From c19d18629370a780ac086bb0edef7ed5210b34e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Laura=20W=C3=BCrz?= Date: Fri, 20 Sep 2019 16:02:56 +0100 Subject: [PATCH 1/2] project deliverables --- .DS_Store | Bin 0 -> 6148 bytes .../Tableau Project-checkpoint.ipynb | 5496 ++++++++ README.md | 128 +- Tableau Project Tourism.twb | 10492 ++++++++++++++++ Tableau Project.ipynb | 5952 +++++++++ datasets/.DS_Store | Bin 0 -> 6148 bytes datasets/Expenditure.xls | Bin 0 -> 27648 bytes datasets/Hotel size.xls | Bin 0 -> 183808 bytes datasets/Occupancy Rate.xlsx | Bin 0 -> 5969 bytes datasets/new hotel perc.xlsx | Bin 0 -> 7922 bytes 10 files changed, 21983 insertions(+), 85 deletions(-) create mode 100644 .DS_Store create mode 100644 .ipynb_checkpoints/Tableau Project-checkpoint.ipynb create mode 100644 Tableau Project Tourism.twb create mode 100644 Tableau Project.ipynb create mode 100644 datasets/.DS_Store create mode 100644 datasets/Expenditure.xls create mode 100644 datasets/Hotel size.xls create mode 100644 datasets/Occupancy Rate.xlsx create mode 100644 datasets/new hotel perc.xlsx diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c9e3b415860bc127c8bf29e98540077f65639530 GIT binary patch literal 6148 zcmeHKO=}ZD7=EXXI*EumC>FfTMeqM}L9;z`s$S`Djf{q#i^>=7o2j`FP)VcAoj#AtKfsMjla_h&WVXp@d|K zsCK0bvSMqLAJZ{1>QGMEP9ob2)~>=a;28MV7*KncQ$zvwZ9p^oH%w%RHXID>9rX5z z7XA4P_Teb&Tl+6zKZ&!vTK&paRtpy{UgEsOOIJ$I{jMDOd5{mYW-xfFO1nYc4Q+l; z%H%8`gpcFSXuo{zflTutPCEnb97i3PJbMzSksLMUFpV-D$2Sd}7kP2Nyfz+t-kpl@ z>YI~_7;kJ;E23VzJDC*uja%ORorB)dtMTi}oA+iW7!EEiS1q2xF)|ASO%6T3Eqnb_ zqh&Td*=kub_{VRbYHyD}Z!Ncre#@@kJ-^SF{vb_cdWe41c#8ChwrPu6^bojCJ&M$k z{9{V#1FUK@Q+kv3F#RDVIKTrMlAv80kfawVx3M$?+9m1-6l0O}5HqI{h4WF_38Ky} zlxH9|1V8$Kw!rC<~P7NA&V)pUDY|qTTP?+u= z@r5=g)-dQ&$ADvCo`F?UEUWu}``7RPc_&wL3^)e?0X={! qQFc><+69Tdj&(*|#cNP4XcH;}Y*?HcL=D9K2xuBy;u!d=415PE>bAE4 literal 0 HcmV?d00001 diff --git a/.ipynb_checkpoints/Tableau Project-checkpoint.ipynb b/.ipynb_checkpoints/Tableau Project-checkpoint.ipynb new file mode 100644 index 0000000..1c89053 --- /dev/null +++ b/.ipynb_checkpoints/Tableau Project-checkpoint.ipynb @@ -0,0 +1,5496 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TABLEAU PROJECT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = pd.read_excel('Occupancy Rate.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TIME201520162017
0Belgium61.54560.62
1Bulgaria48.755.257.1
2Czechia4346.449.8
3Denmark616262
4Germany60.2361.862.07
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" + ], + "text/plain": [ + " TIME 2015 2016 2017\n", + "0 Belgium 61.54 56 0.62\n", + "1 Bulgaria 48.7 55.2 57.1\n", + "2 Czechia 43 46.4 49.8\n", + "3 Denmark 61 62 62\n", + "4 Germany 60.23 61.8 62.07" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupancy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename column names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = occupancy.rename(columns={'TIME':'Country', '2015':'Occupancy Rate in 2015','2016':'Occupancy Rate in 2016','2017':'Occupancy Rate in 2017'})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Correcting Belgium data\n", + "occupancy.at[0,'Occupancy Rate in 2017'] = 62" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Filling Norway data: https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do\n", + "occupancy.at[30,'Occupancy Rate in 2017'] = 57.03" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017
0Belgium61.545662
1Bulgaria48.755.257.1
2Czechia4346.449.8
3Denmark616262
4Germany60.2361.862.07
5Estonia525455
6Ireland6871:
7Greece46.847.750.2
8Spain61.9765.7267.07
9France5958.461.2
10Croatia54.457.359.2
11Italy44.946.248.8
12Cyprus6369.974.6
13Latvia42.442.544.8
14Lithuania49.35153.7
15Luxembourg45.6144.8345.16
16Hungary49.85255
17Malta747476.7
18Netherlands68.168.171.8
19Austria525455
20Poland45.347.648.9
21Portugal48.253.2456.98
22Romania47.4443.9143.97
23Slovenia49.552.255.6
24Slovakia35.4838.8239.94
25Finland51.1352.954.76
26Sweden55.275858.27
27United Kingdom68.570.2:
28Iceland0.5865.3:
29Liechtenstein36.934.937.8
30Norway53.654.457.03
31Switzerland51.551.33:
32Montenegro::38.7
33North Macedonia37.183539.3
34Serbia29.530.5:
\n", + "
" + ], + "text/plain": [ + " Country Occupancy Rate in 2015 Occupancy Rate in 2016 \\\n", + "0 Belgium 61.54 56 \n", + "1 Bulgaria 48.7 55.2 \n", + "2 Czechia 43 46.4 \n", + "3 Denmark 61 62 \n", + "4 Germany 60.23 61.8 \n", + "5 Estonia 52 54 \n", + "6 Ireland 68 71 \n", + "7 Greece 46.8 47.7 \n", + "8 Spain 61.97 65.72 \n", + "9 France 59 58.4 \n", + "10 Croatia 54.4 57.3 \n", + "11 Italy 44.9 46.2 \n", + "12 Cyprus 63 69.9 \n", + "13 Latvia 42.4 42.5 \n", + "14 Lithuania 49.3 51 \n", + "15 Luxembourg 45.61 44.83 \n", + "16 Hungary 49.8 52 \n", + "17 Malta 74 74 \n", + "18 Netherlands 68.1 68.1 \n", + "19 Austria 52 54 \n", + "20 Poland 45.3 47.6 \n", + "21 Portugal 48.2 53.24 \n", + "22 Romania 47.44 43.91 \n", + "23 Slovenia 49.5 52.2 \n", + "24 Slovakia 35.48 38.82 \n", + "25 Finland 51.13 52.9 \n", + "26 Sweden 55.27 58 \n", + "27 United Kingdom 68.5 70.2 \n", + "28 Iceland 0.58 65.3 \n", + "29 Liechtenstein 36.9 34.9 \n", + "30 Norway 53.6 54.4 \n", + "31 Switzerland 51.5 51.33 \n", + "32 Montenegro : : \n", + "33 North Macedonia 37.18 35 \n", + "34 Serbia 29.5 30.5 \n", + "\n", + " Occupancy Rate in 2017 \n", + "0 62 \n", + "1 57.1 \n", + "2 49.8 \n", + "3 62 \n", + "4 62.07 \n", + "5 55 \n", + "6 : \n", + "7 50.2 \n", + "8 67.07 \n", + "9 61.2 \n", + "10 59.2 \n", + "11 48.8 \n", + "12 74.6 \n", + "13 44.8 \n", + "14 53.7 \n", + "15 45.16 \n", + "16 55 \n", + "17 76.7 \n", + "18 71.8 \n", + "19 55 \n", + "20 48.9 \n", + "21 56.98 \n", + "22 43.97 \n", + "23 55.6 \n", + "24 39.94 \n", + "25 54.76 \n", + "26 58.27 \n", + "27 : \n", + "28 : \n", + "29 37.8 \n", + "30 57.03 \n", + "31 : \n", + "32 38.7 \n", + "33 39.3 \n", + "34 : " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupancy" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy.drop(occupancy.index[[6,27,28,31,32,33,34]], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017
0Belgium61.545662
1Bulgaria48.755.257.1
2Czechia4346.449.8
3Denmark616262
4Germany60.2361.862.07
5Estonia525455
7Greece46.847.750.2
8Spain61.9765.7267.07
9France5958.461.2
10Croatia54.457.359.2
11Italy44.946.248.8
12Cyprus6369.974.6
13Latvia42.442.544.8
14Lithuania49.35153.7
15Luxembourg45.6144.8345.16
16Hungary49.85255
17Malta747476.7
18Netherlands68.168.171.8
19Austria525455
20Poland45.347.648.9
21Portugal48.253.2456.98
22Romania47.4443.9143.97
23Slovenia49.552.255.6
24Slovakia35.4838.8239.94
25Finland51.1352.954.76
26Sweden55.275858.27
29Liechtenstein36.934.937.8
30Norway53.654.457.03
\n", + "
" + ], + "text/plain": [ + " Country Occupancy Rate in 2015 Occupancy Rate in 2016 \\\n", + "0 Belgium 61.54 56 \n", + "1 Bulgaria 48.7 55.2 \n", + "2 Czechia 43 46.4 \n", + "3 Denmark 61 62 \n", + "4 Germany 60.23 61.8 \n", + "5 Estonia 52 54 \n", + "7 Greece 46.8 47.7 \n", + "8 Spain 61.97 65.72 \n", + "9 France 59 58.4 \n", + "10 Croatia 54.4 57.3 \n", + "11 Italy 44.9 46.2 \n", + "12 Cyprus 63 69.9 \n", + "13 Latvia 42.4 42.5 \n", + "14 Lithuania 49.3 51 \n", + "15 Luxembourg 45.61 44.83 \n", + "16 Hungary 49.8 52 \n", + "17 Malta 74 74 \n", + "18 Netherlands 68.1 68.1 \n", + "19 Austria 52 54 \n", + "20 Poland 45.3 47.6 \n", + "21 Portugal 48.2 53.24 \n", + "22 Romania 47.44 43.91 \n", + "23 Slovenia 49.5 52.2 \n", + "24 Slovakia 35.48 38.82 \n", + "25 Finland 51.13 52.9 \n", + "26 Sweden 55.27 58 \n", + "29 Liechtenstein 36.9 34.9 \n", + "30 Norway 53.6 54.4 \n", + "\n", + " Occupancy Rate in 2017 \n", + "0 62 \n", + "1 57.1 \n", + "2 49.8 \n", + "3 62 \n", + "4 62.07 \n", + "5 55 \n", + "7 50.2 \n", + "8 67.07 \n", + "9 61.2 \n", + "10 59.2 \n", + "11 48.8 \n", + "12 74.6 \n", + "13 44.8 \n", + "14 53.7 \n", + "15 45.16 \n", + "16 55 \n", + "17 76.7 \n", + "18 71.8 \n", + "19 55 \n", + "20 48.9 \n", + "21 56.98 \n", + "22 43.97 \n", + "23 55.6 \n", + "24 39.94 \n", + "25 54.76 \n", + "26 58.27 \n", + "29 37.8 \n", + "30 57.03 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupancy" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy['Development Occupancy Rates 2015-2017'] = occupancy['Occupancy Rate in 2017']-occupancy['Occupancy Rate in 2015']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
0Belgium61.5456620.46
1Bulgaria48.755.257.18.4
2Czechia4346.449.86.8
3Denmark6162621
4Germany60.2361.862.071.84
5Estonia5254553
7Greece46.847.750.23.4
8Spain61.9765.7267.075.1
9France5958.461.22.2
10Croatia54.457.359.24.8
11Italy44.946.248.83.9
12Cyprus6369.974.611.6
13Latvia42.442.544.82.4
14Lithuania49.35153.74.4
15Luxembourg45.6144.8345.16-0.45
16Hungary49.852555.2
17Malta747476.72.7
18Netherlands68.168.171.83.7
19Austria5254553
20Poland45.347.648.93.6
21Portugal48.253.2456.988.78
22Romania47.4443.9143.97-3.47
23Slovenia49.552.255.66.1
24Slovakia35.4838.8239.944.46
25Finland51.1352.954.763.63
26Sweden55.275858.273
29Liechtenstein36.934.937.80.9
30Norway53.654.457.033.43
\n", + "
" + ], + "text/plain": [ + " Country Occupancy Rate in 2015 Occupancy Rate in 2016 \\\n", + "0 Belgium 61.54 56 \n", + "1 Bulgaria 48.7 55.2 \n", + "2 Czechia 43 46.4 \n", + "3 Denmark 61 62 \n", + "4 Germany 60.23 61.8 \n", + "5 Estonia 52 54 \n", + "7 Greece 46.8 47.7 \n", + "8 Spain 61.97 65.72 \n", + "9 France 59 58.4 \n", + "10 Croatia 54.4 57.3 \n", + "11 Italy 44.9 46.2 \n", + "12 Cyprus 63 69.9 \n", + "13 Latvia 42.4 42.5 \n", + "14 Lithuania 49.3 51 \n", + "15 Luxembourg 45.61 44.83 \n", + "16 Hungary 49.8 52 \n", + "17 Malta 74 74 \n", + "18 Netherlands 68.1 68.1 \n", + "19 Austria 52 54 \n", + "20 Poland 45.3 47.6 \n", + "21 Portugal 48.2 53.24 \n", + "22 Romania 47.44 43.91 \n", + "23 Slovenia 49.5 52.2 \n", + "24 Slovakia 35.48 38.82 \n", + "25 Finland 51.13 52.9 \n", + "26 Sweden 55.27 58 \n", + "29 Liechtenstein 36.9 34.9 \n", + "30 Norway 53.6 54.4 \n", + "\n", + " Occupancy Rate in 2017 Development Occupancy Rates 2015-2017 \n", + "0 62 0.46 \n", + "1 57.1 8.4 \n", + "2 49.8 6.8 \n", + "3 62 1 \n", + "4 62.07 1.84 \n", + "5 55 3 \n", + "7 50.2 3.4 \n", + "8 67.07 5.1 \n", + "9 61.2 2.2 \n", + "10 59.2 4.8 \n", + "11 48.8 3.9 \n", + "12 74.6 11.6 \n", + "13 44.8 2.4 \n", + "14 53.7 4.4 \n", + "15 45.16 -0.45 \n", + "16 55 5.2 \n", + "17 76.7 2.7 \n", + "18 71.8 3.7 \n", + "19 55 3 \n", + "20 48.9 3.6 \n", + "21 56.98 8.78 \n", + "22 43.97 -3.47 \n", + "23 55.6 6.1 \n", + "24 39.94 4.46 \n", + "25 54.76 3.63 \n", + "26 58.27 3 \n", + "29 37.8 0.9 \n", + "30 57.03 3.43 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupancy" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure = pd.read_excel('Expenditure.xls')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
10Croatia342499.97:300298
11Italy3974783.835.36493e+065.96759e+06
12Cyprus222501.91239925300057
13Latvia136220.79121883151176
14Lithuania233245.56280569326529
15Luxembourg351609.13324888361158
16Hungary619193.34638635731482
17Malta103646.87110302114825
18Netherlands3205522.373.49411e+065.07159e+06
19Austria4551468.844.93604e+065.06712e+06
20Poland1632635.301.7733e+062.11174e+06
21Portugal457283.05424443496517
22Romania301553.50291603393141
23Slovenia269009.89270406287334
24Slovakia559150.75567464650462
25Finland2610633.922.7013e+062.86085e+06
26Sweden2606883.783.38111e+06:
27Norway2919968.613.07815e+063.54416e+06
28Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "10 Croatia 342499.97 : 300298\n", + "11 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "12 Cyprus 222501.91 239925 300057\n", + "13 Latvia 136220.79 121883 151176\n", + "14 Lithuania 233245.56 280569 326529\n", + "15 Luxembourg 351609.13 324888 361158\n", + "16 Hungary 619193.34 638635 731482\n", + "17 Malta 103646.87 110302 114825\n", + "18 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "19 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "20 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "21 Portugal 457283.05 424443 496517\n", + "22 Romania 301553.50 291603 393141\n", + "23 Slovenia 269009.89 270406 287334\n", + "24 Slovakia 559150.75 567464 650462\n", + "25 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "26 Sweden 2606883.78 3.38111e+06 :\n", + "27 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "28 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure.drop(expenditure.index[[10,26]], inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
10Italy3974783.835.36493e+065.96759e+06
11Cyprus222501.91239925300057
12Latvia136220.79121883151176
13Lithuania233245.56280569326529
14Luxembourg351609.13324888361158
15Hungary619193.34638635731482
16Malta103646.87110302114825
17Netherlands3205522.373.49411e+065.07159e+06
18Austria4551468.844.93604e+065.06712e+06
19Poland1632635.301.7733e+062.11174e+06
20Portugal457283.05424443496517
21Romania301553.50291603393141
22Slovenia269009.89270406287334
23Slovakia559150.75567464650462
24Finland2610633.922.7013e+062.86085e+06
25Norway2919968.613.07815e+063.54416e+06
26Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "10 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "11 Cyprus 222501.91 239925 300057\n", + "12 Latvia 136220.79 121883 151176\n", + "13 Lithuania 233245.56 280569 326529\n", + "14 Luxembourg 351609.13 324888 361158\n", + "15 Hungary 619193.34 638635 731482\n", + "16 Malta 103646.87 110302 114825\n", + "17 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "18 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "19 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "20 Portugal 457283.05 424443 496517\n", + "21 Romania 301553.50 291603 393141\n", + "22 Slovenia 269009.89 270406 287334\n", + "23 Slovakia 559150.75 567464 650462\n", + "24 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "25 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "26 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
11Italy3974783.835.36493e+065.96759e+06
12Cyprus222501.91239925300057
13Latvia136220.79121883151176
14Lithuania233245.56280569326529
15Luxembourg351609.13324888361158
16Hungary619193.34638635731482
17Malta103646.87110302114825
18Netherlands3205522.373.49411e+065.07159e+06
19Austria4551468.844.93604e+065.06712e+06
20Poland1632635.301.7733e+062.11174e+06
21Portugal457283.05424443496517
22Romania301553.50291603393141
23Slovenia269009.89270406287334
24Slovakia559150.75567464650462
25Finland2610633.922.7013e+062.86085e+06
27Norway2919968.613.07815e+063.54416e+06
28Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "11 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "12 Cyprus 222501.91 239925 300057\n", + "13 Latvia 136220.79 121883 151176\n", + "14 Lithuania 233245.56 280569 326529\n", + "15 Luxembourg 351609.13 324888 361158\n", + "16 Hungary 619193.34 638635 731482\n", + "17 Malta 103646.87 110302 114825\n", + "18 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "19 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "20 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "21 Portugal 457283.05 424443 496517\n", + "22 Romania 301553.50 291603 393141\n", + "23 Slovenia 269009.89 270406 287334\n", + "24 Slovakia 559150.75 567464 650462\n", + "25 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "27 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "28 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename column names" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure = expenditure.rename(columns={'GEO/TIME':'Country', '2015':'Expenditure in 2015','2016':'Expenditure in 2016','2017':'Expenditure in 2017'})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country object\n", + "Expenditure in 2015 float64\n", + "Expenditure in 2016 object\n", + "Expenditure in 2017 object\n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# changing datatypes" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#expenditure = expenditure.astype({\"Expenditure in 2016\": float, \"Expenditure in 2017\": float})\n", + "\n", + "#df = df.astype({\"a\": int, \"b\": complex})\n", + "\n", + "expenditure[\"Expenditure in 2016\"] = pd.to_numeric(expenditure[\"Expenditure in 2016\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure[\"Expenditure in 2017\"] = pd.to_numeric(expenditure[\"Expenditure in 2017\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country object\n", + "Expenditure in 2015 float64\n", + "Expenditure in 2016 float64\n", + "Expenditure in 2017 float64\n", + "dtype: object" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculating Difference in expenditure\n", + "expenditure['Change expenditure 2015-2017 in %'] = 100*((expenditure['Expenditure in 2017']-expenditure['Expenditure in 2015'])/expenditure['Expenditure in 2015'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %
0Belgium2132661.432290696.542784526.4330.565799
1Bulgaria89392.03100088.85149436.0667.169333
2Czechia680150.69845275.761039940.8752.898598
3Denmark2265268.402777677.763607902.2259.270408
4Germany34827677.8637964775.2040557686.4016.452456
5Estonia186741.49220419.19247373.5132.468425
6Ireland1502234.951651493.331723609.7414.736363
7Greece202757.73216346.06249309.6122.959361
8Spain5387633.226727474.536879160.1927.684271
9France8535653.789033252.7310690747.0125.248133
11Italy3974783.835364934.755967589.6250.136206
12Cyprus222501.91239924.64300056.7634.855813
13Latvia136220.79121883.12151175.6510.978398
14Lithuania233245.56280568.55326528.6539.993512
15Luxembourg351609.13324888.07361157.942.715746
16Hungary619193.34638635.07731482.3918.134732
17Malta103646.87110302.40114825.3410.785150
18Netherlands3205522.373494108.795071587.3258.214067
19Austria4551468.844936038.855067124.3111.329430
20Poland1632635.301773295.442111735.3929.345200
21Portugal457283.05424442.67496517.238.579846
22Romania301553.50291603.10393140.7430.371805
23Slovenia269009.89270405.51287334.236.811772
24Slovakia559150.75567463.84650461.8316.330315
25Finland2610633.922701295.722860852.849.584604
27Norway2919968.613078150.753544164.4221.376799
28Switzerland4105694.584522355.104516926.5410.016136
\n", + "
" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 \\\n", + "0 Belgium 2132661.43 2290696.54 \n", + "1 Bulgaria 89392.03 100088.85 \n", + "2 Czechia 680150.69 845275.76 \n", + "3 Denmark 2265268.40 2777677.76 \n", + "4 Germany 34827677.86 37964775.20 \n", + "5 Estonia 186741.49 220419.19 \n", + "6 Ireland 1502234.95 1651493.33 \n", + "7 Greece 202757.73 216346.06 \n", + "8 Spain 5387633.22 6727474.53 \n", + "9 France 8535653.78 9033252.73 \n", + "11 Italy 3974783.83 5364934.75 \n", + "12 Cyprus 222501.91 239924.64 \n", + "13 Latvia 136220.79 121883.12 \n", + "14 Lithuania 233245.56 280568.55 \n", + "15 Luxembourg 351609.13 324888.07 \n", + "16 Hungary 619193.34 638635.07 \n", + "17 Malta 103646.87 110302.40 \n", + "18 Netherlands 3205522.37 3494108.79 \n", + "19 Austria 4551468.84 4936038.85 \n", + "20 Poland 1632635.30 1773295.44 \n", + "21 Portugal 457283.05 424442.67 \n", + "22 Romania 301553.50 291603.10 \n", + "23 Slovenia 269009.89 270405.51 \n", + "24 Slovakia 559150.75 567463.84 \n", + "25 Finland 2610633.92 2701295.72 \n", + "27 Norway 2919968.61 3078150.75 \n", + "28 Switzerland 4105694.58 4522355.10 \n", + "\n", + " Expenditure in 2017 Change expenditure 2015-2017 in % \n", + "0 2784526.43 30.565799 \n", + "1 149436.06 67.169333 \n", + "2 1039940.87 52.898598 \n", + "3 3607902.22 59.270408 \n", + "4 40557686.40 16.452456 \n", + "5 247373.51 32.468425 \n", + "6 1723609.74 14.736363 \n", + "7 249309.61 22.959361 \n", + "8 6879160.19 27.684271 \n", + "9 10690747.01 25.248133 \n", + "11 5967589.62 50.136206 \n", + "12 300056.76 34.855813 \n", + "13 151175.65 10.978398 \n", + "14 326528.65 39.993512 \n", + "15 361157.94 2.715746 \n", + "16 731482.39 18.134732 \n", + "17 114825.34 10.785150 \n", + "18 5071587.32 58.214067 \n", + "19 5067124.31 11.329430 \n", + "20 2111735.39 29.345200 \n", + "21 496517.23 8.579846 \n", + "22 393140.74 30.371805 \n", + "23 287334.23 6.811772 \n", + "24 650461.83 16.330315 \n", + "25 2860852.84 9.584604 \n", + "27 3544164.42 21.376799 \n", + "28 4516926.54 10.016136 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge Datasets on expenditure\n", + "merged_exp_occ = pd.merge(expenditure, occupancy, on='Country')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %Occupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
0Belgium2132661.432290696.542784526.4330.56579961.5456620.46
1Bulgaria89392.03100088.85149436.0667.16933348.755.257.18.4
2Czechia680150.69845275.761039940.8752.8985984346.449.86.8
3Denmark2265268.402777677.763607902.2259.2704086162621
4Germany34827677.8637964775.2040557686.4016.45245660.2361.862.071.84
5Estonia186741.49220419.19247373.5132.4684255254553
6Greece202757.73216346.06249309.6122.95936146.847.750.23.4
7Spain5387633.226727474.536879160.1927.68427161.9765.7267.075.1
8France8535653.789033252.7310690747.0125.2481335958.461.22.2
9Italy3974783.835364934.755967589.6250.13620644.946.248.83.9
10Cyprus222501.91239924.64300056.7634.8558136369.974.611.6
11Latvia136220.79121883.12151175.6510.97839842.442.544.82.4
12Lithuania233245.56280568.55326528.6539.99351249.35153.74.4
13Luxembourg351609.13324888.07361157.942.71574645.6144.8345.16-0.45
14Hungary619193.34638635.07731482.3918.13473249.852555.2
15Malta103646.87110302.40114825.3410.785150747476.72.7
16Netherlands3205522.373494108.795071587.3258.21406768.168.171.83.7
17Austria4551468.844936038.855067124.3111.3294305254553
18Poland1632635.301773295.442111735.3929.34520045.347.648.93.6
19Portugal457283.05424442.67496517.238.57984648.253.2456.988.78
20Romania301553.50291603.10393140.7430.37180547.4443.9143.97-3.47
21Slovenia269009.89270405.51287334.236.81177249.552.255.66.1
22Slovakia559150.75567463.84650461.8316.33031535.4838.8239.944.46
23Finland2610633.922701295.722860852.849.58460451.1352.954.763.63
24Norway2919968.613078150.753544164.4221.37679953.654.457.033.43
\n", + "
" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 \\\n", + "0 Belgium 2132661.43 2290696.54 \n", + "1 Bulgaria 89392.03 100088.85 \n", + "2 Czechia 680150.69 845275.76 \n", + "3 Denmark 2265268.40 2777677.76 \n", + "4 Germany 34827677.86 37964775.20 \n", + "5 Estonia 186741.49 220419.19 \n", + "6 Greece 202757.73 216346.06 \n", + "7 Spain 5387633.22 6727474.53 \n", + "8 France 8535653.78 9033252.73 \n", + "9 Italy 3974783.83 5364934.75 \n", + "10 Cyprus 222501.91 239924.64 \n", + "11 Latvia 136220.79 121883.12 \n", + "12 Lithuania 233245.56 280568.55 \n", + "13 Luxembourg 351609.13 324888.07 \n", + "14 Hungary 619193.34 638635.07 \n", + "15 Malta 103646.87 110302.40 \n", + "16 Netherlands 3205522.37 3494108.79 \n", + "17 Austria 4551468.84 4936038.85 \n", + "18 Poland 1632635.30 1773295.44 \n", + "19 Portugal 457283.05 424442.67 \n", + "20 Romania 301553.50 291603.10 \n", + "21 Slovenia 269009.89 270405.51 \n", + "22 Slovakia 559150.75 567463.84 \n", + "23 Finland 2610633.92 2701295.72 \n", + "24 Norway 2919968.61 3078150.75 \n", + "\n", + " Expenditure in 2017 Change expenditure 2015-2017 in % \\\n", + "0 2784526.43 30.565799 \n", + "1 149436.06 67.169333 \n", + "2 1039940.87 52.898598 \n", + "3 3607902.22 59.270408 \n", + "4 40557686.40 16.452456 \n", + "5 247373.51 32.468425 \n", + "6 249309.61 22.959361 \n", + "7 6879160.19 27.684271 \n", + "8 10690747.01 25.248133 \n", + "9 5967589.62 50.136206 \n", + "10 300056.76 34.855813 \n", + "11 151175.65 10.978398 \n", + "12 326528.65 39.993512 \n", + "13 361157.94 2.715746 \n", + "14 731482.39 18.134732 \n", + "15 114825.34 10.785150 \n", + "16 5071587.32 58.214067 \n", + "17 5067124.31 11.329430 \n", + "18 2111735.39 29.345200 \n", + "19 496517.23 8.579846 \n", + "20 393140.74 30.371805 \n", + "21 287334.23 6.811772 \n", + "22 650461.83 16.330315 \n", + "23 2860852.84 9.584604 \n", + "24 3544164.42 21.376799 \n", + "\n", + " Occupancy Rate in 2015 Occupancy Rate in 2016 Occupancy Rate in 2017 \\\n", + "0 61.54 56 62 \n", + "1 48.7 55.2 57.1 \n", + "2 43 46.4 49.8 \n", + "3 61 62 62 \n", + "4 60.23 61.8 62.07 \n", + "5 52 54 55 \n", + "6 46.8 47.7 50.2 \n", + "7 61.97 65.72 67.07 \n", + "8 59 58.4 61.2 \n", + "9 44.9 46.2 48.8 \n", + "10 63 69.9 74.6 \n", + "11 42.4 42.5 44.8 \n", + "12 49.3 51 53.7 \n", + "13 45.61 44.83 45.16 \n", + "14 49.8 52 55 \n", + "15 74 74 76.7 \n", + "16 68.1 68.1 71.8 \n", + "17 52 54 55 \n", + "18 45.3 47.6 48.9 \n", + "19 48.2 53.24 56.98 \n", + "20 47.44 43.91 43.97 \n", + "21 49.5 52.2 55.6 \n", + "22 35.48 38.82 39.94 \n", + "23 51.13 52.9 54.76 \n", + "24 53.6 54.4 57.03 \n", + "\n", + " Development Occupancy Rates 2015-2017 \n", + "0 0.46 \n", + "1 8.4 \n", + "2 6.8 \n", + "3 1 \n", + "4 1.84 \n", + "5 3 \n", + "6 3.4 \n", + "7 5.1 \n", + "8 2.2 \n", + "9 3.9 \n", + "10 11.6 \n", + "11 2.4 \n", + "12 4.4 \n", + "13 -0.45 \n", + "14 5.2 \n", + "15 2.7 \n", + "16 3.7 \n", + "17 3 \n", + "18 3.6 \n", + "19 8.78 \n", + "20 -3.47 \n", + "21 6.1 \n", + "22 4.46 \n", + "23 3.63 \n", + "24 3.43 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_exp_occ" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure.shape\n", + "listexp = expenditure.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Belgium', 'Bulgaria', 'Czechia', 'Denmark', 'Germany', 'Estonia',\n", + " 'Ireland', 'Greece', 'Spain', 'France', 'Italy', 'Cyprus',\n", + " 'Latvia', 'Lithuania', 'Luxembourg', 'Hungary', 'Malta',\n", + " 'Netherlands', 'Austria', 'Poland', 'Portugal', 'Romania',\n", + " 'Slovenia', 'Slovakia', 'Finland', 'Norway', 'Switzerland'],\n", + " dtype=object)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "listexp" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy.shape\n", + "listocc = occupancy.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "lst3 = [value for value in listexp if value in listocc]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(lst3)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Belgium',\n", + " 'Bulgaria',\n", + " 'Czechia',\n", + " 'Denmark',\n", + " 'Germany',\n", + " 'Estonia',\n", + " 'Greece',\n", + " 'Spain',\n", + " 'France',\n", + " 'Italy',\n", + " 'Cyprus',\n", + " 'Latvia',\n", + " 'Lithuania',\n", + " 'Luxembourg',\n", + " 'Hungary',\n", + " 'Malta',\n", + " 'Netherlands',\n", + " 'Austria',\n", + " 'Poland',\n", + " 'Portugal',\n", + " 'Romania',\n", + " 'Slovenia',\n", + " 'Slovakia',\n", + " 'Finland',\n", + " 'Norway']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst3" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Hotel size\n", + "\n", + "hotelsize = pd.read_excel('Hotel size_clean.xls')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIMELess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Italy181931817018076135741357113488::::::
7Cyprus420423431215212214123120120282829
8Latvia20321521810198100252627:44
9Lithuania252257247136132133282929222
10Luxembourg:145139:7069::::::
11Hungary145614661443580584586119122125303030
12Malta343655616264444441222223
13Poland191719731995151716961763250257268393938
14Romania1351135814539789861015249246249484849
15Sweden385400389913901899442440463252270274
16United Kingdom3027132517:59745318:23291589:1255291:
17Iceland227225225155146152162325433
18Norway293307261505504500225231234596463
19Montenegro::193::89::37::12
20Kosovo::129::50::3::0
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" + ], + "text/plain": [ + " GEO/TIME Less than 25 2015 Less than 25 2016 Less than 25 2017 \\\n", + "0 Bulgaria 512 448 427 \n", + "1 Czechia 4645 4648 4588 \n", + "2 Germany 23425 22808 22394 \n", + "3 Greece 5290 5190 5004 \n", + "4 Spain 12170 12049 12112 \n", + "5 Croatia 396 455 474 \n", + "6 Italy 18193 18170 18076 \n", + "7 Cyprus 420 423 431 \n", + "8 Latvia 203 215 218 \n", + "9 Lithuania 252 257 247 \n", + "10 Luxembourg : 145 139 \n", + "11 Hungary 1456 1466 1443 \n", + "12 Malta 34 36 55 \n", + "13 Poland 1917 1973 1995 \n", + "14 Romania 1351 1358 1453 \n", + "15 Sweden 385 400 389 \n", + "16 United Kingdom 30271 32517 : \n", + "17 Iceland 227 225 225 \n", + "18 Norway 293 307 261 \n", + "19 Montenegro : : 193 \n", + "20 Kosovo : : 129 \n", + "\n", + " 25 to 99 2015 25 to 99 2016 25 to 99 2017 100 to 249 2015 100 to 249 2016 \\\n", + "0 1003 1019 986 341 368 \n", + "1 1164 1190 1195 153 154 \n", + "2 8451 8438 8482 1517 1568 \n", + "3 4032 4008 3981 593 594 \n", + "4 5111 5045 5076 1758 1752 \n", + "5 282 294 305 183 183 \n", + "6 13574 13571 13488 : : \n", + "7 215 212 214 123 120 \n", + "8 101 98 100 25 26 \n", + "9 136 132 133 28 29 \n", + "10 : 70 69 : : \n", + "11 580 584 586 119 122 \n", + "12 61 62 64 44 44 \n", + "13 1517 1696 1763 250 257 \n", + "14 978 986 1015 249 246 \n", + "15 913 901 899 442 440 \n", + "16 5974 5318 : 2329 1589 \n", + "17 155 146 152 16 23 \n", + "18 505 504 500 225 231 \n", + "19 : : 89 : : \n", + "20 : : 50 : : \n", + "\n", + " 100 to 249 2017 more than 250 2015 more than 250 2016 more than 250 2017 \n", + "0 366 324 323 331 \n", + "1 154 30 30 30 \n", + "2 1622 242 247 251 \n", + "3 593 196 195 194 \n", + "4 1755 679 678 687 \n", + "5 183 77 79 75 \n", + "6 : : : : \n", + "7 120 28 28 29 \n", + "8 27 : 4 4 \n", + "9 29 2 2 2 \n", + "10 : : : : \n", + "11 125 30 30 30 \n", + "12 41 22 22 23 \n", + "13 268 39 39 38 \n", + "14 249 48 48 49 \n", + "15 463 252 270 274 \n", + "16 : 1255 291 : \n", + "17 25 4 3 3 \n", + "18 234 59 64 63 \n", + "19 37 : : 12 \n", + "20 3 : : 0 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize.drop(hotelsize.index[[6,10,16,19,20]], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIMELess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
7Cyprus420423431215212214123120120282829
8Latvia20321521810198100252627:44
9Lithuania252257247136132133282929222
11Hungary145614661443580584586119122125303030
12Malta343655616264444441222223
13Poland191719731995151716961763250257268393938
14Romania1351135814539789861015249246249484849
15Sweden385400389913901899442440463252270274
17Iceland227225225155146152162325433
18Norway293307261505504500225231234596463
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CountryLess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
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1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Cyprus420423431215212214123120120282829
7Latvia20321521810198100252627444
8Lithuania252257247136132133282929222
9Hungary145614661443580584586119122125303030
10Malta343655616264444441222223
11Poland191719731995151716961763250257268393938
12Romania1351135814539789861015249246249484849
13Sweden385400389913901899442440463252270274
14Iceland227225225155146152162325433
15Norway293307261505504500225231234596463
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CountryLess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Cyprus420423431215212214123120120282829
7Latvia20321521810198100252627444
8Lithuania252257247136132133282929222
9Hungary145614661443580584586119122125303030
10Malta343655616264444441222223
11Poland191719731995151716961763250257268393938
12Romania1351135814539789861015249246249484849
13Sweden385400389913901899442440463252270274
14Iceland227225225155146152162325433
15Norway293307261505504500225231234596463
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" + ], + "text/plain": [ + " Country Less than 25 2015 Less than 25 2016 Less than 25 2017 \\\n", + "0 Bulgaria 512 448 427 \n", + "1 Czechia 4645 4648 4588 \n", + "2 Germany 23425 22808 22394 \n", + "3 Greece 5290 5190 5004 \n", + "4 Spain 12170 12049 12112 \n", + "5 Croatia 396 455 474 \n", + "6 Cyprus 420 423 431 \n", + "7 Latvia 203 215 218 \n", + "8 Lithuania 252 257 247 \n", + "9 Hungary 1456 1466 1443 \n", + "10 Malta 34 36 55 \n", + "11 Poland 1917 1973 1995 \n", + "12 Romania 1351 1358 1453 \n", + "13 Sweden 385 400 389 \n", + "14 Iceland 227 225 225 \n", + "15 Norway 293 307 261 \n", + "\n", + " 25 to 99 2015 25 to 99 2016 25 to 99 2017 100 to 249 2015 100 to 249 2016 \\\n", + "0 1003 1019 986 341 368 \n", + "1 1164 1190 1195 153 154 \n", + "2 8451 8438 8482 1517 1568 \n", + "3 4032 4008 3981 593 594 \n", + "4 5111 5045 5076 1758 1752 \n", + "5 282 294 305 183 183 \n", + "6 215 212 214 123 120 \n", + "7 101 98 100 25 26 \n", + "8 136 132 133 28 29 \n", + "9 580 584 586 119 122 \n", + "10 61 62 64 44 44 \n", + "11 1517 1696 1763 250 257 \n", + "12 978 986 1015 249 246 \n", + "13 913 901 899 442 440 \n", + "14 155 146 152 16 23 \n", + "15 505 504 500 225 231 \n", + "\n", + " 100 to 249 2017 more than 250 2015 more than 250 2016 more than 250 2017 \n", + "0 366 324 323 331 \n", + "1 154 30 30 30 \n", + "2 1622 242 247 251 \n", + "3 593 196 195 194 \n", + "4 1755 679 678 687 \n", + "5 183 77 79 75 \n", + "6 120 28 28 29 \n", + "7 27 4 4 4 \n", + "8 29 2 2 2 \n", + "9 125 30 30 30 \n", + "10 41 22 22 23 \n", + "11 268 39 39 38 \n", + "12 249 48 48 49 \n", + "13 463 252 270 274 \n", + "14 25 4 3 3 \n", + "15 234 59 64 63 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2017'] = hotelsize['25 to 99 2017']+ hotelsize['100 to 249 2017']+hotelsize['Less than 25 2017']+hotelsize['more than 250 2017']" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2110\n", + "1 5967\n", + "2 32749\n", + "3 9772\n", + "4 19630\n", + "5 1037\n", + "6 794\n", + "7 349\n", + "8 411\n", + "9 2184\n", + "10 183\n", + "11 4064\n", + "12 2766\n", + "13 2025\n", + "14 405\n", + "15 1058\n", + "Name: Sum Hotels 2017, dtype: object" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize['Sum Hotels 2017']" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2016'] = hotelsize['25 to 99 2016']+hotelsize['100 to 249 2016']+hotelsize['Less than 25 2016']+hotelsize['more than 250 2016']" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2015'] = hotelsize['25 to 99 2015']+hotelsize['100 to 249 2015']+hotelsize['Less than 25 2015']+hotelsize['more than 250 2015']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge all 3 datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "final_merge = pd.merge(merged_exp_occ, hotelsize, on='Country')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %Occupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017Less than 25 2015...25 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017Sum Hotels 2017Sum Hotels 2016Sum Hotels 2015
0Bulgaria89392.03100088.85149436.0667.16933348.755.257.18.4512...986341368366324323331211021582180
1Czechia680150.69845275.761039940.8752.8985984346.449.86.84645...1195153154154303030596760225992
2Germany34827677.8637964775.2040557686.4016.45245660.2361.862.071.8423425...8482151715681622242247251327493306133635
3Greece202757.73216346.06249309.6122.95936146.847.750.23.45290...39815935945931961951949772998710111
4Spain5387633.226727474.536879160.1927.68427161.9765.7267.075.112170...5076175817521755679678687196301952419718
5Cyprus222501.91239924.64300056.7634.8558136369.974.611.6420...214123120120282829794783786
6Latvia136220.79121883.12151175.6510.97839842.442.544.82.4203...100252627444349343333
7Lithuania233245.56280568.55326528.6539.99351249.35153.74.4252...133282929222411420418
8Hungary619193.34638635.07731482.3918.13473249.852555.21456...586119122125303030218422022185
9Malta103646.87110302.40114825.3410.785150747476.72.734...64444441222223183164161
10Poland1632635.301773295.442111735.3929.34520045.347.648.93.61917...1763250257268393938406439653723
11Romania301553.50291603.10393140.7430.37180547.4443.9143.97-3.471351...1015249246249484849276626382626
12Norway2919968.613078150.753544164.4221.37679953.654.457.033.43293...500225231234596463105811061082
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For this project, you will choose a data set, explore the it using Tableau, and put together a Story for presentation showing the insights you have derived from the data. You should demonstrate your proficiency using Tableau and the concepts you have learned throughout the chapter. The workbook should be saved to Tableau Public and a link to the workbook should be provided. +## Content +- [Project Description](#project-description) +- [Criteria / Questions](#hypotheses-/-questions) +- [Dataset](#dataset) +- [Workflow](#workflow) +- [Organization](#organization) +- [Links](#links) -**You will be working in pairs for this project** + ---- +## Project Description +For this project, we put ourselves on the shoes of a wealthy expanding hotel chain CEOs who need data support to decide the location of a new hotel to be built in Europe. Sleepy Hotel Group owns medium-sized (25-99 rooms) hotels all over the world that offer accomodation in the low-mid price range. +But we are also wearing the shoes of their data team so we went to the Eurostat Tourism Database and gathered the information we needed for our analysis and to create meaningful dashboards for decision making on the new hotel's location. -## Technical Requirements + -The technical requirements for this project are as follows: +## Criteria / Questions +Criteria to choose the new hotel's location: +- Ocupation rate; +- Changes of accomodation prices; +- Existing types of accomodation. -- You must construct a Tableau Story consisting of at least 5 story points for the data set you have chosen. -- You must use Story features such as captions and annotations. -- You must demonstrate all the concepts we covered in the chapter (sorting, filtering, different visualizations types, aggregations, etc.). -- Your Tableau workbook consisting of at least 5 visualizations and 1 Story should be saved to Tableau Public. -- You should create a Github repo for this project, and your data should be saved to that repo in a folder named data. -- You should also include a README.md file that describes the steps you took, your thought process as you built your visualizations and Story in Tableau, and a link to your workbook on Tableau Public. + -## Necessary Deliverables +## Dataset +We used 3 different datasets: +- [Net occupancy rate of bed-places and bedrooms in hotels and similar accommodation (NACE Rev. 2, I, 55.1) (from 2012 onwards)](https://ec.europa.eu/eurostat/databrowser/view/tin00180/default/table?lang=en) +- [Hotels and similar accommodation (NACE Rev.2, I, 55.1) by size class: number of establishments, bedrooms and bed-places (from 2012 onwards)](https://ec.europa.eu/eurostat/web/products-datasets/-/tour_cap_nats) +- [Expenditure on accommodation (from 2012 onwards)](https://ec.europa.eu/eurostat/web/products-datasets/-/tour_dem_exac) +All our dataset where gathered on Eurostat database for [Tourism](https://ec.europa.eu/eurostat/web/tourism/data/database). -The following deliverables should be pushed to your Github repo for this chapter. + -- **A Tableau workbook uploaded to Tableau Public** that contains the visualizations and Story you created from your data set. -- **An data folder** containing the data set you used for your project. -- **A `README.md` file** containing a detailed explanation of your approach and code for constructing visualizations and organizing them into a Story as well as your results, obstacles encountered, lessons learned, and a link to your completed Tableau workbook. +## Workflow +Before starting to look for data, we put together a case scenario to narrow our data search. +Dealing with the data was a very straightforward process: we found the data in Eurostat and explored the datasets to check if they suited our purposes. +All our data cleaning and manipulation was done on pandas or excel. +Before getting our hands on Tableau, we sketched some visualizations to be sure that we had all the data needed to create the dashboards. +On our tableau workbook, we created our visualizations and built our dashboards and story. +Finally, we drew some results and conclusions. -## Suggested Ways to Get Started + -- **Find a data set to process** - As great places to start looking we recommend [Kaggle](https://www.kaggle.com/datasets), [Pordata](https://www.pordata.pt), and [EuroStat](https://ec.europa.eu/eurostat/data/database). -- **Explore the data set** and come up with a variety of visualizations that you can potentially include in your story. -- **Break the project down into different steps** - identify the entities/dimensions in your data set, explore them each individually, and then progress to analyzing different combinations of them. -- **Use the tools in your tool kit** - the concepts and methods you have learned in the business intelligence chapter as well as some of the things you've learned in previous chapters. This is a great way to start tying everything you've learned together! -- **Work through the lessons in class** & ask questions when you need to! -- **Commit early, commit often**, don’t be afraid of doing something incorrectly because you can always roll back to a previous version. -- **Consult documentation and resources provided** to better understand the tools you are using and how to accomplish what you want. +## Organization +We used Trello to lay out a plan and keep track of all the actions we needed to perform to have the project ready on time. -## Useful Resources + -- [Tableau Getting Started Tutorial](https://onlinehelp.tableau.com/current/guides/get-started-tutorial/en-us/get-started-tutorial-home.html) -- [Tableau Training Videos](https://www.tableau.com/learn/training) -- [Tableau Learning Library](https://onlinehelp.tableau.com/current/guides/get-started-tutorial/en-us/get-started-tutorial-next.html) - -## Evaluation topics - Topics to consider - -**Dataset** - -- You identify clearly the origin of your data -- Clear explanation of your a priori data transformation steps and why you did those -- Mixing/enriching dataset is priority, data should talk by itself before moving to the visualization -- Good data profilling should be done before you jump to visual analysis - get to know the integrity of your data, how it behaves across all variables, clear statement of different relations - -**Problem formulation** - -- After understanding the data, make sure your formulate a good problem/hypothesis -- Place yourself as the final users of your visual analysis -- Do not answer to more than 4 questions on the same dashboard/scream - users will be confused -- Keep it simple - maximize the answers using the minimum number of data points - -**Data Visualization** - -- Data Visualization -- Do simple chart, correlate them if needed (brushing, direct filtering) -- Describe on the documentation how you visualy encode your data (e.g. Sales are encoded on the size of bars, and each bar represents a different Country. It is possible to filter - facet - the entire chart by Year) - -**Text** - -- Your titles must be relevant -- Subtitle and/or annotations provide additional information -- Text is hierarchical in size, readable and horizontal -- Data is labeled directly and labels are used sparingly - -**Arrangement** - -- Data is intentionally ordered -- Is your chart displacement representing the correct data granularity changes (e.g. more aggregated on the top) -- If your charts correlate with each other in the dashboard, place them accordingly (e.g. do not filter from the bottom to the top) - -**Color** - -- Color scheme is intentional, used to highlight key patterns, readable when printed in black & white, and sufficiently contrasts with background - -**Lines** - -- Axes do not have unnecessary tick marks, and graph has one horizontal and one vertical axis - -**Overall** - -- Graphs highlights significant finding or conclusions -- No addition of unnecessary graphs -- Individual chart elements work together to reinforce the overarching takeaway message - -**Feedback** - -- Your feedback is also a part of this project! -- When other groups are presenting, put yourself on the shoes of the final user and comment accordingly with collaborative and constructive feedback +## Links +[Repository](https://github.com/laurawuerz/Project-Week-6-Tableau) +[Tableau](https://www.canva.com/design/DADlSes2lSw/EQwGN7iok2_0vJNTayvIQA/view?utm_content=DADlSes2lSw&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton) diff --git a/Tableau Project Tourism.twb b/Tableau Project Tourism.twb new file mode 100644 index 0000000..70883f5 --- /dev/null +++ b/Tableau Project Tourism.twb @@ -0,0 +1,10492 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + F1 + 20 + [F1] + [Occupancy Rate] + F1 + 0 + integer + Sum + true + + "I8" + + + + Country + 130 + [Country] + [Occupancy Rate] + Country + 1 + string + Count + true + + + "WSTR" + + + + Occupancy Rate in 2015 + 5 + 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+ + + + + + + + + + + + + Where in Europe should + Sleepy Hotel Group + open a new hotel? + Æ + Sleepy Hotel Group is a medium sized hotel chain that offers low to mid price range accomodation worldwide. + Æ + + + + + + + + + + + + + + + + + + + + + Where in Europe should + Sleepy Hotel Group + open a new hotel? + Æ + Sleepy Hotel Group is a medium sized hotel chain that offers low to mid price range accomodation worldwide. + Æ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Low Increase High Increase + + + + + 1. Occupancy Growth + Æ + In which countries did hotel occupancy increase the most? + Æ + + + + + + + + + + + + 1. Occupancy Growth + Æ + In which countries did hotel occupancy increase the most? + Æ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Low Increase High Increase + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2. Spending on Hotel Accomodation + Æ + In which countries did spending increase the most? + Æ + + + + + We filtered out those countries with the lowest spending growth rate and now look at the absolute spending in the remaining 4 countries. + + + + + + + Low Spending High Spending + + + + + Absolute Spending on Hotel Accomodation in 2017 + + + + + + + Lowest absolute spending in Bulgaria, so we will leave that country out of our further decision making process. + + + + + + + + + + + + 2. Spending on Hotel Accomodation + Æ + In which countries did spending increase the most? + Æ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + We filtered out those countries with the lowest spending growth rate and now look at the absolute spending in the remaining 4 countries. + + + + + + + + + + + + Absolute Spending on Hotel Accomodation in 2017 + + + + + + + + + + + + + + + + + + + + + Low Spending High Spending + + + + + + + + + + + + + + + + + + + + + Lowest absolute spending in Bulgaria, so we will leave that country out of our further decision making process. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 3. Hotel Occupancy and Size + Æ + Which country suits our hotel size best? + Æ + After removing the countries with the least growing spending rates and absolute spending we take a closer look into the total number of Hotels and their occupancy in the remaining countries. + + + + + + + + + + + + + + 3. Hotel Occupancy and Size + Æ + Which country suits our hotel size best? + Æ + After removing the countries with the least growing spending rates and absolute spending we take a closer look into the total number of Hotels and their occupancy in the remaining countries. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + And the winner is Cyprus! + Æ + + + + + + + + + + + + + + + + + + + + + + And the winner is Cyprus! + Æ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <formatted-text> + <run bold='true' fontcolor='#0f224b'>Business Case Sleepy Hotel Group</run> + </formatted-text> + + + \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
00Belgium61.5456.062.000.46
11Bulgaria48.7055.257.108.40
22Czechia43.0046.449.806.80
33Denmark61.0062.062.001.00
44Germany60.2361.862.071.84
\n", + "" + ], + "text/plain": [ + " Unnamed: 0 Country Occupancy Rate in 2015 Occupancy Rate in 2016 \\\n", + "0 0 Belgium 61.54 56.0 \n", + "1 1 Bulgaria 48.70 55.2 \n", + "2 2 Czechia 43.00 46.4 \n", + "3 3 Denmark 61.00 62.0 \n", + "4 4 Germany 60.23 61.8 \n", + "\n", + " Occupancy Rate in 2017 Development Occupancy Rates 2015-2017 \n", + "0 62.00 0.46 \n", + "1 57.10 8.40 \n", + "2 49.80 6.80 \n", + "3 62.00 1.00 \n", + "4 62.07 1.84 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "occupancy.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename column names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = occupancy.rename(columns={'TIME':'Country', '2015':'Occupancy Rate in 2015','2016':'Occupancy Rate in 2016','2017':'Occupancy Rate in 2017'})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Correcting Belgium data\n", + "occupancy.at[0,'Occupancy Rate in 2017'] = 62" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Filling Norway data: https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do\n", + "occupancy.at[30,'Occupancy Rate in 2017'] = 57.03" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
00.0Belgium61.5456.0062.000.46
11.0Bulgaria48.7055.2057.108.40
22.0Czechia43.0046.4049.806.80
33.0Denmark61.0062.0062.001.00
44.0Germany60.2361.8062.071.84
55.0Estonia52.0054.0055.003.00
67.0Greece46.8047.7050.203.40
78.0Spain61.9765.7267.075.10
89.0France59.0058.4061.202.20
910.0Croatia54.4057.3059.204.80
1011.0Italy44.9046.2048.803.90
1112.0Cyprus63.0069.9074.6011.60
1213.0Latvia42.4042.5044.802.40
1314.0Lithuania49.3051.0053.704.40
1415.0Luxembourg45.6144.8345.16-0.45
1516.0Hungary49.8052.0055.005.20
1617.0Malta74.0074.0076.702.70
1718.0Netherlands68.1068.1071.803.70
1819.0Austria52.0054.0055.003.00
1920.0Poland45.3047.6048.903.60
2021.0Portugal48.2053.2456.988.78
2122.0Romania47.4443.9143.97-3.47
2223.0Slovenia49.5052.2055.606.10
2324.0Slovakia35.4838.8239.944.46
2425.0Finland51.1352.9054.763.63
2526.0Sweden55.2758.0058.273.00
2629.0Liechtenstein36.9034.9037.800.90
2730.0Norway53.6054.4057.033.43
30NaNNaNNaNNaN57.03NaN
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Unnamed: 0CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
00.0Belgium61.5456.0062.000.46
11.0Bulgaria48.7055.2057.108.40
22.0Czechia43.0046.4049.806.80
33.0Denmark61.0062.0062.001.00
44.0Germany60.2361.8062.071.84
55.0Estonia52.0054.0055.003.00
67.0Greece46.8047.7050.203.40
78.0Spain61.9765.7267.075.10
89.0France59.0058.4061.202.20
910.0Croatia54.4057.3059.204.80
1011.0Italy44.9046.2048.803.90
1112.0Cyprus63.0069.9074.6011.60
1213.0Latvia42.4042.5044.802.40
1314.0Lithuania49.3051.0053.704.40
1415.0Luxembourg45.6144.8345.16-0.45
1516.0Hungary49.8052.0055.005.20
1617.0Malta74.0074.0076.702.70
1718.0Netherlands68.1068.1071.803.70
1819.0Austria52.0054.0055.003.00
1920.0Poland45.3047.6048.903.60
2021.0Portugal48.2053.2456.988.78
2122.0Romania47.4443.9143.97-3.47
2223.0Slovenia49.5052.2055.606.10
2324.0Slovakia35.4838.8239.944.46
2425.0Finland51.1352.9054.763.63
2526.0Sweden55.2758.0058.273.00
2629.0Liechtenstein36.9034.9037.800.90
2730.0Norway53.6054.4057.033.43
30NaNNaNNaNNaN57.03NaN
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Unnamed: 0CountryOccupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
00.0Belgium61.5456.0062.000.46
11.0Bulgaria48.7055.2057.108.40
22.0Czechia43.0046.4049.806.80
33.0Denmark61.0062.0062.001.00
44.0Germany60.2361.8062.071.84
55.0Estonia52.0054.0055.003.00
67.0Greece46.8047.7050.203.40
78.0Spain61.9765.7267.075.10
89.0France59.0058.4061.202.20
910.0Croatia54.4057.3059.204.80
1011.0Italy44.9046.2048.803.90
1112.0Cyprus63.0069.9074.6011.60
1213.0Latvia42.4042.5044.802.40
1314.0Lithuania49.3051.0053.704.40
1415.0Luxembourg45.6144.8345.16-0.45
1516.0Hungary49.8052.0055.005.20
1617.0Malta74.0074.0076.702.70
1718.0Netherlands68.1068.1071.803.70
1819.0Austria52.0054.0055.003.00
1920.0Poland45.3047.6048.903.60
2021.0Portugal48.2053.2456.988.78
2122.0Romania47.4443.9143.97-3.47
2223.0Slovenia49.5052.2055.606.10
2324.0Slovakia35.4838.8239.944.46
2425.0Finland51.1352.9054.763.63
2526.0Sweden55.2758.0058.273.00
2629.0Liechtenstein36.9034.9037.800.90
2730.0Norway53.6054.4057.033.43
30NaNNaNNaNNaN57.03NaN
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
10Croatia342499.97:300298
11Italy3974783.835.36493e+065.96759e+06
12Cyprus222501.91239925300057
13Latvia136220.79121883151176
14Lithuania233245.56280569326529
15Luxembourg351609.13324888361158
16Hungary619193.34638635731482
17Malta103646.87110302114825
18Netherlands3205522.373.49411e+065.07159e+06
19Austria4551468.844.93604e+065.06712e+06
20Poland1632635.301.7733e+062.11174e+06
21Portugal457283.05424443496517
22Romania301553.50291603393141
23Slovenia269009.89270406287334
24Slovakia559150.75567464650462
25Finland2610633.922.7013e+062.86085e+06
26Sweden2606883.783.38111e+06:
27Norway2919968.613.07815e+063.54416e+06
28Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "10 Croatia 342499.97 : 300298\n", + "11 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "12 Cyprus 222501.91 239925 300057\n", + "13 Latvia 136220.79 121883 151176\n", + "14 Lithuania 233245.56 280569 326529\n", + "15 Luxembourg 351609.13 324888 361158\n", + "16 Hungary 619193.34 638635 731482\n", + "17 Malta 103646.87 110302 114825\n", + "18 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "19 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "20 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "21 Portugal 457283.05 424443 496517\n", + "22 Romania 301553.50 291603 393141\n", + "23 Slovenia 269009.89 270406 287334\n", + "24 Slovakia 559150.75 567464 650462\n", + "25 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "26 Sweden 2606883.78 3.38111e+06 :\n", + "27 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "28 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure.drop(expenditure.index[[10,26]], inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
10Italy3974783.835.36493e+065.96759e+06
11Cyprus222501.91239925300057
12Latvia136220.79121883151176
13Lithuania233245.56280569326529
14Luxembourg351609.13324888361158
15Hungary619193.34638635731482
16Malta103646.87110302114825
17Netherlands3205522.373.49411e+065.07159e+06
18Austria4551468.844.93604e+065.06712e+06
19Poland1632635.301.7733e+062.11174e+06
20Portugal457283.05424443496517
21Romania301553.50291603393141
22Slovenia269009.89270406287334
23Slovakia559150.75567464650462
24Finland2610633.922.7013e+062.86085e+06
25Norway2919968.613.07815e+063.54416e+06
26Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "10 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "11 Cyprus 222501.91 239925 300057\n", + "12 Latvia 136220.79 121883 151176\n", + "13 Lithuania 233245.56 280569 326529\n", + "14 Luxembourg 351609.13 324888 361158\n", + "15 Hungary 619193.34 638635 731482\n", + "16 Malta 103646.87 110302 114825\n", + "17 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "18 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "19 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "20 Portugal 457283.05 424443 496517\n", + "21 Romania 301553.50 291603 393141\n", + "22 Slovenia 269009.89 270406 287334\n", + "23 Slovakia 559150.75 567464 650462\n", + "24 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "25 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "26 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIME201520162017
0Belgium2132661.432.2907e+062.78453e+06
1Bulgaria89392.03100089149436
2Czechia680150.698452761.03994e+06
3Denmark2265268.402.77768e+063.6079e+06
4Germany34827677.863.79648e+074.05577e+07
5Estonia186741.49220419247374
6Ireland1502234.951.65149e+061.72361e+06
7Greece202757.73216346249310
8Spain5387633.226.72747e+066.87916e+06
9France8535653.789.03325e+061.06907e+07
11Italy3974783.835.36493e+065.96759e+06
12Cyprus222501.91239925300057
13Latvia136220.79121883151176
14Lithuania233245.56280569326529
15Luxembourg351609.13324888361158
16Hungary619193.34638635731482
17Malta103646.87110302114825
18Netherlands3205522.373.49411e+065.07159e+06
19Austria4551468.844.93604e+065.06712e+06
20Poland1632635.301.7733e+062.11174e+06
21Portugal457283.05424443496517
22Romania301553.50291603393141
23Slovenia269009.89270406287334
24Slovakia559150.75567464650462
25Finland2610633.922.7013e+062.86085e+06
27Norway2919968.613.07815e+063.54416e+06
28Switzerland4105694.584.52236e+064.51693e+06
\n", + "
" + ], + "text/plain": [ + " GEO/TIME 2015 2016 2017\n", + "0 Belgium 2132661.43 2.2907e+06 2.78453e+06\n", + "1 Bulgaria 89392.03 100089 149436\n", + "2 Czechia 680150.69 845276 1.03994e+06\n", + "3 Denmark 2265268.40 2.77768e+06 3.6079e+06\n", + "4 Germany 34827677.86 3.79648e+07 4.05577e+07\n", + "5 Estonia 186741.49 220419 247374\n", + "6 Ireland 1502234.95 1.65149e+06 1.72361e+06\n", + "7 Greece 202757.73 216346 249310\n", + "8 Spain 5387633.22 6.72747e+06 6.87916e+06\n", + "9 France 8535653.78 9.03325e+06 1.06907e+07\n", + "11 Italy 3974783.83 5.36493e+06 5.96759e+06\n", + "12 Cyprus 222501.91 239925 300057\n", + "13 Latvia 136220.79 121883 151176\n", + "14 Lithuania 233245.56 280569 326529\n", + "15 Luxembourg 351609.13 324888 361158\n", + "16 Hungary 619193.34 638635 731482\n", + "17 Malta 103646.87 110302 114825\n", + "18 Netherlands 3205522.37 3.49411e+06 5.07159e+06\n", + "19 Austria 4551468.84 4.93604e+06 5.06712e+06\n", + "20 Poland 1632635.30 1.7733e+06 2.11174e+06\n", + "21 Portugal 457283.05 424443 496517\n", + "22 Romania 301553.50 291603 393141\n", + "23 Slovenia 269009.89 270406 287334\n", + "24 Slovakia 559150.75 567464 650462\n", + "25 Finland 2610633.92 2.7013e+06 2.86085e+06\n", + "27 Norway 2919968.61 3.07815e+06 3.54416e+06\n", + "28 Switzerland 4105694.58 4.52236e+06 4.51693e+06" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename column names" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure = expenditure.rename(columns={'GEO/TIME':'Country', '2015':'Expenditure in 2015','2016':'Expenditure in 2016','2017':'Expenditure in 2017'})" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country object\n", + "Expenditure in 2015 float64\n", + "Expenditure in 2016 object\n", + "Expenditure in 2017 object\n", + "dtype: object" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# changing datatypes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "#expenditure = expenditure.astype({\"Expenditure in 2016\": float, \"Expenditure in 2017\": float})\n", + "\n", + "#df = df.astype({\"a\": int, \"b\": complex})\n", + "\n", + "expenditure[\"Expenditure in 2016\"] = pd.to_numeric(expenditure[\"Expenditure in 2016\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure[\"Expenditure in 2017\"] = pd.to_numeric(expenditure[\"Expenditure in 2017\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country object\n", + "Expenditure in 2015 float64\n", + "Expenditure in 2016 float64\n", + "Expenditure in 2017 float64\n", + "dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculating Difference in expenditure\n", + "expenditure['Change expenditure 2015-2017 in %'] = 100*((expenditure['Expenditure in 2017']-expenditure['Expenditure in 2015'])/expenditure['Expenditure in 2015'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %
0Belgium2132661.432290696.542784526.4330.565799
1Bulgaria89392.03100088.85149436.0667.169333
2Czechia680150.69845275.761039940.8752.898598
3Denmark2265268.402777677.763607902.2259.270408
4Germany34827677.8637964775.2040557686.4016.452456
5Estonia186741.49220419.19247373.5132.468425
6Ireland1502234.951651493.331723609.7414.736363
7Greece202757.73216346.06249309.6122.959361
8Spain5387633.226727474.536879160.1927.684271
9France8535653.789033252.7310690747.0125.248133
11Italy3974783.835364934.755967589.6250.136206
12Cyprus222501.91239924.64300056.7634.855813
13Latvia136220.79121883.12151175.6510.978398
14Lithuania233245.56280568.55326528.6539.993512
15Luxembourg351609.13324888.07361157.942.715746
16Hungary619193.34638635.07731482.3918.134732
17Malta103646.87110302.40114825.3410.785150
18Netherlands3205522.373494108.795071587.3258.214067
19Austria4551468.844936038.855067124.3111.329430
20Poland1632635.301773295.442111735.3929.345200
21Portugal457283.05424442.67496517.238.579846
22Romania301553.50291603.10393140.7430.371805
23Slovenia269009.89270405.51287334.236.811772
24Slovakia559150.75567463.84650461.8316.330315
25Finland2610633.922701295.722860852.849.584604
27Norway2919968.613078150.753544164.4221.376799
28Switzerland4105694.584522355.104516926.5410.016136
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" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 \\\n", + "0 Belgium 2132661.43 2290696.54 \n", + "1 Bulgaria 89392.03 100088.85 \n", + "2 Czechia 680150.69 845275.76 \n", + "3 Denmark 2265268.40 2777677.76 \n", + "4 Germany 34827677.86 37964775.20 \n", + "5 Estonia 186741.49 220419.19 \n", + "6 Ireland 1502234.95 1651493.33 \n", + "7 Greece 202757.73 216346.06 \n", + "8 Spain 5387633.22 6727474.53 \n", + "9 France 8535653.78 9033252.73 \n", + "11 Italy 3974783.83 5364934.75 \n", + "12 Cyprus 222501.91 239924.64 \n", + "13 Latvia 136220.79 121883.12 \n", + "14 Lithuania 233245.56 280568.55 \n", + "15 Luxembourg 351609.13 324888.07 \n", + "16 Hungary 619193.34 638635.07 \n", + "17 Malta 103646.87 110302.40 \n", + "18 Netherlands 3205522.37 3494108.79 \n", + "19 Austria 4551468.84 4936038.85 \n", + "20 Poland 1632635.30 1773295.44 \n", + "21 Portugal 457283.05 424442.67 \n", + "22 Romania 301553.50 291603.10 \n", + "23 Slovenia 269009.89 270405.51 \n", + "24 Slovakia 559150.75 567463.84 \n", + "25 Finland 2610633.92 2701295.72 \n", + "27 Norway 2919968.61 3078150.75 \n", + "28 Switzerland 4105694.58 4522355.10 \n", + "\n", + " Expenditure in 2017 Change expenditure 2015-2017 in % \n", + "0 2784526.43 30.565799 \n", + "1 149436.06 67.169333 \n", + "2 1039940.87 52.898598 \n", + "3 3607902.22 59.270408 \n", + "4 40557686.40 16.452456 \n", + "5 247373.51 32.468425 \n", + "6 1723609.74 14.736363 \n", + "7 249309.61 22.959361 \n", + "8 6879160.19 27.684271 \n", + "9 10690747.01 25.248133 \n", + "11 5967589.62 50.136206 \n", + "12 300056.76 34.855813 \n", + "13 151175.65 10.978398 \n", + "14 326528.65 39.993512 \n", + "15 361157.94 2.715746 \n", + "16 731482.39 18.134732 \n", + "17 114825.34 10.785150 \n", + "18 5071587.32 58.214067 \n", + "19 5067124.31 11.329430 \n", + "20 2111735.39 29.345200 \n", + "21 496517.23 8.579846 \n", + "22 393140.74 30.371805 \n", + "23 287334.23 6.811772 \n", + "24 650461.83 16.330315 \n", + "25 2860852.84 9.584604 \n", + "27 3544164.42 21.376799 \n", + "28 4516926.54 10.016136 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge Datasets on expenditure\n", + "merged_exp_occ = pd.merge(expenditure, occupancy, on='Country')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %Unnamed: 0Occupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017
0Belgium2132661.432290696.542784526.4330.5657990.061.5456.0062.000.46
1Bulgaria89392.03100088.85149436.0667.1693331.048.7055.2057.108.40
2Czechia680150.69845275.761039940.8752.8985982.043.0046.4049.806.80
3Denmark2265268.402777677.763607902.2259.2704083.061.0062.0062.001.00
4Germany34827677.8637964775.2040557686.4016.4524564.060.2361.8062.071.84
5Estonia186741.49220419.19247373.5132.4684255.052.0054.0055.003.00
6Greece202757.73216346.06249309.6122.9593617.046.8047.7050.203.40
7Spain5387633.226727474.536879160.1927.6842718.061.9765.7267.075.10
8France8535653.789033252.7310690747.0125.2481339.059.0058.4061.202.20
9Italy3974783.835364934.755967589.6250.13620611.044.9046.2048.803.90
10Cyprus222501.91239924.64300056.7634.85581312.063.0069.9074.6011.60
11Latvia136220.79121883.12151175.6510.97839813.042.4042.5044.802.40
12Lithuania233245.56280568.55326528.6539.99351214.049.3051.0053.704.40
13Luxembourg351609.13324888.07361157.942.71574615.045.6144.8345.16-0.45
14Hungary619193.34638635.07731482.3918.13473216.049.8052.0055.005.20
15Malta103646.87110302.40114825.3410.78515017.074.0074.0076.702.70
16Netherlands3205522.373494108.795071587.3258.21406718.068.1068.1071.803.70
17Austria4551468.844936038.855067124.3111.32943019.052.0054.0055.003.00
18Poland1632635.301773295.442111735.3929.34520020.045.3047.6048.903.60
19Portugal457283.05424442.67496517.238.57984621.048.2053.2456.988.78
20Romania301553.50291603.10393140.7430.37180522.047.4443.9143.97-3.47
21Slovenia269009.89270405.51287334.236.81177223.049.5052.2055.606.10
22Slovakia559150.75567463.84650461.8316.33031524.035.4838.8239.944.46
23Finland2610633.922701295.722860852.849.58460425.051.1352.9054.763.63
24Norway2919968.613078150.753544164.4221.37679930.053.6054.4057.033.43
\n", + "
" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 \\\n", + "0 Belgium 2132661.43 2290696.54 \n", + "1 Bulgaria 89392.03 100088.85 \n", + "2 Czechia 680150.69 845275.76 \n", + "3 Denmark 2265268.40 2777677.76 \n", + "4 Germany 34827677.86 37964775.20 \n", + "5 Estonia 186741.49 220419.19 \n", + "6 Greece 202757.73 216346.06 \n", + "7 Spain 5387633.22 6727474.53 \n", + "8 France 8535653.78 9033252.73 \n", + "9 Italy 3974783.83 5364934.75 \n", + "10 Cyprus 222501.91 239924.64 \n", + "11 Latvia 136220.79 121883.12 \n", + "12 Lithuania 233245.56 280568.55 \n", + "13 Luxembourg 351609.13 324888.07 \n", + "14 Hungary 619193.34 638635.07 \n", + "15 Malta 103646.87 110302.40 \n", + "16 Netherlands 3205522.37 3494108.79 \n", + "17 Austria 4551468.84 4936038.85 \n", + "18 Poland 1632635.30 1773295.44 \n", + "19 Portugal 457283.05 424442.67 \n", + "20 Romania 301553.50 291603.10 \n", + "21 Slovenia 269009.89 270405.51 \n", + "22 Slovakia 559150.75 567463.84 \n", + "23 Finland 2610633.92 2701295.72 \n", + "24 Norway 2919968.61 3078150.75 \n", + "\n", + " Expenditure in 2017 Change expenditure 2015-2017 in % Unnamed: 0 \\\n", + "0 2784526.43 30.565799 0.0 \n", + "1 149436.06 67.169333 1.0 \n", + "2 1039940.87 52.898598 2.0 \n", + "3 3607902.22 59.270408 3.0 \n", + "4 40557686.40 16.452456 4.0 \n", + "5 247373.51 32.468425 5.0 \n", + "6 249309.61 22.959361 7.0 \n", + "7 6879160.19 27.684271 8.0 \n", + "8 10690747.01 25.248133 9.0 \n", + "9 5967589.62 50.136206 11.0 \n", + "10 300056.76 34.855813 12.0 \n", + "11 151175.65 10.978398 13.0 \n", + "12 326528.65 39.993512 14.0 \n", + "13 361157.94 2.715746 15.0 \n", + "14 731482.39 18.134732 16.0 \n", + "15 114825.34 10.785150 17.0 \n", + "16 5071587.32 58.214067 18.0 \n", + "17 5067124.31 11.329430 19.0 \n", + "18 2111735.39 29.345200 20.0 \n", + "19 496517.23 8.579846 21.0 \n", + "20 393140.74 30.371805 22.0 \n", + "21 287334.23 6.811772 23.0 \n", + "22 650461.83 16.330315 24.0 \n", + "23 2860852.84 9.584604 25.0 \n", + "24 3544164.42 21.376799 30.0 \n", + "\n", + " Occupancy Rate in 2015 Occupancy Rate in 2016 Occupancy Rate in 2017 \\\n", + "0 61.54 56.00 62.00 \n", + "1 48.70 55.20 57.10 \n", + "2 43.00 46.40 49.80 \n", + "3 61.00 62.00 62.00 \n", + "4 60.23 61.80 62.07 \n", + "5 52.00 54.00 55.00 \n", + "6 46.80 47.70 50.20 \n", + "7 61.97 65.72 67.07 \n", + "8 59.00 58.40 61.20 \n", + "9 44.90 46.20 48.80 \n", + "10 63.00 69.90 74.60 \n", + "11 42.40 42.50 44.80 \n", + "12 49.30 51.00 53.70 \n", + "13 45.61 44.83 45.16 \n", + "14 49.80 52.00 55.00 \n", + "15 74.00 74.00 76.70 \n", + "16 68.10 68.10 71.80 \n", + "17 52.00 54.00 55.00 \n", + "18 45.30 47.60 48.90 \n", + "19 48.20 53.24 56.98 \n", + "20 47.44 43.91 43.97 \n", + "21 49.50 52.20 55.60 \n", + "22 35.48 38.82 39.94 \n", + "23 51.13 52.90 54.76 \n", + "24 53.60 54.40 57.03 \n", + "\n", + " Development Occupancy Rates 2015-2017 \n", + "0 0.46 \n", + "1 8.40 \n", + "2 6.80 \n", + "3 1.00 \n", + "4 1.84 \n", + "5 3.00 \n", + "6 3.40 \n", + "7 5.10 \n", + "8 2.20 \n", + "9 3.90 \n", + "10 11.60 \n", + "11 2.40 \n", + "12 4.40 \n", + "13 -0.45 \n", + "14 5.20 \n", + "15 2.70 \n", + "16 3.70 \n", + "17 3.00 \n", + "18 3.60 \n", + "19 8.78 \n", + "20 -3.47 \n", + "21 6.10 \n", + "22 4.46 \n", + "23 3.63 \n", + "24 3.43 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_exp_occ" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "expenditure.shape\n", + "listexp = expenditure.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Belgium', 'Bulgaria', 'Czechia', 'Denmark', 'Germany', 'Estonia',\n", + " 'Ireland', 'Greece', 'Spain', 'France', 'Italy', 'Cyprus',\n", + " 'Latvia', 'Lithuania', 'Luxembourg', 'Hungary', 'Malta',\n", + " 'Netherlands', 'Austria', 'Poland', 'Portugal', 'Romania',\n", + " 'Slovenia', 'Slovakia', 'Finland', 'Norway', 'Switzerland'],\n", + " dtype=object)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "listexp" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy.shape\n", + "listocc = occupancy.Country.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "lst3 = [value for value in listexp if value in listocc]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(lst3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Belgium',\n", + " 'Bulgaria',\n", + " 'Czechia',\n", + " 'Denmark',\n", + " 'Germany',\n", + " 'Estonia',\n", + " 'Greece',\n", + " 'Spain',\n", + " 'France',\n", + " 'Italy',\n", + " 'Cyprus',\n", + " 'Latvia',\n", + " 'Lithuania',\n", + " 'Luxembourg',\n", + " 'Hungary',\n", + " 'Malta',\n", + " 'Netherlands',\n", + " 'Austria',\n", + " 'Poland',\n", + " 'Portugal',\n", + " 'Romania',\n", + " 'Slovenia',\n", + " 'Slovakia',\n", + " 'Finland',\n", + " 'Norway']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst3" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Hotel size\n", + "\n", + "hotelsize = pd.read_excel('Hotel size_clean.xls')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIMELess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Italy181931817018076135741357113488::::::
7Cyprus420423431215212214123120120282829
8Latvia20321521810198100252627:44
9Lithuania252257247136132133282929222
10Luxembourg:145139:7069::::::
11Hungary145614661443580584586119122125303030
12Malta343655616264444441222223
13Poland191719731995151716961763250257268393938
14Romania1351135814539789861015249246249484849
15Sweden385400389913901899442440463252270274
16United Kingdom3027132517:59745318:23291589:1255291:
17Iceland227225225155146152162325433
18Norway293307261505504500225231234596463
19Montenegro::193::89::37::12
20Kosovo::129::50::3::0
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" + ], + "text/plain": [ + " GEO/TIME Less than 25 2015 Less than 25 2016 Less than 25 2017 \\\n", + "0 Bulgaria 512 448 427 \n", + "1 Czechia 4645 4648 4588 \n", + "2 Germany 23425 22808 22394 \n", + "3 Greece 5290 5190 5004 \n", + "4 Spain 12170 12049 12112 \n", + "5 Croatia 396 455 474 \n", + "6 Italy 18193 18170 18076 \n", + "7 Cyprus 420 423 431 \n", + "8 Latvia 203 215 218 \n", + "9 Lithuania 252 257 247 \n", + "10 Luxembourg : 145 139 \n", + "11 Hungary 1456 1466 1443 \n", + "12 Malta 34 36 55 \n", + "13 Poland 1917 1973 1995 \n", + "14 Romania 1351 1358 1453 \n", + "15 Sweden 385 400 389 \n", + "16 United Kingdom 30271 32517 : \n", + "17 Iceland 227 225 225 \n", + "18 Norway 293 307 261 \n", + "19 Montenegro : : 193 \n", + "20 Kosovo : : 129 \n", + "\n", + " 25 to 99 2015 25 to 99 2016 25 to 99 2017 100 to 249 2015 100 to 249 2016 \\\n", + "0 1003 1019 986 341 368 \n", + "1 1164 1190 1195 153 154 \n", + "2 8451 8438 8482 1517 1568 \n", + "3 4032 4008 3981 593 594 \n", + "4 5111 5045 5076 1758 1752 \n", + "5 282 294 305 183 183 \n", + "6 13574 13571 13488 : : \n", + "7 215 212 214 123 120 \n", + "8 101 98 100 25 26 \n", + "9 136 132 133 28 29 \n", + "10 : 70 69 : : \n", + "11 580 584 586 119 122 \n", + "12 61 62 64 44 44 \n", + "13 1517 1696 1763 250 257 \n", + "14 978 986 1015 249 246 \n", + "15 913 901 899 442 440 \n", + "16 5974 5318 : 2329 1589 \n", + "17 155 146 152 16 23 \n", + "18 505 504 500 225 231 \n", + "19 : : 89 : : \n", + "20 : : 50 : : \n", + "\n", + " 100 to 249 2017 more than 250 2015 more than 250 2016 more than 250 2017 \n", + "0 366 324 323 331 \n", + "1 154 30 30 30 \n", + "2 1622 242 247 251 \n", + "3 593 196 195 194 \n", + "4 1755 679 678 687 \n", + "5 183 77 79 75 \n", + "6 : : : : \n", + "7 120 28 28 29 \n", + "8 27 : 4 4 \n", + "9 29 2 2 2 \n", + "10 : : : : \n", + "11 125 30 30 30 \n", + "12 41 22 22 23 \n", + "13 268 39 39 38 \n", + "14 249 48 48 49 \n", + "15 463 252 270 274 \n", + "16 : 1255 291 : \n", + "17 25 4 3 3 \n", + "18 234 59 64 63 \n", + "19 37 : : 12 \n", + "20 3 : : 0 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize.drop(hotelsize.index[[6,10,16,19,20]], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GEO/TIMELess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
7Cyprus420423431215212214123120120282829
8Latvia20321521810198100252627:44
9Lithuania252257247136132133282929222
11Hungary145614661443580584586119122125303030
12Malta343655616264444441222223
13Poland191719731995151716961763250257268393938
14Romania1351135814539789861015249246249484849
15Sweden385400389913901899442440463252270274
17Iceland227225225155146152162325433
18Norway293307261505504500225231234596463
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CountryLess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
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1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Cyprus420423431215212214123120120282829
7Latvia20321521810198100252627444
8Lithuania252257247136132133282929222
9Hungary145614661443580584586119122125303030
10Malta343655616264444441222223
11Poland191719731995151716961763250257268393938
12Romania1351135814539789861015249246249484849
13Sweden385400389913901899442440463252270274
14Iceland227225225155146152162325433
15Norway293307261505504500225231234596463
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CountryLess than 25 2015Less than 25 2016Less than 25 201725 to 99 201525 to 99 201625 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017
0Bulgaria51244842710031019986341368366324323331
1Czechia464546484588116411901195153154154303030
2Germany234252280822394845184388482151715681622242247251
3Greece529051905004403240083981593594593196195194
4Spain121701204912112511150455076175817521755679678687
5Croatia396455474282294305183183183777975
6Cyprus420423431215212214123120120282829
7Latvia20321521810198100252627444
8Lithuania252257247136132133282929222
9Hungary145614661443580584586119122125303030
10Malta343655616264444441222223
11Poland191719731995151716961763250257268393938
12Romania1351135814539789861015249246249484849
13Sweden385400389913901899442440463252270274
14Iceland227225225155146152162325433
15Norway293307261505504500225231234596463
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" + ], + "text/plain": [ + " Country Less than 25 2015 Less than 25 2016 Less than 25 2017 \\\n", + "0 Bulgaria 512 448 427 \n", + "1 Czechia 4645 4648 4588 \n", + "2 Germany 23425 22808 22394 \n", + "3 Greece 5290 5190 5004 \n", + "4 Spain 12170 12049 12112 \n", + "5 Croatia 396 455 474 \n", + "6 Cyprus 420 423 431 \n", + "7 Latvia 203 215 218 \n", + "8 Lithuania 252 257 247 \n", + "9 Hungary 1456 1466 1443 \n", + "10 Malta 34 36 55 \n", + "11 Poland 1917 1973 1995 \n", + "12 Romania 1351 1358 1453 \n", + "13 Sweden 385 400 389 \n", + "14 Iceland 227 225 225 \n", + "15 Norway 293 307 261 \n", + "\n", + " 25 to 99 2015 25 to 99 2016 25 to 99 2017 100 to 249 2015 100 to 249 2016 \\\n", + "0 1003 1019 986 341 368 \n", + "1 1164 1190 1195 153 154 \n", + "2 8451 8438 8482 1517 1568 \n", + "3 4032 4008 3981 593 594 \n", + "4 5111 5045 5076 1758 1752 \n", + "5 282 294 305 183 183 \n", + "6 215 212 214 123 120 \n", + "7 101 98 100 25 26 \n", + "8 136 132 133 28 29 \n", + "9 580 584 586 119 122 \n", + "10 61 62 64 44 44 \n", + "11 1517 1696 1763 250 257 \n", + "12 978 986 1015 249 246 \n", + "13 913 901 899 442 440 \n", + "14 155 146 152 16 23 \n", + "15 505 504 500 225 231 \n", + "\n", + " 100 to 249 2017 more than 250 2015 more than 250 2016 more than 250 2017 \n", + "0 366 324 323 331 \n", + "1 154 30 30 30 \n", + "2 1622 242 247 251 \n", + "3 593 196 195 194 \n", + "4 1755 679 678 687 \n", + "5 183 77 79 75 \n", + "6 120 28 28 29 \n", + "7 27 4 4 4 \n", + "8 29 2 2 2 \n", + "9 125 30 30 30 \n", + "10 41 22 22 23 \n", + "11 268 39 39 38 \n", + "12 249 48 48 49 \n", + "13 463 252 270 274 \n", + "14 25 4 3 3 \n", + "15 234 59 64 63 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize['Total '] = hotelsize" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2017'] = hotelsize['25 to 99 2017']+ hotelsize['100 to 249 2017']+hotelsize['Less than 25 2017']+hotelsize['more than 250 2017']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2110\n", + "1 5967\n", + "2 32749\n", + "3 9772\n", + "4 19630\n", + "5 1037\n", + "6 794\n", + "7 349\n", + "8 411\n", + "9 2184\n", + "10 183\n", + "11 4064\n", + "12 2766\n", + "13 2025\n", + "14 405\n", + "15 1058\n", + "Name: Sum Hotels 2017, dtype: object" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hotelsize['Sum Hotels 2017']" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2016'] = hotelsize['25 to 99 2016']+hotelsize['100 to 249 2016']+hotelsize['Less than 25 2016']+hotelsize['more than 250 2016']" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "hotelsize['Sum Hotels 2015'] = hotelsize['25 to 99 2015']+hotelsize['100 to 249 2015']+hotelsize['Less than 25 2015']+hotelsize['more than 250 2015']" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge all 3 datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "final_merge = pd.merge(merged_exp_occ, hotelsize, on='Country')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %Unnamed: 0Occupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017...25 to 99 2017100 to 249 2015100 to 249 2016100 to 249 2017more than 250 2015more than 250 2016more than 250 2017Sum Hotels 2017Sum Hotels 2016Sum Hotels 2015
0Bulgaria89392.03100088.85149436.0667.1693331.048.7055.2057.108.40...986341368366324323331211021582180
1Czechia680150.69845275.761039940.8752.8985982.043.0046.4049.806.80...1195153154154303030596760225992
2Germany34827677.8637964775.2040557686.4016.4524564.060.2361.8062.071.84...8482151715681622242247251327493306133635
3Greece202757.73216346.06249309.6122.9593617.046.8047.7050.203.40...39815935945931961951949772998710111
4Spain5387633.226727474.536879160.1927.6842718.061.9765.7267.075.10...5076175817521755679678687196301952419718
5Cyprus222501.91239924.64300056.7634.85581312.063.0069.9074.6011.60...214123120120282829794783786
6Latvia136220.79121883.12151175.6510.97839813.042.4042.5044.802.40...100252627444349343333
7Lithuania233245.56280568.55326528.6539.99351214.049.3051.0053.704.40...133282929222411420418
8Hungary619193.34638635.07731482.3918.13473216.049.8052.0055.005.20...586119122125303030218422022185
9Malta103646.87110302.40114825.3410.78515017.074.0074.0076.702.70...64444441222223183164161
10Poland1632635.301773295.442111735.3929.34520020.045.3047.6048.903.60...1763250257268393938406439653723
11Romania301553.50291603.10393140.7430.37180522.047.4443.9143.97-3.47...1015249246249484849276626382626
12Norway2919968.613078150.753544164.4221.37679930.053.6054.4057.033.43...500225231234596463105811061082
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13 rows × 25 columns

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" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 Expenditure in 2017 \\\n", + "0 Bulgaria 89392.03 100088.85 149436.06 \n", + "1 Czechia 680150.69 845275.76 1039940.87 \n", + "2 Germany 34827677.86 37964775.20 40557686.40 \n", + "3 Greece 202757.73 216346.06 249309.61 \n", + "4 Spain 5387633.22 6727474.53 6879160.19 \n", + "5 Cyprus 222501.91 239924.64 300056.76 \n", + "6 Latvia 136220.79 121883.12 151175.65 \n", + "7 Lithuania 233245.56 280568.55 326528.65 \n", + "8 Hungary 619193.34 638635.07 731482.39 \n", + "9 Malta 103646.87 110302.40 114825.34 \n", + "10 Poland 1632635.30 1773295.44 2111735.39 \n", + "11 Romania 301553.50 291603.10 393140.74 \n", + "12 Norway 2919968.61 3078150.75 3544164.42 \n", + "\n", + " Change expenditure 2015-2017 in % Unnamed: 0 Occupancy Rate in 2015 \\\n", + "0 67.169333 1.0 48.70 \n", + "1 52.898598 2.0 43.00 \n", + "2 16.452456 4.0 60.23 \n", + "3 22.959361 7.0 46.80 \n", + "4 27.684271 8.0 61.97 \n", + "5 34.855813 12.0 63.00 \n", + "6 10.978398 13.0 42.40 \n", + "7 39.993512 14.0 49.30 \n", + "8 18.134732 16.0 49.80 \n", + "9 10.785150 17.0 74.00 \n", + "10 29.345200 20.0 45.30 \n", + "11 30.371805 22.0 47.44 \n", + "12 21.376799 30.0 53.60 \n", + "\n", + " Occupancy Rate in 2016 Occupancy Rate in 2017 \\\n", + "0 55.20 57.10 \n", + "1 46.40 49.80 \n", + "2 61.80 62.07 \n", + "3 47.70 50.20 \n", + "4 65.72 67.07 \n", + "5 69.90 74.60 \n", + "6 42.50 44.80 \n", + "7 51.00 53.70 \n", + "8 52.00 55.00 \n", + "9 74.00 76.70 \n", + "10 47.60 48.90 \n", + "11 43.91 43.97 \n", + "12 54.40 57.03 \n", + "\n", + " Development Occupancy Rates 2015-2017 ... 25 to 99 2017 100 to 249 2015 \\\n", + "0 8.40 ... 986 341 \n", + "1 6.80 ... 1195 153 \n", + "2 1.84 ... 8482 1517 \n", + "3 3.40 ... 3981 593 \n", + "4 5.10 ... 5076 1758 \n", + "5 11.60 ... 214 123 \n", + "6 2.40 ... 100 25 \n", + "7 4.40 ... 133 28 \n", + "8 5.20 ... 586 119 \n", + "9 2.70 ... 64 44 \n", + "10 3.60 ... 1763 250 \n", + "11 -3.47 ... 1015 249 \n", + "12 3.43 ... 500 225 \n", + "\n", + " 100 to 249 2016 100 to 249 2017 more than 250 2015 more than 250 2016 \\\n", + "0 368 366 324 323 \n", + "1 154 154 30 30 \n", + "2 1568 1622 242 247 \n", + "3 594 593 196 195 \n", + "4 1752 1755 679 678 \n", + "5 120 120 28 28 \n", + "6 26 27 4 4 \n", + "7 29 29 2 2 \n", + "8 122 125 30 30 \n", + "9 44 41 22 22 \n", + "10 257 268 39 39 \n", + "11 246 249 48 48 \n", + "12 231 234 59 64 \n", + "\n", + " more than 250 2017 Sum Hotels 2017 Sum Hotels 2016 Sum Hotels 2015 \n", + "0 331 2110 2158 2180 \n", + "1 30 5967 6022 5992 \n", + "2 251 32749 33061 33635 \n", + "3 194 9772 9987 10111 \n", + "4 687 19630 19524 19718 \n", + "5 29 794 783 786 \n", + "6 4 349 343 333 \n", + "7 2 411 420 418 \n", + "8 30 2184 2202 2185 \n", + "9 23 183 164 161 \n", + "10 38 4064 3965 3723 \n", + "11 49 2766 2638 2626 \n", + "12 63 1058 1106 1082 \n", + "\n", + "[13 rows x 25 columns]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_merge" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "test = final_merge[['Less than 25 2017','25 to 99 2017','100 to 249 2017','more than 250 2017','Sum Hotels 2017']]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryExpenditure in 2015Expenditure in 2016Expenditure in 2017Change expenditure 2015-2017 in %Unnamed: 0Occupancy Rate in 2015Occupancy Rate in 2016Occupancy Rate in 2017Development Occupancy Rates 2015-2017...more than 250 2015more than 250 2016more than 250 2017Sum Hotels 2017Sum Hotels 2016Sum Hotels 2015Percent 25-99 2017Percent less 25 2017Percent 100-249 2017Percent 250+ 2017
0Bulgaria89392.03100088.85149436.0667.1693331.048.7055.2057.108.40...32432333121102158218046.729920.23717.34615.6872
1Czechia680150.69845275.761039940.8752.8985982.043.0046.4049.806.80...30303059676022599220.026876.88962.580860.502765
2Germany34827677.8637964775.2040557686.4016.4524564.060.2361.8062.071.84...24224725132749330613363525.968.38074.952820.766436
3Greece202757.73216346.06249309.6122.9593617.046.8047.7050.203.40...196195194977299871011140.738851.20756.068361.98526
4Spain5387633.226727474.536879160.1927.6842718.061.9765.7267.075.10...67967868719630195241971825.858461.70158.94043.49975
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5 rows × 29 columns

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" + ], + "text/plain": [ + " Country Expenditure in 2015 Expenditure in 2016 Expenditure in 2017 \\\n", + "0 Bulgaria 89392.03 100088.85 149436.06 \n", + "1 Czechia 680150.69 845275.76 1039940.87 \n", + "2 Germany 34827677.86 37964775.20 40557686.40 \n", + "3 Greece 202757.73 216346.06 249309.61 \n", + "4 Spain 5387633.22 6727474.53 6879160.19 \n", + "\n", + " Change expenditure 2015-2017 in % Unnamed: 0 Occupancy Rate in 2015 \\\n", + "0 67.169333 1.0 48.70 \n", + "1 52.898598 2.0 43.00 \n", + "2 16.452456 4.0 60.23 \n", + "3 22.959361 7.0 46.80 \n", + "4 27.684271 8.0 61.97 \n", + "\n", + " Occupancy Rate in 2016 Occupancy Rate in 2017 \\\n", + "0 55.20 57.10 \n", + "1 46.40 49.80 \n", + "2 61.80 62.07 \n", + "3 47.70 50.20 \n", + "4 65.72 67.07 \n", + "\n", + " Development Occupancy Rates 2015-2017 ... more than 250 2015 \\\n", + "0 8.40 ... 324 \n", + "1 6.80 ... 30 \n", + "2 1.84 ... 242 \n", + "3 3.40 ... 196 \n", + "4 5.10 ... 679 \n", + "\n", + " more than 250 2016 more than 250 2017 Sum Hotels 2017 Sum Hotels 2016 \\\n", + "0 323 331 2110 2158 \n", + "1 30 30 5967 6022 \n", + "2 247 251 32749 33061 \n", + "3 195 194 9772 9987 \n", + "4 678 687 19630 19524 \n", + "\n", + " Sum Hotels 2015 Percent 25-99 2017 Percent less 25 2017 \\\n", + "0 2180 46.7299 20.237 \n", + "1 5992 20.0268 76.8896 \n", + "2 33635 25.9 68.3807 \n", + "3 10111 40.7388 51.2075 \n", + "4 19718 25.8584 61.7015 \n", + "\n", + " Percent 100-249 2017 Percent 250+ 2017 \n", + "0 17.346 15.6872 \n", + "1 2.58086 0.502765 \n", + "2 4.95282 0.766436 \n", + "3 6.06836 1.98526 \n", + "4 8.9404 3.49975 \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_hotel.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "new_hotel.to_excel('new hotel perc.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": 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6148 -> 6148 bytes README.md | 12 +- Tableau Project Tourism.twb | 10492 ----------------------- Tableau Project Tourism.twbx | Bin 0 -> 250800 bytes datasets/.DS_Store | Bin 6148 -> 8196 bytes datasets/Hotel size.xls | Bin 183808 -> 0 bytes datasets/Hotel size_clean.xls | Bin 0 -> 28672 bytes datasets/Occupancy Rate.xlsx | Bin 5969 -> 19894 bytes datasets/tin00180_20190920_164536.xlsx | Bin 0 -> 15288 bytes datasets/tour_cap_nats.tsv.gz | Bin 0 -> 16156 bytes datasets/tour_dem_exac.tsv.gz | Bin 0 -> 370988 bytes 11 files changed, 6 insertions(+), 10498 deletions(-) delete mode 100644 Tableau Project Tourism.twb create mode 100644 Tableau Project Tourism.twbx delete mode 100644 datasets/Hotel size.xls create mode 100644 datasets/Hotel size_clean.xls create mode 100644 datasets/tin00180_20190920_164536.xlsx create mode 100644 datasets/tour_cap_nats.tsv.gz create mode 100644 datasets/tour_dem_exac.tsv.gz diff --git a/.DS_Store b/.DS_Store index c9e3b415860bc127c8bf29e98540077f65639530..3745ccc9602d68b5da8309418aad0f5ed6ef81a4 100644 GIT binary patch delta 117 zcmZoMXffEJ&%*ngfq^0RKNv7DOfF;M8k zW^yK*{A43GaWRGp&z$_^q@4UDps)Y~1LGVZ?Ke4=&6WE;kQD{dw6QRPaWgx|Uw!~s C*C>Mk delta 115 zcmZoMXffEJ&%!&Ifq_xuKNv7DOfF;?sa|ADl| ## Project Description -For this project, we put ourselves on the shoes of a wealthy expanding hotel chain CEOs who need data support to decide the location of a new hotel to be built in Europe. Sleepy Hotel Group owns medium-sized (25-99 rooms) hotels all over the world that offer accomodation in the low-mid price range. -But we are also wearing the shoes of their data team so we went to the Eurostat Tourism Database and gathered the information we needed for our analysis and to create meaningful dashboards for decision making on the new hotel's location. +For this project, we put ourselves on the shoes of a consulting data team who was hired by a wealthy expanding hotel chain. The CEOs need data support to decide the location of a new hotel to be built in Europe. Sleepy Hotel Group owns medium-sized (25-99 rooms) hotels all over the world that offer accomodation in the low-mid price range. +To help them, we went to the Eurostat Tourism Database and gathered the information we needed for our analysis and to create meaningful dashboards for decision making on the new hotel's location. ## Criteria / Questions Criteria to choose the new hotel's location: - Ocupation rate; -- Changes of accomodation prices; -- Existing types of accomodation. +- Spending on hotel accomodation; +- Size of the existing hotels. @@ -41,7 +41,7 @@ All our dataset where gathered on Eurostat database for [Tourism](https://ec.eur ## Workflow Before starting to look for data, we put together a case scenario to narrow our data search. Dealing with the data was a very straightforward process: we found the data in Eurostat and explored the datasets to check if they suited our purposes. -All our data cleaning and manipulation was done on pandas or excel. +All our data cleaning and manipulation was done either on pandas (most of it) or excel. Before getting our hands on Tableau, we sketched some visualizations to be sure that we had all the data needed to create the dashboards. On our tableau workbook, we created our visualizations and built our dashboards and story. Finally, we drew some results and conclusions. @@ -55,4 +55,4 @@ We used Trello to lay out a plan and keep track of all the actions we needed to ## Links [Repository](https://github.com/laurawuerz/Project-Week-6-Tableau) -[Tableau](https://www.canva.com/design/DADlSes2lSw/EQwGN7iok2_0vJNTayvIQA/view?utm_content=DADlSes2lSw&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton) +[Tableau](https://public.tableau.com/views/TableauProjectTourism/StorySleepy?:embed=y&:display_count=yes&publish=yes&:origin=viz_share_link) diff --git a/Tableau Project Tourism.twb b/Tableau Project Tourism.twb deleted file mode 100644 index 70883f5..0000000 --- a/Tableau Project Tourism.twb +++ /dev/null @@ -1,10492 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - F1 - 20 - [F1] - [Occupancy Rate] - F1 - 0 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country] - [Occupancy Rate] - Country - 1 - string - Count - true - - - "WSTR" - - - - Occupancy Rate in 2015 - 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[Final Merge] - Change expenditure 2015-2017 in % - 11 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015] - [Final Merge] - Occupancy Rate in 2015 - 12 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016] - [Final Merge] - Occupancy Rate in 2016 - 13 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017] - [Final Merge] - Occupancy Rate in 2017 - 14 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017] - [Final Merge] - Development Occupancy Rates 2015-2017 - 15 - real - Sum - 15 - true - - "R8" - - - - Less than 25 2015 - 20 - [Less than 25 2015] - [Final Merge] - Less than 25 2015 - 16 - integer - Sum - true - - "I8" - - - - Less than 25 2016 - 20 - [Less than 25 2016] - [Final Merge] - Less than 25 2016 - 17 - integer - Sum - true - - "I8" - - - - Less than 25 2017 - 20 - [Less than 25 2017] - [Final Merge] - Less than 25 2017 - 18 - integer - Sum - true - - "I8" - - - - 25 to 99 2015 - 20 - [25 to 99 2015] - [Final Merge] - 25 to 99 2015 - 19 - integer - Sum - true - - "I8" - - - - 25 to 99 2016 - 20 - [25 to 99 2016] - [Final Merge] - 25 to 99 2016 - 20 - integer - Sum - true - - "I8" - - - - 25 to 99 2017 - 20 - [25 to 99 2017] - [Final Merge] - 25 to 99 2017 - 21 - integer - Sum - true - - "I8" - - - - 100 to 249 2015 - 20 - [100 to 249 2015] - [Final Merge] - 100 to 249 2015 - 22 - integer - Sum - true - - "I8" - - - - 100 to 249 2016 - 20 - [100 to 249 2016] - [Final Merge] - 100 to 249 2016 - 23 - integer - Sum - true - - "I8" - - - - 100 to 249 2017 - 20 - [100 to 249 2017] - [Final Merge] - 100 to 249 2017 - 24 - integer - Sum - true - - "I8" - - - - more than 250 2015 - 20 - [more than 250 2015] - [Final Merge] - more than 250 2015 - 25 - integer - Sum - true - - "I8" - - - - more than 250 2016 - 20 - [more than 250 2016] - [Final Merge] - more than 250 2016 - 26 - integer - Sum - true - - "I8" - - - - more than 250 2017 - 20 - [more than 250 2017] - [Final Merge] - more than 250 2017 - 27 - integer - Sum - true - - "I8" - - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017] - [Final Merge] - Sum Hotels 2017 - 28 - integer - Sum - true - - "I8" - - - - Sum Hotels 2016 - 20 - [Sum Hotels 2016] - [Final Merge] - Sum Hotels 2016 - 29 - integer - Sum - true - - "I8" - - - - Sum Hotels 2015 - 20 - [Sum Hotels 2015] - [Final Merge] - Sum Hotels 2015 - 30 - integer - Sum - true - - "I8" - - - - - 0 - [Final Merge] - - Count - true - - 0 - "A1:Y14:no:A1:Y14:0" - true - 6 - - - - F1 - 20 - [F1 (Sheet12)] - [Occupancy Rate] - F1 - 31 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country (Sheet12)] - [Occupancy Rate] - Country - 32 - string - Count - true - - - "WSTR" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015 (Sheet12)] - [Occupancy Rate] - Occupancy Rate in 2015 - 33 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016 (Sheet12)] - [Occupancy Rate] - Occupancy Rate in 2016 - 34 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017 (Sheet12)] - [Occupancy Rate] - Occupancy Rate in 2017 - 35 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017 (Sheet12)] - [Occupancy Rate] - Development Occupancy Rates 2015-2017 - 36 - real - Sum - 15 - true - - "R8" - - - - - 0 - [Occupancy Rate] - - Count - true - - 0 - "A1:F29:no:A1:F29:0" - true - 6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - F12 - 20 - [F1] - [Extract] - F12 - 0 - Expenditure_night - integer - Sum - 28 - true - - - Country2 - 129 - [Country] - [Extract] - Country2 - 1 - Expenditure_night - string - Count - 28 - true - - - - Expenditure in 20151 - 5 - [Expenditure in 2015] - [Extract] - Expenditure in 20151 - 2 - Expenditure_night - real - Sum - 28 - true - - - Expenditure in 20161 - 5 - [Expenditure in 2016] - [Extract] - Expenditure in 20161 - 3 - Expenditure_night - real - Sum - 28 - true - - - Expenditure in 20171 - 5 - [Expenditure in 2017] - [Extract] - Expenditure in 20171 - 4 - Expenditure_night - real - Sum - 28 - true - - - Change expenditure 2015-2017 in %1 - 5 - [Change expenditure 2015-2017 in %] - [Extract] - Change expenditure 2015-2017 in %1 - 5 - Expenditure_night - real - Sum - 28 - true - - - F1 - 20 - [F1 (Sheet11)] - [Extract] - F1 - 6 - Final Merge - integer - Sum - 14 - true - - - Country - 129 - [Country (Sheet11)] - [Extract] - Country - 7 - Final Merge - string - Count - 14 - true - - - - Expenditure in 2015 - 5 - [Expenditure in 2015 (Sheet11)] - [Extract] - Expenditure in 2015 - 8 - Final Merge - real - Sum - 14 - true - - - Expenditure in 2016 - 5 - [Expenditure in 2016 (Sheet11)] - [Extract] - Expenditure in 2016 - 9 - Final Merge - real - Sum - 14 - true - - - Expenditure in 2017 - 5 - [Expenditure in 2017 (Sheet11)] - [Extract] - Expenditure in 2017 - 10 - Final Merge - real - Sum - 14 - true - - - Change expenditure 2015-2017 in % - 5 - [Change expenditure 2015-2017 in % (Sheet11)] - [Extract] - Change expenditure 2015-2017 in % - 11 - Final Merge - real - Sum - 14 - true - - - Occupancy Rate in 20151 - 5 - [Occupancy Rate in 2015] - [Extract] - Occupancy Rate in 20151 - 12 - Final Merge - real - Sum - 14 - true - - - Occupancy Rate in 20161 - 5 - [Occupancy Rate in 2016] - [Extract] - Occupancy Rate in 20161 - 13 - Final Merge - real - Sum - 14 - true - - - Occupancy Rate in 20171 - 5 - [Occupancy Rate in 2017] - [Extract] - Occupancy Rate in 20171 - 14 - Final Merge - real - Sum - 14 - true - - - Development Occupancy Rates 2015-20171 - 5 - [Development Occupancy Rates 2015-2017] - [Extract] - Development Occupancy Rates 2015-20171 - 15 - Final Merge - real - Sum - 14 - true - - - Less than 25 2015 - 20 - [Less than 25 2015] - [Extract] - Less than 25 2015 - 16 - Final Merge - integer - Sum - 14 - true - - - Less than 25 2016 - 20 - [Less than 25 2016] - [Extract] - Less than 25 2016 - 17 - Final Merge - integer - Sum - 14 - true - - - Less than 25 2017 - 20 - [Less than 25 2017] - [Extract] - Less than 25 2017 - 18 - Final Merge - integer - Sum - 14 - true - - - 25 to 99 2015 - 20 - [25 to 99 2015] - [Extract] - 25 to 99 2015 - 19 - Final Merge - integer - Sum - 14 - true - - - 25 to 99 2016 - 20 - [25 to 99 2016] - [Extract] - 25 to 99 2016 - 20 - Final Merge - integer - Sum - 14 - true - - - 25 to 99 2017 - 20 - [25 to 99 2017] - [Extract] - 25 to 99 2017 - 21 - Final Merge - integer - Sum - 14 - true - - - 100 to 249 2015 - 20 - [100 to 249 2015] - [Extract] - 100 to 249 2015 - 22 - Final Merge - integer - Sum - 14 - true - - - 100 to 249 2016 - 20 - [100 to 249 2016] - [Extract] - 100 to 249 2016 - 23 - Final Merge - integer - Sum - 14 - true - - - 100 to 249 2017 - 20 - [100 to 249 2017] - [Extract] - 100 to 249 2017 - 24 - Final Merge - integer - Sum - 14 - true - - - more than 250 2015 - 20 - [more than 250 2015] - [Extract] - more than 250 2015 - 25 - Final Merge - integer - Sum - 13 - true - - - more than 250 2016 - 20 - [more than 250 2016] - [Extract] - more than 250 2016 - 26 - Final Merge - integer - Sum - 13 - true - - - more than 250 2017 - 20 - [more than 250 2017] - [Extract] - more than 250 2017 - 27 - Final Merge - integer - Sum - 13 - true - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017] - [Extract] - Sum Hotels 2017 - 28 - Final Merge - integer - Sum - 14 - true - - - Sum Hotels 2016 - 20 - [Sum Hotels 2016] - [Extract] - Sum Hotels 2016 - 29 - Final Merge - integer - Sum - 14 - true - - - Sum Hotels 2015 - 20 - [Sum Hotels 2015] - [Extract] - Sum Hotels 2015 - 30 - Final Merge - integer - Sum - 14 - true - - - F11 - 20 - [F1 (Sheet12)] - [Extract] - F11 - 31 - Occupancy Rate - integer - Sum - 29 - true - - - Country1 - 129 - [Country (Sheet12)] - [Extract] - Country1 - 32 - Occupancy Rate - string - Count - 29 - true - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015 (Sheet12)] - [Extract] - Occupancy Rate in 2015 - 33 - Occupancy Rate - real - Sum - 28 - true - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016 (Sheet12)] - [Extract] - Occupancy Rate in 2016 - 34 - Occupancy Rate - real - Sum - 28 - true - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017 (Sheet12)] - [Extract] - Occupancy Rate in 2017 - 35 - Occupancy Rate - real - Sum - 26 - true - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017 (Sheet12)] - [Extract] - Development Occupancy Rates 2015-2017 - 36 - Occupancy Rate - real - Sum - 27 - true - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - F1 - 20 - [F1] - [Sheet1] - F1 - 0 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country] - [Sheet1] - Country - 1 - string - Count - true - - - "WSTR" - - - - Expenditure in 2015 - 5 - [Expenditure in 2015] - [Sheet1] - Expenditure in 2015 - 2 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2016 - 5 - [Expenditure in 2016] - [Sheet1] - Expenditure in 2016 - 3 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2017 - 5 - [Expenditure in 2017] - [Sheet1] - Expenditure in 2017 - 4 - real - Sum - 15 - true - - "R8" - - - - Change expenditure 2015-2017 in % - 5 - [Change expenditure 2015-2017 in %] - [Sheet1] - Change expenditure 2015-2017 in % - 5 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015] - [Sheet1] - Occupancy Rate in 2015 - 6 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016] - [Sheet1] - Occupancy Rate in 2016 - 7 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017] - [Sheet1] - Occupancy Rate in 2017 - 8 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017] - [Sheet1] - Development Occupancy Rates 2015-2017 - 9 - real - Sum - 15 - true - - "R8" - - - - Less than 25 2015 - 20 - [Less than 25 2015] - [Sheet1] - Less than 25 2015 - 10 - integer - Sum - true - - "I8" - - - - Less than 25 2016 - 20 - [Less than 25 2016] - [Sheet1] - Less than 25 2016 - 11 - integer - Sum - true - - "I8" - - - - Less than 25 2017 - 20 - [Less than 25 2017] - [Sheet1] - Less than 25 2017 - 12 - integer - Sum - true - - "I8" - - - - 25 to 99 2015 - 20 - [25 to 99 2015] - [Sheet1] - 25 to 99 2015 - 13 - integer - Sum - true - - "I8" - - - - 25 to 99 2016 - 20 - [25 to 99 2016] - [Sheet1] - 25 to 99 2016 - 14 - integer - Sum - true - - "I8" - - - - 25 to 99 2017 - 20 - [25 to 99 2017] - [Sheet1] - 25 to 99 2017 - 15 - integer - Sum - true - - "I8" - - - - 100 to 249 2015 - 20 - [100 to 249 2015] - [Sheet1] - 100 to 249 2015 - 16 - integer - Sum - true - - "I8" - - - - 100 to 249 2016 - 20 - [100 to 249 2016] - [Sheet1] - 100 to 249 2016 - 17 - integer - Sum - true - - "I8" - - - - 100 to 249 2017 - 20 - [100 to 249 2017] - [Sheet1] - 100 to 249 2017 - 18 - integer - Sum - true - - "I8" - - - - more than 250 2015 - 20 - [more than 250 2015] - [Sheet1] - more than 250 2015 - 19 - integer - Sum - true - - "I8" - - - - more than 250 2016 - 20 - [more than 250 2016] - [Sheet1] - more than 250 2016 - 20 - integer - Sum - true - - "I8" - - - - more than 250 2017 - 20 - [more than 250 2017] - [Sheet1] - more than 250 2017 - 21 - integer - Sum - true - - "I8" - - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017] - [Sheet1] - Sum Hotels 2017 - 22 - integer - Sum - true - - "I8" - - - - Sum Hotels 2016 - 20 - [Sum Hotels 2016] - [Sheet1] - Sum Hotels 2016 - 23 - integer - Sum - true - - "I8" - - - - Sum Hotels 2015 - 20 - [Sum Hotels 2015] - [Sheet1] - Sum Hotels 2015 - 24 - integer - Sum - true - - "I8" - - - - - 0 - [Sheet1] - - Count - true - - 0 - "A1:Y14:no:A1:Y14:0" - true - 6 - - - - F1 - 20 - [F1 (Sheet11)] - [Sheet11] - F1 - 25 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country (Sheet11)] - [Sheet11] - Country - 26 - string - Count - true - - - "WSTR" - - - - Expenditure in 2015 - 5 - [Expenditure in 2015 (Sheet11)] - [Sheet11] - Expenditure in 2015 - 27 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2016 - 5 - [Expenditure in 2016 (Sheet11)] - [Sheet11] - Expenditure in 2016 - 28 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2017 - 5 - [Expenditure in 2017 (Sheet11)] - [Sheet11] - Expenditure in 2017 - 29 - real - Sum - 15 - true - - "R8" - - - - Change expenditure 2015-2017 in % - 5 - [Change expenditure 2015-2017 in % (Sheet11)] - [Sheet11] - Change expenditure 2015-2017 in % - 30 - real - Sum - 15 - true - - "R8" - - - - Unnamed: 0 - 20 - [Unnamed: 0] - [Sheet11] - Unnamed: 0 - 31 - integer - Sum - true - - "I8" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015 (Sheet11)] - [Sheet11] - Occupancy Rate in 2015 - 32 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016 (Sheet11)] - [Sheet11] - Occupancy Rate in 2016 - 33 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017 (Sheet11)] - [Sheet11] - Occupancy Rate in 2017 - 34 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017 (Sheet11)] - [Sheet11] - Development Occupancy Rates 2015-2017 - 35 - real - Sum - 15 - true - - "R8" - - - - Less than 25 2015 - 20 - [Less than 25 2015 (Sheet11)] - [Sheet11] - Less than 25 2015 - 36 - integer - Sum - true - - "I8" - - - - Less than 25 2016 - 20 - [Less than 25 2016 (Sheet11)] - [Sheet11] - Less than 25 2016 - 37 - integer - Sum - true - - "I8" - - - - Less than 25 2017 - 20 - [Less than 25 2017 (Sheet11)] - [Sheet11] - Less than 25 2017 - 38 - integer - Sum - true - - "I8" - - - - 25 to 99 2015 - 20 - [25 to 99 2015 (Sheet11)] - [Sheet11] - 25 to 99 2015 - 39 - integer - Sum - true - - "I8" - - - - 25 to 99 2016 - 20 - [25 to 99 2016 (Sheet11)] - [Sheet11] - 25 to 99 2016 - 40 - integer - Sum - true - - "I8" - - - - 25 to 99 2017 - 20 - [25 to 99 2017 (Sheet11)] - [Sheet11] - 25 to 99 2017 - 41 - integer - Sum - true - - "I8" - - - - 100 to 249 2015 - 20 - [100 to 249 2015 (Sheet11)] - [Sheet11] - 100 to 249 2015 - 42 - integer - Sum - true - - "I8" - - - - 100 to 249 2016 - 20 - [100 to 249 2016 (Sheet11)] - [Sheet11] - 100 to 249 2016 - 43 - integer - Sum - true - - "I8" - - - - 100 to 249 2017 - 20 - [100 to 249 2017 (Sheet11)] - [Sheet11] - 100 to 249 2017 - 44 - integer - Sum - true - - "I8" - - - - more than 250 2015 - 20 - [more than 250 2015 (Sheet11)] - [Sheet11] - more than 250 2015 - 45 - integer - Sum - true - - "I8" - - - - more than 250 2016 - 20 - [more than 250 2016 (Sheet11)] - [Sheet11] - more than 250 2016 - 46 - integer - Sum - true - - "I8" - - - - more than 250 2017 - 20 - [more than 250 2017 (Sheet11)] - [Sheet11] - more than 250 2017 - 47 - integer - Sum - true - - "I8" - - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017 (Sheet11)] - [Sheet11] - Sum Hotels 2017 - 48 - integer - Sum - true - - "I8" - - - - Sum Hotels 2016 - 20 - [Sum Hotels 2016 (Sheet11)] - [Sheet11] - Sum Hotels 2016 - 49 - integer - Sum - true - - "I8" - - - - Sum Hotels 2015 - 20 - [Sum Hotels 2015 (Sheet11)] - [Sheet11] - Sum Hotels 2015 - 50 - integer - Sum - true - - "I8" - - - - Percent 25-99 2017 - 5 - [Percent 25-99 2017] - [Sheet11] - Percent 25-99 2017 - 51 - real - Sum - 15 - true - - "R8" - - - - Percent less 25 2017 - 5 - [Percent less 25 2017] - [Sheet11] - Percent less 25 2017 - 52 - real - Sum - 15 - true - - "R8" - - - - Percent 100-249 2017 - 5 - [Percent 100-249 2017] - [Sheet11] - Percent 100-249 2017 - 53 - real - Sum - 15 - true - - "R8" - - - - Percent 250+ 2017 - 5 - [Percent 250+ 2017] - [Sheet11] - Percent 250+ 2017 - 54 - real - Sum - 15 - true - - "R8" - - - - - 0 - [Sheet11] - - Count - true - - 0 - "A1:AD14:no:A1:AD14:0" - true - 6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - F1 - 20 - [F1] - [Sheet1] - F1 - 0 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country] - [Sheet1] - Country - 1 - string - Count - true - - - "WSTR" - - - - Expenditure in 2015 - 5 - [Expenditure in 2015] - [Sheet1] - Expenditure in 2015 - 2 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2016 - 5 - [Expenditure in 2016] - [Sheet1] - Expenditure in 2016 - 3 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2017 - 5 - [Expenditure in 2017] - [Sheet1] - Expenditure in 2017 - 4 - real - Sum - 15 - true - - "R8" - - - - Change expenditure 2015-2017 in % - 5 - [Change expenditure 2015-2017 in %] - [Sheet1] - Change expenditure 2015-2017 in % - 5 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015] - [Sheet1] - Occupancy Rate in 2015 - 6 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016] - [Sheet1] - Occupancy Rate in 2016 - 7 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017] - [Sheet1] - Occupancy Rate in 2017 - 8 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017] - [Sheet1] - Development Occupancy Rates 2015-2017 - 9 - real - Sum - 15 - true - - "R8" - - - - Less than 25 2015 - 20 - [Less than 25 2015] - [Sheet1] - Less than 25 2015 - 10 - integer - Sum - true - - "I8" - - - - Less than 25 2016 - 20 - [Less than 25 2016] - [Sheet1] - Less than 25 2016 - 11 - integer - Sum - true - - "I8" - - - - Less than 25 2017 - 20 - [Less than 25 2017] - [Sheet1] - Less than 25 2017 - 12 - integer - Sum - true - - "I8" - - - - 25 to 99 2015 - 20 - [25 to 99 2015] - [Sheet1] - 25 to 99 2015 - 13 - integer - Sum - true - - "I8" - - - - 25 to 99 2016 - 20 - [25 to 99 2016] - [Sheet1] - 25 to 99 2016 - 14 - integer - Sum - true - - "I8" - - - - 25 to 99 2017 - 20 - [25 to 99 2017] - [Sheet1] - 25 to 99 2017 - 15 - integer - Sum - true - - "I8" - - - - 100 to 249 2015 - 20 - [100 to 249 2015] - [Sheet1] - 100 to 249 2015 - 16 - integer - Sum - true - - "I8" - - - - 100 to 249 2016 - 20 - [100 to 249 2016] - [Sheet1] - 100 to 249 2016 - 17 - integer - Sum - true - - "I8" - - - - 100 to 249 2017 - 20 - [100 to 249 2017] - [Sheet1] - 100 to 249 2017 - 18 - integer - Sum - true - - "I8" - - - - more than 250 2015 - 20 - [more than 250 2015] - [Sheet1] - more than 250 2015 - 19 - integer - Sum - true - - "I8" - - - - more than 250 2016 - 20 - [more than 250 2016] - [Sheet1] - more than 250 2016 - 20 - integer - Sum - true - - "I8" - - - - more than 250 2017 - 20 - [more than 250 2017] - [Sheet1] - more than 250 2017 - 21 - integer - Sum - true - - "I8" - - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017] - [Sheet1] - Sum Hotels 2017 - 22 - integer - Sum - true - - "I8" - - - - Sum Hotels 2016 - 20 - [Sum Hotels 2016] - [Sheet1] - Sum Hotels 2016 - 23 - integer - Sum - true - - "I8" - - - - Sum Hotels 2015 - 20 - [Sum Hotels 2015] - [Sheet1] - Sum Hotels 2015 - 24 - integer - Sum - true - - "I8" - - - - - 0 - [Sheet1] - - Count - true - - 0 - "A1:Y14:no:A1:Y14:0" - true - 6 - - - - F1 - 20 - [F1 (Sheet11)] - [Sheet11] - F1 - 25 - integer - Sum - true - - "I8" - - - - Country - 130 - [Country (Sheet11)] - [Sheet11] - Country - 26 - string - Count - true - - - "WSTR" - - - - Expenditure in 2015 - 5 - [Expenditure in 2015 (Sheet11)] - [Sheet11] - Expenditure in 2015 - 27 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2016 - 5 - [Expenditure in 2016 (Sheet11)] - [Sheet11] - Expenditure in 2016 - 28 - real - Sum - 15 - true - - "R8" - - - - Expenditure in 2017 - 5 - [Expenditure in 2017 (Sheet11)] - [Sheet11] - Expenditure in 2017 - 29 - real - Sum - 15 - true - - "R8" - - - - Change expenditure 2015-2017 in % - 5 - [Change expenditure 2015-2017 in % (Sheet11)] - [Sheet11] - Change expenditure 2015-2017 in % - 30 - real - Sum - 15 - true - - "R8" - - - - Unnamed: 0 - 20 - [Unnamed: 0] - [Sheet11] - Unnamed: 0 - 31 - integer - Sum - true - - "I8" - - - - Occupancy Rate in 2015 - 5 - [Occupancy Rate in 2015 (Sheet11)] - [Sheet11] - Occupancy Rate in 2015 - 32 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2016 - 5 - [Occupancy Rate in 2016 (Sheet11)] - [Sheet11] - Occupancy Rate in 2016 - 33 - real - Sum - 15 - true - - "R8" - - - - Occupancy Rate in 2017 - 5 - [Occupancy Rate in 2017 (Sheet11)] - [Sheet11] - Occupancy Rate in 2017 - 34 - real - Sum - 15 - true - - "R8" - - - - Development Occupancy Rates 2015-2017 - 5 - [Development Occupancy Rates 2015-2017 (Sheet11)] - [Sheet11] - Development Occupancy Rates 2015-2017 - 35 - real - Sum - 15 - true - - "R8" - - - - Less than 25 2015 - 20 - [Less than 25 2015 (Sheet11)] - [Sheet11] - Less than 25 2015 - 36 - integer - Sum - true - - "I8" - - - - Less than 25 2016 - 20 - [Less than 25 2016 (Sheet11)] - [Sheet11] - Less than 25 2016 - 37 - integer - Sum - true - - "I8" - - - - Less than 25 2017 - 20 - [Less than 25 2017 (Sheet11)] - [Sheet11] - Less than 25 2017 - 38 - integer - Sum - true - - "I8" - - - - 25 to 99 2015 - 20 - [25 to 99 2015 (Sheet11)] - [Sheet11] - 25 to 99 2015 - 39 - integer - Sum - true - - "I8" - - - - 25 to 99 2016 - 20 - [25 to 99 2016 (Sheet11)] - [Sheet11] - 25 to 99 2016 - 40 - integer - Sum - true - - "I8" - - - - 25 to 99 2017 - 20 - [25 to 99 2017 (Sheet11)] - [Sheet11] - 25 to 99 2017 - 41 - integer - Sum - true - - "I8" - - - - 100 to 249 2015 - 20 - [100 to 249 2015 (Sheet11)] - [Sheet11] - 100 to 249 2015 - 42 - integer - Sum - true - - "I8" - - - - 100 to 249 2016 - 20 - [100 to 249 2016 (Sheet11)] - [Sheet11] - 100 to 249 2016 - 43 - integer - Sum - true - - "I8" - - - - 100 to 249 2017 - 20 - [100 to 249 2017 (Sheet11)] - [Sheet11] - 100 to 249 2017 - 44 - integer - Sum - true - - "I8" - - - - more than 250 2015 - 20 - [more than 250 2015 (Sheet11)] - [Sheet11] - more than 250 2015 - 45 - integer - Sum - true - - "I8" - - - - more than 250 2016 - 20 - [more than 250 2016 (Sheet11)] - [Sheet11] - more than 250 2016 - 46 - integer - Sum - true - - "I8" - - - - more than 250 2017 - 20 - [more than 250 2017 (Sheet11)] - [Sheet11] - more than 250 2017 - 47 - integer - Sum - true - - "I8" - - - - Sum Hotels 2017 - 20 - [Sum Hotels 2017 (Sheet11)] - [Sheet11] - Sum Hotels 2017 - 48 - integer - Sum - true - - "I8" - - - - Sum Hotels 2016 - 20 - 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- - - - - - - - - - - - - Where in Europe should - Sleepy Hotel Group - open a new hotel? - Æ - Sleepy Hotel Group is a medium sized hotel chain that offers low to mid price range accomodation worldwide. - Æ - - - - - - - - - - - - - - - - - - - - - Where in Europe should - Sleepy Hotel Group - open a new hotel? - Æ - Sleepy Hotel Group is a medium sized hotel chain that offers low to mid price range accomodation worldwide. - Æ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Low Increase High Increase - - - - - 1. Occupancy Growth - Æ - In which countries did hotel occupancy increase the most? - Æ - - - - - - - - - - - - 1. Occupancy Growth - Æ - In which countries did hotel occupancy increase the most? - Æ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Low Increase High Increase - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 2. Spending on Hotel Accomodation - Æ - In which countries did spending increase the most? - Æ - - - - - We filtered out those countries with the lowest spending growth rate and now look at the absolute spending in the remaining 4 countries. - - - - - - - Low Spending High Spending - - - - - Absolute Spending on Hotel Accomodation in 2017 - - - - - - - Lowest absolute spending in Bulgaria, so we will leave that country out of our further decision making process. - - - - - - - - - - - - 2. Spending on Hotel Accomodation - Æ - In which countries did spending increase the most? - Æ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - We filtered out those countries with the lowest spending growth rate and now look at the absolute spending in the remaining 4 countries. - - - - - - - - - - - - Absolute Spending on Hotel Accomodation in 2017 - - - - - - - - - - - - - - - - - - - - - Low Spending High Spending - - - - - - - - - - - - - - - - - - - - - Lowest absolute spending in Bulgaria, so we will leave that country out of our further decision making process. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 3. Hotel Occupancy and Size - Æ - Which country suits our hotel size best? - Æ - After removing the countries with the least growing spending rates and absolute spending we take a closer look into the total number of Hotels and their occupancy in the remaining countries. - - - - - - - - - - - - - - 3. Hotel Occupancy and Size - Æ - Which country suits our hotel size best? - Æ - After removing the countries with the least growing spending rates and absolute spending we take a closer look into the total number of Hotels and their occupancy in the remaining countries. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - And the winner is Cyprus! - Æ - - - - - - - - - - - - - - - - - - - - - - And the winner is Cyprus! - Æ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - <formatted-text> - <run bold='true' fontcolor='#0f224b'>Business Case Sleepy Hotel Group</run> - </formatted-text> - - -