Merge branch 'master' into arcade-platformer

Author: jaschmitt

python-flask-example-heroku/.gitignore

@@ -0,0 +1,2 @@
+venv
+__pycache__

python-flask-example-heroku/Procfile

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+web: gunicorn app:app

python-flask-example-heroku/README.md

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+Source files supporting the [Deploying a Python Flask Example Application Using Heroku](https://realpython.com/python-flask-example-heroku/) article on [Real Python](https://realpython.com/).

python-flask-example-heroku/app.py

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+import os
+from flask import Flask
+
+app = Flask(__name__)
+env_config = os.getenv("APP_SETTINGS", "config.DevelopmentConfig")
+app.config.from_object(env_config)
+
+
+@app.route("/")
+def index():
+    secret_key = app.config.get("SECRET_KEY")
+    return f"The configured secret key is {secret_key}."

python-flask-example-heroku/config.py

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+import os
+
+
+class Config:
+    DEBUG = False
+    DEVELOPMENT = False
+    SECRET_KEY = os.getenv("SECRET_KEY", "this-is-the-default-key")
+
+
+class ProductionConfig(Config):
+    pass
+
+
+class StagingConfig(Config):
+    DEBUG = True
+
+
+class DevelopmentConfig(Config):
+    DEBUG = True
+    DEVELOPMENT = True

python-flask-example-heroku/requirements.txt

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+click==7.1.2
+Flask==1.1.2
+gunicorn==20.0.4
+itsdangerous==1.1.0
+Jinja2==2.11.2
+MarkupSafe==1.1.1
+Werkzeug==1.0.1

python-interview-problems-parsing-csv/README.md

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+# Python Interview Problems – Parsing CSV Files
+
+Corresponding code for ["Python Interview Problems – Parsing CSV Files."](https://realpython.com/python-interview-problems-parsing-csv-python-files/)
+
+The `skeleton_code` directory contains pytest fixtures and module files to get you set up to run pytest. There are no tests in the files, which pytest tells you when you run it:
+
+```console
+$ pytest test_weather_v1.py 
+======================================= test session starts ========================================
+platform linux -- Python 3.7.1, pytest-6.2.1, py-1.10.0, pluggy-0.13.1
+rootdir: /home/jima/coding/materials_realpy/python-interview-problems-parsing-csv/skeleton_code
+collected 0 items                                                                                  
+
+====================================== no tests ran in 0.00s =======================================
+```
+
+The `full_code` directory contains the source files we used to generate the examples in the article. 
+
+Good luck!

python-interview-problems-parsing-csv/full_code/csv_parser.py

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+#!/usr/bin/env python3
+""" Reusable CSV parser for both football and weather data.  """
+import csv
+
+
+def get_next_result(csv_file, func):
+    for stats in csv.DictReader(csv_file):
+        yield func(stats)

python-interview-problems-parsing-csv/full_code/football_final.py

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+#!/usr/bin/env python3
+""" Find Minimum Goal Differential
+    Write a program that takes a filename on the command line and processes the
+    CSV contents. The contents will be a CSV file with end-of-season football
+    standings for the English Premier League.
+    Determine which team had the smallest goal differential that season.
+    The first line of the CSV file will be column headers:
+
+        Team,Games,Wins,Losses,Draws,Goals For,Goals Against
+
+    Write unit tests with Pytest to test your program.
+"""
+import csv_reader
+
+
+def get_name_and_diff(team_stats):
+    diff = int(team_stats["Goals For"]) - int(team_stats["Goals Against"])
+    return team_stats["Team"], abs(diff)
+
+
+def get_min_score_difference(filename):
+    with open(filename, "r", newline="") as csv_data:
+        return min(
+            csv_reader.get_next_result(csv_data, get_name_and_diff),
+            key=lambda item: item[1],
+        )

python-interview-problems-parsing-csv/full_code/football_pandas.py

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+import pandas as pd
+
+
+def read_data(csv_file):
+    return (
+        pd.read_csv(csv_file)
+        .rename(
+            columns={
+                "Team": "team_name",
+                "Goals For": "goals",
+                "Goals Against": "goals_allowed",
+            }
+        )
+        .assign(goal_difference=lambda df: abs(df.goals - df.goals_allowed))
+    )
+
+
+def get_min_difference(parsed_data):
+    return parsed_data.goal_difference.min()
+
+
+def get_team(parsed_data, min_score_difference):
+    return (
+        parsed_data.query(f"goal_difference == {min_score_difference}")
+        .reset_index()
+        .loc[0, "team_name"]
+    )
+
+
+def get_min_score_difference(csv_file):
+    df = read_data(csv_file)
+    min_diff = get_min_difference(df)
+    team = get_team(df, min_diff)
+    return team, min_diff