EDA Chapter 1 (Pandas).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>39</th>\n",
       "      <th>State-gov</th>\n",
       "      <th>77516</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>13</th>\n",
       "      <th>Never-married</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <th>Not-in-family</th>\n",
       "      <th>White</th>\n",
       "      <th>Male</th>\n",
       "      <th>2174</th>\n",
       "      <th>0</th>\n",
       "      <th>40</th>\n",
       "      <th>United-States</th>\n",
       "      <th>&lt;=50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
       "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
       "1  38            Private  215646     HS-grad    9             Divorced   \n",
       "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
       "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
       "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "\n",
       "    United-States   <=50K  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data')\n",
    "data.head() #initial value of 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>country_of_origin</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship ethnicity   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family     White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband     White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family     White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband     Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife     Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week country_of_origin  income  \n",
       "0          2174             0              40     United-States   <=50K  \n",
       "1             0             0              13     United-States   <=50K  \n",
       "2             0             0              40     United-States   <=50K  \n",
       "3             0             0              40     United-States   <=50K  \n",
       "4             0             0              40              Cuba   <=50K  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  adding titles to features\n",
    "\n",
    "columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
    "    'marital_status', 'occupation', 'relationship', 'ethnicity', 'gender','capital_gain','capital_loss','hours_per_week','country_of_origin','income']\n",
    "data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',names=columns)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DESCRIPTIVE STATISTICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   age                32561 non-null  int64 \n",
      " 1   workclass          32561 non-null  object\n",
      " 2   fnlwgt             32561 non-null  int64 \n",
      " 3   education          32561 non-null  object\n",
      " 4   education_num      32561 non-null  int64 \n",
      " 5   marital_status     32561 non-null  object\n",
      " 6   occupation         32561 non-null  object\n",
      " 7   relationship       32561 non-null  object\n",
      " 8   ethnicity          32561 non-null  object\n",
      " 9   gender             32561 non-null  object\n",
      " 10  capital_gain       32561 non-null  int64 \n",
      " 11  capital_loss       32561 non-null  int64 \n",
      " 12  hours_per_week     32561 non-null  int64 \n",
      " 13  country_of_origin  32561 non-null  object\n",
      " 14  income             32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'ethnicity', 'gender',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'country_of_origin',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours_per_week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays summary statistics for each numerical column in the dataframe\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                                   37\n",
       "workclass                        Private\n",
       "fnlwgt                            280464\n",
       "education                   Some-college\n",
       "education_num                         10\n",
       "marital_status        Married-civ-spouse\n",
       "occupation               Exec-managerial\n",
       "relationship                     Husband\n",
       "ethnicity                          Black\n",
       "gender                              Male\n",
       "capital_gain                           0\n",
       "capital_loss                           0\n",
       "hours_per_week                        80\n",
       "country_of_origin          United-States\n",
       "income                              >50K\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a row\n",
    "data.iloc[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>country_of_origin</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   workclass  fnlwgt      education  education_num  \\\n",
       "10   37     Private  280464   Some-college             10   \n",
       "11   30   State-gov  141297      Bachelors             13   \n",
       "12   23     Private  122272      Bachelors             13   \n",
       "13   32     Private  205019     Assoc-acdm             12   \n",
       "14   40     Private  121772      Assoc-voc             11   \n",
       "\n",
       "         marital_status        occupation    relationship  \\\n",
       "10   Married-civ-spouse   Exec-managerial         Husband   \n",
       "11   Married-civ-spouse    Prof-specialty         Husband   \n",
       "12        Never-married      Adm-clerical       Own-child   \n",
       "13        Never-married             Sales   Not-in-family   \n",
       "14   Married-civ-spouse      Craft-repair         Husband   \n",
       "\n",
       "              ethnicity   gender  capital_gain  capital_loss  hours_per_week  \\\n",
       "10                Black     Male             0             0              80   \n",
       "11   Asian-Pac-Islander     Male             0             0              40   \n",
       "12                White   Female             0             0              30   \n",
       "13                Black     Male             0             0              50   \n",
       "14   Asian-Pac-Islander     Male             0             0              40   \n",
       "\n",
       "   country_of_origin  income  \n",
       "10     United-States    >50K  \n",
       "11             India    >50K  \n",
       "12     United-States   <=50K  \n",
       "13     United-States   <=50K  \n",
       "14                 ?    >50K  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a range of rows \n",
    "data.iloc[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        education  education_num       marital_status\n",
       "10   Some-college             10   Married-civ-spouse\n",
       "11      Bachelors             13   Married-civ-spouse\n",
       "12      Bachelors             13        Never-married\n",
       "13     Assoc-acdm             12        Never-married\n",
       "14      Assoc-voc             11   Married-civ-spouse"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a range of rows with specific columns\n",
    "data.iloc[10:15, 3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        39\n",
       "1        50\n",
       "2        38\n",
       "3        53\n",
       "4        28\n",
       "         ..\n",
       "32556    27\n",
       "32557    40\n",
       "32558    58\n",
       "32559    22\n",
       "32560    52\n",
       "Name: age, Length: 32561, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a column\n",
    "data[\"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>age</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>Bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>HS-grad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>11th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Bachelors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>HS-grad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>HS-grad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>HS-grad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>HS-grad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age    education\n",
       "0       39    Bachelors\n",
       "1       50    Bachelors\n",
       "2       38      HS-grad\n",
       "3       53         11th\n",
       "4       28    Bachelors\n",
       "...    ...          ...\n",
       "32556   27   Assoc-acdm\n",
       "32557   40      HS-grad\n",
       "32558   58      HS-grad\n",
       "32559   22      HS-grad\n",
       "32560   52      HS-grad\n",
       "\n",
       "[32561 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select a range of columns\n",
    "data[[\"age\",\"education\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>F</th>\n",
       "      <th>E</th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.329212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.316280</td>\n",
       "      <td>-0.990810</td>\n",
       "      <td>-1.070816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.438713</td>\n",
       "      <td>0.564417</td>\n",
       "      <td>0.295722</td>\n",
       "      <td>-1.626404</td>\n",
       "      <td>0.219565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.678805</td>\n",
       "      <td>1.889273</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.104011</td>\n",
       "      <td>-0.481165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.850229</td>\n",
       "      <td>1.453425</td>\n",
       "      <td>1.057737</td>\n",
       "      <td>0.165562</td>\n",
       "      <td>0.515018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.336936</td>\n",
       "      <td>0.562861</td>\n",
       "      <td>1.392855</td>\n",
       "      <td>-0.063328</td>\n",
       "      <td>0.121668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.207603</td>\n",
       "      <td>-0.002040</td>\n",
       "      <td>1.627796</td>\n",
       "      <td>0.354493</td>\n",
       "      <td>1.037528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.385684</td>\n",
       "      <td>0.519818</td>\n",
       "      <td>1.686583</td>\n",
       "      <td>-1.325963</td>\n",
       "      <td>1.428984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.089354</td>\n",
       "      <td>-0.129820</td>\n",
       "      <td>0.631523</td>\n",
       "      <td>-0.586538</td>\n",
       "      <td>0.290720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.264103</td>\n",
       "      <td>0.290035</td>\n",
       "      <td>-1.970288</td>\n",
       "      <td>0.803906</td>\n",
       "      <td>1.030550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.118098</td>\n",
       "      <td>-0.021853</td>\n",
       "      <td>0.046841</td>\n",
       "      <td>-1.628753</td>\n",
       "      <td>-0.392361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      F         E         D         C         B         A\n",
       "0   1.0  1.329212       NaN -0.316280 -0.990810 -1.070816\n",
       "1   2.0 -1.438713  0.564417  0.295722 -1.626404  0.219565\n",
       "2   3.0  0.678805  1.889273  0.961538  0.104011 -0.481165\n",
       "3   4.0  0.850229  1.453425  1.057737  0.165562  0.515018\n",
       "4   5.0 -1.336936  0.562861  1.392855 -0.063328  0.121668\n",
       "5   6.0  1.207603 -0.002040  1.627796  0.354493  1.037528\n",
       "6   7.0 -0.385684  0.519818  1.686583 -1.325963  1.428984\n",
       "7   8.0 -2.089354 -0.129820  0.631523 -0.586538  0.290720\n",
       "8   9.0  1.264103  0.290035 -1.970288  0.803906  1.030550\n",
       "9  10.0  0.118098 -0.021853  0.046841 -1.628753 -0.392361"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how to create your own dataframe\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#np.random.seed(24) \n",
    "df = pd.DataFrame({'F': np.linspace(1, 10, 10)})  #create a kinda serial number feature\n",
    "\n",
    "#concatenating another datafram with random values to the previous one\n",
    "df = pd.concat([df, pd.DataFrame(np.random.randn(10, 5), columns=list('EDCBA'))], axis=1)\n",
    "\n",
    "#setting the value in row1 cond colum2 to null\n",
    "df.iloc[0, 2] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Function to colour values\n",
    "*Negative values are RED\n",
    "*Positive values are BLACK\n",
    "*Values equal to zero or null are GREEN\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "def colorNegativeValueToRed(value):\n",
    "  if value < 0:\n",
    "    color = 'red'\n",
    "  elif value > 0:\n",
    "    color = 'black'\n",
    "  else:\n",
    "    color = 'green'\n",
    "\n",
    "  return 'color: %s' % color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col3{\n",
       "            color:  black;\n",
       "        }#T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col2{\n",
       "            color:  green;\n",
       "        }#T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col5,#T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col1,#T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col3,#T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col2,#T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col4,#T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col5{\n",
       "            color:  red;\n",
       "        }</style><table id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F</th>        <th class=\"col_heading level0 col1\" >E</th>        <th class=\"col_heading level0 col2\" >D</th>        <th class=\"col_heading level0 col3\" >C</th>        <th class=\"col_heading level0 col4\" >B</th>        <th class=\"col_heading level0 col5\" >A</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col1\" class=\"data row0 col1\" >1.329212</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col3\" class=\"data row0 col3\" >-0.316280</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col4\" class=\"data row0 col4\" >-0.990810</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row0_col5\" class=\"data row0 col5\" >-1.070816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col0\" class=\"data row1 col0\" >2.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col1\" class=\"data row1 col1\" >-1.438713</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col2\" class=\"data row1 col2\" >0.564417</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col3\" class=\"data row1 col3\" >0.295722</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col4\" class=\"data row1 col4\" >-1.626404</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row1_col5\" class=\"data row1 col5\" >0.219565</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col0\" class=\"data row2 col0\" >3.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col1\" class=\"data row2 col1\" >0.678805</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col2\" class=\"data row2 col2\" >1.889273</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col3\" class=\"data row2 col3\" >0.961538</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col4\" class=\"data row2 col4\" >0.104011</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row2_col5\" class=\"data row2 col5\" >-0.481165</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col0\" class=\"data row3 col0\" >4.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col1\" class=\"data row3 col1\" >0.850229</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col2\" class=\"data row3 col2\" >1.453425</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col3\" class=\"data row3 col3\" >1.057737</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col4\" class=\"data row3 col4\" >0.165562</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row3_col5\" class=\"data row3 col5\" >0.515018</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col1\" class=\"data row4 col1\" >-1.336936</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col2\" class=\"data row4 col2\" >0.562861</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col3\" class=\"data row4 col3\" >1.392855</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col4\" class=\"data row4 col4\" >-0.063328</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row4_col5\" class=\"data row4 col5\" >0.121668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col0\" class=\"data row5 col0\" >6.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col1\" class=\"data row5 col1\" >1.207603</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col2\" class=\"data row5 col2\" >-0.002040</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col3\" class=\"data row5 col3\" >1.627796</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col4\" class=\"data row5 col4\" >0.354493</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row5_col5\" class=\"data row5 col5\" >1.037528</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col0\" class=\"data row6 col0\" >7.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col1\" class=\"data row6 col1\" >-0.385684</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col2\" class=\"data row6 col2\" >0.519818</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col3\" class=\"data row6 col3\" >1.686583</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col4\" class=\"data row6 col4\" >-1.325963</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row6_col5\" class=\"data row6 col5\" >1.428984</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col0\" class=\"data row7 col0\" >8.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col1\" class=\"data row7 col1\" >-2.089354</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col2\" class=\"data row7 col2\" >-0.129820</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col3\" class=\"data row7 col3\" >0.631523</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col4\" class=\"data row7 col4\" >-0.586538</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row7_col5\" class=\"data row7 col5\" >0.290720</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col0\" class=\"data row8 col0\" >9.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col1\" class=\"data row8 col1\" >1.264103</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col2\" class=\"data row8 col2\" >0.290035</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col3\" class=\"data row8 col3\" >-1.970288</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col4\" class=\"data row8 col4\" >0.803906</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row8_col5\" class=\"data row8 col5\" >1.030550</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col0\" class=\"data row9 col0\" >10.000000</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col1\" class=\"data row9 col1\" >0.118098</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col2\" class=\"data row9 col2\" >-0.021853</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col3\" class=\"data row9 col3\" >0.046841</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col4\" class=\"data row9 col4\" >-1.628753</td>\n",
       "                        <td id=\"T_c9dc9971_1b57_11eb_9369_643150a3c607row9_col5\" class=\"data row9 col5\" >-0.392361</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23fa6094ee0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function to the dataframe\n",
    "\n",
    "s = df.style.applymap(colorNegativeValueToRed, subset=['A','B','C','D','E'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_24101070_1b5c_11eb_a746_643150a3c607row0_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row2_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row9_col5{\n",
       "            color:  red;\n",
       "        }#T_24101070_1b5c_11eb_a746_643150a3c607row1_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row3_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row4_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row5_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row6_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row7_col5,#T_24101070_1b5c_11eb_a746_643150a3c607row8_col5{\n",
       "            color:  black;\n",
       "        }</style><table id=\"T_24101070_1b5c_11eb_a746_643150a3c607\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F</th>        <th class=\"col_heading level0 col1\" >E</th>        <th class=\"col_heading level0 col2\" >D</th>        <th class=\"col_heading level0 col3\" >C</th>        <th class=\"col_heading level0 col4\" >B</th>        <th class=\"col_heading level0 col5\" >A</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col1\" class=\"data row0 col1\" >1.329212</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col3\" class=\"data row0 col3\" >-0.316280</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col4\" class=\"data row0 col4\" >-0.990810</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row0_col5\" class=\"data row0 col5\" >-1.070816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col0\" class=\"data row1 col0\" >2.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col1\" class=\"data row1 col1\" >-1.438713</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col2\" class=\"data row1 col2\" >0.564417</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col3\" class=\"data row1 col3\" >0.295722</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col4\" class=\"data row1 col4\" >-1.626404</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row1_col5\" class=\"data row1 col5\" >0.219565</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col0\" class=\"data row2 col0\" >3.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col1\" class=\"data row2 col1\" >0.678805</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col2\" class=\"data row2 col2\" >1.889273</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col3\" class=\"data row2 col3\" >0.961538</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col4\" class=\"data row2 col4\" >0.104011</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row2_col5\" class=\"data row2 col5\" >-0.481165</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col0\" class=\"data row3 col0\" >4.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col1\" class=\"data row3 col1\" >0.850229</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col2\" class=\"data row3 col2\" >1.453425</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col3\" class=\"data row3 col3\" >1.057737</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col4\" class=\"data row3 col4\" >0.165562</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row3_col5\" class=\"data row3 col5\" >0.515018</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col1\" class=\"data row4 col1\" >-1.336936</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col2\" class=\"data row4 col2\" >0.562861</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col3\" class=\"data row4 col3\" >1.392855</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col4\" class=\"data row4 col4\" >-0.063328</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row4_col5\" class=\"data row4 col5\" >0.121668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col0\" class=\"data row5 col0\" >6.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col1\" class=\"data row5 col1\" >1.207603</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col2\" class=\"data row5 col2\" >-0.002040</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col3\" class=\"data row5 col3\" >1.627796</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col4\" class=\"data row5 col4\" >0.354493</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row5_col5\" class=\"data row5 col5\" >1.037528</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col0\" class=\"data row6 col0\" >7.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col1\" class=\"data row6 col1\" >-0.385684</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col2\" class=\"data row6 col2\" >0.519818</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col3\" class=\"data row6 col3\" >1.686583</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col4\" class=\"data row6 col4\" >-1.325963</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row6_col5\" class=\"data row6 col5\" >1.428984</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col0\" class=\"data row7 col0\" >8.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col1\" class=\"data row7 col1\" >-2.089354</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col2\" class=\"data row7 col2\" >-0.129820</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col3\" class=\"data row7 col3\" >0.631523</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col4\" class=\"data row7 col4\" >-0.586538</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row7_col5\" class=\"data row7 col5\" >0.290720</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col0\" class=\"data row8 col0\" >9.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col1\" class=\"data row8 col1\" >1.264103</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col2\" class=\"data row8 col2\" >0.290035</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col3\" class=\"data row8 col3\" >-1.970288</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col4\" class=\"data row8 col4\" >0.803906</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row8_col5\" class=\"data row8 col5\" >1.030550</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_24101070_1b5c_11eb_a746_643150a3c607level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col0\" class=\"data row9 col0\" >10.000000</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col1\" class=\"data row9 col1\" >0.118098</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col2\" class=\"data row9 col2\" >-0.021853</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col3\" class=\"data row9 col3\" >0.046841</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col4\" class=\"data row9 col4\" >-1.628753</td>\n",
       "                        <td id=\"T_24101070_1b5c_11eb_a746_643150a3c607row9_col5\" class=\"data row9 col5\" >-0.392361</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23fa5fab7c0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling the function on a particular feature in the dataframe\n",
    "\n",
    "d = df.style.applymap(colorNegativeValueToRed, subset=['A'])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using background colour instead\n",
    "def colorNegativeValueToRed(value):\n",
    "  if value < 0:\n",
    "    color = 'red'\n",
    "  elif value > 0:\n",
    "    color = 'white'\n",
    "  else:\n",
    "    color = 'green'\n",
    "\n",
    "  return 'background-color: %s' % color "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col3{\n",
       "            background-color:  white;\n",
       "        }#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col2{\n",
       "            background-color:  green;\n",
       "        }#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col5,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col1,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col3,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col2,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col4,#T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col5{\n",
       "            background-color:  red;\n",
       "        }</style><table id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F</th>        <th class=\"col_heading level0 col1\" >E</th>        <th class=\"col_heading level0 col2\" >D</th>        <th class=\"col_heading level0 col3\" >C</th>        <th class=\"col_heading level0 col4\" >B</th>        <th class=\"col_heading level0 col5\" >A</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col1\" class=\"data row0 col1\" >1.329212</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col3\" class=\"data row0 col3\" >-0.316280</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col4\" class=\"data row0 col4\" >-0.990810</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row0_col5\" class=\"data row0 col5\" >-1.070816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col0\" class=\"data row1 col0\" >2.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col1\" class=\"data row1 col1\" >-1.438713</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col2\" class=\"data row1 col2\" >0.564417</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col3\" class=\"data row1 col3\" >0.295722</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col4\" class=\"data row1 col4\" >-1.626404</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row1_col5\" class=\"data row1 col5\" >0.219565</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col0\" class=\"data row2 col0\" >3.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col1\" class=\"data row2 col1\" >0.678805</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col2\" class=\"data row2 col2\" >1.889273</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col3\" class=\"data row2 col3\" >0.961538</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col4\" class=\"data row2 col4\" >0.104011</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row2_col5\" class=\"data row2 col5\" >-0.481165</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col0\" class=\"data row3 col0\" >4.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col1\" class=\"data row3 col1\" >0.850229</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col2\" class=\"data row3 col2\" >1.453425</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col3\" class=\"data row3 col3\" >1.057737</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col4\" class=\"data row3 col4\" >0.165562</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row3_col5\" class=\"data row3 col5\" >0.515018</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col0\" class=\"data row4 col0\" >5.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col1\" class=\"data row4 col1\" >-1.336936</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col2\" class=\"data row4 col2\" >0.562861</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col3\" class=\"data row4 col3\" >1.392855</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col4\" class=\"data row4 col4\" >-0.063328</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row4_col5\" class=\"data row4 col5\" >0.121668</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col0\" class=\"data row5 col0\" >6.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col1\" class=\"data row5 col1\" >1.207603</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col2\" class=\"data row5 col2\" >-0.002040</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col3\" class=\"data row5 col3\" >1.627796</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col4\" class=\"data row5 col4\" >0.354493</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row5_col5\" class=\"data row5 col5\" >1.037528</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col0\" class=\"data row6 col0\" >7.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col1\" class=\"data row6 col1\" >-0.385684</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col2\" class=\"data row6 col2\" >0.519818</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col3\" class=\"data row6 col3\" >1.686583</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col4\" class=\"data row6 col4\" >-1.325963</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row6_col5\" class=\"data row6 col5\" >1.428984</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col0\" class=\"data row7 col0\" >8.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col1\" class=\"data row7 col1\" >-2.089354</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col2\" class=\"data row7 col2\" >-0.129820</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col3\" class=\"data row7 col3\" >0.631523</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col4\" class=\"data row7 col4\" >-0.586538</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row7_col5\" class=\"data row7 col5\" >0.290720</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col0\" class=\"data row8 col0\" >9.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col1\" class=\"data row8 col1\" >1.264103</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col2\" class=\"data row8 col2\" >0.290035</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col3\" class=\"data row8 col3\" >-1.970288</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col4\" class=\"data row8 col4\" >0.803906</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row8_col5\" class=\"data row8 col5\" >1.030550</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col0\" class=\"data row9 col0\" >10.000000</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col1\" class=\"data row9 col1\" >0.118098</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col2\" class=\"data row9 col2\" >-0.021853</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col3\" class=\"data row9 col3\" >0.046841</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col4\" class=\"data row9 col4\" >-1.628753</td>\n",
       "                        <td id=\"T_2ac23427_1b5d_11eb_b4d0_643150a3c607row9_col5\" class=\"data row9 col5\" >-0.392361</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23fa5fab4c0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = df.style.applymap(colorNegativeValueToRed, subset=['A','B','C','D','E'])\n",
    "o"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}