├── LICENSE ├── README.md ├── datasets ├── 2014_world_gdp_with_codes.csv ├── Credit.csv ├── Data.csv ├── FEDFUNDS.csv ├── IMDb 1979 │ └── Ready_to_analysis_1979.csv ├── IMDb 2017 │ ├── Combinded_raw_file_2017.csv │ └── movie_list_2017.csv ├── UN │ ├── SYB58_T25 Index of industrial production.csv │ ├── SYB61_T04_International Migrants and Refugees.csv │ ├── SYB61_T19_Consumer Price Index.csv │ ├── SYB61_T21_Total Imports, Exports and Balance of Trade.csv │ ├── SYB62_T09_201905_Public Expenditure on Education.csv │ ├── SYB62_T12_201904_Intentional homicides and Other Crimes.csv │ ├── SYB62_T13_201904_GDP and GDP Per Capita.csv │ ├── SYB62_T29_201904_Internet Usage.csv │ ├── SYB62_T30_201904_Tourist-Visitors Arrival and Expenditure.csv │ └── trash │ │ ├── SYB60_T03_Population Growth, Fertility and Mortality Indicators.csv │ │ ├── SYB61_T02_Population, Surface Area and Density.csv │ │ ├── SYB61_T03_Population Growth Rates in Urban areas and Capital cities.csv │ │ ├── SYB61_T15_Balance of Payments.csv │ │ ├── SYB61_T16_Exchange Rates.csv │ │ ├── SYB61_T20_Agricultural Production Indices.csv │ │ ├── SYB61_T22_Major Trading Partners.csv │ │ ├── SYB61_T28_Patents.csv │ │ ├── SYB62_T05_201905_Seats held by Women in Parliament.csv │ │ ├── SYB62_T17_201904_Labour Force and Unemployment.csv │ │ ├── SYB62_T23_201904_Production, Trade and Supply of Energy.csv │ │ └── SYB62_T26_201904_Research and Development Staff.csv ├── UN_cleaned.csv ├── countries_2.csv ├── diabetes.csv ├── disease.csv ├── earthquake.csv ├── flowdata.csv ├── gapminderDataFiveYear.csv ├── homeprices.csv ├── homeprices2.csv ├── house_rental_data.csv ├── international-airline-passengers.csv ├── maternal_health.csv ├── migrants.csv ├── monthly-milk-production-pounds.csv ├── significant-earthquakes.csv ├── su5m.csv ├── tennis.csv ├── tesla-stock-price.csv ├── test.csv └── train.csv └── notebooks ├── 1 - Python intro.ipynb ├── 10-stat.ipynb ├── 11-correlations.ipynb ├── 12-hypothesis-test_error-analysis.ipynb ├── 13-deeper_into_stat.ipynb ├── 14- linear_regression.ipynb ├── 15-sklearn-ML.ipynb ├── 16- Clustering.ipynb ├── 17- Anomaly Detection.ipynb ├── 2 - Python intro - missing topics.ipynb ├── 3 - Python Exercise .ipynb ├── 4 - Numpy.ipynb ├── 5 - Numpy Exersice.ipynb ├── 6 - Pandas.ipynb ├── 7 - Pandas, Real case.ipynb ├── 8 - Visualization.ipynb ├── 9 - Real Case - Pandas and visualization.ipynb ├── example_table-analysis.ipynb └── exercise.txt /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Gamein-SUT 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DataMining-With-Python---Gamein2020 2 | 3 | ## **فایلهای مربوط به دوره‌ی آموزشی داده کاوی با پایتون:** 4 | 5 | فایل همه ی کدهای مورد استفاده در طول این دوره در پوشه با نام نوت بوک ها قرار دارد. 6 | 7 | همچنین همه ی دیتاست های مورد استفاده در بخش های مختلف این دوره در پوشه دیتاست ها در دسترس شماست. 8 | 9 | **حتما از این دیتاست های متنوع و بزرگ برای تمرین بیشتر و انجام تحلیل های متنوع تر روی داده ها استفاده کنید.** 10 | 11 | 12 | همچنین ویدئوهای مربوط به این دوره آموزشی از [ایـــنجـــا](https://www.aparat.com/playlist/581395) در دسترس است. 13 | 14 | 15 | ## سرفصل مباحث مورد بحث در این دوره: 16 | 17 | - مبانی برنامه نویسی به زبان پایتون 18 | - کتابخانه ی Numpy 19 | - کتابخانه ی Pandas 20 | - مصورسازی داده ها - Data visualization 21 | - تصمیم گیری داده محور - Data driven decision making 22 | - پیش بینی با استفاده از داده - Data trends and Forecasting 23 | - مبانی یادگیری ماشین - Machine Learning 24 | 25 | ## اساتید این دوره: 26 | 27 | - آقای علیرضا وفائی صدر 28 | - آقای مجید بهرامی 29 | 30 | -------------------------------------------------------------------------------- /datasets/2014_world_gdp_with_codes.csv: -------------------------------------------------------------------------------- 1 | COUNTRY,GDP (BILLIONS),CODE 2 | Afghanistan,21.71,AFG 3 | Albania,13.40,ALB 4 | Algeria,227.80,DZA 5 | American Samoa,0.75,ASM 6 | Andorra,4.80,AND 7 | Angola,131.40,AGO 8 | Anguilla,0.18,AIA 9 | Antigua and Barbuda,1.24,ATG 10 | Argentina,536.20,ARG 11 | Armenia,10.88,ARM 12 | Aruba,2.52,ABW 13 | Australia,1483.00,AUS 14 | Austria,436.10,AUT 15 | Azerbaijan,77.91,AZE 16 | "Bahamas, The",8.65,BHM 17 | Bahrain,34.05,BHR 18 | Bangladesh,186.60,BGD 19 | Barbados,4.28,BRB 20 | Belarus,75.25,BLR 21 | Belgium,527.80,BEL 22 | Belize,1.67,BLZ 23 | Benin,9.24,BEN 24 | Bermuda,5.20,BMU 25 | Bhutan,2.09,BTN 26 | Bolivia,34.08,BOL 27 | Bosnia and Herzegovina,19.55,BIH 28 | Botswana,16.30,BWA 29 | Brazil,2244.00,BRA 30 | British Virgin Islands,1.10,VGB 31 | Brunei,17.43,BRN 32 | Bulgaria,55.08,BGR 33 | Burkina Faso,13.38,BFA 34 | Burma,65.29,MMR 35 | Burundi,3.04,BDI 36 | Cabo Verde,1.98,CPV 37 | Cambodia,16.90,KHM 38 | Cameroon,32.16,CMR 39 | Canada,1794.00,CAN 40 | Cayman Islands,2.25,CYM 41 | Central African Republic,1.73,CAF 42 | Chad,15.84,TCD 43 | Chile,264.10,CHL 44 | China,10360.00,CHN 45 | Colombia,400.10,COL 46 | Comoros,0.72,COM 47 | "Congo, Democratic Republic of the",32.67,COD 48 | "Congo, Republic of the",14.11,COG 49 | Cook Islands,0.18,COK 50 | Costa Rica,50.46,CRI 51 | Cote d'Ivoire,33.96,CIV 52 | Croatia,57.18,HRV 53 | Cuba,77.15,CUB 54 | Curacao,5.60,CUW 55 | Cyprus,21.34,CYP 56 | Czech Republic,205.60,CZE 57 | Denmark,347.20,DNK 58 | Djibouti,1.58,DJI 59 | Dominica,0.51,DMA 60 | Dominican Republic,64.05,DOM 61 | Ecuador,100.50,ECU 62 | Egypt,284.90,EGY 63 | El Salvador,25.14,SLV 64 | Equatorial Guinea,15.40,GNQ 65 | Eritrea,3.87,ERI 66 | Estonia,26.36,EST 67 | Ethiopia,49.86,ETH 68 | Falkland Islands (Islas Malvinas),0.16,FLK 69 | Faroe Islands,2.32,FRO 70 | Fiji,4.17,FJI 71 | Finland,276.30,FIN 72 | France,2902.00,FRA 73 | French Polynesia,7.15,PYF 74 | Gabon,20.68,GAB 75 | "Gambia, The",0.92,GMB 76 | Georgia,16.13,GEO 77 | Germany,3820.00,DEU 78 | Ghana,35.48,GHA 79 | Gibraltar,1.85,GIB 80 | Greece,246.40,GRC 81 | Greenland,2.16,GRL 82 | Grenada,0.84,GRD 83 | Guam,4.60,GUM 84 | Guatemala,58.30,GTM 85 | Guernsey,2.74,GGY 86 | Guinea-Bissau,1.04,GNB 87 | Guinea,6.77,GIN 88 | Guyana,3.14,GUY 89 | Haiti,8.92,HTI 90 | Honduras,19.37,HND 91 | Hong Kong,292.70,HKG 92 | Hungary,129.70,HUN 93 | Iceland,16.20,ISL 94 | India,2048.00,IND 95 | Indonesia,856.10,IDN 96 | Iran,402.70,IRN 97 | Iraq,232.20,IRQ 98 | Ireland,245.80,IRL 99 | Isle of Man,4.08,IMN 100 | Israel,305.00,ISR 101 | Italy,2129.00,ITA 102 | Jamaica,13.92,JAM 103 | Japan,4770.00,JPN 104 | Jersey,5.77,JEY 105 | Jordan,36.55,JOR 106 | Kazakhstan,225.60,KAZ 107 | Kenya,62.72,KEN 108 | Kiribati,0.16,KIR 109 | "Korea, North",28.00,PRK 110 | "Korea, South",1410.00,KOR 111 | Kosovo,5.99,KSV 112 | Kuwait,179.30,KWT 113 | Kyrgyzstan,7.65,KGZ 114 | Laos,11.71,LAO 115 | Latvia,32.82,LVA 116 | Lebanon,47.50,LBN 117 | Lesotho,2.46,LSO 118 | Liberia,2.07,LBR 119 | Libya,49.34,LBY 120 | Liechtenstein,5.11,LIE 121 | Lithuania,48.72,LTU 122 | Luxembourg,63.93,LUX 123 | Macau,51.68,MAC 124 | Macedonia,10.92,MKD 125 | Madagascar,11.19,MDG 126 | Malawi,4.41,MWI 127 | Malaysia,336.90,MYS 128 | Maldives,2.41,MDV 129 | Mali,12.04,MLI 130 | Malta,10.57,MLT 131 | Marshall Islands,0.18,MHL 132 | Mauritania,4.29,MRT 133 | Mauritius,12.72,MUS 134 | Mexico,1296.00,MEX 135 | "Micronesia, Federated States of",0.34,FSM 136 | Moldova,7.74,MDA 137 | Monaco,6.06,MCO 138 | Mongolia,11.73,MNG 139 | Montenegro,4.66,MNE 140 | Morocco,112.60,MAR 141 | Mozambique,16.59,MOZ 142 | Namibia,13.11,NAM 143 | Nepal,19.64,NPL 144 | Netherlands,880.40,NLD 145 | New Caledonia,11.10,NCL 146 | New Zealand,201.00,NZL 147 | Nicaragua,11.85,NIC 148 | Nigeria,594.30,NGA 149 | Niger,8.29,NER 150 | Niue,0.01,NIU 151 | Northern Mariana Islands,1.23,MNP 152 | Norway,511.60,NOR 153 | Oman,80.54,OMN 154 | Pakistan,237.50,PAK 155 | Palau,0.65,PLW 156 | Panama,44.69,PAN 157 | Papua New Guinea,16.10,PNG 158 | Paraguay,31.30,PRY 159 | Peru,208.20,PER 160 | Philippines,284.60,PHL 161 | Poland,552.20,POL 162 | Portugal,228.20,PRT 163 | Puerto Rico,93.52,PRI 164 | Qatar,212.00,QAT 165 | Romania,199.00,ROU 166 | Russia,2057.00,RUS 167 | Rwanda,8.00,RWA 168 | Saint Kitts and Nevis,0.81,KNA 169 | Saint Lucia,1.35,LCA 170 | Saint Martin,0.56,MAF 171 | Saint Pierre and Miquelon,0.22,SPM 172 | Saint Vincent and the Grenadines,0.75,VCT 173 | Samoa,0.83,WSM 174 | San Marino,1.86,SMR 175 | Sao Tome and Principe,0.36,STP 176 | Saudi Arabia,777.90,SAU 177 | Senegal,15.88,SEN 178 | Serbia,42.65,SRB 179 | Seychelles,1.47,SYC 180 | Sierra Leone,5.41,SLE 181 | Singapore,307.90,SGP 182 | Sint Maarten,304.10,SXM 183 | Slovakia,99.75,SVK 184 | Slovenia,49.93,SVN 185 | Solomon Islands,1.16,SLB 186 | Somalia,2.37,SOM 187 | South Africa,341.20,ZAF 188 | South Sudan,11.89,SSD 189 | Spain,1400.00,ESP 190 | Sri Lanka,71.57,LKA 191 | Sudan,70.03,SDN 192 | Suriname,5.27,SUR 193 | Swaziland,3.84,SWZ 194 | Sweden,559.10,SWE 195 | Switzerland,679.00,CHE 196 | Syria,64.70,SYR 197 | Taiwan,529.50,TWN 198 | Tajikistan,9.16,TJK 199 | Tanzania,36.62,TZA 200 | Thailand,373.80,THA 201 | Timor-Leste,4.51,TLS 202 | Togo,4.84,TGO 203 | Tonga,0.49,TON 204 | Trinidad and Tobago,29.63,TTO 205 | Tunisia,49.12,TUN 206 | Turkey,813.30,TUR 207 | Turkmenistan,43.50,TKM 208 | Tuvalu,0.04,TUV 209 | Uganda,26.09,UGA 210 | Ukraine,134.90,UKR 211 | United Arab Emirates,416.40,ARE 212 | United Kingdom,2848.00,GBR 213 | United States,17420.00,USA 214 | Uruguay,55.60,URY 215 | Uzbekistan,63.08,UZB 216 | Vanuatu,0.82,VUT 217 | Venezuela,209.20,VEN 218 | Vietnam,187.80,VNM 219 | Virgin Islands,5.08,VGB 220 | West Bank,6.64,WBG 221 | Yemen,45.45,YEM 222 | Zambia,25.61,ZMB 223 | Zimbabwe,13.74,ZWE 224 | -------------------------------------------------------------------------------- /datasets/Data.csv: -------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | France,44,72000,No 3 | Spain,27,48000,Yes 4 | Germany,30,54000,No 5 | Spain,38,61000,No 6 | Germany,40,,Yes 7 | France,35,58000,Yes 8 | Spain,,52000,No 9 | France,48,79000,Yes 10 | Germany,50,83000,No 11 | France,37,67000,Yes -------------------------------------------------------------------------------- /datasets/FEDFUNDS.csv: -------------------------------------------------------------------------------- 1 | DATE,FEDFUNDS 2 | 1954-07-01,0.80 3 | 1954-08-01,1.22 4 | 1954-09-01,1.06 5 | 1954-10-01,0.85 6 | 1954-11-01,0.83 7 | 1954-12-01,1.28 8 | 1955-01-01,1.39 9 | 1955-02-01,1.29 10 | 1955-03-01,1.35 11 | 1955-04-01,1.43 12 | 1955-05-01,1.43 13 | 1955-06-01,1.64 14 | 1955-07-01,1.68 15 | 1955-08-01,1.96 16 | 1955-09-01,2.18 17 | 1955-10-01,2.24 18 | 1955-11-01,2.35 19 | 1955-12-01,2.48 20 | 1956-01-01,2.45 21 | 1956-02-01,2.50 22 | 1956-03-01,2.50 23 | 1956-04-01,2.62 24 | 1956-05-01,2.75 25 | 1956-06-01,2.71 26 | 1956-07-01,2.75 27 | 1956-08-01,2.73 28 | 1956-09-01,2.95 29 | 1956-10-01,2.96 30 | 1956-11-01,2.88 31 | 1956-12-01,2.94 32 | 1957-01-01,2.84 33 | 1957-02-01,3.00 34 | 1957-03-01,2.96 35 | 1957-04-01,3.00 36 | 1957-05-01,3.00 37 | 1957-06-01,3.00 38 | 1957-07-01,2.99 39 | 1957-08-01,3.24 40 | 1957-09-01,3.47 41 | 1957-10-01,3.50 42 | 1957-11-01,3.28 43 | 1957-12-01,2.98 44 | 1958-01-01,2.72 45 | 1958-02-01,1.67 46 | 1958-03-01,1.20 47 | 1958-04-01,1.26 48 | 1958-05-01,0.63 49 | 1958-06-01,0.93 50 | 1958-07-01,0.68 51 | 1958-08-01,1.53 52 | 1958-09-01,1.76 53 | 1958-10-01,1.80 54 | 1958-11-01,2.27 55 | 1958-12-01,2.42 56 | 1959-01-01,2.48 57 | 1959-02-01,2.43 58 | 1959-03-01,2.80 59 | 1959-04-01,2.96 60 | 1959-05-01,2.90 61 | 1959-06-01,3.39 62 | 1959-07-01,3.47 63 | 1959-08-01,3.50 64 | 1959-09-01,3.76 65 | 1959-10-01,3.98 66 | 1959-11-01,4.00 67 | 1959-12-01,3.99 68 | 1960-01-01,3.99 69 | 1960-02-01,3.97 70 | 1960-03-01,3.84 71 | 1960-04-01,3.92 72 | 1960-05-01,3.85 73 | 1960-06-01,3.32 74 | 1960-07-01,3.23 75 | 1960-08-01,2.98 76 | 1960-09-01,2.60 77 | 1960-10-01,2.47 78 | 1960-11-01,2.44 79 | 1960-12-01,1.98 80 | 1961-01-01,1.45 81 | 1961-02-01,2.54 82 | 1961-03-01,2.02 83 | 1961-04-01,1.49 84 | 1961-05-01,1.98 85 | 1961-06-01,1.73 86 | 1961-07-01,1.17 87 | 1961-08-01,2.00 88 | 1961-09-01,1.88 89 | 1961-10-01,2.26 90 | 1961-11-01,2.61 91 | 1961-12-01,2.33 92 | 1962-01-01,2.15 93 | 1962-02-01,2.37 94 | 1962-03-01,2.85 95 | 1962-04-01,2.78 96 | 1962-05-01,2.36 97 | 1962-06-01,2.68 98 | 1962-07-01,2.71 99 | 1962-08-01,2.93 100 | 1962-09-01,2.90 101 | 1962-10-01,2.90 102 | 1962-11-01,2.94 103 | 1962-12-01,2.93 104 | 1963-01-01,2.92 105 | 1963-02-01,3.00 106 | 1963-03-01,2.98 107 | 1963-04-01,2.90 108 | 1963-05-01,3.00 109 | 1963-06-01,2.99 110 | 1963-07-01,3.02 111 | 1963-08-01,3.49 112 | 1963-09-01,3.48 113 | 1963-10-01,3.50 114 | 1963-11-01,3.48 115 | 1963-12-01,3.38 116 | 1964-01-01,3.48 117 | 1964-02-01,3.48 118 | 1964-03-01,3.43 119 | 1964-04-01,3.47 120 | 1964-05-01,3.50 121 | 1964-06-01,3.50 122 | 1964-07-01,3.42 123 | 1964-08-01,3.50 124 | 1964-09-01,3.45 125 | 1964-10-01,3.36 126 | 1964-11-01,3.52 127 | 1964-12-01,3.85 128 | 1965-01-01,3.90 129 | 1965-02-01,3.98 130 | 1965-03-01,4.04 131 | 1965-04-01,4.09 132 | 1965-05-01,4.10 133 | 1965-06-01,4.04 134 | 1965-07-01,4.09 135 | 1965-08-01,4.12 136 | 1965-09-01,4.01 137 | 1965-10-01,4.08 138 | 1965-11-01,4.10 139 | 1965-12-01,4.32 140 | 1966-01-01,4.42 141 | 1966-02-01,4.60 142 | 1966-03-01,4.65 143 | 1966-04-01,4.67 144 | 1966-05-01,4.90 145 | 1966-06-01,5.17 146 | 1966-07-01,5.30 147 | 1966-08-01,5.53 148 | 1966-09-01,5.40 149 | 1966-10-01,5.53 150 | 1966-11-01,5.76 151 | 1966-12-01,5.40 152 | 1967-01-01,4.94 153 | 1967-02-01,5.00 154 | 1967-03-01,4.53 155 | 1967-04-01,4.05 156 | 1967-05-01,3.94 157 | 1967-06-01,3.98 158 | 1967-07-01,3.79 159 | 1967-08-01,3.90 160 | 1967-09-01,3.99 161 | 1967-10-01,3.88 162 | 1967-11-01,4.13 163 | 1967-12-01,4.51 164 | 1968-01-01,4.60 165 | 1968-02-01,4.71 166 | 1968-03-01,5.05 167 | 1968-04-01,5.76 168 | 1968-05-01,6.11 169 | 1968-06-01,6.07 170 | 1968-07-01,6.02 171 | 1968-08-01,6.03 172 | 1968-09-01,5.78 173 | 1968-10-01,5.91 174 | 1968-11-01,5.82 175 | 1968-12-01,6.02 176 | 1969-01-01,6.30 177 | 1969-02-01,6.61 178 | 1969-03-01,6.79 179 | 1969-04-01,7.41 180 | 1969-05-01,8.67 181 | 1969-06-01,8.90 182 | 1969-07-01,8.61 183 | 1969-08-01,9.19 184 | 1969-09-01,9.15 185 | 1969-10-01,9.00 186 | 1969-11-01,8.85 187 | 1969-12-01,8.97 188 | 1970-01-01,8.98 189 | 1970-02-01,8.98 190 | 1970-03-01,7.76 191 | 1970-04-01,8.10 192 | 1970-05-01,7.94 193 | 1970-06-01,7.60 194 | 1970-07-01,7.21 195 | 1970-08-01,6.61 196 | 1970-09-01,6.29 197 | 1970-10-01,6.20 198 | 1970-11-01,5.60 199 | 1970-12-01,4.90 200 | 1971-01-01,4.14 201 | 1971-02-01,3.72 202 | 1971-03-01,3.71 203 | 1971-04-01,4.15 204 | 1971-05-01,4.63 205 | 1971-06-01,4.91 206 | 1971-07-01,5.31 207 | 1971-08-01,5.56 208 | 1971-09-01,5.55 209 | 1971-10-01,5.20 210 | 1971-11-01,4.91 211 | 1971-12-01,4.14 212 | 1972-01-01,3.50 213 | 1972-02-01,3.29 214 | 1972-03-01,3.83 215 | 1972-04-01,4.17 216 | 1972-05-01,4.27 217 | 1972-06-01,4.46 218 | 1972-07-01,4.55 219 | 1972-08-01,4.80 220 | 1972-09-01,4.87 221 | 1972-10-01,5.04 222 | 1972-11-01,5.06 223 | 1972-12-01,5.33 224 | 1973-01-01,5.94 225 | 1973-02-01,6.58 226 | 1973-03-01,7.09 227 | 1973-04-01,7.12 228 | 1973-05-01,7.84 229 | 1973-06-01,8.49 230 | 1973-07-01,10.40 231 | 1973-08-01,10.50 232 | 1973-09-01,10.78 233 | 1973-10-01,10.01 234 | 1973-11-01,10.03 235 | 1973-12-01,9.95 236 | 1974-01-01,9.65 237 | 1974-02-01,8.97 238 | 1974-03-01,9.35 239 | 1974-04-01,10.51 240 | 1974-05-01,11.31 241 | 1974-06-01,11.93 242 | 1974-07-01,12.92 243 | 1974-08-01,12.01 244 | 1974-09-01,11.34 245 | 1974-10-01,10.06 246 | 1974-11-01,9.45 247 | 1974-12-01,8.53 248 | 1975-01-01,7.13 249 | 1975-02-01,6.24 250 | 1975-03-01,5.54 251 | 1975-04-01,5.49 252 | 1975-05-01,5.22 253 | 1975-06-01,5.55 254 | 1975-07-01,6.10 255 | 1975-08-01,6.14 256 | 1975-09-01,6.24 257 | 1975-10-01,5.82 258 | 1975-11-01,5.22 259 | 1975-12-01,5.20 260 | 1976-01-01,4.87 261 | 1976-02-01,4.77 262 | 1976-03-01,4.84 263 | 1976-04-01,4.82 264 | 1976-05-01,5.29 265 | 1976-06-01,5.48 266 | 1976-07-01,5.31 267 | 1976-08-01,5.29 268 | 1976-09-01,5.25 269 | 1976-10-01,5.02 270 | 1976-11-01,4.95 271 | 1976-12-01,4.65 272 | 1977-01-01,4.61 273 | 1977-02-01,4.68 274 | 1977-03-01,4.69 275 | 1977-04-01,4.73 276 | 1977-05-01,5.35 277 | 1977-06-01,5.39 278 | 1977-07-01,5.42 279 | 1977-08-01,5.90 280 | 1977-09-01,6.14 281 | 1977-10-01,6.47 282 | 1977-11-01,6.51 283 | 1977-12-01,6.56 284 | 1978-01-01,6.70 285 | 1978-02-01,6.78 286 | 1978-03-01,6.79 287 | 1978-04-01,6.89 288 | 1978-05-01,7.36 289 | 1978-06-01,7.60 290 | 1978-07-01,7.81 291 | 1978-08-01,8.04 292 | 1978-09-01,8.45 293 | 1978-10-01,8.96 294 | 1978-11-01,9.76 295 | 1978-12-01,10.03 296 | 1979-01-01,10.07 297 | 1979-02-01,10.06 298 | 1979-03-01,10.09 299 | 1979-04-01,10.01 300 | 1979-05-01,10.24 301 | 1979-06-01,10.29 302 | 1979-07-01,10.47 303 | 1979-08-01,10.94 304 | 1979-09-01,11.43 305 | 1979-10-01,13.77 306 | 1979-11-01,13.18 307 | 1979-12-01,13.78 308 | 1980-01-01,13.82 309 | 1980-02-01,14.13 310 | 1980-03-01,17.19 311 | 1980-04-01,17.61 312 | 1980-05-01,10.98 313 | 1980-06-01,9.47 314 | 1980-07-01,9.03 315 | 1980-08-01,9.61 316 | 1980-09-01,10.87 317 | 1980-10-01,12.81 318 | 1980-11-01,15.85 319 | 1980-12-01,18.90 320 | 1981-01-01,19.08 321 | 1981-02-01,15.93 322 | 1981-03-01,14.70 323 | 1981-04-01,15.72 324 | 1981-05-01,18.52 325 | 1981-06-01,19.10 326 | 1981-07-01,19.04 327 | 1981-08-01,17.82 328 | 1981-09-01,15.87 329 | 1981-10-01,15.08 330 | 1981-11-01,13.31 331 | 1981-12-01,12.37 332 | 1982-01-01,13.22 333 | 1982-02-01,14.78 334 | 1982-03-01,14.68 335 | 1982-04-01,14.94 336 | 1982-05-01,14.45 337 | 1982-06-01,14.15 338 | 1982-07-01,12.59 339 | 1982-08-01,10.12 340 | 1982-09-01,10.31 341 | 1982-10-01,9.71 342 | 1982-11-01,9.20 343 | 1982-12-01,8.95 344 | 1983-01-01,8.68 345 | 1983-02-01,8.51 346 | 1983-03-01,8.77 347 | 1983-04-01,8.80 348 | 1983-05-01,8.63 349 | 1983-06-01,8.98 350 | 1983-07-01,9.37 351 | 1983-08-01,9.56 352 | 1983-09-01,9.45 353 | 1983-10-01,9.48 354 | 1983-11-01,9.34 355 | 1983-12-01,9.47 356 | 1984-01-01,9.56 357 | 1984-02-01,9.59 358 | 1984-03-01,9.91 359 | 1984-04-01,10.29 360 | 1984-05-01,10.32 361 | 1984-06-01,11.06 362 | 1984-07-01,11.23 363 | 1984-08-01,11.64 364 | 1984-09-01,11.30 365 | 1984-10-01,9.99 366 | 1984-11-01,9.43 367 | 1984-12-01,8.38 368 | 1985-01-01,8.35 369 | 1985-02-01,8.50 370 | 1985-03-01,8.58 371 | 1985-04-01,8.27 372 | 1985-05-01,7.97 373 | 1985-06-01,7.53 374 | 1985-07-01,7.88 375 | 1985-08-01,7.90 376 | 1985-09-01,7.92 377 | 1985-10-01,7.99 378 | 1985-11-01,8.05 379 | 1985-12-01,8.27 380 | 1986-01-01,8.14 381 | 1986-02-01,7.86 382 | 1986-03-01,7.48 383 | 1986-04-01,6.99 384 | 1986-05-01,6.85 385 | 1986-06-01,6.92 386 | 1986-07-01,6.56 387 | 1986-08-01,6.17 388 | 1986-09-01,5.89 389 | 1986-10-01,5.85 390 | 1986-11-01,6.04 391 | 1986-12-01,6.91 392 | 1987-01-01,6.43 393 | 1987-02-01,6.10 394 | 1987-03-01,6.13 395 | 1987-04-01,6.37 396 | 1987-05-01,6.85 397 | 1987-06-01,6.73 398 | 1987-07-01,6.58 399 | 1987-08-01,6.73 400 | 1987-09-01,7.22 401 | 1987-10-01,7.29 402 | 1987-11-01,6.69 403 | 1987-12-01,6.77 404 | 1988-01-01,6.83 405 | 1988-02-01,6.58 406 | 1988-03-01,6.58 407 | 1988-04-01,6.87 408 | 1988-05-01,7.09 409 | 1988-06-01,7.51 410 | 1988-07-01,7.75 411 | 1988-08-01,8.01 412 | 1988-09-01,8.19 413 | 1988-10-01,8.30 414 | 1988-11-01,8.35 415 | 1988-12-01,8.76 416 | 1989-01-01,9.12 417 | 1989-02-01,9.36 418 | 1989-03-01,9.85 419 | 1989-04-01,9.84 420 | 1989-05-01,9.81 421 | 1989-06-01,9.53 422 | 1989-07-01,9.24 423 | 1989-08-01,8.99 424 | 1989-09-01,9.02 425 | 1989-10-01,8.84 426 | 1989-11-01,8.55 427 | 1989-12-01,8.45 428 | 1990-01-01,8.23 429 | 1990-02-01,8.24 430 | 1990-03-01,8.28 431 | 1990-04-01,8.26 432 | 1990-05-01,8.18 433 | 1990-06-01,8.29 434 | 1990-07-01,8.15 435 | 1990-08-01,8.13 436 | 1990-09-01,8.20 437 | 1990-10-01,8.11 438 | 1990-11-01,7.81 439 | 1990-12-01,7.31 440 | 1991-01-01,6.91 441 | 1991-02-01,6.25 442 | 1991-03-01,6.12 443 | 1991-04-01,5.91 444 | 1991-05-01,5.78 445 | 1991-06-01,5.90 446 | 1991-07-01,5.82 447 | 1991-08-01,5.66 448 | 1991-09-01,5.45 449 | 1991-10-01,5.21 450 | 1991-11-01,4.81 451 | 1991-12-01,4.43 452 | 1992-01-01,4.03 453 | 1992-02-01,4.06 454 | 1992-03-01,3.98 455 | 1992-04-01,3.73 456 | 1992-05-01,3.82 457 | 1992-06-01,3.76 458 | 1992-07-01,3.25 459 | 1992-08-01,3.30 460 | 1992-09-01,3.22 461 | 1992-10-01,3.10 462 | 1992-11-01,3.09 463 | 1992-12-01,2.92 464 | 1993-01-01,3.02 465 | 1993-02-01,3.03 466 | 1993-03-01,3.07 467 | 1993-04-01,2.96 468 | 1993-05-01,3.00 469 | 1993-06-01,3.04 470 | 1993-07-01,3.06 471 | 1993-08-01,3.03 472 | 1993-09-01,3.09 473 | 1993-10-01,2.99 474 | 1993-11-01,3.02 475 | 1993-12-01,2.96 476 | 1994-01-01,3.05 477 | 1994-02-01,3.25 478 | 1994-03-01,3.34 479 | 1994-04-01,3.56 480 | 1994-05-01,4.01 481 | 1994-06-01,4.25 482 | 1994-07-01,4.26 483 | 1994-08-01,4.47 484 | 1994-09-01,4.73 485 | 1994-10-01,4.76 486 | 1994-11-01,5.29 487 | 1994-12-01,5.45 488 | 1995-01-01,5.53 489 | 1995-02-01,5.92 490 | 1995-03-01,5.98 491 | 1995-04-01,6.05 492 | 1995-05-01,6.01 493 | 1995-06-01,6.00 494 | 1995-07-01,5.85 495 | 1995-08-01,5.74 496 | 1995-09-01,5.80 497 | 1995-10-01,5.76 498 | 1995-11-01,5.80 499 | 1995-12-01,5.60 500 | 1996-01-01,5.56 501 | 1996-02-01,5.22 502 | 1996-03-01,5.31 503 | 1996-04-01,5.22 504 | 1996-05-01,5.24 505 | 1996-06-01,5.27 506 | 1996-07-01,5.40 507 | 1996-08-01,5.22 508 | 1996-09-01,5.30 509 | 1996-10-01,5.24 510 | 1996-11-01,5.31 511 | 1996-12-01,5.29 512 | 1997-01-01,5.25 513 | 1997-02-01,5.19 514 | 1997-03-01,5.39 515 | 1997-04-01,5.51 516 | 1997-05-01,5.50 517 | 1997-06-01,5.56 518 | 1997-07-01,5.52 519 | 1997-08-01,5.54 520 | 1997-09-01,5.54 521 | 1997-10-01,5.50 522 | 1997-11-01,5.52 523 | 1997-12-01,5.50 524 | 1998-01-01,5.56 525 | 1998-02-01,5.51 526 | 1998-03-01,5.49 527 | 1998-04-01,5.45 528 | 1998-05-01,5.49 529 | 1998-06-01,5.56 530 | 1998-07-01,5.54 531 | 1998-08-01,5.55 532 | 1998-09-01,5.51 533 | 1998-10-01,5.07 534 | 1998-11-01,4.83 535 | 1998-12-01,4.68 536 | 1999-01-01,4.63 537 | 1999-02-01,4.76 538 | 1999-03-01,4.81 539 | 1999-04-01,4.74 540 | 1999-05-01,4.74 541 | 1999-06-01,4.76 542 | 1999-07-01,4.99 543 | 1999-08-01,5.07 544 | 1999-09-01,5.22 545 | 1999-10-01,5.20 546 | 1999-11-01,5.42 547 | 1999-12-01,5.30 548 | 2000-01-01,5.45 549 | 2000-02-01,5.73 550 | 2000-03-01,5.85 551 | 2000-04-01,6.02 552 | 2000-05-01,6.27 553 | 2000-06-01,6.53 554 | 2000-07-01,6.54 555 | 2000-08-01,6.50 556 | 2000-09-01,6.52 557 | 2000-10-01,6.51 558 | 2000-11-01,6.51 559 | 2000-12-01,6.40 560 | 2001-01-01,5.98 561 | 2001-02-01,5.49 562 | 2001-03-01,5.31 563 | 2001-04-01,4.8 564 | 2001-05-01,4.21 565 | 2001-06-01,3.97 566 | 2001-07-01,3.77 567 | 2001-08-01,3.65 568 | 2001-09-01,3.07 569 | 2001-10-01,2.49 570 | 2001-11-01,2.09 571 | 2001-12-01,1.82 572 | 2002-01-01,1.73 573 | 2002-02-01,1.74 574 | 2002-03-01,1.73 575 | 2002-04-01,1.75 576 | 2002-05-01,1.75 577 | 2002-06-01,1.75 578 | 2002-07-01,1.73 579 | 2002-08-01,1.74 580 | 2002-09-01,1.75 581 | 2002-10-01,1.75 582 | 2002-11-01,1.34 583 | 2002-12-01,1.24 584 | 2003-01-01,1.24 585 | 2003-02-01,1.26 586 | 2003-03-01,1.25 587 | 2003-04-01,1.26 588 | 2003-05-01,1.26 589 | 2003-06-01,1.22 590 | 2003-07-01,1.01 591 | 2003-08-01,1.03 592 | 2003-09-01,1.01 593 | 2003-10-01,1.01 594 | 2003-11-01,1.00 595 | 2003-12-01,0.98 596 | 2004-01-01,1.00 597 | 2004-02-01,1.01 598 | 2004-03-01,1.00 599 | 2004-04-01,1.00 600 | 2004-05-01,1.00 601 | 2004-06-01,1.03 602 | 2004-07-01,1.26 603 | 2004-08-01,1.43 604 | 2004-09-01,1.61 605 | 2004-10-01,1.76 606 | 2004-11-01,1.93 607 | 2004-12-01,2.16 608 | 2005-01-01,2.28 609 | 2005-02-01,2.50 610 | 2005-03-01,2.63 611 | 2005-04-01,2.79 612 | 2005-05-01,3.00 613 | 2005-06-01,3.04 614 | 2005-07-01,3.26 615 | 2005-08-01,3.50 616 | 2005-09-01,3.62 617 | 2005-10-01,3.78 618 | 2005-11-01,4.00 619 | 2005-12-01,4.16 620 | 2006-01-01,4.29 621 | 2006-02-01,4.49 622 | 2006-03-01,4.59 623 | 2006-04-01,4.79 624 | 2006-05-01,4.94 625 | 2006-06-01,4.99 626 | 2006-07-01,5.24 627 | 2006-08-01,5.25 628 | 2006-09-01,5.25 629 | 2006-10-01,5.25 630 | 2006-11-01,5.25 631 | 2006-12-01,5.24 632 | 2007-01-01,5.25 633 | 2007-02-01,5.26 634 | 2007-03-01,5.26 635 | 2007-04-01,5.25 636 | 2007-05-01,5.25 637 | 2007-06-01,5.25 638 | 2007-07-01,5.26 639 | 2007-08-01,5.02 640 | 2007-09-01,4.94 641 | 2007-10-01,4.76 642 | 2007-11-01,4.49 643 | 2007-12-01,4.24 644 | 2008-01-01,3.94 645 | 2008-02-01,2.98 646 | 2008-03-01,2.61 647 | 2008-04-01,2.28 648 | 2008-05-01,1.98 649 | 2008-06-01,2.00 650 | 2008-07-01,2.01 651 | 2008-08-01,2.00 652 | 2008-09-01,1.81 653 | 2008-10-01,0.97 654 | 2008-11-01,0.39 655 | 2008-12-01,0.16 656 | 2009-01-01,0.15 657 | 2009-02-01,0.22 658 | 2009-03-01,0.18 659 | 2009-04-01,0.15 660 | 2009-05-01,0.18 661 | 2009-06-01,0.21 662 | 2009-07-01,0.16 663 | 2009-08-01,0.16 664 | 2009-09-01,0.15 665 | 2009-10-01,0.12 666 | 2009-11-01,0.12 667 | 2009-12-01,0.12 668 | 2010-01-01,0.11 669 | 2010-02-01,0.13 670 | 2010-03-01,0.16 671 | 2010-04-01,0.20 672 | 2010-05-01,0.20 673 | 2010-06-01,0.18 674 | 2010-07-01,0.18 675 | 2010-08-01,0.19 676 | 2010-09-01,0.19 677 | 2010-10-01,0.19 678 | 2010-11-01,0.19 679 | 2010-12-01,0.18 680 | 2011-01-01,0.17 681 | 2011-02-01,0.16 682 | 2011-03-01,0.14 683 | 2011-04-01,0.10 684 | 2011-05-01,0.09 685 | 2011-06-01,0.09 686 | 2011-07-01,0.07 687 | 2011-08-01,0.10 688 | 2011-09-01,0.08 689 | 2011-10-01,0.07 690 | 2011-11-01,0.08 691 | 2011-12-01,0.07 692 | 2012-01-01,0.08 693 | 2012-02-01,0.10 694 | 2012-03-01,0.13 695 | 2012-04-01,0.14 696 | 2012-05-01,0.16 697 | 2012-06-01,0.16 698 | 2012-07-01,0.16 699 | 2012-08-01,0.13 700 | 2012-09-01,0.14 701 | 2012-10-01,0.16 702 | 2012-11-01,0.16 703 | 2012-12-01,0.16 704 | 2013-01-01,0.14 705 | 2013-02-01,0.15 706 | 2013-03-01,0.14 707 | 2013-04-01,0.15 708 | 2013-05-01,0.11 709 | 2013-06-01,0.09 710 | 2013-07-01,0.09 711 | 2013-08-01,0.08 712 | 2013-09-01,0.08 713 | 2013-10-01,0.09 714 | 2013-11-01,0.08 715 | 2013-12-01,0.09 716 | 2014-01-01,0.07 717 | 2014-02-01,0.07 718 | 2014-03-01,0.08 719 | 2014-04-01,0.09 720 | 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,country,Year,internetusage,CPIG,CPIF,Imports,Exports,homicide,migrants,GDP,PPP,tourexp,tvarrival,education,Manufacturing 2 | 0,Armenia,2010,25.0,100,100,3782,1011,1.9,7.7,9875,3432,694,684,12.4005,101.7 3 | 1,Australia,2010,76.0,100,100,201703,212109,1.0,26.6,1297259,58646,31064,5790,14.3276,100.5 4 | 2,Austria,2010,75.2,100,100,150593,144882,0.7,15.2,391893,46599,18758,22004,10.7891,108.1 5 | 3,Azerbaijan,2010,46.0,100,100,6597,21278,2.3,3.1,52906,5857,792,1280,8.7008,123.5 6 | 4,Belarus,2010,31.8,100,100,34884,25283,4.2,11.5,57232,6042,665,119,11.7781,150.6 7 | 5,Brazil,2010,40.7,100,100,181768,201915,22.0,0.3,2208838,11224,5522,5161,14.5604,114.3 8 | 6,Bulgaria,2010,46.2,100,100,25360,20608,2.0,1,50610,6835,3807,6047,11.1634,96.7 9 | 7,Canada,2010,80.3,100,100,392109,386580,1.6,19.8,1613463,47221,18438,16219,12.336,83.0 10 | 8,Chile,2010,45.0,101.4,102.2,59007,71106,3.2,2.2,218538,12860,2362,2801,17.8436,103.8 11 | 9,China Hong Kong SAR,2010,72.0,100,100,441369,400692,0.5,39.6,228639,32545,27208,20085,19.9094,89.3 12 | 10,Colombia,2010,36.5,100,100,40683,39820,33.7,0.3,287018,6251,3441,1405,16.4186,115.8 13 | 11,Croatia,2010,56.6,100,116.6,20067,11811,1.4,13.2,59866,13832,8299,9111,8.9583,97.3 14 | 12,Cyprus,2010,53.0,100,100,8645,1506,0.7,16.9,25561,30817,2425,2173,15.6495,93.1 15 | 13,Denmark,2010,88.7,100,100,82724,96217,0.8,9.2,321995,57967,5704,9425,15.1033,89.5 16 | 14,Ecuador,2010,29.0,100,100,20591,17490,17.6,2.2,69555,4657,786,1047,12.9966,132.5 17 | 15,Ethiopia,2010,0.8,100,100,8602,2330,8.5,0.6,26311,300,1434,468,26.304,160.9 18 | 16,Finland,2010,86.9,100,100,68767,70117,2.2,4.6,247800,46181,4497,2319,11.9455,98.0 19 | 17,France,2010,77.3,100,100,599172,511651,1.3,11.4,2642610,40565,56187,76647,10.0072,87.2 20 | 18,Germany,2010,82.0,100,100,1066817,1271096,1.0,12.1,3417095,42241,49126,26875,10.3981,105.2 21 | 19,Hungary,2010,65.0,100,100,87432,94749,1.4,4.4,130923,13187,6595,9510,9.6856,109.1 22 | 20,Ireland,2010,69.9,100,100,64601,120645,1.1,15.8,222134,48009,8187,7134,9.284,110.0 23 | 21,Israel,2010,67.5,100,100,59194,58413,2.0,26.3,233733,31475,5621,2803,13.6107,125.1 24 | 22,Italy,2010,53.7,100,100,486984,446840,0.9,9.7,2125058,35578,38438,43626,8.724,88.9 25 | 23,Kenya,2010,7.2,100,100,12093,5169,4.7,2.2,40000,967,1620,1470,20.556,126.3 26 | 24,Kyrgyzstan,2010,16.3,100,100,3223,1488,19.8,4.3,4794,884,212,1224,15.6813,55.2 27 | 25,Lithuania,2010,62.1,100,100,23378,20814,7.0,5.1,37130,11886,958,1507,12.8588,105.2 28 | 26,Malaysia,2010,56.3,100,100,164586,198791,1.9,8.6,255018,9071,18152,24577,18.4062,112.2 29 | 27,Mali,2010,2.0,100,100,4704,1996,12.2,2.2,10679,708,208,169,16.5039,118.7 30 | 28,Mauritius,2010,28.3,100,100,4402,1850,2.6,2,10004,8016,1585,935,14.5732,113.6 31 | 29,Mexico,2010,31.1,100,100,301482,298305,22.0,0.8,1057801,9016,12628,23290,18.6426,104.0 32 | 30,Mongolia,2010,10.2,100,100,3172,2883,8.8,0.6,7189,2650,288,456,14.7111,136.0 33 | 31,Namibia,2010,11.6,100,100,5980,5848,14.4,4.7,11282,5192,473,984,26.1235,129.3 34 | 32,New Zealand,2010,80.5,100,100,30158,30932,1.0,21.7,146584,33543,6523,2435,15.7159,86.4 35 | 33,Norway,2010,93.4,100,100,77330,130657,0.6,10.8,429131,87831,5299,4767,15.2273,110.5 36 | 34,Poland,2010,62.3,100,100,174128,157065,1.1,1.7,479321,12507,10037,12470,11.0646,142.0 37 | 35,Portugal,2010,53.3,100,100,77682,49414,1.2,7.2,238303,22371,12985,6756,10.4266,91.7 38 | 36,Republic of Moldova,2010,32.3,100,100,3855,1541,6.5,3.9,5812,1423,222,64,22.267,79.5 39 | 37,Senegal,2010,8.0,100,100,4782,2088,8.5,2,16725,1295,464,900,24.0464,102.1 40 | 38,Serbia,2010,40.9,100,100,16735,9795,1.4,9.1,39460,5412,950,683,10.0982,95.2 41 | 39,Singapore,2010,71.0,100,100,310791,351867,0.4,42.7,236420,46592,14178,9161,17.1686,141.2 42 | 40,Slovakia,2010,75.7,100,100,64382,63999,1.5,2.7,89501,16561,2335,1327,9.7662,134.6 43 | 41,Slovenia,2010,70.0,100,100,26592,24435,0.7,12.4,48014,23477,2721,1869,12.0802,103.5 44 | 42,South Africa,2010,24.0,100,100,82949,82626,30.8,4.1,375348,7276,10309,8074,18.0444,98.0 45 | 43,Spain,2010,65.8,100,100,315547,246265,0.9,13.4,1431617,30598,54305,52677,10.5619,82.0 46 | 44,Sri Lanka,2010,12.0,100,100,12354,8304,3.8,0.2,56726,2808,1044,654,8.6057,126.5 47 | 45,Switzerland,2010,83.9,100,100,176281,195609,0.7,26.5,583783,74538,17614,8628,15.3909,119.4 48 | 46,Tajikistan,2010,11.6,100,100,2659,1207,2.4,3.6,5642,738,142,160,15.3315,110.8 49 | 47,Thailand,2010,22.4,100,100,182393,195312,5.4,4.8,341105,5075,23796,15936,16.2226,127.7 50 | 48,Togo,2010,3.0,100,100,1205,648,9.6,3.9,3426,527,105,202,19.6158,103.1 51 | 49,Tunisia,2010,36.8,100,100,22215,16427,2.7,0.4,44051,4140,3477,7828,24.8468,114.8 52 | 50,United Kingdom,2010,85.0,100,100,627618,422014,1.2,12,2452900,38746,40526,28295,13.026,94.9 53 | -------------------------------------------------------------------------------- /datasets/countries_2.csv: -------------------------------------------------------------------------------- 1 | ,country,Year,internetusage,Imports,Exports,migrants,GDP,PPP,education 2 | 0,Afghanistan,2010,4.0,5154,388,0.4,16078,558,17.0676 3 | 1,Afghanistan,2015,8.3,7723,571,1.5,20608,611,12.509 4 | 2,Afghanistan,2017,11.4,7384,700,0.4,21993,619,15.6614 5 | 3,Albania,2005,6.0,2614,658,2.1,8052,2615,11.358 6 | 4,Albania,2015,63.3,4320,1930,1.8,11387,3895,11.3177 7 | 5,Angola,2005,1.1,8321,23835,0.3,36971,1891,7.9924 8 | 6,Angola,2010,2.8,18143,52612,0.3,83799,3586,8.6848 9 | 7,Argentina,2005,17.7,28689,40106,4.3,200622,5125,15.8082 10 | 8,Argentina,2010,45.0,56792,68174,4.4,426487,10346,15.0483 11 | 9,Armenia,2005,5.3,1692,937,15.7,5226,1753,13.6481 12 | 10,Armenia,2010,25.0,3782,1011,7.7,9875,3432,12.4005 13 | 11,Australia,2005,63.0,125221,106011,24.1,760397,37571,13.5851 14 | 12,Australia,2010,76.0,201703,212109,26.6,1297259,58646,14.3276 15 | 13,Australia,2015,84.6,200114,187792,28.2,1246800,52388,14.0758 16 | 14,Austria,2005,58.0,119950,117722,13.8,315967,38282,10.2619 17 | 15,Austria,2010,75.2,150593,144882,15.2,391893,46599,10.7891 18 | 16,Austria,2015,83.9,147935,145277,17.2,381806,43994,10.6935 19 | 17,Azerbaijan,2005,8.0,4211,4347,3.5,13245,1551,13.2232 20 | 18,Azerbaijan,2010,46.0,6597,21278,3.1,52906,5857,8.7008 21 | 19,Bahrain,2015,93.5,16378,16684,51.3,31126,22689,7.2974 22 | 20,Bahrain,2017,95.9,12613,9666,48.4,35326,23668,7.1804 23 | 21,Barbados,2005,52.5,1672,361,11.2,3897,14223,18.243 24 | 22,Barbados,2010,65.1,1196,314,11.7,4365,15613,18.0963 25 | 23,Barbados,2017,81.8,1600,485,12.1,4713,16494,12.8792 26 | 24,Belarus,2005,9.0,16699,15977,11.5,31232,3246,12.6201 27 | 25,Belarus,2010,31.8,34884,25283,11.5,57232,6042,11.7781 28 | 26,Belarus,2017,74.4,34235,29207,11.4,54441,5750,12.2817 29 | 27,Belgium,2005,55.8,319085,335692,8.4,387356,36727,11.1742 30 | 28,Belgium,2010,75.0,391256,407596,10.2,483548,44205,12.0132 31 | 29,Belgium,2015,85.1,371025,397739,11.1,455838,40383,12.1689 32 | 30,Belize,2010,28.2,700,282,14.4,1397,4344,23.0521 33 | 31,Belize,2017,47.1,913,278,16,1902,5077,21.681 34 | 32,Benin,2005,1.3,899,288,2.1,4804,602,18.7904 35 | 33,Benin,2010,3.1,2134,534,2.3,6970,758,26.144 36 | 34,Benin,2015,11.3,2475,626,2.3,8454,799,17.4795 37 | 35,Bhutan,2005,3.8,387,258,6.1,819,1247,22.8491 38 | 36,Bhutan,2010,13.6,854,413,6.7,1585,2179,11.0258 39 | 37,Bhutan,2015,39.8,1062,549,6.5,2059,2615,26.3521 40 | 38,Bhutan,2017,48.1,983,520,6.5,2562,3173,24.041 41 | 39,Bolivia (Plurin. State of),2010,22.4,5604,6965,1.2,19650,1981,24.1278 42 | 40,Botswana,2005,3.3,3162,4431,4.8,9931,5351,25.8235 43 | 41,Brazil,2005,21.0,73600,118529,0.3,891634,4770,11.2574 44 | 42,Brazil,2010,40.7,181768,201915,0.3,2208838,11224,14.5604 45 | 43,Brazil,2015,58.3,171446,191127,0.3,1802212,8750,16.2488 46 | 44,Brunei Darussalam,2010,53.0,2539,8908,25.9,13707,35267,5.3021 47 | 45,Bulgaria,2005,20.0,18162,11739,0.8,29636,3857,12.0725 48 | 46,Bulgaria,2010,46.2,25360,20608,1,50610,6835,11.1634 49 | 47,Burkina Faso,2005,0.5,1161,332,4.4,5463,407,19.4942 50 | 48,Burkina Faso,2010,2.4,2048,1288,4.3,8980,575,16.1728 51 | 49,Burkina Faso,2015,11.4,2980,2177,3.9,10416,575,18.0271 52 | 50,Burundi,2005,0.5,258,114,2.3,1117,150,10.9593 53 | 51,Burundi,2010,1.0,404,118,2.7,2032,232,16.5881 54 | 52,Burundi,2017,5.6,725,142,2.8,3155,290,20.4014 55 | 53,Cabo Verde,2010,30.0,731,220,2.9,1664,3313,14.2169 56 | 54,Cabo Verde,2017,57.2,794,50,2.8,1773,3245,16.3829 57 | 55,Cambodia,2010,1.3,4903,5590,0.6,11242,786,7.3375 58 | 56,Cameroon,2005,1.4,2800,2849,1.5,17944,1030,21.3776 59 | 57,Cameroon,2010,4.3,5133,3878,1.4,26144,1309,18.7539 60 | 58,Cameroon,2017,23.2,4224,2433,2.2,34924,1452,15.4686 61 | 59,Canada,2005,71.7,314444,360552,18.8,1169393,36218,12.1781 62 | 60,Canada,2010,80.3,392109,386580,19.8,1613463,47221,12.336 63 | 61,Central African Republic,2005,0.3,185,111,2.3,1413,342,9.6805 64 | 62,Central African Republic,2010,2.0,210,90,2.1,2034,457,6.4754 65 | 63,Chad,2005,0.4,953,3095,3.5,6681,664,14.6728 66 | 64,Chad,2010,1.7,2507,3410,3.5,10970,923,8.1193 67 | 65,Chile,2005,31.2,32927,41973,1.7,122965,7615,16.1991 68 | 66,Chile,2010,45.0,59007,71106,2.2,218538,12860,17.8436 69 | 67,China Hong Kong SAR,2005,56.9,300160,292119,39.9,181569,26593,22.4786 70 | 68,China Hong Kong SAR,2010,72.0,441369,400692,39.6,228639,32545,19.9094 71 | 69,China Hong Kong SAR,2017,89.4,589824,550240,39.1,341659,46390,17.8413 72 | 70,Colombia,2005,11.0,21204,21190,0.2,146566,3386,15.5356 73 | 71,Colombia,2010,36.5,40683,39820,0.3,287018,6251,16.4186 74 | 72,Colombia,2017,62.3,45878,38463,0.3,309191,6302,15.1697 75 | 73,Comoros,2015,7.5,92,9,1.6,988,1271,15.2701 76 | 74,Congo,2005,1.5,1342,4744,8.5,6350,1708,7.5978 77 | 75,Congo,2010,5.0,4369,6918,9.6,13678,3118,24.7278 78 | 76,Congo,2015,7.6,4183,3536,7.9,11092,2220,7.9504 79 | 77,Croatia,2010,56.6,20067,11811,13.2,59866,13832,8.9583 80 | 78,Cyprus,2005,32.8,6382,1546,11.4,18967,25682,15.8394 81 | 79,Cyprus,2010,53.0,8645,1506,16.9,25561,30817,15.6495 82 | 80,Cyprus,2015,71.7,5699,1935,16.5,19681,23236,16.2763 83 | 81,Czechia,2005,35.3,76527,78209,3.1,136281,13285,9.203 84 | 82,Czechia,2010,68.8,125691,132141,3.8,207478,19692,9.3405 85 | 83,Czechia,2015,75.7,140716,157194,3.9,186830,17619,13.8825 86 | 84,Dem. Rep. of the Congo,2010,0.7,4500,5300,0.9,21566,334,9.6611 87 | 85,Dem. Rep. of the Congo,2015,3.8,6196,5789,1.1,37918,498,11.7384 88 | 86,Dem. Rep. of the Congo,2017,8.6,5655,4491,1.1,37642,463,11.7108 89 | 87,Denmark,2005,82.7,72716,82278,8.1,264467,48779,15.7793 90 | 88,Denmark,2010,88.7,82724,96217,9.2,321995,57967,15.1033 91 | 89,Djibouti,2005,1.0,277,39,11.8,674,861,22.7151 92 | 90,Djibouti,2010,6.5,603,470,11.9,1015,1192,12.3284 93 | 91,Dominica,2015,65.0,214,30,9.2,541,7392,10.5221 94 | 92,Ecuador,2010,29.0,20591,17490,2.2,69555,4657,12.9966 95 | 93,Ecuador,2015,48.9,21387,18331,2.4,99290,6150,12.5977 96 | 94,Egypt,2005,12.8,19812,10646,0.4,94456,1230,15.0436 97 | 95,El Salvador,2005,4.2,6809,3436,0.6,14698,2438,14.6558 98 | 96,El Salvador,2010,15.9,8416,4499,0.7,18448,2993,16.1642 99 | 97,El Salvador,2017,31.3,10593,5760,0.7,24805,3889,15.634 100 | 98,Estonia,2005,61.5,11018,8247,17.2,14003,10330,14.2577 101 | 99,Estonia,2010,74.1,13197,12811,16.4,19503,14640,13.6428 102 | 100,Estonia,2015,87.2,15732,13908,14.8,22567,17157,12.9666 103 | 101,Ethiopia,2010,0.8,8602,2330,0.6,26311,300,26.304 104 | 102,Ethiopia,2015,13.9,25815,5028,1.2,63079,632,27.0987 105 | 103,Fiji,2005,8.5,1607,702,1.5,2980,3627,20.2661 106 | 104,Finland,2005,74.5,58473,65238,3.7,204431,38873,12.2497 107 | 105,Finland,2010,86.9,68767,70117,4.6,247800,46181,11.9455 108 | 106,Finland,2015,86.4,60174,59682,5.7,232465,42405,12.4122 109 | 107,France,2005,42.9,475857,434354,11,2196071,34724,10.3555 110 | 108,France,2010,77.3,599172,511651,11.4,2642610,40565,10.0072 111 | 109,France,2015,78.0,563398,493941,12.3,2438208,36574,9.6202 112 | 110,Gabon,2010,13.0,2969,8539,14.9,14359,8754,13.3304 113 | 111,Gambia,2005,3.8,260,7,12.6,944,654,5.3442 114 | 112,Gambia,2010,9.2,284,68,11,1441,851,17.6112 115 | 113,Georgia,2005,6.1,2490,865,1.6,6411,1429,11.1813 116 | 114,Georgia,2017,60.5,7982,2728,2,15159,3875,12.9534 117 | 115,Germany,2010,82.0,1066817,1271096,12.1,3417095,42241,10.3981 118 | 116,Germany,2015,84.4,1057616,1328549,12.5,3381389,41384,10.9935 119 | 117,Ghana,2005,1.8,4878,3060,1.4,22765,1057,23.43 120 | 118,Ghana,2010,7.8,8057,5233,1.4,42587,1737,20.6961 121 | 119,Ghana,2017,37.9,10124,7982,1.4,58996,2046,20.0976 122 | 120,Greece,2005,24.0,54894,17434,10.5,247777,21925,8.7002 123 | 121,Guatemala,2010,10.5,13830,8460,0.5,41338,2825,19.2969 124 | 122,Guatemala,2017,40.7,18190,11108,0.5,75620,4471,23.0881 125 | 123,Guinea,2005,0.5,1648,796,2.4,4063,420,10.9475 126 | 124,Guinea,2010,1.0,1405,1471,1.6,6853,635,12.4383 127 | 125,Guinea,2017,11.4,2065,1942,1,10208,803,13.3768 128 | 126,Guinea-Bissau,2010,2.5,197,120,1.4,849,546,9.0692 129 | 127,Guyana,2005,13.2,778,539,1.4,1315,1752,13.8234 130 | 128,Guyana,2010,29.9,1452,901,1.8,2259,3026,11.8387 131 | 129,Guyana,2017,37.3,1762,1790,2,3543,4555,18.2761 132 | 130,Honduras,2015,27.6,8381,4201,0.4,20980,2341,24.6446 133 | 131,Honduras,2017,32.1,8612,4970,0.4,22979,2480,22.0249 134 | 132,Hungary,2005,39.0,65920,62272,3.6,113035,11207,10.8006 135 | 133,Hungary,2010,65.0,87432,94749,4.4,130923,13187,9.6856 136 | 134,Hungary,2015,72.8,90761,100297,4.9,123074,12579,9.1391 137 | 135,Iceland,2005,87.0,4979,3091,8.6,16813,56996,17.8101 138 | 136,Iceland,2010,93.4,3914,4603,11,13684,42718,14.604 139 | 137,Iceland,2015,98.2,5285,4722,11.8,17344,52519,18.158 140 | 138,India,2005,2.4,140862,100353,0.5,823612,720,11.2085 141 | 139,India,2010,7.5,350029,220408,0.4,1669620,1356,11.8337 142 | 140,Indonesia,2005,3.6,57701,85660,0.1,304372,1343,15.1488 143 | 141,Indonesia,2010,10.9,135663,157779,0.1,755094,3113,16.6542 144 | 142,Indonesia,2015,22.0,142695,150366,0.1,860854,3335,20.5033 145 | 143,Iran (Islamic Republic of),2005,8.1,38869,60012,3.6,226452,3216,22.322 146 | 144,Iran (Islamic Republic of),2010,15.9,54697,83785,3.7,491099,6586,18.8044 147 | 145,Iran (Islamic Republic of),2017,60.4,29519,33103,3.3,460976,5680,20.042 148 | 146,Ireland,2005,41.6,70284,110003,14,211644,50236,13.5624 149 | 147,Ireland,2010,69.9,64601,120645,15.8,222134,48009,9.284 150 | 148,Ireland,2015,83.5,77795,124731,16,291092,61933,13.0293 151 | 149,Israel,2005,25.2,45032,42771,28.6,142411,21568,12.7394 152 | 150,Israel,2010,67.5,59194,58413,26.3,233733,31475,13.6107 153 | 151,Israel,2015,77.4,62068,64062,24.9,300471,37258,15.044 154 | 152,Italy,2005,35.0,384836,372957,6.7,1852616,31503,9.0137 155 | 153,Italy,2010,53.7,486984,446840,9.7,2125058,35578,8.724 156 | 154,Italy,2015,58.1,410933,456989,9.8,1832273,30792,8.113 157 | 155,Jamaica,2005,12.8,4885,1514,0.9,11244,4097,14.2694 158 | 156,Jamaica,2010,27.7,5225,1328,0.8,13219,4692,16.1029 159 | 157,Jamaica,2017,48.8,5818,1310,0.8,14827,5130,18.4025 160 | 158,Jordan,2017,66.8,20407,7469,33.3,40708,4196,12.5294 161 | 159,Kazakhstan,2005,3.0,17333,27846,20,57124,3676,10.2278 162 | 160,Kazakhstan,2017,76.4,29346,48342,20,159407,8756,11.4215 163 | 161,Kenya,2005,3.1,5846,3420,2.1,21506,597,27.4693 164 | 162,Kenya,2010,7.2,12093,5169,2.2,40000,967,20.556 165 | 163,Kenya,2015,16.6,16097,5908,2.3,64008,1355,16.6647 166 | 164,Kenya,2017,17.8,16652,5805,2.2,74938,1508,17.5823 167 | 165,Kuwait,2005,25.9,15801,44869,58.6,80798,35490,13.8541 168 | 166,Kyrgyzstan,2005,10.5,1108,672,6.2,2460,485,16.7684 169 | 167,Kyrgyzstan,2010,16.3,3223,1488,4.3,4794,884,15.6813 170 | 168,Kyrgyzstan,2017,38.2,4474,1784,3.3,7565,1251,18.6443 171 | 169,Lao People's Dem. Rep.,2005,0.9,874,552,0.4,2946,512,13.7316 172 | 170,Lao People's Dem. Rep.,2010,7.0,1837,1909,0.5,7313,1171,7.276 173 | 171,Latvia,2010,68.4,11143,8851,14.8,23765,11216,11.809 174 | 172,Latvia,2015,79.2,13850,11491,13.3,26973,13536,14.1274 175 | 173,Lebanon,2005,10.1,9327,1879,19,21490,5390,8.4213 176 | 174,Lebanon,2010,43.7,17970,4254,18.9,38420,8858,5.5343 177 | 175,Lesotho,2005,2.6,1410,650,0.3,1560,800,32.4049 178 | 176,Liberia,2017,8.0,464,697,2.1,2763,584,7.058 179 | 177,Lithuania,2005,36.2,15704,12070,6,26141,7817,14.5796 180 | 178,Lithuania,2010,62.1,23378,20814,5.1,37130,11886,12.8588 181 | 179,Lithuania,2015,71.4,28176,25411,4.6,41517,14160,12.2883 182 | 180,Luxembourg,2015,96.4,19296,12626,46,57204,100936,9.4276 183 | 181,Madagascar,2005,0.6,1686,836,0.1,5942,324,18.033 184 | 182,Malawi,2010,2.3,2173,1066,1.4,6868,453,12.518 185 | 183,Malawi,2017,13.8,999,941,1.3,6339,340,14.3401 186 | 184,Malaysia,2010,56.3,164586,198791,8.6,255018,9071,18.4062 187 | 185,Malaysia,2017,80.1,193856,216428,8.5,314707,9951,21.0557 188 | 186,Maldives,2005,6.9,745,154,14.1,1163,3649,13.015 189 | 187,Maldives,2010,26.5,1095,74,15,2588,7100,12.4606 190 | 188,Mali,2005,0.5,1544,1075,2,6245,488,16.2803 191 | 189,Mali,2010,2.0,4704,1996,2.2,10679,708,16.5039 192 | 190,Malta,2010,63.0,5732,3717,7.9,8741,21005,15.7192 193 | 191,Malta,2015,76.0,6788,3915,9.9,10563,24703,13.1509 194 | 192,Mauritania,2010,4.0,1708,1819,2.3,4338,1202,16.035 195 | 193,Mauritius,2005,15.2,3160,2144,1.6,6775,5544,17.0881 196 | 194,Mauritius,2010,28.3,4402,1850,2,10004,8016,14.5732 197 | 195,Mauritius,2017,55.6,5269,2103,2.3,13366,10565,19.9341 198 | 196,Mexico,2005,17.2,221819,214207,0.7,877477,8089,21.9038 199 | 197,Mexico,2010,31.1,301482,298305,0.8,1057801,9016,18.6426 200 | 198,Mexico,2015,57.4,395232,380638,0.9,1170567,9298,19.0188 201 | 199,Micronesia (Fed. States of),2015,31.5,68,11,2.6,315,3018,22.3124 202 | 200,Mongolia,2010,10.2,3172,2883,0.6,7189,2650,14.7111 203 | 201,Mongolia,2017,23.7,4295,6112,0.6,11135,3620,13.4912 204 | 202,Mozambique,2005,0.9,2408,1745,1,7724,369,22.7182 205 | 203,Myanmar,2017,30.7,19253,13879,0.1,67102,1257,10.1523 206 | 204,Namibia,2010,11.6,5980,5848,4.7,11282,5192,26.1235 207 | 205,Nepal,2005,0.8,2282,863,2.7,8259,322,22.2942 208 | 206,Nepal,2010,7.9,5116,874,2.1,16281,602,16.0324 209 | 207,Nepal,2015,17.6,6612,660,1.8,20801,726,16.9897 210 | 208,Nepal,2017,21.4,10038,741,1.7,24870,849,15.7465 211 | 209,Netherlands,2005,81.0,310591,349813,10.6,685076,41857,12.4389 212 | 210,Netherlands,2010,90.7,439987,492646,11,846555,50744,11.7416 213 | 211,Netherlands,2015,91.7,424851,473834,11.8,765265,45179,12.2539 214 | 212,New Zealand,2005,62.7,26232,21729,20.3,114721,27741,16.2749 215 | 213,New Zealand,2010,80.5,30158,30932,21.7,146584,33543,15.7159 216 | 214,Niger,2010,0.8,2273,479,0.8,5719,348,18.104 217 | 215,Niger,2017,10.2,1617,639,1.4,8120,378,13.2502 218 | 216,Norway,2005,82.0,55488,103759,7.8,308722,66645,16.5484 219 | 217,Norway,2010,93.4,77330,130657,10.8,429131,87831,15.2273 220 | 218,Norway,2015,96.8,77193,104800,14.4,386663,74361,15.7281 221 | 219,Oman,2005,6.7,8970,18692,26.5,31082,12377,10.0536 222 | 220,Oman,2017,80.2,18893,17652,44.7,70784,15267,15.3437 223 | 221,Pakistan,2005,6.3,25097,16050,2.1,117708,765,13.7775 224 | 222,Pakistan,2010,8.0,37537,21413,2.3,174508,1023,11.8647 225 | 223,Pakistan,2017,15.5,57440,21878,1.7,302139,1534,13.8473 226 | 224,Peru,2005,17.1,12502,17114,0.3,76080,2755,14.2785 227 | 225,Peru,2010,34.8,29966,35807,0.3,147528,5022,13.5556 228 | 226,Peru,2017,48.7,39764,44238,0.3,211403,6572,18.2358 229 | 227,Philippines,2005,5.4,49487,41255,0.3,103072,1195,12.4224 230 | 228,Poland,2005,38.8,101539,89378,1.9,306127,7980,12.2223 231 | 229,Poland,2010,62.3,174128,157065,1.7,479321,12507,11.0646 232 | 230,Poland,2015,68.0,189696,194461,1.6,477577,12481,11.5827 233 | 231,Portugal,2005,35.0,63904,38672,7.3,197300,18674,10.8581 234 | 232,Portugal,2010,53.3,77682,49414,7.2,238303,22371,10.4266 235 | 233,Portugal,2015,68.6,66871,55259,8.3,199420,19141,10.152 236 | 234,Qatar,2005,24.7,10061,25762,74.7,44530,51489,13.7448 237 | 235,Qatar,2010,69.0,23240,74964,81.8,125122,70306,13.8173 238 | 236,Qatar,2017,95.9,29451,67444,65.2,167605,63506,8.8824 239 | 237,Republic of Moldova,2005,14.6,2292,1091,4.2,2988,719,19.2967 240 | 238,Republic of Moldova,2010,32.3,3855,1541,3.9,5812,1423,22.267 241 | 239,Republic of Moldova,2017,76.1,4831,2425,3.5,8128,2006,18.3323 242 | 240,Romania,2005,21.5,40463,27730,0.7,99699,4652,10.8367 243 | 241,Romania,2010,39.9,62007,49413,0.8,166658,8153,9.1291 244 | 242,Romania,2015,55.8,69858,60605,1.4,177913,8951,9.0762 245 | 243,Russian Federation,2005,15.2,98707,241452,8.1,771495,5372,11.9506 246 | 244,Russian Federation,2015,70.1,182782,343908,8.1,1368402,9510,10.8695 247 | 245,Rwanda,2010,8.0,1405,242,4.3,5772,563,17.5481 248 | 246,Rwanda,2015,18.0,1858,579,3.8,8278,712,12.5391 249 | 247,Rwanda,2017,21.8,1794,984,3.6,9136,748,11.0796 250 | 248,Saint Kitts and Nevis,2005,34.0,210,34,13.7,543,11178,11.0562 251 | 249,Saint Kitts and Nevis,2015,75.7,297,32,13.7,865,15937,8.6355 252 | 250,Saint Lucia,2005,21.6,486,64,7,935,5714,16.307 253 | 251,Saint Lucia,2010,32.5,647,215,7,1382,8008,14.2021 254 | 252,Saint Lucia,2015,42.5,583,181,7.2,1622,9156,16.453 255 | 253,Saint Vincent & Grenadines,2005,9.2,240,40,4,551,5065,22.592 256 | 254,Saint Vincent & Grenadines,2010,33.7,379,42,4.1,681,6232,15.4856 257 | 255,Saint Vincent & Grenadines,2017,65.6,315,39,4.2,780,7099,18.9783 258 | 256,Sao Tome and Principe,2005,13.8,50,3,2.2,126,811,12.1265 259 | 257,Sao Tome and Principe,2010,18.8,112,6,1.5,197,1130,19.3236 260 | 258,Sao Tome and Principe,2017,29.9,147,11,1.1,393,1921,18.3994 261 | 259,Saudi Arabia,2005,12.7,57233,180278,27.2,328461,13740,19.2923 262 | 260,Senegal,2005,4.8,3498,1471,2.1,11267,1001,21.771 263 | 261,Senegal,2010,8.0,4782,2088,2,16725,1295,24.0464 264 | 262,Senegal,2017,29.6,6729,2989,1.7,21126,1333,21.5628 265 | 263,Serbia,2010,40.9,16735,9795,9.1,39460,5412,10.0982 266 | 264,Serbia,2015,65.3,18210,13379,9.1,37160,5237,8.8764 267 | 265,Sierra Leone,2005,0.2,341,154,2.6,1650,292,15.5066 268 | 266,Sierra Leone,2010,0.6,776,319,1.5,2578,399,12.8019 269 | 267,Sierra Leone,2017,13.2,893,324,1.3,3740,495,19.9151 270 | 268,Singapore,2005,61.0,200724,230344,38.1,127418,28372,19.817 271 | 269,Singapore,2010,71.0,310791,351867,42.7,236420,46592,17.1686 272 | 270,Slovakia,2005,55.2,34226,32210,2.4,48965,9069,9.4661 273 | 271,Slovakia,2010,75.7,64382,63999,2.7,89501,16561,9.7662 274 | 272,Slovakia,2015,77.6,72958,75051,3.3,87770,16136,10.2806 275 | 273,Slovenia,2005,46.8,19626,17896,9.9,36345,18206,13.2906 276 | 274,Slovenia,2010,70.0,26592,24435,12.4,48014,23477,12.0802 277 | 275,Slovenia,2015,73.1,25870,26587,11.5,43102,20774,11.2185 278 | 276,Solomon Islands,2010,5.0,328,215,0.5,720,1363,17.4785 279 | 277,South Africa,2005,7.5,55033,46991,2.5,257772,5280,19.925 280 | 278,South Africa,2010,24.0,82949,82626,4.1,375348,7276,18.0444 281 | 279,South Africa,2017,56.2,83031,88268,7.1,348872,6151,18.7274 282 | 280,South Sudan,2017,8.0,886,1840,6.7,5694,453,1.0662 283 | 281,Spain,2005,47.9,289611,192798,9.3,1157248,26276,10.7791 284 | 282,Spain,2010,65.8,315547,246265,13.4,1431617,30598,10.5619 285 | 283,Spain,2015,78.7,305266,278122,12.7,1199084,25844,9.771 286 | 284,Sri Lanka,2010,12.0,12354,8304,0.2,56726,2808,8.6057 287 | 285,Sri Lanka,2017,34.1,21316,11741,0.2,87357,4184,14.4998 288 | 286,Eswatini,2005,3.7,1656,1278,2.5,3178,2874,20.8919 289 | 287,Eswatini,2010,11.0,1710,1557,2.5,4439,3690,18.283 290 | 288,Sweden,2005,84.8,111351,130264,12.5,389489,43092,12.7627 291 | 289,Sweden,2010,90.0,148788,158411,14.2,488909,52066,13.3062 292 | 290,Sweden,2015,90.6,138361,140001,16.4,498118,51018,15.5033 293 | 291,Switzerland,2005,70.1,126574,130930,24.4,408697,55153,15.8207 294 | 292,Switzerland,2010,83.9,176281,195609,26.5,583783,74538,15.3909 295 | 293,Switzerland,2015,87.5,253152,291959,29,679832,81713,15.5278 296 | 294,Tajikistan,2005,0.3,1329,905,4.1,2312,337,15.2934 297 | 295,Tajikistan,2010,11.6,2659,1207,3.6,5642,738,15.3315 298 | 296,Tajikistan,2015,19.0,3435,891,3.2,7855,919,16.4434 299 | 297,Thailand,2005,15.0,118164,110110,3.3,189318,2894,20.5468 300 | 298,Thailand,2010,22.4,182393,195312,4.8,341105,5075,16.2226 301 | 299,Timor-Leste,2010,3.0,298,42,1,3999,3604,9.1542 302 | 300,Togo,2005,1.8,593,360,3.6,2281,401,17.6781 303 | 301,Togo,2010,3.0,1205,648,3.9,3426,527,19.6158 304 | 302,Tunisia,2005,9.7,13174,10494,0.3,32272,3194,26.6957 305 | 303,Tunisia,2010,36.8,22215,16427,0.4,44051,4140,24.8468 306 | 304,Tunisia,2015,46.5,20223,14073,0.5,43152,3828,22.8985 307 | 305,Turkey,2015,53.7,207207,143850,5.3,859794,10985,12.839 308 | 306,Uganda,2010,12.5,4664,1619,1.6,19683,580,10.1049 309 | 307,Uganda,2017,23.7,4809,2852,3.9,27699,646,12.0089 310 | 308,Ukraine,2005,3.7,36122,34228,10.8,89239,1903,13.7387 311 | 309,United Kingdom,2005,70.0,528461,392744,9.8,2525013,41883,13.1742 312 | 310,United Kingdom,2010,85.0,627618,422014,12,2452900,38746,13.026 313 | 311,United Rep. of Tanzania,2005,1.1,3247,1672,2,18072,471,18.1431 314 | 312,United Rep. of Tanzania,2010,2.9,8013,4051,0.7,31105,694,19.648 315 | 313,United States of America,2010,71.7,1968260,1278099,14.3,14992052,48574,13.1507 316 | 314,Uruguay,2005,20.1,3879,3422,2.5,17363,5221,9.4367 317 | 315,Uzbekistan,2017,52.3,12998,13894,3.6,49677,1557,19.9572 318 | 316,Vanuatu,2017,25.7,313,38,1.2,864,3128,11.7827 319 | 317,Viet Nam,2010,30.7,84839,72237,0.1,115932,1310,17.1145 320 | 318,Zambia,2005,2.9,2558,1810,2.1,8332,691,7.7297 321 | 319,Zimbabwe,2010,6.4,5852,3199,2.8,10142,720,8.7209 322 | -------------------------------------------------------------------------------- /datasets/flowdata.csv: -------------------------------------------------------------------------------- 1 | Time,L06_347,LS06_347,LS06_348 2 | 2009-01-01 00:00:00,0.1374166666666667,0.09749999999999999,0.01683333333333334 3 | 2009-01-01 03:00:00,0.13125,0.08883333333333332,0.016416666666666673 4 | 2009-01-01 06:00:00,0.11350000000000003,0.09125,0.016750000000000004 5 | 2009-01-01 09:00:00,0.13575,0.09149999999999998,0.016250000000000004 6 | 2009-01-01 12:00:00,0.1409166666666667,0.09616666666666668,0.017000000000000005 7 | 2009-01-01 15:00:00,0.09916666666666667,0.09166666666666667,0.01758333333333334 8 | 2009-01-01 18:00:00,0.13266666666666668,0.09016666666666666,0.016250000000000004 9 | 2009-01-01 21:00:00,0.10941666666666666,0.09116666666666666,0.016000000000000004 10 | 2009-01-02 00:00:00,0.13383333333333336,0.09041666666666666,0.01608333333333334 11 | 2009-01-02 03:00:00,0.09208333333333334,0.08866666666666666,0.016000000000000004 12 | 2009-01-02 06:00:00,0.11291666666666667,0.09141666666666665,0.01633333333333334 13 | 2009-01-02 09:00:00,0.14191666666666666,0.09708333333333331,0.016416666666666666 14 | 2009-01-02 12:00:00,0.14783333333333334,0.10191666666666667,0.016416666666666673 15 | 2009-01-02 15:00:00,0.10791666666666667,0.10025,0.016416666666666673 16 | 2009-01-02 18:00:00,0.14358333333333334,0.09841666666666665,0.016750000000000004 17 | 2009-01-02 21:00:00,0.11308333333333331,0.09808333333333334,0.01683333333333334 18 | 2009-01-03 00:00:00,0.13583333333333333,0.09216666666666666,0.01683333333333334 19 | 2009-01-03 03:00:00,0.08324999999999999,0.07999999999999997,0.01608333333333334 20 | 2009-01-03 06:00:00,0.11941666666666666,0.08024999999999999,0.01541666666666667 21 | 2009-01-03 09:00:00,0.12458333333333334,0.08441666666666664,0.015833333333333338 22 | 2009-01-03 12:00:00,0.09166666666666666,0.08825,0.016250000000000004 23 | 2009-01-03 15:00:00,0.125,0.08466666666666665,0.016500000000000004 24 | 2009-01-03 18:00:00,0.12158333333333332,0.08208333333333331,0.015833333333333338 25 | 2009-01-03 21:00:00,0.10716666666666667,0.09249999999999999,0.016000000000000004 26 | 2009-01-04 00:00:00,0.13525,0.09116666666666666,0.01633333333333334 27 | 2009-01-04 03:00:00,0.13558333333333336,0.09158333333333331,0.01608333333333334 28 | 2009-01-04 06:00:00,0.11716666666666668,0.09516666666666666,0.016000000000000004 29 | 2009-01-04 09:00:00,0.10899999999999999,0.10516666666666667,0.018000000000000002 30 | 2009-01-04 12:00:00,0.15741666666666662,0.11075000000000002,0.01841666666666667 31 | 2009-01-04 15:00:00,0.16041666666666665,0.11375000000000002,0.018416666666666675 32 | 2009-01-04 18:00:00,0.15558333333333332,0.10908333333333332,0.018000000000000006 33 | 2009-01-04 21:00:00,0.15116666666666664,0.105,0.01733333333333334 34 | 2009-01-05 00:00:00,0.14875,0.10283333333333335,0.017000000000000005 35 | 2009-01-05 03:00:00,0.10908333333333332,0.10525000000000001,0.017750000000000005 36 | 2009-01-05 06:00:00,0.1465,0.11516666666666665,0.01766666666666667 37 | 2009-01-05 09:00:00,0.1615,0.11458333333333333,0.02158333333333333 38 | 2009-01-05 12:00:00,0.11566666666666668,0.11175000000000002,0.020166666666666663 39 | 2009-01-05 15:00:00,0.14600000000000002,0.10075,0.0195 40 | 2009-01-05 18:00:00,0.10149999999999999,0.09766666666666667,0.017250000000000005 41 | 2009-01-05 21:00:00,0.10149999999999999,0.08791666666666664,0.01441666666666667 42 | 2009-01-06 00:00:00,0.12191666666666667,0.08224999999999998,0.014000000000000004 43 | 2009-01-06 03:00:00,0.106,0.06983333333333332,0.013499999999999998 44 | 2009-01-06 06:00:00,0.09175,0.06825000000000002,0.014250000000000004 45 | 2009-01-06 09:00:00,0.10008333333333334,0.06558333333333333,0.015500000000000005 46 | 2009-01-06 12:00:00,0.1226666666666667,0.08291666666666665,0.017333333333333336 47 | 2009-01-06 15:00:00,0.12025000000000001,0.08058333333333333,0.017416666666666674 48 | 2009-01-06 18:00:00,0.049249999999999995,0.04725000000000001,0.013166666666666667 49 | 2009-01-06 21:00:00,0.054249999999999986,0.04674999999999999,0.010999999999999998 50 | 2009-01-07 00:00:00,0.07924999999999999,0.05241666666666667,0.011749999999999998 51 | 2009-01-07 03:00:00,0.13933333333333334,0.09575,0.013500000000000003 52 | 2009-01-07 06:00:00,0.12241666666666667,0.09333333333333332,0.014750000000000004 53 | 2009-01-07 09:00:00,0.13849999999999998,0.09391666666666666,0.015000000000000005 54 | 2009-01-07 12:00:00,0.13925,0.09466666666666666,0.016416666666666673 55 | 2009-01-07 15:00:00,0.12725,0.08641666666666666,0.015916666666666673 56 | 2009-01-07 18:00:00,0.11841666666666667,0.07958333333333333,0.014833333333333337 57 | 2009-01-07 21:00:00,0.08475,0.07300000000000001,0.012583333333333335 58 | 2009-01-08 00:00:00,0.09741666666666667,0.06374999999999999,0.012499999999999999 59 | 2009-01-08 03:00:00,0.08483333333333332,0.08166666666666665,0.013083333333333336 60 | 2009-01-08 06:00:00,0.10433333333333335,0.06875000000000002,0.015250000000000005 61 | 2009-01-08 09:00:00,0.10133333333333333,0.06641666666666668,0.01683333333333334 62 | 2009-01-08 12:00:00,0.11516666666666668,0.077,0.01491666666666667 63 | 2009-01-08 15:00:00,0.07625,0.06733333333333334,0.018083333333333337 64 | 2009-01-08 18:00:00,0.06925000000000002,0.06650000000000002,0.015833333333333338 65 | 2009-01-08 21:00:00,0.106,0.06974999999999999,0.014083333333333337 66 | 2009-01-09 00:00:00,0.10041666666666667,0.06591666666666667,0.013666666666666669 67 | 2009-01-09 03:00:00,0.09291666666666666,0.06083333333333335,0.013666666666666669 68 | 2009-01-09 06:00:00,0.06983333333333332,0.05191666666666667,0.013583333333333336 69 | 2009-01-09 09:00:00,0.061750000000000006,0.05941666666666667,0.01516666666666667 70 | 2009-01-09 12:00:00,0.10466666666666667,0.06924999999999999,0.016666666666666673 71 | 2009-01-09 15:00:00,0.0679166666666667,0.061833333333333344,0.016916666666666674 72 | 2009-01-09 18:00:00,0.04658333333333333,0.04474999999999999,0.011500000000000002 73 | 2009-01-09 21:00:00,0.08116666666666665,0.05358333333333334,0.010999999999999998 74 | 2009-01-10 00:00:00,0.11541666666666668,0.077,0.010999999999999998 75 | 2009-01-10 03:00:00,0.12425000000000001,0.12041666666666669,0.011166666666666665 76 | 2009-01-10 06:00:00,0.13658333333333336,0.11341666666666668,0.011666666666666667 77 | 2009-01-10 09:00:00,0.19350000000000003,0.147,0.013000000000000003 78 | 2009-01-10 12:00:00,0.14708333333333332,0.10208333333333332,0.01875 79 | 2009-01-10 15:00:00,0.07475,0.06833333333333332,0.015500000000000005 80 | 2009-01-10 18:00:00,0.09058333333333334,0.059166666666666645,0.013916666666666666 81 | 2009-01-10 21:00:00,0.07716666666666666,0.06333333333333334,0.013583333333333334 82 | 2009-01-11 00:00:00,0.061166666666666675,0.05591666666666668,0.013250000000000003 83 | 2009-01-11 03:00:00,0.09800000000000002,0.06433333333333335,0.014000000000000004 84 | 2009-01-11 06:00:00,0.10283333333333333,0.06766666666666667,0.013166666666666669 85 | 2009-01-11 09:00:00,0.08024999999999999,0.07741666666666665,0.013583333333333336 86 | 2009-01-11 12:00:00,0.11508333333333336,0.07683333333333332,0.013833333333333331 87 | 2009-01-11 15:00:00,0.10675,0.07058333333333333,0.01475 88 | 2009-01-11 18:00:00,0.11191666666666666,0.07441666666666666,0.014083333333333337 89 | 2009-01-11 21:00:00,0.12116666666666669,0.08183333333333331,0.014000000000000004 90 | 2009-01-12 00:00:00,0.12224999999999998,0.08258333333333333,0.014000000000000004 91 | 2009-01-12 03:00:00,0.08908333333333333,0.08583333333333332,0.014000000000000004 92 | 2009-01-12 06:00:00,0.13208333333333336,0.08933333333333332,0.014000000000000004 93 | 2009-01-12 09:00:00,0.13250000000000003,0.08916666666666666,0.01683333333333334 94 | 2009-01-12 12:00:00,0.13899999999999998,0.09441666666666666,0.017083333333333336 95 | 2009-01-12 15:00:00,0.10375000000000001,0.09633333333333333,0.01858333333333333 96 | 2009-01-12 18:00:00,0.15241666666666667,0.10608333333333332,0.01783333333333334 97 | 2009-01-12 21:00:00,0.155,0.10866666666666668,0.017499999999999998 98 | 2009-01-13 00:00:00,0.18175,0.13458333333333336,0.026416666666666668 99 | 2009-01-13 03:00:00,0.16608333333333333,0.16216666666666665,0.021666666666666664 100 | 2009-01-13 06:00:00,0.20933333333333334,0.1795,0.021750000000000002 101 | 2009-01-13 09:00:00,0.19650000000000004,0.19266666666666668,0.04533333333333334 102 | 2009-01-13 12:00:00,0.5295833333333334,0.5974166666666667,0.10491666666666667 103 | 2009-01-13 15:00:00,1.15875,1.62,0.08941666666666666 104 | 2009-01-13 18:00:00,0.9257500000000002,1.2191666666666665,0.053166666666666675 105 | 2009-01-13 21:00:00,0.6546666666666666,0.681,0.0395 106 | 2009-01-14 00:00:00,0.45291666666666663,0.47666666666666674,0.03391666666666668 107 | 2009-01-14 03:00:00,0.39558333333333334,0.39625000000000005,0.03033333333333334 108 | 2009-01-14 06:00:00,0.34158333333333335,0.33516666666666667,0.028666666666666674 109 | 2009-01-14 09:00:00,0.3229166666666667,0.29925,0.02933333333333334 110 | 2009-01-14 12:00:00,0.3000833333333333,0.27016666666666667,0.028166666666666673 111 | 2009-01-14 15:00:00,0.24366666666666661,0.23825,0.028500000000000008 112 | 2009-01-14 18:00:00,0.27499999999999997,0.23883333333333337,0.025583333333333336 113 | 2009-01-14 21:00:00,0.25941666666666663,0.22025000000000003,0.024249999999999997 114 | 2009-01-15 00:00:00,0.2611666666666667,0.22224999999999998,0.023916666666666666 115 | 2009-01-15 03:00:00,0.24400000000000002,0.20216666666666663,0.023416666666666665 116 | 2009-01-15 06:00:00,0.23291666666666666,0.18941666666666668,0.023416666666666665 117 | 2009-01-15 09:00:00,0.21075000000000002,0.16749999999999998,0.024999999999999998 118 | 2009-01-15 12:00:00,0.1085,0.10533333333333333,0.024833333333333332 119 | 2009-01-15 15:00:00,0.2528333333333333,0.24524999999999997,0.025833333333333337 120 | 2009-01-15 18:00:00,0.20291666666666663,0.20224999999999996,0.02233333333333333 121 | 2009-01-15 21:00:00,0.2004166666666667,0.15366666666666665,0.02108333333333333 122 | 2009-01-16 00:00:00,0.19741666666666666,0.15041666666666667,0.020916666666666663 123 | 2009-01-16 03:00:00,0.19474999999999998,0.14775000000000002,0.020999999999999994 124 | 2009-01-16 06:00:00,0.19275,0.14558333333333331,0.020999999999999994 125 | 2009-01-16 09:00:00,0.15783333333333333,0.15391666666666667,0.022999999999999996 126 | 2009-01-16 12:00:00,0.20383333333333334,0.15733333333333333,0.02258333333333333 127 | 2009-01-16 15:00:00,0.15908333333333335,0.15125,0.02333333333333333 128 | 2009-01-16 18:00:00,0.18633333333333332,0.13925,0.019999999999999997 129 | 2009-01-16 21:00:00,0.19766666666666666,0.15058333333333332,0.019999999999999997 130 | 2009-01-17 00:00:00,0.2024166666666667,0.15575,0.02008333333333333 131 | 2009-01-17 03:00:00,0.20825000000000002,0.162,0.020416666666666663 132 | 2009-01-17 06:00:00,0.1895,0.15841666666666662,0.020749999999999998 133 | 2009-01-17 09:00:00,0.21866666666666665,0.1733333333333333,0.022916666666666665 134 | 2009-01-17 12:00:00,0.20483333333333334,0.2011666666666667,0.031833333333333345 135 | 2009-01-17 15:00:00,0.28825,0.256,0.05191666666666667 136 | 2009-01-17 18:00:00,0.2709166666666667,0.2685,0.03433333333333335 137 | 2009-01-17 21:00:00,0.2678333333333333,0.25391666666666673,0.032333333333333346 138 | 2009-01-18 00:00:00,0.2861666666666667,0.2528333333333334,0.031666666666666676 139 | 2009-01-18 03:00:00,0.3095833333333334,0.28333333333333327,0.07633333333333334 140 | 2009-01-18 06:00:00,0.5420833333333334,0.5939166666666666,0.08274999999999999 141 | 2009-01-18 09:00:00,0.633,0.7456666666666667,0.077 142 | 2009-01-18 12:00:00,0.7169166666666666,0.8155000000000001,0.14966666666666664 143 | 2009-01-18 15:00:00,1.4391666666666667,2.018333333333333,0.10216666666666667 144 | 2009-01-18 18:00:00,1.4058333333333335,1.4574999999999998,0.07466666666666667 145 | 2009-01-18 21:00:00,0.6854166666666667,0.8278333333333334,0.06408333333333333 146 | 2009-01-19 00:00:00,0.5694166666666667,0.6484166666666666,0.05866666666666668 147 | 2009-01-19 03:00:00,0.5142499999999999,0.5658333333333333,0.05525 148 | 2009-01-19 06:00:00,0.5187500000000002,0.5530833333333334,0.08141666666666668 149 | 2009-01-19 09:00:00,1.0421666666666667,1.3985,0.13366666666666668 150 | 2009-01-19 12:00:00,2.0483333333333333,2.3775000000000004,0.09616666666666666 151 | 2009-01-19 15:00:00,1.1605,1.5308333333333335,0.10999999999999999 152 | 2009-01-19 18:00:00,1.07875,1.4750000000000003,0.13208333333333336 153 | 2009-01-19 21:00:00,1.1383333333333334,1.58,0.10266666666666667 154 | 2009-01-20 00:00:00,0.9978333333333332,1.3408333333333335,0.08824999999999998 155 | 2009-01-20 03:00:00,0.8119166666666667,1.0305833333333332,0.08033333333333333 156 | 2009-01-20 06:00:00,0.7326666666666667,0.9026666666666667,0.07058333333333333 157 | 2009-01-20 09:00:00,0.7530000000000001,0.773,0.06558333333333334 158 | 2009-01-20 12:00:00,0.58525,0.6724166666666668,0.06158333333333332 159 | 2009-01-20 15:00:00,0.5173333333333333,0.5703333333333332,0.05816666666666667 160 | 2009-01-20 18:00:00,0.4828333333333333,0.5196666666666667,0.05225000000000001 161 | 2009-01-20 21:00:00,0.46866666666666673,0.47533333333333333,0.04841666666666666 162 | 2009-01-21 00:00:00,0.408,0.41324999999999995,0.04549999999999999 163 | 2009-01-21 03:00:00,0.4073333333333334,0.41291666666666665,0.04308333333333333 164 | 2009-01-21 06:00:00,0.3823333333333334,0.3795,0.04191666666666666 165 | 2009-01-21 09:00:00,0.3985,0.3985,0.04249999999999999 166 | 2009-01-21 12:00:00,0.37966666666666665,0.3745,0.03991666666666666 167 | 2009-01-21 15:00:00,0.3673333333333333,0.35766666666666663,0.04041666666666665 168 | 2009-01-21 18:00:00,0.3499999999999999,0.34908333333333325,0.03691666666666668 169 | 2009-01-21 21:00:00,0.34099999999999997,0.3358333333333334,0.03600000000000001 170 | 2009-01-22 00:00:00,0.32558333333333334,0.32408333333333333,0.03591666666666668 171 | 2009-01-22 03:00:00,0.34649999999999986,0.32991666666666664,0.03525000000000001 172 | 2009-01-22 06:00:00,0.35391666666666666,0.3399166666666667,0.036083333333333335 173 | 2009-01-22 09:00:00,0.36241666666666666,0.35125,0.03666666666666668 174 | 2009-01-22 12:00:00,0.37066666666666653,0.36233333333333334,0.06033333333333333 175 | 2009-01-22 15:00:00,0.4685000000000001,0.49966666666666665,0.05475 176 | 2009-01-22 18:00:00,0.49724999999999997,0.54075,0.05041666666666667 177 | 2009-01-22 21:00:00,0.4584999999999999,0.4811666666666667,0.04633333333333333 178 | 2009-01-23 00:00:00,0.4630833333333333,0.4928333333333333,0.14091666666666666 179 | 2009-01-23 03:00:00,2.6984999999999997,3.1049166666666665,0.5429166666666666 180 | 2009-01-23 06:00:00,7.639166666666667,7.918333333333333,0.6375000000000001 181 | 2009-01-23 09:00:00,8.237499999999999,8.56,0.38375 182 | 2009-01-23 12:00:00,6.093333333333334,6.377499999999999,0.27366666666666667 183 | 2009-01-23 15:00:00,5.803333333333334,6.053333333333332,0.5699166666666667 184 | 2009-01-23 18:00:00,9.046666666666667,9.353333333333333,0.4584166666666667 185 | 2009-01-23 21:00:00,7.486666666666667,7.7391666666666685,0.23058333333333336 186 | 2009-01-24 00:00:00,4.357499999999999,4.595,0.1693333333333333 187 | 2009-01-24 03:00:00,2.9550000000000005,3.3000000000000003,0.1366666666666667 188 | 2009-01-24 06:00:00,2.2358333333333333,2.75,0.114 189 | 2009-01-24 09:00:00,1.8575,2.356666666666667,0.09974999999999999 190 | 2009-01-24 12:00:00,1.166,1.6124999999999998,0.09499999999999999 191 | 2009-01-24 15:00:00,0.9210833333333334,1.2091666666666667,0.08549999999999998 192 | 2009-01-24 18:00:00,0.90325,0.9207500000000001,0.07491666666666669 193 | 2009-01-24 21:00:00,0.7539166666666667,0.7971666666666667,0.06866666666666665 194 | 2009-01-25 00:00:00,0.6470833333333333,0.7672500000000001,0.06616666666666669 195 | 2009-01-25 03:00:00,0.6211666666666668,0.7274166666666666,0.06374999999999999 196 | 2009-01-25 06:00:00,0.62125,0.6784166666666667,0.06058333333333334 197 | 2009-01-25 09:00:00,0.5755833333333334,0.65775,0.05900000000000002 198 | 2009-01-25 12:00:00,0.5969166666666667,0.6025833333333332,0.05800000000000002 199 | 2009-01-25 15:00:00,0.5561666666666667,0.5656666666666668,0.05516666666666667 200 | 2009-01-25 18:00:00,0.4830833333333333,0.52025,0.052916666666666674 201 | 2009-01-25 21:00:00,0.46649999999999997,0.49633333333333346,0.04933333333333333 202 | 2009-01-26 00:00:00,0.46174999999999994,0.4890833333333333,0.04666666666666666 203 | 2009-01-26 03:00:00,0.4394166666666666,0.45741666666666664,0.04524999999999999 204 | 2009-01-26 06:00:00,0.4256666666666667,0.4326666666666666,0.04383333333333333 205 | 2009-01-26 09:00:00,0.3054166666666666,0.27991666666666665,0.04416666666666666 206 | 2009-01-26 12:00:00,0.36400000000000005,0.36175,0.04183333333333333 207 | 2009-01-26 15:00:00,0.5044166666666666,0.5025000000000001,0.040666666666666657 208 | 2009-01-26 18:00:00,0.3469166666666667,0.3468333333333333,0.03600000000000001 209 | 2009-01-26 21:00:00,0.3153333333333333,0.30883333333333335,0.03350000000000001 210 | 2009-01-27 00:00:00,0.3345833333333334,0.31433333333333335,0.03291666666666668 211 | 2009-01-27 03:00:00,0.29074999999999995,0.2584166666666667,0.030666666666666675 212 | 2009-01-27 06:00:00,0.28925,0.2565,0.029416666666666674 213 | 2009-01-27 09:00:00,0.27991666666666665,0.2749166666666667,0.03250000000000001 214 | 2009-01-27 12:00:00,0.2725,0.2699166666666667,0.03333333333333335 215 | 2009-01-27 15:00:00,0.28741666666666665,0.2542499999999999,0.03375000000000001 216 | 2009-01-27 18:00:00,0.29691666666666666,0.2659166666666667,0.03266666666666668 217 | 2009-01-27 21:00:00,0.26808333333333334,0.25675000000000003,0.03025000000000001 218 | 2009-01-28 00:00:00,0.28458333333333335,0.25075,0.03000000000000001 219 | 2009-01-28 03:00:00,0.2619166666666667,0.2591666666666667,0.02933333333333334 220 | 2009-01-28 06:00:00,0.26149999999999995,0.2560833333333333,0.029166666666666674 221 | 2009-01-28 09:00:00,0.2870833333333333,0.25383333333333336,0.03108333333333334 222 | 2009-01-28 12:00:00,0.2964166666666667,0.26533333333333337,0.03050000000000001 223 | 2009-01-28 15:00:00,0.2761666666666666,0.24066666666666667,0.02983333333333334 224 | 2009-01-28 18:00:00,0.23749999999999996,0.19458333333333333,0.02516666666666667 225 | 2009-01-28 21:00:00,0.23608333333333334,0.19275,0.023749999999999997 226 | 2009-01-29 00:00:00,0.23808333333333334,0.19533333333333333,0.02358333333333333 227 | 2009-01-29 03:00:00,0.2319166666666667,0.1880833333333333,0.02308333333333333 228 | 2009-01-29 06:00:00,0.22650000000000003,0.18225,0.02333333333333333 229 | 2009-01-29 09:00:00,0.26075,0.22183333333333335,0.028166666666666673 230 | 2009-01-29 12:00:00,0.24091666666666664,0.19858333333333336,0.02900000000000001 231 | 2009-01-29 15:00:00,0.22516666666666665,0.1805,0.029416666666666674 232 | 2009-01-29 18:00:00,0.20533333333333334,0.15916666666666665,0.023666666666666666 233 | 2009-01-29 21:00:00,0.21833333333333335,0.17308333333333334,0.02208333333333333 234 | 2009-01-30 00:00:00,0.21483333333333335,0.16941666666666666,0.022249999999999995 235 | 2009-01-30 03:00:00,0.20675000000000002,0.16024999999999998,0.021749999999999995 236 | 2009-01-30 06:00:00,0.20975,0.16366666666666665,0.022249999999999995 237 | 2009-01-30 09:00:00,0.24200000000000002,0.2001666666666667,0.024749999999999998 238 | 2009-01-30 12:00:00,0.2407500000000000 239 | -------------------------------------------------------------------------------- /datasets/homeprices.csv: -------------------------------------------------------------------------------- 1 | area,price 2 | 2600,550000 3 | 3000,565000 4 | 3200,610000 5 | 3600,680000 6 | 4000,725000 7 | -------------------------------------------------------------------------------- /datasets/homeprices2.csv: -------------------------------------------------------------------------------- 1 | area,bedrooms,age,price 2 | 2600,3,20,550000 3 | 3000,4,15,565000 4 | 3200,,18,610000 5 | 3600,3,30,595000 6 | 4000,5,8,760000 7 | 4100,6,8,810000 8 | -------------------------------------------------------------------------------- /datasets/house_rental_data.csv: -------------------------------------------------------------------------------- 1 | "","Sqft","Floor","TotalFloor","Bedroom","Living.Room","Bathroom","Price" 2 | "1",1177.698,2,7,2,2,2,62000 3 | "2",2134.8,5,7,4,2,2,78000 4 | "3",1138.56,5,7,2,2,1,58000 5 | "4",1458.78,2,7,3,2,2,45000 6 | "5",967.776,11,14,3,2,2,45000 7 | "6",1127.886,11,12,4,2,2,148000 8 | "7",1352.04,5,7,3,2,1,58000 9 | "8",757.854,5,14,1,0,1,48000 10 | "9",1152.792,10,12,3,2,2,45000 11 | "10",1423.2,4,5,4,2,2,65000 12 | "11",668.904,4,11,1,1,1,31000 13 | "12",711.6,4,7,2,1,1,29002 14 | "13",1352.04,9,19,4,2,2,39000 15 | "14",818.34,4,13,2,2,1,48000 16 | "15",2134.8,10,19,3,2,2,55000 17 | "16",2768.124,6,19,3,2,2,100000 18 | "17",711.6,5,13,2,2,1,48000 19 | "18",462.54,5,13,1,1,1,25000 20 | "19",2739.66,6,19,3,2,2,90000 21 | "20",1174.14,3,9,3,2,1,33000 22 | "21",2490.6,19,21,5,3,4,140000 23 | "22",2768.124,6,19,3,2,2,100000 24 | "23",747.18,7,15,2,2,1,55000 25 | "24",1668.702,4,12,3,2,2,105000 26 | "25",3664.74,19,19,3,2,3,200000 27 | "26",1779,10,12,4,2,2,98000 28 | "27",3664.74,19,19,3,2,3,200000 29 | "28",3059.88,21,24,4,2,3,145000 30 | "29",533.7,3,4,1,1,1,25000 31 | "30",853.92,6,7,2,1,1,24028 32 | "31",1732.746,8,12,4,2,2,65000 33 | "32",1245.3,10,12,3,0,0,85000 34 | "33",2134.8,10,19,3,2,2,55000 35 | "34",1206.162,7,7,2,2,1,57000 36 | "35",861.036,5,12,2,2,1,46000 37 | "36",462.54,3,4,1,1,1,52000 38 | "37",434.076,5,12,2,2,1,43500 39 | "38",768.528,4,4,3,2,2,60000 40 | "39",3735.9,19,19,3,2,3,200000 41 | "40",889.5,6,7,3,2,1,36000 42 | "41",3063.438,21,24,5,2,4,145000 43 | "42",925.08,10,14,2,1,1,48000 44 | "43",1174.14,2,14,3,2,2,36000 45 | "44",1352.04,1,4,4,2,2,65000 46 | "45",1771.884,7,15,4,2,2,70000 47 | "46",1601.1,6,7,4,2,2,50000 48 | "47",1487.244,2,6,2,2,2,47999 49 | "48",2277.12,2,25,3,2,2,80000 50 | "49",3735.9,19,19,3,2,3,200000 51 | "50",711.6,4,7,2,1,1,24030 52 | "51",2241.54,7,13,4,2,2,76000 53 | "52",1494.36,1,5,2,2,2,70000 54 | "53",711.6,2,3,2,2,1,32500 55 | "54",889.5,2,6,2,2,1,30032 56 | "55",4643.19,1,6,5,4,4,180000 57 | "56",2191.728,2,7,3,2,2,80000 58 | "57",640.44,5,7,1,1,1,36000 59 | "58",1067.4,12,12,3,2,2,36000 60 | "59",640.44,2,6,2,2,1,29032 61 | "60",1362.714,1,7,3,2,2,43000 62 | "61",604.86,6,10,2,1,1,30000 63 | "62",889.5,2,4,4,1,1,32000 64 | "63",1423.2,5,7,3,2,2,45000 65 | "64",1184.814,2,9,3,2,2,49000 66 | "65",391.38,5,7,1,1,1,27500 67 | "66",426.96,5,13,1,1,1,23000 68 | "67",1067.4,4,5,3,2,1,33500 69 | "68",533.7,2,7,2,1,1,26000 70 | "69",462.54,6,14,1,1,1,27500 71 | "70",1270.206,6,8,3,2,2,50000 72 | "71",889.5,8,12,2,1,1,25000 73 | "72",1284.438,6,12,3,2,2,41000 74 | "73",1892.856,2,7,4,2,3,90000 75 | "74",996.24,2,4,3,2,1,26000 76 | "75",711.6,4,7,2,2,1,38000 77 | "76",1754.094,3,7,3,2,2,75000 78 | "77",2063.64,7,24,3,2,1,56000 79 | "78",1522.824,5,12,4,2,2,40000 80 | "79",1540.614,6,14,4,2,2,89900 81 | "80",914.406,2,4,2,2,1,37000 82 | "81",1558.404,5,7,4,2,2,48800 83 | "82",772.086,6,7,2,1,1,39000 84 | "83",711.6,8,9,2,2,1,45000 85 | "84",747.18,5,9,2,2,2,50000 86 | "85",1227.51,5,12,3,1,2,45000 87 | "86",1451.664,2,5,3,3,2,46000 88 | "87",3255.57,4,7,4,2,4,130000 89 | "88",1779,7,12,4,2,2,60000 90 | "89",1892.856,2,7,4,2,3,78000 91 | "90",2312.7,7,15,4,2,2,70000 92 | "91",1992.48,5,12,3,2,2,70000 93 | "92",2063.64,5,7,4,2,2,70000 94 | "93",1529.94,2,7,2,2,2,49800 95 | "94",2145.474,5,6,4,2,2,79999 96 | "95",2170.38,5,7,4,2,2,60000 97 | "96",1771.884,4,7,4,2,2,57000 98 | "97",1956.9,6,17,4,2,2,66000 99 | "98",1743.42,3,7,4,2,2,75000 100 | "99",1707.84,13,20,2,2,2,55000 101 | "100",2063.64,5,7,4,2,2,70000 102 | "102",2366.07,2,7,3,2,2,75000 103 | "103",1814.58,2,16,3,2,2,60000 104 | "104",2938.908,2,25,4,2,2,95000 105 | "105",3173.736,3,9,3,2,3,150000 106 | "106",2917.56,8,16,4,2,2,150000 107 | "107",1786.116,3,6,4,2,3,98000 108 | "108",3202.2,12,12,5,3,3,80000 109 | "109",583.512,6,6,1,2,1,33000 110 | "110",2099.22,7,15,4,2,2,55000 111 | "111",2063.64,7,15,2,2,2,80000 112 | "112",2099.22,7,15,4,2,2,52000 113 | "113",857.478,6,9,4,2,2,52000 114 | "114",2227.308,2,7,4,2,2,99999 115 | "115",2088.546,8,19,3,2,2,65000 116 | "116",1423.2,7,14,3,2,2,92000 117 | "117",1569.078,6,7,3,2,2,55000 118 | "118",3202.2,5,6,4,2,2,149999 119 | "119",711.6,6,12,2,1,1,35000 120 | "120",391.38,13,14,1,1,1,22000 121 | "121",960.66,2,14,2,2,2,33000 122 | "122",878.826,10,14,2,1,1,48000 123 | "123",1280.88,10,12,3,2,2,53000 124 | "124",2134.8,3,6,3,2,2,68000 125 | "125",2149.032,5,6,4,2,2,80000 126 | "126",2543.97,9,12,3,2,2,120000 127 | "127",1206.162,7,7,2,2,1,57000 128 | "128",1280.88,7,7,2,1,1,57000 129 | "129",2067.198,4,14,4,2,2,100000 130 | "130",1184.814,6,7,3,2,2,38000 131 | "131",732.948,8,11,2,1,1,27000 132 | "132",1529.94,6,12,3,2,2,75000 133 | "133",1572.636,2,5,4,2,2,55000 134 | "134",2540.412,18,18,4,2,2,89000 135 | "135",1135.002,7,12,3,2,1,40000 136 | "136",1544.172,5,16,3,2,2,99999 137 | "137",1921.32,4,7,4,2,2,48000 138 | "138",772.086,6,7,2,1,1,39000 139 | "139",1754.094,3,7,3,2,2,75000 140 | "140",2558.202,2,10,3,2,2,80000 141 | "141",1195.488,10,16,3,2,2,86888 142 | "142",3323.172,19,21,5,3,4,140000 143 | "143",1223.952,6,7,4,2,2,58888 144 | "144",1184.814,7,7,2,2,1,57000 145 | "145",1223.952,3,7,3,2,2,41888 146 | "146",1245.3,5,6,4,2,2,39000 147 | "147",533.7,10,12,2,1,1,29999 148 | "148",2028.06,6,15,4,2,2,88000 149 | "149",4981.2,3,38,3,2,3,225000 150 | "150",1245.3,6,7,3,2,2,40999 151 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"179",1889.298,2,14,4,2,2,100000 179 | "180",1245.3,2,5,3,2,2,35000 180 | "181",1494.36,2,7,2,2,2,49800 181 | "182",1487.244,6,7,4,2,2,33700 182 | "183",1690.05,7,14,3,2,2,40000 183 | "184",796.992,3,12,2,2,2,45000 184 | "185",3255.57,4,7,4,2,5,130000 185 | "186",1063.842,3,7,2,1,1,45000 186 | "187",1263.09,5,7,2,1,2,37000 187 | "188",1352.04,5,5,3,2,2,36000 188 | "189",1287.996,4,5,4,2,2,30000 189 | "190",1167.024,3,12,3,2,2,42000 190 | "191",1167.024,3,12,3,2,2,42000 191 | "192",1839.486,2,13,1,0,1,67000 192 | "193",796.992,3,12,2,2,1,45000 193 | "194",601.302,2,9,1,1,1,38000 194 | "195",2156.148,12,17,3,2,3,75000 195 | "196",2138.358,11,14,4,2,2,55000 196 | "197",1779,2,9,3,2,2,55000 197 | "198",1779,8,15,3,2,2,85000 198 | "199",1732.746,8,12,4,2,2,65000 199 | "200",526.584,13,17,2,1,1,29500 200 | "201",718.716,5,7,2,2,1,43000 201 | "202",1601.1,3,7,3,2,2,33500 202 | "203",1174.14,14,14,3,2,1,26000 203 | "204",1408.968,9,9,2,2,2,65999 204 | "205",1387.62,2,7,3,2,2,45000 205 | "206",604.86,3,12,1,1,1,23800 206 | "207",1380.504,6,13,3,2,2,49999 207 | "208",2134.8,2,13,1,2,2,58000 208 | "209",740.064,10,12,2,2,2,45000 209 | "210",1031.82,5,12,2,2,2,36000 210 | "211",1526.382,8,12,4,2,2,65000 211 | "212",1601.1,1,4,3,2,2,65000 212 | "213",1423.2,13,21,2,2,2,55000 213 | "214",384.264,2,9,1,1,1,35000 214 | "215",1996.038,5,12,3,2,2,75000 215 | "216",718.716,5,7,2,2,1,43000 216 | "217",1366.272,4,11,3,2,2,50000 217 | "218",1743.42,3,12,3,2,2,45000 218 | "219",1636.68,4,4,4,2,2,52000 219 | "220",1903.53,8,13,3,2,2,120000 220 | "221",668.904,5,6,2,1,1,28000 221 | "222",2430.114,12,25,4,2,2,110000 222 | "223",1298.67,7,7,2,2,1,57000 223 | "224",2138.358,11,14,4,2,2,55000 224 | "225",2963.814,2,25,4,2,2,95000 225 | "226",533.7,14,16,1,1,1,25000 226 | "227",2262.888,4,14,3,2,2,120000 227 | "228",590.628,8,9,1,2,1,45000 228 | "229",964.218,5,14,2,1,1,28000 229 | "230",796.992,3,12,2,2,2,45000 230 | "231",2408.766,6,13,4,2,3,80000 231 | "232",1700.724,13,21,2,2,2,55000 232 | "233",1672.26,3,7,3,2,2,75000 233 | "234",1469.454,6,7,2,2,2,75000 234 | "235",2067.198,4,14,4,2,2,100000 235 | "236",1754.094,3,7,3,2,2,75000 236 | "237",1757.652,4,16,4,2,2,55000 237 | "238",587.07,5,7,1,1,1,36000 238 | "239",1487.244,5,7,3,2,2,50000 239 | "240",1487.244,5,7,3,2,2,50000 240 | "241",789.876,7,9,1,1,1,42000 241 | "242",2006.712,3,7,4,2,2,68000 242 | "243",871.71,8,14,2,1,1,41999 243 | "244",1522.824,11,11,4,2,3,60000 244 | "245",3173.736,3,9,4,2,3,160000 245 | "246",1899.972,21,27,4,2,2,57000 246 | "247",2205.96,8,10,3,2,3,120000 247 | "248",1106.538,3,12,1,2,1,50000 248 | "249",2668.5,5,12,4,2,2,138888 249 | "250",882.384,10,14,1,1,1,48800 250 | "251",3255.57,4,7,4,2,5,120000 251 | "252",2138.358,6,7,3,2,2,43000 252 | "253",1579.752,4,15,3,1,2,99990 253 | "254",2768.124,8,19,3,2,2,60000 254 | "255",2081.43,10,17,2,2,2,68000 255 | "256",1700.724,13,21,2,2,2,55000 256 | "257",2134.8,6,7,3,2,2,43000 257 | "258",718.716,5,12,2,1,1,34800 258 | 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"555",1885.74,2,7,4,2,2,55000 554 | "556",1761.21,3,6,3,2,2,45000 555 | "557",1761.21,3,6,4,2,2,45000 556 | "558",889.5,11,18,2,2,1,30500 557 | "559",2668.5,5,12,4,2,2,138888 558 | "560",1334.25,5,11,3,2,2,38000 559 | "561",925.08,1,4,2,2,1,36000 560 | "562",878.826,10,14,1,1,1,47999 561 | "563",1067.4,3,12,3,2,1,35000 562 | "564",1423.2,11,12,2,2,2,38000 563 | "565",1419.642,3,7,3,2,2,45000 564 | "566",711.6,5,5,2,2,1,22000 565 | "567",718.716,5,12,2,2,1,35000 566 | "568",1956.9,9,12,4,2,2,90000 567 | "569",1544.172,10,12,4,2,2,98000 568 | "570",2383.86,1,7,5,3,3,60000 569 | "571",2081.43,10,17,3,2,2,68000 570 | "572",711.6,6,6,2,1,1,17000 571 | "573",700.926,7,11,2,1,1,40000 572 | "574",1181.256,1,4,3,2,2,52000 573 | "575",498.12,11,14,2,1,1,40000 574 | "576",5856.468,7,21,6,2,5,180000 575 | "577",1245.3,8,8,3,2,2,23000 576 | "578",868.152,3,5,3,2,1,26000 577 | "579",533.7,7,7,1,1,1,28800 578 | "580",693.81,2,7,1,1,1,33000 579 | "581",1576.194,3,7,4,2,2,44000 580 | "582",5760.402,9,15,3,2,3,170000 581 | "583",796.992,3,12,2,2,1,45000 582 | "584",2052.966,5,7,4,2,2,78000 583 | "585",1163.466,2,7,3,2,2,38000 584 | "586",1316.46,4,15,3,2,2,45000 585 | "587",1099.422,4,12,2,2,1,38800 586 | "588",1480.128,4,7,2,2,2,99999 587 | "589",2739.66,5,21,4,2,2,150000 588 | "590",800.55,3,4,2,2,1,43000 589 | "591",676.02,6,7,2,1,1,45000 590 | "592",964.218,4,4,3,2,1,32000 591 | "593",1832.37,9,16,4,2,2,69000 592 | "594",782.76,10,14,1,0,1,31000 593 | "595",1551.288,5,5,4,2,2,59800 594 | "596",1102.98,2,4,2,2,2,31000 595 | "597",989.124,9,12,3,2,2,42000 596 | "598",711.6,9,14,2,1,1,27000 597 | "599",1458.78,6,7,3,2,2,36000 598 | "600",1359.156,7,16,3,2,2,45000 599 | "601",2063.64,5,9,4,2,2,60000 600 | "602",846.804,14,14,2,2,1,29000 601 | "603",1458.78,21,27,3,2,2,60000 602 | "604",1127.886,4,5,3,2,2,26000 603 | "605",1355.598,5,19,2,2,2,69999 604 | "606",1892.856,5,12,4,2,2,129999 605 | "607",1031.82,3,6,3,2,2,21000 606 | "608",1707.84,5,7,3,2,2,50000 607 | "609",640.44,9,12,2,1,1,45000 608 | "610",1014.03,8,11,3,2,2,36000 609 | "611",1067.4,6,14,3,2,1,43000 610 | "612",711.6,8,12,1,2,1,30000 611 | "613",996.24,4,5,3,2,1,6100 612 | "614",2134.8,3,6,4,2,3,98000 613 | "615",1647.354,11,14,4,2,2,55000 614 | "616",1138.56,3,12,3,2,2,42000 615 | "617",889.5,5,9,1,1,1,43000 616 | "618",711.6,8,14,1,1,1,36000 617 | "619",1857.276,2,14,3,2,2,45000 618 | "620",1102.98,6,7,3,2,2,65000 619 | "621",925.08,2,4,2,1,1,32000 620 | "622",2312.7,11,14,3,2,2,99999 621 | "623",925.08,4,9,3,2,2,45000 622 | "624",711.6,1,1,3,1,1,12000 623 | "625",4198.44,15,19,7,3,4,180000 624 | "626",3095.46,1,6,4,2,3,100000 625 | "627",2590.224,5,15,3,2,3,110000 626 | "628",1885.74,8,13,3,2,2,120000 627 | "629",1245.3,1,14,3,2,2,55000 628 | "630",1174.14,10,12,3,2,2,41000 629 | "631",1458.78,6,7,2,2,2,75000 630 | "632",1305.786,9,12,3,2,2,42000 631 | "633",1316.46,12,12,3,2,2,45000 632 | "634",1167.024,2,12,3,2,2,38000 633 | "635",996.24,3,5,3,1,1,28000 634 | "636",1099.422,2,7,4,2,2,57777 635 | "637",2042.292,6,18,2,2,2,82000 636 | "638",843.246,2,18,1,1,1,53000 637 | "639",1889.298,18,21,3,2,2,70000 638 | "640",1266.648,6,7,3,2,2,40000 639 | "641",1707.84,9,9,4,2,2,60000 640 | "642",1707.84,9,15,3,2,2,80000 641 | "643",2846.4,5,12,4,2,2,138888 642 | "644",1359.156,7,15,3,2,2,45000 643 | "645",377.148,4,10,1,1,1,24800 644 | "646",740.064,13,14,1,1,1,45000 645 | "647",1707.84,3,14,3,2,2,65000 646 | "648",1376.946,6,7,3,2,1,36000 647 | -------------------------------------------------------------------------------- /datasets/international-airline-passengers.csv: -------------------------------------------------------------------------------- 1 | Month,Passengers 2 | 1949-01,112 3 | 1949-02,118 4 | 1949-03,132 5 | 1949-04,129 6 | 1949-05,121 7 | 1949-06,135 8 | 1949-07,148 9 | 1949-08,148 10 | 1949-09,136 11 | 1949-10,119 12 | 1949-11,104 13 | 1949-12,118 14 | 1950-01,115 15 | 1950-02,126 16 | 1950-03,141 17 | 1950-04,135 18 | 1950-05,125 19 | 1950-06,149 20 | 1950-07,170 21 | 1950-08,170 22 | 1950-09,158 23 | 1950-10,133 24 | 1950-11,114 25 | 1950-12,140 26 | 1951-01,145 27 | 1951-02,150 28 | 1951-03,178 29 | 1951-04,163 30 | 1951-05,172 31 | 1951-06,178 32 | 1951-07,199 33 | 1951-08,199 34 | 1951-09,184 35 | 1951-10,162 36 | 1951-11,146 37 | 1951-12,166 38 | 1952-01,171 39 | 1952-02,180 40 | 1952-03,193 41 | 1952-04,181 42 | 1952-05,183 43 | 1952-06,218 44 | 1952-07,230 45 | 1952-08,242 46 | 1952-09,209 47 | 1952-10,191 48 | 1952-11,172 49 | 1952-12,194 50 | 1953-01,196 51 | 1953-02,196 52 | 1953-03,236 53 | 1953-04,235 54 | 1953-05,229 55 | 1953-06,243 56 | 1953-07,264 57 | 1953-08,272 58 | 1953-09,237 59 | 1953-10,211 60 | 1953-11,180 61 | 1953-12,201 62 | 1954-01,204 63 | 1954-02,188 64 | 1954-03,235 65 | 1954-04,227 66 | 1954-05,234 67 | 1954-06,264 68 | 1954-07,302 69 | 1954-08,293 70 | 1954-09,259 71 | 1954-10,229 72 | 1954-11,203 73 | 1954-12,229 74 | 1955-01,242 75 | 1955-02,233 76 | 1955-03,267 77 | 1955-04,269 78 | 1955-05,270 79 | 1955-06,315 80 | 1955-07,364 81 | 1955-08,347 82 | 1955-09,312 83 | 1955-10,274 84 | 1955-11,237 85 | 1955-12,278 86 | 1956-01,284 87 | 1956-02,277 88 | 1956-03,317 89 | 1956-04,313 90 | 1956-05,318 91 | 1956-06,374 92 | 1956-07,413 93 | 1956-08,405 94 | 1956-09,355 95 | 1956-10,306 96 | 1956-11,271 97 | 1956-12,306 98 | 1957-01,315 99 | 1957-02,301 100 | 1957-03,356 101 | 1957-04,348 102 | 1957-05,355 103 | 1957-06,422 104 | 1957-07,465 105 | 1957-08,467 106 | 1957-09,404 107 | 1957-10,347 108 | 1957-11,305 109 | 1957-12,336 110 | 1958-01,340 111 | 1958-02,318 112 | 1958-03,362 113 | 1958-04,348 114 | 1958-05,363 115 | 1958-06,435 116 | 1958-07,491 117 | 1958-08,505 118 | 1958-09,404 119 | 1958-10,359 120 | 1958-11,310 121 | 1958-12,337 122 | 1959-01,360 123 | 1959-02,342 124 | 1959-03,406 125 | 1959-04,396 126 | 1959-05,420 127 | 1959-06,472 128 | 1959-07,548 129 | 1959-08,559 130 | 1959-09,463 131 | 1959-10,407 132 | 1959-11,362 133 | 1959-12,405 134 | 1960-01,417 135 | 1960-02,391 136 | 1960-03,419 137 | 1960-04,461 138 | 1960-05,472 139 | 1960-06,535 140 | 1960-07,622 141 | 1960-08,606 142 | 1960-09,508 143 | 1960-10,461 144 | 1960-11,390 145 | 1960-12,432 146 | , 147 | International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60, -------------------------------------------------------------------------------- /datasets/migrants.csv: -------------------------------------------------------------------------------- 1 | ,ccode,country,number1000,percentarea,under18 2 | 0,AFG,Afghanistan,134,0.0,40 3 | 1,ALB,Albania,52,2.0,47 4 | 2,DZA,Algeria,249,1.0,20 5 | 3,AND,Andorra,41,53.0,7 6 | 4,AGO,Angola,638,2.0,47 7 | 5,AIA,Anguilla,6,37.0,22 8 | 6,ATG,Antigua and Barbuda,29,28.0,11 9 | 7,ARG,Argentina,2165,5.0,8 10 | 8,ARM,Armenia,191,7.0,8 11 | 9,AUS,Australia,7036,29.0,7 12 | 10,AUT,Austria,1660,19.0,5 13 | 11,AZE,Azerbaijan,259,3.0,11 14 | 12,BHS,Bahamas,62,16.0,12 15 | 13,BHR,Bahrain,723,48.0,14 16 | 14,BGD,Bangladesh,1501,1.0,18 17 | 15,BRB,Barbados,35,12.0,14 18 | 16,BLR,Belarus,1079,11.0,3 19 | 17,BEL,Belgium,1268,11.0,18 20 | 18,BLZ,Belize,60,16.0,11 21 | 19,BEN,Benin,253,2.0,26 22 | 20,BTN,Bhutan,52,6.0,11 23 | 21,BOL,Bolivia (Plurinational State of),149,1.0,32 24 | 22,BIH,Bosnia and Herzegovina,37,1.0,17 25 | 23,BWA,Botswana,166,7.0,23 26 | 24,BRA,Brazil,736,0.0,14 27 | 25,BRN,Brunei Darussalam,109,25.0,17 28 | 26,BGR,Bulgaria,154,2.0,31 29 | 27,BFA,Burkina Faso,709,4.0,21 30 | 28,BDI,Burundi,300,3.0,28 31 | 29,CPV,Cabo Verde,15,3.0,10 32 | 30,KHM,Cambodia,76,0.0,9 33 | 31,CMR,Cameroon,540,2.0,32 34 | 32,CAN,Canada,7861,21.0,8 35 | 33,CAF,Central African Republic,89,2.0,26 36 | 34,TCD,Chad,490,3.0,36 37 | 35,CHL,Chile,489,3.0,21 38 | 36,CHN,China,1000,0.0,21 39 | 37,COL,Colombia,142,0.0,31 40 | 38,COM,Comoros,13,2.0,15 41 | 39,COG,Congo,399,8.0,27 42 | 40,CRI,Costa Rica,414,8.0,9 43 | 41,CIV,C√¥te d'Ivoire,2197,9.0,14 44 | 42,HRV,Croatia,560,13.0,4 45 | 43,CUB,Cuba,13,0.0,9 46 | 44,CYP,Cyprus,189,16.0,9 47 | 45,CZE,Czechia,433,4.0,5 48 | 46,PRK,Democratic People's Republic of Korea,49,0.0,18 49 | 47,COD,Democratic Republic of the Congo,879,1.0,29 50 | 48,DNK,Denmark,657,11.0,9 51 | 49,DJI,Djibouti,116,12.0,21 52 | 50,DMA,Dominica,7,9.0,36 53 | 51,DOM,Dominican Republic,425,4.0,18 54 | 52,ECU,Ecuador,399,2.0,34 55 | 53,EGY,Egypt,478,0.0,16 56 | 54,SLV,El Salvador,42,1.0,17 57 | 55,GNQ,Equatorial Guinea,222,18.0,1 58 | 56,ERI,Eritrea,16,0.0,24 59 | 57,EST,Estonia,193,15.0,3 60 | 58,SWZ,Eswatini,33,2.0,20 61 | 59,ETH,Ethiopia,1227,1.0,43 62 | 60,FJI,Fiji,14,2.0,19 63 | 61,FIN,Finland,344,6.0,11 64 | 62,FRA,France,7903,12.0,7 65 | 63,GAB,Gabon,280,14.0,24 66 | 64,GMB,Gambia,205,10.0,26 67 | 65,GEO,Georgia,78,2.0,21 68 | 66,DEU,Germany,12165,15.0,5 69 | 67,GHA,Ghana,418,1.0,31 70 | 68,GRC,Greece,1220,11.0,6 71 | 69,GRD,Grenada,7,7.0,25 72 | 70,GTM,Guatemala,82,0.0,20 73 | 71,GIN,Guinea,123,1.0,31 74 | 72,GNB,Guinea-Bissau,23,1.0,38 75 | 73,GUY,Guyana,16,2.0,20 76 | 74,HTI,Haiti,41,0.0,23 77 | 75,VAT,Holy See,1,100.0,- 78 | 76,HND,Honduras,39,0.0,18 79 | 77,HUN,Hungary,504,5.0,11 80 | 78,ISL,Iceland,42,12.0,14 81 | 79,IND,India,5189,0.0,6 82 | 80,IDN,Indonesia,346,0.0,27 83 | 81,IRN,Iran (Islamic Republic of),2699,3.0,23 84 | 82,IRQ,Iraq,367,1.0,13 85 | 83,IRL,Ireland,807,17.0,14 86 | 84,ISR,Israel,1962,24.0,7 87 | 85,ITA,Italy,5907,10.0,7 88 | 86,JAM,Jamaica,23,1.0,28 89 | 87,JPN,Japan,2321,2.0,11 90 | 88,JOR,Jordan,3234,33.0,45 91 | 89,KAZ,Kazakhstan,3635,20.0,11 92 | 90,KEN,Kenya,1079,2.0,38 93 | 91,KIR,Kiribati,3,3.0,22 94 | 92,KWT,Kuwait,3123,76.0,17 95 | 93,KGZ,Kyrgyzstan,200,3.0,7 96 | 94,LAO,Lao People's Democratic Republic,45,1.0,15 97 | 95,LVA,Latvia,257,13.0,3 98 | 96,LBN,Lebanon,1939,32.0,41 99 | 97,LSO,Lesotho,7,0.0,17 100 | 98,LBR,Liberia,99,2.0,29 101 | 99,LBY,Libya,788,12.0,24 102 | 100,LIE,Liechtenstein,25,65.0,13 103 | 101,LTU,Lithuania,125,4.0,10 104 | 102,LUX,Luxembourg,264,45.0,9 105 | 103,MDG,Madagascar,34,0.0,18 106 | 104,MWI,Malawi,237,1.0,22 107 | 105,MYS,Malaysia,2704,9.0,10 108 | 106,MDV,Maldives,67,15.0,5 109 | 107,MLI,Mali,384,2.0,27 110 | 108,MLT,Malta,46,11.0,7 111 | 109,MHL,Marshall Islands,3,6.0,24 112 | 110,MRT,Mauritania,168,4.0,37 113 | 111,MUS,Mauritius,29,2.0,11 114 | 112,MEX,Mexico,1224,1.0,60 115 | 113,FSM,Micronesia (Federated States of),3,3.0,21 116 | 114,MCO,Monaco,21,55.0,8 117 | 115,MNG,Mongolia,18,1.0,11 118 | 116,MNE,Montenegro,71,11.0,9 119 | 117,MAR,Morocco,96,0.0,21 120 | 118,MOZ,Mozambique,247,1.0,29 121 | 119,MMR,Myanmar,75,0.0,19 122 | 120,NAM,Namibia,95,4.0,13 123 | 121,NRU,Nauru,4,33.0,20 124 | 122,NPL,Nepal,503,2.0,10 125 | 123,NLD,Netherlands,2057,12.0,7 126 | 124,NZL,New Zealand,1067,23.0,10 127 | 125,NIC,Nicaragua,41,1.0,32 128 | 126,NER,Niger,296,1.0,36 129 | 127,NGA,Nigeria,1235,1.0,48 130 | 128,NIU,Niue,1,34.0,42 131 | 129,NOR,Norway,799,15.0,11 132 | 130,OMN,Oman,2073,45.0,5 133 | 131,PAK,Pakistan,3398,2.0,5 134 | 132,PLW,Palau,5,23.0,9 135 | 133,PAN,Panama,191,5.0,10 136 | 134,PNG,Papua New Guinea,32,0.0,32 137 | 135,PRY,Paraguay,161,2.0,16 138 | 136,PER,Peru,94,0.0,26 139 | 137,PHL,Philippines,219,0.0,27 140 | 138,POL,Poland,641,2.0,13 141 | 139,PRT,Portugal,880,9.0,6 142 | 140,QAT,Qatar,1721,65.0,14 143 | 141,KOR,Republic of Korea,1152,2.0,5 144 | 142,MDA,Republic of Moldova,140,3.0,17 145 | 143,ROU,Romania,371,2.0,42 146 | 144,RUS,Russian Federation,11652,8.0,6 147 | 145,RWA,Rwanda,443,4.0,20 148 | 146,KNA,Saint Kitts and Nevis,8,14.0,25 149 | 147,LCA,Saint Lucia,13,7.0,27 150 | 148,VCT,Saint Vincent and the Grenadines,5,4.0,33 151 | 149,WSM,Samoa,5,2.0,40 152 | 150,SMR,San Marino,5,16.0,12 153 | 151,STP,Sao Tome and Principe,2,1.0,17 154 | 152,SAU,Saudi Arabia,12185,37.0,20 155 | 153,SEN,Senegal,266,2.0,24 156 | 154,SRB,Serbia,802,9.0,3 157 | 155,SYC,Seychelles,13,14.0,9 158 | 156,SLE,Sierra Leone,95,1.0,30 159 | 157,SGP,Singapore,2623,46.0,10 160 | 158,SVK,Slovakia,185,3.0,12 161 | 159,SVN,Slovenia,245,12.0,6 162 | 160,SLB,Solomon Islands,3,0.0,19 163 | 161,SOM,Somalia,45,0.0,34 164 | 162,ZAF,South Africa,4037,7.0,16 165 | 163,SSD,South Sudan,845,7.0,28 166 | 164,ESP,Spain,5947,13.0,8 167 | 165,LKA,Sri Lanka,40,0.0,36 168 | 166,PSE,State of Palestine,254,5.0,14 169 | 167,SDN,Sudan,736,2.0,31 170 | 168,SUR,Suriname,48,8.0,30 171 | 169,SWE,Sweden,1748,18.0,10 172 | 170,CHE,Switzerland,2506,30.0,6 173 | 171,SYR,Syrian Arab Republic,1014,6.0,17 174 | 172,TJK,Tajikistan,273,3.0,6 175 | 173,THA,Thailand,3589,5.0,14 176 | 174,MKD,The former Yugoslav Republic of Macedonia,131,6.0,14 177 | 175,TLS,Timor-Leste,12,1.0,18 178 | 176,TGO,Togo,284,4.0,39 179 | 177,TON,Tonga,5,5.0,28 180 | 178,TTO,Trinidad and Tobago,50,4.0,17 181 | 179,TUN,Tunisia,58,1.0,17 182 | 180,TUR,Turkey,4882,6.0,26 183 | 181,TKM,Turkmenistan,195,3.0,7 184 | 182,TUV,Tuvalu,0,1.0,20 185 | 183,UGA,Uganda,1692,4.0,27 186 | 184,UKR,Ukraine,4964,11.0,5 187 | 185,ARE,United Arab Emirates,8313,88.0,14 188 | 186,GBR,United Kingdom of Great Britain and Northern Ireland,8842,13.0,8 189 | 187,TZA,United Republic of Tanzania,493,1.0,21 190 | 188,USA,United States of America,49777,15.0,5 191 | 189,URY,Uruguay,80,2.0,19 192 | 190,UZB,Uzbekistan,1159,4.0,8 193 | 191,VUT,Vanuatu,3,1.0,24 194 | 192,VEN,Venezuela (Bolivarian Republic of),1426,4.0,14 195 | 193,VNM,Viet Nam,76,0.0,15 196 | 194,YEM,Yemen,384,1.0,30 197 | 195,ZMB,Zambia,157,1.0,18 198 | 196,ZWE,Zimbabwe,404,2.0,13 199 | -------------------------------------------------------------------------------- /datasets/monthly-milk-production-pounds.csv: -------------------------------------------------------------------------------- 1 | Month,Monthly milk production (pounds per cow) 2 | 1962-01,589 3 | 1962-02,561 4 | 1962-03,640 5 | 1962-04,656 6 | 1962-05,727 7 | 1962-06,697 8 | 1962-07,640 9 | 1962-08,599 10 | 1962-09,568 11 | 1962-10,577 12 | 1962-11,553 13 | 1962-12,582 14 | 1963-01,600 15 | 1963-02,566 16 | 1963-03,653 17 | 1963-04,673 18 | 1963-05,742 19 | 1963-06,716 20 | 1963-07,660 21 | 1963-08,617 22 | 1963-09,583 23 | 1963-10,587 24 | 1963-11,565 25 | 1963-12,598 26 | 1964-01,628 27 | 1964-02,618 28 | 1964-03,688 29 | 1964-04,705 30 | 1964-05,770 31 | 1964-06,736 32 | 1964-07,678 33 | 1964-08,639 34 | 1964-09,604 35 | 1964-10,611 36 | 1964-11,594 37 | 1964-12,634 38 | 1965-01,658 39 | 1965-02,622 40 | 1965-03,709 41 | 1965-04,722 42 | 1965-05,782 43 | 1965-06,756 44 | 1965-07,702 45 | 1965-08,653 46 | 1965-09,615 47 | 1965-10,621 48 | 1965-11,602 49 | 1965-12,635 50 | 1966-01,677 51 | 1966-02,635 52 | 1966-03,736 53 | 1966-04,755 54 | 1966-05,811 55 | 1966-06,798 56 | 1966-07,735 57 | 1966-08,697 58 | 1966-09,661 59 | 1966-10,667 60 | 1966-11,645 61 | 1966-12,688 62 | 1967-01,713 63 | 1967-02,667 64 | 1967-03,762 65 | 1967-04,784 66 | 1967-05,837 67 | 1967-06,817 68 | 1967-07,767 69 | 1967-08,722 70 | 1967-09,681 71 | 1967-10,687 72 | 1967-11,660 73 | 1967-12,698 74 | 1968-01,717 75 | 1968-02,696 76 | 1968-03,775 77 | 1968-04,796 78 | 1968-05,858 79 | 1968-06,826 80 | 1968-07,783 81 | 1968-08,740 82 | 1968-09,701 83 | 1968-10,706 84 | 1968-11,677 85 | 1968-12,711 86 | 1969-01,734 87 | 1969-02,690 88 | 1969-03,785 89 | 1969-04,805 90 | 1969-05,871 91 | 1969-06,845 92 | 1969-07,801 93 | 1969-08,764 94 | 1969-09,725 95 | 1969-10,723 96 | 1969-11,690 97 | 1969-12,734 98 | 1970-01,750 99 | 1970-02,707 100 | 1970-03,807 101 | 1970-04,824 102 | 1970-05,886 103 | 1970-06,859 104 | 1970-07,819 105 | 1970-08,783 106 | 1970-09,740 107 | 1970-10,747 108 | 1970-11,711 109 | 1970-12,751 110 | 1971-01,804 111 | 1971-02,756 112 | 1971-03,860 113 | 1971-04,878 114 | 1971-05,942 115 | 1971-06,913 116 | 1971-07,869 117 | 1971-08,834 118 | 1971-09,790 119 | 1971-10,800 120 | 1971-11,763 121 | 1971-12,800 122 | 1972-01,826 123 | 1972-02,799 124 | 1972-03,890 125 | 1972-04,900 126 | 1972-05,961 127 | 1972-06,935 128 | 1972-07,894 129 | 1972-08,855 130 | 1972-09,809 131 | 1972-10,810 132 | 1972-11,766 133 | 1972-12,805 134 | 1973-01,821 135 | 1973-02,773 136 | 1973-03,883 137 | 1973-04,898 138 | 1973-05,957 139 | 1973-06,924 140 | 1973-07,881 141 | 1973-08,837 142 | 1973-09,784 143 | 1973-10,791 144 | 1973-11,760 145 | 1973-12,802 146 | 1974-01,828 147 | 1974-02,778 148 | 1974-03,889 149 | 1974-04,902 150 | 1974-05,969 151 | 1974-06,947 152 | 1974-07,908 153 | 1974-08,867 154 | 1974-09,815 155 | 1974-10,812 156 | 1974-11,773 157 | 1974-12,813 158 | 1975-01,834 159 | 1975-02,782 160 | 1975-03,892 161 | 1975-04,903 162 | 1975-05,966 163 | 1975-06,937 164 | 1975-07,896 165 | 1975-08,858 166 | 1975-09,817 167 | 1975-10,827 168 | 1975-11,797 169 | 1975-12,843 -------------------------------------------------------------------------------- /datasets/su5m.csv: -------------------------------------------------------------------------------- 1 | ,ccode,country,m1990,m2000,m2010,m2015,m2017,f1990,f2000,f2010,f2015,f2017 2 | 0,AFG,Afghanistan,180.1,133.5,94.2,77.2,71.8,169.7,123.8,85.5,68.8,63.7 3 | 1,ALB,Albania,43.7,26.7,13.0,10.1,9.5,36.4,22.2,11.0,8.6,8.1 4 | 2,DZA,Algeria,53.7,42.5,28.9,26.4,25.5,45.3,36.7,25.8,23.5,22.5 5 | 3,AND,Andorra,9.4,5.4,4.3,3.9,3.6,7.6,4.5,3.6,3.2,3.0 6 | 4,AGO,Angola,235.0,217.5,129.2,95.1,86.9,211.6,194.5,113.1,82.4,74.9 7 | 5,ATG,Antigua and Barbuda,29.0,16.5,10.8,8.7,8.1,23.4,13.2,8.8,7.2,6.7 8 | 6,ARG,Argentina,31.8,21.9,16.0,12.5,11.3,25.7,17.6,13.0,10.3,9.4 9 | 7,ARM,Armenia,54.3,33.1,20.0,15.5,14.0,44.6,26.7,16.0,12.4,11.2 10 | 8,AUS,Australia,10.3,6.8,5.3,4.1,3.8,8.1,5.5,4.3,3.5,3.2 11 | 9,AUS,Austria,10.6,6.0,4.7,4.1,3.9,8.4,4.9,3.9,3.3,3.2 12 | 10,AZE,Azerbaijan,101.1,79.9,41.0,29.2,25.4,89.2,69.2,33.7,23.5,20.5 13 | 11,BHS,Bahamas,25.2,17.1,12.5,8.6,7.7,21.7,14.8,10.8,7.5,6.7 14 | 12,BHR,Bahrain,23.8,12.9,8.8,7.9,7.6,22.2,12.0,8.2,7.3,7.1 15 | 13,BGD,Bangladesh,147.1,90.8,52.0,38.8,34.7,140.2,84.0,46.3,33.8,30.0 16 | 14,BRB,Barbados,19.6,16.5,15.8,14.3,13.6,16.1,13.8,13.1,11.9,11.2 17 | 15,BLR,Belarus,17.4,14.6,6.2,4.5,4.2,12.9,10.8,4.8,3.6,3.3 18 | 16,BEL,Belgium,11.3,6.5,5.0,4.4,4.2,8.6,5.2,4.0,3.5,3.3 19 | 17,BLZ,Belize,42.0,26.0,20.8,17.1,15.6,34.8,21.2,17.1,14.1,12.8 20 | 18,BEN,Benin,185.1,149.2,119.4,109.3,103.7,170.0,137.0,107.4,97.8,92.6 21 | 19,BTN,Bhutan,132.7,81.8,45.8,36.2,33.6,122.8,73.4,39.0,30.3,27.9 22 | 20,BOL,Bolivia (Plurinational State of),130.1,84.4,49.9,40.9,38.1,117.2,74.6,41.9,33.9,31.4 23 | 21,BIH,Bosnia and Herzegovina,20.1,10.9,7.9,6.6,6.2,16.1,8.7,6.4,5.4,5.2 24 | 22,BWA,Botswana,55.7,91.3,53.9,44.2,40.9,46.7,82.4,45.8,36.8,34.0 25 | 23,BRA,Brazil,69.0,38.6,20.9,17.6,16.5,56.9,30.6,16.4,13.8,13.0 26 | 24,BRN,Brunei Darussalam,14.5,12.7,11.5,11.4,11.3,12.2,10.8,9.7,9.6,9.6 27 | 25,BGR,Bulgaria,20.5,19.5,11.8,9.2,8.2,16.2,15.6,9.5,7.4,6.7 28 | 26,BFA,Burkina Faso,207.0,186.5,120.7,92.5,85.3,192.4,174.1,111.4,83.9,76.9 29 | 27,BDI,Burundi,182.1,163.9,96.2,72.0,65.7,166.6,148.9,84.8,62.1,56.3 30 | 28,KHM,Cambodia,123.7,114.3,49.0,35.5,32.5,107.7,99.2,39.6,28.2,25.7 31 | 29,CMR,Cameroon,146.1,158.2,117.0,95.7,89.5,130.8,142.0,103.5,84.1,78.3 32 | 30,CAN,Canada,9.1,6.8,6.1,5.7,5.5,7.3,5.6,5.2,4.9,4.7 33 | 31,CPV,Cabo Verde,67.0,38.5,27.4,21.6,19.1,58.2,32.4,22.7,17.8,15.7 34 | 32,CAF,Central African Republic,183.1,182.1,157.5,136.8,128.0,167.9,167.8,143.7,123.3,114.8 35 | 33,TCD,Chad,222.4,195.4,157.9,137.8,129.9,202.5,177.5,142.2,123.6,116.1 36 | 34,CHL,Chile,21.0,11.9,9.5,8.6,8.0,17.2,9.9,8.0,7.2,6.8 37 | 35,CHN,China,56.0,38.7,16.8,11.4,9.9,51.5,34.7,14.6,10.0,8.7 38 | 36,COL,Colombia,38.9,27.8,20.6,17.5,16.4,31.2,22.1,16.4,13.9,13.0 39 | 37,COM,Comoros,132.1,107.5,91.4,78.8,74.3,117.2,94.3,79.2,67.3,63.4 40 | 38,COG,Congo,95.4,120.5,67.6,55.5,51.6,84.2,107.7,58.3,46.9,43.2 41 | 39,COD,Democratic Republic of the Congo,194.6,170.5,123.9,104.8,97.9,177.6,151.6,107.9,90.2,84.0 42 | 40,COK,Cook Islands,27.4,18.6,10.9,8.9,8.3,21.9,14.9,8.9,7.3,6.9 43 | 41,CRI,Costa Rica,18.7,14.3,11.0,10.2,9.8,14.8,11.5,9.0,8.5,8.1 44 | 42,CIV,Cote d'Ivoire,163.3,157.9,119.4,103.4,96.8,139.8,135.0,99.8,86.0,80.3 45 | 43,HRV,Croatia,14.4,9.1,5.9,5.2,5.0,11.2,7.5,5.0,4.4,4.2 46 | 44,CUB,Cuba,15.0,9.0,6.7,6.2,5.9,11.4,7.1,5.4,5.0,4.8 47 | 45,CYP,Cyprus,12.1,7.2,3.9,3.1,2.9,9.9,6.1,3.3,2.7,2.5 48 | 46,CZE,Czechia,13.7,6.1,3.8,3.5,3.7,10.5,4.8,3.0,2.8,2.9 49 | 47,DNK,Denmark,10.0,6.0,4.5,4.6,4.6,7.8,4.9,3.7,3.8,3.9 50 | 48,DJI,Djibouti,126.1,108.2,82.5,71.1,66.7,110.7,94.5,70.6,60.1,56.3 51 | 49,DMA,Dominica,18.5,16.6,22.5,33.6,36.6,15.7,14.1,19.1,28.6,31.3 52 | 50,DOM,Dominican Republic,64.5,44.5,37.5,34.5,32.7,55.6,37.4,31.1,28.5,26.9 53 | 51,ECU,Ecuador,59.3,32.5,20.4,16.8,16.1,48.8,25.5,16.0,13.3,12.8 54 | 52,EGY,Egypt,85.6,48.4,30.5,25.1,23.4,85.8,45.1,27.4,22.3,20.7 55 | 53,SLV,El Salvador,64.3,36.0,21.4,17.1,15.9,54.8,29.5,17.2,13.8,12.9 56 | 54,GNQ,Equatorial Guinea,187.7,164.9,120.4,102.4,95.7,169.2,147.5,106.1,89.2,83.2 57 | 55,ERI,Eritrea,161.7,95.8,60.7,50.9,47.8,139.0,80.6,49.5,41.0,38.2 58 | 56,EST,Estonia,19.9,12.3,5.0,3.4,3.0,15.4,9.7,4.1,2.8,2.5 59 | 57,ETH,Ethiopia,214.8,153.1,91.3,70.5,64.5,188.5,131.6,75.7,57.4,52.1 60 | 58,FSM,Micronesia (Federated States of),59.7,57.9,43.9,37.6,35.3,50.3,48.9,36.2,30.6,28.8 61 | 59,FJI,Fiji,31.4,24.7,25.9,26.7,27.5,26.6,21.0,21.9,22.5,23.1 62 | 60,FIN,Finland,7.4,4.7,3.3,2.7,2.5,6.0,3.8,2.7,2.2,2.0 63 | 61,FRA,France,10.2,6.0,4.6,4.6,4.6,7.7,4.7,3.8,3.8,3.8 64 | 62,GAB,Gabon,99.0,89.2,68.4,57.1,52.8,85.5,76.7,57.9,47.5,43.5 65 | 63,GMB,Gambia,179.2,125.2,86.2,72.9,68.4,160.5,111.2,75.1,62.7,58.5 66 | 64,GEO,Georgia,52.8,39.5,18.6,13.5,12.1,42.3,31.0,14.4,10.6,9.5 67 | 65,DEU,Germany,9.6,5.9,4.6,4.2,4.1,7.4,4.8,3.8,3.5,3.4 68 | 66,GHA,Ghana,134.1,106.0,78.3,59.6,53.9,118.5,92.1,66.3,49.6,44.4 69 | 67,GRC,Greece,11.3,6.9,4.2,5.1,5.7,9.5,5.9,3.6,4.4,4.9 70 | 68,GRD,Grenada,25.2,17.0,15.5,17.6,18.1,21.4,14.4,13.1,14.9,15.3 71 | 69,GTM,Guatemala,86.8,56.0,38.8,32.5,30.4,75.7,47.5,31.9,26.4,24.6 72 | 70,GIN,Guinea,242.5,172.8,114.6,96.9,90.8,226.4,157.5,102.8,86.2,80.5 73 | 71,GNB,Guinea-Bissau,235.0,185.8,121.4,97.3,90.3,212.2,165.3,105.8,83.9,77.8 74 | 72,GUY,Guyana,66.9,52.0,42.0,37.4,35.2,53.7,41.1,32.8,29.0,27.2 75 | 73,HTI,Haiti,153.2,111.0,217.1,81.0,77.3,136.1,96.7,204.4,68.9,65.7 76 | 74,HND,Honduras,63.1,41.0,26.2,21.7,20.2,53.0,33.6,21.0,17.4,16.1 77 | 75,HUN,Hungary,19.1,11.1,6.5,5.5,4.9,15.3,9.2,5.5,4.7,4.2 78 | 76,ISL,Iceland,6.9,4.5,2.9,2.5,2.3,5.7,3.8,2.4,2.1,1.9 79 | 77,IND,India,122.0,87.0,55.6,42.8,38.5,130.3,96.6,61.4,45.5,40.4 80 | 78,IDN,Indonesia,90.3,56.7,36.8,30.3,28.3,77.3,47.2,29.5,24.0,22.3 81 | 79,IRN,Iran (Islamic Republic of),56.4,35.2,20.4,16.7,15.6,55.8,33.2,18.8,15.3,14.2 82 | 80,IRQ,Iraq,57.9,48.5,40.1,35.2,33.3,49.6,41.1,33.5,29.1,27.3 83 | 81,IRL,Ireland,10.2,7.8,4.6,4.1,3.9,8.2,6.4,3.8,3.4,3.2 84 | 82,ISR,Israel,12.3,7.4,4.9,4.1,3.8,10.8,6.4,4.2,3.5,3.3 85 | 83,ITA,Italy,10.6,6.0,4.3,3.8,3.6,8.7,5.1,3.6,3.2,3.1 86 | 84,JAM,Jamaica,34.1,25.1,20.9,18.1,17.0,26.9,19.5,16.3,14.2,13.3 87 | 85,JPN,Japan,6.9,4.9,3.4,3.2,2.7,5.7,4.1,3.0,2.8,2.4 88 | 86,JOR,Jordan,37.8,29.1,22.1,19.0,17.9,34.9,26.4,19.9,17.1,16.1 89 | 87,KAZ,Kazakhstan,58.2,47.8,22.7,13.3,11.1,46.6,38.0,18.0,10.6,8.9 90 | 88,KEN,Kenya,109.4,110.4,62.7,52.7,49.5,97.6,98.4,53.9,44.5,41.5 91 | 89,KIR,Kiribati,101.9,76.6,70.0,62.6,59.2,89.1,65.5,59.5,52.8,49.8 92 | 90,PRK,Democratic People's Republic of Korea,48.1,65.4,33.1,23.6,21.3,40.0,55.8,26.9,19.0,17.2 93 | 91,KOR,Republic of Korea,16.8,8.0,4.4,3.8,3.5,14.1,6.9,3.8,3.2,3.0 94 | 92,KWT,Kuwait,19.1,13.7,11.6,9.4,8.7,16.1,11.7,9.9,8.1,7.5 95 | 93,KGZ,Kyrgyzstan,71.5,54.3,32.9,24.8,22.2,59.7,44.3,26.2,19.6,17.6 96 | 94,LAO,Lao People's Democratic Republic,162.9,120.3,86.8,73.6,68.9,145.0,104.4,73.7,61.8,57.7 97 | 95,LVA,Latvia,18.9,15.7,8.5,5.4,4.5,14.7,12.8,7.1,4.6,3.8 98 | 96,LBN,Lebanon,33.9,21.0,10.8,8.7,8.2,30.8,18.9,9.7,7.9,7.4 99 | 97,LSO,Lesotho,95.8,124.9,106.7,100.9,92.7,81.4,108.6,91.4,86.3,78.7 100 | 98,LBR,Liberia,273.7,195.7,103.9,85.8,80.2,246.6,176.1,90.8,74.1,69.0 101 | 99,LBY,Libya,45.4,30.9,18.3,14.7,13.6,37.9,25.2,14.8,11.9,11.1 102 | 100,LTU,Lithuania,16.9,11.8,6.6,5.4,4.6,13.4,9.6,5.5,4.5,3.9 103 | 101,LUX,Luxembourg,9.5,5.2,3.2,2.9,2.8,7.8,4.3,2.7,2.4,2.3 104 | 102,MKD,The former Yugoslav Republic of Macedonia,38.5,17.1,11.1,13.0,14.5,34.9,14.8,9.7,11.4,12.7 105 | 103,MDG,Madagascar,167.7,113.1,66.4,52.0,48.2,152.1,100.8,56.9,43.4,40.0 106 | 104,MWI,Malawi,245.4,180.9,95.1,67.0,60.2,223.3,162.4,81.8,56.2,50.3 107 | 105,MYS,Malaysia,18.3,11.2,8.4,8.2,8.5,14.8,9.1,6.9,6.9,7.2 108 | 106,MDV,Maldives,99.6,47.8,14.4,9.7,8.7,87.6,39.6,11.6,7.9,7.1 109 | 107,MLI,Mali,263.0,227.8,141.8,119.0,111.3,244.7,210.9,129.5,107.8,100.5 110 | 108,MLT,Malta,12.4,8.4,7.2,7.1,7.0,10.2,7.0,6.1,6.0,5.9 111 | 109,MHL,Marshall Islands,53.5,45.0,43.3,41.2,39.6,44.4,36.6,34.9,33.0,31.5 112 | 110,MRT,Mauritania,125.2,121.2,104.4,90.2,84.7,110.4,106.0,90.9,77.8,72.9 113 | 111,MUS,Mauritius,25.7,20.7,16.6,15.8,14.4,20.0,16.2,13.4,12.8,11.7 114 | 112,MEX,Mexico,49.1,29.3,18.9,16.2,14.7,41.6,24.1,15.6,13.3,12.1 115 | 113,MDA,Republic of Moldova,36.6,34.6,18.9,17.6,17.1,29.5,28.2,15.3,14.3,13.8 116 | 114,MCO,Monaco,8.6,5.7,4.4,3.9,3.7,6.9,4.6,3.6,3.1,3.0 117 | 115,MNG,Mongolia,122.0,73.5,31.1,22.3,20.4,92.8,53.7,21.2,15.1,13.9 118 | 116,MNE,Montenegro,17.7,15.1,7.2,4.4,3.7,15.5,13.1,6.3,3.9,3.3 119 | 117,MAR,Morocco,84.1,53.8,35.1,27.9,25.6,74.6,45.5,28.8,22.8,20.9 120 | 118,MOZ,Mozambique,248.5,176.8,108.3,83.2,76.9,229.9,163.8,97.6,73.6,67.6 121 | 119,MMR,Myanmar,123.0,96.1,69.2,57.0,53.0,107.9,83.2,58.6,47.6,44.0 122 | 120,NAM,Namibia,77.5,81.2,57.8,51.1,48.1,67.7,71.0,49.0,42.9,40.2 123 | 121,NRU,Nauru,64.2,45.2,42.5,38.4,36.4,54.6,37.3,34.9,31.4,29.5 124 | 122,NPL,Nepal,141.0,83.4,49.5,38.8,35.8,139.6,79.5,44.6,34.2,31.3 125 | 123,NLD,Netherlands,9.4,6.9,4.9,4.4,4.3,7.3,5.5,4.0,3.6,3.5 126 | 124,NZL,New Zealand,12.5,8.1,6.8,6.1,5.8,9.8,6.6,5.5,5.0,4.7 127 | 125,NIC,Nicaragua,72.4,42.6,25.2,20.6,19.2,61.0,34.5,19.9,16.3,15.2 128 | 126,NER,Niger,328.9,227.7,127.5,97.1,88.0,323.9,219.5,119.4,89.6,80.8 129 | 127,NGA,Nigeria,222.2,194.7,136.8,114.0,106.1,200.9,177.2,121.9,100.8,93.8 130 | 128,NIU,Niue,15.3,25.7,29.1,25.4,23.8,12.4,20.7,23.3,20.3,19.1 131 | 129,NOR,Norway,9.7,5.4,3.6,3.0,2.8,7.6,4.3,2.9,2.4,2.3 132 | 130,OMN,Oman,42.6,18.1,12.7,12.3,12.4,35.7,14.8,10.4,10.1,10.2 133 | 131,PAK,Pakistan,141.3,115.3,94.3,83.0,78.3,136.6,109.6,87.1,75.9,71.2 134 | 132,PLW,Palau,39.7,29.5,21.3,18.1,17.0,32.2,23.7,17.1,14.5,13.6 135 | 133,PAN,Panama,33.8,28.8,22.2,19.0,17.9,27.4,23.2,17.8,15.3,14.3 136 | 134,PNG,Papua New Guinea,93.1,83.0,71.3,61.7,57.6,81.7,72.2,61.4,52.4,48.9 137 | 135,PRY,Paraguay,49.1,37.0,28.7,24.6,23.1,41.7,30.6,23.5,20.0,18.7 138 | 136,PER,Peru,84.9,41.8,22.4,17.8,16.4,76.4,35.4,18.2,14.5,13.4 139 | 137,PHL,Philippines,62.9,42.8,34.8,32.3,31.2,52.0,34.5,27.7,25.7,24.8 140 | 138,POL,Poland,19.4,10.2,6.5,5.3,5.2,15.2,8.4,5.4,4.4,4.3 141 | 139,PRT,Portugal,16.4,7.9,4.2,4.0,4.1,13.0,6.5,3.5,3.3,3.3 142 | 140,QAT,Qatar,22.6,13.5,9.8,8.7,8.2,19.0,11.4,8.3,7.4,7.0 143 | 141,ROU,Romania,34.3,24.2,12.7,10.2,8.6,27.7,19.5,10.2,8.2,7.0 144 | 142,RUS,Russian Federation,24.8,21.9,11.6,9.5,8.3,18.4,16.6,9.1,7.6,6.7 145 | 143,RWA,Rwanda,159.1,189.3,68.7,45.6,41.3,142.8,173.1,59.8,38.2,34.3 146 | 144,KNA,Saint Kitts and Nevis,34.8,24.8,15.6,15.1,14.8,28.2,20.0,12.7,12.6,12.5 147 | 145,LCA,Saint Lucia,24.7,19.9,20.3,19.1,18.2,19.9,16.2,16.5,15.5,14.8 148 | 146,WSM,Samoa,33.0,23.6,20.3,18.8,17.9,27.8,19.9,16.9,15.8,14.9 149 | 147,SMR,San Marino,11.8,6.4,3.5,2.7,2.4,9.6,5.4,3.0,2.2,2.0 150 | 148,STP,Sao Tome and Principe,115.0,90.3,49.7,38.7,35.8,101.9,79.2,41.1,31.3,28.8 151 | 149,SAU,Saudi Arabia,46.7,23.3,12.7,8.9,7.7,42.1,20.6,11.3,8.0,7.0 152 | 150,SEN,Senegal,145.9,138.1,71.1,54.0,49.4,132.4,125.0,61.7,45.5,41.3 153 | 151,SRB,Serbia,29.9,14.0,8.4,6.9,6.2,26.3,11.2,6.8,5.7,5.2 154 | 152,SYC,Seychelles,18.0,14.6,15.4,15.6,15.3,15.3,12.5,13.1,13.2,13.0 155 | 153,SLE,Sierra Leone,272.9,242.6,169.9,128.3,116.3,250.1,223.0,155.9,115.9,104.4 156 | 154,SGP,Singapore,8.3,4.2,3.0,3.0,3.1,7.0,3.5,2.6,2.5,2.6 157 | 155,SVK,Slovakia,16.5,10.8,7.7,6.5,6.2,12.8,8.7,6.2,5.4,5.1 158 | 156,SVN,Slovenia,11.5,6.0,3.5,2.6,2.3,9.2,5.0,3.0,2.2,2.0 159 | 157,SLB,Solomon Islands,41.5,32.9,28.0,24.0,22.5,35.0,27.5,23.2,19.8,18.5 160 | 158,SOM,Somalia,187.7,180.4,165.4,141.8,133.2,171.6,165.5,151.0,128.4,120.5 161 | 159,ZAF,South Africa,66.5,84.2,63.9,44.4,40.9,55.0,72.4,54.4,36.0,33.0 162 | 160,SSD,South Sudan,260.7,188.4,111.8,100.6,100.6,246.1,177.0,102.4,91.6,91.7 163 | 161,ESP,Spain,10.1,5.9,4.1,3.5,3.3,8.2,4.9,3.5,3.0,2.8 164 | 162,LKA,Sri Lanka,23.1,17.9,12.6,10.4,9.6,19.4,15.1,10.6,8.7,8.0 165 | 163,VCT,Saint Vincent and the Grenadines,26.5,24.5,22.7,19.1,17.8,22.0,20.3,18.6,15.6,14.6 166 | 164,PSE,State of Palestine,47.2,32.4,25.4,23.7,22.7,41.9,27.8,21.5,20.0,19.1 167 | 165,SDN,Sudan,139.2,111.8,82.5,72.4,68.3,123.5,97.5,70.7,61.7,57.9 168 | 166,SUR,Suriname,52.4,38.2,27.3,23.3,21.8,43.0,30.7,21.7,18.4,17.3 169 | 167,SWZ,Eswatini,74.2,133.3,98.6,62.5,58.4,62.6,118.6,86.9,52.9,49.1 170 | 168,SWE,Sweden,7.7,4.5,3.3,3.2,3.1,6.2,3.7,2.8,2.6,2.6 171 | 169,CHE,Switzerland,9.1,6.2,4.9,4.7,4.6,7.2,5.1,4.1,3.9,3.8 172 | 170,SYR,Syrian Arab Republic,40.2,25.6,17.5,18.7,18.5,34.3,21.1,14.4,15.6,15.5 173 | 171,TJK,Tajikistan,112.3,94.5,47.2,39.6,37.2,97.1,80.4,38.2,31.8,29.7 174 | 172,TZA,United Republic of Tanzania,171.6,135.4,77.2,62.0,57.6,159.2,125.1,69.3,54.5,50.3 175 | 173,THA,Thailand,40.6,24.3,14.7,11.5,10.5,33.0,19.3,11.8,9.3,8.5 176 | 174,TLS,Timor-Leste,180.4,113.9,66.6,55.2,51.5,166.2,102.2,57.6,47.0,43.5 177 | 175,TGO,Togo,155.2,129.3,96.9,83.5,78.6,136.6,111.6,83.5,71.4,66.9 178 | 176,TON,Tonga,19.8,15.5,15.4,14.9,14.2,24.5,19.4,19.2,18.5,17.6 179 | 177,TTO,Trinidad and Tobago,35.6,35.6,34.2,30.2,28.5,29.8,29.7,28.5,25.0,23.5 180 | 178,TUN,Tunisia,59.7,34.5,18.9,15.2,14.2,52.8,28.8,15.7,12.7,11.8 181 | 179,TUR,Turkey,76.4,41.1,20.2,14.2,12.3,71.5,37.3,18.1,12.6,11.0 182 | 180,TKM,Turkmenistan,93.6,89.6,66.5,56.6,53.3,74.7,71.2,52.0,43.9,41.1 183 | 181,TUV,Tuvalu,60.8,45.4,34.1,29.2,27.3,52.8,38.3,28.1,23.9,22.3 184 | 182,UGA,Uganda,193.1,156.8,84.6,60.5,53.8,168.7,134.8,71.1,49.8,43.8 185 | 183,UKR,Ukraine,21.5,20.5,13.0,10.4,9.7,17.1,16.4,10.4,8.4,7.8 186 | 184,ARE,United Arab Emirates,18.6,12.5,9.3,9.7,10.1,14.6,9.9,7.5,7.7,8.1 187 | 185,GBR,United Kingdom,10.4,7.2,5.6,4.9,4.7,8.1,5.8,4.6,4.0,3.9 188 | 186,USA,United States,12.5,9.3,8.0,7.4,7.2,10.0,7.6,6.6,6.2,6.0 189 | 187,URY,Uruguay,25.5,19.0,11.8,9.9,9.0,20.4,15.0,9.5,7.9,7.3 190 | 188,UZB,Uzbekistan,80.3,69.6,41.0,29.1,25.5,63.4,54.5,31.1,21.9,19.2 191 | 189,VUT,Vanuatu,38.5,31.0,31.5,30.5,29.2,32.6,26.1,26.7,25.8,24.5 192 | 190,VEN,Venezuela (Bolivarian Republic of),32.5,24.1,18.4,25.0,34.2,26.6,19.1,14.8,19.9,27.4 193 | 191,VNM,Viet Nam,59.0,34.5,26.6,25.1,24.2,43.7,24.7,19.0,18.0,17.3 194 | 192,YEM,Yemen,131.4,99.8,60.3,59.4,59.3,120.4,90.4,52.2,51.3,51.4 195 | 193,ZMB,Zambia,193.5,173.8,88.1,69.8,64.7,176.0,155.9,76.4,59.7,55.1 196 | 194,ZWE,Zimbabwe,83.8,108.5,93.7,62.0,55.0,70.9,94.6,81.0,51.8,45.4 197 | -------------------------------------------------------------------------------- /datasets/tennis.csv: -------------------------------------------------------------------------------- 1 | outlook,temp,humidity,windy,play 2 | sunny,hot,high,false,no 3 | sunny,hot,high,true,no 4 | overcast,hot,high,false,yes 5 | rainy,mild,high,false,yes 6 | rainy,cool,normal,false,yes 7 | rainy,cool,normal,true,no 8 | overcast,cool,normal,true,yes 9 | sunny,mild,high,false,no 10 | sunny,cool,normal,false,yes 11 | rainy,mild,normal,false,yes 12 | sunny,mild,normal,true,yes 13 | overcast,mild,high,true,yes 14 | overcast,hot,normal,false,yes 15 | rainy,mild,high,true,no 16 | -------------------------------------------------------------------------------- /notebooks/10-stat.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# mean, median, mode, variance, standard deviation" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 3, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "%matplotlib inline\n", 17 | "import numpy as np\n", 18 | "import pylab as plt\n", 19 | "from scipy.stats import mode,skew,kurtosis" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 4, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "data = np.random.normal(0,1,1000)" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 9, 34 | "metadata": {}, 35 | "outputs": [ 36 | { 37 | "data": { 38 | "text/plain": [ 39 | "(1000,)" 40 | ] 41 | }, 42 | "execution_count": 9, 43 | "metadata": {}, 44 | "output_type": "execute_result" 45 | } 46 | ], 47 | "source": [ 48 | "data.shape" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 21, 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "data = np.random.uniform(0,1,1000)" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 22, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "data": { 67 | "text/plain": [ 68 | "8.6" 69 | ] 70 | }, 71 | "execution_count": 22, 72 | "metadata": {}, 73 | "output_type": "execute_result" 74 | } 75 | ], 76 | "source": [ 77 | "100*np.mean((0" 230 | ] 231 | }, 232 | "metadata": { 233 | "needs_background": "light" 234 | }, 235 | "output_type": "display_data" 236 | } 237 | ], 238 | "source": [ 239 | "data = np.random.gumbel(1,1,100000)\n", 240 | "data = np.floor(10*data)/10\n", 241 | "plt.hist(data,20);" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 31, 247 | "metadata": {}, 248 | "outputs": [ 249 | { 250 | "data": { 251 | "text/plain": [ 252 | "(1.5282270000000002, 1.3, ModeResult(mode=array([0.9]), count=array([3743])))" 253 | ] 254 | }, 255 | "execution_count": 31, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "data.mean(),np.median(data),mode(data)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 32, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "data": { 271 | "text/plain": [ 272 | "1.1118582897566018" 273 | ] 274 | }, 275 | "execution_count": 32, 276 | "metadata": {}, 277 | "output_type": "execute_result" 278 | } 279 | ], 280 | "source": [ 281 | "skew(data)" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 58, 287 | "metadata": {}, 288 | "outputs": [], 289 | "source": [ 290 | "def ruler1(truth):\n", 291 | " obs = truth+np.random.normal(0,0.03)\n", 292 | " return obs\n", 293 | "\n", 294 | "def ruler2(truth):\n", 295 | " obs = truth+np.random.normal(0.05,0.01)\n", 296 | " return obs\n", 297 | "\n", 298 | "def ruler3(truth):\n", 299 | " obs = truth+np.random.normal(0,0.01)\n", 300 | " return obs" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": 59, 306 | "metadata": {}, 307 | "outputs": [ 308 | { 309 | "name": "stdout", 310 | "output_type": "stream", 311 | "text": [ 312 | "9.976812685880443\n" 313 | ] 314 | } 315 | ], 316 | "source": [ 317 | "truth = 10\n", 318 | "\n", 319 | "obs = ruler1(truth)\n", 320 | "print(obs)" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": 60, 326 | "metadata": {}, 327 | "outputs": [], 328 | "source": [ 329 | "ntry = 100\n", 330 | "obss1 = []\n", 331 | "obss2 = []\n", 332 | "obss3 = []\n", 333 | "for i in range(ntry):\n", 334 | " obss1.append(ruler1(truth))\n", 335 | " obss2.append(ruler2(truth))\n", 336 | " obss3.append(ruler3(truth))" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 61, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "[]" 348 | ] 349 | }, 350 | "execution_count": 61, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | }, 354 | { 355 | "data": { 356 | "image/png": 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\n", 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" 359 | ] 360 | }, 361 | "metadata": { 362 | "needs_background": "light" 363 | }, 364 | "output_type": "display_data" 365 | } 366 | ], 367 | "source": [ 368 | "plt.plot([10,10],[0,4],'k--')\n", 369 | "plt.plot(obss1,[1]*ntry,'b.')\n", 370 | "plt.plot(obss2,[2]*ntry,'r.')\n", 371 | "plt.plot(obss3,[3]*ntry,'g.')" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": null, 377 | "metadata": {}, 378 | "outputs": [], 379 | "source": [] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "metadata": {}, 385 | "outputs": [], 386 | "source": [] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": null, 398 | "metadata": {}, 399 | "outputs": [], 400 | "source": [] 401 | } 402 | ], 403 | "metadata": { 404 | "kernelspec": { 405 | "display_name": "Python 3", 406 | "language": "python", 407 | "name": "python3" 408 | }, 409 | "language_info": { 410 | "codemirror_mode": { 411 | "name": "ipython", 412 | "version": 3 413 | }, 414 | "file_extension": ".py", 415 | "mimetype": "text/x-python", 416 | "name": "python", 417 | "nbconvert_exporter": "python", 418 | "pygments_lexer": "ipython3", 419 | "version": "3.6.6" 420 | } 421 | }, 422 | "nbformat": 4, 423 | "nbformat_minor": 2 424 | } 425 | -------------------------------------------------------------------------------- /notebooks/12-hypothesis-test_error-analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 11, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "%matplotlib inline\n", 10 | "\n", 11 | "import numpy as np\n", 12 | "import pylab as plt" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "### Confidence interval" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 22, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "mu = 10\n", 29 | "sigma = 5\n", 30 | "\n", 31 | "means = []\n", 32 | "for _ in range(1000):\n", 33 | " dd = np.random.normal(mu,sigma,100)\n", 34 | " means.append(np.mean(dd))" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 23, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "data": { 44 | "text/plain": [ 45 | "16.16" 46 | ] 47 | }, 48 | "execution_count": 23, 49 | "metadata": {}, 50 | "output_type": "execute_result" 51 | } 52 | ], 53 | "source": [ 54 | "dd = np.random.normal(mu,sigma,10000)\n", 55 | "100*np.mean(dd>15)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 25, 61 | "metadata": {}, 62 | "outputs": [ 63 | { 64 | "data": { 65 | "text/plain": [ 66 | "(9.999797067998665, 0.5204453695769921)" 67 | ] 68 | }, 69 | "execution_count": 25, 70 | "metadata": {}, 71 | "output_type": "execute_result" 72 | } 73 | ], 74 | "source": [ 75 | "gm = np.mean(means)\n", 76 | "ss = np.std(means)\n", 77 | "gm,ss" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 26, 83 | "metadata": {}, 84 | "outputs": [ 85 | { 86 | "name": "stderr", 87 | "output_type": "stream", 88 | "text": [ 89 | "/home/gf/packages/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", 90 | "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", 91 | " alternative=\"'density'\", removal=\"3.1\")\n" 92 | ] 93 | }, 94 | { 95 | "data": { 96 | "text/plain": [ 97 | "[]" 98 | ] 99 | }, 100 | "execution_count": 26, 101 | "metadata": {}, 102 | "output_type": "execute_result" 103 | }, 104 | { 105 | "data": { 106 | "image/png": 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\n", 107 | "text/plain": [ 108 | "
" 109 | ] 110 | }, 111 | "metadata": { 112 | "needs_background": "light" 113 | }, 114 | "output_type": "display_data" 115 | } 116 | ], 117 | "source": [ 118 | "plt.hist(means,25,normed=1);\n", 119 | "plt.plot(2*[gm-ss],[0,1],'k')\n", 120 | "plt.plot(2*[gm+ss],[0,1],'k')" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 33, 126 | "metadata": {}, 127 | "outputs": [ 128 | { 129 | "name": "stdout", 130 | "output_type": "stream", 131 | "text": [ 132 | "67.4\n", 133 | "9.479351698421674 10.520242437575657\n" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "means = np.array(means)\n", 139 | "print(100*np.mean((means>gm-ss) & (meansgm-2*ss) & (meansgm-3*ss) & (means" 241 | ] 242 | }, 243 | "metadata": { 244 | "needs_background": "light" 245 | }, 246 | "output_type": "display_data" 247 | } 248 | ], 249 | "source": [ 250 | "plt.hist(diffs);" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": 107, 256 | "metadata": {}, 257 | "outputs": [ 258 | { 259 | "data": { 260 | "text/plain": [ 261 | "6.18" 262 | ] 263 | }, 264 | "execution_count": 107, 265 | "metadata": {}, 266 | "output_type": "execute_result" 267 | } 268 | ], 269 | "source": [ 270 | "np.mean(diffs>=diff_obs)*100" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": 108, 276 | "metadata": {}, 277 | "outputs": [], 278 | "source": [ 279 | "before_opt = np.array([230, 210, 190, 240, 350, 170, 180, 240, 330, 270, 210, 230])\n", 280 | "after_opt = np.array([310, 180, 190, 240, 220, 240, 160, 410, 130, 320, 290, 210])" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 109, 286 | "metadata": {}, 287 | "outputs": [ 288 | { 289 | "data": { 290 | "text/plain": [ 291 | "4.166666666666657" 292 | ] 293 | }, 294 | "execution_count": 109, 295 | "metadata": {}, 296 | "output_type": "execute_result" 297 | } 298 | ], 299 | "source": [ 300 | "diff_obs = after_opt.mean()-before_opt.mean()\n", 301 | "diff_obs" 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 110, 307 | "metadata": {}, 308 | "outputs": [ 309 | { 310 | "data": { 311 | "text/plain": [ 312 | "array([230, 210, 190, 240, 350, 170, 180, 240, 330, 270, 210, 230, 310,\n", 313 | " 180, 190, 240, 220, 240, 160, 410, 130, 320, 290, 210])" 314 | ] 315 | }, 316 | "execution_count": 110, 317 | "metadata": {}, 318 | "output_type": "execute_result" 319 | } 320 | ], 321 | "source": [ 322 | "experiment = np.append(before_opt,after_opt)\n", 323 | "experiment" 324 | ] 325 | }, 326 | { 327 | "cell_type": "code", 328 | "execution_count": 111, 329 | "metadata": {}, 330 | "outputs": [], 331 | "source": [ 332 | "diffs = []\n", 333 | "for i in range(10000):\n", 334 | " np.random.shuffle(experiment)\n", 335 | " set1 = experiment[:12]\n", 336 | " set2 = experiment[12:]\n", 337 | " diffs.append(set1.mean()-set2.mean())\n", 338 | "diffs = np.array(diffs)" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 114, 344 | "metadata": {}, 345 | "outputs": [ 346 | { 347 | "data": { 348 | "text/plain": [ 349 | "45.61" 350 | ] 351 | }, 352 | "execution_count": 114, 353 | "metadata": {}, 354 | "output_type": "execute_result" 355 | } 356 | ], 357 | "source": [ 358 | "np.mean(diffs>=diff_obs)*100" 359 | ] 360 | }, 361 | { 362 | "cell_type": "markdown", 363 | "metadata": {}, 364 | "source": [ 365 | "# Confidence Interval" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": 115, 371 | "metadata": {}, 372 | "outputs": [], 373 | "source": [ 374 | "before_opt = np.array([23, 21, 19, 24, 35, 17, 18, 24, 33, 27, 21, 23])\n", 375 | "after_opt = np.array([31, 28, 19, 24, 32, 27, 16, 41, 23, 32, 29, 33])" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | "execution_count": 116, 381 | "metadata": {}, 382 | "outputs": [ 383 | { 384 | "data": { 385 | "text/plain": [ 386 | "array([17, 27, 23, 21, 21, 18, 21, 21, 33, 23])" 387 | ] 388 | }, 389 | "execution_count": 116, 390 | "metadata": {}, 391 | "output_type": "execute_result" 392 | } 393 | ], 394 | "source": [ 395 | "random_before_opt = np.random.choice(before_opt, size=10, replace=True)\n", 396 | "random_before_opt" 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": 117, 402 | "metadata": {}, 403 | "outputs": [], 404 | "source": [ 405 | "diffs = []\n", 406 | "for i in range(1000):\n", 407 | " random_before_opt = np.random.choice(before_opt, size=10, replace=True)\n", 408 | " random_after_opt = np.random.choice(after_opt, size=10, replace=True)\n", 409 | " diffs.append(np.mean(random_after_opt) - np.mean(random_before_opt))" 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 123, 415 | "metadata": {}, 416 | "outputs": [ 417 | { 418 | "data": { 419 | "text/plain": [ 420 | "array([ 0. , 11.4])" 421 | ] 422 | }, 423 | "execution_count": 123, 424 | "metadata": {}, 425 | "output_type": "execute_result" 426 | } 427 | ], 428 | "source": [ 429 | "np.percentile(diffs, [5.2,100])" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 44, 435 | "metadata": {}, 436 | "outputs": [ 437 | { 438 | "data": { 439 | "text/plain": [ 440 | "(array([ 5., 20., 72., 152., 196., 234., 176., 102., 33., 10.]),\n", 441 | " array([-3.7 , -2.17, -0.64, 0.89, 2.42, 3.95, 5.48, 7.01, 8.54,\n", 442 | " 10.07, 11.6 ]),\n", 443 | " )" 444 | ] 445 | }, 446 | "execution_count": 44, 447 | "metadata": {}, 448 | "output_type": "execute_result" 449 | }, 450 | { 451 | "data": { 452 | "image/png": "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\n", 453 | "text/plain": [ 454 | "
" 455 | ] 456 | }, 457 | "metadata": { 458 | "needs_background": "light" 459 | }, 460 | "output_type": "display_data" 461 | } 462 | ], 463 | "source": [ 464 | "plt.hist(diffs)" 465 | ] 466 | }, 467 | { 468 | "cell_type": "code", 469 | "execution_count": null, 470 | "metadata": {}, 471 | "outputs": [], 472 | "source": [] 473 | } 474 | ], 475 | "metadata": { 476 | "kernelspec": { 477 | "display_name": "Python 3", 478 | "language": "python", 479 | "name": "python3" 480 | }, 481 | "language_info": { 482 | "codemirror_mode": { 483 | "name": "ipython", 484 | "version": 3 485 | }, 486 | "file_extension": ".py", 487 | "mimetype": "text/x-python", 488 | "name": "python", 489 | "nbconvert_exporter": "python", 490 | "pygments_lexer": "ipython3", 491 | "version": "3.6.6" 492 | } 493 | }, 494 | "nbformat": 4, 495 | "nbformat_minor": 2 496 | } 497 | -------------------------------------------------------------------------------- /notebooks/2 - Python intro - missing topics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Dictionary" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "a = {'a':1, 'b':3, 'c':4}" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": {}, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/plain": [ 27 | "dict_keys(['a', 'b', 'c'])" 28 | ] 29 | }, 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "output_type": "execute_result" 33 | } 34 | ], 35 | "source": [ 36 | "a.keys()" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "dict_values([1, 3, 4])" 48 | ] 49 | }, 50 | "execution_count": 3, 51 | "metadata": {}, 52 | "output_type": "execute_result" 53 | } 54 | ], 55 | "source": [ 56 | "a.values()" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 4, 62 | "metadata": {}, 63 | "outputs": [ 64 | { 65 | "data": { 66 | "text/plain": [ 67 | "dict_items([('a', 1), ('b', 3), ('c', 4)])" 68 | ] 69 | }, 70 | "execution_count": 4, 71 | "metadata": {}, 72 | "output_type": "execute_result" 73 | } 74 | ], 75 | "source": [ 76 | "a.items()" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "# String" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 6, 89 | "metadata": {}, 90 | "outputs": [ 91 | { 92 | "data": { 93 | "text/plain": [ 94 | "['m', 'b', 'n']" 95 | ] 96 | }, 97 | "execution_count": 6, 98 | "metadata": {}, 99 | "output_type": "execute_result" 100 | } 101 | ], 102 | "source": [ 103 | "'m b n'.split()" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": 8, 109 | "metadata": {}, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/plain": [ 114 | "'ma'" 115 | ] 116 | }, 117 | "execution_count": 8, 118 | "metadata": {}, 119 | "output_type": "execute_result" 120 | } 121 | ], 122 | "source": [ 123 | "'ma@gmail.com'.split('@')[0]" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 10, 129 | "metadata": {}, 130 | "outputs": [ 131 | { 132 | "data": { 133 | "text/plain": [ 134 | "'USA'" 135 | ] 136 | }, 137 | "execution_count": 10, 138 | "metadata": {}, 139 | "output_type": "execute_result" 140 | } 141 | ], 142 | "source": [ 143 | "'@USA@'.strip('@')" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "# Raising error" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 11, 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "ename": "KeyError", 160 | "evalue": "'d'", 161 | "output_type": "error", 162 | "traceback": [ 163 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 164 | "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", 165 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 166 | "\u001b[1;31mKeyError\u001b[0m: 'd'" 167 | ] 168 | } 169 | ], 170 | "source": [ 171 | "a['d']" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 12, 177 | "metadata": {}, 178 | "outputs": [], 179 | "source": [ 180 | "try:\n", 181 | " a['d']\n", 182 | "except:\n", 183 | " a['d'] = 10" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 13, 189 | "metadata": {}, 190 | "outputs": [ 191 | { 192 | "data": { 193 | "text/plain": [ 194 | "{'a': 1, 'b': 3, 'c': 4, 'd': 10}" 195 | ] 196 | }, 197 | "execution_count": 13, 198 | "metadata": {}, 199 | "output_type": "execute_result" 200 | } 201 | ], 202 | "source": [ 203 | "a" 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "metadata": {}, 209 | "source": [ 210 | "# Regex" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": null, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "'2h 30min'" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 16, 225 | "metadata": {}, 226 | "outputs": [], 227 | "source": [ 228 | "import re" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 29, 234 | "metadata": {}, 235 | "outputs": [ 236 | { 237 | "data": { 238 | "text/plain": [ 239 | "[('3', '0')]" 240 | ] 241 | }, 242 | "execution_count": 29, 243 | "metadata": {}, 244 | "output_type": "execute_result" 245 | } 246 | ], 247 | "source": [ 248 | "re.findall(r'(\\d*)\\w*\\s*(\\d+)min','30min')" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "metadata": {}, 255 | "outputs": [], 256 | "source": [] 257 | } 258 | ], 259 | "metadata": { 260 | "kernelspec": { 261 | "display_name": "Python 3", 262 | "language": "python", 263 | "name": "python3" 264 | }, 265 | "language_info": { 266 | "codemirror_mode": { 267 | "name": "ipython", 268 | "version": 3 269 | }, 270 | "file_extension": ".py", 271 | "mimetype": "text/x-python", 272 | "name": "python", 273 | "nbconvert_exporter": "python", 274 | "pygments_lexer": "ipython3", 275 | "version": "3.8.3" 276 | } 277 | }, 278 | "nbformat": 4, 279 | "nbformat_minor": 4 280 | } 281 | -------------------------------------------------------------------------------- /notebooks/3 - Python Exercise .ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Write your codes as a function (as much as possible\n", 8 | "# Use google a lot" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "# 1Q\n", 16 | "### Dragon name:\n", 17 | "Get user first and last name, use first three letter of each, random shuffle it and return it as his/her dragon name\n", 18 | "[How to randon shuffle a list](http://letmegooglethat.com/?q=random+shuffle+list+python)" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "## 2Q\n", 26 | "### Number less than ten\n", 27 | "Get a list, return the number less than 10 \n" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": null, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "## 3Q\n", 42 | "### Divisors (مقسوم علیه!)\n", 43 | "Get a number - list all the divisors" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "## 4Q \n", 51 | "### Prime number\n", 52 | "Get a number and list all the prime number between two number" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "## 5Q\n", 67 | "### Check the elements\n", 68 | "get list, checi if an specific element is there" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "metadata": {}, 74 | "source": [ 75 | "# 6Q\n", 76 | "### Choose elements \"N ta miyan!\"\n", 77 | "Get a list and a number (like N) and choose element N ta darmiyan" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "# 7Q\n", 92 | "### Calculate your age in different year\n", 93 | "Write a funtion to use your year of birth and return your are in different years" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": null, 99 | "metadata": {}, 100 | "outputs": [], 101 | "source": [] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "## Q8\n", 108 | "### Reverse a list \n", 109 | "reverse a list in as many as way you can" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "## Q9\n", 117 | "### Fibonacci\n", 118 | "every element in this series is sum of the last two elements:0, 1, 1, 2, 3, 5, 8, 13" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [] 127 | }, 128 | { 129 | "cell_type": "code", 130 | "execution_count": null, 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "metadata": {}, 138 | "source": [ 139 | "## Q10\n", 140 | "### List Remove Duplicates \n", 141 | "get a list and remove the duplicates" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 71, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "metadata": {}, 154 | "source": [ 155 | "# Q11\n", 156 | "### Reverse a name!\n", 157 | "get a name and reverse it" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 74, 163 | "metadata": {}, 164 | "outputs": [ 165 | { 166 | "data": { 167 | "text/plain": [ 168 | "'dijam'" 169 | ] 170 | }, 171 | "execution_count": 74, 172 | "metadata": {}, 173 | "output_type": "execute_result" 174 | } 175 | ], 176 | "source": [] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "metadata": {}, 181 | "source": [ 182 | "## Q12\n", 183 | "### produce a strong password\n", 184 | "It should be a combination of uppercase, lowercase, number, symbole and must be long enough" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 75, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "## Q13\n", 199 | "### Write to a file\n", 200 | "produce these pattern and write them into a file:\n", 201 | "\n", 202 | "**1)**\n", 203 | "\n", 204 | "@@@@@@\n", 205 | "\n", 206 | "@@@@@\n", 207 | "\n", 208 | "@@@@\n", 209 | "\n", 210 | "@@@\n", 211 | "\n", 212 | "@@\n", 213 | "\n", 214 | "@\n", 215 | "\n", 216 | "**2)**\n", 217 | "\n", 218 | "@\n", 219 | "\n", 220 | "@@\n", 221 | "\n", 222 | "@@@\n", 223 | "\n", 224 | "@@@@\n", 225 | "\n", 226 | "@@@@@\n", 227 | "\n", 228 | "@@@@@@\n", 229 | "\n", 230 | "@@@@@@@\n", 231 | "\n" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": null, 237 | "metadata": {}, 238 | "outputs": [], 239 | "source": [] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": null, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": null, 251 | "metadata": {}, 252 | "outputs": [], 253 | "source": [] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "metadata": {}, 258 | "source": [ 259 | "## Q14\n", 260 | "### mean, max, min a list\n", 261 | "Get a list and caclulate mean, max, mean of them" 262 | ] 263 | }, 264 | { 265 | "cell_type": "markdown", 266 | "metadata": {}, 267 | "source": [ 268 | "# Q15\n", 269 | "### store and recall eveyone birthday\n", 270 | "Get everyone birthday, store it somehow that you can access their birthday using their name:" 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": {}, 276 | "source": [ 277 | "# Q16\n", 278 | "## Area of a circle\n", 279 | "get radius of a circle and a percision, caculate the area with that percision" 280 | ] 281 | }, 282 | { 283 | "cell_type": "markdown", 284 | "metadata": {}, 285 | "source": [ 286 | "# Q17\n", 287 | "### Report about element of a list\n", 288 | "get a list and say how many str, float, int, etc are in that list" 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "metadata": {}, 294 | "source": [ 295 | "# Q18\n", 296 | "### Check if a list is empty or not\n", 297 | "get a list check if it is empty or not" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "# Q19\n", 305 | "### Ban some members! remove specific element from the list\n", 306 | "Get a list of name and ban members, check if they are in the list, if they were remove them" 307 | ] 308 | }, 309 | { 310 | "cell_type": "markdown", 311 | "metadata": {}, 312 | "source": [ 313 | "# Q20\n", 314 | "### Find the combination password:\n", 315 | "there is a locked briefcase, there are three digit wheels from 0-9. generate all the conbination untill you get the correct one" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "# Q21\n", 323 | "Imagine you have x and y coordinate of points in two different list, x,y! find the y elements which have positive x" 324 | ] 325 | }, 326 | { 327 | "cell_type": "markdown", 328 | "metadata": {}, 329 | "source": [ 330 | "# Q22\n", 331 | "Find the second largest of an element in a list" 332 | ] 333 | }, 334 | { 335 | "cell_type": "markdown", 336 | "metadata": {}, 337 | "source": [ 338 | "# Q23\n", 339 | "Sort a dictionary by their values" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": null, 345 | "metadata": {}, 346 | "outputs": [], 347 | "source": [] 348 | } 349 | ], 350 | "metadata": { 351 | "kernelspec": { 352 | "display_name": "Python 3", 353 | "language": "python", 354 | "name": "python3" 355 | }, 356 | "language_info": { 357 | "codemirror_mode": { 358 | "name": "ipython", 359 | "version": 3 360 | }, 361 | "file_extension": ".py", 362 | "mimetype": "text/x-python", 363 | "name": "python", 364 | "nbconvert_exporter": "python", 365 | "pygments_lexer": "ipython3", 366 | "version": "3.8.3" 367 | } 368 | }, 369 | "nbformat": 4, 370 | "nbformat_minor": 4 371 | } 372 | -------------------------------------------------------------------------------- /notebooks/5 - Numpy Exersice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Q1 \n", 8 | "create of:\n", 9 | "\n", 10 | "zero\n", 11 | "\n", 12 | "ones\n", 13 | "\n", 14 | "tens\n", 15 | "\n", 16 | "with desireable dimention" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "# Q2\n", 24 | "array from one to 100 3 ta darmiyan" 25 | ] 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "metadata": {}, 30 | "source": [ 31 | "# Q3\n", 32 | "create a diagonal matrix out of and array, e.g:\n", 33 | "\n", 34 | "array = np.array([1,2])\n", 35 | "\n", 36 | "matrix = np.array([1,0],[0,2])" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 10, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "array([[1, 0],\n", 48 | " [0, 2]])" 49 | ] 50 | }, 51 | "execution_count": 10, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "np.diag([1,2])" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "# Q4\n", 65 | "Plot a histogram\n", 66 | "\n", 67 | "np.random.rand\n", 68 | "\n", 69 | "np.random.randn\n", 70 | "\n", 71 | "np.random.randint" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 12, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "(array([ 92., 98., 99., 89., 102., 98., 110., 97., 116., 99.]),\n", 83 | " array([3.84255854e-04, 1.00269360e-01, 2.00154463e-01, 3.00039567e-01,\n", 84 | " 3.99924671e-01, 4.99809775e-01, 5.99694879e-01, 6.99579982e-01,\n", 85 | " 7.99465086e-01, 8.99350190e-01, 9.99235294e-01]),\n", 86 | " )" 87 | ] 88 | }, 89 | "execution_count": 12, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | }, 93 | { 94 | "data": { 95 | "image/png": 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BZ4BrqmrZt9TNgiG/z78HfDjJFxicsri8qmb2apFJrgfOBTYkOQBcCTwHJtcvP6EqSQ2aldMykqRVMO6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1KD/Bg8X4JAlRkOPAAAAAElFTkSuQmCC\n", 96 | "text/plain": [ 97 | "
" 98 | ] 99 | }, 100 | "metadata": { 101 | "needs_background": "light" 102 | }, 103 | "output_type": "display_data" 104 | } 105 | ], 106 | "source": [ 107 | "import matplotlib.pylab as plt\n", 108 | "plt.hist(np.random.rand(1000))" 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "# Q5\n", 116 | "creat the following:" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 14, 122 | "metadata": {}, 123 | "outputs": [ 124 | { 125 | "data": { 126 | "text/plain": [ 127 | "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", 128 | " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", 129 | " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", 130 | " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", 131 | " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", 132 | " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", 133 | " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", 134 | " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", 135 | " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", 136 | " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" 137 | ] 138 | }, 139 | "execution_count": 14, 140 | "metadata": {}, 141 | "output_type": "execute_result" 142 | } 143 | ], 144 | "source": [ 145 | "np.arange(1,101).reshape(10,10)/100" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "# Q6\n", 153 | "creat an array of 100 points, with equal distance, from 0-10, with two methods" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 20, 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "data": { 163 | "text/plain": [ 164 | "array([ 0. , 0.1010101 , 0.2020202 , 0.3030303 , 0.4040404 ,\n", 165 | " 0.50505051, 0.60606061, 0.70707071, 0.80808081, 0.90909091,\n", 166 | " 1.01010101, 1.11111111, 1.21212121, 1.31313131, 1.41414141,\n", 167 | " 1.51515152, 1.61616162, 1.71717172, 1.81818182, 1.91919192,\n", 168 | " 2.02020202, 2.12121212, 2.22222222, 2.32323232, 2.42424242,\n", 169 | " 2.52525253, 2.62626263, 2.72727273, 2.82828283, 2.92929293,\n", 170 | " 3.03030303, 3.13131313, 3.23232323, 3.33333333, 3.43434343,\n", 171 | " 3.53535354, 3.63636364, 3.73737374, 3.83838384, 3.93939394,\n", 172 | " 4.04040404, 4.14141414, 4.24242424, 4.34343434, 4.44444444,\n", 173 | " 4.54545455, 4.64646465, 4.74747475, 4.84848485, 4.94949495,\n", 174 | " 5.05050505, 5.15151515, 5.25252525, 5.35353535, 5.45454545,\n", 175 | " 5.55555556, 5.65656566, 5.75757576, 5.85858586, 5.95959596,\n", 176 | " 6.06060606, 6.16161616, 6.26262626, 6.36363636, 6.46464646,\n", 177 | " 6.56565657, 6.66666667, 6.76767677, 6.86868687, 6.96969697,\n", 178 | " 7.07070707, 7.17171717, 7.27272727, 7.37373737, 7.47474747,\n", 179 | " 7.57575758, 7.67676768, 7.77777778, 7.87878788, 7.97979798,\n", 180 | " 8.08080808, 8.18181818, 8.28282828, 8.38383838, 8.48484848,\n", 181 | " 8.58585859, 8.68686869, 8.78787879, 8.88888889, 8.98989899,\n", 182 | " 9.09090909, 9.19191919, 9.29292929, 9.39393939, 9.49494949,\n", 183 | " 9.5959596 , 9.6969697 , 9.7979798 , 9.8989899 , 10. ])" 184 | ] 185 | }, 186 | "execution_count": 20, 187 | "metadata": {}, 188 | "output_type": "execute_result" 189 | } 190 | ], 191 | "source": [ 192 | "(np.linspace(0,10,100))" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 19, 198 | "metadata": {}, 199 | "outputs": [ 200 | { 201 | "data": { 202 | "text/plain": [ 203 | "array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,\n", 204 | " 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1,\n", 205 | " 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2,\n", 206 | " 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3,\n", 207 | " 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4,\n", 208 | " 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5,\n", 209 | " 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6,\n", 210 | " 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7,\n", 211 | " 8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,\n", 212 | " 9.9, 10. ])" 213 | ] 214 | }, 215 | "execution_count": 19, 216 | "metadata": {}, 217 | "output_type": "execute_result" 218 | } 219 | ], 220 | "source": [ 221 | "np.arange(0,10.1, 10/100)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "markdown", 226 | "metadata": {}, 227 | "source": [ 228 | "# Q7\n", 229 | "create return sum. mean, std a matrix in\n", 230 | "\n", 231 | "each row\n", 232 | "\n", 233 | "each column\n", 234 | "\n", 235 | "all the matrix" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 22, 241 | "metadata": { 242 | "scrolled": true 243 | }, 244 | "outputs": [], 245 | "source": [ 246 | "a = np.array([[1,2],[3,4]])" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 25, 252 | "metadata": {}, 253 | "outputs": [ 254 | { 255 | "data": { 256 | "text/plain": [ 257 | "array([[1, 2],\n", 258 | " [3, 4]])" 259 | ] 260 | }, 261 | "execution_count": 25, 262 | "metadata": {}, 263 | "output_type": "execute_result" 264 | } 265 | ], 266 | "source": [ 267 | "a" 268 | ] 269 | }, 270 | { 271 | "cell_type": "code", 272 | "execution_count": 27, 273 | "metadata": {}, 274 | "outputs": [ 275 | { 276 | "data": { 277 | "text/plain": [ 278 | "array([1., 1.])" 279 | ] 280 | }, 281 | "execution_count": 27, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "a.std(axis=0)" 288 | ] 289 | }, 290 | { 291 | "cell_type": "markdown", 292 | "metadata": {}, 293 | "source": [ 294 | "# Q8\n", 295 | "\n", 296 | "closest value to an scalar in a array" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": 28, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "m = 4\n", 306 | "l = np.arange(10)" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 33, 312 | "metadata": {}, 313 | "outputs": [ 314 | { 315 | "data": { 316 | "text/plain": [ 317 | "4" 318 | ] 319 | }, 320 | "execution_count": 33, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "min(abs(l-m))+m" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": null, 332 | "metadata": {}, 333 | "outputs": [], 334 | "source": [] 335 | } 336 | ], 337 | "metadata": { 338 | "kernelspec": { 339 | "display_name": "Python 3", 340 | "language": "python", 341 | "name": "python3" 342 | }, 343 | "language_info": { 344 | "codemirror_mode": { 345 | "name": "ipython", 346 | "version": 3 347 | }, 348 | "file_extension": ".py", 349 | "mimetype": "text/x-python", 350 | "name": "python", 351 | "nbconvert_exporter": "python", 352 | "pygments_lexer": "ipython3", 353 | "version": "3.8.3" 354 | } 355 | }, 356 | "nbformat": 4, 357 | "nbformat_minor": 4 358 | } 359 | -------------------------------------------------------------------------------- /notebooks/exercise.txt: -------------------------------------------------------------------------------- 1 | 1. Bob and Alice are playing a game. There are three doors. 2 | Two of them open to a sheep and one of the doors leads to a brand new car! 3 | Here is the rule, once Alice/Bob picks a door, one of the unpicked doors reveals a sheep. 4 | Then, Alice/Bob have the option of either staying with the selected door or switching to the remaining unopened door. 5 | Bob always stays (of course he's a man!) and Alice always switches! 6 | Who has more chance to win? 7 | 8 | input: 9 | 10 | output: 11 | Bob's chance (float) 12 | Alice's chance (float) 13 | 14 | 2. For the data frame produced by SS02-02.ipynb, write a 15 | code that computes correlation coefficient for any two given columns. 16 | Then make a matrix that includes the coefficients. 17 | Note that each element presents correlation coefficient of two columns, so the matrix is symmetric. 18 | 19 | input: 20 | relative path to UN_cleaned.csv (string) 21 | output: 22 | correlation matrix 23 | 24 | 3. write a code to fit a sinusoidal function like f(x)=a*sin(b*x) to a given data. 25 | 26 | input: 27 | The data file name (string) 28 | (like the example in datasets directory, exercise.npy) 29 | output: 30 | a (float) 31 | b (float) 32 | 33 | Some more exercises just for fun (DO NOT INCLUDE THEM IN YOUR SENT ANSWERS!): 34 | 35 | - Imagine 366 people in some place. There should be two people who have the same birthday dates. 36 | since there are only 365 possible days in a year (the probability of this event is one). 37 | How many people is needed to have 99% chance of this event? 38 | 39 | - One hundred people line up to board an airplane. Each has a boarding pass including the assigned seat. 40 | However, the first person to board has lost his boarding pass and takes a random seat. 41 | After that, each person takes the assigned seat if it is unoccupied, and one of unoccupied seats at random otherwise. 42 | What is the probability that the last person to board gets to sit in his assigned seat? 43 | 44 | - Two real numbers X and Y are chosen at random in the interval (0,1). 45 | Compute the probability that the closest integer to X/Y is even. 46 | 47 | --------------------------------------------------------------------------------