refactor: align naming for clean_ohlcv_dataframe

freqtrade/data/converter/converter.py

@@ -59,14 +59,14 @@ def ohlcv_to_dataframe(
 
 
 def clean_ohlcv_dataframe(
-    data: DataFrame, timeframe: str, pair: str, *, fill_missing: bool, drop_incomplete: bool
+    dataframe: DataFrame, timeframe: str, pair: str, *, fill_missing: bool, drop_incomplete: bool
 ) -> DataFrame:
     """
     Cleanse a OHLCV dataframe by
       * Grouping it by date (removes duplicate tics)
       * dropping last candles if requested
       * Filling up missing data (if requested)
-    :param data: DataFrame containing candle (OHLCV) data.
+    :param dataframe: DataFrame containing candle (OHLCV) data.
     :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
@@ -75,7 +75,7 @@ def clean_ohlcv_dataframe(
     :return: DataFrame
     """
     # group by index and aggregate results to eliminate duplicate ticks
-    data = data.groupby(by="date", as_index=False, sort=True).agg(
+    dataframe = dataframe.groupby(by="date", as_index=False, sort=True).agg(
         {
             "open": "first",
             "high": "max",
@@ -86,13 +86,13 @@ def clean_ohlcv_dataframe(
     )
     # eliminate partial candle
     if drop_incomplete:
-        data.drop(data.tail(1).index, inplace=True)
+        dataframe.drop(dataframe.tail(1).index, inplace=True)
         logger.debug("Dropping last candle")
 
     if fill_missing:
-        return ohlcv_fill_up_missing_data(data, timeframe, pair)
+        return ohlcv_fill_up_missing_data(dataframe, timeframe, pair)
     else:
-        return data
+        return dataframe
 
 
 def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame: