example/pandas_validator_example_en.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# pandas-validator example\n",
    "\n",
    "This is example of pandas-validator in English."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Please install this package using following command.\n",
    "# $ pip install pandas-validator\n",
    "import pandas_validator as pv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series Validator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create validator's instance\n",
    "validator = pv.IntegerSeriesValidator(min_value=0, max_value=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "series = pd.Series([0, 3, 6, 9])  # This series is valid.\n",
    "print(validator.is_valid(series))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "series = pd.Series([0, 4, 8, 12])  # This series is invalid. because that includes 12 number.\n",
    "print(validator.is_valid(series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame Validator\n",
    "\n",
    "DataFrameValidator class can validate panda's dataframe object.\n",
    "It can define easily like Django's model definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define validator\n",
    "class SampleDataFrameValidator(pv.DataFrameValidator):\n",
    "    row_num = 5\n",
    "    column_num = 2\n",
    "    label1 = pv.IntegerColumnValidator('label1', min_value=0, max_value=10)\n",
    "    label2 = pv.FloatColumnValidator('label2', min_value=0, max_value=10)\n",
    "\n",
    "# Create validator's instance\n",
    "validator = SampleDataFrameValidator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'label1': [0, 1, 2, 3, 4], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})  # This data frame is valid.\n",
    "print(validator.is_valid(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'label1': [11, 12, 13, 14, 15], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})  # This data frame is invalid.\n",
    "print(validator.is_valid(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'label1': [0, 1, 2], 'label2': [5.0, 6.0, 7.0]})  # This data frame is invalid.\n",
    "print(validator.is_valid(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
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