Adds Python 3.11 support, by fixing pandas.groupby.mean() removing kwarg 'level'

.github/workflows/python-package.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2
@@ -47,4 +47,3 @@ jobs:
       uses: codecov/codecov-action@v2
       with:
         fail_ci_if_error: true
-

.github/workflows/pythonpublish.yml

@@ -12,7 +12,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v1
       with:
-        python-version: '3.7'
+        python-version: '3.11'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

README.md

@@ -76,7 +76,7 @@ Only one Waiver claim (for Jordon Howard) increases discounted points.
 
 ## Contribution
 
-Please add Issues or submit Pull requests!
+Please add Issues or submit Pull Requests!
 
 For local development, install optional testing dependencies and pre-commit hooks using
 

ffbot/constants.py

@@ -1 +1 @@
-VERSION = "1.2.2"
+VERSION = "1.2.3"

ffbot/scraper.py

@@ -1,4 +1,5 @@
 from datetime import datetime
+from io import StringIO
 
 import numpy as np
 import pandas as pd
@@ -123,7 +124,7 @@
         row["% Owned"] = playerinfo.select_one("dd.owned").text.split()[0]
 
         # Weekly projections
-        df2 = pd.read_html(html)[0]
+        df2 = pd.read_html(StringIO(html))[0]
         for _, row2 in df2.iterrows():
             week = "Week {}".format(row2["Week"])
             points = row2["Fan Pts"]
@@ -157,7 +158,9 @@
     columns = ["Week {}".format(i) for i in range(current_week(), 18)]
     df["Remaining"] = df[columns].sum(axis=1)
     available = df.loc[df["Owner ID"].isnull()]
-    means = available.groupby(["Position"])["Remaining"].nlargest(3).mean(level=0)
+    means = (
+        available.groupby(["Position"])["Remaining"].nlargest(3).groupby(level=0).mean()
+    )
     for positions in means.index:
         if "," in positions:
             for position in positions.split(","):