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Temperature Prediction (Hacker Rank challenge)
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#!/usr/bin/env python3 | |
# Challenge available at: | |
# https://www.hackerrank.com/challenges/temperature-predictions/problem | |
import pandas | |
import numpy | |
count = int(input().strip()) | |
input() | |
months = { | |
'Jan': '1', | |
'Feb': '2', | |
'Mar': '3', | |
'Apr': '4', | |
'May': '5', | |
'Jun': '6', | |
'Jul': '7', | |
'Aug': '8', | |
'Sep': '9', | |
'Oct': '10', | |
'Nov': '11', | |
'Dec': '12', | |
} | |
def normalize_value(value): | |
if value[0:7] == 'Missing': | |
return numpy.nan | |
else: | |
return float(value) | |
data = [] | |
missing = [] | |
for index in range(count): | |
[year, month, tmax, tmin] = input().split("\t") | |
date = months[month[0:3]] + "/1/" + year | |
data.append([ | |
date, | |
normalize_value(tmax), | |
normalize_value(tmin) | |
]) | |
if tmax[0:7] == 'Missing': | |
[_, order] = tmax.split('Missing_') | |
entry = [ index, 1, int(order) ] | |
missing.append(entry) | |
else: | |
pass | |
if tmin[0:7] == 'Missing': | |
[_, order] = tmin.split('Missing_') | |
entry = [ index, 2, int(order) ] | |
missing.append(entry) | |
else: | |
pass | |
date_series = [date for [date, _, _] in data] | |
tmax_series = [tmax for [_, tmax, _] in data] | |
tmin_series = [tmin for [_, _, tmin] in data] | |
dataframe = pandas.DataFrame({ | |
'date': pandas.Series(date_series), | |
'tmax': pandas.Series(tmax_series), | |
'tmin': pandas.Series(tmin_series), | |
}) | |
dataframe = dataframe.assign( | |
DIdx=pandas.to_datetime( | |
dataframe.date, | |
format="%m/%d/%Y" | |
) | |
) | |
dataframe = dataframe.set_index('DIdx') | |
dataframe = dataframe.assign( | |
IMax=dataframe.tmax.interpolate( | |
method='time' | |
) | |
) | |
dataframe = dataframe.assign( | |
IMin=dataframe.tmin.interpolate( | |
method='time' | |
) | |
) | |
result = [None for _ in range(len(missing))] | |
for [line, column, order] in missing: | |
value = dataframe.iloc[line, column + 2] | |
result[order - 1] = float(str(value)) | |
for value in result: | |
print(value) | |
# end |
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