Classwork 16

Pandas Fundamental V-2: Joining DataFrames

import pandas as pd 
import numpy as np

# Below is for an interactive display of DataFrame in Colab
from google.colab import data_table
data_table.enable_dataframe_formatter()

Below DataFrames are related, as described above.

flights = pd.read_csv("https://bcdanl.github.io/data/flights.zip")
airlines = pd.read_csv("https://bcdanl.github.io/data/flights-airlines.csv")
airports = pd.read_csv("https://bcdanl.github.io/data/flights-airports.csv")
planes = pd.read_csv("https://bcdanl.github.io/data/flights-planes.csv")
weather = pd.read_csv("https://bcdanl.github.io/data/flights-weather.csv")

Variables in flights DataFrame

• year, month, day: Date of departure.

• dep_time, arr_time: Actual departure and arrival times (format HHMM or HMM), local tz.

• sched_dep_time, sched_arr_time: Scheduled departure and arrival times (format HHMM or HMM), local tz.

• dep_delay, arr_delay: Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.

• carrier: Two letter carrier abbreviation. See airlines DataFrame to get full names.

• flight: Flight number.

• tailnum: Plane tail number. See planes DataFrame for additional metadata.

• origin, dest: Origin and destination. See airports DataFrame for additional metadata.

• air_time: Amount of time spent in the air, in minutes.

• distance: Distance between airports, in miles.

• hour, minute: Time of scheduled departure broken into hour and minutes.

• time_hour: Scheduled date and hour of the flight as a datetime64. Along with origin, can be used to join flights data to weather DataFrame

Question 1

The following is the flights DataFrame:

The following is the weather DataFrame:

Merge the flights and weather DataFrames so that all observations from flights are retained in the resulting DataFrame.

Answer:

Question 2

The following is the airlines DataFrame:

Identify the airlines (by full name) that have the top five dep_delay values in the flights DataFrame.

Answer:

Question 3

• Consider the following two airlines:

  • Delta Air Lines Inc. (DL)
  • United Air Lines Inc. (UA)

• Determine which airline has a higher proportion of flights with a dep_delay greater than 30 minutes.

• Hint: np.where() can be very useful.

Answer:

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