Temperature Prediction (Hacker Rank challenge)

temp-predict.py

#!/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