diff --git a/examples/XAI Tabular Data Example Usage.ipynb b/examples/XAI Tabular Data Example Usage.ipynb
index d5f5c3d..0a9abf8 100644
--- a/examples/XAI Tabular Data Example Usage.ipynb	
+++ b/examples/XAI Tabular Data Example Usage.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 692,
+   "execution_count": 781,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 694,
+   "execution_count": 782,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 695,
+   "execution_count": 783,
    "metadata": {},
    "outputs": [
     {
@@ -205,7 +205,7 @@
        "32560             0              40   True  "
       ]
      },
-     "execution_count": 695,
+     "execution_count": 783,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 696,
+   "execution_count": 784,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,30 +228,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 709,
+   "execution_count": 785,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ims = xai.show_imbalance(df, \"gender\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 719,
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -274,26 +254,23 @@
     }
    ],
    "source": [
-    "xai = reload(xai)\n",
-    "ims = xai.show_imbalance(df, \"gender\")"
+    "df_groups = xai.show_imbalance(df, \"gender\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 720,
+   "execution_count": 786,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "show_imbalance() got an unexpected keyword argument 'cross'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-720-a1d6165bfb0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbal_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupsample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Programming/ethical/xai/xai/__init__.py\u001b[0m in \u001b[0;36mbalance\u001b[0;34m(df, column_name, cross, upsample, downsample, bins, categorical_cols, plot)\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0mcross\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcross\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m             categorical_cols=categorical_cols)\n\u001b[0m\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtmp_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: show_imbalance() got an unexpected keyword argument 'cross'"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -302,12 +279,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 700,
+   "execution_count": 791,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No categorical_cols passed so inferred using np.object, np.int8 and np.bool: Index(['workclass', 'education', 'marital-status', 'occupation',\n",
+      "       'relationship', 'ethnicity', 'gender', 'loan'],\n",
+      "      dtype='object'). If you see an error these are not correct, please provide them as a string array as: categorical_cols=['col1', 'col2', ...]\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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lZp7R0hXrLjvvrTt2adxNwxQd1atBQy3efggAAAAAfzL33D1e1/eP0qHDP2vthgSVlpVp6A39dedtY8y2d3N11dP/fUCtW7XQzqRkrV63ybAayMXFWU88ep/Gjh6qyspKbdy8TZu27JSHu5vu/fttumnEDUZ9jRk5WH+/6y9q1MhD23bu0YpVG7T3wGFdd52nevboajK2nZ2dJv37H+oRFqLklIPasGmbHB0dNO6mYRo5bKBJ+zEjB+u+f9yhAD8fbdm2S6vXbZJko7Gjh2rSv/4hO9vLfw22t7fTow//Uzdc30/nzxdq5Zp4HTx0VCOGDtCt40aYrfHyaqzPp71uOHi+vuzs7BQV0UOVlZVaH7/VopoWQc3UskWgss7k1Lr10MPDTXfdNlY7d6UoYVvt521JUlr6aUlSSHBH2djYGN0LDekkSXUOMq7zbKTY6AgNGxyn2OgIBTbzr7Vtpw7V54wl7z1oci855aBRm8txcXZWyxaBOn4yTaVlZbW28/VuqgGxkRo2OE7Rkb3k69PUbDt7ezu1bd1CJSWlOnTkZzPzO1Azv7YWza+uz1qf8bNzzsrDw01NGl9nuNayRaBKSkoNgVa/vuHq0L6NPvviO1VaEKRm55xVTu5ZBXdsZ8ljXjWs1AIAAACAPxmfpl566vk3VFCzMmTewmV64alHFBXRQ3MXLDPZvhYU6K9NW3Zq+hffmnzhHX/zSLVoHqhv5y/RsotWTtnb2+tfE+/SsMFx2p6YrJOp1ed6xUZHKCf3rJ5+YYpJyODu5moy18bXeepk6ik99c7HKiurXpWycMmPevX5/2rQgGj9sGy1YTVTm1YtNHzIAGXn5OqFV98zPMfcBcv04L13qlvXzho8KFZLlq+55Odzw/Uxat2yuXYkJmvqJ18aVistWbFGzz7x0CVrf6ueYSHy8HBXyr5DyjqTY1HNha2H6+O31noO0l23jZWNrY2++GreZfvbnbxfOxKT1bN7iF58+hHtO3C4+kyt5oFq17alVq6J16q1myx/KEldOrdXl87tja7tP3hEn86Yo5zcXw6dd3R0UJPG16mouFh5ZrZRns7MkiT5+npbNG6b1i1kZ2enY8dPXrJdn97d1ad3d6Nr2xP3aMasuUYrqHy8m8rOzk7pGZlmw5/TmWcsnl99nrU+4ydsTdTwIXF6bNJE7UxKVmCAnzp3bKflK9dLqj5z65abhmvp8rWG/59a4ufjqerRrYsC/H3qdL7alUSoBQAAAOAP5dZNa9Q8u2G+YFnqhJePvo6Ka7Dxv/t+qSHQkqTS0jJt2ZakkcMHqlWLQJM3rpWVlWvOvMUmX6Ld3FzVJzxMPx87aRRoSVJ5ebm+m79EIU8/oojwMKMvyxUVlaqsMv1Cfr6g0Ox8v5qz0BBoSVJ+foGSdu9VVJ+e8vPzVtqp6pVF0VG9JEmLl642CuYqKyv1zdzF6tqlo/pFhV821IqO7KnKykp9O3+JUUh0JjtXq9bGa9TwQSY1Z3Pz9Pizr6miwrLtgrWJia7eRrg+vvZthBdzcnJU717dVF5RoY2bzZ8ZFR3ZS927ddHUT77UufzzFvX7wcdfaOSwgbpx6AA1C/AzXN+7/7C2bE+qNTz7tdLSUi1cslJJu/Yq80y2JCmomb9GDR+kTh3bavLD/9QzL72l0tLqgNPVxUWSVFRUbLa/C9cvtLscrybVq5PO5pk/Zyw/v0DfzV+i3Sn7dSY7Vw729mrZIkhjRw1Wr+5d5dnIQ69O+dDwvK4u1YfrF9Y2v+Jio3aXUp9nrc/4uWfz9MbbH+uWMcMVE91b588XavGy1Vq0pPoMsTvHj1FObp4WLV2lwGb+uu2WkWrbuoWKS0qVsHWn5sxbYvYtiXk1n2mTxo0JtQAAAIDfU/feUxt6CvgNnnqsvfbtN/8lKie3SF6FtW8zqg83V4cr2l9D+9nMqpULq2VcXU3DgjPZOcrPLzC53qpFkOzs7FSlKo0cbroV0M7OTpIU4OdjuJawLUkD4/rqpWf+ox2Je3Tg0FH9dPS44cv4rxUWFikzK/sS8/1ldVeLoGaSqlcA/drpzDPKyc2Tj7eXXFycaw0SnJ2c5OvjreycXGWdMR23trcVVlRWKuN0ltl7lvL1aaoO7VqbPSC+Nr17dZOLi7N2JCabPSDey6uxbh03Qtt27tb2nXss6tPe3l7/+NtfFBLcQbO+WaDE3XtVWlqqdm1a6rZbRurxSRM19ZNZFr3hLz+/QAsWG599dejIz3rj3U/0xH/uU5vWLRTTt7dWrom3aG6/sCxUu7D67+LVVhc7lX5ap2q2W0pSSUmpUvYd1JGjx/T8kw+rfdtW6ta1c53eZnjlWfasl3L02Em9MuVDk+t9endXSHAHvfz6B7KztdUjD96tgoIivfvhDPl4N9UtY4arvLzC5C2JklRQWB1Ce7i7/eb51RehFgAAAIA/lLfa9LrifXbu5HP5RlbEXKBzYQufrZkzp8xtjZIkd/fqwKB1y+Zq3bJ5reM5OTka/v3r7xYp60y2+kb20rDBcRo2OE7lFRVKTjmgb+YuNgmwCovMhxG/zPeXM59calannM07Z7YmL++cmno1luslQq0LfZw7Z35FU14tK36uhJi+vWVra1unA+Jj+17Yemh+Zdfdd9ys0rIyfTl7vsXzGDa4v8J7hOqrOQuMDp5P3ntQH3z0pV54+hGNv3nEbwp6KisrtWHTNrVp3ULt27Y2hFoXft4utax0crnMSqVfu7DF1cGhbvFHcXGJtm5P0o1Dr1f7tq0Mz1pYdOmVWC7Ols+vPs96Jcdv5OGu8eNG6MfVG/XzsZPq1zdcja/z1IefzNLhn45JOqjmQQG6vn+UFiz+0WS7sKNDddh/qbPKrjZCLQAAAABAvVwIhlas2qBv5i62qKaqqkor18Rr5Zp4eXi4qX2bVgrv1U3hPUIV4O+rp154w+I3FNY2H89GjcyusvL0bCTp0l/4L/TRqJH5t/t5enrUa26XU31AfM86HRAfFBigVi2DlJmVrZR9h8y2adG8mVxdXfTelOfN3r9x6PW6cej1StyVovemzZT0y2HwBw6arko7mZau8+cL1NSridzcXFVQy5ZRS1w4pPzi0LO0tEw5uWfVpPF18mzkYRKo+vpUnxV12sJVcfk12y3NnddWn/llZp1RRUWFvJt6ydbW1mRL7oUD5i2ZX32e9UqOf/uto3W+oFDfL14hSQrw85UkHT+RZmhz7ESq+kWFy8fbS6mnMozqL3ymlm5pvRp4+yEAAAAAoF6OHjuhyspKtW/bql71+fkF2rkrRR9+Mkv7DhyWr09To/Ob6urEyeov4x3btza55+PtpSaNPZWZlV3rKi1JKi4p0enMLDW+zlPeTb1M7ndsb9lb9+qqR7cuatTIXfsOHKnzAfEbNm2rtc2mLTu1IX6ryT8XtlEeP5GmDfFbjd6a6GBfvf7Fw8N0W5m9vZ2ca1YJVdQzfLygTavq1X2/DiD314RpIcEdTGpCunQwanM5J9PSJUl+fnVfbdnaML9ffh7l5RU6cvS4nJwczf6+D+nSsWZ+pltgzanrs16p8Xt2D1GPbl00/cvvfjmvrmbRo/1Fq9oc7Gvfeu3n56PKykql1nzGDYFQCwAAAABQL/n5BUrYlqRWLYN049DrzW5d9G7qpaZejSVVByKdOrQ1aWNnayu3mrOxLhwYXh8XDkq/cej1Ruf82NjY6JYxw2Vra6uNm2sPgH7pZ4dsbW017qahsrH5ZXtjU6/Gur5/X7M1dra28vP1NhuEWeLCAfEXb/e7FEdHB0WEX/qAeEma/e1CfT5rrsk/8Qk7JEl7Uvbr81lztWb9ZkPNoSM/S5KGDR4ge3s7o/5GDR8kezs7HT12QsUlJYbrLs7O8vP1lmcj45VsrVsGGc5Wu1inDm00aEC0pOq3811s3YYESdLwIQOMznjz8mqsATGRKisr08aE2p/5YmmnTuvcufOGAO3X2rZpafQzvqBPeHeF9whVWVm5tu3cbXRv7frq+d00crDs7X8JgFq1CFR4j1CdO3deO5KSjWo8G3nIz9fbsD3wtzxrfca/mJubq27/y2itWb9Zh2t+1pJ0quaFC91COhuudQvppLKyMpNtwfb2dmoeGKDjJ9MuGRJfbWw/BAAAAADU26xvvpevT1PdNOIGRfbursNHfta5/PO6zrOR/P191Lplc3346azqN8s5OGjyw/co60yOjv58Qtk5uXJwsFfnTu3VzN9XSbv3Kj2j/m9RO3L0uJauWKuhN/TXi89M0o7EZJWUlqprcAcFNvPXocNHtezH9ZftZ8Wq9ereLVi9uneV7xMPKWXfQbm4OCu8R6gOHflZYaHBJjXXNfbUK89P1pnsHD365Ct1mrePt5c6tm+jvLx87bLwnKrePbvJ1cWl1gPif4vFy1arW0hnBXdqp/89N1nJew+qrKxMbdu0VJtWzVVSWqrZcxYZ1XQP66K/33mL4hN26LOZcwzXx900TM38fXXg0E/KOZsnqfrth507tpMkzV+4XEeOHjfq68jR41q+cr0GD4zRi089ou1Je2RvZ6/wnqFyd3fTrG++V3Z2rsXPk7grRbH9IhTg72t0KLwk3TPhVtnY2OjIT8eVezZPDg7Vbz9s06q5yisqNHP2PJOxtu7YpR5hXdSrR6ief/Ih7UreL3c3V4X3CJWtrY1mfDVXxcUlRjVjRw9V3z499enMOdpUEyjW91nrM/7FbrtlpEpLyzR3wTKj6wnbEjVy+ED9dfxNat2quXy8vdSpY1st/XGdyblZHdu3kYODvXYm1h6e/R4ItQAAAAAA9VZcXKJXp3yo2OjeiugVph7dQ+Rg76Bz+fk6nXlGs79daNjaVlpSqm/nL1HH9m3Utk0Lde8WrOLiEmVmZWvmV/MuueLIUt99v1THT6ZpQGyUIiO6y97OTplZ2Zq3cJmWr9ygiorLb5krL6/Q629/rFHDBym8Z6gGxvXVmexcLV62Wom7UsyGWr9FfQ6Ij4m+9AHxv8XZs+f03P/e1tAb+qtrl46KjuwpGxsbnc3L18bN27V0xVqL3/S4ectOde/WRa1aBinEraPs7Ox0Lj9f23bs0qp1xiuFLjZn3g9KTUvXgNgoxfaNUGVVpY6fSNPyleu1O3l/nZ5nzfrNiu0XoaiIHvru+6VG99auT1DnTu3Urk1Lubu7ycZGyj17Ths3b9fK1RsN2xd/bdpns3Xk6HFFR/bS9bFRKisr06EjR7V46WqTkO5y6vOs9R2/a5eO6hPeXa+//bFKSkqN7pWVlevN9z7V+JtHKjqyl0pKSrRi9QbNX7jcpJ+oiJ4qKyvXBgtWPl5NNhH9R//2d0P+CSWssfzNEX8mvBobdZG49b6GngIA4E+MP7dYt6cea6//e/vL3228P9rbD4E/m0kP/l1BgQF69Kn//XKGFOrFw8NNr7/8hLZuS9Lns+ZaXPfPO8eqT5/el223/+ARTZg42aI+OVMLAAAAAAD8oc2Z94Pc3V0VFxPZ0FOxesMHD1BlZaXmL1rR0FMh1AIAAAAAAH9sqacyNP2L71ildQXk5Z3TJ59/o7wrfJZbfXCmFgAAAAAA+MPbvHVnQ0/hD2Hpj+saegoGrNQCAAAAAACA1SHUAgAAAAAAgNUh1AIAAAAAAIDVIdQCAAAAAACA1SHUAgAAAAAAgNUh1AIAAAAAAIDVIdQCAAAAAACA1SHUAgAAAAAAgNUh1AIAAAAAAIDVsbe0Yf9+fRQWGqx2bVqqXZuWcnNz1fKV6/X8K+9YVP/Ef+7TjUOvlySNu/0+pZ7KMGlja2urcaOHavjgOAUF+qukpFQp+w9pxqy5St570Gy/To6OuuPW0bo+rq/8fL1VUFCkpN0p+mTGNzp+Is1sTSMPd034683qFxUuryaNlXcuX1u3J+njz79R1plsCz8RAAAAAAAANBSLV2rddftYjRs9VO3atlLWmZw6DdK3T0/dOPR6FRQWXbLdi08/oofunyAHB3vNXbBM6+O3qlvXzpr69kuKjuxl0t7BwV7vvP6s7r7zFhUUFOnbeUu0I3G3Yvr21ucfvq7OHduZ1DRq5K6P339Ft4wZrrRTGfpm3mLtO3BYw4cM0IxpryvA37dOzwYAAAAAsMzn017Xfx+5t6H3NLReAAAgAElEQVSnAeAPwuKVWu9M/VyZWdlKTUtXWGiwpr71okV113k20mOTJmrlmnh5NblO3bt1MdtuYFxfxcVEak/KAT046VmVlpVJkr5f/KOmvfOyHpt0n3YmTVRhUbGh5taxIxQa0klr1m/WUy9MUVVVlSRp1dpNeu2lx/Xk5Pt1+90PG65L0sS7b1eLoGb6+rtFevfDGYbr40YP1SMP/l2P/vufevgxy54NAAAAwLWprLj3Fe1vd9IV7U6hYT9f2Q4B4E/I4pVaibtSlJqWXucBHps0UZL0xrsfX7Ld6BE3SJI+mj7bEGhJ0v6DR7R63SY1aeyp/jGRRjWjamre/+gLo+Bq4+btStqzV61bNldYaLDhurOzkwYPjFFhUZE+nfGNUV9zFyzTqfTTiggPY7UWAAAAAADANe6qHhQ/9Ib+iunbW6+99ZHOnTtfazsHB3uFBHdUUVGxdu/ZZ3I/YVuiJKlH2C+rvAID/OTv663jJ9OUnpFpUrNla/VfpfQMCzFcC+ncQc7OTtqTcsBoxZckVVVVaduO3dXj1LKaDAAAAAAAANcGi7cf1pWfr7cevn+Clq1cpw2btl2ybWAzf9nb2el4eqoqKitN7p9MrV4h1jwwwHCteVCzmnunzPZ5smZVWZBRTYBRf5bUAAAAAMC1bGBcX8VER8inaROdLyhU4q4UzVuwXC88/bAk6dEnXzFq37tnN8VE91bzwAA5Ojoo60yOtmxL0rKV61ReXmHU9vNpr+vAoZ/0wUdfaMyoIerWtbPcXF2VmXVGy1euV3zCDpP52NnZaegN/dW3T081vs5TZ/POacu2RC1auqrWZ7C1tVVMdG9F9e6hAH9f2drZKiMjSxs3b9Oa9QlGO3O8vBrrjZefUHzCDv2wbLVuGnGDOrZvK3d3V7329kc6eOjob/k4AViRqxJq2djY6On/Pqii4mK99d5nl23v7uYqSTpfUGj2/oXr7u5upjXnL1fjelGNW829glpqqq97XDQOAAAAAFyr7rh1tOJiIpV7Nk/rNm5VRUWFunXtrFYtg2RnZ6eKCuOQ6m93jFO/qHDl5JzVzl0pKiwsUpvWLXTTyMHq1LGt3njnE1X+aqGBq4uLnnj0flVUVGhH4h45ODioZ1iI7r7zFlVVVWnTlp1G7e/7x+3q3q2LTmee0ep1m2Rvb6e+kb0U2Mzf7DPY2drq3/dPUEhwB6VnZGrL9iSVlZWrY4c2uv0vo9W6ZXN98qvjYyTJp2kTPf3fB5WReUYJ2xPl6OCg4qKS3/iJArAmVyXU+svYG9W9Wxc98vhLyj9vPkCqCxubmn+5KJ23vKYO48imZhjzRSOHDdTI4QMt7xAAAAAArpJ2bVspLiZS6RmZevH/3lNRzRErcxcs06MP/VONr/PUmexf3lwf1aen+kWFa2dSsj6aPltlZeWGeyOHD9So4YM0IDZSK9fEG43TPChAG+K3asZX8wzflX5cvUEvPPWIhtzQ3yjUiujVTd27ddGRo8f1f29OU3l59RgLFv+oZx7/t9nnGD50gEKCO2jV2njN/naRYQwbGxvddftY9YsK146kZCXt3mtU175da/2wbLXmLVxe348QgJW74mdqBTbz1z13j9cPy1YrYWuiRTWGVVVurmbvu7maruQytxLr8jUFNeOYX4nldpkVYwuXrNSEiZM1YeJk8w8CAAAAAL+TvhE9JEk/LFtjCLQkqaKiQnMXLDNpP7B/X5VXVOizL741CrQkadGSVco/X6CI8DCTupKSUn09d7HRX/6fSs/U4Z+OqZm/r5ydnH6ZU2QvSdK8BcsMgZYkFRQWmd1+aGNjowGxUTqbd05ff2c8RlVVlb6Zu1iVlZVm55WXl6+FS1aafjAA/jSu+Eqt1i2D5OToqOFDBmj4kAFm23w3a6ok6b9Pv6oNm7YpNS1d5RUVCvD3lZ2trcm5WkGB1ctUT1x0ftaJk2k198yffxVUs7T1pFHNKaP+LKkBAAAAgGvRhXOGD//0s8m9n44eV/lFWw8dHRwUFOiv8+cLNWhAtNn+ysvL5e/nY3L9dOYZFRebbuvLzc2TJLm6Oqu4pPp+i6Bmqqys1OEjpnM6cOgnk2u+Pk3l4e6mjNNZunGo+e+PZWXlCjAzr5Opp0zOAAPw53LFQ630jEwtWmL+AMDIiB5q6tVYq9dtUkFBkeGthWVl5Uree0BhXYMV2rWzEnelGNX1Ce8uSdqZ9Mv11FMZSj+dpRZBzeTv52PyBsSI3tVJ/o6kZMO1lH2HVFxcoq5dOsrVxdnoDYg2NjYK7xlaPc6vxgcAAACAa42Li7MkKe9cvsm9qqoqFVx0/rCrm4tsbW3VqJG7Rg0fVKdxCouKzF6/sBjBxvaXDUAuLs4qKCg0+wKwvDzTeV44N9nP1/uS83K6aDWYoT8zzw3gz+WKh1qHfzqmV6ZMNXvvgzdfUFOvxpr26VdKPZVhdO/7RSsU1jVY90wYrwcnPavSsjJJUqcObTUgNko5uXlauyHBqGbBohWa+I/b9cA9f9VTL0wxLFWNjuylsK7BOnrshNG+66LiYi1fuV6jbhyku+/8i96bNsNwb+yoIQrw99WWbUk6lX76SnwUAAAAAHDVFBdX/yW9ZyMPZZ3JMbpnY2MjN3dXnT1bvZrqwvbE4ydS9dz/3rlqcyoqKpabm6vZHTienh5m2lcHZjuTkvX+R1/Uaaw6HJ8M4A/K4lCrX1S4+kWFS5K8mjSWJHUJ7qCnJj8gqTolf2/azHpPZOWaeMVGRyguJlIzP56i+ITt8mzkoQH9o2RrZ6tXp0xVYaHx3xB8PXeRovr0VFxMpD79wEc7kvbIz6ep4mIiVVRUrJdf+8Dk0PcPP5ulsG7BGn/zCLVv21L7DhxWi+aBiunbWzk5Z/XGux/X+xkAAAAA4Pdy/OQptWgeqHZtWpmEWm1at5C9nZ3h1yUlpUpNy1CAv5/cXF1UUGh+9dVvn1Oagju1V7u2rUy2G3Zs38akfXpGlgoKCtW6VXOzQRgAXIrFB8W3a9tKwwbHadjgOMMhfYEBfoZr/fv1+c2TeebFN/XO1OmqqKjQuNFDFRMdoV179um+h57Sxs3bTdqXlZXrX/95Tp/NnCMPd1f9ZcyN6tUjVBs2bdOE+yZr34HDJjXnzp3XPx94XHPm/aBmzfx167gRCu7UXj8sW6277n1UaadYpQUAAADg2re55q2Dw4fEycXZ2XDdzs5OY0YONmn/4+oNcnCw14S/3mzYungxV1cXtag5p6u+4mu+t40ZOVj29r+soXBzddGNZs5crqys1Kp1m9T4Ok+Nv2WUHBxM1114NvJQgL/pmVoAYPFKrc9mztFnM+f8psHuf+SZS96vqKzUN3N/0Ddzf7C4z5LSUn06c44+rcPczuWf19sfTNfbH0y3uAYAAAAAriUHDx/Vug1bFNsvQi89O0k7k5JVUVGp0JBOKiouVu7ZPFVW/rJzZePm7WrRPFADYiP1WttWStl3SNk5uXJzc5V30yZq37a14hO264vZ8+s9py3bdym8ZzeFhQbrpWcmKWn3XtnZ2aln9xAdO54qX5+mJjWLl6xS88AAxcX0UbeunbT/4E86ezZPHh7u8vVpqnZtWmrewuU6lZ5pZkQAf2ZX/EwtAAAAAMDv44uv5yv9dKZioyMUG91H5wsKlLhrr+YtWKYprz6p/PwCo/azvvleyXsPKDY6Qp07tpOrq7MKCoqUnZur5SvXafPWxN88pw8+/lLDbuivqD49NSA2Snnnzik+YYcWLVmpT95/1aR9RWWl3v1whvr07q6+fXoqNKSTnJ0clX++QFlncjR/0Qpt2fbb5wXgj4dQCwAAAMAfjoPz1ivaX+dO1+b2t6qqKv24eqN+XL3R6LqvT1O5ODvrVIb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\n",
       "text/plain": [
        "<Figure size 1440x504 with 1 Axes>"
       ]
@@ -317,27 +303,17 @@
     }
    ],
    "source": [
-    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
+    "groups = xai.imbalance_plot(bal_df, \"gender\", \"loan\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 701,
+   "execution_count": 793,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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hMjgY9O33s039a1T3kiT1C++qrKwcPffKl9qxc5+qV/fWv+4ZrkE3henTD57WmHtfNtua+Z8n7pKPt6cefvKDv/Q854PKyv5uc/Mqvg89Pd0tavLyCiqtcXd3laeH2yVDrTp+NSVJ6emWAZQkFRYW6cep87V+Y7QSzoWITRvX0/33Dlfnjq31zWfPa+x9r6qwsNh8brmVzC3X8nkuxs3NVZJUUGB9pVNBYaFcXV3k5uaq4pISubu5Xbz/ufbz/aSKwPOjLybpjlFD1bN7ZxWXlGjN+ij9Nm+xJGnUiJvl5Oik6bMWqIZvdd11xwi1btlMZcYyRe/ap2kz5qmw0PItidlZFT9jNXyr2/SsVwOhFgAAAAD8jzlxynJbVUbmWUmSu5W3251JzzCdu3OhRg3qycHBQeUq17DB/S2unz+APLCOn6ktaluM+kf01Duv/Uc7ovfo4OHjOnb8VKXbl/LzC5Saln6R+f4RHjSoV1eSdODQUYv+KalnlJGZJb/aNeXm5lppKODq4iJ/v9pKz8hU2hnL+x48bP0cpzKjUckpaRfd4nYp9er6K6R9i4seEL/+T2/vS0nN0MLF63Xo8En9+O1ruvO2gZo+a5lpK6LBwSCpYlvjux9N1qaoiu2defmFevO9SWrYIECtWzZWRJ8uWrF6iyQpvFcnDbopTB9+NvWiq8WqQrVq5/5gfQfkRWtsKfHx8ZQkZVv5/pWkzLM5mjTZfJvgrj2H9Ph/PtKkr15Wm+CmGnZzn8terVbZls4rdfmjmVckp6Tps69/tOjVvGkj9e3dXRO+n6a8/AI9++S/5eXlqe8mT5eLi7PG3jZczk6OFm9JlKTc/Iqg0svT47JnV1UItQAAAAD8o3zWpEuVj9m6ld+lO9kRa4HO+S18BoPB4pq1bU7SH6tRGjesr8YN61d6PxcXZ9Off/1todLOpKtnaBfdPCBCNw+IUGlZmWL3HtSM2b9bBFj5BdZXxPwx32qmtvOrXs5mZVutycrKVq2avnK/SKh1fozsbMvzqSrGsP5ZVIXhQ8JlMBiu6ID4Q0dOaf+B42rfrrnaBjdV5OaKA/pzciqCh6KiYtN5VBfaEBmj1i0bq3XLxlqxeou8vTz0wn/u0fad+zRn/sVXtNni/Aota1tLJcnjXIh64Uqu3Lx8+Vb3loeHm7KzLcOo8zWVreS6UOG5lVwuzpe32rKszKgFizeoTXBThbRvYQq1zs/Tw9My/JUkT0/b5yZV/Cx6eXnKzc1VeVZWs7m5Vnw/Fp77fj3/83D++9Si/7n2/Eq+vy/k5OSoe+8apZ0xsdoZE6vWrZqpQf0gTZr8q6J3VbwQoVbNGrpl6E2qPW+pRcjr7FTxmf55G/HfiVALAAAAAHBFzgdDy1dt0IzZv9tUU15erpVrIrVyTaS8vDzUvEkjde3SQV07tVdggL9eeetjm99QWNl8fLy9ra6y8vHxlnTxf/CfH8Pb29PqdR8fryua26U4Ojpo0ICelzwg/mIyzwVubq4uprZTpyvOqcovKLS6euj8CjwXl4qAwt+/pnyre6tLp2BtXT/V6n2+/vR5SdJnX/2iGbNXXHROp04nq3XLxqpfr44OHj5pds3BwaDAgNoqLS1VQuIfK8JOn06Wb3Vv1a9Xx+INhzVr+Mjd3VUpqemX3HooSZmZFQFnZX+fF3P2bEWt6wWfZ2FhsVJTM+TnV0M1a/hYnKtVL8i/4hniLc8DsyYpJU1eXp6q41fb7A2HkuTj7SVXVxdlZJw1BUfFxSXKyDyrGr7V5ePtZRE4+/tVnKWVknLpFXa3DB0gDw93/XdGxUq18ysqT8XFm/qcOl3x58AAP4ufKU+PihVa2TnWA+C/g2UEDwAAAACADY6fPC2j0ajmTRtdUX1OTp527tqrb7+fpv0Hj8jfr5bqBtp2wLY1p+MSJMnqGwr9atdUDV8fpaalV7pKS5IKi4qUkpom3+o+ql2rpsX1ls2bXPH8Lia8VyfV8PXW9p37r2jLn4ODg1o0ayBJZgHR0eNxyjybLd/q3qrh621R17hxxVvwkpLPSJKysnK1YNF6q/+dP7h985bdWrBovY6diLcY7892xuyXJHXv2tbiWod2LeTm5qI9e4+anZm241xNj67tLGpCu1e07Yg+YHHNmuMnE1RaWqaG9QNs6n+hNq0r3jqY8Ke/jx0xFffu0c3K/Lpd3vzOb5VtG9zC4lq7Ni3N+vxRc6zSmrZtWpj1qUzjhvV0Y79emj5rgSnYrHZuX6ej4x/rn5wcK18LFVCnIkCLi0ustM/VRqgFAAAAALgiOTl5itoWo0YN62nIoBusbl2sXaumatX0lVSxGqlVi6YWfRwMBnmcOxuruPjKtzJt3LxdkjRk0A1m5/xUq1ZNt40cLIPBoI2bt9kwzg4ZDAaNumWQ6R/6klSrpq9u6NvTao2DwaA6/rVVN/DKtqqePyB+3kLrB8RLkrubq5o1tdzm6ejooKcfG6OAOrV04lSiDhw6YbpWVmbUvIXrJEmPPnib2fM0aRykmwf0VGlpqdasr/jsUtMy9N5Hk63+t2fvEUnS9FnL9N5Hk7V9537TWB4ebmpQP0A1a/iYzW3Nuu3KPJut/hHd1LJFQ1O7s7OT6Q2OcxeYb3P8fclGFRUVa9SIGxRQp5ap3cvTXePuHFJRs9C2rZF5eQU6cvS0mjapZ3ULYnCrxnJ0dLBo7xTSSrePqnghwLIVm82unZ/vPWOHyOuCA+ED6tTSrcNvUFFRsRYt3WhW4+PjqQb1A0xnfJ0XuXm7SkpK1C88VDXP/ZxIFWfb3Tyg4mUSazdsMatZtyFKkjR4YD+zM/Bq1vRVvz6hKikp0cao7ZV8IhUB6Pi7R2vP3oPasu2Ps9vOv3ChQ9vWprb27Sr+nJhkeVZc40YNlJOTq/hE21alXQ1sPwQAAAAAXLFpM+bJ36+Wbhl6k0K7ddSRoyeUnZOr6j7eCgjwU+OG9fXtD9N0Jj1TTk5Oeu6pB5R2JkPHT5xWekamnJwc1bpVc9UN8FfM7n1KSr7yg9aPHj+lJcvXatBNffX2a89oR3SsioqL1S64hYLqBujwkeNaumL9JcdZvmq9OnYIVpeO7eT/0pPau/+Q3Nxc1bVTex0+ekIh7YMtaqr7+uj9N59TYlKaRtz+n8uad1BdP3UKaaX09LPasMn6AfFSRTAy7ce3dejIKR09Fqcz6WflW91LnUJaqW6gnzLPZuvVt7612GY4Zdrv6tKptW4e0FNNGgcpetdB+Vb3Ut/eneXq4qzPv5mu+IQr/9ylipVmr714vxYt3ai3P/jB1J6XX6j3PvpJ77/5qL79/EWtXLNV2Tm56hUaooYNArV63TatXLPVbKyk5DP6auJM/eeJuzTluze0au1WlZSUKSK8s/z9auqXGUsttiVezNoNO9SqZSN17tham7bsNrv2yAOj1bhRXUXvOqjU1ExJUtMmQerSqeLveOIPcxS7z3ylVOy+o/pl5lLdedtA/fLTO1qzboecnBx0Q99u8vHx1Eef/9e08u28USNu0P33jtD3P83TD1Pmm9rPpGdq5pzFGnv7cL3+4hPatmO3SstK1SWknWrUqK5lK9dbbEs8evyUlq1crwH9++jtV57W9pg9cnRwVNfO7eXp6aFpM+YpPT2z0s9j2OD+8q3uo4+//N6sff/BIzpxKk5Db75BNWv6ytXFWV07d9C2nbstth7W8a+tWjV9te5PgdvfjVALAAAAAHDFCguL9MEn3yq8Vzd17xKiTh3bysnRSdk5OUpJPaPpsxZo34GKFT7FRcWaNXexWjZvoqZNGqhjh2AVFhYpNS1dU3+ZY1pp9Vf8Nm+JTsUlqF94mEK7d5Sjg4NS09I1Z8FSLVu5waYD2EtLy/TR55M0fPCN6tq5vfpH9NSZ9Ez9vnS1onfttRpq/RXDBtt2QHx2dq5mzlmh4JaN1b1LG3l7e6qkpFQJiama+ssi/TprmTLPWh5kX1RUrEee+j/ddccg9Y/opluH91NxcYn27D2q6bOWKWqr5QHyVWlDZLQeeuJ93XvXEPXt01nOzk6KT0jRZ19P16w51s/k+m3uKiUln9Gdtw3UwJvCZKhm0IlTCZr4wxwtWb7psu6/cPF6/eue4Rp4U5hFqLV0xWaF9+qkVi0aqUfXdnJ0dFBGZrZWrtmq2fNWadeew1bH/HLCDB09FqdRt9yg4UPCZSw36tDhU5o2Y4npDZO2Wr1uk86kZ2hA/z4K7d5RhmoGJSalaO7CZdq0ZafVmplzFik+IUn9wsMU3rO7jOVGnTqdoGUr12t3bOVbH+sFBWrgjeH6+Zc5pjPDLvTVt1M19vbh6tqpvYxGoyKjdmj6zAUW/cK6d5IkrTm3auxaqda974iqfc/k/4ioNXOv9RSuSx27TbjWU4Adid768LWeAuwEv1twOfjdAlvxu8W+vfJCc/3f5//92+73T3v7Ia6e/Qf+2oonXB0vPHOPBt0UpuG3/0cZfzrc/Vqyx98tjo4O+vCdF5WYlKqPv5hkc92/x92qHj26XbLfgUNHNf6h52wakzO1AAAAAADAP9qkyXNVWlqme+8acq2nYvf69u4hH28vzbTxjadXE6EWAAAAAAD4R8vIzNbr73ynM2fOmh2Wj8tXWlqmyf/9TXEJSdd6KpypBQAAAAAA/vk2bo7Rxs2VH8QP26y9xudoXYiVWgAAAAAAALA7Nq/U6tu7h0LaB6tZk4Zq1qShPDzctWzler35/hc21b/0n4c1ZNANkqRRYx9WfGKyRR+DwaBRIwZp8IAI1QsKUFFRsfYeOKwp02Yrdt8hq+O6ODvrrjtG6IaInqrjX1t5eQWK2b1X30+ZoVOnE6zWeHt5avzdo9U7rKtq1vBVVnaOtm6P0aSfZli8phIAAAAAAADXH5tXat0z9laNGjFIzZo2UtqZjMu6Sc8enTVk0A3Kyy+4aL+3X31aTz4yXk5Ojpo9f6nWR25Vh3atNeHzd9QrtItFfycnR33x0eu6b9xtyssr0Kw5i7Ujerf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\n",
-      "text/plain": [
-       "<Figure size 1440x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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hMjgY9O33s039a1T3kiT1C++qrKwcPffKl9qxc5+qV/fWv+4ZrkE3henTD57WmHtfNtua+Z8n7pKPt6cefvKDv/Q854PKyv5uc/Mqvg89Pd0tavLyCiqtcXd3laeH2yVDrTp+NSVJ6emWAZQkFRYW6cep87V+Y7QSzoWITRvX0/33Dlfnjq31zWfPa+x9r6qwsNh8brmVzC3X8nkuxs3NVZJUUGB9pVNBYaFcXV3k5uaq4pISubu5Xbz/ufbz/aSKwPOjLybpjlFD1bN7ZxWXlGjN+ij9Nm+xJGnUiJvl5Oik6bMWqIZvdd11xwi1btlMZcYyRe/ap2kz5qmw0PItidlZFT9jNXyr2/SsVwOhFgAAAAD8jzlxynJbVUbmWUmSu5W3251JzzCdu3OhRg3qycHBQeUq17DB/S2unz+APLCOn6ktaluM+kf01Duv/Uc7ovfo4OHjOnb8VKXbl/LzC5Saln6R+f4RHjSoV1eSdODQUYv+KalnlJGZJb/aNeXm5lppKODq4iJ/v9pKz8hU2hnL+x48bP0cpzKjUckpaRfd4nYp9er6K6R9i4seEL/+T2/vS0nN0MLF63Xo8En9+O1ruvO2gZo+a5lpK6LBwSCpYlvjux9N1qaoiu2defmFevO9SWrYIECtWzZWRJ8uWrF6iyQpvFcnDbopTB9+NvWiq8WqQrVq5/5gfQfkRWtsKfHx8ZQkZVv5/pWkzLM5mjTZfJvgrj2H9Ph/PtKkr15Wm+CmGnZzn8terVbZls4rdfmjmVckp6Tps69/tOjVvGkj9e3dXRO+n6a8/AI9++S/5eXlqe8mT5eLi7PG3jZczk6OFm9JlKTc/Iqg0svT47JnV1UItQAAAAD8o3zWpEuVj9m6ld+lO9kRa4HO+S18BoPB4pq1bU7SH6tRGjesr8YN61d6PxcXZ9Off/1todLOpKtnaBfdPCBCNw+IUGlZmWL3HtSM2b9bBFj5BdZXxPwx32qmtvOrXs5mZVutycrKVq2avnK/SKh1fozsbMvzqSrGsP5ZVIXhQ8JlMBiu6ID4Q0dOaf+B42rfrrnaBjdV5OaKA/pzciqCh6KiYtN5VBfaEBmj1i0bq3XLxlqxeou8vTz0wn/u0fad+zRn/sVXtNni/Aota1tLJcnjXIh64Uqu3Lx8+Vb3loeHm7KzLcOo8zWVreS6UOG5lVwuzpe32rKszKgFizeoTXBThbRvYQq1zs/Tw9My/JUkT0/b5yZV/Cx6eXnKzc1VeVZWs7m5Vnw/Fp77fj3/83D++9Si/7n2/Eq+vy/k5OSoe+8apZ0xsdoZE6vWrZqpQf0gTZr8q6J3VbwQoVbNGrpl6E2qPW+pRcjr7FTxmf55G/HfiVALAAAAAHBFzgdDy1dt0IzZv9tUU15erpVrIrVyTaS8vDzUvEkjde3SQV07tVdggL9eeetjm99QWNl8fLy9ra6y8vHxlnTxf/CfH8Pb29PqdR8fryua26U4Ojpo0ICelzwg/mIyzwVubq4uprZTpyvOqcovKLS6euj8CjwXl4qAwt+/pnyre6tLp2BtXT/V6n2+/vR5SdJnX/2iGbNXXHROp04nq3XLxqpfr44OHj5pds3BwaDAgNoqLS1VQuIfK8JOn06Wb3Vv1a9Xx+INhzVr+Mjd3VUpqemX3HooSZmZFQFnZX+fF3P2bEWt6wWfZ2FhsVJTM+TnV0M1a/hYnKtVL8i/4hniLc8DsyYpJU1eXp6q41fb7A2HkuTj7SVXVxdlZJw1BUfFxSXKyDyrGr7V5ePtZRE4+/tVnKWVknLpFXa3DB0gDw93/XdGxUq18ysqT8XFm/qcOl3x58AAP4ufKU+PihVa2TnWA+C/g2UEDwAAAACADY6fPC2j0ajmTRtdUX1OTp527tqrb7+fpv0Hj8jfr5bqBtp2wLY1p+MSJMnqGwr9atdUDV8fpaalV7pKS5IKi4qUkpom3+o+ql2rpsX1ls2bXPH8Lia8VyfV8PXW9p37r2jLn4ODg1o0ayBJZgHR0eNxyjybLd/q3qrh621R17hxxVvwkpLPSJKysnK1YNF6q/+dP7h985bdWrBovY6diLcY7892xuyXJHXv2tbiWod2LeTm5qI9e4+anZm241xNj67tLGpCu1e07Yg+YHHNmuMnE1RaWqaG9QNs6n+hNq0r3jqY8Ke/jx0xFffu0c3K/Lpd3vzOb5VtG9zC4lq7Ni3N+vxRc6zSmrZtWpj1qUzjhvV0Y79emj5rgSnYrHZuX6ej4x/rn5wcK18LFVCnIkCLi0ustM/VRqgFAAAAALgiOTl5itoWo0YN62nIoBusbl2sXaumatX0lVSxGqlVi6YWfRwMBnmcOxuruPjKtzJt3LxdkjRk0A1m5/xUq1ZNt40cLIPBoI2bt9kwzg4ZDAaNumWQ6R/6klSrpq9u6NvTao2DwaA6/rVVN/DKtqqePyB+3kLrB8RLkrubq5o1tdzm6ejooKcfG6OAOrV04lSiDhw6YbpWVmbUvIXrJEmPPnib2fM0aRykmwf0VGlpqdasr/jsUtMy9N5Hk63+t2fvEUnS9FnL9N5Hk7V9537TWB4ebmpQP0A1a/iYzW3Nuu3KPJut/hHd1LJFQ1O7s7OT6Q2OcxeYb3P8fclGFRUVa9SIGxRQp5ap3cvTXePuHFJRs9C2rZF5eQU6cvS0mjapZ3ULYnCrxnJ0dLBo7xTSSrePqnghwLIVm82unZ/vPWOHyOuCA+ED6tTSrcNvUFFRsRYt3WhW4+PjqQb1A0xnfJ0XuXm7SkpK1C88VDXP/ZxIFWfb3Tyg4mUSazdsMatZtyFKkjR4YD+zM/Bq1vRVvz6hKikp0cao7ZV8IhUB6Pi7R2vP3oPasu2Ps9vOv3ChQ9vWprb27Sr+nJhkeVZc40YNlJOTq/hE21alXQ1sPwQAAAAAXLFpM+bJ36+Wbhl6k0K7ddSRoyeUnZOr6j7eCgjwU+OG9fXtD9N0Jj1TTk5Oeu6pB5R2JkPHT5xWekamnJwc1bpVc9UN8FfM7n1KSr7yg9aPHj+lJcvXatBNffX2a89oR3SsioqL1S64hYLqBujwkeNaumL9JcdZvmq9OnYIVpeO7eT/0pPau/+Q3Nxc1bVTex0+ekIh7YMtaqr7+uj9N59TYlKaRtz+n8uad1BdP3UKaaX09LPasMn6AfFSRTAy7ce3dejIKR09Fqcz6WflW91LnUJaqW6gnzLPZuvVt7612GY4Zdrv6tKptW4e0FNNGgcpetdB+Vb3Ut/eneXq4qzPv5mu+IQr/9ylipVmr714vxYt3ai3P/jB1J6XX6j3PvpJ77/5qL79/EWtXLNV2Tm56hUaooYNArV63TatXLPVbKyk5DP6auJM/eeJuzTluze0au1WlZSUKSK8s/z9auqXGUsttiVezNoNO9SqZSN17tham7bsNrv2yAOj1bhRXUXvOqjU1ExJUtMmQerSqeLveOIPcxS7z3ylVOy+o/pl5lLdedtA/fLTO1qzboecnBx0Q99u8vHx1Eef/9e08u28USNu0P33jtD3P83TD1Pmm9rPpGdq5pzFGnv7cL3+4hPatmO3SstK1SWknWrUqK5lK9dbbEs8evyUlq1crwH9++jtV57W9pg9cnRwVNfO7eXp6aFpM+YpPT2z0s9j2OD+8q3uo4+//N6sff/BIzpxKk5Db75BNWv6ytXFWV07d9C2nbstth7W8a+tWjV9te5PgdvfjVALAAAAAHDFCguL9MEn3yq8Vzd17xKiTh3bysnRSdk5OUpJPaPpsxZo34GKFT7FRcWaNXexWjZvoqZNGqhjh2AVFhYpNS1dU3+ZY1pp9Vf8Nm+JTsUlqF94mEK7d5Sjg4NS09I1Z8FSLVu5waYD2EtLy/TR55M0fPCN6tq5vfpH9NSZ9Ez9vnS1onfttRpq/RXDBtt2QHx2dq5mzlmh4JaN1b1LG3l7e6qkpFQJiama+ssi/TprmTLPWh5kX1RUrEee+j/ddccg9Y/opluH91NxcYn27D2q6bOWKWqr5QHyVWlDZLQeeuJ93XvXEPXt01nOzk6KT0jRZ19P16w51s/k+m3uKiUln9Gdtw3UwJvCZKhm0IlTCZr4wxwtWb7psu6/cPF6/eue4Rp4U5hFqLV0xWaF9+qkVi0aqUfXdnJ0dFBGZrZWrtmq2fNWadeew1bH/HLCDB09FqdRt9yg4UPCZSw36tDhU5o2Y4npDZO2Wr1uk86kZ2hA/z4K7d5RhmoGJSalaO7CZdq0ZafVmplzFik+IUn9wsMU3rO7jOVGnTqdoGUr12t3bOVbH+sFBWrgjeH6+Zc5pjPDLvTVt1M19vbh6tqpvYxGoyKjdmj6zAUW/cK6d5IkrTm3auxaqda974iqfc/k/4ioNXOv9RSuSx27TbjWU4Adid768LWeAuwEv1twOfjdAlvxu8W+vfJCc/3f5//92+73T3v7Ia6e/Qf+2oonXB0vPHOPBt0UpuG3/0cZfzrc/Vqyx98tjo4O+vCdF5WYlKqPv5hkc92/x92qHj26XbLfgUNHNf6h52wakzO1AAAAAADAP9qkyXNVWlqme+8acq2nYvf69u4hH28vzbTxjadXE6EWAAAAAAD4R8vIzNbr73ynM2fOmh2Wj8tXWlqmyf/9TXEJSdd6KpypBQAAAAAA/vk2bo7Rxs2VH8QP26y9xudoXYiVWgAAAAAAALA7Nq/U6tu7h0LaB6tZk4Zq1qShPDzctWzler35/hc21b/0n4c1ZNANkqRRYx9WfGKyRR+DwaBRIwZp8IAI1QsKUFFRsfYeOKwp02Yrdt8hq+O6ODvrrjtG6IaInqrjX1t5eQWK2b1X30+ZoVOnE6zWeHt5avzdo9U7rKtq1vBVVnaOtm6P0aSfZli8phIAAAAAAADXH5tXat0z9laNGjFIzZo2UtqZjMu6Sc8enTVk0A3Kyy+4aL+3X31aTz4yXk5Ojpo9f6nWR25Vh3atNeHzd9QrtItFfycnR33x0eu6b9xtyssr0Kw5i7Ujerf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hMjgY9O33s039a1T3kiT1C++qrKwcPffKl9qxc5+qV/fWv+4ZrkE3henTD57WmHtfNtua+Z8n7pKPt6cefvKDv/Q854PKyv5uc/Mqvg89Pd0tavLyCiqtcXd3laeH2yVDrTp+NSVJ6emWAZQkFRYW6cep87V+Y7QSzoWITRvX0/33Dlfnjq31zWfPa+x9r6qwsNh8brmVzC3X8nkuxs3NVZJUUGB9pVNBYaFcXV3k5uaq4pISubu5Xbz/ufbz/aSKwPOjLybpjlFD1bN7ZxWXlGjN+ij9Nm+xJGnUiJvl5Oik6bMWqIZvdd11xwi1btlMZcYyRe/ap2kz5qmw0PItidlZFT9jNXyr2/SsVwOhFgAAAAD8jzlxynJbVUbmWUmSu5W3251JzzCdu3OhRg3qycHBQeUq17DB/S2unz+APLCOn6ktaluM+kf01Duv/Uc7ovfo4OHjOnb8VKXbl/LzC5Saln6R+f4RHjSoV1eSdODQUYv+KalnlJGZJb/aNeXm5lppKODq4iJ/v9pKz8hU2hnL+x48bP0cpzKjUckpaRfd4nYp9er6K6R9i4seEL/+T2/vS0nN0MLF63Xo8En9+O1ruvO2gZo+a5lpK6LBwSCpYlvjux9N1qaoiu2defmFevO9SWrYIECtWzZWRJ8uWrF6iyQpvFcnDbopTB9+NvWiq8WqQrVq5/5gfQfkRWtsKfHx8ZQkZVv5/pWkzLM5mjTZfJvgrj2H9Ph/PtKkr15Wm+CmGnZzn8terVbZls4rdfmjmVckp6Tps69/tOjVvGkj9e3dXRO+n6a8/AI9++S/5eXlqe8mT5eLi7PG3jZczk6OFm9JlKTc/Iqg0svT47JnV1UItQAAAAD8o3zWpEuVj9m6ld+lO9kRa4HO+S18BoPB4pq1bU7SH6tRGjesr8YN61d6PxcXZ9Off/1todLOpKtnaBfdPCBCNw+IUGlZmWL3HtSM2b9bBFj5BdZXxPwx32qmtvOrXs5mZVutycrKVq2avnK/SKh1fozsbMvzqSrGsP5ZVIXhQ8JlMBiu6ID4Q0dOaf+B42rfrrnaBjdV5OaKA/pzciqCh6KiYtN5VBfaEBmj1i0bq3XLxlqxeou8vTz0wn/u0fad+zRn/sVXtNni/Aota1tLJcnjXIh64Uqu3Lx8+Vb3loeHm7KzLcOo8zWVreS6UOG5lVwuzpe32rKszKgFizeoTXBThbRvYQq1zs/Tw9My/JUkT0/b5yZV/Cx6eXnKzc1VeVZWs7m5Vnw/Fp77fj3/83D++9Si/7n2/Eq+vy/k5OSoe+8apZ0xsdoZE6vWrZqpQf0gTZr8q6J3VbwQoVbNGrpl6E2qPW+pRcjr7FTxmf55G/HfiVALAAAAAHBFzgdDy1dt0IzZv9tUU15erpVrIrVyTaS8vDzUvEkjde3SQV07tVdggL9eeetjm99QWNl8fLy9ra6y8vHxlnTxf/CfH8Pb29PqdR8fryua26U4Ojpo0ICelzwg/mIyzwVubq4uprZTpyvOqcovKLS6euj8CjwXl4qAwt+/pnyre6tLp2BtXT/V6n2+/vR5SdJnX/2iGbNXXHROp04nq3XLxqpfr44OHj5pds3BwaDAgNoqLS1VQuIfK8JOn06Wb3Vv1a9Xx+INhzVr+Mjd3VUpqemX3HooSZmZFQFnZX+fF3P2bEWt6wWfZ2FhsVJTM+TnV0M1a/hYnKtVL8i/4hniLc8DsyYpJU1eXp6q41fb7A2HkuTj7SVXVxdlZJw1BUfFxSXKyDyrGr7V5ePtZRE4+/tVnKWVknLpFXa3DB0gDw93/XdGxUq18ysqT8XFm/qcOl3x58AAP4ufKU+PihVa2TnWA+C/g2UEDwAAAACADY6fPC2j0ajmTRtdUX1OTp527tqrb7+fpv0Hj8jfr5bqBtp2wLY1p+MSJMnqGwr9atdUDV8fpaalV7pKS5IKi4qUkpom3+o+ql2rpsX1ls2bXPH8Lia8VyfV8PXW9p37r2jLn4ODg1o0ayBJZgHR0eNxyjybLd/q3qrh621R17hxxVvwkpLPSJKysnK1YNF6q/+dP7h985bdWrBovY6diLcY7892xuyXJHXv2tbiWod2LeTm5qI9e4+anZm241xNj67tLGpCu1e07Yg+YHHNmuMnE1RaWqaG9QNs6n+hNq0r3jqY8Ke/jx0xFffu0c3K/Lpd3vzOb5VtG9zC4lq7Ni3N+vxRc6zSmrZtWpj1qUzjhvV0Y79emj5rgSnYrHZuX6ej4x/rn5wcK18LFVCnIkCLi0ustM/VRqgFAAAAALgiOTl5itoWo0YN62nIoBusbl2sXaumatX0lVSxGqlVi6YWfRwMBnmcOxuruPjKtzJt3LxdkjRk0A1m5/xUq1ZNt40cLIPBoI2bt9kwzg4ZDAaNumWQ6R/6klSrpq9u6NvTao2DwaA6/rVVN/DKtqqePyB+3kLrB8RLkrubq5o1tdzm6ejooKcfG6OAOrV04lSiDhw6YbpWVmbUvIXrJEmPPnib2fM0aRykmwf0VGlpqdasr/jsUtMy9N5Hk63+t2fvEUnS9FnL9N5Hk7V9537TWB4ebmpQP0A1a/iYzW3Nuu3KPJut/hHd1LJFQ1O7s7OT6Q2OcxeYb3P8fclGFRUVa9SIGxRQp5ap3cvTXePuHFJRs9C2rZF5eQU6cvS0mjapZ3ULYnCrxnJ0dLBo7xTSSrePqnghwLIVm82unZ/vPWOHyOuCA+ED6tTSrcNvUFFRsRYt3WhW4+PjqQb1A0xnfJ0XuXm7SkpK1C88VDXP/ZxIFWfb3Tyg4mUSazdsMatZtyFKkjR4YD+zM/Bq1vRVvz6hKikp0cao7ZV8IhUB6Pi7R2vP3oPasu2Ps9vOv3ChQ9vWprb27Sr+nJhkeVZc40YNlJOTq/hE21alXQ1sPwQAAAAAXLFpM+bJ36+Wbhl6k0K7ddSRoyeUnZOr6j7eCgjwU+OG9fXtD9N0Jj1TTk5Oeu6pB5R2JkPHT5xWekamnJwc1bpVc9UN8FfM7n1KSr7yg9aPHj+lJcvXatBNffX2a89oR3SsioqL1S64hYLqBujwkeNaumL9JcdZvmq9OnYIVpeO7eT/0pPau/+Q3Nxc1bVTex0+ekIh7YMtaqr7+uj9N59TYlKaRtz+n8uad1BdP3UKaaX09LPasMn6AfFSRTAy7ce3dejIKR09Fqcz6WflW91LnUJaqW6gnzLPZuvVt7612GY4Zdrv6tKptW4e0FNNGgcpetdB+Vb3Ut/eneXq4qzPv5mu+IQr/9ylipVmr714vxYt3ai3P/jB1J6XX6j3PvpJ77/5qL79/EWtXLNV2Tm56hUaooYNArV63TatXLPVbKyk5DP6auJM/eeJuzTluze0au1WlZSUKSK8s/z9auqXGUsttiVezNoNO9SqZSN17tham7bsNrv2yAOj1bhRXUXvOqjU1ExJUtMmQerSqeLveOIPcxS7z3ylVOy+o/pl5lLdedtA/fLTO1qzboecnBx0Q99u8vHx1Eef/9e08u28USNu0P33jtD3P83TD1Pmm9rPpGdq5pzFGnv7cL3+4hPatmO3SstK1SWknWrUqK5lK9dbbEs8evyUlq1crwH9++jtV57W9pg9cnRwVNfO7eXp6aFpM+YpPT2z0s9j2OD+8q3uo4+//N6sff/BIzpxKk5Db75BNWv6ytXFWV07d9C2nbstth7W8a+tWjV9te5PgdvfjVALAAAAAHDFCguL9MEn3yq8Vzd17xKiTh3bysnRSdk5OUpJPaPpsxZo34GKFT7FRcWaNXexWjZvoqZNGqhjh2AVFhYpNS1dU3+ZY1pp9Vf8Nm+JTsUlqF94mEK7d5Sjg4NS09I1Z8FSLVu5waYD2EtLy/TR55M0fPCN6tq5vfpH9NSZ9Ez9vnS1onfttRpq/RXDBtt2QHx2dq5mzlmh4JaN1b1LG3l7e6qkpFQJiama+ssi/TprmTLPWh5kX1RUrEee+j/ddccg9Y/opluH91NxcYn27D2q6bOWKWqr5QHyVWlDZLQeeuJ93XvXEPXt01nOzk6KT0jRZ19P16w51s/k+m3uKiUln9Gdtw3UwJvCZKhm0IlTCZr4wxwtWb7psu6/cPF6/eue4Rp4U5hFqLV0xWaF9+qkVi0aqUfXdnJ0dFBGZrZWrtmq2fNWadeew1bH/HLCDB09FqdRt9yg4UPCZSw36tDhU5o2Y4npDZO2Wr1uk86kZ2hA/z4K7d5RhmoGJSalaO7CZdq0ZafVmplzFik+IUn9wsMU3rO7jOVGnTqdoGUr12t3bOVbH+sFBWrgjeH6+Zc5pjPDLvTVt1M19vbh6tqpvYxGoyKjdmj6zAUW/cK6d5IkrTm3auxaqda974iqfc/k/4ioNXOv9RSuSx27TbjWU4Adid768LWeAuwEv1twOfjdAlvxu8W+vfJCc/3f5//92+73T3v7Ia6e/Qf+2oonXB0vPHOPBt0UpuG3/0cZfzrc/Vqyx98tjo4O+vCdF5WYlKqPv5hkc92/x92qHj26XbLfgUNHNf6h52wakzO1AAAAAADAP9qkyXNVWlqme+8acq2nYvf69u4hH28vzbTxjadXE6EWAAAAAAD4R8vIzNbr73ynM2fOmh2Wj8tXWlqmyf/9TXEJSdd6KpypBQAAAAAA/vk2bo7Rxs2VH8QP26y9xudoXYiVWgAAAAAAALA7Nq/U6tu7h0LaB6tZk4Zq1qShPDzctWzler35/hc21b/0n4c1ZNANkqRRYx9WfGKyRR+DwaBRIwZp8IAI1QsKUFFRsfYeOKwp02Yrdt8hq+O6ODvrrjtG6IaInqrjX1t5eQWK2b1X30+ZoVOnE6zWeHt5avzdo9U7rKtq1vBVVnaOtm6P0aSfZli8phIAAAAAAADXH5tXat0z9laNGjFIzZo2UtqZjMu6Sc8enTVk0A3Kyy+4aL+3X31aTz4yXk5Ojpo9f6nWR25Vh3atNeHzd9QrtItFfycnR33x0eu6b9xtyssr0Kw5i7Ujerf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\n",
       "text/plain": [
        "<Figure size 1440x504 with 1 Axes>"
       ]
@@ -347,8 +323,7 @@
     }
    ],
    "source": [
-    "bal_df = xai.balance(df, \"gender\", cross=[\"loan\"], upsample=1)\n",
-    "im = xai.show_imbalance(bal_df, \"gender\", cross=[\"loan\"])"
+    "bal_df = xai.balance(df, \"gender\", \"loan\", upsample=1)"
    ]
   },
   {
@@ -381,11 +356,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 680,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 780,
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/alejandro/anaconda3/envs/xai/lib/python3.7/site-packages/scipy/stats/stats.py:245: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.\n",
+      "  \"values. nan values will be ignored.\", RuntimeWarning)\n"
+     ]
+    },
     {
      "data": {
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diff --git a/xai/__init__.py b/xai/__init__.py
index 9d4275c..199fba5 100644
--- a/xai/__init__.py
+++ b/xai/__init__.py
@@ -26,8 +26,9 @@ def normalize_numeric(
 
     :Example:
 
-    import xai
-    norm_df = xai.normalize_numeric(df)
+    norm_df = xai.normalize_numeric(
+                df,
+                normalize_numeric=["age", "other_numeric_attribute"])
 
     :param df: Pandas Dataframe containing data (inputs and target)
     :type df: pd.DataFrame
@@ -87,19 +88,25 @@ def group_by_columns(
         bins: int = 6,
         categorical_cols: List[str] = []):
     """
-    Groups dataframe by columns provided. If categorical it uses categories,
-        if numeric, it uses bins. If more than one column is provided, the function
-        creates crossed sub-groups.
+    Groups dataframe by the categories (or bucketized values) for all columns provided. 
+        If categorical it uses categories,
+        if numeric, it uses bins. If more than one column is provided, the columns
+        provided are, for example, age and binary_target_label, then the result 
+        would be a pandas DataFrame that is grouped by age groups for each of the
+        positive and negative/positive labels.
 
     :Example:
 
-    import xai
-    columns=["loan", "gender", "age"]
-    cat_df = xai.group_by_columns(
+    columns=["loan", "gender"]
+    df_groups = xai.group_by_columns(
         df, 
         columns=columns,
         bins=10,
-        categorical_cols=["gender"])
+        categorical_cols=["gender", "loan"])
+
+    for group, df_group in df_groups:
+        print(group) 
+        print(grouped_df.head())
 
     :param df: Pandas Dataframe containing data (inputs and target)
     :type df: pandas.DataFrame
@@ -136,7 +143,7 @@ def group_by_columns(
     return grouped 
 
 
-def show_imbalance( 
+def imbalance_plot( 
         df: pd.DataFrame, 
         *cross_cols: str, 
         categorical_cols: List[str] = [],
@@ -144,11 +151,14 @@ def show_imbalance(
         threshold: float = 0.5):
     """
     Shows the number of examples provided for each of the values across the
-    product tuples in the columns provided.
+    product tuples in the columns provided. If you would like to do processing
+    with the sub-groups created by this class please see the 
+    group_by_columns function.
+
     :Example:
 
     import xai
-    cat_df = xai.show_imbalance(
+    class_counts = xai.imbalance_plot(
         df, 
         "gender", "loan",
         bins=10,
@@ -167,13 +177,13 @@ def show_imbalance(
     :type bins: int
     :param threshold: [Default: 0.5] Threshold to display in the chart.
     :type bins: float
-    :returns: GroupsCounts, List of imbalance percent, List where imbalances found
-    :rtype: Tuple[pandas...DataFrameGroupBy, List[float], List[bool]]
+    :returns: Null
+    :rtype: None
 
     """
 
     if not cross_cols:
-        raise TypeError("show_imbalance requires at least 1 string column name")
+        raise TypeError("imbalance_plot requires at least 1 string column name")
 
     grouped = group_by_columns(
             df,
@@ -196,8 +206,6 @@ def show_imbalance(
     lp.set_label(f"Threshold: {threshold*count_max:.2f} ({threshold*100:.2f}%)")
     plt.legend()
     plt.show()
-        
-    return count_grp, ratios, imbalances
 
 def balance(
         df: pd.DataFrame,
@@ -271,7 +279,7 @@ def norm(x):
                 .reset_index(drop=True)
 
     if plot:
-        imbalance = show_imbalance(
+        imbalance_plot(
             tmp_df,
             *cross_cols,
             bins=bins,
@@ -279,12 +287,22 @@ def norm(x):
 
     return tmp_df
     
-def plot_dendogram(
+def _plot_correlation_dendogram(
         corr: pd.DataFrame, 
         cols: List[str],
         plt_kwargs={}):
     """
-    Plot dendogram of a correlation matrix, using the columns provided.
+    Plot dendogram of a correlation matrix, using the columns provided. 
+    This consists of a chart that that shows hierarchically the variables
+    that are most correlated by the connecting trees. The closer to the right
+    that the connection is, the more correlated the features are.
+    If you would like to visualise this as a tree, please 
+    see the function _plot_correlation_dendogram.
+
+    :Example:
+
+    columns_to_include=["age", "loan", "gender"]
+    xai._plot_correlation_dendogram(df, cols=columns_to_include)
 
     :returns: Null
     :rtype: None
@@ -299,10 +317,27 @@ def plot_dendogram(
         z, labels=cols, orientation="left", leaf_font_size=16)
     plt.show()
 
-def plot_matrix(
+def _plot_correlation_matrix(
         corr, 
         cols: List[str], 
         plt_kwargs={}):
+    """
+    Plot a matrix of the correlation matrix, using the columns provided in params. 
+    This visualisation contains all the columns in the X and Y axis, where the 
+    intersection of the column and row displays the correlation value. 
+    The closer this correlation factor is to 1, the more correlated the features
+    are. If you would like to visualise this as a tree, please see 
+    the function _plot_correlation_dendogram.
+
+    :Example:
+
+    columns_to_include=["age", "loan", "gender"]
+    xai._plot_correlation_matrix(df, cols=columns_to_include)
+
+    :returns: Null
+    :rtype: None
+
+    """
     fig = plt.figure(**plt_kwargs)
     ax = fig.add_subplot(111)
     cax = ax.matshow(
@@ -325,6 +360,7 @@ def correlations(
         plot_type: str = "dendogram",
         plt_kwargs={},
         categorical_cols: List[str] = []):
+
     corr = None
     cols: List = []
     if include_categorical:
@@ -341,16 +377,14 @@ def correlations(
         cols = corr.columns
 
     if plot_type == "dendogram":
-        plot_dendogram(corr, cols, plt_kwargs=plt_kwargs)
+        _plot_correlation_dendogram(corr, cols, plt_kwargs=plt_kwargs)
     elif plot_type == "matrix":
-        plot_matrix(corr, cols, plt_kwargs=plt_kwargs)
+        _plot_correlation_matrix(corr, cols, plt_kwargs=plt_kwargs)
     else:
         raise ValueError(f"Variable plot_type not valid. Provided: {plot_type}")
 
     return corr
 
-
-
 def confusion_matrix_plot(
         y_test, 
         pred,