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May 9, 2020 16:26
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TestDome-DataScience
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import pandas as pd | |
import numpy as np | |
def most_corr(prices): | |
""" | |
:param prices: (pandas.DataFrame) A dataframe containing each ticker's | |
daily closing prices. | |
:returns: (container of strings) A container, containing the two tickers that | |
are the most highly (linearly) correlated by daily percentage change. | |
""" | |
s = prices.pct_change().corr().unstack() | |
s = s[s!=1] | |
return s.idxmax() | |
#For example, the code below should print: ('FB', 'MSFT') | |
print(most_corr(pd.DataFrame.from_dict({ | |
'GOOG' : [ | |
742.66, 738.40, 738.22, 741.16, | |
739.98, 747.28, 746.22, 741.80, | |
745.33, 741.29, 742.83, 750.50 | |
], | |
'FB' : [ | |
108.40, 107.92, 109.64, 112.22, | |
109.57, 113.82, 114.03, 112.24, | |
114.68, 112.92, 113.28, 115.40 | |
], | |
'MSFT' : [ | |
55.40, 54.63, 54.98, 55.88, | |
54.12, 59.16, 58.14, 55.97, | |
61.20, 57.14, 56.62, 59.25 | |
], | |
'AAPL' : [ | |
106.00, 104.66, 104.87, 105.69, | |
104.22, 110.16, 109.84, 108.86, | |
110.14, 107.66, 108.08, 109.90 | |
] | |
}))) |
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