Skip to content

Instantly share code, notes, and snippets.

@KFoxder

KFoxder/pychartssetup.ipynb

Created May 23, 2018 15:54
Show Gist options
  • Save KFoxder/db7ad64ca240eba4544c35a48ad4ad44 to your computer and use it in GitHub Desktop.
Save KFoxder/db7ad64ca240eba4544c35a48ad4ad44 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
pychartssetup.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pycharts import CompanyClient, IndicatorClient, MutualFundClient\n",
"\n",
"ycharts_api_key = 'G/frzo6YEeCaBxS0/HhfaA'\n",
"\n",
"company_client = CompanyClient(ycharts_api_key)\n",
"mutual_fund_client = MutualFundClient(ycharts_api_key)\n",
"indicator_client = IndicatorClient(ycharts_api_key)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"companies = company_client.get_securities(exchange='NYSE')\n",
"mutual_funds = mutual_fund_client.get_securities(category='Technology')\n",
"indicators = indicator_client.get_securities(region='USA')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Queries the latest price values for AAPL and MSFT\n",
"point_rsp = company_client.get_points(['AAPL', 'MSFT'], ['price'])\n",
"# Queries the latest net asset value for M:FCNTX\n",
"point_rsp = mutual_fund_client.get_points('M:FCNTX', ['net_asset_value'])\n",
"# Queries the latest value for I:USICUI\n",
"point_rsp = indicator_client.get_points('I:USICUI')\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import datetime\n",
"now = datetime.datetime.now()\n",
"past = now - datetime.timedelta(days=100)\n",
"\n",
"series_rsp = company_client.get_series(['AAPL', 'MSFT'], ['price'],\n",
" query_start_date=past , query_end_date=now)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'meta': {u'url': u'http://ycharts.com/api/v3/indicators/I:USICUI/points', u'status': u'ok'}, u'response': {u'I:USICUI': {u'meta': {u'status': u'ok'}, u'results': {u'meta': {u'status': u'ok'}, u'data': [u'2018-05-12', 222000.0]}}}}\n"
]
}
],
"source": [
"print point_rsp\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
"import pandas as pd\n",
"start_date = datetime.datetime(2015, 1, 1)\n",
"dividend_rsp = company_client.get_dividends(['F', 'MSFT'], ex_start_date=start_date, \n",
" dividend_type='special')\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'meta': {u'url': u'http://ycharts.com/api/v3/companies/F,MSFT/dividends?dividend_type=special&start_date=2015-01-01', u'status': u'ok'}, u'response': {u'MSFT': {u'meta': {u'status': u'ok'}, u'results': []}, u'F': {u'meta': {u'status': u'ok'}, u'results': [{u'dividend_type': u'special', u'declared_date': u'2016-01-12', u'pay_date': u'2016-03-01', u'dividend_amount': 0.25, u'ex_date': u'2016-01-27', u'adjusted_dividend_amount': 0.25, u'currency_code': u'USD', u'record_date': u'2016-01-29'}, {u'dividend_type': u'special', u'declared_date': u'2017-01-10', u'pay_date': u'2017-03-01', u'dividend_amount': 0.05, u'ex_date': u'2017-01-18', u'adjusted_dividend_amount': 0.05, u'currency_code': u'USD', u'record_date': u'2017-01-20'}, {u'dividend_type': u'special', u'declared_date': u'2018-01-17', u'pay_date': u'2018-03-01', u'dividend_amount': 0.13, u'ex_date': u'2018-01-29', u'adjusted_dividend_amount': 0.13, u'currency_code': u'USD', u'record_date': u'2018-01-30'}]}}}\n"
]
}
],
"source": [
"print dividend_rsp\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"ford = dividend_rsp[\"response\"][\"F\"][\"results\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"\n",
"resp = pd.DataFrame.from_dict((ford), orient='columns')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2016-01-27\n",
"1 2017-01-18\n",
"2 2018-01-29\n",
"Name: ex_date, dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resp[\"ex_date\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>adjusted_dividend_amount</th>\n",
" <th>currency_code</th>\n",
" <th>declared_date</th>\n",
" <th>dividend_amount</th>\n",
" <th>dividend_type</th>\n",
" <th>ex_date</th>\n",
" <th>pay_date</th>\n",
" <th>record_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.25</td>\n",
" <td>USD</td>\n",
" <td>2016-01-12</td>\n",
" <td>0.25</td>\n",
" <td>special</td>\n",
" <td>2016-01-27</td>\n",
" <td>2016-03-01</td>\n",
" <td>2016-01-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.05</td>\n",
" <td>USD</td>\n",
" <td>2017-01-10</td>\n",
" <td>0.05</td>\n",
" <td>special</td>\n",
" <td>2017-01-18</td>\n",
" <td>2017-03-01</td>\n",
" <td>2017-01-20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.13</td>\n",
" <td>USD</td>\n",
" <td>2018-01-17</td>\n",
" <td>0.13</td>\n",
" <td>special</td>\n",
" <td>2018-01-29</td>\n",
" <td>2018-03-01</td>\n",
" <td>2018-01-30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" adjusted_dividend_amount currency_code declared_date dividend_amount \\\n",
"0 0.25 USD 2016-01-12 0.25 \n",
"1 0.05 USD 2017-01-10 0.05 \n",
"2 0.13 USD 2018-01-17 0.13 \n",
"\n",
" dividend_type ex_date pay_date record_date \n",
"0 special 2016-01-27 2016-03-01 2016-01-29 \n",
"1 special 2017-01-18 2017-03-01 2017-01-20 \n",
"2 special 2018-01-29 2018-03-01 2018-01-30 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resp"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{u'meta': {u'status': u'ok',\n",
" u'url': u'http://ycharts.com/api/v3/indicators/I:USICUI/points'},\n",
" u'response': {u'I:USICUI': {u'meta': {u'status': u'ok'},\n",
" u'results': {u'data': [u'2018-05-12', 222000.0],\n",
" u'meta': {u'status': u'ok'}}}}}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"point_rsp"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{u'meta': {u'status': u'ok',\n",
" u'url': u'http://ycharts.com/api/v3/companies/AAPL,MSFT/series/price?start_date=2018-02-11&end_date=2018-05-22'},\n",
" u'response': {u'AAPL': {u'meta': {u'status': u'ok'},\n",
" u'results': {u'price': {u'data': [[u'2018-02-12', 162.71],\n",
" [u'2018-02-13', 164.34],\n",
" [u'2018-02-14', 167.37],\n",
" [u'2018-02-15', 172.99],\n",
" [u'2018-02-16', 172.43],\n",
" [u'2018-02-20', 171.85],\n",
" [u'2018-02-21', 171.07],\n",
" [u'2018-02-22', 172.5],\n",
" [u'2018-02-23', 175.5],\n",
" [u'2018-02-26', 178.97],\n",
" [u'2018-02-27', 178.39],\n",
" [u'2018-02-28', 178.12],\n",
" [u'2018-03-01', 175.0],\n",
" [u'2018-03-02', 176.21],\n",
" [u'2018-03-05', 176.82],\n",
" [u'2018-03-06', 176.67],\n",
" [u'2018-03-07', 175.03],\n",
" [u'2018-03-08', 176.94],\n",
" [u'2018-03-09', 179.98],\n",
" [u'2018-03-12', 181.72],\n",
" [u'2018-03-13', 179.97],\n",
" [u'2018-03-14', 178.44],\n",
" [u'2018-03-15', 178.65],\n",
" [u'2018-03-16', 178.02],\n",
" [u'2018-03-19', 175.3],\n",
" [u'2018-03-20', 175.24],\n",
" [u'2018-03-21', 171.27],\n",
" [u'2018-03-22', 168.85],\n",
" [u'2018-03-23', 164.94],\n",
" [u'2018-03-26', 172.77],\n",
" [u'2018-03-27', 168.34],\n",
" [u'2018-03-28', 166.48],\n",
" [u'2018-03-29', 167.78],\n",
" [u'2018-04-02', 166.68],\n",
" [u'2018-04-03', 168.39],\n",
" [u'2018-04-04', 171.61],\n",
" [u'2018-04-05', 172.8],\n",
" [u'2018-04-06', 168.38],\n",
" [u'2018-04-09', 170.05],\n",
" [u'2018-04-10', 173.25],\n",
" [u'2018-04-11', 172.44],\n",
" [u'2018-04-12', 174.14],\n",
" [u'2018-04-13', 174.73],\n",
" [u'2018-04-16', 175.82],\n",
" [u'2018-04-17', 178.24],\n",
" [u'2018-04-18', 177.84],\n",
" [u'2018-04-19', 172.8],\n",
" [u'2018-04-20', 165.72],\n",
" [u'2018-04-23', 165.24],\n",
" [u'2018-04-24', 162.94],\n",
" [u'2018-04-25', 163.65],\n",
" [u'2018-04-26', 164.22],\n",
" [u'2018-04-27', 162.32],\n",
" [u'2018-04-30', 165.26],\n",
" [u'2018-05-01', 169.1],\n",
" [u'2018-05-02', 176.57],\n",
" [u'2018-05-03', 176.89],\n",
" [u'2018-05-04', 183.83],\n",
" [u'2018-05-07', 185.16],\n",
" [u'2018-05-08', 186.05],\n",
" [u'2018-05-09', 187.36],\n",
" [u'2018-05-10', 190.04],\n",
" [u'2018-05-11', 188.59],\n",
" [u'2018-05-14', 188.15],\n",
" [u'2018-05-15', 186.44],\n",
" [u'2018-05-16', 188.18],\n",
" [u'2018-05-17', 186.99],\n",
" [u'2018-05-18', 186.31],\n",
" [u'2018-05-21', 187.63],\n",
" [u'2018-05-22', 187.29]],\n",
" u'meta': {u'status': u'ok'}}}},\n",
" u'MSFT': {u'meta': {u'status': u'ok'},\n",
" u'results': {u'price': {u'data': [[u'2018-02-12', 89.13],\n",
" [u'2018-02-13', 89.83],\n",
" [u'2018-02-14', 90.81],\n",
" [u'2018-02-15', 92.66],\n",
" [u'2018-02-16', 92.0],\n",
" [u'2018-02-20', 92.72],\n",
" [u'2018-02-21', 91.49],\n",
" [u'2018-02-22', 91.73],\n",
" [u'2018-02-23', 94.06],\n",
" [u'2018-02-26', 95.42],\n",
" [u'2018-02-27', 94.2],\n",
" [u'2018-02-28', 93.77],\n",
" [u'2018-03-01', 92.85],\n",
" [u'2018-03-02', 93.05],\n",
" [u'2018-03-05', 93.64],\n",
" [u'2018-03-06', 93.32],\n",
" [u'2018-03-07', 93.86],\n",
" [u'2018-03-08', 94.43],\n",
" [u'2018-03-09', 96.54],\n",
" [u'2018-03-12', 96.77],\n",
" [u'2018-03-13', 94.41],\n",
" [u'2018-03-14', 93.85],\n",
" [u'2018-03-15', 94.18],\n",
" [u'2018-03-16', 94.6],\n",
" [u'2018-03-19', 92.89],\n",
" [u'2018-03-20', 93.13],\n",
" [u'2018-03-21', 92.48],\n",
" [u'2018-03-22', 89.79],\n",
" [u'2018-03-23', 87.18],\n",
" [u'2018-03-26', 93.78],\n",
" [u'2018-03-27', 89.47],\n",
" [u'2018-03-28', 89.39],\n",
" [u'2018-03-29', 91.27],\n",
" [u'2018-04-02', 88.52],\n",
" [u'2018-04-03', 89.71],\n",
" [u'2018-04-04', 92.33],\n",
" [u'2018-04-05', 92.38],\n",
" [u'2018-04-06', 90.23],\n",
" [u'2018-04-09', 90.77],\n",
" [u'2018-04-10', 92.88],\n",
" [u'2018-04-11', 91.86],\n",
" [u'2018-04-12', 93.58],\n",
" [u'2018-04-13', 93.08],\n",
" [u'2018-04-16', 94.17],\n",
" [u'2018-04-17', 96.07],\n",
" [u'2018-04-18', 96.44],\n",
" [u'2018-04-19', 96.11],\n",
" [u'2018-04-20', 95.0],\n",
" [u'2018-04-23', 95.35],\n",
" [u'2018-04-24', 93.12],\n",
" [u'2018-04-25', 92.31],\n",
" [u'2018-04-26', 94.26],\n",
" [u'2018-04-27', 95.82],\n",
" [u'2018-04-30', 93.52],\n",
" [u'2018-05-01', 95.0],\n",
" [u'2018-05-02', 93.51],\n",
" [u'2018-05-03', 94.07],\n",
" [u'2018-05-04', 95.16],\n",
" [u'2018-05-07', 96.22],\n",
" [u'2018-05-08', 95.81],\n",
" [u'2018-05-09', 96.94],\n",
" [u'2018-05-10', 97.91],\n",
" [u'2018-05-11', 97.7],\n",
" [u'2018-05-14', 98.03],\n",
" [u'2018-05-15', 97.32],\n",
" [u'2018-05-16', 97.15],\n",
" [u'2018-05-17', 96.18],\n",
" [u'2018-05-18', 96.36],\n",
" [u'2018-05-21', 97.6],\n",
" [u'2018-05-22', 97.56]],\n",
" u'meta': {u'status': u'ok'}}}}}}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series_rsp"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"\n",
"resp = pd.DataFrame.from_dict((series_rsp)[\"response\"][\"AAPL\"][\"results\"])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"aapl = series_rsp[\"response\"][\"AAPL\"][\"results\"][\"price\"][\"data\"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"aaple = pd.DataFrame.from_dict(aapl)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2018-02-12</td>\n",
" <td>162.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-02-13</td>\n",
" <td>164.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-02-14</td>\n",
" <td>167.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2018-02-15</td>\n",
" <td>172.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2018-02-16</td>\n",
" <td>172.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2018-02-20</td>\n",
" <td>171.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2018-02-21</td>\n",
" <td>171.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2018-02-22</td>\n",
" <td>172.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2018-02-23</td>\n",
" <td>175.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2018-02-26</td>\n",
" <td>178.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2018-02-27</td>\n",
" <td>178.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2018-02-28</td>\n",
" <td>178.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2018-03-01</td>\n",
" <td>175.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2018-03-02</td>\n",
" <td>176.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2018-03-05</td>\n",
" <td>176.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2018-03-06</td>\n",
" <td>176.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2018-03-07</td>\n",
" <td>175.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2018-03-08</td>\n",
" <td>176.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2018-03-09</td>\n",
" <td>179.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2018-03-12</td>\n",
" <td>181.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2018-03-13</td>\n",
" <td>179.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2018-03-14</td>\n",
" <td>178.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2018-03-15</td>\n",
" <td>178.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2018-03-16</td>\n",
" <td>178.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2018-03-19</td>\n",
" <td>175.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2018-03-20</td>\n",
" <td>175.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2018-03-21</td>\n",
" <td>171.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2018-03-22</td>\n",
" <td>168.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018-03-23</td>\n",
" <td>164.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2018-03-26</td>\n",
" <td>172.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>2018-04-11</td>\n",
" <td>172.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2018-04-12</td>\n",
" <td>174.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2018-04-13</td>\n",
" <td>174.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2018-04-16</td>\n",
" <td>175.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2018-04-17</td>\n",
" <td>178.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2018-04-18</td>\n",
" <td>177.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2018-04-19</td>\n",
" <td>172.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2018-04-20</td>\n",
" <td>165.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>2018-04-23</td>\n",
" <td>165.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2018-04-24</td>\n",
" <td>162.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2018-04-25</td>\n",
" <td>163.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>2018-04-26</td>\n",
" <td>164.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>2018-04-27</td>\n",
" <td>162.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>2018-04-30</td>\n",
" <td>165.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>2018-05-01</td>\n",
" <td>169.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>2018-05-02</td>\n",
" <td>176.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>2018-05-03</td>\n",
" <td>176.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2018-05-04</td>\n",
" <td>183.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>2018-05-07</td>\n",
" <td>185.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>2018-05-08</td>\n",
" <td>186.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>2018-05-09</td>\n",
" <td>187.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>2018-05-10</td>\n",
" <td>190.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2018-05-11</td>\n",
" <td>188.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2018-05-14</td>\n",
" <td>188.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>2018-05-15</td>\n",
" <td>186.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2018-05-16</td>\n",
" <td>188.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>2018-05-17</td>\n",
" <td>186.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>2018-05-18</td>\n",
" <td>186.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>2018-05-21</td>\n",
" <td>187.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>2018-05-22</td>\n",
" <td>187.29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 2018-02-12 162.71\n",
"1 2018-02-13 164.34\n",
"2 2018-02-14 167.37\n",
"3 2018-02-15 172.99\n",
"4 2018-02-16 172.43\n",
"5 2018-02-20 171.85\n",
"6 2018-02-21 171.07\n",
"7 2018-02-22 172.50\n",
"8 2018-02-23 175.50\n",
"9 2018-02-26 178.97\n",
"10 2018-02-27 178.39\n",
"11 2018-02-28 178.12\n",
"12 2018-03-01 175.00\n",
"13 2018-03-02 176.21\n",
"14 2018-03-05 176.82\n",
"15 2018-03-06 176.67\n",
"16 2018-03-07 175.03\n",
"17 2018-03-08 176.94\n",
"18 2018-03-09 179.98\n",
"19 2018-03-12 181.72\n",
"20 2018-03-13 179.97\n",
"21 2018-03-14 178.44\n",
"22 2018-03-15 178.65\n",
"23 2018-03-16 178.02\n",
"24 2018-03-19 175.30\n",
"25 2018-03-20 175.24\n",
"26 2018-03-21 171.27\n",
"27 2018-03-22 168.85\n",
"28 2018-03-23 164.94\n",
"29 2018-03-26 172.77\n",
".. ... ...\n",
"40 2018-04-11 172.44\n",
"41 2018-04-12 174.14\n",
"42 2018-04-13 174.73\n",
"43 2018-04-16 175.82\n",
"44 2018-04-17 178.24\n",
"45 2018-04-18 177.84\n",
"46 2018-04-19 172.80\n",
"47 2018-04-20 165.72\n",
"48 2018-04-23 165.24\n",
"49 2018-04-24 162.94\n",
"50 2018-04-25 163.65\n",
"51 2018-04-26 164.22\n",
"52 2018-04-27 162.32\n",
"53 2018-04-30 165.26\n",
"54 2018-05-01 169.10\n",
"55 2018-05-02 176.57\n",
"56 2018-05-03 176.89\n",
"57 2018-05-04 183.83\n",
"58 2018-05-07 185.16\n",
"59 2018-05-08 186.05\n",
"60 2018-05-09 187.36\n",
"61 2018-05-10 190.04\n",
"62 2018-05-11 188.59\n",
"63 2018-05-14 188.15\n",
"64 2018-05-15 186.44\n",
"65 2018-05-16 188.18\n",
"66 2018-05-17 186.99\n",
"67 2018-05-18 186.31\n",
"68 2018-05-21 187.63\n",
"69 2018-05-22 187.29\n",
"\n",
"[70 rows x 2 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aaple"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "expected string or buffer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-a512ae25dfa7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mford_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mford\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Ale/anaconda2/lib/python2.7/json/__init__.pyc\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 338\u001b[0m parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 339\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 340\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Ale/anaconda2/lib/python2.7/json/decoder.pyc\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 363\u001b[0m \"\"\"\n\u001b[0;32m--> 364\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 365\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 366\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: expected string or buffer"
]
}
],
"source": [
"ford_data=json.loads(ford)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment