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4. Historical Stock Prices Data Set

from datascience import *
import numpy as np

%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')

Warning!! Do not execute code block below unless you have a good bandwidth connection and are prepared to wait 60+ seconds.

The file stocks.csv includes thousands of historical stock prices from 1970 to 2018. The file is 2GB of data which, given time, Python can read into this notebook. I have created some subsets of the table, however, which you may want to work with first before downloading the big daddy table.

#stocks =  Table.read_table('http://faculty.ung.edu/rsinn/stocks.csv')

### The following subsets are much smaller files

microsoft = Table.read_table('http://faculty.ung.edu/rsinn/msft.csv')
microsoft

ticker	open	close	adj_close	low	high	volume	date
MSFT	0.0885417	0.0972222	0.0708597	0.0885417	0.101562	1031788800	1986-03-13
MSFT	0.0972222	0.100694	0.0733905	0.0972222	0.102431	308160000	1986-03-14
MSFT	0.100694	0.102431	0.0746558	0.100694	0.103299	133171200	1986-03-17
MSFT	0.102431	0.0998264	0.0727578	0.0989583	0.103299	67766400	1986-03-18
MSFT	0.0998264	0.0980903	0.0714925	0.0972222	0.100694	47894400	1986-03-19
MSFT	0.0980903	0.0954861	0.0695944	0.0946181	0.0980903	58435200	1986-03-20
MSFT	0.0954861	0.0928819	0.0676964	0.0911458	0.0972222	59990400	1986-03-21
MSFT	0.0928819	0.0902778	0.0657983	0.0894097	0.0928819	65289600	1986-03-24
MSFT	0.0902778	0.0920139	0.0670637	0.0894097	0.0920139	32083200	1986-03-25
MSFT	0.0920139	0.0946181	0.0689617	0.0911458	0.0954861	22752000	1986-03-26

... (8172 rows omitted)

To create a CSV file from a table and store in current directory

# microsoft.to_csv('msft.csv')

faang = Table.read_table('http://faculty.ung.edu/rsinn/faang.csv')
faang

ticker	open	close	adj_close	low	high	volume	date
AAPL	0.513393	0.513393	0.0231864	0.513393	0.515625	117258400	1980-12-12
AAPL	0.488839	0.486607	0.0219766	0.486607	0.488839	43971200	1980-12-15
AAPL	0.453125	0.450893	0.0203637	0.450893	0.453125	26432000	1980-12-16
AAPL	0.462054	0.462054	0.0208677	0.462054	0.464286	21610400	1980-12-17
AAPL	0.475446	0.475446	0.0214726	0.475446	0.477679	18362400	1980-12-18
AAPL	0.504464	0.504464	0.0227831	0.504464	0.506696	12157600	1980-12-19
AAPL	0.529018	0.529018	0.023892	0.529018	0.53125	9340800	1980-12-22
AAPL	0.551339	0.551339	0.0249001	0.551339	0.553571	11737600	1980-12-23
AAPL	0.580357	0.580357	0.0262107	0.580357	0.582589	12000800	1980-12-24
AAPL	0.633929	0.633929	0.0286301	0.633929	0.636161	13893600	1980-12-26

... (24055 rows omitted)

facebook = faang.where('ticker','FB')
apple = faang.where('ticker','AAPL')
amazon = faang.where('ticker','AMZN')
netflix = faang.where('ticker','NFLX')
google = faang.where('ticker','GOOGL')

facebook

ticker	open	close	adj_close	low	high	volume	date
FB	42.05	38.23	38.23	38	45	573576400	2012-05-18
FB	36.53	34.03	34.03	33	36.66	168192700	2012-05-21
FB	32.61	31	31	30.94	33.59	101786600	2012-05-22
FB	31.37	32	32	31.36	32.5	73600000	2012-05-23
FB	32.95	33.03	33.03	31.77	33.21	50237200	2012-05-24
FB	32.9	31.91	31.91	31.11	32.95	37149800	2012-05-25
FB	31.48	28.84	28.84	28.65	31.69	78063400	2012-05-29
FB	28.7	28.19	28.19	27.86	29.55	57267900	2012-05-30
FB	28.55	29.6	29.6	26.83	29.67	111639200	2012-05-31
FB	28.89	27.72	27.72	27.39	29.15	41855500	2012-06-01

... (1568 rows omitted)

apple

ticker	open	close	adj_close	low	high	volume	date
AAPL	0.513393	0.513393	0.0231864	0.513393	0.515625	117258400	1980-12-12
AAPL	0.488839	0.486607	0.0219766	0.486607	0.488839	43971200	1980-12-15
AAPL	0.453125	0.450893	0.0203637	0.450893	0.453125	26432000	1980-12-16
AAPL	0.462054	0.462054	0.0208677	0.462054	0.464286	21610400	1980-12-17
AAPL	0.475446	0.475446	0.0214726	0.475446	0.477679	18362400	1980-12-18
AAPL	0.504464	0.504464	0.0227831	0.504464	0.506696	12157600	1980-12-19
AAPL	0.529018	0.529018	0.023892	0.529018	0.53125	9340800	1980-12-22
AAPL	0.551339	0.551339	0.0249001	0.551339	0.553571	11737600	1980-12-23
AAPL	0.580357	0.580357	0.0262107	0.580357	0.582589	12000800	1980-12-24
AAPL	0.633929	0.633929	0.0286301	0.633929	0.636161	13893600	1980-12-26

... (9497 rows omitted)

amazon

ticker	open	close	adj_close	low	high	volume	date
AMZN	2.4375	1.95833	1.95833	1.92708	2.5	72156000	1997-05-15
AMZN	1.96875	1.72917	1.72917	1.70833	1.97917	14700000	1997-05-16
AMZN	1.76042	1.70833	1.70833	1.625	1.77083	6106800	1997-05-19
AMZN	1.72917	1.63542	1.63542	1.63542	1.75	5467200	1997-05-20
AMZN	1.63542	1.42708	1.42708	1.375	1.64583	18853200	1997-05-21
AMZN	1.4375	1.39583	1.39583	1.3125	1.44792	11776800	1997-05-22
AMZN	1.40625	1.5	1.5	1.33333	1.52083	15937200	1997-05-23
AMZN	1.51042	1.58333	1.58333	1.45833	1.64583	8697600	1997-05-27
AMZN	1.625	1.53125	1.53125	1.53125	1.63542	4574400	1997-05-28
AMZN	1.54167	1.50521	1.50521	1.47917	1.54167	3472800	1997-05-29

... (5346 rows omitted)

netflix

ticker	open	close	adj_close	low	high	volume	date
NFLX	1.15643	1.19643	1.19643	1.14571	1.24286	104790000	2002-05-23
NFLX	1.21429	1.21	1.21	1.19714	1.225	11104800	2002-05-24
NFLX	1.21357	1.15714	1.15714	1.15714	1.23214	6609400	2002-05-28
NFLX	1.16429	1.10357	1.10357	1.08571	1.16429	6757800	2002-05-29
NFLX	1.10786	1.07143	1.07143	1.07143	1.10786	10154200	2002-05-30
NFLX	1.07857	1.07643	1.07643	1.07143	1.07857	8464400	2002-05-31
NFLX	1.08	1.12857	1.12857	1.07643	1.14929	3151400	2002-06-03
NFLX	1.13571	1.11786	1.11786	1.11071	1.14	3105200	2002-06-04
NFLX	1.11071	1.14714	1.14714	1.10714	1.15929	1531600	2002-06-05
NFLX	1.15	1.18214	1.18214	1.14857	1.23214	2305800	2002-06-06

... (4084 rows omitted)

google

ticker	open	close	adj_close	low	high	volume	date
GOOGL	50.05	50.2202	50.2202	48.028	52.0821	44659000	2004-08-19
GOOGL	50.5556	54.2092	54.2092	50.3003	54.5946	22834300	2004-08-20
GOOGL	55.4304	54.7548	54.7548	54.5796	56.7968	18256100	2004-08-23
GOOGL	55.6757	52.4875	52.4875	51.8368	55.8559	15247300	2004-08-24
GOOGL	52.5325	53.0531	53.0531	51.992	54.0541	9188600	2004-08-25
GOOGL	52.5275	54.009	54.009	52.3824	54.029	7094800	2004-08-26
GOOGL	54.1041	53.1281	53.1281	52.8979	54.3644	6211700	2004-08-27
GOOGL	52.6927	51.0561	51.0561	51.0561	52.7978	5196700	2004-08-30
GOOGL	51.2012	51.2362	51.2362	51.1311	51.9069	4917800	2004-08-31
GOOGL	51.4014	50.1752	50.1752	49.8849	51.5365	9138200	2004-09-01

... (3520 rows omitted)

faang.sort('date', descending = True)

ticker	open	close	adj_close	low	high	volume	date
AAPL	216.6	216.16	216.16	215.11	216.9	18469900	2018-08-24
AMZN	1910.51	1905.39	1905.39	1902.54	1916.01	2792600	2018-08-24
NFLX	346	358.82	358.82	344.54	359.15	14711000	2018-08-24
GOOGL	1226	1236.75	1236.75	1221.42	1237.4	1222000	2018-08-24
FB	173.7	174.65	174.65	172.92	174.82	14617600	2018-08-24
AAPL	214.65	215.49	215.49	214.6	217.05	18883200	2018-08-23
AMZN	1907.17	1902.9	1902.9	1900.76	1919.5	3563000	2018-08-23
NFLX	348.11	339.17	339.17	337.65	350.08	11336400	2018-08-23
GOOGL	1219.88	1221.16	1221.16	1219.35	1235.17	1233300	2018-08-23
FB	173.09	172.9	172.9	172.83	175.55	18053600	2018-08-23

... (24055 rows omitted)

To create a CSV file from a table and store in current directory

# faang.to_csv('faang.csv')

3. Writing Functions in Python 5. Subsetting Tables using World Airport Data and GIS Maps