transaction_count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "block1 = pd.read_csv('blocks.csv',sep=',')\n",
    "block2 = pd.read_csv('blocks2.csv',sep=',')\n",
    "block3 = pd.read_csv('blocks3.csv',sep=',')\n",
    "block4 = pd.read_csv('blocks4.csv',sep=',')\n",
    "block5 = pd.read_csv('blocks5.csv',sep=',')\n",
    "block6 = pd.read_csv('blocks6.csv',sep=',')\n",
    "block7 = pd.read_csv('blocks7.csv',sep=',')\n",
    "block8 = pd.read_csv('blocks8.csv',sep=',')\n",
    "block9 = pd.read_csv('blocks9.csv',sep=',')\n",
    "block10 = pd.read_csv('blocks10.csv',sep=',')\n",
    "block11 = pd.read_csv('blocks11.csv',sep=',')\n",
    "block12 = pd.read_csv('blocks12.csv',sep=',')\n",
    "block13= pd.read_csv('blocks13.csv',sep=',')\n",
    "price = pd.read_csv('ether_price.csv',sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = block1[[\"timestamp\",\"transaction_count\"]]\n",
    "df2 = block2[[\"timestamp\",\"transaction_count\"]]\n",
    "df3 = block3[[\"timestamp\",\"transaction_count\"]]\n",
    "df4 = block4[[\"timestamp\",\"transaction_count\"]]\n",
    "df5 = block5[[\"timestamp\",\"transaction_count\"]]\n",
    "df6 = block6[[\"timestamp\",\"transaction_count\"]]\n",
    "df7 = block7[[\"timestamp\",\"transaction_count\"]]\n",
    "df8 = block8[[\"timestamp\",\"transaction_count\"]]\n",
    "df9 = block9[[\"timestamp\",\"transaction_count\"]]\n",
    "df10 = block10[[\"timestamp\",\"transaction_count\"]]\n",
    "df11 = block11[[\"timestamp\",\"transaction_count\"]]\n",
    "df12 = block12[[\"timestamp\",\"transaction_count\"]]\n",
    "df13 = block13[[\"timestamp\",\"transaction_count\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "/home/o0o0/Data_analysis_ethereum/env/lib/python3.5/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "df1[\"date\"] = pd.to_datetime(df1['timestamp'],unit='s')\n",
    "df2[\"date\"] = pd.to_datetime(df2['timestamp'],unit='s')\n",
    "df3[\"date\"] = pd.to_datetime(df3['timestamp'],unit='s')\n",
    "df4[\"date\"] = pd.to_datetime(df4['timestamp'],unit='s')\n",
    "df5[\"date\"] = pd.to_datetime(df5['timestamp'],unit='s')\n",
    "df6[\"date\"] = pd.to_datetime(df6['timestamp'],unit='s')\n",
    "df7[\"date\"] = pd.to_datetime(df7['timestamp'],unit='s')\n",
    "df8[\"date\"] = pd.to_datetime(df8['timestamp'],unit='s')\n",
    "df9[\"date\"] = pd.to_datetime(df9['timestamp'],unit='s')\n",
    "df10[\"date\"] = pd.to_datetime(df10['timestamp'],unit='s')\n",
    "df11[\"date\"] = pd.to_datetime(df11['timestamp'],unit='s')\n",
    "df12[\"date\"] = pd.to_datetime(df12['timestamp'],unit='s')\n",
    "df13[\"date\"] = pd.to_datetime(df13['timestamp'],unit='s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.sort_values([\"date\"],ascending=[True])\n",
    "df2 = df2.sort_values([\"date\"],ascending=[True])\n",
    "df3 = df3.sort_values([\"date\"],ascending=[True])\n",
    "df4 = df4.sort_values([\"date\"],ascending=[True])\n",
    "df5 = df5.sort_values([\"date\"],ascending=[True])\n",
    "df6 = df6.sort_values([\"date\"],ascending=[True])\n",
    "df7 = df7.sort_values([\"date\"],ascending=[True])\n",
    "df8 = df8.sort_values([\"date\"],ascending=[True])\n",
    "df9 = df9.sort_values([\"date\"],ascending=[True])\n",
    "df10 = df10.sort_values([\"date\"],ascending=[True])\n",
    "df11 = df11.sort_values([\"date\"],ascending=[True])\n",
    "df12 = df12.sort_values([\"date\"],ascending=[True])\n",
    "df13 = df13.sort_values([\"date\"],ascending=[True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1970-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1438269988</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1438270017</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>1438270048</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>1438270077</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp  transaction_count                date\n",
       "2300           0                  0 1970-01-01 00:00:00\n",
       "2301  1438269988                  0 2015-07-30 15:26:28\n",
       "2302  1438270017                  0 2015-07-30 15:26:57\n",
       "2303  1438270048                  0 2015-07-30 15:27:28\n",
       "2304  1438270077                  0 2015-07-30 15:27:57"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1.drop(df1.index[0])\n",
    "df2=df2.drop(df2.index[0])\n",
    "df3=df3.drop(df3.index[0])\n",
    "df4=df4.drop(df4.index[0])\n",
    "df5=df5.drop(df5.index[0])\n",
    "df6=df6.drop(df6.index[0])\n",
    "df7=df7.drop(df7.index[0])\n",
    "df8=df8.drop(df8.index[0])\n",
    "df9=df9.drop(df9.index[0])\n",
    "df10=df10.drop(df10.index[0])\n",
    "df11=df11.drop(df10.index[0])\n",
    "df12=df12.drop(df10.index[0])\n",
    "df13=df13.drop(df10.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'date'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3077\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3078\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'date'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-667c7f2def09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf5\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   3907\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3908\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3909\u001b[0;31m                 \u001b[0mlevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3910\u001b[0m                 \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3911\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2686\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2688\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2690\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2693\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2695\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2488\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2489\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   4113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4114\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4115\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4116\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3078\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'date'"
     ]
    }
   ],
   "source": [
    "df1=df1.set_index('date',inplace=False)\n",
    "df2=df2.set_index('date',inplace=False)\n",
    "df3=df3.set_index('date',inplace=False)\n",
    "df4=df4.set_index('date',inplace=False)\n",
    "df5=df5.set_index('date',inplace=False)\n",
    "df6=df6.set_index('date',inplace=False)\n",
    "df7=df7.set_index('date',inplace=False)\n",
    "df8=df8.set_index('date',inplace=False)\n",
    "df9=df9.set_index('date',inplace=False)\n",
    "df10=df10.set_index('date',inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1509953803</td>\n",
       "      <td>269</td>\n",
       "      <td>2017-11-06 07:36:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1509953856</td>\n",
       "      <td>37</td>\n",
       "      <td>2017-11-06 07:37:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>1509953864</td>\n",
       "      <td>113</td>\n",
       "      <td>2017-11-06 07:37:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1509953889</td>\n",
       "      <td>246</td>\n",
       "      <td>2017-11-06 07:38:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1509953924</td>\n",
       "      <td>166</td>\n",
       "      <td>2017-11-06 07:38:44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp  transaction_count                date\n",
       "301  1509953803                269 2017-11-06 07:36:43\n",
       "302  1509953856                 37 2017-11-06 07:37:36\n",
       "303  1509953864                113 2017-11-06 07:37:44\n",
       "304  1509953889                246 2017-11-06 07:38:09\n",
       "305  1509953924                166 2017-11-06 07:38:44"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df1.append(df2)\n",
    "df = df.append(df3)\n",
    "df = df.append(df4)\n",
    "df = df.append(df5)\n",
    "df = df.append(df6)\n",
    "df = df.append(df7)\n",
    "df = df.append(df8)\n",
    "df = df.append(df9)\n",
    "df = df.append(df10)\n",
    "df = df.append(df11)\n",
    "df = df.append(df12)\n",
    "df = df.append(df13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1438269988</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1438270017</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>1438270048</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>1438270077</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>1438270083</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:28:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp  transaction_count                date\n",
       "2301  1438269988                  0 2015-07-30 15:26:28\n",
       "2302  1438270017                  0 2015-07-30 15:26:57\n",
       "2303  1438270048                  0 2015-07-30 15:27:28\n",
       "2304  1438270077                  0 2015-07-30 15:27:57\n",
       "2305  1438270083                  0 2015-07-30 15:28:03"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6500000 entries, 2301 to 499800\n",
      "Data columns (total 3 columns):\n",
      "timestamp            int64\n",
      "transaction_count    int64\n",
      "date                 datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 198.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2016-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2017-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "Freq: A-DEC, Name: transaction_count, dtype: object"
      ]
     },
     "execution_count": 30,
     "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": [
    "df['transaction_count'].resample('A').plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 500001 entries, 2015-07-30 15:40:48 to 2015-11-06 16:04:34\n",
      "Data columns (total 2 columns):\n",
      "timestamp            500001 non-null int64\n",
      "transaction_count    500001 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 11.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1ba8e8f908>]"
      ]
     },
     "execution_count": 28,
     "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": [
    "plt.plot(pd.to_datetime(df['timestamp'],unit='s'),df['transaction_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values([\"date\"],ascending=[True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-09 20:52:47</th>\n",
       "      <td>1499633567</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09 20:52:51</th>\n",
       "      <td>1499633571</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09 20:52:54</th>\n",
       "      <td>1499633574</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09 20:53:01</th>\n",
       "      <td>1499633581</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09 20:53:06</th>\n",
       "      <td>1499633586</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp  transaction_count\n",
       "date                                              \n",
       "2017-07-09 20:52:47  1499633567                 69\n",
       "2017-07-09 20:52:51  1499633571                 16\n",
       "2017-07-09 20:52:54  1499633574                  9\n",
       "2017-07-09 20:53:01  1499633581                 21\n",
       "2017-07-09 20:53:06  1499633586                 14"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3077\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3078\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-ad11118bc8f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2686\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2688\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2690\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2693\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2695\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2488\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2489\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   4113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4114\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4115\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4116\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3078\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1970-01-01 00:00:00')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(df.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4765046 entries, 2301 to 265046\n",
      "Data columns (total 3 columns):\n",
      "timestamp            int64\n",
      "transaction_count    int64\n",
      "date                 datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 145.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.get_value of 2301     2015-07-30 15:26:28\n",
       "2302     2015-07-30 15:26:57\n",
       "2303     2015-07-30 15:27:28\n",
       "2304     2015-07-30 15:27:57\n",
       "2305     2015-07-30 15:28:03\n",
       "2306     2015-07-30 15:28:27\n",
       "2307     2015-07-30 15:28:30\n",
       "2308     2015-07-30 15:28:32\n",
       "2309     2015-07-30 15:28:35\n",
       "2310     2015-07-30 15:28:48\n",
       "2311     2015-07-30 15:28:56\n",
       "2312     2015-07-30 15:29:04\n",
       "2313     2015-07-30 15:29:18\n",
       "2314     2015-07-30 15:29:21\n",
       "2315     2015-07-30 15:29:28\n",
       "2316     2015-07-30 15:29:34\n",
       "2317     2015-07-30 15:29:38\n",
       "2318     2015-07-30 15:29:40\n",
       "2319     2015-07-30 15:29:47\n",
       "2320     2015-07-30 15:29:49\n",
       "2321     2015-07-30 15:29:53\n",
       "2322     2015-07-30 15:30:04\n",
       "2323     2015-07-30 15:30:08\n",
       "2324     2015-07-30 15:30:13\n",
       "2325     2015-07-30 15:30:14\n",
       "2326     2015-07-30 15:30:16\n",
       "2327     2015-07-30 15:30:23\n",
       "2328     2015-07-30 15:30:28\n",
       "2329     2015-07-30 15:30:39\n",
       "2330     2015-07-30 15:30:41\n",
       "                 ...        \n",
       "265017   2017-12-20 10:34:06\n",
       "265018   2017-12-20 10:34:22\n",
       "265019   2017-12-20 10:34:37\n",
       "265020   2017-12-20 10:34:59\n",
       "265021   2017-12-20 10:35:06\n",
       "265022   2017-12-20 10:35:10\n",
       "265023   2017-12-20 10:35:15\n",
       "265024   2017-12-20 10:35:57\n",
       "265025   2017-12-20 10:36:09\n",
       "265026   2017-12-20 10:36:12\n",
       "265027   2017-12-20 10:37:01\n",
       "265028   2017-12-20 10:37:12\n",
       "265029   2017-12-20 10:37:39\n",
       "265030   2017-12-20 10:37:54\n",
       "265031   2017-12-20 10:38:26\n",
       "265032   2017-12-20 10:38:45\n",
       "265033   2017-12-20 10:38:51\n",
       "265034   2017-12-20 10:38:56\n",
       "265035   2017-12-20 10:39:11\n",
       "265036   2017-12-20 10:39:43\n",
       "265037   2017-12-20 10:39:55\n",
       "265038   2017-12-20 10:40:03\n",
       "265039   2017-12-20 10:40:10\n",
       "265040   2017-12-20 10:40:45\n",
       "265041   2017-12-20 10:41:00\n",
       "265042   2017-12-20 10:41:08\n",
       "265043   2017-12-20 10:41:34\n",
       "265044   2017-12-20 10:41:46\n",
       "265045   2017-12-20 10:41:49\n",
       "265046   2017-12-20 10:42:05\n",
       "Name: date, Length: 4765046, dtype: datetime64[ns]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.get_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-30 15:26:28</th>\n",
       "      <td>1438269988</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30 15:26:57</th>\n",
       "      <td>1438270017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30 15:27:28</th>\n",
       "      <td>1438270048</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30 15:27:57</th>\n",
       "      <td>1438270077</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30 15:28:03</th>\n",
       "      <td>1438270083</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp  transaction_count\n",
       "date                                              \n",
       "2015-07-30 15:26:28  1438269988                  0\n",
       "2015-07-30 15:26:57  1438270017                  0\n",
       "2015-07-30 15:27:28  1438270048                  0\n",
       "2015-07-30 15:27:57  1438270077                  0\n",
       "2015-07-30 15:28:03  1438270083                  0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eacd59dd8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['transaction_count'].resample('M').mean().plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-07-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2015-08-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2015-09-30    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2015-10-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2015-11-30    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "Freq: M, Name: transaction_count, dtype: object"
      ]
     },
     "execution_count": 54,
     "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": [
    "df['transaction_count'].resample('M').plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3eb479ebe0>]"
      ]
     },
     "execution_count": 52,
     "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": [
    "plt.plot(pd.to_datetime(df['timestamp'],unit='s'),df['transaction_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3e940df160>,\n",
       " <matplotlib.lines.Line2D at 0x7f3e940f2128>]"
      ]
     },
     "execution_count": 53,
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 500000 entries, 2015-07-30 15:26:28 to 2015-11-06 18:01:05\n",
      "Data columns (total 2 columns):\n",
      "timestamp            500000 non-null int64\n",
      "transaction_count    500000 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 11.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2016-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "2017-12-31    AxesSubplot(0.125,0.2;0.775x0.68)\n",
       "Freq: A-DEC, Name: transaction_count, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['transaction_count'].resample('A').plot(kind='line',figsize=(20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of                       timestamp  transaction_count\n",
       "date                                              \n",
       "2015-07-30 15:26:57  1438270017                  0\n",
       "2015-07-30 15:27:28  1438270048                  0\n",
       "2015-07-30 15:27:57  1438270077                  0\n",
       "2015-07-30 15:28:03  1438270083                  0\n",
       "2015-07-30 15:28:27  1438270107                  0\n",
       "2015-07-30 15:28:30  1438270110                  0\n",
       "2015-07-30 15:28:32  1438270112                  0\n",
       "2015-07-30 15:28:35  1438270115                  0\n",
       "2015-07-30 15:28:48  1438270128                  0\n",
       "2015-07-30 15:28:56  1438270136                  0\n",
       "2015-07-30 15:29:04  1438270144                  0\n",
       "2015-07-30 15:29:18  1438270158                  0\n",
       "2015-07-30 15:29:21  1438270161                  0\n",
       "2015-07-30 15:29:28  1438270168                  0\n",
       "2015-07-30 15:29:34  1438270174                  0\n",
       "2015-07-30 15:29:38  1438270178                  0\n",
       "2015-07-30 15:29:40  1438270180                  0\n",
       "2015-07-30 15:29:47  1438270187                  0\n",
       "2015-07-30 15:29:49  1438270189                  0\n",
       "2015-07-30 15:29:53  1438270193                  0\n",
       "2015-07-30 15:30:04  1438270204                  0\n",
       "2015-07-30 15:30:08  1438270208                  0\n",
       "2015-07-30 15:30:13  1438270213                  0\n",
       "2015-07-30 15:30:14  1438270214                  0\n",
       "2015-07-30 15:30:16  1438270216                  0\n",
       "2015-07-30 15:30:23  1438270223                  0\n",
       "2015-07-30 15:30:28  1438270228                  0\n",
       "2015-07-30 15:30:39  1438270239                  0\n",
       "2015-07-30 15:30:41  1438270241                  0\n",
       "2015-07-30 15:30:42  1438270242                  0\n",
       "...                         ...                ...\n",
       "2017-12-10 23:23:59  1512948239                121\n",
       "2017-12-10 23:24:22  1512948262                198\n",
       "2017-12-10 23:24:51  1512948291                121\n",
       "2017-12-10 23:24:56  1512948296                107\n",
       "2017-12-10 23:24:59  1512948299                115\n",
       "2017-12-10 23:25:19  1512948319                 89\n",
       "2017-12-10 23:25:29  1512948329                 62\n",
       "2017-12-10 23:25:39  1512948339                 70\n",
       "2017-12-10 23:26:14  1512948374                115\n",
       "2017-12-10 23:26:35  1512948395                117\n",
       "2017-12-10 23:26:41  1512948401                 68\n",
       "2017-12-10 23:26:51  1512948411                 53\n",
       "2017-12-10 23:27:11  1512948431                 85\n",
       "2017-12-10 23:27:20  1512948440                158\n",
       "2017-12-10 23:27:24  1512948444                 30\n",
       "2017-12-10 23:27:28  1512948448                193\n",
       "2017-12-10 23:27:37  1512948457                135\n",
       "2017-12-10 23:27:42  1512948462                134\n",
       "2017-12-10 23:28:28  1512948508                147\n",
       "2017-12-10 23:28:33  1512948513                170\n",
       "2017-12-10 23:29:06  1512948546                157\n",
       "2017-12-10 23:29:15  1512948555                119\n",
       "2017-12-10 23:29:50  1512948590                150\n",
       "2017-12-10 23:30:02  1512948602                149\n",
       "2017-12-10 23:30:06  1512948606                155\n",
       "2017-12-10 23:30:13  1512948613                156\n",
       "2017-12-10 23:30:24  1512948624                117\n",
       "2017-12-10 23:30:30  1512948630                198\n",
       "2017-12-10 23:30:58  1512948658                151\n",
       "2017-12-10 23:31:03  1512948663                123\n",
       "\n",
       "[4710946 rows x 2 columns]>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"date2\"] = pd.to_datetime(df1['timestamp'],unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values([\"date\"],ascending=[True])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date</th>\n",
       "      <th>date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1438269988</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:28</td>\n",
       "      <td>2015-07-30 15:26:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1438270017</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:57</td>\n",
       "      <td>2015-07-30 15:26:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>1438270048</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:28</td>\n",
       "      <td>2015-07-30 15:27:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>1438270077</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:57</td>\n",
       "      <td>2015-07-30 15:27:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>1438270083</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:28:03</td>\n",
       "      <td>2015-07-30 15:28:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp  transaction_count                date               date2\n",
       "2301  1438269988                  0 2015-07-30 15:26:28 2015-07-30 15:26:28\n",
       "2302  1438270017                  0 2015-07-30 15:26:57 2015-07-30 15:26:57\n",
       "2303  1438270048                  0 2015-07-30 15:27:28 2015-07-30 15:27:28\n",
       "2304  1438270077                  0 2015-07-30 15:27:57 2015-07-30 15:27:57\n",
       "2305  1438270083                  0 2015-07-30 15:28:03 2015-07-30 15:28:03"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4765046 entries, 2301 to 265046\n",
      "Data columns (total 3 columns):\n",
      "timestamp            int64\n",
      "transaction_count    int64\n",
      "date                 datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 145.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date2']=df['date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date</th>\n",
       "      <th>date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>1438269988</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:28</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>1438270017</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:26:57</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>1438270048</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:28</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>1438270077</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:27:57</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>1438270083</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-07-30 15:28:03</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timestamp  transaction_count                date       date2\n",
       "2301  1438269988                  0 2015-07-30 15:26:28  2015-07-30\n",
       "2302  1438270017                  0 2015-07-30 15:26:57  2015-07-30\n",
       "2303  1438270048                  0 2015-07-30 15:27:28  2015-07-30\n",
       "2304  1438270077                  0 2015-07-30 15:27:57  2015-07-30\n",
       "2305  1438270083                  0 2015-07-30 15:28:03  2015-07-30"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-1ad35468eb4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'transaction_count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'A'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base, on, level)\u001b[0m\n\u001b[1;32m   7108\u001b[0m                      \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7109\u001b[0m                      \u001b[0mconvention\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvention\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7110\u001b[0;31m                      base=base, key=on, level=level)\n\u001b[0m\u001b[1;32m   7111\u001b[0m         return _maybe_process_deprecations(r,\n\u001b[1;32m   7112\u001b[0m                                            \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(obj, kind, **kwds)\u001b[0m\n\u001b[1;32m   1146\u001b[0m     \u001b[0;34m\"\"\" create a TimeGrouper and return our resampler \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \u001b[0mtg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Data_analysis_ethereum/env/lib/python3.5/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m   1274\u001b[0m         raise TypeError(\"Only valid with DatetimeIndex, \"\n\u001b[1;32m   1275\u001b[0m                         \u001b[0;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1276\u001b[0;31m                         \"but got an instance of %r\" % type(ax).__name__)\n\u001b[0m\u001b[1;32m   1277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df[[\"date2\",\"transaction_count\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date2</th>\n",
       "      <th>transaction_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>2015-07-30</td>\n",
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       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date2  transaction_count\n",
       "2301  2015-07-30                  0\n",
       "2302  2015-07-30                  0\n",
       "2303  2015-07-30                  0\n",
       "2304  2015-07-30                  0\n",
       "2305  2015-07-30                  0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregation_functions = {'transaction_count': 'sum', 'date2': 'first'}\n",
    "df_new2=df_new.groupby(df['date2']).aggregate(aggregation_functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_count</th>\n",
       "      <th>date2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date2</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-16</th>\n",
       "      <td>899857</td>\n",
       "      <td>2017-12-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-17</th>\n",
       "      <td>876574</td>\n",
       "      <td>2017-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>984021</td>\n",
       "      <td>2017-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>1092234</td>\n",
       "      <td>2017-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>429065</td>\n",
       "      <td>2017-12-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            transaction_count       date2\n",
       "date2                                    \n",
       "2017-12-16             899857  2017-12-16\n",
       "2017-12-17             876574  2017-12-17\n",
       "2017-12-18             984021  2017-12-18\n",
       "2017-12-19            1092234  2017-12-19\n",
       "2017-12-20             429065  2017-12-20"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb6f07b2ac8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(df_new2['date2'],df_new2['transaction_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Date(UTC)</th>\n",
       "      <th>UnixTimeStamp</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7/30/2015</td>\n",
       "      <td>1438214400</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/31/2015</td>\n",
       "      <td>1438300800</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8/1/2015</td>\n",
       "      <td>1438387200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/2/2015</td>\n",
       "      <td>1438473600</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/3/2015</td>\n",
       "      <td>1438560000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date(UTC)  UnixTimeStamp  Value\n",
       "0  7/30/2015     1438214400    0.0\n",
       "1  7/31/2015     1438300800    0.0\n",
       "2   8/1/2015     1438387200    0.0\n",
       "3   8/2/2015     1438473600    0.0\n",
       "4   8/3/2015     1438560000    0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "price[\"date\"] = pd.to_datetime(price['UnixTimeStamp'],unit='s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "price['date2']=price['date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date(UTC)</th>\n",
       "      <th>UnixTimeStamp</th>\n",
       "      <th>Value</th>\n",
       "      <th>date</th>\n",
       "      <th>date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7/30/2015</td>\n",
       "      <td>1438214400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>2015-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/31/2015</td>\n",
       "      <td>1438300800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>2015-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8/1/2015</td>\n",
       "      <td>1438387200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/2/2015</td>\n",
       "      <td>1438473600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>2015-08-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/3/2015</td>\n",
       "      <td>1438560000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>2015-08-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date(UTC)  UnixTimeStamp  Value       date       date2\n",
       "0  7/30/2015     1438214400    0.0 2015-07-30  2015-07-30\n",
       "1  7/31/2015     1438300800    0.0 2015-07-31  2015-07-31\n",
       "2   8/1/2015     1438387200    0.0 2015-08-01  2015-08-01\n",
       "3   8/2/2015     1438473600    0.0 2015-08-02  2015-08-02\n",
       "4   8/3/2015     1438560000    0.0 2015-08-03  2015-08-03"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8faa0d37b8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"transaction and price\", fontsize=16, fontweight='bold')\n",
    "plt.xlabel(\"date\")\n",
    "\n",
    "plt.plot(df_new2['date2'],df_new2['transaction_count'])\n",
    "plt.plot(price['date2'],price['Value'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8f9e92d6a0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(df_new2['date2'], df_new2['transaction_count'])\n",
    "ax1.set_ylabel('count')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(price['date2'], price['Value'], 'r-')\n",
    "ax2.set_ylabel('price $', color='r')\n",
    "for tl in ax2.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "plt.legend()"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}