ta-data-lis · joaopq · Aug 8, 2020
diff --git a/your-project/Ironhack_week 6_project_HDI.pptx b/your-project/Ironhack_week 6_project_HDI.pptx
diff --git a/your-project/dt selection and cleaning criterias/backups/elements to drop in dfs - Copy.csv b/your-project/dt selection and cleaning criterias/backups/elements to drop in dfs - Copy.csv
@@ -0,0 +1,79 @@
+East Asia and Pacific
+Europe and Central Asia
+Latin America and Caribbean
+Middle East and North Africa
+South Asia
+Sub-Saharan Africa
+World
+European Union (28 countries)
+Reunion
+Western Sahara
+Arab World
+Caribbean small states
+Central Europe and the Baltics
+Early-demographic dividend
+East Asia & Pacific
+East Asia & Pacific (IDA & IBRD)
+East Asia & Pacific (excluding high income)
+Euro area
+Europe & Central Asia
+Europe & Central Asia (IDA & IBRD)
+Europe & Central Asia (excluding high income)
+European Union
+Fragile and conflict affected situations
+Heavily indebted poor countries (HIPC)
+High income
+IBRD only
+IDA & IBRD total
+IDA blend
+IDA only
+IDA total
+Late-demographic dividend
+Latin America & Caribbean
+Latin America & Caribbean (IDA & IBRD)
+Latin America & Caribbean (excluding high income)
+Least developed countries: UN classification
+Low & middle income
+Low income
+Lower middle income
+Middle East & North Africa
+Middle East & North Africa (IDA & IBRD)
+Middle East & North Africa (excluding high income)
+Middle income
+OECD members
+Other small states
+Pacific island small states
+Post-demographic dividend
+Pre-demographic dividend
+Saint Martin (French part)
+Small states
+South Asia (IDA & IBRD)
+Sub-Saharan Africa (IDA & IBRD)
+Sub-Saharan Africa (excluding high income)
+Upper middle income
+Andean Latin America
+Australasia
+Central Asia
+Central Europe
+"Central Europe, Eastern Europe, and Central Asia"
+Central Latin America
+Central Sub-Saharan Africa
+East Asia
+Eastern Europe
+Eastern Sub-Saharan Africa
+High SDI
+High-income
+High-income Asia Pacific
+High-middle SDI
+Low SDI
+Low-middle SDI
+Middle SDI
+North Africa and Middle East
+Northern Ireland
+Southeast Asia
+"Southeast Asia, East Asia, and Oceania"
+Southern Latin America
+Southern Sub-Saharan Africa
+Tropical Latin America
+Western Europe
+Western Sub-Saharan Africa
diff --git a/your-project/dt selection and cleaning criterias/elements to drop in dfs.csv b/your-project/dt selection and cleaning criterias/elements to drop in dfs.csv
@@ -0,0 +1,79 @@
+East Asia and Pacific
+Europe and Central Asia
+Latin America and Caribbean
+Middle East and North Africa
+South Asia
+Sub-Saharan Africa
+World
+European Union (28 countries)
+Reunion
+Western Sahara
+Arab World
+Caribbean small states
+Central Europe and the Baltics
+Early-demographic dividend
+East Asia & Pacific
+East Asia & Pacific (IDA & IBRD)
+East Asia & Pacific (excluding high income)
+Euro area
+Europe & Central Asia
+Europe & Central Asia (IDA & IBRD)
+Europe & Central Asia (excluding high income)
+European Union
+Fragile and conflict affected situations
+Heavily indebted poor countries (HIPC)
+High income
+IBRD only
+IDA & IBRD total
+IDA blend
+IDA only
+IDA total
+Late-demographic dividend
+Latin America & Caribbean
+Latin America & Caribbean (IDA & IBRD)
+Latin America & Caribbean (excluding high income)
+Least developed countries: UN classification
+Low & middle income
+Low income
+Lower middle income
+Middle East & North Africa
+Middle East & North Africa (IDA & IBRD)
+Middle East & North Africa (excluding high income)
+Middle income
+OECD members
+Other small states
+Pacific island small states
+Post-demographic dividend
+Pre-demographic dividend
+Saint Martin (French part)
+Small states
+South Asia (IDA & IBRD)
+Sub-Saharan Africa (IDA & IBRD)
+Sub-Saharan Africa (excluding high income)
+Upper middle income
+Andean Latin America
+Australasia
+Central Asia
+Central Europe
+"Central Europe, Eastern Europe, and Central Asia"
+Central Latin America
+Central Sub-Saharan Africa
+East Asia
+Eastern Europe
+Eastern Sub-Saharan Africa
+High SDI
+High-income
+High-income Asia Pacific
+High-middle SDI
+Low SDI
+Low-middle SDI
+Middle SDI
+North Africa and Middle East
+Northern Ireland
+Southeast Asia
+"Southeast Asia, East Asia, and Oceania"
+Southern Latin America
+Southern Sub-Saharan Africa
+Tropical Latin America
+Western Europe
+Western Sub-Saharan Africa
diff --git a/your-project/dt selection and cleaning criterias/elements to drop in dfs.xlsx b/your-project/dt selection and cleaning criterias/elements to drop in dfs.xlsx
diff --git a/your-project/dt selection and cleaning criterias/range years per var.xlsx b/your-project/dt selection and cleaning criterias/range years per var.xlsx
diff --git a/your-project/from python to excel/OLSmodel_selected_vars.xlsx b/your-project/from python to excel/OLSmodel_selected_vars.xlsx
diff --git a/your-project/from python to excel/corr_kendaltau_matrix.png b/your-project/from python to excel/corr_kendaltau_matrix.png
diff --git a/your-project/from python to excel/corr_kendaltau_matrix.xlsx b/your-project/from python to excel/corr_kendaltau_matrix.xlsx
diff --git a/your-project/from python to excel/corr_pearson_matrix.png b/your-project/from python to excel/corr_pearson_matrix.png
diff --git a/your-project/from python to excel/corr_pearson_matrix.xlsx b/your-project/from python to excel/corr_pearson_matrix.xlsx
diff --git a/your-project/from python to excel/corr_pearson_matrix_analyzed.xlsx b/your-project/from python to excel/corr_pearson_matrix_analyzed.xlsx
diff --git a/your-project/from python to excel/corr_pearson_matrix_analyzed_final.xlsx b/your-project/from python to excel/corr_pearson_matrix_analyzed_final.xlsx
diff --git a/your-project/from python to excel/corr_spearman_matrix.png b/your-project/from python to excel/corr_spearman_matrix.png
diff --git a/your-project/from python to excel/corr_spearman_matrix.xlsx b/your-project/from python to excel/corr_spearman_matrix.xlsx
diff --git a/your-project/from python to excel/df_OLS_all.xlsx b/your-project/from python to excel/df_OLS_all.xlsx
diff --git a/your-project/from python to excel/df_OLSmodel_neg_vars.xlsx b/your-project/from python to excel/df_OLSmodel_neg_vars.xlsx
diff --git a/your-project/from python to excel/df_OLSmodel_pos_vars.xlsx b/your-project/from python to excel/df_OLSmodel_pos_vars.xlsx
diff --git a/your-project/from python to excel/df_analysis - Copy (2).xlsx b/your-project/from python to excel/df_analysis - Copy (2).xlsx
diff --git a/your-project/from python to excel/df_analysis - Copy.xlsx b/your-project/from python to excel/df_analysis - Copy.xlsx
diff --git a/your-project/from python to excel/df_analysis.xlsx b/your-project/from python to excel/df_analysis.xlsx
diff --git a/your-project/from python to excel/df_analysis_filtr countries.xlsx b/your-project/from python to excel/df_analysis_filtr countries.xlsx
diff --git a/your-project/from python to excel/diff_pearson_spearman.png b/your-project/from python to excel/diff_pearson_spearman.png
diff --git a/your-project/from python to excel/diff_pearson_spearman.xlsx b/your-project/from python to excel/diff_pearson_spearman.xlsx
diff --git a/your-project/from python to excel/hdi.xlsx b/your-project/from python to excel/hdi.xlsx
diff --git a/your-project/from python to excel/old/corr_matrix_analyzed.csv b/your-project/from python to excel/old/corr_matrix_analyzed.csv
@@ -0,0 +1,27 @@
+,HDI_x,Trust in others (%)_x,Avg Height (cm),"Crude divorce rate (per 1,000 inhabitants)",Forest area (% of land area),Gini coeff," homicides per 100,000",HDI_y,Human Rights Protection Scores,Military expenditure (% of GDP),Schizophrenia (%),Bipolar disorder (%),Eating disorders (%),Anxiety disorders (%),Drug use disorders (%),Depression (%),Alcohol use disorders (%),Trust in others (%)_y,Share of Top 1% in Pre-tax national income (%),Share with Mental and Substance disorders,Suicide rate,Taxes  goods and services (% GDP),Corruption Perception Index,Freedom score,Unnamed: 4,Unnamed: 5
+Year,0.241485692,-0.1133278,0.573523407,0.187590488,-0.010674767,-0.0341413,-0.021271235,0.241485692,0.203362439,-0.200753455,0.039163261,0.012272871,0.086792573,0.012516933,0.058543368,-0.035426924,0.017216598,-0.1133278,-0.299300056,-0.026695728,-0.024162815,0.242618849,0.002761237,0.028792547,,
+HDI_x,1,0.364147175,0.679586552,0.360343436,0.017859236,-0.24438168,-0.123210242,1,0.574652339,0.006580022,0.674009947,0.566776423,0.756705718,0.492064749,0.436412736,-0.032316181,0.181838097,0.364147175,-0.282966661,0.316391479,0.132137991,0.53058427,0.730664763,0.466437373,,
+Trust in others (%)_x,0.364147175,1,0.183520186,0.380432039,0.059003964,-0.25114818,-0.291553915,0.364147175,0.327147279,-0.024774344,0.598279977,0.035548283,0.362864846,0.26243603,0.244066675,0.275973869,-0.019843504,1,-0.556779563,0.28285192,0.198024619,0.087522224,0.469979977,0.191240379,,
+Avg Height (cm),0.679586552,0.183520186,1,0.296429092,-0.084998081,-0.373294335,-0.149509612,0.679586552,0.422030845,0.004400259,0.408599424,0.470052274,0.553224568,0.381984164,0.255069314,0.1160764,0.343572469,0.183520186,-0.482569794,0.23972616,0.411875136,0.560676233,,0.390749223,,
+"Crude divorce rate (per 1,000 inhabitants)",0.360343436,0.380432039,0.296429092,1,0.057778496,-0.121923937,-0.02942117,0.360343436,0.219003267,-0.098345192,0.208378236,0.035972725,0.178843895,0.107316774,0.233955339,0.39896291,0.378287693,0.380432039,0.115094405,0.2064133,0.459912031,0.032996761,0.322231825,0.250873665,,
+Forest area (% of land area),0.017859236,0.059003964,-0.084998081,0.057778496,1,-0.20193338,0.084216435,0.017859236,0.149006286,-0.223658323,0.047461362,-0.143540167,0.002696841,-0.158967311,-0.23942587,-0.231736159,0.093726091,0.059003964,0.007867737,-0.271104931,0.138088413,-0.075006703,0.070674926,0.21573186,,
+Gini coeff,-0.24438168,-0.25114818,-0.373294335,-0.121923937,-0.20193338,1,0.289736397,-0.24438168,-0.477218762,0.220046011,0.22556246,0.024362598,0.108962308,0.123999825,0.422989479,0.100380542,-0.233731406,-0.25114818,0.500519379,0.364044881,-0.644111178,-0.145182328,-0.448741565,-0.302765696,,
+"homicides per 100,000",-0.123210242,-0.291553915,-0.149509612,-0.02942117,0.084216435,0.289736397,1,-0.123210242,-0.220965168,-0.071042546,-0.254795877,0.049271808,-0.12708669,-0.160388757,-0.10246824,-0.119867022,0.298205394,-0.291553915,0.47933891,-0.183156151,0.019330304,-0.064488343,-0.184605825,-0.006119172,,
+HDI_y,1,0.364147175,0.679586552,0.360343436,0.017859236,-0.24438168,-0.123210242,1,0.574652339,0.006580022,0.674009947,0.566776423,0.756705718,0.492064749,0.436412736,-0.032316181,0.181838097,0.364147175,-0.282966661,0.316391479,0.132137991,0.53058427,0.730664763,0.466437373,,
+Human Rights Protection Scores,0.574652339,0.327147279,0.422030845,0.219003267,0.149006286,-0.477218762,-0.220965168,0.574652339,1,-0.215125137,0.446845268,0.294289615,0.538688704,0.301746182,0.087369831,-0.034824535,0.090215995,0.327147279,-0.444558754,0.131627209,0.146100679,0.360253171,0.76901164,0.610881972,,
+Military expenditure (% of GDP),0.006580022,-0.024774344,0.004400259,-0.098345192,-0.223658323,0.220046011,-0.071042546,0.006580022,-0.215125137,1,-0.031946061,0.005671509,-0.037906844,0.046656551,0.169672056,0.070286631,-0.1316979,-0.024774344,0.197083128,0.08457278,-0.116901188,-0.157341477,-0.098757295,-0.231890611,,
+Schizophrenia (%),0.674009947,0.598279977,0.408599424,0.208378236,0.047461362,0.22556246,-0.254795877,0.674009947,0.446845268,-0.031946061,1,0.19845551,0.668815546,0.469324663,0.475202689,0.167477532,-0.059206939,0.598279977,-0.382558146,0.387764526,-0.029306353,0.292762796,0.661736975,0.41231483,,
+Bipolar disorder (%),0.566776423,0.035548283,0.470052274,0.035972725,-0.143540167,0.024362598,0.049271808,0.566776423,0.294289615,0.005671509,0.19845551,1,0.708055326,0.663745285,0.363870618,0.148557216,0.079694317,0.035548283,-0.133592808,0.601410519,-0.138699411,0.357258258,0.500708653,0.3392874,,
+Eating disorders (%),0.756705718,0.362864846,0.553224568,0.178843895,0.002696841,0.108962308,-0.12708669,0.756705718,0.538688704,-0.037906844,0.668815546,0.708055326,1,0.698026136,0.494187478,0.24071598,0.03525434,0.362864846,-0.383193402,0.603767547,-0.061615865,0.362218023,0.757939261,0.504648966,,
+Anxiety disorders (%),0.492064749,0.26243603,0.381984164,0.107316774,-0.158967311,0.123999825,-0.160388757,0.492064749,0.301746182,0.046656551,0.469324663,0.663745285,0.698026136,1,0.61607729,0.379011551,-0.136502328,0.26243603,-0.18091977,0.889176765,-0.163314088,0.242135511,0.482896043,0.316328004,,
+Drug use disorders (%),0.436412736,0.244066675,0.255069314,0.233955339,-0.23942587,0.422989479,-0.10246824,0.436412736,0.087369831,0.169672056,0.475202689,0.363870618,0.494187478,0.61607729,1,0.366538754,-0.113244921,0.244066675,0.209063646,0.642753628,-0.073924905,0.060183932,0.2761726,0.040610789,,
+Depression (%),-0.032316181,0.275973869,0.1160764,0.39896291,-0.231736159,0.100380542,-0.119867022,-0.032316181,-0.034824535,0.070286631,0.167477532,0.148557216,0.24071598,0.379011551,0.366538754,1,0.050145561,0.275973869,-0.119800923,0.652032808,0.27413125,0.008391144,0.117825578,-0.024968212,,
+Alcohol use disorders (%),0.181838097,-0.019843504,0.343572469,0.378287693,0.093726091,-0.233731406,0.298205394,0.181838097,0.090215995,-0.1316979,-0.059206939,0.079694317,0.03525434,-0.136502328,-0.113244921,0.050145561,1,-0.019843504,0.212948777,-0.136007712,0.63072996,0.316290722,0.080357263,0.151149236,,
+Trust in others (%)_y,0.364147175,1,0.183520186,0.380432039,0.059003964,-0.25114818,-0.291553915,0.364147175,0.327147279,-0.024774344,0.598279977,0.035548283,0.362864846,0.26243603,0.244066675,0.275973869,-0.019843504,1,-0.556779563,0.28285192,0.198024619,0.087522224,0.469979977,0.191240379,,
+Share of Top 1% in Pre-tax national income (%),-0.282966661,-0.556779563,-0.482569794,0.115094405,0.007867737,0.500519379,0.47933891,-0.282966661,-0.444558754,0.197083128,-0.382558146,-0.133592808,-0.383193402,-0.18091977,0.209063646,-0.119800923,0.212948777,-0.556779563,1,-0.133697441,-0.149072349,-0.211245057,-0.679953527,-0.447243614,,
+Share with Mental and Substance disorders,0.316391479,0.28285192,0.23972616,0.2064133,-0.271104931,0.364044881,-0.183156151,0.316391479,0.131627209,0.08457278,0.387764526,0.601410519,0.603767547,0.889176765,0.642753628,0.652032808,-0.136007712,0.28285192,-0.133697441,1,-0.059454176,0.159548326,0.386520289,0.191683639,,
+Suicide rate,0.132137991,0.198024619,0.411875136,0.459912031,0.138088413,-0.644111178,0.019330304,0.132137991,0.146100679,-0.116901188,-0.029306353,-0.138699411,-0.061615865,-0.163314088,-0.073924905,0.27413125,0.63072996,0.198024619,-0.149072349,-0.059454176,1,0.308622874,,0.033810065,,
+Taxes  goods and services (% GDP),0.53058427,0.087522224,0.560676233,0.032996761,-0.075006703,-0.145182328,-0.064488343,0.53058427,0.360253171,-0.157341477,0.292762796,0.357258258,0.362218023,0.242135511,0.060183932,0.008391144,0.316290722,0.087522224,-0.211245057,0.159548326,0.308622874,1,0.314912981,0.341982394,,
+Corruption Perception Index,0.730664763,0.469979977,,0.322231825,0.070674926,-0.448741565,-0.184605825,0.730664763,0.76901164,-0.098757295,0.661736975,0.500708653,0.757939261,0.482896043,0.2761726,0.117825578,0.080357263,0.469979977,-0.679953527,0.386520289,,0.314912981,1,0.664157905,,
+Freedom score,0.466437373,0.191240379,0.390749223,0.250873665,0.21573186,-0.302765696,-0.006119172,0.466437373,0.610881972,-0.231890611,0.41231483,0.3392874,0.504648966,0.316328004,0.040610789,-0.024968212,0.151149236,0.191240379,-0.447243614,0.191683639,0.033810065,0.341982394,0.664157905,1,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/your-project/from python to excel/~$corr_pearson_matrix_analyzed.xlsx b/your-project/from python to excel/~$corr_pearson_matrix_analyzed.xlsx
diff --git a/your-project/from python to excel/~$df_OLS_all.xlsx b/your-project/from python to excel/~$df_OLS_all.xlsx