chore(interpolation): change back to bfill, change forward to ffill

README.md

@@ -187,8 +187,8 @@ Interpolate a series to fill in missing values using a specified function. The f
 
 - Zero Fill : `zero`
 - Null Fill: `null`
-- Backwards Fill: `back`
-- Forwards Fill: `forward`
+- Backwards Fill: `bfill`
+- Forwards Fill: `ffill`
 - Linear Fill: `linear`
 
 This method automatically first re-samples the input dataset into a given frequency, then performs interpolation on the sampled time-series dataset

python/README.md

@@ -188,8 +188,8 @@ Interpolate a series to fill in missing values using a specified function. The f
 
 - Zero Fill : `zero`
 - Null Fill: `null`
-- Backwards Fill: `back`
-- Forwards Fill: `forward`
+- Backwards Fill: `bfill`
+- Forwards Fill: `ffill`
 - Linear Fill: `linear`
 
 This method automatically first re-samples the input dataset into a given frequency, then performs interpolation on the sampled time-series dataset

python/tempo/interpol.py

@@ -6,7 +6,7 @@
 from pyspark.sql.window import Window
 
 # Interpolation fill options
-method_options = ["zero", "null", "back", "forward", "linear"]
+method_options = ["zero", "null", "bfill", "ffill", "linear"]
 supported_target_col_types = ["int", "bigint", "float", "double"]
 
 
@@ -15,7 +15,7 @@ def __validate_fill(self, method: str):
         """
         Validate if the fill provided is within the allowed list of values.
 
-        :param fill - Fill type e.g. "zero", "null", "back", "forward", "linear"
+        :param fill - Fill type e.g. "zero", "null", "bfill", "ffill", "linear"
         """
         if method not in method_options:
             raise ValueError(
@@ -137,7 +137,7 @@ def __interpolate_column(
             )
 
         # Handle forward fill
-        if method == "forward":
+        if method == "ffill":
             output_df = output_df.withColumn(
                 target_col,
                 when(
@@ -146,7 +146,7 @@ def __interpolate_column(
                 ).otherwise(col(target_col)),
             )
         # Handle backwards fill 
-        if method == "back":
+        if method == "bfill":
             output_df = output_df.withColumn(
                 target_col,
                 # Handle case when subsequent value is null

python/tempo/tsdf.py

@@ -593,7 +593,7 @@ def interpolate(self, freq: str, func: str, method: str, target_cols: List[str]
 
     :param freq: frequency for upsample - valid inputs are "hr", "min", "sec" corresponding to hour, minute, or second
     :param func: function used to aggregate input
-    :param method: function used to fill missing values e.g. linear, null, zero, back, forward
+    :param method: function used to fill missing values e.g. linear, null, zero, bfill, ffill
     :param target_cols [optional]: columns that should be interpolated, by default interpolates all numeric columns
     :param ts_col [optional]: specify other ts_col, by default this uses the ts_col within the TSDF object
     :param partition_cols [optional]: specify other partition_cols, by default this uses the partition_cols within the TSDF object

python/tests/interpol_tests.py

@@ -263,7 +263,7 @@ def test_back_fill_interpolation(self):
             freq="30 seconds",
             ts_col="event_ts",
             func="mean",
-            method="back",
+            method="bfill",
             show_interpolated=True,
         )
 
@@ -303,7 +303,7 @@ def test_forward_fill_interpolation(self):
             freq="30 seconds",
             ts_col="event_ts",
             func="mean",
-            method="forward",
+            method="ffill",
             show_interpolated=True,
         )