From 0a113fdd8ac1c19ac31cd755b0e9b8a47a8eca06 Mon Sep 17 00:00:00 2001
From: Gourav Kumar <gourav2711@gmail.com>
Date: Tue, 24 May 2022 21:11:34 +0530
Subject: [PATCH] completed transform function

---
 fincal/fincal.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/fincal/fincal.py b/fincal/fincal.py
index 3cc7bfb..28fd039 100644
--- a/fincal/fincal.py
+++ b/fincal/fincal.py
@@ -784,7 +784,7 @@ class TimeSeries(TimeSeriesCore):
         return statistics.mean(self.values)
 
     def transform(
-        self, to_frequency: Literal["W", "M", "Q", "H", "Y"], method: Literal["sum", "mean"], eomonth: bool
+        self, to_frequency: Literal["W", "M", "Q", "H", "Y"], method: Literal["sum", "mean"], eomonth: bool = False
     ) -> TimeSeries:
         """Transform a time series object into a lower frequency object with an aggregation function.
 
@@ -822,6 +822,28 @@ class TimeSeries(TimeSeriesCore):
         if method not in ["sum", "mean"]:
             raise ValueError(f"Method not recognised: {method}")
 
+        dates = create_date_series(
+            self.start_date,
+            self.end_date
+            + datetime.timedelta(to_frequency.days),  # need extra date at the end for calculation of last value
+            to_frequency.symbol,
+            ensure_coverage=True,
+        )
+        prev_date = dates[0]
+
+        new_ts_dict = {}
+        for date in dates[1:]:
+            cur_data = self[(self.dates >= prev_date) & (self.dates < date)]
+            if method == "sum":
+                value = sum(cur_data.values)
+            elif method == "mean":
+                value = cur_data.mean()
+
+            new_ts_dict.update({prev_date: value})
+            prev_date = date
+
+        return self.__class__(new_ts_dict, to_frequency.symbol)
+
 
 def _preprocess_csv(file_path: str | pathlib.Path, delimiter: str = ",", encoding: str = "utf-8") -> List[list]:
     """Preprocess csv data"""