03_Data_Exploration.ipynb

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   "source": [
    "import snowflake.snowpark as snp\n",
    "from snowflake.snowpark import functions as F\n",
    "from snowflake.snowpark import types as T\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration\n",
    "In this example notebook we show how to start data exploration using popular python libraries, such as pandas or seaborn. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load the Credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from steps.snowpark_connection import snowpark_connect\n",
    "session, state_dict = snowpark_connect('./include/state.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "snowdf = session.table(state_dict['table_name'])\n",
    "print(\"Count records: \" + str(snowdf.count()))\n",
    "snowdf.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Sample Data and explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = snowdf.sample(n=500).to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Below are just some basic plots"
   ]
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    "plt.style.use('seaborn-darkgrid')\n",
    "plt.figure(figsize=(6,3))\n",
    "df.groupby([\"AGE\"]).size().plot(kind='bar');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(4,2))\n",
    "activity_data = (\n",
    "    df[[\"RUNNING\", \"CHASING\", \"CLIMBING\", \"EATING\", \"FORAGING\"]].sum().reset_index()\n",
    "    )\n",
    "activity_data.columns = [\"Activity\", \"Count\"]\n",
    "activity_data.groupby([\"Activity\"]).agg({'Count':'sum'}).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "df.groupby([\"PRIMARY_FUR_COLOR\"]).size().plot(kind='area');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()"
   ]
  }
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