Merge pull request #46 from devonfw-forge/data-sharks-machine-learning

Data sharks machine learning

Author: lpinon

Notebook-Cluster.ipynb

@@ -19067,9 +19067,9 @@
     }
    ],
    "source": [
-    "def tableOut(Threshold, Country):\n",
+    "def tableOut(threshold, country):\n",
     "\n",
-    "    df = search(Threshold, col_country, Country)\n",
+    "    df = search(threshold, col_country, country)\n",
     "    if df.empty:\n",
     "        return print(\"No indicators have been found.\")\n",
     "          \n",
@@ -19088,10 +19088,10 @@
     "\n",
     "\n",
     "@interact(\n",
-    "    Country = sorted(corr_df.index.tolist()),\n",
-    "    Threshold = (0, 1, 0.05))\n",
-    "def g(Country = 'Afghanistan', Threshold = 0.7):\n",
-    "    return tableOut(Threshold,Country)"
+    "    country = sorted(corr_df.index.tolist()),\n",
+    "    threshold = (0, 1, 0.05))\n",
+    "def g(country = 'Afghanistan', threshold = 0.7):\n",
+    "    return tableOut(threshold ,country)"
    ]
   },
   {
@@ -19134,9 +19134,9 @@
     }
    ],
    "source": [
-    "def tableRegAgg(Threshold, Region):\n",
+    "def tableRegAgg(threshold, region):\n",
     "\n",
-    "    df = search(Threshold, col_region, Region)\n",
+    "    df = search(threshold, col_region, region)\n",
     "    if df.empty:\n",
     "        return print(\"No indicators have been found.\")\n",
     "\n",
@@ -19154,10 +19154,10 @@
     "\n",
     "\n",
     "@interact(\n",
-    "    Region = region_list,\n",
-    "    Threshold = (0, 1, 0.05))\n",
-    "def g(Region = region_list[0], Threshold = 0.7):\n",
-    "    return tableRegAgg(Threshold, Region)\n"
+    "    region = region_list,\n",
+    "    threshold = (0, 1, 0.05))\n",
+    "def g(region = region_list[0], threshold = 0.7):\n",
+    "    return tableRegAgg(threshold, region)\n"
    ]
   },
   {
@@ -19191,13 +19191,13 @@
     "median_corr_df_pearson_region = pd.merge(corr_df_pearson, region_df, how = 'inner', left_index = True, right_index = True).groupby(level = col_region).median()\n",
     "median_corr_df_spearman_region = pd.merge(corr_df_spearman, region_df, how = 'inner', left_index = True, right_index = True).groupby(level = col_region).median()\n",
     "\n",
-    "def tableRegMed(Region, Threshold):    \n",
-    "    median_corr_series_pearson_region = median_corr_df_pearson_region.loc[Region]\n",
+    "def tableRegMed(region, threshold):    \n",
+    "    median_corr_series_pearson_region = median_corr_df_pearson_region.loc[region]\n",
     "    median_corr_series_pearson_region.name = 'GDP Pearson Corr'\n",
-    "    median_corr_series_spearman_region = median_corr_df_spearman_region.loc[Region]\n",
+    "    median_corr_series_spearman_region = median_corr_df_spearman_region.loc[region]\n",
     "    median_corr_series_spearman_region.name = 'GDP Spearman Corr'\n",
     "    df = pd.concat([median_corr_series_pearson_region, median_corr_series_spearman_region], axis = 1)\n",
-    "    df = df.loc[(abs(df['GDP Pearson Corr']) >= Threshold) & (abs(df['GDP Spearman Corr']) >= Threshold)]\n",
+    "    df = df.loc[(abs(df['GDP Pearson Corr']) >= threshold) & (abs(df['GDP Spearman Corr']) >= threshold)]\n",
     "\n",
     "    if df.empty:\n",
     "        return print(\"No indicators have been found.\")\n",
@@ -19216,10 +19216,10 @@
     "\n",
     "\n",
     "@interact(\n",
-    "    Region = region_list,\n",
-    "    Threshold = (0, 1, 0.05))\n",
-    "def g(Region = region_list[0], Threshold = 0.7):\n",
-    "    return tableRegMed(Region, Threshold)\n"
+    "    region = region_list,\n",
+    "    threshold = (0, 1, 0.05))\n",
+    "def g(region = region_list[0], threshold = 0.7):\n",
+    "    return tableRegMed(region, threshold)\n"
    ]
   },
   {
@@ -19260,9 +19260,9 @@
     }
    ],
    "source": [
-    "def tableWorldAgg(Threshold):\n",
+    "def tableWorldAgg(threshold):\n",
     "\n",
-    "    df = search(Threshold, 'Global')\n",
+    "    df = search(threshold, 'Global')\n",
     "    if df.empty:\n",
     "        return print(\"No indicators have been found.\")\n",
     "\n",
@@ -19280,9 +19280,9 @@
     "\n",
     "\n",
     "@interact(\n",
-    "    Threshold = (0, 1, 0.05))\n",
-    "def g(Threshold = 0.7):\n",
-    "    return tableWorldAgg(Threshold)\n"
+    "    threshold = (0, 1, 0.05))\n",
+    "def g(threshold = 0.7):\n",
+    "    return tableWorldAgg(threshold)\n"
    ]
   },
   {
@@ -19316,9 +19316,9 @@
     "median_corr_df_pearson = pd.merge(corr_df_pearson, region_df, how = 'inner', left_index = True, right_index = True).median().rename('GDP Pearson Corr')\n",
     "median_corr_df_spearman = pd.merge(corr_df_spearman, region_df, how = 'inner', left_index = True, right_index = True).median().rename('GDP Spearman Corr')\n",
     "\n",
-    "def tableWorldMed(Threshold):\n",
+    "def tableWorldMed(threshold):\n",
     "    df = pd.concat([median_corr_df_pearson, median_corr_df_spearman], axis = 1)\n",
-    "    df = df.loc[(abs(df['GDP Pearson Corr']) >= Threshold) & (abs(df['GDP Spearman Corr']) >= Threshold)]\n",
+    "    df = df.loc[(abs(df['GDP Pearson Corr']) >= threshold) & (abs(df['GDP Spearman Corr']) >= threshold)]\n",
     "\n",
     "    if df.empty:\n",
     "        return print(\"No indicators have been found.\")\n",
@@ -19336,9 +19336,9 @@
     "\n",
     "\n",
     "@interact(\n",
-    "    Threshold = (0, 1, 0.05))\n",
-    "def g(Threshold = 0.7):\n",
-    "    return tableWorldMed(Threshold)"
+    "    threshold = (0, 1, 0.05))\n",
+    "def g(threshold = 0.7):\n",
+    "    return tableWorldMed(threshold)"
    ]
   }
  ],