style: Applies linters

Author: alvarofpp

extract.py

@@ -1,7 +1,8 @@
 import os.path
+
 import requests
-from tqdm import tqdm
 from bs4 import BeautifulSoup
+from tqdm import tqdm
 
 path = 'data/extract/'
 
@@ -23,22 +24,21 @@
     if os.path.exists(filename):
         continue
 
-    with requests.get(link) as file_response:
-        with open(filename, 'wb') as file_to_save:
-            file_to_save.write(file_response.content)
+    with requests.get(link) as file_response, open(filename, 'wb') as file_to_save:
+        file_to_save.write(file_response.content)
 
 # Public aerodromes (ANAC)
 public_aerodromes_filepath = path + 'public_aerodromes.csv'
 if not os.path.exists(public_aerodromes_filepath):
     url = 'https://sistemas.anac.gov.br/dadosabertos/Aerodromos/Aer%C3%B3dromos%20P%C3%BAblicos/Lista%20de%20aer%C3%B3dromos%20p%C3%BAblicos/AerodromosPublicos.csv'
-    with requests.get(url) as file_response:
-        with open(public_aerodromes_filepath, 'wb') as file_to_save:
-            file_to_save.write(file_response.content)
+    with requests.get(url) as file_response, \
+            open(public_aerodromes_filepath, 'wb') as file_to_save:
+        file_to_save.write(file_response.content)
 
 # Airport Codes (DataHub.io)
 airport_codes_filepath = path + 'airport_codes.csv'
 if not os.path.exists(airport_codes_filepath):
     url = 'https://datahub.io/core/airport-codes/r/airport-codes.csv'
-    with requests.get(url) as file_response:
-        with open(airport_codes_filepath, 'wb') as file_to_save:
-            file_to_save.write(file_response.content)
+    with requests.get(url) as file_response, \
+            open(airport_codes_filepath, 'wb') as file_to_save:
+        file_to_save.write(file_response.content)

transform_to_airports_csv.py

@@ -1,4 +1,5 @@
 import os.path
+
 import geocoder
 import pandas as pd
 
@@ -91,14 +92,19 @@
     'ident',
     'coordinates',
 ]
-df_others_airports = pd.read_csv(path + 'airport_codes.csv', usecols=others_airports_columns)
+df_others_airports = pd.read_csv(
+    path + 'airport_codes.csv',
+    usecols=others_airports_columns
+)
 
 # Rename columns
 columns_map = {
     'ident': 'code',
 }
 df_others_airports = df_others_airports.rename(columns=columns_map)
-df_others_airports[['lon_geo_point', 'lat_geo_point']] = df_others_airports.coordinates.str.split(', ', expand=True, )
+df_others_airports[
+    ['lon_geo_point', 'lat_geo_point']
+] = df_others_airports.coordinates.str.split(', ', expand=True, )
 df_others_airports = df_others_airports.drop(columns=['coordinates'])
 
 # Coordinates dataframe
@@ -123,7 +129,7 @@
     'continent',
 ]
 airports_to_find = []
-for index, row in df_merge[df_merge.lat_geo_point.isnull()].iterrows():
+for index, row in df_merge[df_merge.lat_geo_point.isnull()].iterrows(): # noqa: B007
     label = ''
 
     for column in columns:

transform_to_anac_csv.py

@@ -1,5 +1,6 @@
-import os.path
 import glob
+import os.path
+
 import pandas as pd
 
 if os.path.exists('data/anac.csv'):
@@ -9,7 +10,9 @@
 files = glob.glob('data/extract/resumo_anual_*.csv')
 
 # Combine all files in the list
-combined_csv = pd.concat([pd.read_csv(file, sep=';', encoding='ISO-8859-1') for file in files])
+combined_csv = pd.concat(
+    [pd.read_csv(file, sep=';', encoding='ISO-8859-1') for file in files]
+)
 
 # Rename columns
 columns_map = {

transform_to_graphml.py

@@ -1,4 +1,5 @@
 import os.path
+
 import networkx as nx
 import pandas as pd
 
@@ -13,7 +14,7 @@
 G = nx.Graph()
 
 # Add nodes
-for index, row in df_airports.iterrows():
+for index, row in df_airports.iterrows(): # noqa: B007
     G.add_node(row['code'],
                name=row['name'],
                country=row['country'],
@@ -27,10 +28,14 @@
     'destination_airport_abbreviation',
 ]].dropna()
 df_edges = df_edges.groupby(df_edges.columns.tolist(), as_index=False).size()
-for index, row in df_edges.iterrows():
+for index, row in df_edges.iterrows(): # noqa: B007
     if row['origin_airport_abbreviation'] == row['destination_airport_abbreviation']:
         continue
-    G.add_edge(row['origin_airport_abbreviation'], row['destination_airport_abbreviation'], flight_count=row['size'])
+    G.add_edge(
+        row['origin_airport_abbreviation'],
+        row['destination_airport_abbreviation'],
+        flight_count=row['size']
+    )
 
 # Export to graphml
 nx.write_graphml(G, 'data/air_traffic.graphml')