.6 211 391 \n", + "24 82.1 115 314 \n", + "25 95.5 287 499 \n", + "26 53.1 348 555 \n", + "27 47.0 130 335 \n", + "28 12.4 207 598 \n", + "29 19.4 205 680 \n", + "30 83.5 382 607 \n", + "31 38.5 529 891 \n", + "32 23.7 459 1082 \n", + "33 14.3 234 627 \n", + "34 52.1 279 699 \n", + "35 39.4 143 238 \n", + "36 39.4 335 802 \n", + "37 18.4 345 630 \n", + "38 18.1 40 379 \n", + "39 11.4 25 448 \n", + "40 8.0 79 724 \n", + "41 6.1 34 100 \n", + "42 63.4 87 422 \n", + "43 29.4 451 870 \n", + "44 35.2 297 710 \n", + "45 13.4 437 1152 \n", + "46 6.1 190 375 \n", + "47 22.0 249 691 \n", + "48 11.1 87 618 \n", + "49 18.2 243 856 \n", + "50 14.3 520 867 \n", + "51 27.3 444 965 \n", + "52 12.6 130 578 \n", + "53 21.2 16 135 \n", + "54 15.1 291 683 \n", + "55 6.8 23 203 \n", + "56 12.7 123 786 \n", + "57 3.9 110 649 \n", + "58 17.9 67 702 \n", + "59 76.6 311 746 \n", + "60 39.5 299 736 \n", + "61 19.1 347 737 \n", + "62 22.5 523 984 \n", + "63 43.0 515 888 \n", + "64 23.6 389 1145 \n", + "65 20.0 136 758 \n", + "66 24.2 99 387 \n", + "67 42.6 219 795 \n", + "68 23.0 110 592 \n", + "69 26.0 181 885 \n", + "70 13.8 185 1490 \n", + "71 23.4 351 1116 \n", + "72 40.1 256 716 " + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Import file\n", + "data = pd.read_csv(path)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of duplicate records dropped: 0\n" + ] + } + ], + "source": [ + "#DROP DUPLICATES\n", + "before_data = len(data)\n", + "data_nodup = data.drop_duplicates()\n", + "after_data = len(data_nodup)\n", + "print('Number of duplicate records dropped: ', str(before_data - after_data))\n", + "\n", + "data = data.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "#CHANGE RELEVANT COLUMN NAMES (translate if not in English) IF NEEDED\n", + "col_names = [\"Year\",\"Distr-Code\",\"District-Name\",\"Neigh-Name\",\n", + " \"Neigh-Name\",\"Population\",\"Area (ha)\",\"Residential Area (ha)\",\"Density (hab/ha)\",\n", + " \"Net Density (hab/ha)\"]\n", + "data.columns = col_names" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationArea (ha)Residential Area (ha)Density (hab/ha)Net Density (hab/ha)
District-Name
Ciutat Vella101387436.8129.89423237
Eixample266416747.6372.122994802
Gràcia121347418.6200.814533111
Horta-Guinardó1687511194.7294.320606151
Les Corts82033601.8180.24971586
Nou Barris166579804.1232.733788703
Sant Andreu147594656.5192.217805340
Sant Martí2355131052.4279.724418772
Sants-Montjuïc1819102294.2249.223446060
Sarrià-Sant Gervasi1492792009.2459.010472071
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" + ], + "text/plain": [ + " Population Area (ha) Residential Area (ha) \\\n", + "District-Name \n", + "Ciutat Vella 101387 436.8 129.8 \n", + "Eixample 266416 747.6 372.1 \n", + "Gràcia 121347 418.6 200.8 \n", + "Horta-Guinardó 168751 1194.7 294.3 \n", + "Les Corts 82033 601.8 180.2 \n", + "Nou Barris 166579 804.1 232.7 \n", + "Sant Andreu 147594 656.5 192.2 \n", + "Sant Martí 235513 1052.4 279.7 \n", + "Sants-Montjuïc 181910 2294.2 249.2 \n", + "Sarrià-Sant Gervasi 149279 2009.2 459.0 \n", + "\n", + " Density (hab/ha) Net Density (hab/ha) \n", + "District-Name \n", + "Ciutat Vella 942 3237 \n", + "Eixample 2299 4802 \n", + "Gràcia 1453 3111 \n", + "Horta-Guinardó 2060 6151 \n", + "Les Corts 497 1586 \n", + "Nou Barris 3378 8703 \n", + "Sant Andreu 1780 5340 \n", + "Sant Martí 2441 8772 \n", + "Sants-Montjuïc 2344 6060 \n", + "Sarrià-Sant Gervasi 1047 2071 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#EXTRACT RELEVANT PARAMETERS by RELEVANT COLUMN\n", + "result = data.groupby('District-Name', as_index=True).agg({'Population':'sum','Area (ha)':'sum',\n", + " \"Residential Area (ha)\":\"sum\",\n", + " \"Density (hab/ha)\":\"sum\",\n", + " \"Net Density (hab/ha)\":\"sum\"})\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationArea (ha)Residential Area (ha)Density (hab/ha)Net Density (hab/ha)Year
District-Name
Ciutat Vella101387436.8129.894232372017
Eixample266416747.6372.1229948022017
Gràcia121347418.6200.8145331112017
Horta-Guinardó1687511194.7294.3206061512017
Les Corts82033601.8180.249715862017
Nou Barris166579804.1232.7337887032017
Sant Andreu147594656.5192.2178053402017
Sant Martí2355131052.4279.7244187722017
Sants-Montjuïc1819102294.2249.2234460602017
Sarrià-Sant Gervasi1492792009.2459.0104720712017
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" + ], + "text/plain": [ + " Population Area (ha) Residential Area (ha) \\\n", + "District-Name \n", + "Ciutat Vella 101387 436.8 129.8 \n", + "Eixample 266416 747.6 372.1 \n", + "Gràcia 121347 418.6 200.8 \n", + "Horta-Guinardó 168751 1194.7 294.3 \n", + "Les Corts 82033 601.8 180.2 \n", + "Nou Barris 166579 804.1 232.7 \n", + "Sant Andreu 147594 656.5 192.2 \n", + "Sant Martí 235513 1052.4 279.7 \n", + "Sants-Montjuïc 181910 2294.2 249.2 \n", + "Sarrià-Sant Gervasi 149279 2009.2 459.0 \n", + "\n", + " Density (hab/ha) Net Density (hab/ha) Year \n", + "District-Name \n", + "Ciutat Vella 942 3237 2017 \n", + "Eixample 2299 4802 2017 \n", + "Gràcia 1453 3111 2017 \n", + "Horta-Guinardó 2060 6151 2017 \n", + "Les Corts 497 1586 2017 \n", + "Nou Barris 3378 8703 2017 \n", + "Sant Andreu 1780 5340 2017 \n", + "Sant Martí 2441 8772 2017 \n", + "Sants-Montjuïc 2344 6060 2017 \n", + "Sarrià-Sant Gervasi 1047 2071 2017 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#ADD REFERENCE YEAR OF DATA\n", + "year = [2017]*len(result[\"Population\"])\n", + "result[\"Year\"] = year\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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District-NameCiutat VellaEixampleGràciaHorta-GuinardóLes CortsNou BarrisSant AndreuSant MartíSants-MontjuïcSarrià-Sant Gervasi
Population101387.0266416.0121347.0168751.082033.0166579.0147594.0235513.0181910.0149279.0
Area (ha)436.8747.6418.61194.7601.8804.1656.51052.42294.22009.2
Residential Area (ha)129.8372.1200.8294.3180.2232.7192.2279.7249.2459.0
Density (hab/ha)942.02299.01453.02060.0497.03378.01780.02441.02344.01047.0
Net Density (hab/ha)3237.04802.03111.06151.01586.08703.05340.08772.06060.02071.0
Year2017.02017.02017.02017.02017.02017.02017.02017.02017.02017.0
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" + ], + "text/plain": [ + "District-Name Ciutat Vella Eixample Gràcia Horta-Guinardó \\\n", + "Population 101387.0 266416.0 121347.0 168751.0 \n", + "Area (ha) 436.8 747.6 418.6 1194.7 \n", + "Residential Area (ha) 129.8 372.1 200.8 294.3 \n", + "Density (hab/ha) 942.0 2299.0 1453.0 2060.0 \n", + "Net Density (hab/ha) 3237.0 4802.0 3111.0 6151.0 \n", + "Year 2017.0 2017.0 2017.0 2017.0 \n", + "\n", + "District-Name Les Corts Nou Barris Sant Andreu Sant Martí \\\n", + "Population 82033.0 166579.0 147594.0 235513.0 \n", + "Area (ha) 601.8 804.1 656.5 1052.4 \n", + "Residential Area (ha) 180.2 232.7 192.2 279.7 \n", + "Density (hab/ha) 497.0 3378.0 1780.0 2441.0 \n", + "Net Density (hab/ha) 1586.0 8703.0 5340.0 8772.0 \n", + "Year 2017.0 2017.0 2017.0 2017.0 \n", + "\n", + "District-Name Sants-Montjuïc Sarrià-Sant Gervasi \n", + "Population 181910.0 149279.0 \n", + "Area (ha) 2294.2 2009.2 \n", + "Residential Area (ha) 249.2 459.0 \n", + "Density (hab/ha) 2344.0 1047.0 \n", + "Net Density (hab/ha) 6060.0 2071.0 \n", + "Year 2017.0 2017.0 " + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#TRANSPOSE DATA\n", + "result = result.transpose()\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "#EXPORT TO csv FILE WITH APPROPRIATE NAME and PATH\n", + "\n", + "result.to_csv('DataSet/clean-pop-density.csv', index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2d54b655dfb10328e6d5c536cbeff238fce8b7ed Mon Sep 17 00:00:00 2001 From: Juanlacalle Date: Wed, 30 Oct 2019 18:19:58 +0000 Subject: [PATCH 3/4] [drug] --- .../Antisystem-checkpoint.ipynb | 72 +++++++++++++++++++ your-project/Antisystem.ipynb | 38 ++++++---- .../{crime 2018.cvs.csv => crime 2018.csv} | 0 your-project/test drug.csv | 43 +++++++++++ 4 files changed, 138 insertions(+), 15 deletions(-) create mode 100644 your-project/.ipynb_checkpoints/Antisystem-checkpoint.ipynb rename your-project/{crime 2018.cvs.csv => crime 2018.csv} (100%) create mode 100644 your-project/test drug.csv diff --git a/your-project/.ipynb_checkpoints/Antisystem-checkpoint.ipynb b/your-project/.ipynb_checkpoints/Antisystem-checkpoint.ipynb new file mode 100644 index 0000000..2caa0ed --- /dev/null +++ b/your-project/.ipynb_checkpoints/Antisystem-checkpoint.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "ParserError", + "evalue": "Error tokenizing data. C error: EOF inside string starting at row 10", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Perception.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 436\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nrows'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1993\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1994\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1995\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1996\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1997\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: EOF inside string starting at row 10" + ] + } + ], + "source": [ + "data=pd.read_csv('Perception.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/Antisystem.ipynb b/your-project/Antisystem.ipynb index 2de7ef7..f58e1e8 100644 --- a/your-project/Antisystem.ipynb +++ b/your-project/Antisystem.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -12,31 +12,39 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] File b'Perception' does not exist: b'Perception'", + "ename": "ParserError", + "evalue": "Error tokenizing data. C error: EOF inside string starting at row 10", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Perception'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Perception.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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C error: EOF inside string starting at row 10" ] } ], "source": [ - "data=pd.read_csv('Perception.csv')\n", - "data.head()" + "data=pd.read_csv('Perception.csv')\n" ] }, { diff --git a/your-project/crime 2018.cvs.csv b/your-project/crime 2018.csv similarity index 100% rename from your-project/crime 2018.cvs.csv rename to your-project/crime 2018.csv diff --git a/your-project/test drug.csv b/your-project/test drug.csv new file mode 100644 index 0000000..c85429f --- /dev/null +++ b/your-project/test drug.csv @@ -0,0 +1,43 @@ +;;;;;;;;;;;;;; +;;;;;;;;;;;;;; +2. Guardia Urbana;;;;;;;;;;;;;; +;;;;;;;;;;;;;; +2.4. Pruebas drogo test por distritos. 2014-2018;;;;;;;;;;;;;; +;;;;;;;;;;;;;; +;;;BARCELONA;"1.Ciutat   +Vella";2.Eixample;"3.Sants-   +Montjuïc";4.Les Corts;"5.Sarrià-   +Sant Gervasi";6.Gràcia;"7.Horta-   +Guinardó";"8.Nou   +Barris";"9.Sant   +Andreu";"10.Sant   +Martí";"Distrito   +desconocido" +;;;;;;;;;;;;;; +;;;;;;;;;;;;;; +2014;;;2.367;146;282;836;83;96;167;102;127;235;220;73 +2015;;;2.162;179;191;108;132;54;105;262;309;642;109;71 +2016;;;3.042;357;397;115;133;127;168;184;581;732;171;77 +2017;;;3.964;501;455;703;98;277;205;289;285;395;588;168 +;;;;;;;;;;;;;; +2018;;;5.149;676;657;1.059;135;420;238;372;263;477;823;29 +Control preventivo;;;;;;;;;;;;;; +Negativo;;;1.770;244;166;370;52;172;103;158;65;167;266;7 +Positivo;;;1.625;159;125;405;28;166;63;139;59;171;297;13 +Negarse;;;8;1;0;0;0;0;0;1;1;0;5;0 +Accidente;;;;;;;;;;;;;; +Negativo;;;42;0;11;6;4;2;2;2;2;4;7;2 +Positivo;;;108;6;24;23;6;9;2;7;8;8;15;0 +Negarse;;;5;0;0;2;0;0;0;2;0;0;1;0 +Infracción;;;;;;;;;;;;;; +Negativo;;;172;31;28;38;13;12;6;10;5;9;19;1 +Positivo;;;864;129;201;129;22;37;45;39;83;84;93;2 +Negarse;;;13;3;2;3;0;1;0;1;0;0;3;0 +Síntomas;;;;;;;;;;;;;; +Negativo;;;68;12;14;9;1;2;1;0;0;2;27;0 +Positivo;;;473;91;85;74;9;19;16;13;40;32;90;4 +Negarse;;;1;0;1;0;0;0;0;0;0;0;0;0 +;;;;;;;;;;;;;; +;;;;;;;;;;;;;; +Departament d'Estadística i Difusió de Dades. Ajuntament de Barcelona.;;;;;;;;;;;;;; +Fuente: Ajuntament de Barcelona. Àrea de Seguretat i Prevenció. Galileo/Sistemes d'Informació.;;;;;;;;;;;;;; From 4cdcd7d7423d732e366170eebd0289e2e18dc923 Mon Sep 17 00:00:00 2001 From: Juanlacalle Date: Wed, 30 Oct 2019 18:53:29 +0000 Subject: [PATCH 4/4] [mierda lista] JUan --- your-project/Antisystem.ipynb | 146 +++++++++++++++++++++++++++----- your-project/Perception.csv | 26 ------ your-project/Perception2018.csv | 11 +++ your-project/crime 2018.csv | 58 ++++++------- your-project/test drug.csv | 52 ++++-------- 5 files changed, 175 insertions(+), 118 deletions(-) delete mode 100644 your-project/Perception.csv create mode 100644 your-project/Perception2018.csv diff --git a/your-project/Antisystem.ipynb b/your-project/Antisystem.ipynb index f58e1e8..6a400a8 100644 --- a/your-project/Antisystem.ipynb +++ b/your-project/Antisystem.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "import csv" ] }, { @@ -19,32 +20,135 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#data=pd.read_csv('Perception2018.csv', sep=';')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "data=pd.read_csv('crime 2018.csv', sep=';')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": {}, "outputs": [ { - "ename": "ParserError", - "evalue": "Error tokenizing data. C error: EOF inside string starting at row 10", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Perception.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 436\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nrows'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/Estelle/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1993\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1994\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1995\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1996\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1997\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: EOF inside string starting at row 10" + "name": "stdout", + "output_type": "stream", + "text": [ + " Unnamed: 0 Unnamed: 1 Unnamed: 2 \\\n", + "0 NaN Total 2018 NaN \n", + "1 NaN Homicide NaN \n", + "2 NaN Omission of S.O.S. duty NaN \n", + "3 NaN Falsehoods NaN \n", + "4 NaN Lesions NaN \n", + "5 NaN Torture NaN \n", + "6 NaN Human traffic NaN \n", + "7 NaN Economics crimes NaN \n", + "8 NaN Against foreign NaN \n", + "9 NaN Against honor NaN \n", + "10 NaN Against Constitution NaN \n", + "11 NaN Against privacy NaN \n", + "12 NaN Against freedom NaN \n", + "13 NaN Against sexual freedom NaN \n", + "14 NaN Against Justice NaN \n", + "15 NaN Against public administration NaN \n", + "16 NaN Against familiar relationships NaN \n", + "17 NaN Crimes against public order NaN \n", + "18 NaN Crimes related to genetic manipulation NaN \n", + "19 NaN Workers' rights NaN \n", + "20 NaN Against the Public Treasury and against Social... NaN \n", + "21 NaN Against collective security NaN \n", + "22 NaN Crimes related to urban planning, the protecti... NaN \n", + "\n", + " Unnamed: 3 BARCELONA 1.Ciutat  \\nVella 2.Eixample \\\n", + "0 NaN 219.521 54.056 56.231 \n", + "1 NaN 73.000 15.000 7.000 \n", + "2 NaN 1.000 0.000 0.000 \n", + "3 NaN 820.000 99.000 167.000 \n", + "4 NaN 5.699 1.090 1.035 \n", + "5 NaN 162.000 18.000 25.000 \n", + "6 NaN 10.000 2.000 1.000 \n", + "7 NaN 205.231 51.228 53.832 \n", + "8 NaN 1.000 0.000 1.000 \n", + "9 NaN 24.000 0.000 5.000 \n", + "10 NaN 67.000 9.000 6.000 \n", + "11 NaN 491.000 65.000 99.000 \n", + "12 NaN 3.313 444.000 497.000 \n", + "13 NaN 743.000 128.000 131.000 \n", + "14 NaN 647.000 96.000 95.000 \n", + "15 NaN 8.000 2.000 1.000 \n", + "16 NaN 173.000 16.000 17.000 \n", + "17 NaN 974.000 317.000 216.000 \n", + "18 NaN 1.000 0.000 0.000 \n", + "19 NaN 8.000 0.000 2.000 \n", + "20 NaN 2.000 0.000 0.000 \n", + "21 NaN 1.044 523.000 92.000 \n", + "22 NaN 29.000 4.000 2.000 \n", + "\n", + " 3.Sants-  \\nMontjuïc 4.Les Corts 5.Sarrià-  \\nSant Gervasi 6.Gràcia \\\n", + "0 24.065 7.794 10.231 10.30 \n", + "1 14.000 2.000 2.000 6.00 \n", + "2 0.000 0.000 0.000 0.00 \n", + "3 155.000 57.000 31.000 35.00 \n", + "4 821.000 159.000 267.000 226.00 \n", + "5 20.000 6.000 5.000 12.00 \n", + "6 0.000 3.000 0.000 1.00 \n", + "7 21.985 7.313 9.591 9.71 \n", + "8 0.000 0.000 0.000 0.00 \n", + "9 4.000 2.000 5.000 2.00 \n", + "10 8.000 2.000 0.000 4.00 \n", + "11 66.000 35.000 35.000 28.00 \n", + "12 495.000 125.000 178.000 164.00 \n", + "13 113.000 18.000 36.000 31.00 \n", + "14 114.000 19.000 16.000 13.00 \n", + "15 3.000 0.000 0.000 0.00 \n", + "16 22.000 3.000 8.000 7.00 \n", + "17 117.000 30.000 38.000 25.00 \n", + "18 0.000 0.000 0.000 0.00 \n", + "19 4.000 0.000 0.000 0.00 \n", + "20 0.000 0.000 1.000 0.00 \n", + "21 119.000 19.000 18.000 34.00 \n", + "22 5.000 1.000 0.000 2.00 \n", + "\n", + " 7.Horta-  \\nGuinardó 8.Nou  \\nBarris 9.Sant  \\nAndreu 10.Sant  \\nMartí \n", + "0 8.852 8.865 11.406 25.650 \n", + "1 5.000 8.000 3.000 11.000 \n", + "2 0.000 0.000 0.000 1.000 \n", + "3 51.000 55.000 66.000 94.000 \n", + "4 374.000 512.000 445.000 747.000 \n", + "5 14.000 22.000 17.000 22.000 \n", + "6 0.000 1.000 0.000 2.000 \n", + "7 7.920 7.545 10.323 23.817 \n", + "8 0.000 0.000 0.000 0.000 \n", + "9 2.000 3.000 0.000 0.000 \n", + "10 1.000 8.000 5.000 3.000 \n", + "11 29.000 51.000 37.000 43.000 \n", + "12 271.000 372.000 292.000 462.000 \n", + "13 45.000 64.000 61.000 102.000 \n", + "14 30.000 78.000 65.000 118.000 \n", + "15 0.000 0.000 1.000 1.000 \n", + "16 32.000 29.000 21.000 18.000 \n", + "17 30.000 55.000 34.000 100.000 \n", + "18 1.000 0.000 0.000 0.000 \n", + "19 1.000 1.000 0.000 0.000 \n", + "20 0.000 0.000 1.000 0.000 \n", + "21 46.000 55.000 34.000 102.000 \n", + "22 0.000 6.000 1.000 7.000 \n" ] } ], "source": [ - "data=pd.read_csv('Perception.csv')\n" + "print(data)" ] }, { diff --git a/your-project/Perception.csv b/your-project/Perception.csv deleted file mode 100644 index 3957c38..0000000 --- a/your-project/Perception.csv +++ /dev/null @@ -1,26 +0,0 @@ -;;;;;;;;;;;; -;;;;;;;;;;;; -5. Encuesta de victimización de Barcelona;;;;;;;;;;;; -;;;;;;;;;;;; -5.5. Percepción de la seguridad en la ciudad y el barrio por distritos. 2015-2019;;;;;;;;;;;; -;;;;;;;;;;;; -;;"Nivel de seguridad en la ciudad    -(puntuación de 0 a 10) -";;;;;;"Nivel de seguridad en el barrio    -(puntuación de 0 a 10) -";;;; -Distritos;;2015;2016 (1);2017;2018;2019 (1);;2015;2016 (1);2017;2018;2019 (1) -;;;;;;;;;;;; -;;;;;;;;;;;; -BARCELONA;;6,1;6,2;6,3;6,2;5,2;;6,4;6,3;6,5;6,4;5,9 -;;;;;;;;;;;; -1. Ciutat Vella;;6,3;-;6,6;6,2;-;;5,2;-;5,7;5,2;- -2. Eixample;;6,2;-;6,3;6,3;-;;6,8;-;7,1;6,9;- -3. Sants-Montjuïc;;6,1;-;6,1;6,1;-;;6,0;-;6,1;6,2;- -4. Les Corts;;6,2;-;6,2;6,4;-;;7,1;-;7,3;7,3;- -5. Sarrià-Sant Gervasi;;6,0;-;6,2;6,0;-;;7,0;-;7,0;6,8;- -6. Gràcia;;6,1;-;6,2;6,3;-;;6,8;-;6,9;7,0;- -7. Horta-Guinardó;;6,1;-;6,4;6,2;-;;6,4;-;6,5;6,3;- -8. Nou Barris;;6,2;-;6,3;6,2;-;;5,9;-;5,7;5,9;- -9. Sant Andreu;;5,8;-;6,5;6,2;-;;6,1;-;6,3;6,3;- -10. Sant Martí;;6,1;-;6,4;6,2;-;;6,2;-;6,4;6,1;- diff --git a/your-project/Perception2018.csv b/your-project/Perception2018.csv new file mode 100644 index 0000000..a8d43bd --- /dev/null +++ b/your-project/Perception2018.csv @@ -0,0 +1,11 @@ +BARCELONA;6,4 +1. Ciutat Vella;5,2 +2. Eixample;6,9 +3. Sants-Montjuïc;6,2 +4. Les Corts;7,3 +5. Sarrià-Sant Gervasi;6,8 +6. Gràcia;7,0 +7. Horta-Guinardó;6,3 +8. Nou Barris;5,9 +9. Sant Andreu;6,3 +10. Sant Martí;6,1 \ No newline at end of file diff --git a/your-project/crime 2018.csv b/your-project/crime 2018.csv index e8fc177..f42b42a 100644 --- a/your-project/crime 2018.csv +++ b/your-project/crime 2018.csv @@ -1,8 +1,3 @@ -;;;;;;;;;;;;;;; -;Catalonia Police [Policía de la Generalitat - Mossos d’Esquadra];;;;;;;;;;;;;; -;;;;;;;;;;;;;;; -;Type of facts known per district in Barcelona. 2014-2018;;;;;;;;;;;;;; -;;;;;;;;;;;;;;; ;;;;BARCELONA;"1.Ciutat   Vella";2.Eixample;"3.Sants-   Montjuïc";4.Les Corts;"5.Sarrià-   @@ -10,32 +5,27 @@ Sant Gervasi";6.Gràcia;"7.Horta-   Guinardó";"8.Nou   Barris";"9.Sant   Andreu";"10.Sant   -Martí";"Distrito   -desconocido" -;;;;;;;;;;;;;;; -;2018;;;219.521;54.056;56.231;24.065;7.794;10.231;10.300;8.852;8.865;11.406;25.650;2.071 -;;;;;;;;;;;;;;; -;Homicide;;;73;15;7;14;2;2;6;5;8;3;11;0 -;Omission of S.O.S. duty;;;1;0;0;0;0;0;0;0;0;0;1;0 -;Falsehoods;;;820;99;167;155;57;31;35;51;55;66;94;10 -;Lesions;;;5.699;1.090;1.035;821;159;267;226;374;512;445;747;23 -;Torture;;;162;18;25;20;6;5;12;14;22;17;22;1 -;Human traffic;;;10;2;1;0;3;0;1;0;1;0;2;0 -;Economics crimes;;;205.231;51.228;53.832;21.985;7.313;9.591;9.710;7.920;7.545;10.323;23.817;1.967 -;Against foreign;;;1;0;1;0;0;0;0;0;0;0;0;0 -;Against honor;;;24;0;5;4;2;5;2;2;3;0;0;1 -;Against Constitution;;;67;9;6;8;2;0;4;1;8;5;3;21 -;Against privacy;;;491;65;99;66;35;35;28;29;51;37;43;3 -;Against freedom;;;3.313;444;497;495;125;178;164;271;372;292;462;13 -;Against sexual freedom;;;743;128;131;113;18;36;31;45;64;61;102;14 -;Against Justice;;;647;96;95;114;19;16;13;30;78;65;118;3 -;Against public administration;;;8;2;1;3;0;0;0;0;0;1;1;0 -;Against familiar relationships;;;173;16;17;22;3;8;7;32;29;21;18;0 -;Crimes against public order;;;974;317;216;117;30;38;25;30;55;34;100;12 -;Crimes related to genetic manipulation;;;1;0;0;0;0;0;0;1;0;0;0;0 -;Workers' rights;;;8;0;2;4;0;0;0;1;1;0;0;0 -;Against the Public Treasury and against Social Security;;;2;0;0;0;0;1;0;0;0;1;0;0 -;Against collective security;;;1.044;523;92;119;19;18;34;46;55;34;102;2 -;Crimes related to urban planning, the protection of the historical heritage and the environment;;;29;4;2;5;1;0;2;0;6;1;7;1 -;Departament d'Estadística i Difusió de Dades. Ajuntament de Barcelona.;;;;;;;;;;;;;; -;Fuente: Generalitat de Catalunya. Departament d'Interior. Direcció General de la Policia.;;;;;;;;;;;;;; \ No newline at end of file +Martí" +;Total 2018;;;219.521;54.056;56.231;24.065;7.794;10.231;10.300;8.852;8.865;11.406;25.650 +;Homicide;;;73;15;7;14;2;2;6;5;8;3;11 +;Omission of S.O.S. duty;;;1;0;0;0;0;0;0;0;0;0;1 +;Falsehoods;;;820;99;167;155;57;31;35;51;55;66;94 +;Lesions;;;5.699;1.090;1.035;821;159;267;226;374;512;445;747 +;Torture;;;162;18;25;20;6;5;12;14;22;17;22 +;Human traffic;;;10;2;1;0;3;0;1;0;1;0;2 +;Economics crimes;;;205.231;51.228;53.832;21.985;7.313;9.591;9.710;7.920;7.545;10.323;23.817 +;Against foreign;;;1;0;1;0;0;0;0;0;0;0;0 +;Against honor;;;24;0;5;4;2;5;2;2;3;0;0 +;Against Constitution;;;67;9;6;8;2;0;4;1;8;5;3 +;Against privacy;;;491;65;99;66;35;35;28;29;51;37;43 +;Against freedom;;;3.313;444;497;495;125;178;164;271;372;292;462 +;Against sexual freedom;;;743;128;131;113;18;36;31;45;64;61;102 +;Against Justice;;;647;96;95;114;19;16;13;30;78;65;118 +;Against public administration;;;8;2;1;3;0;0;0;0;0;1;1 +;Against familiar relationships;;;173;16;17;22;3;8;7;32;29;21;18 +;Crimes against public order;;;974;317;216;117;30;38;25;30;55;34;100 +;Crimes related to genetic manipulation;;;1;0;0;0;0;0;0;1;0;0;0 +;Workers' rights;;;8;0;2;4;0;0;0;1;1;0;0 +;Against the Public Treasury and against Social Security;;;2;0;0;0;0;1;0;0;0;1;0 +;Against collective security;;;1.044;523;92;119;19;18;34;46;55;34;102 +;Crimes related to urban planning, the protection of the historical heritage and the environment;;;29;4;2;5;1;0;2;0;6;1;7 \ No newline at end of file diff --git a/your-project/test drug.csv b/your-project/test drug.csv index c85429f..411d7b1 100644 --- a/your-project/test drug.csv +++ b/your-project/test drug.csv @@ -1,43 +1,21 @@ -;;;;;;;;;;;;;; -;;;;;;;;;;;;;; -2. Guardia Urbana;;;;;;;;;;;;;; -;;;;;;;;;;;;;; -2.4. Pruebas drogo test por distritos. 2014-2018;;;;;;;;;;;;;; -;;;;;;;;;;;;;; -;;;BARCELONA;"1.Ciutat   +;BARCELONA;"1.Ciutat   Vella";2.Eixample;"3.Sants-   Montjuïc";4.Les Corts;"5.Sarrià-   Sant Gervasi";6.Gràcia;"7.Horta-   Guinardó";"8.Nou   Barris";"9.Sant   Andreu";"10.Sant   -Martí";"Distrito   -desconocido" -;;;;;;;;;;;;;; -;;;;;;;;;;;;;; -2014;;;2.367;146;282;836;83;96;167;102;127;235;220;73 -2015;;;2.162;179;191;108;132;54;105;262;309;642;109;71 -2016;;;3.042;357;397;115;133;127;168;184;581;732;171;77 -2017;;;3.964;501;455;703;98;277;205;289;285;395;588;168 -;;;;;;;;;;;;;; -2018;;;5.149;676;657;1.059;135;420;238;372;263;477;823;29 -Control preventivo;;;;;;;;;;;;;; -Negativo;;;1.770;244;166;370;52;172;103;158;65;167;266;7 -Positivo;;;1.625;159;125;405;28;166;63;139;59;171;297;13 -Negarse;;;8;1;0;0;0;0;0;1;1;0;5;0 -Accidente;;;;;;;;;;;;;; -Negativo;;;42;0;11;6;4;2;2;2;2;4;7;2 -Positivo;;;108;6;24;23;6;9;2;7;8;8;15;0 -Negarse;;;5;0;0;2;0;0;0;2;0;0;1;0 -Infracción;;;;;;;;;;;;;; -Negativo;;;172;31;28;38;13;12;6;10;5;9;19;1 -Positivo;;;864;129;201;129;22;37;45;39;83;84;93;2 -Negarse;;;13;3;2;3;0;1;0;1;0;0;3;0 -Síntomas;;;;;;;;;;;;;; -Negativo;;;68;12;14;9;1;2;1;0;0;2;27;0 -Positivo;;;473;91;85;74;9;19;16;13;40;32;90;4 -Negarse;;;1;0;1;0;0;0;0;0;0;0;0;0 -;;;;;;;;;;;;;; -;;;;;;;;;;;;;; -Departament d'Estadística i Difusió de Dades. Ajuntament de Barcelona.;;;;;;;;;;;;;; -Fuente: Ajuntament de Barcelona. Àrea de Seguretat i Prevenció. Galileo/Sistemes d'Informació.;;;;;;;;;;;;;; +Martí" +2018;5.149;676;657;1.059;135;420;238;372;263;477;823 +Negativo;1.770;244;166;370;52;172;103;158;65;167;266 +Positivo;1.625;159;125;405;28;166;63;139;59;171;297 +Negarse;8;1;0;0;0;0;0;1;1;0;5 +Negativo;42;0;11;6;4;2;2;2;2;4;7 +Positivo;108;6;24;23;6;9;2;7;8;8;15 +Negarse;5;0;0;2;0;0;0;2;0;0;1 +Negativo;172;31;28;38;13;12;6;10;5;9;19 +Positivo;864;129;201;129;22;37;45;39;83;84;93 +Negarse;13;3;2;3;0;1;0;1;0;0;3 +Negativo;68;12;14;9;1;2;1;0;0;2;27 +Positivo;473;91;85;74;9;19;16;13;40;32;90 +Negarse;1;0;1;0;0;0;0;0;0;0;0 \ No newline at end of file