diff --git a/Benford for Python IP Addresses.ipynb b/Benford for Python IP Addresses.ipynb
index e430698..a9ec7ad 100644
--- a/Benford for Python IP Addresses.ipynb	
+++ b/Benford for Python IP Addresses.ipynb	
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
@@ -50,13 +50,12 @@
     "%matplotlib inline\n",
     "\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pandas_datareader.data as web # Not a dependency, but we'll need it now."
+    "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -75,19 +74,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
    "source": [
-    "good_ips = pd.read_csv('/Users/donajstewart/good_ips_index.csv',encoding='latin-1', index_col=\"Record\")"
+    "good_ips = pd.read_csv('good_ips_index.csv',\n",
+    "                       encoding='latin-1',\n",
+    "                       index_col=\"Record\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -119,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -128,6 +129,19 @@
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -181,7 +195,7 @@
        "5        172.217.6.142      1"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,52 +232,274 @@
     "## Please see error below, I've tried to pass an index parameter in the bf.first_digits function but it does not accept it."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### It's something with the constructor of the DataFrame inside pandas that the function calls. I found it very weird, since there is no way we could be using a *scalar* value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First, I forgot the `first` column and converted the `Server Address` column into ints after removing all the IPs '.'. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Server Address</th>\n",
+       "      <th>first</th>\n",
+       "      <th>ip_numbers</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Record</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34.239.253.124</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34239253124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>151.101.64.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1511016470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.101.64.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1511016470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>173.194.78.189</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17319478189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>172.217.6.142</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1722176142</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Server Address  first   ip_numbers\n",
+       "Record                                    \n",
+       "1       34.239.253.124      3  34239253124\n",
+       "2        151.101.64.70      1   1511016470\n",
+       "3        151.101.64.70      1   1511016470\n",
+       "4       173.194.78.189      1  17319478189\n",
+       "5        172.217.6.142      1   1722176142"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "good_ips['ip_numbers'] = good_ips['Server Address'].str.replace('.','').astype(int)\n",
+    "good_ips.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Then I ran the function on it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "If using all scalar values, you must pass an index",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-18d4f99baf9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgood_ips\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst_digits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgood_ips\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdigs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfidence\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/benford/benford.py\u001b[0m in \u001b[0;36mfirst_digits\u001b[0;34m(data, digs, decimals, sign, inform, confidence, high_Z, limit_N, MAD, MSE, chi_square, KS, show_plot)\u001b[0m\n\u001b[1;32m   1134\u001b[0m     '''\n\u001b[1;32m   1135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAnalysis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1136\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAnalysis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     data = data.first_digits(digs, inform=inform, confidence=confidence,\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/benford/benford.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, decimals, sign, sec_order, inform)\u001b[0m\n\u001b[1;32m    173\u001b[0m  or 'neg'.\")\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m         \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'Seq'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'float'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'int'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    264\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    265\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    400\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   5396\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5397\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5398\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5399\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5400\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m   5435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5436\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraw_lengths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5437\u001b[0;31m             raise ValueError('If using all scalar values, you must pass'\n\u001b[0m\u001b[1;32m   5438\u001b[0m                              ' an index')\n\u001b[1;32m   5439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized sequence with 9867 registries.\n",
+      "\n",
+      "Test performed on 9867 registries.\n",
+      "Discarded 0 records < 1 after preparation.\n",
+      "\n",
+      "The entries with the significant deviations are:\n",
+      "\n",
+      "             Expected     Found     Z_score\n",
+      "First_1_Dig                                \n",
+      "1            0.301030  0.899868  129.667516\n",
+      "2            0.176091  0.027972   38.614192\n",
+      "3            0.124939  0.006284   35.630828\n",
+      "4            0.096910  0.000101   32.488493\n",
+      "6            0.066947  0.001926   25.822050\n",
+      "7            0.057992  0.001419   24.021605\n",
+      "8            0.051153  0.001419   22.401054\n",
+      "9            0.045757  0.000507   21.486776\n",
+      "5            0.079181  0.060505    6.851894\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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APPzII3z5K1/h0UceeUfbuveee7h44kS+8uUv16p9SomUUq4LEGvLnmpJkiQ1ii2bN9Ol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\nmwAORAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fec496d35c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "good_ips=bf.first_digits(good_ips.first, digs=1, decimals=0, confidence=99) "
+    "ips_first = bf.first_digits(good_ips.ip_numbers, digs=1, decimals=0, confidence=99, high_Z='all') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Everything went fine, except for the non-compliant dataset :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But it still doesn't explain why the other column wouldn't work. So I thought, maybe pandas was getting confused when you called `good_ips.first` and throwing a scalar value of some sort. Indeed, when I called it like that bellow, surprise(!), it is already a DataFrame method (somethign to handle initial periods of time series), which means that to use it like that you would have to change the column name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mgood_ips\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Convenience method for subsetting initial periods of time series data\n",
+       "based on a date offset.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "offset : string, DateOffset, dateutil.relativedelta\n",
+       "\n",
+       "Examples\n",
+       "--------\n",
+       "ts.first('10D') -> First 10 days\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "subset : type of caller\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\n",
+       "\u001b[0;31mType:\u001b[0m      method\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "good_ips.first?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Or use the ['`column`'] notation, like bellow, which rendered the same result."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized sequence with 9867 registries.\n",
+      "\n",
+      "Test performed on 9867 registries.\n",
+      "Discarded 0 records < 1 after preparation.\n",
+      "\n",
+      "The entries with the significant deviations are:\n",
+      "\n",
+      "             Expected     Found     Z_score\n",
+      "First_1_Dig                                \n",
+      "1            0.301030  0.899868  129.667516\n",
+      "2            0.176091  0.027972   38.614192\n",
+      "3            0.124939  0.006284   35.630828\n",
+      "4            0.096910  0.000101   32.488493\n",
+      "6            0.066947  0.001926   25.822050\n",
+      "7            0.057992  0.001419   24.021605\n",
+      "8            0.051153  0.001419   22.401054\n",
+      "9            0.045757  0.000507   21.486776\n",
+      "5            0.079181  0.060505    6.851894\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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APPzII3z5K1/h0UceeUfbuveee7h44kS+8uUv16p9SomUUq4LEGvLnmpJkiQ1ii2bN9Ol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\nmwAORAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fec489a2240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ips_scalar = bf.first_digits(good_ips['first'], digs=1, decimals=0, confidence=99, high_Z='all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hope it helped!"
+   ]
   }
  ],
  "metadata": {
@@ -283,9 +519,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }