Merge pull request #96 from washingtonpost/remove-duplicate-code

Removing duplicate code in `Estimandizer` class

Author: dmnapolitano

.github/workflows/integration.yml

@@ -14,7 +14,7 @@ jobs:
     timeout-minutes: 5
     strategy:
       matrix:
-        python-version: ['3.10']
+        python-version: ['3.11']
     steps:
       - uses: actions/checkout@v2
       - name: Setup Python

.github/workflows/pre-commit.yml

@@ -10,5 +10,5 @@ jobs:
     - uses: actions/checkout@v2
     - uses: actions/setup-python@v2
       with:
-        python-version: '3.9'
+        python-version: '3.11'
     - uses: pre-commit/action@v2.0.3

.github/workflows/test.yml

@@ -14,7 +14,7 @@ jobs:
     timeout-minutes: 5
     strategy:
       matrix:
-        python-version: ['3.10']
+        python-version: ['3.11']
     steps:
       - uses: actions/checkout@v2
       - name: Setup Python

src/elexmodel/handlers/data/Estimandizer.py

@@ -95,20 +95,6 @@ def add_turnout_factor(self, data_df):
         )
         return data_df
 
-    def add_weights(self, data_df, col_prefix):
-        data_df[f"{col_prefix}weights"] = data_df[f"{col_prefix}turnout"]
-        return data_df
-
-    def add_turnout_factor(self, data_df):
-        # posinf and neginf are also set to zero because dividing by zero can lead to nan/posinf/neginf depending
-        # on the type of the numeric in the numpy array. Assume that if baseline_weights is zero then turnout
-        # would be incredibly low in this election too (ie. this is effectively an empty precinct) and so setting
-        # the turnout factor to zero is fine
-        data_df["turnout_factor"] = np.nan_to_num(
-            data_df.results_weights / data_df.baseline_weights, nan=0, posinf=0, neginf=0
-        )
-        return data_df
-
 
 # custom estimands