Please written by computer source Assignment 4 In this assignment you will be using the dataset released by The Department of Transportation. This dataset lists flights that occurred in 2015, along with other information such as delays, flight time etc. In this assignment, you will be showing good practices to manipulate data using Python's most popular libraries to accomplish the following: cleaning data with pandas make specific changes with numpy handling date-related values with datetime Note: please consider the flights departing from BOS, JFK, SFO and LAX. Each question is equally weighted for the total grade. import os
Please written by computer source
Assignment 4 In this assignment you will be using the dataset released by The Department of Transportation. This dataset lists flights that occurred in 2015, along with other information such as delays, flight time etc.
In this assignment, you will be showing good practices to manipulate data using Python's most popular libraries to accomplish the following:
cleaning data with pandas make specific changes with numpy handling date-related values with datetime Note: please consider the flights departing from BOS, JFK, SFO and LAX.
Each question is equally weighted for the total grade.
import os
import pandas as pd
import pandas.api.types as ptypes
import numpy as np
import datetime as dt
airlines_df= pd.read_csv('assets\airlines.csv')
airports_df = pd.read_csv('assets\airports.csv')
flights_df_raw = pd.read_csv('assets\flights.csv', low_memory = False)
Question 1: Data Preprocessing
For this question, perform the following:
remove rows with missing values
keep flights departing from airports (ORIGIN_AIRPORT) that we want to look at (BOS, JFK, SFO and LAX)
filter out the flights that have more than 1 day delay (DEPARTURE_DELAY)
convert FLIGHT_NUMBER column type to string
SCHEDULED_DEPARTURE is coded as a float where the first two digits indicate the hour and the last two indicate the minutes. Convert this column to datetime format by combining existing columns DAY, MONTH, YEAR and SCHEDULED_DEPARTURE
add IS_DELAYEDcolumn by considering any flight above 15 minutes delay (DEPARTURE_DELAY) are delayed, and any other flight is not delayed
remove YEAR, MONTH, DAY columns
def data_preprocess(flights_df):
# YOUR CODE HERE
#raise NotImplementedError()
return df
flights_df = data_preprocess(flights_df_raw.copy())
assert len(flights_df) == 535744, "Q1: There should be 535744 observations in the flights dataframe"
Question 2
NOTE: The column to merge both dataframes are flights_df['ORIGIN_AIRPORT'] and airports_df['IATA_CODE'] and there is no ['NUM_FLIGHTS'] column in the dataframe
PLEASE MAKE SURE that the shape of the dataframe return as (4,1) AND number of counts are not equal to 105276
Merge flights_df dataframe with airports_df dataframe and return the number of departing flights (NUM_FLIGHTS) per airport (IATA_CODE) across the year.
def flights_per_airport(flights_df, airports_df):
# YOUR CODE HERE
raise NotImplementedError()
return df
num_flights_df=flights_per_airport(flights_df_raw.copy(), airports_df.copy())
assert num_flights_df.shape==(4,1), "Shape of DataFrame should be (4,1)"
assert num_flights_df.columns[0]=='NUM_FLIGHTS', "DataFrame should have a column which is called NUM_FLIGHTS"
assert num_flights_df.loc["BOS", "NUM_FLIGHTS"] == 105276, "The NUM_FLIGHTS for BOS is wrong"
PLEASE MAKE SURE that the shape of the dataframe return as (4,1)
Question 3
For this question, find the top three airline names which have high number of flights and the least percentage of delay compared to other airlines. The result should be a dataframe which has three columns AIRLINE_NAME, NUM_FLIGHTS and PERC_DELAY.
NOTE: THERE ARE NO COLUMNS NAMED AIRLINE_NAME AND PERC_DELAY ND NUM_FLIGHTS so you have create them
Hint:
percentage of delay for each airline is obtained using groupby and apply methods
merge flights_df with airlines_df to get the names of top three airlines
def top_three_airlines(flights_df, airlines_df):
# YOUR CODE HERE
raise NotImplementedError()
return df
top_three_airlines_df = top_three_airlines(flights_df_raw.copy(), airlines_df.copy())
assert sorted(list(top_three_airlines_df.columns)) == sorted(['NUM_FLIGHTS', 'PERC_DELAY', 'AIRLINE_NAME']), "Dataframe doesn't have required columns"
assert top_three_airlines_df.loc[0, 'AIRLINE_NAME'] == 'United Air Lines Inc.', "Top airline name doesn't match"
Question 4
For this question, obtain the monthly percentage of delays for each ORIGIN_AIRPORT.
Example Result:
MONTH BOS JFK LAX SFO
0 January 0.1902 0.2257 0.1738 0.xxxx
1 February 0.3248 0.xxxx 0.xxxx 0.xxxx
2 March 0.1984 0.xxxx 0.xxxx 0.xxxx
3 April 0.xxxx 0.xxxx 0.xxxx 0.xxxx
def monthly_airport_delays(flights_df):
# YOUR CODE HERE
raise NotImplementedError()
return df
monthly_airport_delays_df = monthly_airport_delays(flights_df_raw.copy())
I would like to add the csv files but can't.
I need help with this assignment.
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