AceDigital_Q1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82729cd2",
   "metadata": {},
   "source": [
    "# Import all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1946e34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f20548",
   "metadata": {},
   "source": [
    " ## Descriptive analysis of the additives (columns named as “a” to “i”)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2dd95692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       214 non-null    float64\n",
      " 1   b       214 non-null    float64\n",
      " 2   c       214 non-null    float64\n",
      " 3   d       214 non-null    float64\n",
      " 4   e       214 non-null    float64\n",
      " 5   f       214 non-null    float64\n",
      " 6   g       214 non-null    float64\n",
      " 7   h       214 non-null    float64\n",
      " 8   i       214 non-null    float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 15.2 KB\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"Downloads\\\\PreScreen_r3_pw - 1234\\\\PreScreen_r3\\\\ingredient.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f340f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51735</td>\n",
       "      <td>13.02</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.69</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.54</td>\n",
       "      <td>8.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.53125</td>\n",
       "      <td>10.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>69.81</td>\n",
       "      <td>0.58</td>\n",
       "      <td>13.30</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.52300</td>\n",
       "      <td>13.31</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.82</td>\n",
       "      <td>71.99</td>\n",
       "      <td>0.12</td>\n",
       "      <td>10.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51768</td>\n",
       "      <td>12.56</td>\n",
       "      <td>3.52</td>\n",
       "      <td>1.43</td>\n",
       "      <td>73.15</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51813</td>\n",
       "      <td>13.43</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1.18</td>\n",
       "      <td>72.49</td>\n",
       "      <td>0.58</td>\n",
       "      <td>8.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1.52152</td>\n",
       "      <td>13.12</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.90</td>\n",
       "      <td>72.20</td>\n",
       "      <td>0.23</td>\n",
       "      <td>9.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1.51848</td>\n",
       "      <td>13.64</td>\n",
       "      <td>3.87</td>\n",
       "      <td>1.27</td>\n",
       "      <td>71.96</td>\n",
       "      <td>0.54</td>\n",
       "      <td>8.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>1.51784</td>\n",
       "      <td>12.68</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.16</td>\n",
       "      <td>73.11</td>\n",
       "      <td>0.61</td>\n",
       "      <td>8.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1.51841</td>\n",
       "      <td>12.93</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1.11</td>\n",
       "      <td>72.28</td>\n",
       "      <td>0.64</td>\n",
       "      <td>8.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>1.51321</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.02</td>\n",
       "      <td>70.70</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           a      b     c     d      e     f      g     h     i\n",
       "0    1.51735  13.02  3.54  1.69  72.73  0.54   8.44  0.00  0.07\n",
       "1    1.53125  10.73  0.00  2.10  69.81  0.58  13.30  3.15  0.28\n",
       "2    1.52300  13.31  3.58  0.82  71.99  0.12  10.17  0.00  0.03\n",
       "3    1.51768  12.56  3.52  1.43  73.15  0.57   8.54  0.00  0.00\n",
       "4    1.51813  13.43  3.98  1.18  72.49  0.58   8.15  0.00  0.00\n",
       "..       ...    ...   ...   ...    ...   ...    ...   ...   ...\n",
       "209  1.52152  13.12  3.58  0.90  72.20  0.23   9.82  0.00  0.16\n",
       "210  1.51848  13.64  3.87  1.27  71.96  0.54   8.32  0.00  0.32\n",
       "211  1.51784  12.68  3.67  1.16  73.11  0.61   8.70  0.00  0.00\n",
       "212  1.51841  12.93  3.74  1.11  72.28  0.64   8.96  0.00  0.22\n",
       "213  1.51321  13.00  0.00  3.02  70.70  6.21   6.93  0.00  0.00\n",
       "\n",
       "[214 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c25b01db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(count    214.000000\n",
       " mean       1.518365\n",
       " std        0.003037\n",
       " min        1.511150\n",
       " 25%        1.516522\n",
       " 50%        1.517680\n",
       " 75%        1.519157\n",
       " max        1.533930\n",
       " Name: a, dtype: float64,\n",
       " count    214.000000\n",
       " mean      13.407850\n",
       " std        0.816604\n",
       " min       10.730000\n",
       " 25%       12.907500\n",
       " 50%       13.300000\n",
       " 75%       13.825000\n",
       " max       17.380000\n",
       " Name: b, dtype: float64,\n",
       " count    214.000000\n",
       " mean       2.684533\n",
       " std        1.442408\n",
       " min        0.000000\n",
       " 25%        2.115000\n",
       " 50%        3.480000\n",
       " 75%        3.600000\n",
       " max        4.490000\n",
       " Name: c, dtype: float64,\n",
       " count    214.000000\n",
       " mean       1.444907\n",
       " std        0.499270\n",
       " min        0.290000\n",
       " 25%        1.190000\n",
       " 50%        1.360000\n",
       " 75%        1.630000\n",
       " max        3.500000\n",
       " Name: d, dtype: float64,\n",
       " count    214.000000\n",
       " mean      72.650935\n",
       " std        0.774546\n",
       " min       69.810000\n",
       " 25%       72.280000\n",
       " 50%       72.790000\n",
       " 75%       73.087500\n",
       " max       75.410000\n",
       " Name: e, dtype: float64,\n",
       " count    214.000000\n",
       " mean       0.497056\n",
       " std        0.652192\n",
       " min        0.000000\n",
       " 25%        0.122500\n",
       " 50%        0.555000\n",
       " 75%        0.610000\n",
       " max        6.210000\n",
       " Name: f, dtype: float64,\n",
       " count    214.000000\n",
       " mean       8.956963\n",
       " std        1.423153\n",
       " min        5.430000\n",
       " 25%        8.240000\n",
       " 50%        8.600000\n",
       " 75%        9.172500\n",
       " max       16.190000\n",
       " Name: g, dtype: float64,\n",
       " count    214.000000\n",
       " mean       0.175047\n",
       " std        0.497219\n",
       " min        0.000000\n",
       " 25%        0.000000\n",
       " 50%        0.000000\n",
       " 75%        0.000000\n",
       " max        3.150000\n",
       " Name: h, dtype: float64,\n",
       " count    214.000000\n",
       " mean       0.057009\n",
       " std        0.097439\n",
       " min        0.000000\n",
       " 25%        0.000000\n",
       " 50%        0.000000\n",
       " 75%        0.100000\n",
       " max        0.510000\n",
       " Name: i, dtype: float64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_desc=df['a'].describe()\n",
    "b_desc=df['b'].describe()\n",
    "c_desc=df['c'].describe()\n",
    "d_desc=df['d'].describe()\n",
    "#e_desc=df['e'].descibe()  #output: Series' object has no attribute 'descibe'\n",
    "#e_series=pd.Series(['e'])\n",
    "e_desc=df['e'].describe()  #output: dtype: object\n",
    "\n",
    "#e_series=pd.to_numeric(['e'], errors='coerce')\n",
    "#e_desc=e_series.describe(include=numpy.object)\n",
    "\n",
    "f_desc=df['f'].describe()\n",
    "g_desc=df['g'].describe()\n",
    "h_desc=df['h'].describe()\n",
    "i_desc=df['i'].describe()\n",
    "\n",
    "a_desc, b_desc, c_desc, d_desc,e_desc, f_desc, g_desc, h_desc, i_desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cc29b9",
   "metadata": {},
   "source": [
    "## Distribution Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d4c045c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_skew: 1.6254305060087642, b_skew: 0.45418145365774937, c_skew: -1.152559317580235, d_skew: 0.9072898090246341, e_skew= -0.7304472251318592, f_skew: 6.551648309604964, g_skew: 2.047053913118311, h_skew: 3.416424569302559, i_skew: 1.7543274704358307\n"
     ]
    }
   ],
   "source": [
    "a_skew=df['a'].skew()\n",
    "b_skew=df['b'].skew()\n",
    "c_skew=df['c'].skew()\n",
    "d_skew=df['d'].skew()\n",
    "e_skew=df['e'].skew()\n",
    "f_skew=df['f'].skew()\n",
    "g_skew=df['g'].skew()\n",
    "h_skew=df['h'].skew()\n",
    "i_skew=df['i'].skew()  #Skewness is a measure of symmetry, or more precisely, the lack of symmetry\n",
    "\n",
    "print(f\"a_skew: {a_skew}, b_skew: {b_skew}, c_skew: {c_skew}, d_skew: {d_skew}, e_skew= {e_skew}, f_skew: {f_skew}, g_skew: {g_skew}, h_skew: {h_skew}, i_skew: {i_skew}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "756b9907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_kurt: 4.931737386095595, b_kurt: 3.0522324094919226, c_kurt: -0.4103189628915689, d_kurt: 2.060568968946122, e_kurt= 2.9679029559660095, f_kurt: 54.68969852994255, g_kurt: 6.681977951411167, h_kurt: 12.541083582809158, i_kurt: 2.662015617441456\n"
     ]
    }
   ],
   "source": [
    "a_kurt=df['a'].kurtosis()\n",
    "b_kurt=df['b'].kurtosis()\n",
    "c_kurt=df['c'].kurtosis()\n",
    "d_kurt=df['d'].kurtosis()\n",
    "e_kurt=df['e'].kurtosis()\n",
    "f_kurt=df['f'].kurtosis()\n",
    "g_kurt=df['g'].kurtosis()\n",
    "h_kurt=df['h'].kurtosis()\n",
    "i_kurt=df['i'].kurtosis()  #Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution.\n",
    "\n",
    "print(f\"a_kurt: {a_kurt}, b_kurt: {b_kurt}, c_kurt: {c_kurt}, d_kurt: {d_kurt}, e_kurt= {e_kurt}, f_kurt: {f_kurt}, g_kurt: {g_kurt}, h_kurt: {h_kurt}, i_kurt: {i_kurt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3129e4c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1932456ee20>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.hist(df['a'], bins=100, alpha=0.5, label=\"a\")\n",
    "plt.hist(df['b'], bins=100, alpha=0.5, label=\"b\")\n",
    "plt.hist(df['c'], bins=100, alpha=0.5, label=\"c\")\n",
    "plt.hist(df['d'], bins=100, alpha=0.5, label=\"d\")\n",
    "plt.hist(df['e'], bins=100, alpha=0.5, label=\"e\")\n",
    "plt.hist(df['f'], bins=100, alpha=0.5, label=\"f\")\n",
    "plt.hist(df['g'], bins=100, alpha=0.5, label=\"g\")\n",
    "plt.hist(df['h'], bins=100, alpha=0.5, label=\"h\")\n",
    "plt.hist(df['i'], bins=100, alpha=0.5, label=\"i\")\n",
    "\n",
    "\n",
    "plt.ylim([0,25])\n",
    "plt.xlabel(\"Data\", size=10)\n",
    "plt.ylabel(\"Count\", size=10)\n",
    "plt.title(\"Multiple Histograms with ingredients\")\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "579ff7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
    "    nunique = df.nunique() #Count no of unique elements in col\n",
    "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df.shape\n",
    "    columnNames = list(df)  #Create column value based on numbers in element of list df\n",
    "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0ebeb40a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-42-c4d51ae7f98d>:9: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 4800x704 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df, 10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0858296f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCorrelationMatrix(df, graphWidth):\n",
    "    filename = \"ingredient plot\"\n",
    "    df = df.dropna('columns') # drop columns with NaN\n",
    "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    if df.shape[1] < 2:\n",
    "        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n",
    "        return\n",
    "    corr = df.corr()\n",
    "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
    "    corrMat = plt.matshow(corr, fignum = 1)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
    "    plt.gca().xaxis.tick_bottom()\n",
    "    plt.colorbar(corrMat)\n",
    "    plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a63382a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plotCorrelationMatrix(df, 8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440d422c",
   "metadata": {},
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}