Add a simple function for recoding proximity.

exploration/expl_app_categories.py

@@ -42,7 +42,9 @@ df_app_categories.head()
 df_app_categories["play_store_genre"].value_counts()
 
 # %%
-df_category_not_found = df_app_categories[df_app_categories["play_store_genre"] == "not_found"]
+df_category_not_found = df_app_categories[
+    df_app_categories["play_store_genre"] == "not_found"
+]
 
 # %%
 df_category_not_found["play_store_response"].value_counts()
@@ -51,13 +53,24 @@ df_category_not_found["play_store_response"].value_counts()
 df_category_not_found["package_name"].value_counts()
 
 # %%
-manufacturers = ["samsung","oneplus","huawei","xiaomi","lge","motorola","miui","lenovo","oppo","mediatek"]
+manufacturers = [
+    "samsung",
+    "oneplus",
+    "huawei",
+    "xiaomi",
+    "lge",
+    "motorola",
+    "miui",
+    "lenovo",
+    "oppo",
+    "mediatek",
+]
 custom_rom = ["coloros", "lineageos", "myos", "cyanogenmod", "foundation.e"]
 other = ["android", "wssyncmldm"]
-rows_os_manufacturer = df_category_not_found["package_name"].str.contains("|".join(manufacturers + custom_rom + other), case=False)
+rows_os_manufacturer = df_category_not_found["package_name"].str.contains(
+    "|".join(manufacturers + custom_rom + other), case=False
+)
 
 # %%
-with pd.option_context(
-    "display.max_rows", None, "display.max_columns", None
-): 
+with pd.option_context("display.max_rows", None, "display.max_columns", None):
     display(df_category_not_found.loc[~rows_os_manufacturer])

features/proximity.py

@@ -28,3 +28,28 @@ def get_proximity_data(usernames: Collection) -> pd.DataFrame:
     with db_engine.connect() as connection:
         df_proximity = pd.read_sql(query_proximity.statement, connection)
     return df_proximity
+
+
+def recode_proximity(df_proximity: pd.DataFrame) -> pd.DataFrame:
+    """
+    This function recodes proximity from a double to a boolean value.
+    Different proximity sensors report different values,
+        but in our data only several distinct values have ever been found.
+    These are therefore converted into "near" and "far" binary values.
+    See expl_proximity.ipynb for additional info.
+
+    Parameters
+    ----------
+    df_proximity: pd.DataFrame
+        A dataframe of proximity data.
+
+    Returns
+    -------
+    df_proximity: pd.DataFrame
+        The same dataframe with an additional column bool_prox_near,
+            indicating whether "near" proximity was reported.
+        False values correspond to "far" reported by this sensor.
+
+    """
+    df_proximity = df_proximity.assign(bool_prox_near=lambda x: x.double_proximity == 0)
+    return df_proximity