import math
import re
import pandas as pd
from tqdm import tqdm
from woodwork.logical_types import Boolean, Categorical, Ordinal
import featuretools as ft
[docs]def load_flight(
month_filter=None,
categorical_filter=None,
nrows=None,
demo=True,
return_single_table=False,
verbose=False,
):
"""
Download, clean, and filter flight data from 2017.
The original dataset can be found `here <https://www.transtats.bts.gov/ot_delay/ot_delaycause1.asp>`_.
Args:
month_filter (list[int]): Only use data from these months (example is ``[1, 2]``).
To skip, set to None.
categorical_filter (dict[str->str]): Use only specified categorical values.
Example is ``{'dest_city': ['Boston, MA'], 'origin_city': ['Boston, MA']}``
which returns all flights in OR out of Boston. To skip, set to None.
nrows (int): Passed to nrows in ``pd.read_csv``. Used before filtering.
demo (bool): Use only two months of data. If False, use the whole year.
return_single_table (bool): Exit the function early and return a dataframe.
verbose (bool): Show a progress bar while loading the data.
Examples:
.. ipython::
:verbatim:
In [1]: import featuretools as ft
In [2]: es = ft.demo.load_flight(verbose=True,
...: month_filter=[1],
...: categorical_filter={'origin_city':['Boston, MA']})
100%|xxxxxxxxxxxxxxxxxxxxxxxxx| 100/100 [01:16<00:00, 1.31it/s]
In [3]: es
Out[3]:
Entityset: Flight Data
DataFrames:
airports [Rows: 55, Columns: 3]
flights [Rows: 613, Columns: 9]
trip_logs [Rows: 9456, Columns: 22]
airlines [Rows: 10, Columns: 1]
Relationships:
trip_logs.flight_id -> flights.flight_id
flights.carrier -> airlines.carrier
flights.dest -> airports.dest
"""
filename, csv_length = get_flight_filename(demo=demo)
print("Downloading data ...")
url = "https://api.featurelabs.com/datasets/{}?library=featuretools&version={}".format(
filename, ft.__version__
)
chunksize = math.ceil(csv_length / 99)
pd.options.display.max_columns = 200
iter_csv = pd.read_csv(
url, compression="zip", iterator=True, nrows=nrows, chunksize=chunksize
)
if verbose:
iter_csv = tqdm(iter_csv, total=100)
partial_df_list = []
for chunk in iter_csv:
df = filter_data(
_clean_data(chunk),
month_filter=month_filter,
categorical_filter=categorical_filter,
)
partial_df_list.append(df)
data = pd.concat(partial_df_list)
if return_single_table:
return data
es = make_es(data)
return es
def make_es(data):
es = ft.EntitySet("Flight Data")
arr_time_columns = [
"arr_delay",
"dep_delay",
"carrier_delay",
"weather_delay",
"national_airspace_delay",
"security_delay",
"late_aircraft_delay",
"canceled",
"diverted",
"taxi_in",
"taxi_out",
"air_time",
"dep_time",
]
logical_types = {
"flight_num": Categorical,
"distance_group": Ordinal(order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),
"canceled": Boolean,
"diverted": Boolean,
}
es.add_dataframe(
data,
dataframe_name="trip_logs",
index="trip_log_id",
make_index=True,
time_index="date_scheduled",
secondary_time_index={"arr_time": arr_time_columns},
logical_types=logical_types,
)
es.normalize_dataframe(
"trip_logs",
"flights",
"flight_id",
additional_columns=[
"origin",
"origin_city",
"origin_state",
"dest",
"dest_city",
"dest_state",
"distance_group",
"carrier",
"flight_num",
],
)
es.normalize_dataframe("flights", "airlines", "carrier", make_time_index=False)
es.normalize_dataframe(
"flights",
"airports",
"dest",
additional_columns=["dest_city", "dest_state"],
make_time_index=False,
)
return es
def _clean_data(data):
# Make column names snake case
clean_data = data.rename(columns={col: convert(col) for col in data})
# Chance crs -> "scheduled" and other minor clarifications
clean_data = clean_data.rename(
columns={
"crs_arr_time": "scheduled_arr_time",
"crs_dep_time": "scheduled_dep_time",
"crs_elapsed_time": "scheduled_elapsed_time",
"nas_delay": "national_airspace_delay",
"origin_city_name": "origin_city",
"dest_city_name": "dest_city",
"cancelled": "canceled",
}
)
# Combine strings like 0130 (1:30 AM) with dates (2017-01-01)
clean_data["scheduled_dep_time"] = clean_data["scheduled_dep_time"].apply(
lambda x: str(x)
) + clean_data["flight_date"].astype("str")
# Parse combined string as a date
clean_data.loc[:, "scheduled_dep_time"] = pd.to_datetime(
clean_data["scheduled_dep_time"], format="%H%M%Y-%m-%d", errors="coerce"
)
clean_data["scheduled_elapsed_time"] = pd.to_timedelta(
clean_data["scheduled_elapsed_time"], unit="m"
)
clean_data = _reconstruct_times(clean_data)
# Create a time index 6 months before scheduled_dep
clean_data.loc[:, "date_scheduled"] = clean_data[
"scheduled_dep_time"
].dt.date - pd.Timedelta("120d")
# A null entry for a delay means no delay
clean_data = _fill_labels(clean_data)
# Nulls for scheduled values are too problematic. Remove them.
clean_data = clean_data.dropna(
axis="rows", subset=["scheduled_dep_time", "scheduled_arr_time"]
)
# Make a flight id. Define a flight as a combination of:
# 1. carrier 2. flight number 3. origin airport 4. dest airport
clean_data.loc[:, "flight_id"] = (
clean_data["carrier"]
+ "-"
+ clean_data["flight_num"].apply(lambda x: str(x))
+ ":"
+ clean_data["origin"]
+ "->"
+ clean_data["dest"]
)
column_order = [
"flight_id",
"flight_num",
"date_scheduled",
"scheduled_dep_time",
"scheduled_arr_time",
"carrier",
"origin",
"origin_city",
"origin_state",
"dest",
"dest_city",
"dest_state",
"distance_group",
"dep_time",
"arr_time",
"dep_delay",
"taxi_out",
"taxi_in",
"arr_delay",
"diverted",
"scheduled_elapsed_time",
"air_time",
"distance",
"carrier_delay",
"weather_delay",
"national_airspace_delay",
"security_delay",
"late_aircraft_delay",
"canceled",
]
clean_data = clean_data[column_order]
return clean_data
def _fill_labels(clean_data):
labely_columns = [
"arr_delay",
"dep_delay",
"carrier_delay",
"weather_delay",
"national_airspace_delay",
"security_delay",
"late_aircraft_delay",
"canceled",
"diverted",
"taxi_in",
"taxi_out",
"air_time",
]
for col in labely_columns:
clean_data.loc[:, col] = clean_data[col].fillna(0)
return clean_data
def _reconstruct_times(clean_data):
"""Reconstruct departure_time, scheduled_dep_time,
arrival_time and scheduled_arr_time by adding known delays
to known times. We do:
- dep_time is scheduled_dep + dep_delay
- arr_time is dep_time + taxiing and air_time
- scheduled arrival is scheduled_dep + scheduled_elapsed
"""
clean_data.loc[:, "dep_time"] = clean_data["scheduled_dep_time"] + pd.to_timedelta(
clean_data["dep_delay"], unit="m"
)
clean_data.loc[:, "arr_time"] = clean_data["dep_time"] + pd.to_timedelta(
clean_data["taxi_out"] + clean_data["air_time"] + clean_data["taxi_in"],
unit="m",
)
clean_data.loc[:, "scheduled_arr_time"] = (
clean_data["scheduled_dep_time"] + clean_data["scheduled_elapsed_time"]
)
return clean_data
def filter_data(clean_data, month_filter=None, categorical_filter=None):
if month_filter is not None:
tmp = clean_data["scheduled_dep_time"].dt.month.isin(month_filter)
clean_data = clean_data[tmp]
if categorical_filter is not None:
tmp = False
for key, values in categorical_filter.items():
tmp = tmp | clean_data[key].isin(values)
clean_data = clean_data[tmp]
return clean_data
def convert(name):
# Rename columns to underscore
# Code via SO https://stackoverflow.com/questions/1175208/elegant-python-function-to-convert-camelcase-to-snake-case
s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower()
def get_flight_filename(demo=True):
if demo:
filename = SMALL_FLIGHT_CSV
rows = 860457
else:
filename = BIG_FLIGHT_CSV
rows = 5162742
return filename, rows
SMALL_FLIGHT_CSV = "data_2017_jan_feb.csv.zip"
BIG_FLIGHT_CSV = "data_all_2017.csv.zip"