From 8f3bc6115f83359039a1bad432ebe098e84f0701 Mon Sep 17 00:00:00 2001
From: Phaeton <808865+Phaeton@users.noreply.github.com>
Date: Sat, 8 Feb 2025 14:06:31 -0500
Subject: [PATCH] linting

---
 scripts/export_categories.py | 10 ++++----
 scripts/update_readme.py     | 45 ++++++++++++++++++------------------
 2 files changed, 28 insertions(+), 27 deletions(-)

diff --git a/scripts/export_categories.py b/scripts/export_categories.py
index a9a43b68..0e43dbac 100644
--- a/scripts/export_categories.py
+++ b/scripts/export_categories.py
@@ -1,4 +1,4 @@
-""""script to export current categories, this time sorted descending by frequency"""
+""" "script to export current categories, sorted descending by frequency"""
 
 import logging
 import pandas as pd
@@ -11,11 +11,13 @@
     logging.info("Reading the main csv file...")
     df = pd.read_csv("plane-alert-db.csv")
 
-    logging.info("Sorting categories by frequency...")
-    category_sorted = df["Category"].value_counts().index.to_series().reset_index(drop=True)
+    logging.info("Identifying and sorting categories by frequency...")
+    category_sorted = (
+        df["Category"].value_counts().index.to_series().reset_index(drop=True)
+    )
 
     logging.info(f"Total Unique Categories: ({category_sorted.shape[0]}).")
-    logging.info("Creating the plane-alert-categories.csv file sorted by frequency.")
+    logging.info("Creating the plane-alert-categories.csv file, sorted by frequency.")
 
     category_sorted.to_csv(
         "plane-alert-categories.csv",
diff --git a/scripts/update_readme.py b/scripts/update_readme.py
index fcb96d0f..d91f92a9 100644
--- a/scripts/update_readme.py
+++ b/scripts/update_readme.py
@@ -1,5 +1,4 @@
-"""Script that performs several counts to update the README
-"""
+"""Script that performs several counts to update the README"""
 
 import logging
 import pandas as pd
@@ -20,23 +19,13 @@
     images_df = pd.read_csv("plane_images.csv")
     logging.info("All csv files read successfully.")
 
-    plane_count_df = (
-        df["$ICAO"]
-        .drop_duplicates()
-        .reset_index(drop=True)
-    )
+    plane_count_df = df["$ICAO"].drop_duplicates().reset_index(drop=True)
     logging.info(f"Total Planes Count: ({plane_count_df.shape[0]}).")
 
-    category_unique_df = (
-        df["Category"]
-        .drop_duplicates()
-        .reset_index(drop=True)
-    )
+    category_unique_df = df["Category"].drop_duplicates().reset_index(drop=True)
     logging.info(f"Total Categories Count: ({category_unique_df.shape[0]}).")
 
-    category_df = df["Category"].reset_index(
-        drop=False
-    )
+    category_df = df["Category"].reset_index(drop=False)
 
     logging.info("Generating Counts to update README.md via mustache template.")
 
@@ -102,7 +91,9 @@
                     "flying_doctors_count": category_df[
                         category_df["Category"] == "Flying Doctors"
                     ].shape[0],
-                    "gaf_count": category_df[category_df["Category"] == "GAF"].shape[0],
+                    "gaf_count": category_df[
+                        category_df["Category"] == "GAF"
+                    ].shape[0],
                     "gas_bags_count": category_df[
                         category_df["Category"] == "Gas Bags"
                     ].shape[0],
@@ -142,7 +133,9 @@
                     "psa_count": category_df[
                         category_df["Category"] == "Perfectly Serviceable Aircraft"
                     ].shape[0],
-                    "pia_count": category_df[category_df["Category"] == "PIA"].shape[0],
+                    "pia_count": category_df[
+                        category_df["Category"] == "PIA"
+                    ].shape[0],
                     "police_forces_count": category_df[
                         category_df["Category"] == "Police Forces"
                     ].shape[0],
@@ -158,7 +151,9 @@
                     "radiohead_count": category_df[
                         category_df["Category"] == "Radiohead"
                     ].shape[0],
-                    "raf_count": category_df[category_df["Category"] == "RAF"].shape[0],
+                    "raf_count": category_df[category_df[
+                        "Category"] == "RAF"
+                    ].shape[0],
                     "royal_aircraft_count": category_df[
                         category_df["Category"] == "Royal Aircraft"
                     ].shape[0],
@@ -171,20 +166,24 @@
                     "special_forces_count": category_df[
                         category_df["Category"] == "Special Forces"
                     ].shape[0],
-                    "uav_count": category_df[category_df["Category"] == "UAV"].shape[0],
+                    "uav_count": category_df[
+                        category_df["Category"] == "UAV"
+                    ].shape[0],
                     "uk_police_count": category_df[
                         category_df["Category"] == "UK National Police Air Service"
                     ].shape[0],
-                    "ukraine_count": category_df[category_df["Category"] == "Ukraine"].shape[0],
+                    "ukraine_count": category_df[
+                        category_df["Category"] == "Ukraine"
+                    ].shape[0],
                     "us_marines_count": category_df[
                         category_df["Category"] == "United States Marine Corps"
                     ].shape[0],
                     "us_navy_count": category_df[
                         category_df["Category"] == "United States Navy"
                     ].shape[0],
-                    "usaf_count": category_df[category_df["Category"] == "USAF"].shape[
-                        0
-                    ],
+                    "usaf_count": category_df[
+                        category_df["Category"] == "USAF"
+                    ].shape[0],
                     "vanity_plate_count": category_df[
                         category_df["Category"] == "Vanity Plate"
                     ].shape[0],