transaccions_boxplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "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>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987000</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.5</td>\n",
       "      <td>W</td>\n",
       "      <td>13926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>discover</td>\n",
       "      <td>142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 394 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ... V330  V331  V332  V333  V334 V335  \\\n",
       "0    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "2  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
       "\n",
       "  V336  V337  V338  V339  \n",
       "0  NaN   NaN   NaN   NaN  \n",
       "1  NaN   NaN   NaN   NaN  \n",
       "2  NaN   NaN   NaN   NaN  \n",
       "3  NaN   NaN   NaN   NaN  \n",
       "4  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 394 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction = pd.read_csv('train_transaction.csv')\n",
    "transaction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>TransactionID</th>\n",
       "      <th>id_01</th>\n",
       "      <th>id_02</th>\n",
       "      <th>id_03</th>\n",
       "      <th>id_04</th>\n",
       "      <th>id_05</th>\n",
       "      <th>id_06</th>\n",
       "      <th>id_07</th>\n",
       "      <th>id_08</th>\n",
       "      <th>id_09</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70787.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987008</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>98945.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>mobile safari 11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1334x750</td>\n",
       "      <td>match_status:1</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS Device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987010</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>191631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987011</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>221832.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  id_08  \\\n",
       "0        2987004    0.0   70787.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        2987008   -5.0   98945.0    NaN    NaN    0.0   -5.0    NaN    NaN   \n",
       "2        2987010   -5.0  191631.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
       "3        2987011   -5.0  221832.0    NaN    NaN    0.0   -6.0    NaN    NaN   \n",
       "4        2987016    0.0    7460.0    0.0    0.0    1.0    0.0    NaN    NaN   \n",
       "\n",
       "   id_09  ...                id_31  id_32      id_33           id_34  id_35  \\\n",
       "0    NaN  ...  samsung browser 6.2   32.0  2220x1080  match_status:2      T   \n",
       "1    NaN  ...   mobile safari 11.0   32.0   1334x750  match_status:1      T   \n",
       "2    0.0  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "3    NaN  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "4    0.0  ...          chrome 62.0   24.0   1280x800  match_status:2      T   \n",
       "\n",
       "  id_36 id_37  id_38  DeviceType                     DeviceInfo  \n",
       "0     F     T      T      mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
       "1     F     F      T      mobile                     iOS Device  \n",
       "2     F     T      T     desktop                        Windows  \n",
       "3     F     T      T     desktop                            NaN  \n",
       "4     F     T      T     desktop                          MacOS  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity = pd.read_csv('train_identity.csv')\n",
    "identity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debit              439938\n",
       "credit             148986\n",
       "debit or credit        30\n",
       "charge card            15\n",
       "Name: card6, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction['card6'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_short = transaction.loc[transaction['TransactionAmt'] < 200]"
   ]
  },
  {
   "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>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987000</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.50</td>\n",
       "      <td>W</td>\n",
       "      <td>13926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>discover</td>\n",
       "      <td>142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.00</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.00</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.00</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.00</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590535</th>\n",
       "      <td>3577535</td>\n",
       "      <td>0</td>\n",
       "      <td>15811047</td>\n",
       "      <td>49.00</td>\n",
       "      <td>W</td>\n",
       "      <td>6550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590536</th>\n",
       "      <td>3577536</td>\n",
       "      <td>0</td>\n",
       "      <td>15811049</td>\n",
       "      <td>39.50</td>\n",
       "      <td>W</td>\n",
       "      <td>10444</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590537</th>\n",
       "      <td>3577537</td>\n",
       "      <td>0</td>\n",
       "      <td>15811079</td>\n",
       "      <td>30.95</td>\n",
       "      <td>W</td>\n",
       "      <td>12037</td>\n",
       "      <td>595.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590538</th>\n",
       "      <td>3577538</td>\n",
       "      <td>0</td>\n",
       "      <td>15811088</td>\n",
       "      <td>117.00</td>\n",
       "      <td>W</td>\n",
       "      <td>7826</td>\n",
       "      <td>481.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590539</th>\n",
       "      <td>3577539</td>\n",
       "      <td>0</td>\n",
       "      <td>15811131</td>\n",
       "      <td>279.95</td>\n",
       "      <td>W</td>\n",
       "      <td>15066</td>\n",
       "      <td>170.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>582960 rows × 394 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  \\\n",
       "0             2987000        0          86400           68.50         W   \n",
       "1             2987001        0          86401           29.00         W   \n",
       "2             2987002        0          86469           59.00         W   \n",
       "3             2987003        0          86499           50.00         W   \n",
       "4             2987004        0          86506           50.00         H   \n",
       "...               ...      ...            ...             ...       ...   \n",
       "590535        3577535        0       15811047           49.00         W   \n",
       "590536        3577536        0       15811049           39.50         W   \n",
       "590537        3577537        0       15811079           30.95         W   \n",
       "590538        3577538        0       15811088          117.00         W   \n",
       "590539        3577539        0       15811131          279.95         W   \n",
       "\n",
       "        card1  card2  card3       card4  card5  ... V330  V331  V332  V333  \\\n",
       "0       13926    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   \n",
       "1        2755  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   \n",
       "2        4663  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   \n",
       "3       18132  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   \n",
       "4        4497  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   \n",
       "...       ...    ...    ...         ...    ...  ...  ...   ...   ...   ...   \n",
       "590535   6550    NaN  150.0        visa  226.0  ...  NaN   NaN   NaN   NaN   \n",
       "590536  10444  225.0  150.0  mastercard  224.0  ...  NaN   NaN   NaN   NaN   \n",
       "590537  12037  595.0  150.0  mastercard  224.0  ...  NaN   NaN   NaN   NaN   \n",
       "590538   7826  481.0  150.0  mastercard  224.0  ...  NaN   NaN   NaN   NaN   \n",
       "590539  15066  170.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   \n",
       "\n",
       "        V334 V335 V336  V337  V338  V339  \n",
       "0        NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "1        NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "2        NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "3        NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "4        0.0  0.0  0.0   0.0   0.0   0.0  \n",
       "...      ...  ...  ...   ...   ...   ...  \n",
       "590535   NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "590536   NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "590537   NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "590538   NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "590539   NaN  NaN  NaN   NaN   NaN   NaN  \n",
       "\n",
       "[582960 rows x 394 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Tiempo que demoró la transaccion ')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.dpi\":100,'figure.figsize':(12.8,9.6)})\n",
    "\n",
    "ax = sns.boxplot(x=\"card6\", y=\"TransactionAmt\",hue=\"isFraud\",data=transaction_short,palette='hls',orient='v')\n",
    "ax.set_title(\"Estadísticas sobre monto de las transacciones por caso\",fontdict={'fontsize':10})\n",
    "ax.set_xlabel(\"Tipo\")\n",
    "ax.set_ylabel(\"Monto la transaccion \")\n"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}