Statement of Completion#e6349fbf
Matthew Gray has successfully completed this project.
Advanced collections
easy
List Comprehensions Practice
Resolution
Activities
In [1]:
import numpy as np
import pandas as pd
Datasets¶
First, start by looking at the data we'll use for our List Comprehension exercises. The data comes from CSVs that we parse into Lists.
In [2]:
# this is just to show you the structure of the data
# we'll be working with
pd.read_csv('taylor.csv', index_col=0).head()
Out[2]:
| x | |
|---|---|
| 1 | 22262 |
| 2 | 21756 |
| 3 | 22247 |
| 4 | 22759 |
| 5 | 22549 |
In [5]:
electricity_demand = list(pd.read_csv('taylor.csv', index_col=0).squeeze("columns").values)
electricity_demand[:10]
Out[5]:
[22262, 21756, 22247, 22759, 22549, 22313, 22128, 21860, 21751, 21336]
Dataset 2: S&P Companies¶
Companies in the S&P500 index. This is not comprehensive as the data is from 2018 and some might have changed.
We'll read the list in the variable sp_companies.
In [6]:
pd.read_csv('sp_companies.csv').head()
--------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[6], line 1 ----> 1 pd.read_csv('sp_companies.csv').head() File /usr/local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend) 899 kwds_defaults = _refine_defaults_read( 900 dialect, 901 delimiter, (...) 908 dtype_backend=dtype_backend, 909 ) 910 kwds.update(kwds_defaults) --> 912 return _read(filepath_or_buffer, kwds) File /usr/local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:577, in _read(filepath_or_buffer, kwds) 574 _validate_names(kwds.get("names", None)) 576 # Create the parser. --> 577 parser = TextFileReader(filepath_or_buffer, **kwds) 579 if chunksize or iterator: 580 return parser File /usr/local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds) 1404 self.options["has_index_names"] = kwds["has_index_names"] 1406 self.handles: IOHandles | None = None -> 1407 self._engine = self._make_engine(f, self.engine) File /usr/local/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1661, in TextFileReader._make_engine(self, f, engine) 1659 if "b" not in mode: 1660 mode += "b" -> 1661 self.handles = get_handle( 1662 f, 1663 mode, 1664 encoding=self.options.get("encoding", None), 1665 compression=self.options.get("compression", None), 1666 memory_map=self.options.get("memory_map", False), 1667 is_text=is_text, 1668 errors=self.options.get("encoding_errors", "strict"), 1669 storage_options=self.options.get("storage_options", None), 1670 ) 1671 assert self.handles is not None 1672 f = self.handles.handle File /usr/local/lib/python3.11/site-packages/pandas/io/common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options) 854 elif isinstance(handle, str): 855 # Check whether the filename is to be opened in binary mode. 856 # Binary mode does not support 'encoding' and 'newline'. 857 if ioargs.encoding and "b" not in ioargs.mode: 858 # Encoding --> 859 handle = open( 860 handle, 861 ioargs.mode, 862 encoding=ioargs.encoding, 863 errors=errors, 864 newline="", 865 ) 866 else: 867 # Binary mode 868 handle = open(handle, ioargs.mode) FileNotFoundError: [Errno 2] No such file or directory: 'sp_companies.csv'
In [7]:
# this is just to show you the structure of the data
# we'll be working with
pd.read_csv('sp500.csv', usecols=['Name']).head()
Out[7]:
| Name | |
|---|---|
| 0 | A |
| 1 | AAL |
| 2 | AAP |
| 3 | AAPL |
| 4 | ABBV |
In [8]:
sp_companies = list(pd.read_csv('sp500.csv', usecols=['Name']).squeeze("columns").values)
sp_companies[:10]
Out[8]:
['A', 'AAL', 'AAP', 'AAPL', 'ABBV', 'ABC', 'ABT', 'ACN', 'ADBE', 'ADI']
Dataset 3: S&P Companies Stock Price¶
This is going to be a list of tuples, containing the ticker of the company and their latest recorded stock price (for February 2018). In the form: [(ticker, price), (ticker, price), ...]
We'll read the list in the variable sp_prices.
In [9]:
# this is just to show you the structure of the data
# we'll be working with
pd.read_csv('sp500.csv').head(5)
Out[9]:
| Name | Price | |
|---|---|---|
| 0 | A | 68.06 |
| 1 | AAL | 51.40 |
| 2 | AAP | 109.93 |
| 3 | AAPL | 159.54 |
| 4 | ABBV | 113.62 |
In [10]:
sp_prices = [(row['Name'], row['Price']) for idx, row in pd.read_csv('sp500.csv').iterrows()]
sp_prices[:10]
Out[10]:
[('A', 68.06),
('AAL', 51.4),
('AAP', 109.93),
('AAPL', 159.54),
('ABBV', 113.62),
('ABC', 94.22),
('ABT', 58.67),
('ACN', 155.15),
('ADBE', 192.34),
('ADI', 85.35)]
Activities¶
Use the following cells to complete the activities.
1. Lower-case all the companies in sp_companies¶
In [12]:
sp_companies[:5]
Out[12]:
['A', 'AAL', 'AAP', 'AAPL', 'ABBV']
In [11]:
[comp.lower() for comp in sp_companies]
Out[11]:
['a', 'aal', 'aap', 'aapl', 'abbv', 'abc', 'abt', 'acn', 'adbe', 'adi', 'adm', 'adp', 'ads', 'adsk', 'aee', 'aep', 'aes', 'aet', 'afl', 'agn', 'aig', 'aiv', 'aiz', 'ajg', 'akam', 'alb', 'algn', 'alk', 'all', 'alle', 'alxn', 'amat', 'amd', 'ame', 'amg', 'amgn', 'amp', 'amt', 'amzn', 'andv', 'anss', 'antm', 'aon', 'aos', 'apa', 'apc', 'apd', 'aph', 'aptv', 'are', 'arnc', 'atvi', 'avb', 'avgo', 'avy', 'awk', 'axp', 'ayi', 'azo', 'ba', 'bac', 'bax', 'bbt', 'bby', 'bdx', 'ben', 'bf.b', 'bhf', 'bhge', 'biib', 'bk', 'blk', 'bll', 'bmy', 'brk.b', 'bsx', 'bwa', 'bxp', 'c', 'ca', 'cag', 'cah', 'cat', 'cb', 'cbg', 'cboe', 'cbs', 'cci', 'ccl', 'cdns', 'celg', 'cern', 'cf', 'cfg', 'chd', 'chk', 'chrw', 'chtr', 'ci', 'cinf', 'cl', 'clx', 'cma', 'cmcsa', 'cme', 'cmg', 'cmi', 'cms', 'cnc', 'cnp', 'cof', 'cog', 'col', 'coo', 'cop', 'cost', 'coty', 'cpb', 'crm', 'csco', 'csra', 'csx', 'ctas', 'ctl', 'ctsh', 'ctxs', 'cvs', 'cvx', 'cxo', 'd', 'dal', 'de', 'dfs', 'dg', 'dgx', 'dhi', 'dhr', 'dis', 'disca', 'disck', 'dish', 'dlr', 'dltr', 'dov', 'dps', 'dre', 'dri', 'dte', 'duk', 'dva', 'dvn', 'dwdp', 'dxc', 'ea', 'ebay', 'ecl', 'ed', 'efx', 'eix', 'el', 'emn', 'emr', 'eog', 'eqix', 'eqr', 'eqt', 'es', 'esrx', 'ess', 'etfc', 'etn', 'etr', 'evhc', 'ew', 'exc', 'expd', 'expe', 'exr', 'f', 'fast', 'fb', 'fbhs', 'fcx', 'fdx', 'fe', 'ffiv', 'fis', 'fisv', 'fitb', 'fl', 'flir', 'flr', 'fls', 'fmc', 'fox', 'foxa', 'frt', 'fti', 'ftv', 'gd', 'ge', 'ggp', 'gild', 'gis', 'glw', 'gm', 'goog', 'googl', 'gpc', 'gpn', 'gps', 'grmn', 'gs', 'gt', 'gww', 'hal', 'has', 'hban', 'hbi', 'hca', 'hcn', 'hcp', 'hd', 'hes', 'hig', 'hii', 'hlt', 'hog', 'holx', 'hon', 'hp', 'hpe', 'hpq', 'hrb', 'hrl', 'hrs', 'hsic', 'hst', 'hsy', 'hum', 'ibm', 'ice', 'idxx', 'iff', 'ilmn', 'incy', 'info', 'intc', 'intu', 'ip', 'ipg', 'iqv', 'ir', 'irm', 'isrg', 'it', 'itw', 'ivz', 'jbht', 'jci', 'jec', 'jnj', 'jnpr', 'jpm', 'jwn', 'k', 'key', 'khc', 'kim', 'klac', 'kmb', 'kmi', 'kmx', 'ko', 'kors', 'kr', 'kss', 'ksu', 'l', 'lb', 'leg', 'len', 'lh', 'lkq', 'lll', 'lly', 'lmt', 'lnc', 'lnt', 'low', 'lrcx', 'luk', 'luv', 'lyb', 'm', 'ma', 'maa', 'mac', 'mar', 'mas', 'mat', 'mcd', 'mchp', 'mck', 'mco', 'mdlz', 'mdt', 'met', 'mgm', 'mhk', 'mkc', 'mlm', 'mmc', 'mmm', 'mnst', 'mo', 'mon', 'mos', 'mpc', 'mrk', 'mro', 'ms', 'msft', 'msi', 'mtb', 'mtd', 'mu', 'myl', 'navi', 'nbl', 'nclh', 'ndaq', 'nee', 'nem', 'nflx', 'nfx', 'ni', 'nke', 'nlsn', 'noc', 'nov', 'nrg', 'nsc', 'ntap', 'ntrs', 'nue', 'nvda', 'nwl', 'nws', 'nwsa', 'o', 'oke', 'omc', 'orcl', 'orly', 'oxy', 'payx', 'pbct', 'pcar', 'pcg', 'pcln', 'pdco', 'peg', 'pep', 'pfe', 'pfg', 'pg', 'pgr', 'ph', 'phm', 'pkg', 'pki', 'pld', 'pm', 'pnc', 'pnr', 'pnw', 'ppg', 'ppl', 'prgo', 'pru', 'psa', 'psx', 'pvh', 'pwr', 'px', 'pxd', 'pypl', 'qcom', 'qrvo', 'rcl', 're', 'reg', 'regn', 'rf', 'rhi', 'rht', 'rjf', 'rl', 'rmd', 'rok', 'rop', 'rost', 'rrc', 'rsg', 'rtn', 'sbac', 'sbux', 'scg', 'schw', 'see', 'shw', 'sig', 'sjm', 'slb', 'slg', 'sna', 'sni', 'snps', 'so', 'spg', 'spgi', 'srcl', 'sre', 'sti', 'stt', 'stx', 'stz', 'swk', 'swks', 'syf', 'syk', 'symc', 'syy', 't', 'tap', 'tdg', 'tel', 'tgt', 'tif', 'tjx', 'tmk', 'tmo', 'tpr', 'trip', 'trow', 'trv', 'tsco', 'tsn', 'tss', 'twx', 'txn', 'txt', 'ua', 'uaa', 'ual', 'udr', 'uhs', 'ulta', 'unh', 'unm', 'unp', 'ups', 'uri', 'usb', 'utx', 'v', 'var', 'vfc', 'viab', 'vlo', 'vmc', 'vno', 'vrsk', 'vrsn', 'vrtx', 'vtr', 'vz', 'wat', 'wba', 'wdc', 'wec', 'wfc', 'whr', 'wltw', 'wm', 'wmb', 'wmt', 'wrk', 'wu', 'wy', 'wyn', 'wynn', 'xec', 'xel', 'xl', 'xlnx', 'xom', 'xray', 'xrx', 'xyl', 'yum', 'zbh', 'zion', 'zts']
2. Standarize all the values in electricity_demand¶
In [13]:
np.mean(electricity_demand)
Out[13]:
29617.136160714286
In [14]:
np.std(electricity_demand)
Out[14]:
5566.669346535028
In [17]:
[(sample - 29617)/ 5566 for sample in electricity_demand]
Out[17]:
[-1.321415738411786, -1.4123248293208768, -1.3241106719367588, -1.2321236076176787, -1.2698526769673015, -1.3122529644268774, -1.345490477901545, -1.3936399568810636, -1.4132231404958677, -1.4877829680201222, -1.4829320876751706, -1.3368666906216313, -0.8925619834710744, -0.38645346748113546, 0.25206611570247933, 0.7028386633129716, 1.04401724757456, 1.1528925619834711, 1.2966223499820337, 1.3796263025512037, 1.3871721164211284, 1.4356809198706433, 1.4507725476104922, 1.4960474308300395, 1.4845490477901546, 1.4150197628458498, 1.3381243262666187, 1.300395256916996, 1.3032698526769673, 1.2971613366870285, 1.2551203736974488, 1.3232123607617678, 1.3927416457060726, 1.4432267337405678, 1.4006467840459935, 1.2321236076176787, 1.0544376572044556, 0.8686669062163134, 0.7342795544376572, 0.5867768595041323, 0.41735537190082644, 0.46676248652533237, 0.530901904419691, 0.5808480057491915, 0.4732303269852677, 0.22960833632770392, -0.20786920589292132, -0.5470715055695293, -0.8127919511318721, 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3. Filter the values in electricity_demand that are greater than the mean¶
In [18]:
[sample for sample in electricity_demand if sample > 29617]
Out[18]:
[31020, 33529, 35428, 36034, 36834, 37296, 37338, 37608, 37692, 37944, 37880, 37493, 37065, 36855, 36871, 36837, 36603, 36982, 37369, 37650, 37413, 36475, 35486, 34452, 33704, 32883, 31940, 32215, 32572, 32850, 32251, 30895, 32023, 34315, 35927, 36367, 37087, 37184, 37009, 37212, 37531, 37890, 37982, 37540, 37160, 36920, 37060, 36930, 36640, 36910, 37220, 37530, 37320, 36331, 35206, 34203, 33273, 32376, 31838, 31211, 31505, 31838, 32185, 30881, 32034, 34235, 35694, 35978, 36475, 36675, 36720, 36842, 36890, 36917, 36854, 36525, 36431, 36212, 36061, 35931, 35835, 36148, 36523, 36859, 36731, 35804, 34721, 34035, 33234, 32532, 31474, 31957, 32500, 32586, 31917, 30636, 31876, 34363, 35819, 36295, 36949, 37095, 37086, 37520, 37540, 37671, 37695, 37314, 37113, 36796, 36705, 36634, 36357, 36664, 37020, 37152, 36836, 36008, 34981, 34265, 33385, 32477, 31966, 31402, 31703, 32192, 32322, 30972, 31838, 34308, 36305, 36743, 37705, 37980, 38084, 38379, 38526, 38429, 38344, 37785, 37495, 37190, 37010, 36641, 36296, 36309, 36532, 36662, 36229, 35321, 34409, 33425, 32708, 31460, 30955, 30274, 30302, 30427, 30154, 29915, 30895, 31273, 31390, 31287, 31163, 30811, 30310, 29767, 29721, 30973, 33497, 35192, 35624, 36444, 36903, 36944, 37223, 37345, 37710, 37641, 37391, 37116, 37017, 37024, 36882, 36605, 36861, 37241, 37550, 37215, 36203, 35035, 34112, 33482, 32349, 31186, 30920, 30270, 31869, 31651, 30821, 31600, 34011, 35567, 35946, 36733, 36963, 37009, 37100, 37265, 37478, 37462, 37040, 36778, 36679, 36570, 36509, 36433, 36730, 37125, 37359, 36995, 36149, 34845, 33986, 33353, 32595, 31833, 31382, 31264, 31805, 32067, 30723, 32093, 34598, 36118, 36505, 37295, 37562, 37546, 37734, 38071, 38204, 38233, 37710, 37459, 37224, 37208, 36962, 36837, 37151, 37602, 38055, 37780, 36832, 35861, 35202, 34347, 33562, 32860, 32606, 32578, 32552, 32256, 30893, 32017, 34569, 36166, 36510, 37250, 37372, 37318, 37333, 37611, 37736, 37756, 37275, 37010, 36926, 36837, 36726, 36449, 36734, 37067, 37174, 36824, 35980, 34953, 34235, 33350, 32664, 31923, 31603, 31736, 32134, 31993, 30956, 31647, 34117, 35590, 36167, 36741, 36768, 36783, 36991, 37115, 37224, 37439, 36822, 36375, 36126, 35805, 35478, 35161, 35123, 35226, 35435, 35062, 34357, 33239, 32583, 31586, 30604, 30091, 29893, 30100, 30152, 31325, 31720, 31770, 31694, 31507, 31267, 30679, 30227, 31136, 33625, 35517, 36628, 37545, 37995, 38086, 38389, 38715, 38777, 38699, 38307, 37894, 37832, 37717, 37563, 37294, 37604, 37640, 37584, 37196, 36090, 35053, 34283, 33378, 32754, 31904, 31817, 31795, 32467, 32401, 31574, 31749, 34325, 35968, 36745, 37427, 37718, 37880, 38206, 38488, 38657, 38762, 38230, 37990, 37673, 37673, 37530, 37455, 37622, 37993, 38264, 37712, 36707, 35667, 34737, 33728, 32967, 31725, 31408, 30991, 31696, 32234, 30990, 31602, 34031, 35538, 36017, 36993, 37420, 37409, 37412, 37547, 37664, 37564, 37271, 36915, 36791, 36625, 36357, 36265, 36582, 37072, 37238, 36884, 35890, 34723, 33724, 32888, 32110, 31263, 30907, 30953, 31413, 31264, 30239, 31222, 33533, 35135, 35717, 36555, 36843, 36885, 37138, 37311, 37550, 37619, 37199, 36951, 36844, 36785, 36624, 36350, 36614, 37167, 37388, 36880, 36185, 35197, 34300, 33586, 32645, 32269, 31671, 31998, 31890, 31643, 30227, 31219, 33642, 35220, 35757, 36569, 36876, 36928, 37087, 37191, 37439, 37385, 36914, 36253, 35983, 35707, 35269, 34879, 34885, 35075, 35346, 35131, 34313, 33544, 32824, 32102, 31035, 30421, 30052, 30107, 30249, 30060, 30652, 31175, 31370, 31497, 31436, 31377, 30833, 30254, 29743, 30032, 29798, 30507, 33060, 34783, 35322, 36130, 36494, 36593, 36826, 36985, 37098, 37200, 36654, 36464, 36257, 36157, 36098, 35959, 36207, 36559, 36790, 36577, 35727, 34602, 33578, 32714, 31926, 31142, 30833, 31109, 31310, 31236, 30184, 31278, 33530, 35240, 35783, 36372, 36610, 36651, 36835, 37248, 37128, 37230, 36841, 36529, 36500, 36445, 36348, 36152, 36282, 36853, 37137, 36844, 35875, 34589, 33987, 32959, 32015, 31847, 31404, 31501, 31824, 31759, 30395, 31383, 33907, 35592, 36022, 36643, 36976, 36985, 37123, 37144, 37385, 37334, 36914, 36630, 36433, 36461, 36246, 36131, 36534, 37099, 37346, 37098, 36145, 34997, 34342, 33431, 33088, 32201, 32185, 31966, 32214, 31736, 30325, 31494, 33985, 35754, 35972, 36855, 37135, 37159, 37282, 37347, 37408, 37444, 37066, 36874, 36696, 36707, 36456, 36362, 36506, 36831, 37268, 36851, 36027, 34950, 34149, 33515, 32938, 32777, 32443, 32620, 32453, 31486, 30006, 31283, 33772, 35443, 35929, 36898, 37272, 37270, 37363, 37445, 37550, 37543, 36995, 36620, 36032, 35843, 35606, 35131, 35079, 35341, 35546, 35305, 34459, 33526, 32835, 32000, 31136, 30696, 30054, 29993, 30178, 29786, 29687, 30737, 31531, 31785, 31805, 31710, 31593, 31228, 30907, 30011, 29983, 30335, 30650, 30474, 29654, 30833, 33448, 35245, 35900, 36798, 37251, 37335, 37677, 37887, 38066, 38091, 37784, 37622, 37408, 37354, 37172, 36995, 37311, 37693, 37887, 37446, 36451, 35267, 34434, 33733, 32812, 32061, 32089, 32345, 32564, 32096, 30702, 31617, 34140, 35901, 36473, 37122, 37559, 37703, 37933, 38059, 38274, 38303, 37876, 37688, 37414, 37312, 37234, 37086, 37421, 37923, 38363, 38127, 37433, 36363, 35617, 34829, 34036, 33520, 33163, 33322, 32823, 32221, 30611, 31571, 33923, 35446, 35973, 36833, 37164, 37342, 37642, 37713, 37898, 37963, 37603, 37290, 36955, 36956, 36815, 36637, 37003, 37526, 37775, 37630, 36722, 35559, 34742, 34002, 33230, 32809, 32446, 32614, 32715, 32150, 30568, 31552, 34093, 35828, 36337, 37142, 37407, 37471, 37632, 37920, 38095, 38118, 37735, 37411, 37205, 37064, 36870, 36749, 37029, 37542, 37684, 37470, 36532, 35290, 34597, 33819, 33104, 32718, 32512, 32693, 32784, 32151, 30654, 31460, 33905, 35501, 36128, 36898, 37056, 37165, 37294, 37516, 37537, 37478, 36905, 36316, 36081, 35478, 35101, 34944, 34929, 35327, 35498, 35065, 34315, 33258, 32375, 31716, 30629, 29779, 29776, 29963, 30662, 31096, 31292, 31464, 31393, 31269, 30742, 30282, 30153, 30634, 31033, 30704, 29870, 30787, 33457, 35273, 36011, 36866, 37346, 37510, 37825, 38248, 38518, 38621, 38270, 37907, 37766, 37646, 37477, 37261, 37766, 38302, 38496, 38279, 37451, 36435, 35601, 34543, 33925, 33161, 32709, 32881, 32719, 32030, 30569, 31634, 34080, 35646, 36079, 36758, 36958, 37169, 37410, 37601, 37773, 37672, 37245, 36995, 36519, 36533, 36464, 36274, 36577, 37042, 37242, 36996, 36020, 34759, 33932, 33001, 32254, 31581, 31270, 31776, 32287, 32117, 30604, 31689, 33984, 35449, 35832, 36526, 36882, 36959, 37105, 37355, 37510, 37519, 37159, 36810, 36474, 36565, 36459, 36267, 36585, 37062, 37301, 37110, 36312, 35274, 34483, 33804, 33187, 32918, 32800, 32820, 32686, 31877, 30754, 32005, 34393, 35901, 36370, 37303, 37585, 37565, 37943, 37990, 38062, 38124, 37692, 37429, 37067, 37005, 36949, 36646, 37019, 37402, 37476, 37062, 36292, 35247, 34449, 33822, 32999, 32569, 32485, 32879, 32866, 32106, 30590, 31541, 33932, 35554, 36040, 36630, 36857, 36971, 37020, 37181, 37304, 37487, 36880, 36159, 35654, 35564, 35302, 34901, 34910, 35229, 35529, 35253, 34636, 33774, ...]
4. Filter only companies that end with an X¶
In [22]:
[comp for comp in sp_companies if comp.endswith('X')]
Out[22]:
['BAX', 'BDX', 'BSX', 'CLX', 'CSX', 'CVX', 'DGX', 'EFX', 'EIX', 'EQIX', 'ESRX', 'FCX', 'FDX', 'FOX', 'HOLX', 'IDXX', 'KMX', 'LRCX', 'NFLX', 'NFX', 'PAYX', 'PSX', 'PX', 'SBUX', 'STX', 'TJX', 'TWX', 'UTX', 'VRTX', 'XLNX', 'XRX']
5. Return the length of the ticker for companies that end with an L¶
In [23]:
[len(comp) for comp in sp_companies if comp.endswith('L')]
Out[23]:
[3, 4, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 5, 3, 3, 1, 3, 3, 3, 3, 4, 3, 4, 3, 2, 4, 3, 3, 3, 2, 3]
6. Return the ticker and its length (in characters) for companies that contain an X in its ticker¶
In [25]:
[(ticker, len(ticker)) for ticker in sp_companies if 'X' in ticker]
Out[25]:
[('ALXN', 4),
('AXP', 3),
('BAX', 3),
('BDX', 3),
('BSX', 3),
('BXP', 3),
('CLX', 3),
('CSX', 3),
('CTXS', 4),
('CVX', 3),
('CXO', 3),
('DGX', 3),
('DXC', 3),
('EFX', 3),
('EIX', 3),
('EQIX', 4),
('ESRX', 4),
('EXC', 3),
('EXPD', 4),
('EXPE', 4),
('EXR', 3),
('FCX', 3),
('FDX', 3),
('FOX', 3),
('FOXA', 4),
('HOLX', 4),
('IDXX', 4),
('KMX', 3),
('LRCX', 4),
('NFLX', 4),
('NFX', 3),
('OXY', 3),
('PAYX', 4),
('PSX', 3),
('PX', 2),
('PXD', 3),
('SBUX', 4),
('STX', 3),
('TJX', 3),
('TWX', 3),
('TXN', 3),
('TXT', 3),
('UTX', 3),
('VRTX', 4),
('XEC', 3),
('XEL', 3),
('XL', 2),
('XLNX', 4),
('XOM', 3),
('XRAY', 4),
('XRX', 3),
('XYL', 3)]
7. Format the result from the previous activity to return a single string with the ticker and its length¶
In [ ]:
8. What's the mean of the prices in sp_prices?¶
In [30]:
np.mean([price for ticker, price in sp_prices])
Out[30]:
107.76220792079208
9. Return ticker and stock price of companies in sp_prices whose price is above the mean¶
In [33]:
[(ticker, price) for ticker, price in sp_prices if price > 107]
Out[33]:
[('AAP', 109.93),
('AAPL', 159.54),
('ABBV', 113.62),
('ACN', 155.15),
('ADBE', 192.34),
('ADP', 113.56),
('ADS', 246.75),
('ADSK', 110.54),
('AET', 181.49),
('AGN', 170.39),
('ALGN', 234.33),
('ALXN', 117.15),
('AMG', 187.87),
('AMGN', 177.47),
('AMP', 160.11),
('AMT', 140.0),
('AMZN', 1416.78),
('ANSS', 155.03),
('ANTM', 238.44),
('AON', 140.55),
('APD', 158.21),
('ARE', 118.96),
('AVB', 158.28),
('AVGO', 237.38),
('AVY', 114.78),
('AYI', 148.07),
('AZO', 728.33),
('BA', 348.12),
('BDX', 221.92),
('BIIB', 326.89),
('BLK', 532.26),
('BRK.B', 200.37),
('BXP', 115.34),
('CAT', 154.34),
('CB', 147.52),
('CBOE', 114.565),
('CCI', 108.75),
('CHTR', 363.55),
('CI', 195.44),
('CLX', 127.82),
('CME', 159.93),
('CMG', 272.21),
('CMI', 172.74),
('COL', 135.4),
('COO', 230.47),
('COST', 183.19),
('CRM', 110.05),
('CTAS', 156.6),
('CVX', 115.29),
('CXO', 148.1),
('DE', 161.44),
('DPS', 118.0),
('EA', 123.05),
('ECL', 133.12),
('EFX', 117.6),
('EL', 134.71),
('EQIX', 426.08),
('ESS', 217.81),
('EW', 129.7),
('EXPE', 129.33),
('FB', 180.18),
('FDX', 251.15),
('FFIV', 141.39),
('FISV', 129.17),
('FRT', 111.34),
('GD', 214.95),
('GOOG', 1048.58),
('GOOGL', 1055.41),
('GS', 257.1),
('GWW', 259.89),
('HD', 191.29),
('HII', 234.82),
('HON', 151.19),
('HRS', 152.79),
('HUM', 264.9),
('IBM', 153.85),
('IDXX', 176.83),
('IFF', 142.7),
('ILMN', 221.2),
('INTU', 161.29),
('ISRG', 399.53),
('IT', 119.45),
('ITW', 163.44),
('JBHT', 117.95),
('JNJ', 131.42),
('JPM', 112.87),
('KMB', 112.36),
('KSU', 107.1),
('LH', 169.66),
('LLL', 207.69),
('LMT', 345.43),
('LRCX', 171.63),
('LYB', 110.27),
('MA', 168.73),
('MAR', 139.64),
('MCD', 165.71),
('MCK', 152.83),
('MCO', 157.98),
('MHK', 267.66),
('MLM', 218.85),
('MMM', 233.19),
('MON', 120.41),
('MTB', 184.97),
('MTD', 641.96),
('NEE', 148.09),
('NFLX', 264.56),
('NOC', 337.66),
('NSC', 142.68),
('NVDA', 228.8),
('ORLY', 252.08),
('PCLN', 1882.68),
('PEP', 113.5),
('PH', 183.31),
('PKG', 117.13),
('PNC', 155.0),
('PPG', 114.13),
('PRU', 111.17),
('PSA', 185.35),
('PVH', 149.21),
('PX', 150.01),
('PXD', 176.2),
('RCL', 128.52),
('RE', 246.7),
('REGN', 334.61),
('RHT', 128.81),
('RL', 107.7),
('ROK', 188.78),
('ROP', 266.19),
('RTN', 204.32),
('SBAC', 166.51),
('SHW', 403.48),
('SJM', 119.83),
('SNA', 166.63),
('SPG', 154.86),
('SPGI', 182.69),
('STZ', 214.15),
('SWK', 159.74),
('SYK', 153.67),
('TDG', 292.1),
('TMO', 207.55),
('TRV', 142.14),
('UHS', 117.12),
('ULTA', 218.06),
('UNH', 225.82),
('UNP', 128.96),
('UPS', 111.91),
('URI', 173.58),
('UTX', 131.97),
('V', 119.65),
('VAR', 116.92),
('VMC', 128.24),
('VRSN', 109.11),
('VRTX', 155.67),
('WAT', 201.98),
('WHR', 171.52),
('WLTW', 157.38),
('WYN', 118.72),
('WYNN', 177.32),
('ZBH', 120.78)]
10. Return the price of the stocks of the FAANG companies¶
In [34]:
faang_tickers = ['FB', 'AMZN', 'AAPL', 'NFLX', 'GOOGL']
In [37]:
[price for ticker, price in sp_prices if ticker in faang_tickers]
Out[37]:
[159.54, 1416.78, 180.18, 1055.41, 264.56]
11. Return the price and the ticker of those copanies that start with a B and contain an X (anywhere in the ticker)¶
In [ ]:
