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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "simplified-office",
+   "metadata": {},
+   "source": [
+    "## Some example mock APIs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "concerned-holiday",
+   "metadata": {},
+   "source": [
+    "Looking for some high level feedback on these data mocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "cordless-prevention",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlfaker.generators import NormalGenerator, CategoricalGenerator, BaseGenerator\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "aerial-subscriber",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_df(generators, size):\n",
+    "    return pd.DataFrame([g.generate(size) for g in generators]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "sustained-timber",
+   "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>foo</th>\n",
+       "      <th>bar</th>\n",
+       "      <th>fizz</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.624345</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.611756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.528172</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.072969</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.865408</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-2.301539</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.744812</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        foo  ... fizz\n",
+       "0  1.624345  ...  NaN\n",
+       "1 -0.611756  ...    a\n",
+       "2 -0.528172  ...  NaN\n",
+       "3 -1.072969  ...  NaN\n",
+       "4  0.865408  ...    b\n",
+       "5 -2.301539  ...    a\n",
+       "6  1.744812  ...  NaN\n",
+       "\n",
+       "[7 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_df(\n",
+    "    [\n",
+    "        NormalGenerator(\"foo\"),\n",
+    "        CategoricalGenerator(\"bar\", fillrate=0.5), \n",
+    "        CategoricalGenerator(\"fizz\", classes=[\"a\", \"b\", \"c\"], fillrate=0.5)\n",
+    "    ],\n",
+    "    7\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "damaged-memory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DataFramer():\n",
+    "    def __init__(self, generators):\n",
+    "        self.generators = generators\n",
+    "        \n",
+    "    def generate(self, size):\n",
+    "        return pd.DataFrame([g.generate(size) for g in self.generators]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "applied-shopping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataframer = DataFramer(\n",
+    "    [\n",
+    "        NormalGenerator(\"mike\"),\n",
+    "        CategoricalGenerator(\"jeff\", fillrate=0.5, seed=10),\n",
+    "        NormalGenerator(\"tom\", fillrate=0.9, loc=3),\n",
+    "        CategoricalGenerator(\"target\", classes=[0, 1], fillrate=0.5)\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "sacred-sodium",
+   "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>mike</th>\n",
+       "      <th>jeff</th>\n",
+       "      <th>tom</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.900856</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.705072</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.683728</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.320058</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.122890</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.999976</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.935769</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.267888</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.193118</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.530355</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.446993</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       mike  ...  target\n",
+       "0  0.900856  ...     0.0\n",
+       "1 -0.683728  ...     1.0\n",
+       "2 -0.122890  ...     0.0\n",
+       "3 -0.935769  ...     NaN\n",
+       "4 -0.267888  ...     NaN\n",
+       "5  0.530355  ...     NaN\n",
+       "\n",
+       "[6 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataframer.generate(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "serial-doctor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_df_from_cols(cols_types , size=10):\n",
+    "    lookup = {\"normal\": NormalGenerator, \"categorical\": CategoricalGenerator}\n",
+    "    return pd.DataFrame([lookup[v](k).generate(size) for k, v in cols_types.items()]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "continuing-crime",
+   "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>foo</th>\n",
+       "      <th>bar</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.624345</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.611756</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.528172</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.072969</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.865408</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        foo  bar\n",
+       "0  1.624345  1.0\n",
+       "1 -0.611756  1.0\n",
+       "2 -0.528172  0.0\n",
+       "3 -1.072969  0.0\n",
+       "4  0.865408  1.0"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_df_from_cols({\"foo\": \"normal\", \"bar\": \"categorical\"}, size=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "searching-technology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_df_from_num_cat(nums, cats, size=10):\n",
+    "    gens = []\n",
+    "    counter = 0\n",
+    "    for col_type, gen in zip([nums, cats], [NormalGenerator, CategoricalGenerator]):\n",
+    "        if isinstance(col_type, int):\n",
+    "            gen_holder = [gen(f\"col{counter+i}\") for i in range(col_type)]\n",
+    "        else:\n",
+    "            gen_holder = [gen(col) for col in col_type]\n",
+    "        counter += len(gen_holder)\n",
+    "        gens.extend(gen_holder)\n",
+    "    \n",
+    "    return pd.DataFrame([gen.generate(size) for gen in gens]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "affiliated-burden",
+   "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>col0</th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.624345</td>\n",
+       "      <td>1.624345</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.611756</td>\n",
+       "      <td>-0.611756</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.528172</td>\n",
+       "      <td>-0.528172</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.072969</td>\n",
+       "      <td>-1.072969</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.865408</td>\n",
+       "      <td>0.865408</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col0  ...  col3\n",
+       "0  1.624345  ...   1.0\n",
+       "1 -0.611756  ...   1.0\n",
+       "2 -0.528172  ...   0.0\n",
+       "3 -1.072969  ...   0.0\n",
+       "4  0.865408  ...   1.0\n",
+       "\n",
+       "[5 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_df_from_num_cat(nums=2, cats=2, size=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "decreased-extension",
+   "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>foo</th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.624345</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.611756</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.528172</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        foo  ...  col2\n",
+       "0  1.624345  ...   1.0\n",
+       "1 -0.611756  ...   1.0\n",
+       "2 -0.528172  ...   0.0\n",
+       "\n",
+       "[3 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_df_from_num_cat(nums=[\"foo\"], cats=2, size=3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
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+   "version": "3.8.5"
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}