Feat: Minor thing

recopytex/csv_extraction.py

@@ -8,6 +8,12 @@ from .config import NO_ST_COLUMNS, COLUMNS, VALIDSCORE
 
 pd.set_option("Precision", 2)
 
+def try_replace(x, old, new):
+    try:
+        return str(x).replace(old, new)
+    except ValueError:
+        return x
+
 
 def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
     """ Extract the list of students from df 
@@ -44,9 +50,9 @@ def flat_df_students(df, no_student_columns=NO_ST_COLUMNS.values()):
                 value_vars=st,
                 var_name=COLUMNS["student"],
                 value_name=COLUMNS["score"],
-            )
+            ).dropna(subset=[COLUMNS["score"]])
         )
-    return pd.concat(scores).dropna(subset=[COLUMNS["score"]])
+    return pd.concat(scores)
 
 
 def flat_clear_csv(csv_df, no_student_columns=NO_ST_COLUMNS.values()):
@@ -67,11 +73,12 @@ def flat_clear_csv(csv_df, no_student_columns=NO_ST_COLUMNS.values()):
     df[COLUMNS["score"]] = pd.to_numeric(
         df[COLUMNS["score"]]
         .replace(VALIDSCORE["NOANSWER"], -1)
-        .apply(lambda x: str(x).replace(",", "."))
+        .apply(lambda x: try_replace(x, ",", "."))
     )
     df[COLUMNS["score_rate"]] = pd.to_numeric(
         df[COLUMNS["score_rate"]]
-        .apply(lambda x: str(x).replace(",", "."))
+        .apply(lambda x: try_replace(x, ",", ".")),
+        errors="coerce"
     )
 
     return df

recopytex/df_marks_manip.py

@@ -77,14 +77,14 @@ def score_to_level(x):
     >>> score_to_level(df.loc[10])
     2
     """
-    # -1 is no answer
-    if x[COLUMNS["score"]] == -1:
-        return x[COLUMNS["score"]]
+    # negatives are no answer or negatives points
+    if x[COLUMNS["score"]] <= -1:
+        return np.nan
 
     if x[COLUMNS["is_leveled"]]:
         return int(x[COLUMNS["score"]])
-    else:
-        return int(ceil(x[COLUMNS["score"]] / x[COLUMNS["score_rate"]] * 3))
+
+    return int(ceil(x[COLUMNS["score"]] / x[COLUMNS["score_rate"]] * 3))
 
 
 # DataFrame columns manipulations