Methods for Normality Tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normality Tests\n",
    "\n",
    "### There are many normality tests. Some are given here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries and reading data \n",
    "\n",
    "### For this UCI Abalone data is used from http://archive.ics.uci.edu/ml/datasets/Abalone. The size of data is 4177 instances and 9 variables. Below we explored it further. There are no null values in this dataset. All variables are numeric except 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rows and columns are (4177, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"The rows and columns are\",df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4177 entries, 0 to 4176\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Sex             4177 non-null   object \n",
      " 1   Length          4177 non-null   float64\n",
      " 2   Diameter        4177 non-null   float64\n",
      " 3   Height          4177 non-null   float64\n",
      " 4   Whole weight    4177 non-null   float64\n",
      " 5   Shucked weight  4177 non-null   float64\n",
      " 6   Viscera weight  4177 non-null   float64\n",
      " 7   Shell weight    4177 non-null   float64\n",
      " 8   Rings           4177 non-null   int64  \n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 293.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex               0\n",
       "Length            0\n",
       "Diameter          0\n",
       "Height            0\n",
       "Whole weight      0\n",
       "Shucked weight    0\n",
       "Viscera weight    0\n",
       "Shell weight      0\n",
       "Rings             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Histogram Plots for all Numeric Variables')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(layout=(5,4),color='blue', figsize=(15,12), grid=False)\n",
    "plt.suptitle(\"Histogram Plots for all Numeric Variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q-Q Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.drop(columns=\"Sex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Length\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Diameter\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Height\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Whole weight\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Shucked weight\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Viscera weight\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Shell weight\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QQ plot of: Rings\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import pylab\n",
    "\n",
    "for i, val in enumerate(df1):\n",
    "    print('QQ plot of:', val)\n",
    "    sm.qqplot(df[val], line='s')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df1.drop(columns=\"Rings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bit = dict(markerfacecolor='b', marker='p')\n",
    "boxplot = df2.boxplot(figsize=(18,10), grid=False, flierprops=bit)\n",
    "plt.title('Boxplot of Continuous Variables', fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test whether a sample differs from a normal distribution.\n",
    "\n",
    "This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino and Pearson’s [1], [2] test that combines skew and kurtosis to produce an omnibus test of normality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Tests\n",
    "\n",
    "### D'Agostino's K-squared test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "k2, p = stats.normaltest(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length']\n",
      "[242.15893836] [2.60528154e-53]\n",
      "The null hypothesis can be rejected\n",
      "['Diameter']\n",
      "[222.13059303] [5.8204236e-49]\n",
      "The null hypothesis can be rejected\n",
      "['Height']\n",
      "[3528.1390401] [0.]\n",
      "The null hypothesis can be rejected\n",
      "['Whole weight']\n",
      "[174.21558397] [1.4776331e-38]\n",
      "The null hypothesis can be rejected\n",
      "['Shucked weight']\n",
      "[332.14071399] [7.52593305e-73]\n",
      "The null hypothesis can be rejected\n",
      "['Viscera weight']\n",
      "[212.14436441] [8.57899837e-47]\n",
      "The null hypothesis can be rejected\n",
      "['Shell weight']\n",
      "[261.12003993] [1.98840008e-57]\n",
      "The null hypothesis can be rejected\n",
      "['Rings']\n",
      "[813.44917871] [2.30014487e-177]\n",
      "The null hypothesis can be rejected\n"
     ]
    }
   ],
   "source": [
    "for i in columns:\n",
    "    print ([i])\n",
    "    a,b= stats.normaltest(df[[i]])\n",
    "    print (a, b)\n",
    "    if b < alpha:  \n",
    "        print(\"The null hypothesis can be rejected\")\n",
    "    else:\n",
    "        print(\"The null hypothesis cannot be rejected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jarque–Bera test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight',\n",
       "       'Viscera weight', 'Shell weight', 'Rings'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length']\n",
      "Statistics 285.52537806031535 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Diameter']\n",
      "Statistics 258.55960781595996 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Height']\n",
      "Statistics 1010305.8446662541 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Whole weight']\n",
      "Statistics 196.22949102469235 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Shucked weight']\n",
      "Statistics 420.9270297928447 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Viscera weight']\n",
      "Statistics 244.86823035422486 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Shell weight']\n",
      "Statistics 317.07566717674916 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Rings']\n",
      "Statistics 1805.4653813095663 p-value 0.0\n",
      "The null hypothesis can be rejected\n"
     ]
    }
   ],
   "source": [
    "for i in columns:\n",
    "    print ([i])\n",
    "    a,b= stats.jarque_bera(df[[i]])\n",
    "    print (\"Statistics\", a, \"p-value\", b)\n",
    "    if b < alpha:  \n",
    "        print(\"The null hypothesis can be rejected\")\n",
    "    else:\n",
    "        print(\"The null hypothesis cannot be rejected\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anderson-Darling Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AndersonResult(statistic=78.6001457878765, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.anderson(df1.Rings, dist='norm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length']\n",
      "AndersonResult(statistic=36.6212136651302, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Diameter']\n",
      "AndersonResult(statistic=36.43777467948166, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Height']\n",
      "AndersonResult(statistic=14.493110733826143, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Whole weight']\n",
      "AndersonResult(statistic=20.992667027511743, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Shucked weight']\n",
      "AndersonResult(statistic=27.229221575566953, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Viscera weight']\n",
      "AndersonResult(statistic=25.711089536228428, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Shell weight']\n",
      "AndersonResult(statistic=18.5606736029049, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n",
      "['Rings']\n",
      "AndersonResult(statistic=78.6001457878765, critical_values=array([0.575, 0.655, 0.786, 0.917, 1.091]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))\n",
      "The null hypothesis can be rejected at 5.0 % significance level\n"
     ]
    }
   ],
   "source": [
    "for i in columns:\n",
    "    print ([i])\n",
    "    a= stats.anderson(df[i], dist='norm')\n",
    "    print (a)\n",
    "    if a[0] > a[1][2]:  \n",
    "        print(\"The null hypothesis can be rejected at\", a[2][2],\"% significance level\")\n",
    "    else:\n",
    "        print(\"The null hypothesis cannot be rejected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kolmogorov–Smirnov test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length']\n",
      "Statistics 0.7924638449586122 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Diameter']\n",
      "Statistics 0.7421538891941353 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Height']\n",
      "Statistics 0.8707618877599821 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Whole weight']\n",
      "Statistics 0.9976396556000835 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Shucked weight']\n",
      "Statistics 0.9316245531838383 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Viscera weight']\n",
      "Statistics 0.7763727075624006 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Shell weight']\n",
      "Statistics 0.8425515750696722 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Rings']\n",
      "Statistics 1.0 p-value 0.0\n",
      "The null hypothesis can be rejected\n"
     ]
    }
   ],
   "source": [
    "for i in columns:\n",
    "    print ([i])\n",
    "    a,b= stats.kstest(df[[i]], 'norm')\n",
    "    print (\"Statistics\", a, \"p-value\", b)\n",
    "    if b < alpha:  \n",
    "        print(\"The null hypothesis can be rejected\")\n",
    "    else:\n",
    "        print(\"The null hypothesis cannot be rejected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shapiro-Wilk Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length']\n",
      "Statistics 0.9696492552757263 p-value 7.377629143455696e-29\n",
      "The null hypothesis can be rejected\n",
      "['Diameter']\n",
      "Statistics 0.970473051071167 p-value 1.6414551656441721e-28\n",
      "The null hypothesis can be rejected\n",
      "['Height']\n",
      "Statistics 0.8896051645278931 p-value 0.0\n",
      "The null hypothesis can be rejected\n",
      "['Whole weight']\n",
      "Statistics 0.9722852110862732 p-value 1.0143092052495122e-27\n",
      "The null hypothesis can be rejected\n",
      "['Shucked weight']\n",
      "Statistics 0.962066650390625 p-value 9.362563887244321e-32\n",
      "The null hypothesis can be rejected\n",
      "['Viscera weight']\n",
      "Statistics 0.9681379199028015 p-value 1.774917175594725e-29\n",
      "The null hypothesis can be rejected\n",
      "['Shell weight']\n",
      "Statistics 0.9704266786575317 p-value 1.568463383716868e-28\n",
      "The null hypothesis can be rejected\n",
      "['Rings']\n",
      "Statistics 0.9311649203300476 p-value 3.2763058745146386e-40\n",
      "The null hypothesis can be rejected\n"
     ]
    }
   ],
   "source": [
    "for i in columns:\n",
    "    print ([i])\n",
    "    a,b= stats.shapiro(df[[i]])\n",
    "    print (\"Statistics\", a, \"p-value\", b)\n",
    "    if b < alpha:  \n",
    "        print(\"The null hypothesis can be rejected\")\n",
    "    else:\n",
    "        print(\"The null hypothesis cannot be rejected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}