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EsmailELBoBDev2/beat_the_street.ipynb

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beat_the_street.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Libraries imported.\n"
]
}
],
"source": [
"# Let's first import our libraries!\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from pandas.io.json import json_normalize\n",
"\n",
"\n",
"import matplotlib.pyplot as plt \n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"%matplotlib inline \n",
"\n",
"from sklearn.cluster import KMeans \n",
"from sklearn.datasets.samples_generator import make_blobs\n",
"\n",
"print('Libraries imported.')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Let's import our datasets\n",
"dfBar = pd.read_csv(\"bar.csv\")\n",
"dfEvent = pd.read_csv('event.csv')\n",
"dfNews = pd.read_csv('news.csv')\n",
"dfQuote = pd.read_csv('quote.csv')\n",
"dfRating = pd.read_csv('rating.csv')\n",
"dfTarget = pd.read_csv('target.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Let's make sure that its 30 rows\n",
"dfBar = dfBar.head(30)\n",
"dfEvent = dfEvent.head(30)\n",
"dfNews = dfNews.head(30)\n",
"dfQuote = dfQuote.head(30)\n",
"dfRating = dfRating.head(30)\n",
"dfTarget = dfTarget.head(30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Let's explore each "
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>time</th>\n",
" <th>symbol</th>\n",
" <th>volume</th>\n",
" <th>accumulated_volume</th>\n",
" <th>VWAP</th>\n",
" <th>open_price</th>\n",
" <th>high_price</th>\n",
" <th>low_price</th>\n",
" <th>close_price</th>\n",
" <th>average_price</th>\n",
" <th>epoch_time_at_the_beginning</th>\n",
" <th>epoch_time_at_the_ending</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-09-02 22:46:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5477495</td>\n",
" <td>131.5890</td>\n",
" <td>131.589</td>\n",
" <td>131.589</td>\n",
" <td>131.589</td>\n",
" <td>131.589</td>\n",
" <td>131.5506</td>\n",
" <td>1.599087e+12</td>\n",
" <td>1.599087e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-09-02 22:45:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5477395</td>\n",
" <td>131.5190</td>\n",
" <td>131.519</td>\n",
" <td>131.519</td>\n",
" <td>131.519</td>\n",
" <td>131.519</td>\n",
" <td>131.5506</td>\n",
" <td>1.599087e+12</td>\n",
" <td>1.599087e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-09-02 22:43:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5477295</td>\n",
" <td>131.5590</td>\n",
" <td>131.559</td>\n",
" <td>131.559</td>\n",
" <td>131.559</td>\n",
" <td>131.559</td>\n",
" <td>131.5506</td>\n",
" <td>1.599087e+12</td>\n",
" <td>1.599087e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-09-02 21:15:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5477167</td>\n",
" <td>131.5440</td>\n",
" <td>131.544</td>\n",
" <td>131.544</td>\n",
" <td>131.544</td>\n",
" <td>131.544</td>\n",
" <td>131.5506</td>\n",
" <td>1.599081e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-09-02 21:12:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5477067</td>\n",
" <td>131.4390</td>\n",
" <td>131.439</td>\n",
" <td>131.439</td>\n",
" <td>131.439</td>\n",
" <td>131.439</td>\n",
" <td>131.5506</td>\n",
" <td>1.599081e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2020-09-02 21:08:00+00:00</td>\n",
" <td>FANG</td>\n",
" <td>100</td>\n",
" <td>202172</td>\n",
" <td>36.6400</td>\n",
" <td>36.640</td>\n",
" <td>36.640</td>\n",
" <td>36.640</td>\n",
" <td>36.640</td>\n",
" <td>37.1381</td>\n",
" <td>1.599081e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2020-09-02 21:06:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5476967</td>\n",
" <td>131.3510</td>\n",
" <td>131.351</td>\n",
" <td>131.351</td>\n",
" <td>131.351</td>\n",
" <td>131.351</td>\n",
" <td>131.5506</td>\n",
" <td>1.599081e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2020-09-02 21:05:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>199</td>\n",
" <td>5476867</td>\n",
" <td>131.4140</td>\n",
" <td>131.414</td>\n",
" <td>131.414</td>\n",
" <td>131.414</td>\n",
" <td>131.414</td>\n",
" <td>131.5506</td>\n",
" <td>1.599081e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2020-09-02 21:01:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>300</td>\n",
" <td>5476668</td>\n",
" <td>131.4030</td>\n",
" <td>131.404</td>\n",
" <td>131.404</td>\n",
" <td>131.401</td>\n",
" <td>131.401</td>\n",
" <td>131.5506</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599081e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2020-09-02 21:00:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>200</td>\n",
" <td>5476368</td>\n",
" <td>131.3740</td>\n",
" <td>131.374</td>\n",
" <td>131.374</td>\n",
" <td>131.374</td>\n",
" <td>131.374</td>\n",
" <td>131.5506</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2020-09-02 20:59:00+00:00</td>\n",
" <td>BBY</td>\n",
" <td>200</td>\n",
" <td>364484</td>\n",
" <td>113.8300</td>\n",
" <td>113.830</td>\n",
" <td>113.830</td>\n",
" <td>113.830</td>\n",
" <td>113.830</td>\n",
" <td>113.3237</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2020-09-02 20:58:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>396</td>\n",
" <td>13364445</td>\n",
" <td>6.4000</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.3711</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2020-09-02 20:56:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>360</td>\n",
" <td>13364049</td>\n",
" <td>6.4000</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.3711</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2020-09-02 20:53:00+00:00</td>\n",
" <td>COG</td>\n",
" <td>278</td>\n",
" <td>721180</td>\n",
" <td>18.8300</td>\n",
" <td>18.830</td>\n",
" <td>18.830</td>\n",
" <td>18.830</td>\n",
" <td>18.830</td>\n",
" <td>18.3008</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2020-09-02 20:52:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>1262</td>\n",
" <td>13363519</td>\n",
" <td>6.3900</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.3711</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2020-09-02 20:51:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>100</td>\n",
" <td>5476109</td>\n",
" <td>131.4500</td>\n",
" <td>131.450</td>\n",
" <td>131.450</td>\n",
" <td>131.450</td>\n",
" <td>131.450</td>\n",
" <td>131.5506</td>\n",
" <td>1.599080e+12</td>\n",
" <td>1.599080e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2020-09-02 20:42:00+00:00</td>\n",
" <td>PVH</td>\n",
" <td>400</td>\n",
" <td>272904</td>\n",
" <td>61.9500</td>\n",
" <td>61.950</td>\n",
" <td>61.950</td>\n",
" <td>61.950</td>\n",
" <td>61.950</td>\n",
" <td>59.3275</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2020-09-02 20:41:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>3596</td>\n",
" <td>13362089</td>\n",
" <td>6.3900</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.3711</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2020-09-02 20:38:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>400</td>\n",
" <td>5475922</td>\n",
" <td>131.5395</td>\n",
" <td>131.524</td>\n",
" <td>131.586</td>\n",
" <td>131.524</td>\n",
" <td>131.524</td>\n",
" <td>131.5506</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2020-09-02 20:36:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>204</td>\n",
" <td>13358493</td>\n",
" <td>6.3900</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.3711</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2020-09-02 20:33:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>2878</td>\n",
" <td>13358289</td>\n",
" <td>6.3900</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.390</td>\n",
" <td>6.3711</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2020-09-02 20:32:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>376</td>\n",
" <td>5475451</td>\n",
" <td>131.6427</td>\n",
" <td>131.624</td>\n",
" <td>131.660</td>\n",
" <td>131.624</td>\n",
" <td>131.660</td>\n",
" <td>131.5506</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2020-09-02 20:31:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>412</td>\n",
" <td>5475075</td>\n",
" <td>131.6773</td>\n",
" <td>131.690</td>\n",
" <td>131.695</td>\n",
" <td>131.630</td>\n",
" <td>131.630</td>\n",
" <td>131.5506</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2020-09-02 20:31:00+00:00</td>\n",
" <td>PVH</td>\n",
" <td>212</td>\n",
" <td>272334</td>\n",
" <td>63.3547</td>\n",
" <td>63.370</td>\n",
" <td>63.370</td>\n",
" <td>63.370</td>\n",
" <td>63.370</td>\n",
" <td>59.3211</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>GE</td>\n",
" <td>200</td>\n",
" <td>13355289</td>\n",
" <td>6.4000</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.400</td>\n",
" <td>6.3711</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>PVH</td>\n",
" <td>600</td>\n",
" <td>272122</td>\n",
" <td>62.8807</td>\n",
" <td>62.880</td>\n",
" <td>62.881</td>\n",
" <td>62.880</td>\n",
" <td>62.881</td>\n",
" <td>59.3179</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>CDNS</td>\n",
" <td>206</td>\n",
" <td>101085</td>\n",
" <td>115.1110</td>\n",
" <td>115.111</td>\n",
" <td>115.111</td>\n",
" <td>115.111</td>\n",
" <td>115.111</td>\n",
" <td>115.8448</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>209</td>\n",
" <td>5474663</td>\n",
" <td>131.6797</td>\n",
" <td>131.740</td>\n",
" <td>131.740</td>\n",
" <td>131.620</td>\n",
" <td>131.620</td>\n",
" <td>131.5506</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2020-09-02 20:29:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>200</td>\n",
" <td>5474454</td>\n",
" <td>131.7600</td>\n",
" <td>131.780</td>\n",
" <td>131.780</td>\n",
" <td>131.740</td>\n",
" <td>131.740</td>\n",
" <td>131.5506</td>\n",
" <td>1.599079e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2020-09-02 20:28:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>214</td>\n",
" <td>5474254</td>\n",
" <td>131.7687</td>\n",
" <td>131.770</td>\n",
" <td>131.770</td>\n",
" <td>131.770</td>\n",
" <td>131.770</td>\n",
" <td>131.5506</td>\n",
" <td>1.599078e+12</td>\n",
" <td>1.599079e+12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time symbol volume accumulated_volume VWAP \\\n",
"0 2020-09-02 22:46:00+00:00 AAPL 100 5477495 131.5890 \n",
"1 2020-09-02 22:45:00+00:00 AAPL 100 5477395 131.5190 \n",
"2 2020-09-02 22:43:00+00:00 AAPL 100 5477295 131.5590 \n",
"3 2020-09-02 21:15:00+00:00 AAPL 100 5477167 131.5440 \n",
"4 2020-09-02 21:12:00+00:00 AAPL 100 5477067 131.4390 \n",
"5 2020-09-02 21:08:00+00:00 FANG 100 202172 36.6400 \n",
"6 2020-09-02 21:06:00+00:00 AAPL 100 5476967 131.3510 \n",
"7 2020-09-02 21:05:00+00:00 AAPL 199 5476867 131.4140 \n",
"8 2020-09-02 21:01:00+00:00 AAPL 300 5476668 131.4030 \n",
"9 2020-09-02 21:00:00+00:00 AAPL 200 5476368 131.3740 \n",
"10 2020-09-02 20:59:00+00:00 BBY 200 364484 113.8300 \n",
"11 2020-09-02 20:58:00+00:00 GE 396 13364445 6.4000 \n",
"12 2020-09-02 20:56:00+00:00 GE 360 13364049 6.4000 \n",
"13 2020-09-02 20:53:00+00:00 COG 278 721180 18.8300 \n",
"14 2020-09-02 20:52:00+00:00 GE 1262 13363519 6.3900 \n",
"15 2020-09-02 20:51:00+00:00 AAPL 100 5476109 131.4500 \n",
"16 2020-09-02 20:42:00+00:00 PVH 400 272904 61.9500 \n",
"17 2020-09-02 20:41:00+00:00 GE 3596 13362089 6.3900 \n",
"18 2020-09-02 20:38:00+00:00 AAPL 400 5475922 131.5395 \n",
"19 2020-09-02 20:36:00+00:00 GE 204 13358493 6.3900 \n",
"20 2020-09-02 20:33:00+00:00 GE 2878 13358289 6.3900 \n",
"21 2020-09-02 20:32:00+00:00 AAPL 376 5475451 131.6427 \n",
"22 2020-09-02 20:31:00+00:00 AAPL 412 5475075 131.6773 \n",
"23 2020-09-02 20:31:00+00:00 PVH 212 272334 63.3547 \n",
"24 2020-09-02 20:30:00+00:00 GE 200 13355289 6.4000 \n",
"25 2020-09-02 20:30:00+00:00 PVH 600 272122 62.8807 \n",
"26 2020-09-02 20:30:00+00:00 CDNS 206 101085 115.1110 \n",
"27 2020-09-02 20:30:00+00:00 AAPL 209 5474663 131.6797 \n",
"28 2020-09-02 20:29:00+00:00 AAPL 200 5474454 131.7600 \n",
"29 2020-09-02 20:28:00+00:00 AAPL 214 5474254 131.7687 \n",
"\n",
" open_price high_price low_price close_price average_price \\\n",
"0 131.589 131.589 131.589 131.589 131.5506 \n",
"1 131.519 131.519 131.519 131.519 131.5506 \n",
"2 131.559 131.559 131.559 131.559 131.5506 \n",
"3 131.544 131.544 131.544 131.544 131.5506 \n",
"4 131.439 131.439 131.439 131.439 131.5506 \n",
"5 36.640 36.640 36.640 36.640 37.1381 \n",
"6 131.351 131.351 131.351 131.351 131.5506 \n",
"7 131.414 131.414 131.414 131.414 131.5506 \n",
"8 131.404 131.404 131.401 131.401 131.5506 \n",
"9 131.374 131.374 131.374 131.374 131.5506 \n",
"10 113.830 113.830 113.830 113.830 113.3237 \n",
"11 6.400 6.400 6.400 6.400 6.3711 \n",
"12 6.400 6.400 6.400 6.400 6.3711 \n",
"13 18.830 18.830 18.830 18.830 18.3008 \n",
"14 6.390 6.390 6.390 6.390 6.3711 \n",
"15 131.450 131.450 131.450 131.450 131.5506 \n",
"16 61.950 61.950 61.950 61.950 59.3275 \n",
"17 6.390 6.390 6.390 6.390 6.3711 \n",
"18 131.524 131.586 131.524 131.524 131.5506 \n",
"19 6.390 6.390 6.390 6.390 6.3711 \n",
"20 6.390 6.390 6.390 6.390 6.3711 \n",
"21 131.624 131.660 131.624 131.660 131.5506 \n",
"22 131.690 131.695 131.630 131.630 131.5506 \n",
"23 63.370 63.370 63.370 63.370 59.3211 \n",
"24 6.400 6.400 6.400 6.400 6.3711 \n",
"25 62.880 62.881 62.880 62.881 59.3179 \n",
"26 115.111 115.111 115.111 115.111 115.8448 \n",
"27 131.740 131.740 131.620 131.620 131.5506 \n",
"28 131.780 131.780 131.740 131.740 131.5506 \n",
"29 131.770 131.770 131.770 131.770 131.5506 \n",
"\n",
" epoch_time_at_the_beginning epoch_time_at_the_ending \n",
"0 1.599087e+12 1.599087e+12 \n",
"1 1.599087e+12 1.599087e+12 \n",
"2 1.599087e+12 1.599087e+12 \n",
"3 1.599081e+12 1.599081e+12 \n",
"4 1.599081e+12 1.599081e+12 \n",
"5 1.599081e+12 1.599081e+12 \n",
"6 1.599081e+12 1.599081e+12 \n",
"7 1.599081e+12 1.599081e+12 \n",
"8 1.599080e+12 1.599081e+12 \n",
"9 1.599080e+12 1.599080e+12 \n",
"10 1.599080e+12 1.599080e+12 \n",
"11 1.599080e+12 1.599080e+12 \n",
"12 1.599080e+12 1.599080e+12 \n",
"13 1.599080e+12 1.599080e+12 \n",
"14 1.599080e+12 1.599080e+12 \n",
"15 1.599080e+12 1.599080e+12 \n",
"16 1.599079e+12 1.599079e+12 \n",
"17 1.599079e+12 1.599079e+12 \n",
"18 1.599079e+12 1.599079e+12 \n",
"19 1.599079e+12 1.599079e+12 \n",
"20 1.599079e+12 1.599079e+12 \n",
"21 1.599079e+12 1.599079e+12 \n",
"22 1.599079e+12 1.599079e+12 \n",
"23 1.599079e+12 1.599079e+12 \n",
"24 1.599079e+12 1.599079e+12 \n",
"25 1.599079e+12 1.599079e+12 \n",
"26 1.599079e+12 1.599079e+12 \n",
"27 1.599079e+12 1.599079e+12 \n",
"28 1.599079e+12 1.599079e+12 \n",
"29 1.599078e+12 1.599079e+12 "
]
},
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" <td>Aug 31 18:06:44 2020</td>\n",
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" <td>Aug 31 18:06:44 2020</td>\n",
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" <th>2</th>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>Aug 31 18:06:44 2020</td>\n",
" <td>CTLT</td>\n",
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" <td>Aug 31 18:06:44 2020</td>\n",
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" system_time symbol reportDate\n",
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"1 Aug 31 18:06:44 2020 CUE 2020-08-31\n",
"2 Aug 31 18:06:44 2020 SCSC 2020-08-31\n",
"3 Aug 31 18:06:44 2020 CTLT 2020-08-31\n",
"4 Aug 31 18:06:44 2020 KRKR 2020-08-31"
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" <td>2020-09-03 03:24:00+00:00</td>\n",
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" <td>2020-09-03</td>\n",
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" <th>2</th>\n",
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" <td>2020-09-03</td>\n",
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" <td>2020-09-03 02:47:00+00:00</td>\n",
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" <td>2020-09-03</td>\n",
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" <th>4</th>\n",
" <td>2020-09-03 02:45:00+00:00</td>\n",
" <td>FB</td>\n",
" <td>The Daily Beast : Sources: Mark Zuckerberg sai...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2020-09-03 02:45:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>Apple And Google Update Contact Tracing Softwa...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2020-09-03 02:35:00+00:00</td>\n",
" <td>TWTR</td>\n",
" <td>Twitter said it was aware of the activity with...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2020-09-03 02:23:15+00:00</td>\n",
" <td>AMZN</td>\n",
" <td>Cyber Security NSW said Amazon (pictured) 'won...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2020-09-03 02:19:15+00:00</td>\n",
" <td>NDAQ</td>\n",
" <td>US Markets blast overnight, Dow up 454 Points,...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2020-09-03 02:15:17+00:00</td>\n",
" <td>FB</td>\n",
" <td>Sarah Perez / TechCrunch : Facebook's introduc...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2020-09-03 02:11:08+00:00</td>\n",
" <td>AMZN</td>\n",
" <td>MacKenzie Scott — philanthropist, author and e...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2020-09-03 02:01:06+00:00</td>\n",
" <td>WFC</td>\n",
" <td>Rocket Companies, Inc. (NYSE:RKT) Q2 2020 Earn...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2020-09-03 02:00:00+00:00</td>\n",
" <td>V</td>\n",
" <td>Le SUV 7 places de Peugeot profite des mêmes é...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2020-09-03 01:51:14+00:00</td>\n",
" <td>FB</td>\n",
" <td>Facebook removed a post by Rep. Clay Higgins, ...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2020-09-03 01:41:33+00:00</td>\n",
" <td>JPM</td>\n",
" <td>There's three constraints weighing on the pote...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2020-09-03 01:39:13+00:00</td>\n",
" <td>FB</td>\n",
" <td>Summary List Placement PayPal has terminated a...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2020-09-03 01:30:10+00:00</td>\n",
" <td>XOM</td>\n",
" <td>Since my capital is limited, it sometimes mean...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2020-09-03 01:25:23+00:00</td>\n",
" <td>FB</td>\n",
" <td>Summary List Placement A recent internal debat...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2020-09-03 01:24:14+00:00</td>\n",
" <td>C</td>\n",
" <td>China maintains pace in the opening of its fin...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2020-09-03 01:18:11+00:00</td>\n",
" <td>GOOG</td>\n",
" <td>The plan includes up to 1,850 residential homes.</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2020-09-03 01:17:00+00:00</td>\n",
" <td>TWTR</td>\n",
" <td>The incident comes after several Twitter accou...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2020-09-03 01:03:50+00:00</td>\n",
" <td>VZ</td>\n",
" <td>The stake-sale talks were paused because the o...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2020-09-03 00:48:42+00:00</td>\n",
" <td>EQIX</td>\n",
" <td>Asia Pacific’s fast-developing market for serv...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2020-09-03 00:48:35+00:00</td>\n",
" <td>DISCA</td>\n",
" <td>The “Star Trek” umbrella is becoming even more...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2020-09-03 00:47:00+00:00</td>\n",
" <td>TWTR</td>\n",
" <td>Read more about Twitter confirms account of PM...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2020-09-03 00:36:00+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>Every day, Macworld brings you the essential d...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2020-09-03 00:30:12+00:00</td>\n",
" <td>GM</td>\n",
" <td>Ivanka, 38, met with General Motors CEO Mary B...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2020-09-03 00:23:00+00:00</td>\n",
" <td>DIS</td>\n",
" <td>According to an announcement on the Star Wars ...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2020-09-03 00:17:51+00:00</td>\n",
" <td>FB</td>\n",
" <td>Summary List Placement Google has resumed empl...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2020-09-03 00:17:36+00:00</td>\n",
" <td>KR</td>\n",
" <td>Kroger is set to report fiscal second quarter ...</td>\n",
" <td>2020-09-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime stock \\\n",
"0 2020-09-03 03:24:00+00:00 FB \n",
"1 2020-09-03 03:07:51+00:00 VZ \n",
"2 2020-09-03 03:00:00+00:00 AMZN \n",
"3 2020-09-03 02:47:00+00:00 MCK \n",
"4 2020-09-03 02:45:00+00:00 FB \n",
"5 2020-09-03 02:45:00+00:00 AAPL \n",
"6 2020-09-03 02:35:00+00:00 TWTR \n",
"7 2020-09-03 02:23:15+00:00 AMZN \n",
"8 2020-09-03 02:19:15+00:00 NDAQ \n",
"9 2020-09-03 02:15:17+00:00 FB \n",
"10 2020-09-03 02:11:08+00:00 AMZN \n",
"11 2020-09-03 02:01:06+00:00 WFC \n",
"12 2020-09-03 02:00:00+00:00 V \n",
"13 2020-09-03 01:51:14+00:00 FB \n",
"14 2020-09-03 01:41:33+00:00 JPM \n",
"15 2020-09-03 01:39:13+00:00 FB \n",
"16 2020-09-03 01:30:10+00:00 XOM \n",
"17 2020-09-03 01:25:23+00:00 FB \n",
"18 2020-09-03 01:24:14+00:00 C \n",
"19 2020-09-03 01:18:11+00:00 GOOG \n",
"20 2020-09-03 01:17:00+00:00 TWTR \n",
"21 2020-09-03 01:03:50+00:00 VZ \n",
"22 2020-09-03 00:48:42+00:00 EQIX \n",
"23 2020-09-03 00:48:35+00:00 DISCA \n",
"24 2020-09-03 00:47:00+00:00 TWTR \n",
"25 2020-09-03 00:36:00+00:00 AAPL \n",
"26 2020-09-03 00:30:12+00:00 GM \n",
"27 2020-09-03 00:23:00+00:00 DIS \n",
"28 2020-09-03 00:17:51+00:00 FB \n",
"29 2020-09-03 00:17:36+00:00 KR \n",
"\n",
" summary date \n",
"0 The sources claim that discussions with Facebo... 2020-09-03 \n",
"1 Firms in talks to buy a stake in struggling te... 2020-09-03 \n",
"2 Die Plattform eBay wird 25: Gebrauchtes findet... 2020-09-03 \n",
"3 US News: Dallas-based wholesaler McKesson Corp... 2020-09-03 \n",
"4 The Daily Beast : Sources: Mark Zuckerberg sai... 2020-09-03 \n",
"5 Apple And Google Update Contact Tracing Softwa... 2020-09-03 \n",
"6 Twitter said it was aware of the activity with... 2020-09-03 \n",
"7 Cyber Security NSW said Amazon (pictured) 'won... 2020-09-03 \n",
"8 US Markets blast overnight, Dow up 454 Points,... 2020-09-03 \n",
"9 Sarah Perez / TechCrunch : Facebook's introduc... 2020-09-03 \n",
"10 MacKenzie Scott — philanthropist, author and e... 2020-09-03 \n",
"11 Rocket Companies, Inc. (NYSE:RKT) Q2 2020 Earn... 2020-09-03 \n",
"12 Le SUV 7 places de Peugeot profite des mêmes é... 2020-09-03 \n",
"13 Facebook removed a post by Rep. Clay Higgins, ... 2020-09-03 \n",
"14 There's three constraints weighing on the pote... 2020-09-03 \n",
"15 Summary List Placement PayPal has terminated a... 2020-09-03 \n",
"16 Since my capital is limited, it sometimes mean... 2020-09-03 \n",
"17 Summary List Placement A recent internal debat... 2020-09-03 \n",
"18 China maintains pace in the opening of its fin... 2020-09-03 \n",
"19 The plan includes up to 1,850 residential homes. 2020-09-03 \n",
"20 The incident comes after several Twitter accou... 2020-09-03 \n",
"21 The stake-sale talks were paused because the o... 2020-09-03 \n",
"22 Asia Pacific’s fast-developing market for serv... 2020-09-03 \n",
"23 The “Star Trek” umbrella is becoming even more... 2020-09-03 \n",
"24 Read more about Twitter confirms account of PM... 2020-09-03 \n",
"25 Every day, Macworld brings you the essential d... 2020-09-03 \n",
"26 Ivanka, 38, met with General Motors CEO Mary B... 2020-09-03 \n",
"27 According to an announcement on the Star Wars ... 2020-09-03 \n",
"28 Summary List Placement Google has resumed empl... 2020-09-03 \n",
"29 Kroger is set to report fiscal second quarter ... 2020-09-03 "
]
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" <th>ask_price</th>\n",
" <th>ask_size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AKAM</td>\n",
" <td>112.89</td>\n",
" <td>1</td>\n",
" <td>113.05</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AIZ</td>\n",
" <td>107.26</td>\n",
" <td>1</td>\n",
" <td>113.65</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AES</td>\n",
" <td>15.11</td>\n",
" <td>1</td>\n",
" <td>15.13</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADP</td>\n",
" <td>134.37</td>\n",
" <td>2</td>\n",
" <td>134.72</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>435.79</td>\n",
" <td>1</td>\n",
" <td>439.84</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AKAM</td>\n",
" <td>112.69</td>\n",
" <td>8</td>\n",
" <td>113.05</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AME</td>\n",
" <td>94.90</td>\n",
" <td>1</td>\n",
" <td>95.08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ALLE</td>\n",
" <td>100.01</td>\n",
" <td>8</td>\n",
" <td>100.59</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADI</td>\n",
" <td>116.47</td>\n",
" <td>2</td>\n",
" <td>116.62</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AEP</td>\n",
" <td>85.74</td>\n",
" <td>1</td>\n",
" <td>85.80</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ALLE</td>\n",
" <td>100.01</td>\n",
" <td>8</td>\n",
" <td>100.57</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AKAM</td>\n",
" <td>112.89</td>\n",
" <td>1</td>\n",
" <td>113.03</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>LNT</td>\n",
" <td>53.50</td>\n",
" <td>2</td>\n",
" <td>55.86</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>APH</td>\n",
" <td>107.36</td>\n",
" <td>1</td>\n",
" <td>107.48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AEE</td>\n",
" <td>79.27</td>\n",
" <td>1</td>\n",
" <td>79.37</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADBE</td>\n",
" <td>447.21</td>\n",
" <td>1</td>\n",
" <td>448.32</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADM</td>\n",
" <td>42.90</td>\n",
" <td>2</td>\n",
" <td>42.94</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADP</td>\n",
" <td>134.37</td>\n",
" <td>1</td>\n",
" <td>134.72</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADBE</td>\n",
" <td>447.85</td>\n",
" <td>1</td>\n",
" <td>448.25</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADP</td>\n",
" <td>134.33</td>\n",
" <td>1</td>\n",
" <td>134.42</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADM</td>\n",
" <td>42.91</td>\n",
" <td>3</td>\n",
" <td>42.96</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AAPL</td>\n",
" <td>435.41</td>\n",
" <td>1</td>\n",
" <td>439.84</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADM</td>\n",
" <td>42.93</td>\n",
" <td>1</td>\n",
" <td>42.95</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADBE</td>\n",
" <td>447.85</td>\n",
" <td>2</td>\n",
" <td>448.11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AKAM</td>\n",
" <td>112.69</td>\n",
" <td>8</td>\n",
" <td>118.30</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AKAM</td>\n",
" <td>112.91</td>\n",
" <td>1</td>\n",
" <td>112.98</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AMAT</td>\n",
" <td>64.93</td>\n",
" <td>4</td>\n",
" <td>65.05</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ADSK</td>\n",
" <td>239.25</td>\n",
" <td>1</td>\n",
" <td>239.28</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>AMT</td>\n",
" <td>256.39</td>\n",
" <td>9</td>\n",
" <td>256.95</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>ALGN</td>\n",
" <td>286.92</td>\n",
" <td>1</td>\n",
" <td>287.42</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time ticker bid_price bid_size ask_price ask_size\n",
"0 2020-08-03 19:59:59+00:00 AKAM 112.89 1 113.05 3\n",
"1 2020-08-03 19:59:59+00:00 AIZ 107.26 1 113.65 1\n",
"2 2020-08-03 19:59:59+00:00 AES 15.11 1 15.13 3\n",
"3 2020-08-03 19:59:59+00:00 ADP 134.37 2 134.72 7\n",
"4 2020-08-03 19:59:59+00:00 AAPL 435.79 1 439.84 1\n",
"5 2020-08-03 19:59:59+00:00 AKAM 112.69 8 113.05 3\n",
"6 2020-08-03 19:59:59+00:00 AME 94.90 1 95.08 1\n",
"7 2020-08-03 19:59:59+00:00 ALLE 100.01 8 100.59 3\n",
"8 2020-08-03 19:59:59+00:00 ADI 116.47 2 116.62 2\n",
"9 2020-08-03 19:59:59+00:00 AEP 85.74 1 85.80 1\n",
"10 2020-08-03 19:59:59+00:00 ALLE 100.01 8 100.57 3\n",
"11 2020-08-03 19:59:59+00:00 AKAM 112.89 1 113.03 4\n",
"12 2020-08-03 19:59:59+00:00 LNT 53.50 2 55.86 1\n",
"13 2020-08-03 19:59:59+00:00 APH 107.36 1 107.48 1\n",
"14 2020-08-03 19:59:59+00:00 AEE 79.27 1 79.37 2\n",
"15 2020-08-03 19:59:59+00:00 ADBE 447.21 1 448.32 1\n",
"16 2020-08-03 19:59:59+00:00 ADM 42.90 2 42.94 1\n",
"17 2020-08-03 19:59:59+00:00 ADP 134.37 1 134.72 7\n",
"18 2020-08-03 19:59:59+00:00 ADBE 447.85 1 448.25 1\n",
"19 2020-08-03 19:59:59+00:00 ADP 134.33 1 134.42 1\n",
"20 2020-08-03 19:59:59+00:00 ADM 42.91 3 42.96 1\n",
"21 2020-08-03 19:59:59+00:00 AAPL 435.41 1 439.84 1\n",
"22 2020-08-03 19:59:59+00:00 ADM 42.93 1 42.95 1\n",
"23 2020-08-03 19:59:59+00:00 ADBE 447.85 2 448.11 1\n",
"24 2020-08-03 19:59:59+00:00 AKAM 112.69 8 118.30 1\n",
"25 2020-08-03 19:59:59+00:00 AKAM 112.91 1 112.98 2\n",
"26 2020-08-03 19:59:59+00:00 AMAT 64.93 4 65.05 2\n",
"27 2020-08-03 19:59:59+00:00 ADSK 239.25 1 239.28 1\n",
"28 2020-08-03 19:59:59+00:00 AMT 256.39 9 256.95 2\n",
"29 2020-08-03 19:59:59+00:00 ALGN 286.92 1 287.42 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfQuote"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>ratingOverweight</th>\n",
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" <td>BMY</td>\n",
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" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DRI</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TMUS</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>VIAC</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.625000</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
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" <th>6</th>\n",
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" <td>8</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.529412</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>O</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.526316</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>FIS</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.222222</td>\n",
" <td>2020-08-25 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>DLR</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.478261</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>LUV</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.450000</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>HAS</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.323529</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>MRK</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.294118</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>SBUX</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.588235</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ORCL</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.775862</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NLOK</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.781250</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>BAC</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.480769</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>D</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.781250</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>CSCO</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.480769</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" <td>2020-09-02 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>WM</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.600000</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>CXO</td>\n",
" <td>28</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.157143</td>\n",
" <td>2020-08-21 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>HUM</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.291667</td>\n",
" <td>2020-08-21 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>MRK</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.312500</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>NLOK</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.750000</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>BMY</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.437500</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>DLR</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.521739</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>TJX</td>\n",
" <td>23</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.155172</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>MOS</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.400000</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>EVRG</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.500000</td>\n",
" <td>2020-08-19 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NUE</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.708333</td>\n",
" <td>2020-08-19 00:00:00+00:00</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol ratingBuy ratingOverweight ratingHold ratingUnderweight \\\n",
"0 ABC 9 1 7 1 \n",
"1 BMY 7 2 6 0 \n",
"2 O 7 1 9 0 \n",
"3 DRI 19 1 11 0 \n",
"4 TMUS 19 2 5 1 \n",
"5 VIAC 11 0 16 1 \n",
"6 ABC 8 1 7 1 \n",
"7 O 8 2 9 0 \n",
"8 FIS 27 3 5 1 \n",
"9 DLR 12 1 9 1 \n",
"10 LUV 11 0 9 0 \n",
"11 HAS 11 1 5 0 \n",
"12 MRK 11 2 4 0 \n",
"13 SBUX 13 2 19 0 \n",
"14 ORCL 6 2 20 1 \n",
"15 NLOK 4 1 9 2 \n",
"16 BAC 13 1 12 0 \n",
"17 D 4 0 11 1 \n",
"18 CSCO 13 1 12 0 \n",
"19 WM 5 2 8 0 \n",
"20 CXO 28 3 4 0 \n",
"21 HUM 17 0 7 0 \n",
"22 MRK 10 2 4 0 \n",
"23 NLOK 5 1 10 2 \n",
"24 BMY 8 2 6 0 \n",
"25 DLR 11 1 10 1 \n",
"26 TJX 23 3 3 0 \n",
"27 MOS 11 2 7 0 \n",
"28 EVRG 3 1 3 0 \n",
"29 NUE 4 0 7 1 \n",
"\n",
" ratingSell ratingNone ratingScaleMark consensusStartDate \\\n",
"0 0 0 1.500000 2020-08-28 00:00:00+00:00 \n",
"1 0 0 1.466667 2020-08-27 00:00:00+00:00 \n",
"2 0 0 1.558824 2020-08-27 00:00:00+00:00 \n",
"3 0 0 1.370968 2020-08-26 00:00:00+00:00 \n",
"4 0 0 1.277778 2020-08-26 00:00:00+00:00 \n",
"5 0 0 1.625000 2020-08-26 00:00:00+00:00 \n",
"6 0 0 1.529412 2020-08-26 00:00:00+00:00 \n",
"7 0 0 1.526316 2020-08-26 00:00:00+00:00 \n",
"8 0 0 1.222222 2020-08-25 00:00:00+00:00 \n",
"9 0 0 1.478261 2020-08-24 00:00:00+00:00 \n",
"10 0 0 1.450000 2020-08-24 00:00:00+00:00 \n",
"11 0 0 1.323529 2020-08-24 00:00:00+00:00 \n",
"12 0 0 1.294118 2020-08-24 00:00:00+00:00 \n",
"13 0 0 1.588235 2020-08-24 00:00:00+00:00 \n",
"14 0 0 1.775862 2020-08-24 00:00:00+00:00 \n",
"15 0 0 1.781250 2020-08-23 00:00:00+00:00 \n",
"16 0 0 1.480769 2020-08-23 00:00:00+00:00 \n",
"17 0 1 1.781250 2020-08-23 00:00:00+00:00 \n",
"18 0 0 1.480769 2020-08-22 00:00:00+00:00 \n",
"19 0 0 1.600000 2020-08-22 00:00:00+00:00 \n",
"20 0 0 1.157143 2020-08-21 00:00:00+00:00 \n",
"21 0 0 1.291667 2020-08-21 00:00:00+00:00 \n",
"22 0 0 1.312500 2020-08-20 00:00:00+00:00 \n",
"23 0 0 1.750000 2020-08-20 00:00:00+00:00 \n",
"24 0 0 1.437500 2020-08-20 00:00:00+00:00 \n",
"25 0 0 1.521739 2020-08-20 00:00:00+00:00 \n",
"26 0 0 1.155172 2020-08-20 00:00:00+00:00 \n",
"27 0 0 1.400000 2020-08-20 00:00:00+00:00 \n",
"28 0 0 1.500000 2020-08-19 00:00:00+00:00 \n",
"29 0 0 1.708333 2020-08-19 00:00:00+00:00 \n",
"\n",
" consensusEndDate \n",
"0 2020-08-31 00:00:00+00:00 \n",
"1 2020-08-27 00:00:00+00:00 \n",
"2 2020-08-31 00:00:00+00:00 \n",
"3 2020-08-26 00:00:00+00:00 \n",
"4 2020-08-26 00:00:00+00:00 \n",
"5 2020-08-30 00:00:00+00:00 \n",
"6 2020-08-27 00:00:00+00:00 \n",
"7 2020-08-26 00:00:00+00:00 \n",
"8 2020-09-01 00:00:00+00:00 \n",
"9 2020-08-26 00:00:00+00:00 \n",
"10 2020-08-30 00:00:00+00:00 \n",
"11 2020-08-27 00:00:00+00:00 \n",
"12 2020-08-27 00:00:00+00:00 \n",
"13 2020-08-27 00:00:00+00:00 \n",
"14 2020-09-01 00:00:00+00:00 \n",
"15 2020-08-31 00:00:00+00:00 \n",
"16 2020-09-01 00:00:00+00:00 \n",
"17 2020-08-27 00:00:00+00:00 \n",
"18 2020-09-02 00:00:00+00:00 \n",
"19 2020-08-23 00:00:00+00:00 \n",
"20 2020-08-30 00:00:00+00:00 \n",
"21 2020-08-26 00:00:00+00:00 \n",
"22 2020-08-23 00:00:00+00:00 \n",
"23 2020-08-22 00:00:00+00:00 \n",
"24 2020-08-26 00:00:00+00:00 \n",
"25 2020-08-23 00:00:00+00:00 \n",
"26 2020-09-01 00:00:00+00:00 \n",
"27 2020-08-31 00:00:00+00:00 \n",
"28 2020-08-30 00:00:00+00:00 \n",
"29 2020-08-20 00:00:00+00:00 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfRating"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>symbol</th>\n",
" <th>updatedDate</th>\n",
" <th>priceTargetAverage</th>\n",
" <th>priceTargetHigh</th>\n",
" <th>priceTargetLow</th>\n",
" <th>numberOfAnalysts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>COO</td>\n",
" <td>2020-09-04</td>\n",
" <td>337.27</td>\n",
" <td>360.00</td>\n",
" <td>315.00</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CF</td>\n",
" <td>2020-09-04</td>\n",
" <td>38.06</td>\n",
" <td>45.00</td>\n",
" <td>31.00</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>PVH</td>\n",
" <td>2020-09-04</td>\n",
" <td>64.50</td>\n",
" <td>107.00</td>\n",
" <td>39.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DD</td>\n",
" <td>2020-09-04</td>\n",
" <td>65.15</td>\n",
" <td>77.00</td>\n",
" <td>55.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AVGO</td>\n",
" <td>2020-09-04</td>\n",
" <td>387.52</td>\n",
" <td>450.00</td>\n",
" <td>335.00</td>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>CNP</td>\n",
" <td>2020-09-04</td>\n",
" <td>20.62</td>\n",
" <td>25.00</td>\n",
" <td>12.25</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>WBA</td>\n",
" <td>2020-09-03</td>\n",
" <td>41.56</td>\n",
" <td>46.00</td>\n",
" <td>37.00</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>COST</td>\n",
" <td>2020-09-03</td>\n",
" <td>352.29</td>\n",
" <td>410.00</td>\n",
" <td>235.00</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>DPZ</td>\n",
" <td>2020-09-03</td>\n",
" <td>429.77</td>\n",
" <td>512.00</td>\n",
" <td>350.00</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>HCA</td>\n",
" <td>2020-09-03</td>\n",
" <td>146.90</td>\n",
" <td>175.00</td>\n",
" <td>124.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>DOW</td>\n",
" <td>2020-09-03</td>\n",
" <td>45.05</td>\n",
" <td>58.00</td>\n",
" <td>36.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>BF.B</td>\n",
" <td>2020-09-03</td>\n",
" <td>71.19</td>\n",
" <td>100.00</td>\n",
" <td>51.00</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>CPRT</td>\n",
" <td>2020-09-03</td>\n",
" <td>101.17</td>\n",
" <td>120.00</td>\n",
" <td>74.00</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>UHS</td>\n",
" <td>2020-09-03</td>\n",
" <td>131.13</td>\n",
" <td>145.00</td>\n",
" <td>117.00</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>EL</td>\n",
" <td>2020-09-03</td>\n",
" <td>219.90</td>\n",
" <td>244.00</td>\n",
" <td>147.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>EA</td>\n",
" <td>2020-09-03</td>\n",
" <td>155.17</td>\n",
" <td>170.00</td>\n",
" <td>140.00</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>RMD</td>\n",
" <td>2020-09-03</td>\n",
" <td>182.85</td>\n",
" <td>216.41</td>\n",
" <td>145.00</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>MSFT</td>\n",
" <td>2020-09-03</td>\n",
" <td>233.13</td>\n",
" <td>260.00</td>\n",
" <td>208.00</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>SWKS</td>\n",
" <td>2020-09-03</td>\n",
" <td>144.78</td>\n",
" <td>170.00</td>\n",
" <td>103.00</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>EMN</td>\n",
" <td>2020-09-03</td>\n",
" <td>81.58</td>\n",
" <td>92.00</td>\n",
" <td>72.00</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>QRVO</td>\n",
" <td>2020-09-03</td>\n",
" <td>137.20</td>\n",
" <td>160.00</td>\n",
" <td>115.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>LLY</td>\n",
" <td>2020-09-03</td>\n",
" <td>174.67</td>\n",
" <td>190.00</td>\n",
" <td>148.00</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>BEN</td>\n",
" <td>2020-09-03</td>\n",
" <td>21.75</td>\n",
" <td>25.00</td>\n",
" <td>19.00</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>CPB</td>\n",
" <td>2020-09-03</td>\n",
" <td>53.11</td>\n",
" <td>63.00</td>\n",
" <td>39.00</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>NI</td>\n",
" <td>2020-09-03</td>\n",
" <td>26.15</td>\n",
" <td>28.00</td>\n",
" <td>24.00</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>MDT</td>\n",
" <td>2020-09-02</td>\n",
" <td>117.71</td>\n",
" <td>130.00</td>\n",
" <td>110.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>SLG</td>\n",
" <td>2020-09-02</td>\n",
" <td>58.73</td>\n",
" <td>105.00</td>\n",
" <td>47.00</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>PNC</td>\n",
" <td>2020-09-02</td>\n",
" <td>114.73</td>\n",
" <td>148.00</td>\n",
" <td>83.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>MCD</td>\n",
" <td>2020-09-02</td>\n",
" <td>218.46</td>\n",
" <td>245.00</td>\n",
" <td>185.00</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>KEY</td>\n",
" <td>2020-09-02</td>\n",
" <td>13.73</td>\n",
" <td>16.00</td>\n",
" <td>12.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol updatedDate priceTargetAverage priceTargetHigh priceTargetLow \\\n",
"0 COO 2020-09-04 337.27 360.00 315.00 \n",
"1 CF 2020-09-04 38.06 45.00 31.00 \n",
"2 PVH 2020-09-04 64.50 107.00 39.00 \n",
"3 DD 2020-09-04 65.15 77.00 55.00 \n",
"4 AVGO 2020-09-04 387.52 450.00 335.00 \n",
"5 CNP 2020-09-04 20.62 25.00 12.25 \n",
"6 WBA 2020-09-03 41.56 46.00 37.00 \n",
"7 COST 2020-09-03 352.29 410.00 235.00 \n",
"8 DPZ 2020-09-03 429.77 512.00 350.00 \n",
"9 HCA 2020-09-03 146.90 175.00 124.00 \n",
"10 DOW 2020-09-03 45.05 58.00 36.00 \n",
"11 BF.B 2020-09-03 71.19 100.00 51.00 \n",
"12 CPRT 2020-09-03 101.17 120.00 74.00 \n",
"13 UHS 2020-09-03 131.13 145.00 117.00 \n",
"14 EL 2020-09-03 219.90 244.00 147.00 \n",
"15 EA 2020-09-03 155.17 170.00 140.00 \n",
"16 RMD 2020-09-03 182.85 216.41 145.00 \n",
"17 MSFT 2020-09-03 233.13 260.00 208.00 \n",
"18 SWKS 2020-09-03 144.78 170.00 103.00 \n",
"19 EMN 2020-09-03 81.58 92.00 72.00 \n",
"20 QRVO 2020-09-03 137.20 160.00 115.00 \n",
"21 LLY 2020-09-03 174.67 190.00 148.00 \n",
"22 BEN 2020-09-03 21.75 25.00 19.00 \n",
"23 CPB 2020-09-03 53.11 63.00 39.00 \n",
"24 NI 2020-09-03 26.15 28.00 24.00 \n",
"25 MDT 2020-09-02 117.71 130.00 110.00 \n",
"26 SLG 2020-09-02 58.73 105.00 47.00 \n",
"27 PNC 2020-09-02 114.73 148.00 83.00 \n",
"28 MCD 2020-09-02 218.46 245.00 185.00 \n",
"29 KEY 2020-09-02 13.73 16.00 12.00 \n",
"\n",
" numberOfAnalysts \n",
"0 11.0 \n",
"1 17.0 \n",
"2 20.0 \n",
"3 20.0 \n",
"4 29.0 \n",
"5 15.0 \n",
"6 18.0 \n",
"7 28.0 \n",
"8 26.0 \n",
"9 21.0 \n",
"10 20.0 \n",
"11 16.0 \n",
"12 6.0 \n",
"13 15.0 \n",
"14 20.0 \n",
"15 27.0 \n",
"16 14.0 \n",
"17 27.0 \n",
"18 23.0 \n",
"19 19.0 \n",
"20 20.0 \n",
"21 12.0 \n",
"22 13.0 \n",
"23 18.0 \n",
"24 13.0 \n",
"25 21.0 \n",
"26 15.0 \n",
"27 21.0 \n",
"28 28.0 \n",
"29 21.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfTarget"
]
},
{
"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>symbol</th>\n",
" <th>updatedDate</th>\n",
" <th>priceTargetAverage</th>\n",
" <th>priceTargetHigh</th>\n",
" <th>priceTargetLow</th>\n",
" <th>numberOfAnalysts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>KEY</td>\n",
" <td>2020-09-02</td>\n",
" <td>13.73</td>\n",
" <td>16.00</td>\n",
" <td>12.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>PNC</td>\n",
" <td>2020-09-02</td>\n",
" <td>114.73</td>\n",
" <td>148.00</td>\n",
" <td>83.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>SLG</td>\n",
" <td>2020-09-02</td>\n",
" <td>58.73</td>\n",
" <td>105.00</td>\n",
" <td>47.00</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>MDT</td>\n",
" <td>2020-09-02</td>\n",
" <td>117.71</td>\n",
" <td>130.00</td>\n",
" <td>110.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>MCD</td>\n",
" <td>2020-09-02</td>\n",
" <td>218.46</td>\n",
" <td>245.00</td>\n",
" <td>185.00</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>NI</td>\n",
" <td>2020-09-03</td>\n",
" <td>26.15</td>\n",
" <td>28.00</td>\n",
" <td>24.00</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>CPB</td>\n",
" <td>2020-09-03</td>\n",
" <td>53.11</td>\n",
" <td>63.00</td>\n",
" <td>39.00</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>BEN</td>\n",
" <td>2020-09-03</td>\n",
" <td>21.75</td>\n",
" <td>25.00</td>\n",
" <td>19.00</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>LLY</td>\n",
" <td>2020-09-03</td>\n",
" <td>174.67</td>\n",
" <td>190.00</td>\n",
" <td>148.00</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>QRVO</td>\n",
" <td>2020-09-03</td>\n",
" <td>137.20</td>\n",
" <td>160.00</td>\n",
" <td>115.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>EMN</td>\n",
" <td>2020-09-03</td>\n",
" <td>81.58</td>\n",
" <td>92.00</td>\n",
" <td>72.00</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>SWKS</td>\n",
" <td>2020-09-03</td>\n",
" <td>144.78</td>\n",
" <td>170.00</td>\n",
" <td>103.00</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>MSFT</td>\n",
" <td>2020-09-03</td>\n",
" <td>233.13</td>\n",
" <td>260.00</td>\n",
" <td>208.00</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>RMD</td>\n",
" <td>2020-09-03</td>\n",
" <td>182.85</td>\n",
" <td>216.41</td>\n",
" <td>145.00</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>EA</td>\n",
" <td>2020-09-03</td>\n",
" <td>155.17</td>\n",
" <td>170.00</td>\n",
" <td>140.00</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>EL</td>\n",
" <td>2020-09-03</td>\n",
" <td>219.90</td>\n",
" <td>244.00</td>\n",
" <td>147.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>CPRT</td>\n",
" <td>2020-09-03</td>\n",
" <td>101.17</td>\n",
" <td>120.00</td>\n",
" <td>74.00</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>BF.B</td>\n",
" <td>2020-09-03</td>\n",
" <td>71.19</td>\n",
" <td>100.00</td>\n",
" <td>51.00</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>DOW</td>\n",
" <td>2020-09-03</td>\n",
" <td>45.05</td>\n",
" <td>58.00</td>\n",
" <td>36.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>HCA</td>\n",
" <td>2020-09-03</td>\n",
" <td>146.90</td>\n",
" <td>175.00</td>\n",
" <td>124.00</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>DPZ</td>\n",
" <td>2020-09-03</td>\n",
" <td>429.77</td>\n",
" <td>512.00</td>\n",
" <td>350.00</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>COST</td>\n",
" <td>2020-09-03</td>\n",
" <td>352.29</td>\n",
" <td>410.00</td>\n",
" <td>235.00</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>WBA</td>\n",
" <td>2020-09-03</td>\n",
" <td>41.56</td>\n",
" <td>46.00</td>\n",
" <td>37.00</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>UHS</td>\n",
" <td>2020-09-03</td>\n",
" <td>131.13</td>\n",
" <td>145.00</td>\n",
" <td>117.00</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>CNP</td>\n",
" <td>2020-09-04</td>\n",
" <td>20.62</td>\n",
" <td>25.00</td>\n",
" <td>12.25</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AVGO</td>\n",
" <td>2020-09-04</td>\n",
" <td>387.52</td>\n",
" <td>450.00</td>\n",
" <td>335.00</td>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DD</td>\n",
" <td>2020-09-04</td>\n",
" <td>65.15</td>\n",
" <td>77.00</td>\n",
" <td>55.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>PVH</td>\n",
" <td>2020-09-04</td>\n",
" <td>64.50</td>\n",
" <td>107.00</td>\n",
" <td>39.00</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CF</td>\n",
" <td>2020-09-04</td>\n",
" <td>38.06</td>\n",
" <td>45.00</td>\n",
" <td>31.00</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>COO</td>\n",
" <td>2020-09-04</td>\n",
" <td>337.27</td>\n",
" <td>360.00</td>\n",
" <td>315.00</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol updatedDate priceTargetAverage priceTargetHigh priceTargetLow \\\n",
"29 KEY 2020-09-02 13.73 16.00 12.00 \n",
"27 PNC 2020-09-02 114.73 148.00 83.00 \n",
"26 SLG 2020-09-02 58.73 105.00 47.00 \n",
"25 MDT 2020-09-02 117.71 130.00 110.00 \n",
"28 MCD 2020-09-02 218.46 245.00 185.00 \n",
"24 NI 2020-09-03 26.15 28.00 24.00 \n",
"23 CPB 2020-09-03 53.11 63.00 39.00 \n",
"22 BEN 2020-09-03 21.75 25.00 19.00 \n",
"21 LLY 2020-09-03 174.67 190.00 148.00 \n",
"20 QRVO 2020-09-03 137.20 160.00 115.00 \n",
"19 EMN 2020-09-03 81.58 92.00 72.00 \n",
"18 SWKS 2020-09-03 144.78 170.00 103.00 \n",
"17 MSFT 2020-09-03 233.13 260.00 208.00 \n",
"16 RMD 2020-09-03 182.85 216.41 145.00 \n",
"15 EA 2020-09-03 155.17 170.00 140.00 \n",
"14 EL 2020-09-03 219.90 244.00 147.00 \n",
"12 CPRT 2020-09-03 101.17 120.00 74.00 \n",
"11 BF.B 2020-09-03 71.19 100.00 51.00 \n",
"10 DOW 2020-09-03 45.05 58.00 36.00 \n",
"9 HCA 2020-09-03 146.90 175.00 124.00 \n",
"8 DPZ 2020-09-03 429.77 512.00 350.00 \n",
"7 COST 2020-09-03 352.29 410.00 235.00 \n",
"6 WBA 2020-09-03 41.56 46.00 37.00 \n",
"13 UHS 2020-09-03 131.13 145.00 117.00 \n",
"5 CNP 2020-09-04 20.62 25.00 12.25 \n",
"4 AVGO 2020-09-04 387.52 450.00 335.00 \n",
"3 DD 2020-09-04 65.15 77.00 55.00 \n",
"2 PVH 2020-09-04 64.50 107.00 39.00 \n",
"1 CF 2020-09-04 38.06 45.00 31.00 \n",
"0 COO 2020-09-04 337.27 360.00 315.00 \n",
"\n",
" numberOfAnalysts \n",
"29 21.0 \n",
"27 21.0 \n",
"26 15.0 \n",
"25 21.0 \n",
"28 28.0 \n",
"24 13.0 \n",
"23 18.0 \n",
"22 13.0 \n",
"21 12.0 \n",
"20 20.0 \n",
"19 19.0 \n",
"18 23.0 \n",
"17 27.0 \n",
"16 14.0 \n",
"15 27.0 \n",
"14 20.0 \n",
"12 6.0 \n",
"11 16.0 \n",
"10 20.0 \n",
"9 21.0 \n",
"8 26.0 \n",
"7 28.0 \n",
"6 18.0 \n",
"13 15.0 \n",
"5 15.0 \n",
"4 29.0 \n",
"3 20.0 \n",
"2 20.0 \n",
"1 17.0 \n",
"0 11.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBar.sort_values(by=['time'])\n",
"dfEvent.sort_values(by=['system_time'])\n",
"dfNews.sort_values(by=['datetime'])\n",
"dfQuote.sort_values(by=['time'])\n",
"dfRating.sort_values(by=['consensusStartDate'])\n",
"dfTarget.sort_values(by=['updatedDate'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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</th>\n",
" <th>bidPrice</th>\n",
" <th>askPrice</th>\n",
" <th>avgPrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>112.89</td>\n",
" <td>113.05</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>107.26</td>\n",
" <td>113.65</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>15.11</td>\n",
" <td>15.13</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>134.37</td>\n",
" <td>134.72</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-08-03 19:59:59+00:00</td>\n",
" <td>435.79</td>\n",
" <td>439.84</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date bidPrice askPrice avgPrice\n",
"0 2020-08-03 19:59:59+00:00 112.89 113.05 131.5506\n",
"1 2020-08-03 19:59:59+00:00 107.26 113.65 131.5506\n",
"2 2020-08-03 19:59:59+00:00 15.11 15.13 131.5506\n",
"3 2020-08-03 19:59:59+00:00 134.37 134.72 131.5506\n",
"4 2020-08-03 19:59:59+00:00 435.79 439.84 131.5506"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"empty = []\n",
"dfBarQoute = pd.DataFrame(empty)\n",
"dfBarQoute.reset_index(inplace=True)\n",
"dfBarQoute.columns = ['Date']\n",
"dfBarQoute['Date'] = dfQuote[\"time\"]\n",
"dfBarQoute['bidPrice'] = dfQuote[\"bid_price\"]\n",
"dfBarQoute['askPrice'] = dfQuote[\"ask_price\"]\n",
"dfBarQoute['avgPrice'] = dfBar[\"average_price\"]\n",
"dfBarQoute.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"bidPrice float64\n",
"askPrice float64\n",
"avgPrice float64\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBarQoute.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Date VS avgPrice'}, ylabel='Date'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarQoute.plot(x= \"Date\", y= \"avgPrice\", kind = \"barh\", title= \"Date VS avgPrice\", figsize=(10,8))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Date VS avgPrice'}, xlabel='Date'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqZLuLVUScHxF3RcTXJty3X0RcHhFrh38XTHjsbRFxa0TcEhEnz1RxSZKk1uzIEauPAadMuu8cYHVmHgGsHm4TEUcBZwBHD6/5UETMK9ZWkiSpYdtdrDLz74HvT7p7GbByuL4SOG3C/Rdm5kOZeRtwK3BcmaqSJEltG/ccq4My806A4d8Dh/sPAdZNeN764T5JkqTulT55Paa4L6d8YsRZEbEmItZs2LChcA1JkqT6xl2svhsRTwMY/r1ruH89cOiE5y0C7pgqIDPPy8ylmbl04cKFY9aQJElqx7iL1aXA8uH6cuCzE+4/IyKeGhGHA0cA/zi9ipIkSXPDLtt7QkRcAJwAHBAR64F3AOcCF0fEmcC3gdcCZObXI+Ji4BvAZuDXMvORGeouSZLUlO0uVpn5um08dOI2nv9e4L3TKSVJkjQX+c3rkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBUyrcUqIn4rIr4eEV+LiAsiYn5E7BcRl0fE2uHfBaXKSpIktWzsxSoiDgF+A1iamc8G5gFnAOcAqzPzCGD1cFuSJKl7030rcBdgt4jYBdgduANYBqwcHl8JnDbNnyFJkjQnjL1YZea/AO8Dvg3cCdyXmauAgzLzzuE5dwIHligqSZLUuum8FbiA0dGpw4GfBPaIiF/aidefFRFrImLNhg0bxq0hSZLUjOm8FfgS4LbM3JCZDwN/Dfw88N2IeBrA8O9dU704M8/LzKWZuXThwoXTqCFJktSG6SxW3waeHxG7R0QAJwI3AZcCy4fnLAc+O72KkiRJc8Mu474wM78cEZ8CrgM2A9cD5wF7AhdHxJmMlq/XligqSZLUurEXK4DMfAfwjkl3P8To6JUkSdKTit+8LkmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIh01qsImLfiPhURNwcETdFxM9FxH4RcXlErB3+XVCqrCRJUsume8Tq/cDnM/NI4DnATcA5wOrMPAJYPdyWJEnq3tiLVUTsDbwQWAGQmT/KzHuBZcDK4WkrgdOmV1GSJGlumM4Rq6cDG4D/ERHXR8RfRsQewEGZeSfA8O+BBXpKkiQ1bzqL1S7AEuDDmfkzwA/Yibf9IuKsiFgTEWs2bNgwjRqSJEltmM5itR5Yn5lfHm5/itGi9d2IeBrA8O9dU704M8/LzKWZuXThwoXTqCFJktSGXcZ9YWZ+JyLWRcQzM/MW4ETgG8NlOXDu8O9nizSVpMYsPuey7T7n9nNPrdBEs6nF/x202OnJYuzFanA28PGIeArwz8C/Z3QU7OKIOBP4NvDaaf4MSZKkOWFai1Vm3gAsneKhE6eTK0mSNBf5zeuSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFTLtxSoi5kXE9RHxN8Pt/SLi8ohYO/y7YPo1JUmS2lfiiNVvAjdNuH0OsDozjwBWD7clSZK6N63FKiIWAacCfznh7mXAyuH6SuC06fwMSZKkuWK6R6z+FHgr8OiE+w7KzDsBhn8PnObPkCRJmhPGXqwi4uXAXZl57ZivPysi1kTEmg0bNoxbQ5IkqRnTOWJ1PPDKiLgduBD4hYj4n8B3I+JpAMO/d0314sw8LzOXZubShQsXTqOGJElSG8ZerDLzbZm5KDMXA2cA/39m/hJwKbB8eNpy4LPTbilJkjQHzMT3WJ0LnBQRa4GThtuSJEnd26VESGZeCVw5XL8bOLFEriRJ0lziN69LkiQV4mIlSZJUiIuVJElSIS5WkiRJhRQ5eV19WHzOZdt9zu3nnlqhiSRJc5NHrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgoZe7GKiEMj4u8i4qaI+HpE/OZw/34RcXlErB3+XVCuriRJUrumc8RqM/A7mfks4PnAr0XEUcA5wOrMPAJYPdyWJEnq3tiLVWbemZnXDdc3AjcBhwDLgJXD01YCp02zoyRJ0pxQ5ByriFgM/AzwZeCgzLwTRssXcGCJnyFJktS6aS9WEbEn8GngP2Xm/TvxurMiYk1ErNmwYcN0a0iSJM26aS1WEbEro6Xq45n518Pd342Ipw2PPw24a6rXZuZ5mbk0M5cuXLhwOjUkSZKaMJ1PBQawArgpM/9kwkOXAsuH68uBz45fT5Ikae7YZRqvPR54PfDViLhhuO/3gHOBiyPiTODbwGun1VCSJGmOGHuxyswvArGNh08cN1eSJGmums4RK0l6QovPuWy7z7n93FMrNJGkOvyTNpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiEuVpIkSYW4WEmSJBXiYiVJklSIi5UkSVIhLlaSJEmFuFhJkiQV4mIlSZJUiIuVJElSIS5WkiRJhbhYSZIkFeJiJUmSVIiLlSRJUiG7zHYBSWUsPuey7T7n9nNPrZZT0o50gvq9Sun9d97ifC3q/X/nTxYesZIkSSrExUqSJKkQFytJkqRCXKwkSZIKcbGSJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgpxsZIkSSrExUqSJKkQFytJkqRCXKwkSZIKmbHFKiJOiYhbIuLWiDhnpn6OJElSK2ZksYqIecCfAy8DjgJeFxFHzcTPkiRJasVMHbE6Drg1M/85M38EXAgsm6GfJUmS1ISZWqwOAdZNuL1+uE+SJKlbkZnlQyNeC5ycmb883H49cFxmnj3hOWcBZw03nwncsgPRBwDfK1CxVE7JrBY7lcyyU/0sO9XPslP9LDvVz7ITHJaZC6d6YJdCJSZbDxw64fYi4I6JT8jM84DzdiY0ItZk5tLpliuV03unkll2qp9lp/pZdqqfZaf6WXZ6YjP1VuA1wBERcXhEPAU4A7h0hn6WJElSE2bkiFVmbo6IXwe+AMwDzs/Mr8/Ez5IkSWrFTL0VSGb+L+B/FY7dqbcOK+SUzGqxU8ksO9XPslP9LDvVz7JT/Sw7PYEZOXldkiTpycg/aSNJklSIi5UkSVIhM3aO1XRFxD7AKYy+WDQZfV3DFzLz3tnI6b1TySw71c+yU/0sO9XPslP9LDvtvCaPWEXEG4DrgBOA3YE9gBcD1w6PVc3pvVPJLDvVz7JT/Sw71c+yU/0sO40pM5u7MPoW9n2nuH8B8M3aOb136n2+Fjv1Pl+LnXqfr8VOvc/XYqfe52ux0+RLk0esgGB0SG6yR4fHauf03qlklp3qZ9mpfpad6mfZqX6WncbQ6jlW7wWui4hVPPbHnH8KOAl49yzk9N6pZJad6mfZqX6Wnepn2al+lp3G0Oz3WEXEAuBkRieUBaO/P/iFzLxnNnJ671Qyy071s+xUP8tO9bPsVD/LTjuv2cVqi4jYD8jpDFkyp/dOJbPsVD/LTvWz7FQ/y071s+y0E8Y9OWsmL4wOxV0I3AWsBW4drl8ILK6d03un3udrsVPv87XYqff5WuzU+3wtdup9vhY7bZU77gtn8gJcBZwOzJtw3zzgDODq2jm9d+p9vhY79T5fi516n6/FTr3P12Kn3udrsdNWueO+cCYvwNpxHpupnN479T5fi516n6/FTr3P12Kn3udrsVPv87XYafKl1U8FXhsRHwJW8tiZ+ocCy4HrZyGn904ls+xUP8tO9bPsVD/LTvWz7DSGJk9ej4inAGcCy3jsTP11wOeAFZn5UM2c3juVzLKT87XaqWSWnZyv1U4ls+y041mPy21xsZIkSZqLWv3m9R+LiJc/0e3aOb13Kpllp/pZdqqfZaf6WXaqn2WnHdf8YgUcu53btXNKZrXYqWSWnepn2al+lp3qZ9mpfpaddpBvBUqSJBXS6qcCiYh9gFMYnVCWwB2Mvmb+3tnI6b1TySw71c+yU/0sO9XPslP9LDvtvCbfCoyINwDXAScAuwN7AC9m9NHIN9TO6b1TySw71c+yU/0sO9XPslP9LDuNadwvwJrJC3ALsO8U9y8Avlk7p/dOvc/XYqfe52uxU+/ztdip9/la7NT7fC12mnxp8ogVo++SmOrkr0eHx2rn9N6pZJad6mfZqX6Wnepn2al+lp3G0Oo5Vu8FrouIVTz2bag/BZwEvHsWcnrvVDLLTvWz7FQ/y071s+xUP8tOY2j2U4ERsQA4mce+DXU9oxPK7pmNnN47lcyyU/0sO9XPslP9LDvVz7LTzmt2sdoiIvYDcjpDlszpvVPJLDvVz7JT/Sw71c+yU/0sO+2EcU/OmskLo0NxFwJ3AWuBW4frFwKLa+f03qn3+Vrs1Pt8LXbqfb4WO/U+X4udep+vxU5b5Y77wpm8AFcBpwPzJtw3DzgDuLp2Tu+dep+vxU69z9dip97na7FT7/O12Kn3+VrstFXuuC+cyQuwdpzHZiqn9069z9dip97na7FT7/O12Kn3+Vrs1Pt8LXaafGn1U4HXRsSHgJU8dqb+ocBy4PpZyOm9U8ksO9XPslP9LDvVz7JT/Sw7jaHJk9cj4inAmcAyHjtTfx3wOWBFZj5UM6f3TiWz7OR8rXYqmWUn52u1U8ksO+141uNyW1ysJEmS5qJWv3n9cSJieUs5JbNa7FQyy071s+xUP8tO9bPsVD/LTjuYMxeOWEXEdZm5pJWc3juVzLJT/Sw71c+yU/0sO9XPstOOmRNHrCRJkuaCZo9YRcTfMfrjiAE8D1gzXM/M/IXaOb13Kpllp/pZdqqfZaf6WXaqn2WnndfyYnXYlqvAZcC/2fJYZn6rdk7vnUpm2al+lp3qZ9mpfpad6mfZaee1+j1WjxsoIh4ad8BSOb13Kpllp/pZdqqfZaf6WXaqn2WnnTdXzrG6vbGcklmlclrNKpVTMqtUTqtZpXJKZpXKaTWrVE7JrFI5rWaVyimZVSqn1axSOSWzSuUUy2r2rUBJkqS5ptm3AgEiYimjr5ffzOjv9tw8mzm9dyqZZaf6WXaqn2Wn+ll2qp9lp53Ma/GIVUS8CPhj4F5GZ+l/CVgAPAy8PjPXbfvV5XN671Qyy07O12qnkll2cr5WO5XMstOOZz1OjvnXm2fywuiPHy4crh8OXDJcPwlYVTun9069z9dip97na7FT7/O12Kn3+Vrs1Pt8LXaafGn15PV5mblhuP5t4DCAzLyc0R9KrJ3Te6eSWXaqn2Wn+ll2qp9lp/pZdhpDq+dYrYmIFcBqRn91+kqAiNgdmDcLOb13Kpllp/pZdqqfZaf6WXaqn2WnMbR6jtWuwK8ARwFfAc7PzEciYjfgwNzB75koldN7p97na7FT7/O12Kn3+Vrs1Pt8LXbqfb4WO22V2+JiJUmSNBc1eY5VRJwy4fo+EbEiIm6MiE9ExEG1c3rvVDLLTvWz7FQ/y071s+xUP8tO42lysQL+YML1PwbuBF4BXAN8dBZyeu9UMstO9bPsVD/LTvWz7FQ/y07j2NbHBWfzAlw34foNkx67oXZO7516n6/FTr3P12Kn3udrsVPv87XYqff5Wuw0+dLqpwIPjIjfBgLYOyIih0nZuaNspXJ671Qyy071s+xUP8tO9bPsVD/LTmNo9a3AvwD2AvYEVgIHAETEwcANs5DTe6eSWXaqn2Wn+ll2qp9lp/pZdhqDnwqUJEkqpNUjVj8WEUue6HbtnN47lcyyU/0sO9XPslP9LDvVz7LTjmt+sQLevJ3btXNKZrXYqWSWnepn2al+lp3qZ9mpfpaddpBvBUqSJBXS6qcCAYiIpcChwGZgbWbePJs5vXcqmWWn+ll2qp9lp/pZdqqfZaedzGvxiFVEvIjRl3XdCzwP+BKwAHgYeH1mrquZ03unkll2cr5WO5XMspPztdqpZJaddjzrcab6cqvZvgDXAwuH64cDlwzXTwJW1c7pvVPv87XYqff5WuzU+3wtdup9vhY79T5fi50mX1o9eX1eZm4Yrn8bOAwgMy8HDpmFnN47lcyyU/0sO9XPslP9LDvVz7LTGFo9x2pNRKwAVgPLgCsBImJ3YN4s5PTeqWSWnepn2al+lp3qZ9mpfpadxtDqOVa7Ar8CHAV8BTg/Mx+JiN2AAzPzWzVzeu/U+3wtdup9vhY79T5fi516n6/FTr3P12KnrXJbXKwkSZLmoibPsYqIUyZc3yciVkTEjRHxiYg4qHZO751KZtmpfpad6mfZqX6Wnepn2Wk8TS5WwB9MuP7HwJ3AK4BrgI/OQk7vnUpm2al+lp3qZ9mpfpad6mfZaRzb+rjgbF6A6yZcv2HSYzfUzum9U+/ztdip9/la7NT7fC126n2+Fjv1Pl+LnSZfWv1U4IER8dtAAHtHROQwKTt3lK1UTu+dSmbZqX6Wnepn2al+lp3qZ9lpDK2+FfgXwF7AnsBK4ACAiDgYuGEWcnrvVDLLTvWz7FQ/y071s+xUP8tOY/BTgZIkSYW0esTqxyJiyRPdrp3Te6eSWXaqn2Wn+ll2qp9lp/pZdtpxzS9WwJu3c7t2TsmsFjuVzLJT/Sw71c+yU/0sO9XPstMO8q1ASZKkQlr9VCAAEbEUOBTYDKzNzJtnM6f3TiWz7FQ/y071s+xUP8tO9bPstJN5LR6xiogXMfqyrnuB5wFfAhYADwOvz8x1NXN671Qyy07O12qnkll2cr5WO5XMstOOZz3OVF9uNdsX4Hpg4XD9cOCS4fpJwKraOb136n2+Fjv1Pl+LnXqfr8VOvc/XYqfe52ux0+RLqyevz8vMDcP1bwOHAWTm5cAhs5DTe6eSWXaqn2Wn+ll2qp9lp/pZdhpDq+dYrYmIFcBqYBlwJUBE7A7Mm4Wc3juVzLJT/Sw71c+yU/0sO9XPstMYWj3HalfgV4CjgK8A52fmIxGxG3BgZn6rZk7vnXqfr8VOvc/XYqfe52uxU+/ztdip9/la7LRVbouLlSRJ0lzU6jlW2xQRf9tSTsmsFjuVzLJT/Sw71c+yU/0sO9XPstO2NXmOVWz7q+QDeG7tnN47lcyyU/0sO9XPslP9LDvVz7LTeJpcrIBrgP/NaLjJ9p2FnN47lcyyU/0sO9XPslP9LDvVz7LTOLb1PQyzeQG+BhyxjcfW1c7pvVPv87XYqff5WuzU+3wtdup9vhY79T5fi50mX1o9x+qdbPv8r7NnIadkVqmcVrNK5ZTMKpXTalapnJJZpXJazSqVUzKrVE6rWaVySmaVymk1q1ROyaxSOaWzfsxPBUqSJBXS6hErSZKkOcfFSpIkqRAXK0mSpEKaXawi4uCIOHi4vjAiXh0RR89WTu+dSmbZqX6Wnepn2al+lp3qZ9lpDON+nHAmL8CbgNuA24E3A18GzgduAc6sndN7p97na7FT7/O12Kn3+Vrs1Pt8LXbqfb4WO22VO+4LZ/ICfBXYHdgfeAA4eLh/AXBD7ZzeO/U+X4udep+vxU69z9dip97na7FT7/O12GnypdVvXn84M38I/DAi/ikzvwOQmfdExM58P0SpnN47lcyyU/0sO9XPslP9LDvVz7LTGFo9x+rRiNh1uH7qljsjYj4717lUTu+dSmbZqX6Wnepn2al+lp3qZ9lpHOMe6prJC/BTwK5T3H8I8JLaOb136n2+Fjv1Pl+LnXqfr8VOvc/XYqfe52ux0+RL89+8HhH7AZmZ97SQ03unkll2qp9lp/pZdqqfZaf6WXbaCeNuZDN5YbRFXghsANYCtwJ3Dfctrp3Te6fe52uxU+/ztdip9/la7NT7fC126n2+FjttlTvuC2fyAlwFnA7Mm3DfPOAM4OraOb136n2+Fjv1Pl+LnXqfr8VOvc/XYqfe52ux01a5475wJi/A2nEem6mc3jv1Pl+LnXqfr8VOvc/XYqfe52uxU+/ztdhp8qXVr1u4NiI+BKwE1g33HQosB66fhZzeO5XMslP9LDvVz7JT/Sw71c+y0xiaPHk9Ip4CnAksY3R2fgDrgUuBFZn5UM2c3juVzLKT87XaqWSWnZyv1U4ls+y041mPy21xsZIkSZqLWv2C0K1ExHUt5ZTMarFTySw71c+yU/0sO9XPslP9LDtt35xZrBgdomspp2RWi51KZtmpfpad6mfZqX6Wnepn2Wk75tJidVljOSWzWuxUMstO9bPsVD/LTvWz7FQ/y07bMWfOsYqIAzLze63k9N6pZJad6mfZqX6Wnepn2al+lp12wLjf0zCTF+BlwG3AF4GfAb4O/BOjs/VPrJ3Te6fe52uxU+/ztdip9/la7NT7fC126n2+FjttlTvuC2fyAtwAPAv4OeBu4PnD/c8Crqud03un3udrsVPv87XYqff5WuzU+3wtdup9vhY7Tb60+gWhj2bmTQAR8cPMvBogM2+KiJ05L6xUTu+dSmbZqX6Wnepn2al+lp3qZ9lpDK0uVvdGxJuAvYF7IuK3gIuBlwAPzEJO751KZtmpfpad6mfZqX6Wnepn2WkMrX4qcDmwBPhXwEuH+74A/FvgV2Yhp/dOJbPsVD/LTvWz7FQ/y071s+w0hjnzqUBJkqTWtfpWIBFxMnAao7/fk8AdwGcy8wuzkdN7p5JZdqqfZaf6WXaqn2Wn+ll22nlNHrGKiD8FngH8FaOPPQIsAt4ArM3M36yZ03unkll2cr5WO5XMspPztdqpZJaddjzrcXLMjxPO5AX45jbuj2HYqjm9d+p9vhY79T5fi516n6/FTr3P12Kn3udrsdPkS6snr2+KiOOmuP9YYNMs5PTeqWSWnepn2al+lp3qZ9mpfpadxtDqOVZvBD4cEXvx2OG5Q4H7h8dq5/TeqWSWnepn2al+lp3qZ9mpfpadxtDkOVZbRMTBjE4oC2B9Zn5nNnN671Qyy071s+xUP8tO9bPsVD/LTjtp3PcQa1+Ad7aU03un3udrsVPv87XYqff5WuzU+3wtdup9vtY6tXqO1VRe2VhOyawWO5XMslP9LDvVz7JT/Sw71c+y03bMpcUqGsspmdVip5JZdqqfZaf6WXaqn2Wn+ll22l7AcOireRHxE5n5aCs5vXcqmWWn+ll2qp9lp/pZdqqfZacdyGh1sYqpvw31s5n5+dnI6b1TySw71c+yU/0sO9XPslP9LDvtvCYXq+j4G1pb7FQyy07O12qnkll2cr5WO5XMspPfvD5jOb136n2+Fjv1Pl+LnXqfr8VOvc/XYqfe52ux0+RLqyev9/wNrS12Kpllp/pZdqqfZaf6WXaqn2WnMfjN63YqnWWn+ll2qp9lp/pZdqqfZacxNHmO1RbR8Te0ttipZJad6mfZqX6Wnepn2al+lp12Mq/xxWrXzHx40n0HZOb3ZiOn904ls+xUP8tO9bPsVD/LTvWz7LRzmjzHKiJeHBHrgTsiYlVELJ7w8KraOb13Kpllp/pZdqqfZaf6WXaqn2WnMY171vtMXoBrgKOH668B1gLPH25fXzun9069z9dip97na7FT7/O12Kn3+Vrs1Pt8LXbaKnfcF87kBfjKpNtHA7cArwKuq53Te6fe52uxU+/ztdip9/la7NT7fC126n2+FjttlTvuC2fyAqwBDp503yLgBmBj7ZzeO/U+X4udep+vxU69z9dip97na7FT7/O12Gmr3HFfOJMX4CXAc6a4f1/g7bVzeu/U+3wtdup9vhY79T5fi516n6/FTr3P12KnyZemPxUoSZI0l7T6qcB9IuLciLg5Iu4eLjcN9+1bO6f3Tr3P12Kn3udrsVPv87XYqff5WuzU+3wtdpqsycUKuBi4BzghM/fPzP2BFw/3fXIWcnrvVDLLTvWz7FQ/y071s+xUP8tO4xj3PcSZvAC3jPPYTOX03qn3+Vrs1Pt8LXbqfb4WO/U+X4udep+vxU6TL60esfpWRLw1Ig7ackdEHBQRvwusm4Wc3juVzLJT/Sw71c+yU/0sO9XPstMYWl2sTgf2B/53RHw/Ir4PXAnsB/zbWcjpvVPJLDvVz7JT/Sw71c+yU/0sO43BTwVKkiQV0uoRKyLiyIg4MSL2mHT/KbOR03unkll2qp9lp/pZdqqfZaf6WXYaw7gnZ83kBfgNRl8r/xngdmDZhMd25qvvi+T03qn3+Vrs1Pt8LXbqfb4WO/U+X4udep+vxU5b5Y77wpm8AF8F9hyuL2b0tfO/Ody+vnZO7516n6/FTr3P12Kn3udrsVPv87XYqff5Wuw0+bILbZqXmQ8AZObtEXEC8KmIOAyIWcjpvVPJLDvVz7JT/Sw71c+yU/0sO42h1XOsvhMRz91yYxj85cABwE/PQk7vnUpm2al+lp3qZ9mpfpad6mfZaRzjHuqayQujvy598DYeO752Tu+dep+vxU69z9dip97na7FT7/O12Kn3+VrsNPni1y1IkiQV0upbgZIkSXOOi5UkSVIhLlaS5pSIeCQiboiIr0fEVyLityPiCf9bFhGLI+Lf1eoo6cnLxUrSXPNgZj43M48GTgL+DfCO7bxmMeBiJWnGefK6pDklIh7IzD0n3H46cA2jj0gfBvx/wJY/T/HrmfkPEXE18CzgNmAl8AHgXOAE4KnAn2fmR6sNIalbLlaS5pTJi9Vw3z3AkcBG4NHM3BQRRwAXZObS4Yv//nNmvnx4/lnAgZn5noh4KvAl4LWZeVvNWST1p9VvXpeknbHlW5J3Bf5s+NK/R4BnbOP5LwWOiYjXDLf3AY5gdERLksbmYiVpThveCnwEuIvRuVbfBZ7D6BzSTdt6GXB2Zn6hSklJTxqevC5pzoqIhcBHgD/L0XkN+wB3ZuajwOuBecNTNwJ7TXjpF4A3R8SuQ84zImIPJGmaPGIlaa7ZLSJuYPS232ZGJ6v/yfDYh4BPR8Rrgb8DfjDcfyOwOSK+AnwMeD+jTwpeFxEBbABOq1NfUs88eV2SJKkQ3wqUJEkqxMVKkiSpEBcrSZKkQlysJEmSCnGxkiRJKsTFSpIkqRAXK0mSpEJcrCRJkgr5v+lmQXe3aA97AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarQoute.plot(x= \"Date\", y= \"avgPrice\", kind = \"bar\", title= \"Date VS avgPrice\", figsize=(10,8))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Date VS avgPrice'}, xlabel='Date'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarQoute.plot(x= \"Date\", y= \"avgPrice\", kind = \"line\", title= \"Date VS avgPrice\", figsize=(10,8))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Date VS avgPrice'}>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarQoute.plot(x= \"Date\", y= \"avgPrice\", kind = \"box\", title= \"Date VS avgPrice\", figsize=(10,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next Task"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>symbol</th>\n",
" <th>ratingBuy</th>\n",
" <th>ratingScaleMark</th>\n",
" <th>avgPriceMean</th>\n",
" <th>startDate</th>\n",
" <th>endDate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ABC</td>\n",
" <td>9</td>\n",
" <td>1.500000</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-28 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BMY</td>\n",
" <td>7</td>\n",
" <td>1.466667</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>O</td>\n",
" <td>7</td>\n",
" <td>1.558824</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DRI</td>\n",
" <td>19</td>\n",
" <td>1.370968</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TMUS</td>\n",
" <td>19</td>\n",
" <td>1.277778</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>VIAC</td>\n",
" <td>11</td>\n",
" <td>1.625000</td>\n",
" <td>37.1381</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ABC</td>\n",
" <td>8</td>\n",
" <td>1.529412</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>O</td>\n",
" <td>8</td>\n",
" <td>1.526316</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>FIS</td>\n",
" <td>27</td>\n",
" <td>1.222222</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-25 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>DLR</td>\n",
" <td>12</td>\n",
" <td>1.478261</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>LUV</td>\n",
" <td>11</td>\n",
" <td>1.450000</td>\n",
" <td>113.3237</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>HAS</td>\n",
" <td>11</td>\n",
" <td>1.323529</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>MRK</td>\n",
" <td>11</td>\n",
" <td>1.294118</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>SBUX</td>\n",
" <td>13</td>\n",
" <td>1.588235</td>\n",
" <td>18.3008</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ORCL</td>\n",
" <td>6</td>\n",
" <td>1.775862</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-24 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NLOK</td>\n",
" <td>4</td>\n",
" <td>1.781250</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>BAC</td>\n",
" <td>13</td>\n",
" <td>1.480769</td>\n",
" <td>59.3275</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>D</td>\n",
" <td>4</td>\n",
" <td>1.781250</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" <td>2020-08-27 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>CSCO</td>\n",
" <td>13</td>\n",
" <td>1.480769</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" <td>2020-09-02 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>WM</td>\n",
" <td>5</td>\n",
" <td>1.600000</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>CXO</td>\n",
" <td>28</td>\n",
" <td>1.157143</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-21 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>HUM</td>\n",
" <td>17</td>\n",
" <td>1.291667</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-21 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>MRK</td>\n",
" <td>10</td>\n",
" <td>1.312500</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>NLOK</td>\n",
" <td>5</td>\n",
" <td>1.750000</td>\n",
" <td>59.3211</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-22 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>BMY</td>\n",
" <td>8</td>\n",
" <td>1.437500</td>\n",
" <td>6.3711</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-26 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>DLR</td>\n",
" <td>11</td>\n",
" <td>1.521739</td>\n",
" <td>59.3179</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-23 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>TJX</td>\n",
" <td>23</td>\n",
" <td>1.155172</td>\n",
" <td>115.8448</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-09-01 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>MOS</td>\n",
" <td>11</td>\n",
" <td>1.400000</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" <td>2020-08-31 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>EVRG</td>\n",
" <td>3</td>\n",
" <td>1.500000</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-19 00:00:00+00:00</td>\n",
" <td>2020-08-30 00:00:00+00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NUE</td>\n",
" <td>4</td>\n",
" <td>1.708333</td>\n",
" <td>131.5506</td>\n",
" <td>2020-08-19 00:00:00+00:00</td>\n",
" <td>2020-08-20 00:00:00+00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol ratingBuy ratingScaleMark avgPriceMean \\\n",
"0 ABC 9 1.500000 131.5506 \n",
"1 BMY 7 1.466667 131.5506 \n",
"2 O 7 1.558824 131.5506 \n",
"3 DRI 19 1.370968 131.5506 \n",
"4 TMUS 19 1.277778 131.5506 \n",
"5 VIAC 11 1.625000 37.1381 \n",
"6 ABC 8 1.529412 131.5506 \n",
"7 O 8 1.526316 131.5506 \n",
"8 FIS 27 1.222222 131.5506 \n",
"9 DLR 12 1.478261 131.5506 \n",
"10 LUV 11 1.450000 113.3237 \n",
"11 HAS 11 1.323529 6.3711 \n",
"12 MRK 11 1.294118 6.3711 \n",
"13 SBUX 13 1.588235 18.3008 \n",
"14 ORCL 6 1.775862 6.3711 \n",
"15 NLOK 4 1.781250 131.5506 \n",
"16 BAC 13 1.480769 59.3275 \n",
"17 D 4 1.781250 6.3711 \n",
"18 CSCO 13 1.480769 131.5506 \n",
"19 WM 5 1.600000 6.3711 \n",
"20 CXO 28 1.157143 6.3711 \n",
"21 HUM 17 1.291667 131.5506 \n",
"22 MRK 10 1.312500 131.5506 \n",
"23 NLOK 5 1.750000 59.3211 \n",
"24 BMY 8 1.437500 6.3711 \n",
"25 DLR 11 1.521739 59.3179 \n",
"26 TJX 23 1.155172 115.8448 \n",
"27 MOS 11 1.400000 131.5506 \n",
"28 EVRG 3 1.500000 131.5506 \n",
"29 NUE 4 1.708333 131.5506 \n",
"\n",
" startDate endDate \n",
"0 2020-08-28 00:00:00+00:00 2020-08-31 00:00:00+00:00 \n",
"1 2020-08-27 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"2 2020-08-27 00:00:00+00:00 2020-08-31 00:00:00+00:00 \n",
"3 2020-08-26 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"4 2020-08-26 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"5 2020-08-26 00:00:00+00:00 2020-08-30 00:00:00+00:00 \n",
"6 2020-08-26 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"7 2020-08-26 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"8 2020-08-25 00:00:00+00:00 2020-09-01 00:00:00+00:00 \n",
"9 2020-08-24 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"10 2020-08-24 00:00:00+00:00 2020-08-30 00:00:00+00:00 \n",
"11 2020-08-24 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"12 2020-08-24 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"13 2020-08-24 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"14 2020-08-24 00:00:00+00:00 2020-09-01 00:00:00+00:00 \n",
"15 2020-08-23 00:00:00+00:00 2020-08-31 00:00:00+00:00 \n",
"16 2020-08-23 00:00:00+00:00 2020-09-01 00:00:00+00:00 \n",
"17 2020-08-23 00:00:00+00:00 2020-08-27 00:00:00+00:00 \n",
"18 2020-08-22 00:00:00+00:00 2020-09-02 00:00:00+00:00 \n",
"19 2020-08-22 00:00:00+00:00 2020-08-23 00:00:00+00:00 \n",
"20 2020-08-21 00:00:00+00:00 2020-08-30 00:00:00+00:00 \n",
"21 2020-08-21 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"22 2020-08-20 00:00:00+00:00 2020-08-23 00:00:00+00:00 \n",
"23 2020-08-20 00:00:00+00:00 2020-08-22 00:00:00+00:00 \n",
"24 2020-08-20 00:00:00+00:00 2020-08-26 00:00:00+00:00 \n",
"25 2020-08-20 00:00:00+00:00 2020-08-23 00:00:00+00:00 \n",
"26 2020-08-20 00:00:00+00:00 2020-09-01 00:00:00+00:00 \n",
"27 2020-08-20 00:00:00+00:00 2020-08-31 00:00:00+00:00 \n",
"28 2020-08-19 00:00:00+00:00 2020-08-30 00:00:00+00:00 \n",
"29 2020-08-19 00:00:00+00:00 2020-08-20 00:00:00+00:00 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBarRating = pd.DataFrame(empty)\n",
"#dfBarRating.reset_index(inplace=True)\n",
"#dfBarRating.columns = ['Symbol']\n",
"dfBarRating['symbol'] = dfRating[\"symbol\"]\n",
"dfBarRating['ratingBuy'] = dfRating[\"ratingBuy\"]\n",
"dfBarRating['ratingScaleMark'] = dfRating[\"ratingScaleMark\"]\n",
"dfBarRating['avgPriceMean'] = dfBar[[\"average_price\"]].mean(axis=1)\n",
"dfBarRating['startDate'] = dfRating[\"consensusStartDate\"]\n",
"dfBarRating['endDate'] = dfRating[\"consensusEndDate\"]\n",
"dfBarRating"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"symbol object\n",
"ratingBuy int64\n",
"ratingScaleMark float64\n",
"avgPriceMean float64\n",
"startDate object\n",
"endDate object\n",
"dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBarRating.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'avgPriceMean VS consensusStartDate'}, ylabel='startDate'>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarRating.plot(x= \"startDate\", y= \"avgPriceMean\", kind = \"barh\", title= \"avgPriceMean VS consensusStartDate\", figsize=(10,8))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'avgPriceMean VS consensusEndDate'}, ylabel='endDate'>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarRating.plot(x= \"endDate\", y= \"avgPriceMean\", kind = \"barh\", title= \"avgPriceMean VS consensusEndDate\", figsize=(10,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"# Another way because i'm not sure what you mean by \"the mean\""
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"#still working on it\n",
"mean = []\n",
"for i in dfBar[\"average_price\"]:\n",
" i = \n",
" mean.append(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>reportDate</th>\n",
" <th>average_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-08-31</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-08-31</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-08-31</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-08-31</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-08-31</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" reportDate average_price\n",
"0 2020-08-31 131.5506\n",
"1 2020-08-31 131.5506\n",
"2 2020-08-31 131.5506\n",
"3 2020-08-31 131.5506\n",
"4 2020-08-31 131.5506"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBarEvent = pd.DataFrame(empty)\n",
"dfBarEvent.reset_index(inplace=True)\n",
"dfBarEvent.columns = ['reportDate']\n",
"dfBarEvent['reportDate'] = dfEvent[\"reportDate\"]\n",
"dfBarEvent['average_price'] = dfBar[\"average_price\"]\n",
"dfBarEvent"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Report Date VS Avg Price'}, ylabel='reportDate'>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dfBarEvent.plot(x= \"reportDate\", y= \"average_price\", kind = \"barh\", title= \"Report Date VS Avg Price\", figsize=(10,8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next task"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"dfBar[\"priceTargetAverage\"] = dfTarget.loc[dfTarget[\"updatedDate\"] == \"2020-08-31\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next task"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <td>2020-09-02 20:36:00+00:00</td>\n",
" <td>6.3711</td>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>2020-09-02 20:41:00+00:00</td>\n",
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" <tr>\n",
" <th>24</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>6.3711</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>2020-09-02 20:53:00+00:00</td>\n",
" <td>18.3008</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>2020-09-02 21:08:00+00:00</td>\n",
" <td>37.1381</td>\n",
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" <tr>\n",
" <th>25</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>59.3179</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>2020-09-02 20:31:00+00:00</td>\n",
" <td>59.3211</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>2020-09-02 20:42:00+00:00</td>\n",
" <td>59.3275</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>2020-09-02 20:59:00+00:00</td>\n",
" <td>113.3237</td>\n",
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" <tr>\n",
" <th>26</th>\n",
" <td>2020-09-02 20:30:00+00:00</td>\n",
" <td>115.8448</td>\n",
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" <tr>\n",
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" <td>131.5506</td>\n",
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" <tr>\n",
" <th>18</th>\n",
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" <td>131.5506</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>2020-09-02 20:32:00+00:00</td>\n",
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" <tr>\n",
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" <td>131.5506</td>\n",
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" <tr>\n",
" <th>28</th>\n",
" <td>2020-09-02 20:29:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>9</th>\n",
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" <td>131.5506</td>\n",
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" <tr>\n",
" <th>8</th>\n",
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" <td>131.5506</td>\n",
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" <tr>\n",
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" <td>2020-09-02 21:05:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>2020-09-02 21:06:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>2020-09-02 21:12:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>2020-09-02 21:15:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
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" <td>2020-09-02 22:43:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2020-09-02 22:45:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>2020-09-02 20:51:00+00:00</td>\n",
" <td>131.5506</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>2020-09-02 20:28:00+00:00</td>\n",
" <td>131.5506</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time average_price\n",
"14 2020-09-02 20:52:00+00:00 6.3711\n",
"19 2020-09-02 20:36:00+00:00 6.3711\n",
"12 2020-09-02 20:56:00+00:00 6.3711\n",
"11 2020-09-02 20:58:00+00:00 6.3711\n",
"20 2020-09-02 20:33:00+00:00 6.3711\n",
"17 2020-09-02 20:41:00+00:00 6.3711\n",
"24 2020-09-02 20:30:00+00:00 6.3711\n",
"13 2020-09-02 20:53:00+00:00 18.3008\n",
"5 2020-09-02 21:08:00+00:00 37.1381\n",
"25 2020-09-02 20:30:00+00:00 59.3179\n",
"23 2020-09-02 20:31:00+00:00 59.3211\n",
"16 2020-09-02 20:42:00+00:00 59.3275\n",
"10 2020-09-02 20:59:00+00:00 113.3237\n",
"26 2020-09-02 20:30:00+00:00 115.8448\n",
"22 2020-09-02 20:31:00+00:00 131.5506\n",
"27 2020-09-02 20:30:00+00:00 131.5506\n",
"18 2020-09-02 20:38:00+00:00 131.5506\n",
"21 2020-09-02 20:32:00+00:00 131.5506\n",
"0 2020-09-02 22:46:00+00:00 131.5506\n",
"28 2020-09-02 20:29:00+00:00 131.5506\n",
"9 2020-09-02 21:00:00+00:00 131.5506\n",
"8 2020-09-02 21:01:00+00:00 131.5506\n",
"7 2020-09-02 21:05:00+00:00 131.5506\n",
"6 2020-09-02 21:06:00+00:00 131.5506\n",
"4 2020-09-02 21:12:00+00:00 131.5506\n",
"3 2020-09-02 21:15:00+00:00 131.5506\n",
"2 2020-09-02 22:43:00+00:00 131.5506\n",
"1 2020-09-02 22:45:00+00:00 131.5506\n",
"15 2020-09-02 20:51:00+00:00 131.5506\n",
"29 2020-09-02 20:28:00+00:00 131.5506"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBar[[\"time\", 'average_price']].sort_values(by=\"average_price\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next task"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" dateTime stock summary \\\n",
"0 NaN FB The sources claim that discussions with Facebo... \n",
"1 NaN VZ Firms in talks to buy a stake in struggling te... \n",
"2 NaN AMZN Die Plattform eBay wird 25: Gebrauchtes findet... \n",
"3 NaN MCK US News: Dallas-based wholesaler McKesson Corp... \n",
"4 NaN FB The Daily Beast : Sources: Mark Zuckerberg sai... \n",
"5 NaN AAPL Apple And Google Update Contact Tracing Softwa... \n",
"6 NaN TWTR Twitter said it was aware of the activity with... \n",
"7 NaN AMZN Cyber Security NSW said Amazon (pictured) 'won... \n",
"8 NaN NDAQ US Markets blast overnight, Dow up 454 Points,... \n",
"9 NaN FB Sarah Perez / TechCrunch : Facebook's introduc... \n",
"10 NaN AMZN MacKenzie Scott — philanthropist, author and e... \n",
"11 NaN WFC Rocket Companies, Inc. (NYSE:RKT) Q2 2020 Earn... \n",
"12 NaN V Le SUV 7 places de Peugeot profite des mêmes é... \n",
"13 NaN FB Facebook removed a post by Rep. Clay Higgins, ... \n",
"14 NaN JPM There's three constraints weighing on the pote... \n",
"15 NaN FB Summary List Placement PayPal has terminated a... \n",
"16 NaN XOM Since my capital is limited, it sometimes mean... \n",
"17 NaN FB Summary List Placement A recent internal debat... \n",
"18 NaN C China maintains pace in the opening of its fin... \n",
"19 NaN GOOG The plan includes up to 1,850 residential homes. \n",
"20 NaN TWTR The incident comes after several Twitter accou... \n",
"21 NaN VZ The stake-sale talks were paused because the o... \n",
"22 NaN EQIX Asia Pacific’s fast-developing market for serv... \n",
"23 NaN DISCA The “Star Trek” umbrella is becoming even more... \n",
"24 NaN TWTR Read more about Twitter confirms account of PM... \n",
"25 NaN AAPL Every day, Macworld brings you the essential d... \n",
"26 NaN GM Ivanka, 38, met with General Motors CEO Mary B... \n",
"27 NaN DIS According to an announcement on the Star Wars ... \n",
"28 NaN FB Summary List Placement Google has resumed empl... \n",
"29 NaN KR Kroger is set to report fiscal second quarter ... \n",
"\n",
" averageprice \n",
"0 131.5506 \n",
"1 131.5506 \n",
"2 131.5506 \n",
"3 131.5506 \n",
"4 131.5506 \n",
"5 37.1381 \n",
"6 131.5506 \n",
"7 131.5506 \n",
"8 131.5506 \n",
"9 131.5506 \n",
"10 113.3237 \n",
"11 6.3711 \n",
"12 6.3711 \n",
"13 18.3008 \n",
"14 6.3711 \n",
"15 131.5506 \n",
"16 59.3275 \n",
"17 6.3711 \n",
"18 131.5506 \n",
"19 6.3711 \n",
"20 6.3711 \n",
"21 131.5506 \n",
"22 131.5506 \n",
"23 59.3211 \n",
"24 6.3711 \n",
"25 59.3179 \n",
"26 115.8448 \n",
"27 131.5506 \n",
"28 131.5506 \n",
"29 131.5506 "
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfBarNews = pd.DataFrame(empty)\n",
"dfBarNews.reset_index(inplace=True)\n",
"dfBarNews.columns = ['dateTime']\n",
"dfBarNews['dateTime'] = dfNews.loc[dfNews[\"datetime\"] == \"2020-08-12 11:31\"]\n",
"dfBarNews['stock'] = dfNews[\"stock\"]\n",
"dfBarNews['summary'] = dfNews[\"summary\"]\n",
"dfBarNews['averageprice'] = dfBar[\"average_price\"]\n",
"\n",
"dfBarNews"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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