Question 1.1 It looks like the departure and arrival in flights were read in as floating-point numbers. Write two functions, extract_hour and extract_mins that converts military time to hours and minutes, respectively. Hint: You may want to use modular arithmetic and integer division. Keep in mind that the data has not been cleaned and you need to check whether the extracted values are valid. Replace all the invalid values with NaN . The documentation for pandas.Series.where provided here should be helpful. In [ ]: def extract_hour(time): H Extracts hour information from military time. Args: time (float64): series of time given in military format. Takes on values in 0.0-2359.0 due to float64 representation. Returns: array (float64): series of input dimension with hour information. Should only take on integer values in 0-23 *** # [YOUR CODE HERE] # You MUST add comments explaining your thought process, # and the resources you used to solve this question (if any) hour return hour 'blabla' # test your code test_ser = pd.Series ([1030.0, 1259.0, np.nan, 2400], dtype='float64') extract_hour(test_ser) 10.0 12.0 NaN NaN # 0 # 1 # 2 # 3 #dtype: float64

Question 1.1 It looks like the departure and arrival in flights were read in as floating-point numbers. Write two functions, extract_hour and extract_mins that converts military time to hours and minutes, respectively. Hint: You may want to use modular arithmetic and integer division. Keep in mind that the data has not been cleaned and you need to check whether the extracted values are valid. Replace all the invalid values with NaN . The documentation for pandas.Series.where provided here should be helpful. In [ ]: def extract_hour(time): H Extracts hour information from military time. Args: time (float64): series of time given in military format. Takes on values in 0.0-2359.0 due to float64 representation. Returns: array (float64): series of input dimension with hour information. Should only take on integer values in 0-23 *** # [YOUR CODE HERE] # You MUST add comments explaining your thought process, # and the resources you used to solve this question (if any) hour return hour 'blabla' # test your code test_ser = pd.Series ([1030.0, 1259.0, np.nan, 2400], dtype='float64') extract_hour(test_ser) 10.0 12.0 NaN NaN # 0 # 1 # 2 # 3 #dtype: float64

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

airports.dat and airlines.dat, respectively. Both were sourced from http://openflights.org/data.html.

In [ ]: def extract_mins(time):
Extracts minute information from military time
Args:
time (float64): series of time given in military format.
Takes on values in 0.0-2359.0 due to float64 representation.
Returns:
array (float64): series of input dimension with minute information.
Should only take on integer values in 0-59
|||||
# [YOUR CODE HERE]
# You MUST add comments explaining your thought process,
# and the resources you used to solve this question (if any)
mins='blabla'
return mins
# test your code
test_ser = pd.Series ([1030.0, 1259.0, np.nan, 2475], dtype='float64')
extract_mins (test_ser)
# 0
# 1
# 2
# 3
#dtype: float64
30.0
59.0
NaN
NaN

Question 1.1
It looks like the departure and arrival in flights were read in as floating-point numbers. Write two functions, extract_hour and extract_mins that
converts military time to hours and minutes, respectively. Hint: You may want to use modular arithmetic and integer division. Keep in mind that the data has not
been cleaned and you need to check whether the extracted values are valid. Replace all the invalid values with NaN. The documentation for
pandas.Series. where provided here should be helpful.
In [ ]: def extract_hour(time):
Extracts hour information from military time.
Args:
time (float64): series of time given in military format.
Takes on values in 0.0-2359.0 due to float64 representation.
Returns:
array (float64): series of input dimension with hour information.
Should only take on integer values in 0-23
# [YOUR CODE HERE]
# You MUST add comments explaining your thought process,
# and the resources you used to solve this question (if any)
hour 'blabla'
return hour
=
# test your code
test_ser = pd.Series ([1030.0, 1259.0, np.nan, 2400], dtype='float64')
extract_hour(test_ser)
10.0
12.0
NaN
NaN
# 0
# 1
# 2
# 3
#dtype: float64

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