Source code for featuretools.demo.flight

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"