01 Tabular Data.ipynb

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   "source": [
    "<img src=\"http://bigdataexperience.org/BX/wp-content/uploads/2016/05/BX-FULL.png\" width=\"200\" height=\"200\" alt=\"Big Data Experience Center, King Mongkut's University of Technology Thonburi\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Data Science</center>\n",
    "# <center>2 - Tabular Data</center>\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load and preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7590-VHVEG</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5575-GNVDE</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3668-QPYBK</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7795-CFOCW</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9237-HQITU</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "customerID                                                                  \n",
       "7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "7795-CFOCW    Male              0      No         No      45           No   \n",
       "9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "               MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "customerID                                                                 \n",
       "7590-VHVEG  No phone service             DSL             No          Yes   \n",
       "5575-GNVDE                No             DSL            Yes           No   \n",
       "3668-QPYBK                No             DSL            Yes          Yes   \n",
       "7795-CFOCW  No phone service             DSL            Yes           No   \n",
       "9237-HQITU                No     Fiber optic             No           No   \n",
       "\n",
       "           DeviceProtection TechSupport StreamingTV StreamingMovies  \\\n",
       "customerID                                                            \n",
       "7590-VHVEG               No          No          No              No   \n",
       "5575-GNVDE              Yes          No          No              No   \n",
       "3668-QPYBK               No          No          No              No   \n",
       "7795-CFOCW              Yes         Yes          No              No   \n",
       "9237-HQITU               No          No          No              No   \n",
       "\n",
       "                  Contract PaperlessBilling              PaymentMethod  \\\n",
       "customerID                                                               \n",
       "7590-VHVEG  Month-to-month              Yes           Electronic check   \n",
       "5575-GNVDE        One year               No               Mailed check   \n",
       "3668-QPYBK  Month-to-month              Yes               Mailed check   \n",
       "7795-CFOCW        One year               No  Bank transfer (automatic)   \n",
       "9237-HQITU  Month-to-month              Yes           Electronic check   \n",
       "\n",
       "            MonthlyCharges  TotalCharges Churn  \n",
       "customerID                                      \n",
       "7590-VHVEG           29.85         29.85    No  \n",
       "5575-GNVDE           56.95       1889.50    No  \n",
       "3668-QPYBK           53.85        108.15   Yes  \n",
       "7795-CFOCW           42.30       1840.75    No  \n",
       "9237-HQITU           70.70        151.65   Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "telcoData = pd.read_csv('./Telco-Churn.csv', index_col='customerID', na_values=' ')\n",
    "telcoData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Check missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Fix missing data by filling with 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Validate with there are no missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              False\n",
       "SeniorCitizen       False\n",
       "Partner             False\n",
       "Dependents          False\n",
       "tenure              False\n",
       "PhoneService        False\n",
       "MultipleLines       False\n",
       "InternetService     False\n",
       "OnlineSecurity      False\n",
       "OnlineBackup        False\n",
       "DeviceProtection    False\n",
       "TechSupport         False\n",
       "StreamingTV         False\n",
       "StreamingMovies     False\n",
       "Contract            False\n",
       "PaperlessBilling    False\n",
       "PaymentMethod       False\n",
       "MonthlyCharges      False\n",
       "TotalCharges         True\n",
       "Churn               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcoData.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate analysis : Categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Frequency table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>5174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1869</td>\n",
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      "text/plain": [
       "col_0  Count\n",
       "Churn       \n",
       "No      5174\n",
       "Yes     1869"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(telcoData['Churn'], columns='Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Normalized frequency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.73463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.26537</td>\n",
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      "text/plain": [
       "col_0    Count\n",
       "Churn         \n",
       "No     0.73463\n",
       "Yes    0.26537"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize by column ค่าร้อยละในคอลัมน์เดียวกันเอาแต่ละแถวมารวมกันได้ 1\n",
    "pd.crosstab(telcoData['Churn'], columns='Count', normalize='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='PaymentMethod'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# นับความถี่แยกตาม category ที่กำหนด\n",
    "sns.countplot(data = telcoData, y = 'PaymentMethod')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='PaymentMethod'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tIeCnwBnAVZLmkI1leL2OGN5L16xvlNSBrMv82w3E/HyK6+60/SLgPyLiiYaOk67nHw/cLunbEfF+CfUyM7NmUkTxy5eSfgM8FhG1r/uaNdrgvl3jtqM2qHQYueEBcGYGIGl6RAyta12pzzMXcJKkL8laaQLCzzM3MzOrvJKeZ25mZmZtV6n3Zq/r5iBfW2ZmZmatr1g3exXQDVhD0up8dXexVYC+ZY7NzMzMSlBsNPtRwFiyh6vMKFj+MdCoB4CYmZlZeRS7Zn4+cL6kYyLiglaKyczMzBqh1JvGTJJ0iqQJAJI2lLRPGeMyMzOzEpWczIGFwA5p/k3gN2WJyMzMzBql1GS+fkScQ/YbcyLiM+p51KaZmZm1rlKT+cL0fOoAkLQ+yz+gw8zMzCqk1HuznwbcCawraQqwI6U9HMTMzMzKrNTnmU+VNIPsedYCjvVDNMzMzNqGYjeN2arWorfT3/6S+kfEjNr7mJmZWesq1jKfBswGalrhhYPeAhhejqDMzMysdMWS+XHA/sDnwNXATRHxadmjMjMzs5I1OJo9Iv4YETsBxwDrAvdKulbSkNYIzszMzIordQDcK5JuBroCo4GNgGfLGJe1Y136bEb/U6dVOgwzs3aj2AC49YAfAiOBN8i62n8bEZ+3QmxmZmZWgmIt85eBWcDNZE9K6w8cLWXj4CLi92WNzszMzIoqlszPJN31Dehea11gZmZmFVfsEainA0jaMSIeLVwnaccyxmVmZmYlKvXe7HU9y9zPNzczM2sDig2A257ssae9JR1XsGoVoGM5AzMzM7PSFLtm3oXsWnknoEfB8o/JbiZjZmZmFVbsmvmDwIOSLo+I11opJjMzM2uEUh+BupKkCcCAwn0iwvdmNzMzq7BSk/l1wHjgUmBJ+cIxMzOzxio1mS+OiIvKGomtMOZWz2frE66sdBhmZmU1/dxDWq2sUn+adqukn0nqI6lnzauskZmZmVlJSm2ZH5r+nlCwLID1WjYcMzMza6xSn5o2sNyBmJmZWdOU1M0uaWVJp6QR7UjaUNI+5Q3NzMzMSlHqNfPLgIVkd4MDeBP4TVkiMjMzs0YpNZmvHxHnAIsAIuIzQGWLyszMzEpWajJfKKkr6bGnktYHvixbVGZmZlayUkeznwbcCawraQqwI3BYuYIyMzOz0pU6mn2qpBnAdmTd68dGxPtljczMzMxKUmo3O0BfsseedgF2kbRfeUIyMzOzxiipZS5pEjAYmAMsTYsDuLFMcZmZmVmJSr1mvl1EDCprJGZmZtYkpXazPy7JydzMzKwNKrVlfiVZQv8n2U/SBEREDC5bZGZmZlaSUpP5RGA08BxfXTM3MzOzNqDUZP5eRNxS1kjMzMysSUpN5s9I+gtwKwV3fosIj2Y3MzOrsFKTeVeyJL57wTL/NM3MzKwNKHU0+/ERcXit1xEN7SBpiaRnJc2UNEPSDg1tX+RYD0gaWmSbsZJWbmoZjYhljKS56ba2TT1GV0kPSurYxP0Pk7ROU8svsYzVJP2sYH4dSdcX2edqSRuWMy4zM/u6UpP5E5Kuk7SXpFKflvZ5RAyJiC2AXwK/a1qIJRsL1JnMm5o06/Ez4NsRMaqUjSXV1ftxBHBjRCxpYgyHAWVN5sBqZHUFICLeioj9i+xzEXBiOYMyM7OvK7WbfSNgBFkSGifpWuDyiPh7ifuvAvwLQFJ34GZgdaAzcEpE3CxpAPA34BGy56a/CYyMiM9rDiKpAzAJqI6IUwqWjyFLbvdLej8ihkn6FLg4xf0fkoYD3yW7ZPAYcFREhKQHgCeBYWQJ7N8i4mFJm5E9x70L2ZeeHwD/BawH/C3dFW8CcAGwearL6akuhwH7Ad3JboH7/2qdj1HAj0s4H7dFxOZpu+PT8WYDQ4Epkj4Htk/n6zyy9/Np4OiI+FLSPOAqYE9gMXAk2ZeqDYBzI2J8feUD/wOsL+lZYCpwYU086cvR2cAeZL9uuCQiLgAeBi6X1CkiFhdWWNKRqXz6rtqZm3qcy4qo/6nPVToEM2uHSmqZR2ZqRPwI+AlwKPBU6irevp7duqZu9heAS4Ffp+VfAN+PiK3IEuj/FrT2NwQujIjNgA/JEmiNTsAU4KXCRJ7iGwe8BQyLiGFpcTfgyYjYIiIeAf4UEduk5NgV2Kfw2BGxLVnr/rS07KfA+RExhCx5VkfETwvK+QNwMnBf2ncYcK6kbmn/rYD9I2K5RC6pC7BeRMwr4Xx8TURcD0wDRqXYArgcOCgivpnO09EFu7yetns4bbc/2QNzzihS/i+Af6TelRNqhXEkMAAYku41MCXFthR4GdiijrgnRMTQiBjas1tLdpSYmVmp92bvBRxM9lvzd4BjgFuAIcB1wMA6dvs8JRFSwr9S0uZkN5z5raRdyFp1fYG10j6vRsSzaXo6WcKocTFwbUScVVrVWALcUDA/TNKJZF3xPcnuM39rWlczkK+wzMeBkyX1I+sSf6mOMnYH9k2tZoAqoH+anhoRH9SxzxpkX1RqNHQ+SrEx2Xmr6SW5AvgP4I9pvuYnhc8B3SPiE+ATSV9KWg1Y0ITyRwDja1rfter5LlkvyfRG1MHMzJqh5Nu5knWVfy8i9o6IGyNicURMA8YX2zkiHidLYr3Juph7A1unZP8OWRKEgp+9kSXjwi8bj5El5CpK80XNNem0z5/JWsrfBC4pKLOw3GVlRsRfgH2Bz4E7Ujd9bQJ+kFqvQyKif0TMTesW1BPX57XKru98LGb596fUetdWU7elLH9+l5LVtaH3oymqyOpoZmatpNRkvnFE/DoiqmuviIizi+0saROya8fzgVWBdyNikaRhwDdKjGEicAdwbT2Dyj4BetSzb01yej9dIy42kAtJ6wGvpC78m8meGlfbXcAxNd3ikrYsdtyI+BfQseBLSX3n4x1gTUm9JK3E8pcFCuv6IjBA0gZpfjTwYLE4CtRXfkPncypwVM37IKlnwbqNyK7rm5lZKyl1ANwaqYt6MwpabRFRV2u1Rtc0eAqyFuyhEbEk/aTrVknPkV37faHUYCPi95JWBSZLGpWu0daYANwp6a2C6+Y1+30o6RKyJPNPskFixRwIjJa0KO3z2zq2+TVZd/asNDjvVZZPuvW5G9gJuIfsevPXzkdKrmcCT5ENBiw8T5cD4wsGwB0OXJeS69OU0FtSoL7y50t6VNJssoGJFxbscylZ0p6Vzs8lwJ8krUV2eeWfjSjfzMyaSRFRfCPpbuAa4HiygWGHkt3i9b/LG177JGkr4OcRMbrSsbQkST8HPo6IiQ1tN7hv17jtqA0a2qTd8mh2M2sqSdMjos57rpTazd4rfUAviogH0w1jGmqVWwMiYgbZz+ja27DuD8kG4JmZWSsqtZt9Ufr7tqS9yX6e1bOB7a2IiJhU6RhaWkRcVukYzMxWRKUm89+ka9X/RXaTlFWAn5ctKjMzMytZg8k8jbj+Kdkdw/oCE2sPLjMzM7PKKnbN/Aqyu589R3ZL0P8te0RmZmbWKMW62Qelm6wgaSLZz6TMzMysDSnWMq8Z+EbtB2eYmZlZ21CsZb6FpI/TtMhuBPNxmo6IWKWs0ZmZmVlRDSbziGhvv4M2MzNrd0q9aYyZmZm1UU7mZmZmOedkbmZmlnNO5mZmZjnnZG5mZpZzTuZmZmY5V+qDVsxaTJc+m9H/1GmVDsPMrN1wy9zMzCznnMzNzMxyzsnczMws55zMzczMcs7J3MzMLOeczM3MzHLOydzMzCznnMzNzMxyzsnczMws55zMzczMcs63c7VWN7d6PlufcGWlwzAzK7vp5x7SKuW4ZW5mZpZzTuZmZmY552RuZmaWc07mZmZmOedkbmZmlnNO5mZmZjnnZG5mZpZzTuZmZmY552RuZmaWc07mZmZmOedkbmZmlnNO5mZmZjnnZG5mZpZzTuZmZmY552RuZmaWc07mZmZmOedkbmZmlnNO5mZmZjnXppK5pLUlXS3pH5KmS7pD0kbNON7lkvZP05dKGpSmT2qpmIuU/4CkofWsu17Sek087q6SdmhedCWVM1bSygXzd0harYHt/1PSEeWOy8zMltdmkrkkATcBD0TE+hGxNfBLYK1a23VqyvEj4t8j4vk02+LJvDFxSdoM6BgRrzSxuF2BsidzYCywLJlHxF4R8WED208CjilzTGZmVkubSebAMGBRRIyvWRARMyPi4dQSfVjSLcDzkjpKOlfS05JmSToKsi8Ekv4k6UVJ9wBr1hyrppUs6X+ArpKelTSldhCS9pA0Q9JMSfemZdtKelzSM5Iek7RxWn6YpFsk3QfcK6lr6lmYK+kmoGs9dR0F3FxQ5kWSpkmaI+mMguXzJK2RpoemOgwAfgr8PNVhZ0kDJN2XzsW9kvqnfS5Px35C0ivpPE5K8V3eUPmSxgDrAPdLur+OeA5J5c2UNDm9X58B8yRtW/TdNjOzFtOkVm6ZbA5Mb2D9VsDmEfGqpCOBjyJiG0krAY9KuhvYEtgYGETWon+erLW4TET8QtJ/RsSQ2gVI6g1cAuySyumZVr0A7BwRiyWNAH4L/KAgrsER8YGk44DPImJTSYOBGfXUZUfgqoL5k9P+Hcm+FAyOiFl17RgR8ySNBz6NiPNS3LcCV0TEFambexzwvbTL6sD2wL7ALansfweeljQkIp6tp/xxqT7DIuL9WudpM+AUYIeIeL/gPAFMA3YGnqq1z5HAkQB9V+3MTT3OrefUtA/9T32u0iGY2QqkLbXMi3kqIl5N07sDh0h6FngS6AVsCOwCXBURSyLiLeC+RpaxHfBQTTkR8UFavipwnaTZwB+AzQr2mVqw3S7A/6V9ZwF1JmSgD/BewfyBkmYAz6RjD2pk3NsDf0nTk4GdCtbdGhEBPAe8ExHPRcRSYA4woInlDweuq0nyBfUHeJesRb+ciJgQEUMjYmjPbh0bVTkzM2tYW2qZzwH2b2D9goJpAcdExF2FG0jaqxyBAb8G7o+I76du7gfqiatUnwNVAJIGAscD20TEv1L3d1XabjFffeGqqn2QEn2Z/i4tmK6Z71Sk/KaoIqufmZm1krbUMr8PWCl1xwIgabCknevY9i7gaEmd03YbSeoGPAQclK6p9yG7Dl+XRTX71vIEsEtKcBR0H68KvJmmD2ugDg8BP077bg4Mrme7ucAGaXoVsi8EH0laC9izYLt5wNZp+gcFyz8BehTMPwb8ME2PAh5uIMbaGiq/djk17gMOkNQLljtPABsBsxtRvpmZNVObSeapK/j7wAhlP02bA/wO+Gcdm19Kdj18Rur6vpisl+Em4KW07krg8XqKmwDMqj0ALiLeI7uue6OkmcA1adU5wO8kPUPDvRkXAd0lzQXOpP4xALeTjUgnImaSdW+/QNZV/mjBdmcA50uaBiwpWH4r8P2aAXBkI8gPlzQLGA0c20CMyylS/gTgzpoBcAX7zAHOAh5M5+n3Bat3BKaWWr6ZmTWfshxqrUlSV+B+YMeIWFJs+7yQtCVwXESMbmi7wX27xm1HbdDQJrnnAXBm1tIkTY+IOu9d0mZa5iuSiPgcOA3oW+lYWtgawK8qHYSZ2YqmLQ2AW6HUHrzXHkSEu9fNzCrALXMzM7OcczI3MzPLOSdzMzOznHMyNzMzyzknczMzs5xzMjczM8s5J3MzM7OcczI3MzPLOSdzMzOznHMyNzMzyzknczMzs5xzMjczM8s5J3MzM7Oc81PTrNV16bMZ/U+dVukwzMzaDbfMzczMcs7J3MzMLOeczM3MzHLOydzMzCznnMzNzMxyThFR6RhsBSPpE+DFSsfRTGsA71c6iGZyHdoG16FtyEMdvhERveta4Z+mWSW8GBFDKx1Ec0ia5jpUnuvQNrgOledudjMzs5xzMjczM8s5J3OrhAmVDqAFuA5tg+vQNrgOFeYBcGZmZjnnlrmZmVnOOZmbmZnlnJO5tSpJe0h6UdLLkn5R6XjqI2ldSfdLel7SHEnHpuU9JU2V9FL6u3paLknjUr1mSdqqsjXISOoo6RlJt6X5gZKeTHFeI6lLWr5Smn85rR9Q0cATSatJul7SC5LmSto+h+/Bz9O/odmSrpJU1dbfB0mTJL0raXbBskafd0mHpu1fknRoG6jDuenf0ixJN0larWDdL1MdXpT0nYLlufjMIiL88qtVXkBH4B/AekAXYCYwqNJx1RNrH2CrNN0D+DswCDgH+EVa/gvg7DS9F/A3QMB2wJOVrkOK6zjgL8Btaf5a4IdpejxwdJr+GTA+Tf8QuKbSsadYrgD+PU13AVbL03sA9AVeBboWnP/D2vr7AOwCbAXMLljWqPMO9AReSX9XT9OrV7gOuwOd0vTZBXUYlD6PVgIGps+pjnn6zHLL3FrTtsDLEfFKRCwErgZGVjimOkXE2xExI01/Aswl+2AeSZZgSH+/l6ZHAldG5glgNUl9Wjfq5UnqB+wNXJrmBQwHrk+b1I6/pl7XA7ul7StG0qpkH8gTASJiYUR8SI7eg6QT0FVSJ2Bl4G3a+PsQEQ8BH9Ra3Njz/h1gakR8EBH/AqYCe5Q9+KSuOkTE3RGxOM0+AfRL0yOBqyPiy4h4FXiZ7PMqN59ZTubWmvoCbxTMV6dlbVrq6twSeBJYKyLeTqv+CayVptti3f4InAgsTfO9gA8LPswKY1wWf1r/Udq+kgYC7wGXpUsFl0rqRo7eg4h4EzgPeJ0siX8ETCdf70ONxp73Nvd+1HIEWY8C5LcOyziZmzVAUnfgBmBsRHxcuC6y/rk2+dtOSfsA70bE9ErH0gydyLpJL4qILYEFZN27y7Tl9wAgXVceSfbFZB2gG63YOi2Xtn7ei5F0MrAYmFLpWFqKk7m1pjeBdQvm+6VlbZKkzmSJfEpE3JgWv1PTdZv+vpuWt7W67QjsK2keWdfgcOB8si7QmmcyFMa4LP60flVgfmsGXIdqoDoinkzz15Ml97y8BwAjgFcj4r2IWATcSPbe5Ol9qNHY894W3w8kHQbsA4xKX0ogZ3Woi5O5taangQ3TSN4uZAN8bqlwTHVK1yknAnMj4vcFq24BakblHgrcXLD8kDSydzvgo4IuyVYXEb+MiH4RMYDsPN8XEaOA+4H902a146+p1/5p+4q2vCLin8AbkjZOi3YDnicn70HyOrCdpJXTv6maOuTmfSjQ2PN+F7C7pNVTD8XuaVnFSNqD7NLTvhHxWcGqW4Afpl8TDAQ2BJ4iR59ZFR+B59eK9SIb+fp3shGiJ1c6ngbi3ImsG3EW8Gx67UV2/fJe4CXgHqBn2l7AhalezwFDK12Hgrrsylej2dcj+5B6GbgOWCktr0rzL6f161U67hTXEGBaeh/+SjYqOlfvAXAG8AIwG5hMNmK6Tb8PwFVk1/gXkfWQ/FtTzjvZdemX0+vwNlCHl8mugdf8nx5fsP3JqQ4vAnsWLM/FZ5Zv52pmZpZz7mY3MzPLOSdzMzOznHMyNzMzyzknczMzs5xzMjczM8s5J3MzsyaQNFbSypWOwwzwT9PMzJoi3V1vaES8X+lYzNwyN7N2S9Ih6dnVMyVNljRA0n1p2b2S+qftLpe0f8F+n6a/u0p6QF89U31KutPZGLJ7rd8v6f7K1M7sK52Kb2Jmlj+SNgNOAXaIiPcl9SR7dOcVEXGFpCOAcXz1KM/6bAlsBrwFPArsGBHjJB0HDHPL3NoCt8zNrL0aDlxXk2wj4gNge+Avaf1kstv2FvNURFRHxFKyW4AOaPlQzZrHydzMLHscZgcASR2ALgXrviyYXoJ7NK0NcjI3s/bqPuAASb0AUjf7Y2RPvgIYBTycpucBW6fpfYHOJRz/E6BHSwVr1hz+hmlm7VJEzJF0FvCgpCXAM8AxwGWSTgDeAw5Pm18C3CxpJnAnsKCEIiYAd0p6KyKGtXwNzErnn6aZmZnlnLvZzczMcs7J3MzMLOeczM3MzHLOydzMzCznnMzNzMxyzsnczMws55zMzczMcu7/A1FoQ6Jm5+3dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot with multiple x variables\n",
    "# นับแยกความถี่ตาม category \n",
    "sns.countplot(data = telcoData, y = 'PaymentMethod', hue = 'Churn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate analysis : Numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Descriptive statisitcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>2283.300441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>2266.771362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>401.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>1397.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>3794.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>8684.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges  TotalCharges\n",
       "count    7043.000000  7043.000000     7043.000000   7032.000000\n",
       "mean        0.162147    32.371149       64.761692   2283.300441\n",
       "std         0.368612    24.559481       30.090047   2266.771362\n",
       "min         0.000000     0.000000       18.250000     18.800000\n",
       "25%         0.000000     9.000000       35.500000    401.450000\n",
       "50%         0.000000    29.000000       70.350000   1397.475000\n",
       "75%         0.000000    55.000000       89.850000   3794.737500\n",
       "max         1.000000    72.000000      118.750000   8684.800000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean, median, varaince, standard deviation, quartiles\n",
    "telcoData.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdHUlEQVR4nO3dfZRddX3v8ffHxBAgloDoJGUCiSUlC0FoZuRhWbtmQDEol9B18QrLh+jF5lqjF65kCdje4m1LL7k3Iwpal7GkgLWMig/kplgMkZFlV3kaRAJBZAQiiWQSBaWDmHHi9/6xf9Mcp2dmn3Pm7PPAfF5rnTV7//Zv7/0552Tmm/2siMDMzGwqL2t2ADMza30uFmZmlsvFwszMcrlYmJlZLhcLMzPLNbvZAYpw5JFHxuLFi2ue/4UXXuDQQw+tX6CCOGd9tUtOaJ+szll/RWYdHBz8aUS8quzEiHjJvbq6umI67rzzzmnN3yjOWV/tkjOifbI6Z/0VmRW4Pyb5u+rdUGZmlsvFwszMcrlYmJlZLhcLMzPL5WJhZma5XCzMzCyXi4WZmeUqrFhI2ihpj6SHJ7R/WNIPJD0i6f+UtF8haUjSY5LeUtK+IrUNSbq8qLxmZja5Iq/gvgH4NHDTeIOkXmAlcFJE7JP06tR+PHAB8Frgd4E7JP1+mu0zwJuBncB9kjZFxPYCc5uZ2QSFbVlExF3AsxOa/xS4OiL2pT57UvtKoD8i9kXEk8AQcEp6DUXEExExCvSnvoV66KFtSGr4a2Hn0UW/NTOzmigKfFKepMXA5og4IY0/CNwKrAB+BayNiPskfRq4OyL+IfW7HvhmWsyKiHh/an83cGpEfKjMulYDqwE6Ojq6+vv7a849PDzMnrG5Nc9fq9HdQ3R1dVXcf2RkhHnz5hWYqD6cs/7aJatz1l+RWXt7ewcjorvctEbfSHA2cARwGvB64MuSXlOPBUfEBmADQHd3d/T09NS8rL6+Pq7bu6wesaqyY91aqineAwMDTOd9Nopz1l+7ZHXO+mtW1kYXi53A19INq+6V9BvgSGAXsKikX2dqY4p2MzNrkEafOvsNoBcgHcCeA/wU2ARcIOkgSUuApcC9wH3AUklLJM0hOwi+qcGZzcxmvMK2LCTdDPQAR0raCVwJbAQ2ptNpR4FVaSvjEUlfBrYDY8CaiNiflvMh4HZgFrAxIh4pKrOZmZVXWLGIiAsnmfSuSfpfBVxVpv024LY6RjMzsyr5Cm4zM8vlYmFmZrlcLMzMLJeLhZmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszM8vlYmFmZrlcLMzMLJeLhZmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszM8tVWLGQtFHSnvQI1YnTLpUUko5M45J0raQhSQ9JWl7Sd5Wkx9NrVVF5zcxsckVuWdwArJjYKGkRcBbw45Lms4Gl6bUa+GzqewTZs7tPBU4BrpR0eIGZzcysjMKKRUTcBTxbZtI1wEeBKGlbCdwUmbuB+ZIWAm8BtkTEsxHxHLCFMgXIzMyKNbuRK5O0EtgVEd+XVDrpKODpkvGdqW2y9nLLXk22VUJHRwcDAwM15+zs7OTSBWM1z1+r0fXrq8o9MjIyrffZKM5Zf+2S1Tnrr1lZG1YsJB0CfIxsF1TdRcQGYANAd3d39PT01Lysvr4+rtu7rE7JKrdj3VoiIr9jMjAwwHTeZ6M4Z/21S1bnrL9mZW3k2VC/BywBvi/pKaATeEDSAmAXsKikb2dqm6zdzMwaqGHFIiK2RcSrI2JxRCwm26W0PCJ2A5uA96Szok4DfhERzwC3A2dJOjwd2D4rtZmZWQMVeerszcC/AsdJ2inpoim63wY8AQwBnwc+CBARzwJ/BdyXXn+Z2szMrIEKO2YRERfmTF9cMhzAmkn6bQQ21jWcmZlVxVdwm5lZLhcLMzPL5WJhZma5XCzMzCyXi4WZmeVysTAzs1wuFmZmlsvFwszMcrlYmJlZLhcLMzPL5WJhZma5XCzMzCyXi4WZmeVysTAzs1wuFmZmlsvFwszMchX5pLyNkvZIerik7f9K+oGkhyR9XdL8kmlXSBqS9Jikt5S0r0htQ5IuLyqvmZlNrsgtixuAFRPatgAnRMTrgB8CVwBIOh64AHhtmudvJc2SNAv4DHA2cDxwYeprZmYNVFixiIi7gGcntH0rIsbS6N1AZxpeCfRHxL6IeJLsWdynpNdQRDwREaNAf+prZmYN1MxjFv8V+GYaPgp4umTaztQ2WbuZmTXQ7GasVNKfAWPAF+u4zNXAaoCOjg4GBgZqXlZnZyeXLhjL71hno+vXV5V7ZGRkWu+zUZyz/tolq3PWX7OyNrxYSHovcA5wZkREat4FLCrp1pnamKL9t0TEBmADQHd3d/T09NScsa+vj+v2Lqt5/lrtWLeWAx9JvoGBAabzPhvFOeuvXbI6Z/01K2tDd0NJWgF8FDg3In5ZMmkTcIGkgyQtAZYC9wL3AUslLZE0h+wg+KZGZjYzswK3LCTdDPQAR0raCVxJdvbTQcAWSQB3R8QHIuIRSV8GtpPtnloTEfvTcj4E3A7MAjZGxCNFZTYzs/IKKxYRcWGZ5uun6H8VcFWZ9tuA2+oYzczMquQruM3MLJeLhZmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszswIs7DwaSXV/DQ4OTjl9YefRhbyfptwbyszspW73rqc55rLNdV/unAVjUy53x7pz6r5O8JaFmZlVwMXCzMxyuViYmVkuFwszM8vlYmFmZrlcLMzMLJeLhZmZ5XKxMDOzXC4WZmaWq6JiIenEahcsaaOkPZIeLmk7QtIWSY+nn4endkm6VtKQpIckLS+ZZ1Xq/7ikVdXmMDOz6at0y+JvJd0r6YOSDqtwnhuAFRPaLge2RsRSYGsaBzgbWJpeq4HPQlZcyJ7dfSpwCnDleIExM7PGqahYRMQbgXcCi4BBSf8o6c0589wFPDuheSVwYxq+ETivpP2myNwNzJe0EHgLsCUino2I54At/McCZGZmBVNEVN5ZmkX2B/5a4HlAwMci4muT9F8MbI6IE9L4zyNifhoW8FxEzJe0Gbg6Ir6bpm0FLgN6gLkR8dep/X8CL0bE+jLrWk22VUJHR0dXf39/xe9rouHhYfaMza15/lqN7h6iq6ur4v4jIyPMmzevwET14Zz11y5ZZ3LOwcFB5iw4tq7LBOg4GIZfnHx6tX9HSvX29g5GRHe5aRXddVbS64D3AW8j+9/9f4qIByT9LvCvQNliMZWICEmVV6r85W0ANgB0d3dHT09Pzcvq6+vjur3L6pSscjvWraWa4j0wMMB03mejOGf9tUvWmZyzt7e3kLvOXnriGH3bJv/TXe3fkUpVesziOuAB4KSIWBMRDwBExE+AP69ifcNp9xLp557UvotsF9e4ztQ2WbuZmTVQpcXibcA/RsSLAJJeJukQgIj4QhXr2wSMn9G0Cri1pP096ayo04BfRMQzwO3AWZIOTwe2z0ptZmbWQJUWizuAg0vGD0ltk5J0M9kuquMk7ZR0EXA18GZJjwNvSuMAtwFPAEPA54EPAkTEs8BfAfel11+mNjMza6BKn5Q3NyJGxkciYmR8y2IyEXHhJJPOLNM3gDWTLGcjsLHCnGZmVoBKtyxemHChXBcwxfF4MzN7Kal0y+IS4CuSfkJ2uuwC4B1FhTIzs9ZSUbGIiPskLQOOS02PRcSvi4tlZmatpNItC4DXA4vTPMslERE3FZLKzMxaSqUX5X0B+D3gQWB/ag7AxcLMbAaodMuiGzg+irgs0MzMWl6lZ0M9THZQ28zMZqBKtyyOBLZLuhfYN94YEecWksrMzFpKpcXi40WGMDOz1lbpqbPfkXQMsDQi7khXb88qNpqZmbWKSh+r+ifALcDnUtNRwDcKymRmZi2m0gPca4A3kD3wiIh4HHh1UaHMzKy1VFos9kXE6PiIpNlk11mYmdkMUGmx+I6kjwEHp2dvfwX4f8XFMjOzVlJpsbgc2AtsA/4b2fMnqnlCnpmZtbFKz4b6DdlDiT5fbBwzM2tFld4b6knKHKOIiNfUPZGZmbWcau4NNW4u8HbgiFpXKul/AO8nK0DbgPcBC4F+4JXAIPDuiBiVdBDZDQu7gJ8B74iIp2pdt5mZVa+iYxYR8bOS166I+CTwtlpWKOko4L8D3RFxAtnFfRcA64BrIuJY4DngojTLRcBzqf2a1M/MzBqo0ovylpe8uiV9gOqehTHRbLIzq2YDhwDPAGeQXfgHcCNwXhpemcZJ08+UpGms28zMqqRK7jou6c6S0THgKWB9RDxW00qli4GryJ7j/S3gYuDutPWApEXANyPiBEkPAysiYmea9iPg1Ij46YRlrgZWA3R0dHT19/fXEg2A4eFh9ozNrXn+Wo3uHqKrq6vi/iMjI8ybN6/ARPXhnPXXLllncs7BwUHmLDi2rssE6DgYhl+cfHq1f0dK9fb2DkZEd7lplZ4N1VvTmsuQdDjZ1sIS4Odk12ysmO5yI2IDsAGgu7s7enp6al5WX18f1+1dNt1IVduxbi3VPDJkYGCA6bzPRnHO+muXrDM5Z29vL8dctrmuywS49MQx+rZN/qe72r8jlar0bKiPTDU9Ij5RxTrfBDwZEXvTsr9GdiuR+ZJmR8QY0AnsSv13AYuAnWm31WFkB7rNzKxBKr0orxv4U7IbCB4FfABYDrwivarxY+A0SYekYw9nAtuBO4HzU59VwK1peFMaJ03/tp/YZ2bWWJUepO4ElkfEvwFI+jjwTxHxrmpXGBH3SLoFeIDs+Mf3yHYf/RPQL+mvU9v1aZbrgS9IGgKeJTtzyszMGqjSLYsOYLRkfDS11SQiroyIZRFxQkS8OyL2RcQTEXFKRBwbEW+PiH2p76/S+LFp+hO1rrflzXo5kip+DQ4OVtV/qtfCzqOb/e7NrIVVumVxE3CvpK+n8fM4cDqr1cv+X1d1QGzOgrG6HUDbse6cuizHzF6aKj0b6ipJ3wTemJreFxHfKy6WmZm1kkp3Q0F28dzzEfEpsjOTlhSUyczMWkylV3BfCVwGXJGaXg78Q1GhzMystVS6ZfHHwLnACwAR8ROqP2XWzMzaVKXFYjRd2xAAkg4tLpKZmbWaSovFlyV9juwq6z8B7sAPQjIzmzFyz4ZKV1l/CVgGPA8cB/xFRGwpOJuZmbWI3GIRESHptog4EXCBMDObgSrdDfWApNcXmsTMzFpWpVdwnwq8S9JTZGdEiWyj43VFBTMzs9YxZbGQdHRE/Bh4S4PymJlZC8rbsvgG2d1md0j6akT85wZkMjOzFpN3zKL0WdevKTKImZm1rrxiEZMMm5nZDJK3G+okSc+TbWEcnIbhwAHu3yk0nZmZtYQpi0VEzGpUEDMza13V3KK8biTNl3SLpB9IelTS6ZKOkLRF0uPp5+GpryRdK2lI0kOSljcjs5nZTNaUYgF8CvjniFgGnAQ8ClwObI2IpcDWNA5wNrA0vVYDn218XDOzma3hxULSYcAfAdcDRMRoRPwcWMmBR7XeSPboVlL7TZG5m+xmhgsbGtrMbIZTdufxBq5QOhnYAGwn26oYBC4GdkXE/NRHwHMRMV/SZuDqiPhumrYVuCwi7p+w3NVkWx50dHR09ff315xxeHiYPWNza56/VqO7h5iz4NiK+3ccDMMv1m/dXV1d9VnYBCMjI8ybN6+QZddTu+SE9sk6k3MODg5W9ftcqbzf++n8Lvf29g5GRHe5aZXe7qOeZgPLgQ9HxD2SPsWBXU7Av9+8sKoqFhEbyIoQ3d3d0dPTU3PAvr4+rtu7rOb5a7Vj3VqOuWxzxf0vPXGMvm31+Qp3rFtLUf9xGBgYYDrfR6O0S05on6wzOWdvb29Vv8+Vyvu9L+p3uRnHLHYCOyPinjR+C1nxGB7fvZR+7knTdwGLSubvTG1mZtYgDS8WEbEbeFrScanpTLJdUpuAValtFXBrGt4EvCedFXUa8IuIeKaRmc3MZrpmnQ31YeCLkh4CTgb+BrgaeLOkx4E3pXGA24AngCGyp/N9sOFpZ4JZL0dSIa/BwcFJpy3sPLrZ79zMKtCMYxZExINAuYMoZ5bpG8CaojPNePt/Xcj+VYA5C8YmXfaO9X9Mdj5DYy04ahHP7Pxxw9dr1q6aUizM/l2BRWoqO9ad0/B1mrWzZu2GMjOzNuJiYWZmuVwszMwsl4uFmZnlcrEwM7NcLhZm9pK1sPPoaV8LVOvrpcanztrMlC5CLLV+/Xp6e3uLX/Wcuewf/dW0llFL1mZeW7Kw82h273q6Keuu5NTsqa4FqtVL7fRsFwubmcpc31HEH4xydqw7Z9rrqSVrM/947d71tK+naXPeDWVmZrlcLMzMLJeLhZmZ5XKxMDOzXC4WZjNFgbehzzsl1dqfz4YymymacIff8bO2fFZS+/OWhZmZ5WpasZA0S9L3JG1O40sk3SNpSNKXJM1J7Qel8aE0fXGzMpuZzVTN3LK4GHi0ZHwdcE1EHAs8B1yU2i8Cnkvt16R+ZmbWQE0pFpI6gbcBf5fGBZwB3JK63Aicl4ZXpnHS9DPlI2ZmZg2l7BHXDV6pdAvwv4FXAGuB9wJ3p60HJC0CvhkRJ0h6GFgRETvTtB8Bp0bETycsczWwGqCjo6Orv7+/5nzDw8PsGZtb8/y1Gt09xJwFx1bcv+NgGH6xOeuuxlQ5i1zvVMqtt56fZ7XrrlYtWZvxWY/nbKXvuZwivvui3nNe1tHdQ3R1ddW07N7e3sGI6C43reFnQ0k6B9gTEYOSeuq13IjYAGwA6O7ujp6e2hfd19fHdXuX1SlZ5XasW1vV2SqXnjhG37b6fIXVrrsaU+Uscr1TKbfeen6e1a67WrVkbcZnPZ6zlb7ncor47ot6z3lZd6xbSxEbAc04dfYNwLmS3grMBX4H+BQwX9LsiBgDOoFdqf8uYBGwU9Js4DDgZ42PbWY2czX8mEVEXBERnRGxGLgA+HZEvBO4Ezg/dVsF3JqGN6Vx0vRvRzP2nZmZzWCtdJ3FZcBHJA0BrwSuT+3XA69M7R8BLm9SPjOzGaupV3BHxAAwkIafAE4p0+dXwNsbGszMzH5LK21ZmJlZi3KxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszM8vlYmFmZrlcLMzMLJeLhZmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszM8vlYmFmZrlcLMzMLJeLhZmZ5Wp4sZC0SNKdkrZLekTSxan9CElbJD2efh6e2iXpWklDkh6StLzRmc3MZrpmbFmMAZdGxPHAacAaSceTPVt7a0QsBbZy4FnbZwNL02s18NnGRzYzm9kaXiwi4pmIeCAN/xvwKHAUsBK4MXW7ETgvDa8EborM3cB8SQsbm9rMbGZTRDRv5dJi4C7gBODHETE/tQt4LiLmS9oMXB0R303TtgKXRcT9E5a1mmzLg46Ojq7+/v6acw0PD7NnbG7N89dqdPcQcxYcW3H/joNh+MXmrLsaU+Uscr1TKbfeen6e1a67WrVkbcZnPZ6zlb7ncor47ot6z3lZR3cP0dXVVdOye3t7ByOiu9y02TUtsQ4kzQO+ClwSEc9n9SETESGpqioWERuADQDd3d3R09NTc7a+vj6u27us5vlrtWPdWo65bHPF/S89cYy+bfX5CqtddzWmylnkeqdSbr31/DyrXXe1asnajM96PGcrfc/lFPHdF/We87LuWLeWIjYCmnI2lKSXkxWKL0bE11Lz8PjupfRzT2rfBSwqmb0ztZmZWYM042woAdcDj0bEJ0ombQJWpeFVwK0l7e9JZ0WdBvwiIp5pWGAzM2vKbqg3AO8Gtkl6MLV9DLga+LKki4AdwH9J024D3goMAb8E3tfQtGZm1vhikQ5Ua5LJZ5bpH8CaQkOZmdmUfAW3mZnlcrEwM7NcLhZmZpbLxcLMzHK5WJiZWS4XCzMzy+ViYWZmuVwszMwsl4uFmZnlcrEwM7NcLhZmZpbLxcLMzHK5WJiZWS4XCzMzy+ViYWZmuVwszMwsl4uFmZnlaptiIWmFpMckDUm6vNl5zMxmkrYoFpJmAZ8BzgaOBy6UdHxzU5mZzRxtUSyAU4ChiHgiIkaBfmBlkzOZmc0YiohmZ8gl6XxgRUS8P42/Gzg1Ij5U0mc1sDqNHgc8No1VHgn8dBrzN4pz1le75IT2yeqc9Vdk1mMi4lXlJswuaIUNFxEbgA31WJak+yOiux7LKpJz1le75IT2yeqc9desrO2yG2oXsKhkvDO1mZlZA7RLsbgPWCppiaQ5wAXApiZnMjObMdpiN1REjEn6EHA7MAvYGBGPFLjKuuzOagDnrK92yQntk9U5668pWdviALeZmTVXu+yGMjOzJnKxMDOzXC4WJVr5liKSNkraI+nhkrYjJG2R9Hj6eXgzM6ZMiyTdKWm7pEckXdyKWSXNlXSvpO+nnP8rtS+RdE/6N/CldEJF00maJel7kjan8VbN+ZSkbZIelHR/amup7z5lmi/pFkk/kPSopNNbLaek49LnOP56XtIlzcrpYpG0wS1FbgBWTGi7HNgaEUuBrWm82caASyPieOA0YE36HFst6z7gjIg4CTgZWCHpNGAdcE1EHAs8B1zUvIi/5WLg0ZLxVs0J0BsRJ5dcC9Bq3z3Ap4B/johlwElkn21L5YyIx9LneDLQBfwS+DrNyhkRfmUH+U8Hbi8ZvwK4otm5JmRcDDxcMv4YsDANLwQea3bGMplvBd7cylmBQ4AHgFPJroydXe7fRBPzdZL9UTgD2AyoFXOmLE8BR05oa6nvHjgMeJJ0gk+r5pyQ7SzgX5qZ01sWBxwFPF0yvjO1tbKOiHgmDe8GOpoZZiJJi4E/AO6hBbOmXTsPAnuALcCPgJ9HxFjq0ir/Bj4JfBT4TRp/Ja2ZEyCAb0kaTLfggdb77pcAe4G/T7v2/k7SobRezlIXADen4abkdLF4iYjsvxktcx60pHnAV4FLIuL50mmtkjUi9ke2id9JdrPKZc1N9B9JOgfYExGDzc5SoT+MiOVku3PXSPqj0okt8t3PBpYDn42IPwBeYMKunBbJCUA6HnUu8JWJ0xqZ08XigHa8pciwpIUA6eeeJucBQNLLyQrFFyPia6m5JbMCRMTPgTvJdufMlzR+sWor/Bt4A3CupKfI7rZ8Btn+9lbLCUBE7Eo/95DtXz+F1vvudwI7I+KeNH4LWfFotZzjzgYeiIjhNN6UnC4WB7TjLUU2AavS8Cqy4wNNJUnA9cCjEfGJkkktlVXSqyTNT8MHkx1XeZSsaJyfujU9Z0RcERGdEbGY7N/ktyPinbRYTgBJh0p6xfgw2X72h2mx7z4idgNPSzouNZ0JbKfFcpa4kAO7oKBZOZt94KaVXsBbgR+S7bv+s2bnmZDtZuAZ4Ndk/zO6iGzf9VbgceAO4IgWyPmHZJvFDwEPptdbWy0r8Drgeynnw8BfpPbXAPcCQ2Sb/Qc1+zMtydwDbG7VnCnT99PrkfHfoVb77lOmk4H70/f/DeDwFs15KPAz4LCStqbk9O0+zMwsl3dDmZlZLhcLMzPL5WJhZma5XCzMzCyXi4WZmeVqiyflmbUKSeOnLQIsAPaT3ToC4JSIGG1KMLOC+dRZsxpJ+jgwEhHrC1r+rIjYX8Syzarl3VBm0ySpS9J30s3zbi+5FcOApHXpuRk/lPTG1P5eSZ8umX+zpJ40PCKpT9L3gdMlvSvN/6Ckz6Vb6Zs1nIuF2fQIuA44PyK6gI3AVSXTZ0fEKcAlwJUVLO9Q4J7InrPxM+AdwBsiu+HhfuCd9YtuVjkfszCbnoOAE4At2W2xmEV2W5Zx4zdSHCR7Hkme/WQ3YYTsnkVdwH1p2QfTOje3sxnGxcJsegQ8EhGnTzJ9X/q5nwO/b2P89lb93JLhX5UcpxBwY0RcUa+wZrXybiiz6dkHvErS6ZDdnl3Sa3PmeQo4WdLLJC0iu413OVuB8yW9Oi37CEnH1Cm3WVW8ZWE2Pb8hu1X4tZIOI/ud+iTZXVcn8y9kj/XcTnZb9AfKdYqI7ZL+nOzJcy8ju+PwGmBH3dKbVcinzpqZWS7vhjIzs1wuFmZmlsvFwszMcrlYmJlZLhcLMzPL5WJhZma5XCzMzCzX/weufyRW1vMEHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of numerical data\n",
    "import matplotlib.pyplot as plt\n",
    "telcoData['tenure'].hist(edgecolor='black')\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 with seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Roaming\\Python\\Python38\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tenure'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# be careful choosing suitable bins for data\n",
    "sns.distplot(telcoData['tenure'], \n",
    "             bins=20, \n",
    "             kde=False, \n",
    "             hist_kws={'edgecolor':'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate analysis : Categorical vs Categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month-to-month</th>\n",
       "      <td>2220</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One year</th>\n",
       "      <td>1307</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two year</th>\n",
       "      <td>1647</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn             No   Yes\n",
       "Contract                  \n",
       "Month-to-month  2220  1655\n",
       "One year        1307   166\n",
       "Two year        1647    48"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ใส่ตัวแปรนึงเป็น index อีกตัวแปรเป็น column \n",
    "pd.crosstab( index = telcoData['Contract'],\n",
    "             columns = telcoData['Churn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Normalized crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month-to-month</th>\n",
       "      <td>0.315207</td>\n",
       "      <td>0.234985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One year</th>\n",
       "      <td>0.185574</td>\n",
       "      <td>0.023570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two year</th>\n",
       "      <td>0.233849</td>\n",
       "      <td>0.006815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn                 No       Yes\n",
       "Contract                          \n",
       "Month-to-month  0.315207  0.234985\n",
       "One year        0.185574  0.023570\n",
       "Two year        0.233849  0.006815"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize=True -> ร้อยละของแต่ละ combination \n",
    "# 100% = เอาค่าทุกแถวทุกคอลัมน์มาบวกกัน\n",
    "pd.crosstab( index = telcoData['Contract'],\n",
    "             columns = telcoData['Churn'],\n",
    "             normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Pivot table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "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>Churn</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Month-to-month</th>\n",
       "      <th>Bank transfer (automatic)</th>\n",
       "      <td>0.658744</td>\n",
       "      <td>0.341256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit card (automatic)</th>\n",
       "      <td>0.672192</td>\n",
       "      <td>0.327808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic check</th>\n",
       "      <td>0.462703</td>\n",
       "      <td>0.537297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed check</th>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">One year</th>\n",
       "      <th>Bank transfer (automatic)</th>\n",
       "      <td>0.902813</td>\n",
       "      <td>0.097187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit card (automatic)</th>\n",
       "      <td>0.896985</td>\n",
       "      <td>0.103015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic check</th>\n",
       "      <td>0.815562</td>\n",
       "      <td>0.184438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed check</th>\n",
       "      <td>0.931751</td>\n",
       "      <td>0.068249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Two year</th>\n",
       "      <th>Bank transfer (automatic)</th>\n",
       "      <td>0.966312</td>\n",
       "      <td>0.033688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit card (automatic)</th>\n",
       "      <td>0.977625</td>\n",
       "      <td>0.022375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic check</th>\n",
       "      <td>0.922619</td>\n",
       "      <td>0.077381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mailed check</th>\n",
       "      <td>0.992147</td>\n",
       "      <td>0.007853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn                                           No       Yes\n",
       "Contract       PaymentMethod                                \n",
       "Month-to-month Bank transfer (automatic)  0.658744  0.341256\n",
       "               Credit card (automatic)    0.672192  0.327808\n",
       "               Electronic check           0.462703  0.537297\n",
       "               Mailed check               0.684211  0.315789\n",
       "One year       Bank transfer (automatic)  0.902813  0.097187\n",
       "               Credit card (automatic)    0.896985  0.103015\n",
       "               Electronic check           0.815562  0.184438\n",
       "               Mailed check               0.931751  0.068249\n",
       "Two year       Bank transfer (automatic)  0.966312  0.033688\n",
       "               Credit card (automatic)    0.977625  0.022375\n",
       "               Electronic check           0.922619  0.077381\n",
       "               Mailed check               0.992147  0.007853"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize by row\n",
    "import numpy as np\n",
    "pd.pivot_table(data = telcoData,\n",
    "               index = ['Contract','PaymentMethod'],\n",
    "               columns = 'Churn',\n",
    "               values = 'TotalCharges',\n",
    "               aggfunc= np.size)\\\n",
    "  .apply(lambda x: x/sum(x), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate analysis : Categorical vs Numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qcut เปลี่ยนตัวเลขเป็น categorical โดยแบ่งเป็นช่วงตาม quantiles\n",
    "telcoData['tenure_bin'] = pd.qcut(telcoData['tenure'], q = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f8dbcc1c10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# norminal vs numeric\n",
    "# proportion\n",
    "churnByTenure = pd.crosstab(index = telcoData['tenure_bin'],\n",
    "                            columns = telcoData['Churn'])\\\n",
    "                  .apply(lambda x: x/sum(x), axis = 1)\n",
    "churnByTenure.plot.bar(stacked = True).legend(bbox_to_anchor=(1.2, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Contract', ylabel='TotalCharges'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# category vs numerical\n",
    "sns.boxplot(data = telcoData, x = 'Contract', y = 'TotalCharges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.1 Non-aggregated scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numerical vs numerical\n",
    "# graphical\n",
    "# ดูทุกๆจุดของข้อมูล เพื่อดูภาพรวม แนวโน้มข้อมูล\n",
    "telcoData.plot.scatter(x = 'tenure', y = 'TotalCharges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.2 Aggregated scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numerical vs numerical\n",
    "# graphical\n",
    "# ordered pairs\n",
    "telcoData1 = telcoData.groupby(['Contract','Churn'], as_index=False)\\\n",
    "                      .agg({'tenure':'mean', 'TotalCharges':'mean'})\n",
    "sns.scatterplot(data = telcoData1, \n",
    "                x = 'tenure', \n",
    "                y = 'TotalCharges', \n",
    "                hue = 'Churn', \n",
    "                style = 'Contract')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016567</td>\n",
       "      <td>0.220173</td>\n",
       "      <td>0.102411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.016567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.247900</td>\n",
       "      <td>0.825880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.220173</td>\n",
       "      <td>0.247900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.102411</td>\n",
       "      <td>0.825880</td>\n",
       "      <td>0.651065</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SeniorCitizen    tenure  MonthlyCharges  TotalCharges\n",
       "SeniorCitizen        1.000000  0.016567        0.220173      0.102411\n",
       "tenure               0.016567  1.000000        0.247900      0.825880\n",
       "MonthlyCharges       0.220173  0.247900        1.000000      0.651065\n",
       "TotalCharges         0.102411  0.825880        0.651065      1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical vs numerical\n",
    "# non-graphical\n",
    "telcoData.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plott correlation into heatmap\n",
    "sns.heatmap(telcoData.corr(), cmap = 'RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabular data lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Select a data source : Avocado Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/neuromusic/avocado-prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4           4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4   75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  \n",
       "3  2015  Albany  \n",
       "4  2015  Albany  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado = pd.read_csv('./avocado.csv')\n",
    "avocado.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Identify what form of data it has : Time series table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Select one aspect/question of the data, create a visualization to describe the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q : What region is the highest avocado production?\n",
    "#### A : Louisville"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avo_sum = avocado.groupby('region')['Total Volume'].sum()\n",
    "plt.figure(figsize=(15,3))\n",
    "avo_sum.sort_values().head(5).plot.barh()\n",
    "plt.title('Top 5 Production per region (2015-2018)', fontsize=15)\n",
    "plt.xlabel('Volume')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = [1,2,3]\n",
    "[a+5 for a in A]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"int\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_21680/3647163654.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
     ]
    }
   ],
   "source": [
    "[a for a in A +5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add(x) :\n",
    "    return (x+1)\n",
    "y=0\n",
    "x=y+5\n",
    "add(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}