refactor: don't use pandas for basic reports (#30597)

erpnext/crm/report/opportunity_summary_by_sales_stage/opportunity_summary_by_sales_stage.py

@@ -1,9 +1,9 @@
 # Copyright (c) 2013, Frappe Technologies Pvt. Ltd. and contributors
 # For license information, please see license.txt
 import json
+from itertools import groupby
 
 import frappe
-import pandas
 from frappe import _
 from frappe.utils import flt
 
@@ -101,18 +101,19 @@ class OpportunitySummaryBySalesStage(object):
 
 			self.convert_to_base_currency()
 
-			dataframe = pandas.DataFrame.from_records(self.query_result)
+			for row in self.query_result:
-			dataframe.replace(to_replace=[None], value="Not Assigned", inplace=True)
+				if not row.get(based_on):
-			result = dataframe.groupby(["sales_stage", based_on], as_index=False)["amount"].sum()
+					row[based_on] = "Not Assigned"
 
 			self.grouped_data = []
 
-			for i in range(len(result["amount"])):
+			grouping_key = lambda o: (o["sales_stage"], o[based_on])  # noqa
+			for (sales_stage, _based_on), rows in groupby(self.query_result, grouping_key):
 				self.grouped_data.append(
 					{
-						"sales_stage": result["sales_stage"][i],
+						"sales_stage": sales_stage,
-						based_on: result[based_on][i],
+						based_on: _based_on,
-						"amount": result["amount"][i],
+						"amount": sum(flt(r["amount"]) for r in rows),
 					}
 				)
 

erpnext/crm/report/sales_pipeline_analytics/sales_pipeline_analytics.py

@@ -3,9 +3,9 @@
 
 import json
 from datetime import date
+from itertools import groupby
 
 import frappe
-import pandas
 from dateutil.relativedelta import relativedelta
 from frappe import _
 from frappe.utils import cint, flt
@@ -109,18 +109,15 @@ class SalesPipelineAnalytics(object):
 
 			self.convert_to_base_currency()
 
-			dataframe = pandas.DataFrame.from_records(self.query_result)
-			dataframe.replace(to_replace=[None], value="Not Assigned", inplace=True)
-			result = dataframe.groupby([self.pipeline_by, self.period_by], as_index=False)["amount"].sum()
-
 			self.grouped_data = []
 
-			for i in range(len(result["amount"])):
+			grouping_key = lambda o: (o.get(self.pipeline_by) or "Not Assigned", o[self.period_by])  # noqa
+			for (pipeline_by, period_by), rows in groupby(self.query_result, grouping_key):
 				self.grouped_data.append(
 					{
-						self.pipeline_by: result[self.pipeline_by][i],
+						self.pipeline_by: pipeline_by,
-						self.period_by: result[self.period_by][i],
+						self.period_by: period_by,
-						"amount": result["amount"][i],
+						"amount": sum(flt(r["amount"]) for r in rows),
 					}
 				)
 

erpnext/selling/page/sales_funnel/sales_funnel.py

@@ -1,10 +1,11 @@
 # Copyright (c) 2018, Frappe Technologies Pvt. Ltd. and Contributors
 # License: GNU General Public License v3. See license.txt
 
+from itertools import groupby
 
 import frappe
-import pandas as pd
 from frappe import _
+from frappe.utils import flt
 
 from erpnext.accounts.report.utils import convert
 
@@ -89,28 +90,21 @@ def get_opp_by_lead_source(from_date, to_date, company):
 			for x in opportunities
 		]
 
-		df = (
+		summary = {}
-			pd.DataFrame(cp_opportunities)
+		sales_stages = set()
-			.groupby(["source", "sales_stage"], as_index=False)
+		group_key = lambda o: (o["source"], o["sales_stage"])  # noqa
-			.agg({"compound_amount": "sum"})
+		for (source, sales_stage), rows in groupby(cp_opportunities, group_key):
-		)
+			summary.setdefault(source, {})[sales_stage] = sum(r["compound_amount"] for r in rows)
+			sales_stages.add(sales_stage)
 
-		result = {}
+		pivot_table = []
-		result["labels"] = list(set(df.source.values))
+		for sales_stage in sales_stages:
-		result["datasets"] = []
+			row = []
-
+			for source, sales_stage_values in summary.items():
-		for s in set(df.sales_stage.values):
+				row.append(flt(sales_stage_values.get(sales_stage)))
-			result["datasets"].append(
+			pivot_table.append({"chartType": "bar", "name": sales_stage, "values": row})
-				{"name": s, "values": [0] * len(result["labels"]), "chartType": "bar"}
-			)
-
-		for row in df.itertuples():
-			source_index = result["labels"].index(row.source)
-
-			for dataset in result["datasets"]:
-				if dataset["name"] == row.sales_stage:
-					dataset["values"][source_index] = row.compound_amount
 
+		result = {"datasets": pivot_table, "labels": list(summary.keys())}
 		return result
 
 	else:
@@ -148,20 +142,14 @@ def get_pipeline_data(from_date, to_date, company):
 			for x in opportunities
 		]
 
-		df = (
+		summary = {}
-			pd.DataFrame(cp_opportunities)
+		for sales_stage, rows in groupby(cp_opportunities, lambda o: o["sales_stage"]):
-			.groupby(["sales_stage"], as_index=True)
+			summary[sales_stage] = sum(flt(r["compound_amount"]) for r in rows)
-			.agg({"compound_amount": "sum"})
-			.to_dict()
-		)
-
-		result = {}
-		result["labels"] = df["compound_amount"].keys()
-		result["datasets"] = []
-		result["datasets"].append(
-			{"name": _("Total Amount"), "values": df["compound_amount"].values(), "chartType": "bar"}
-		)
 
+		result = {
+			"labels": list(summary.keys()),
+			"datasets": [{"name": _("Total Amount"), "values": list(summary.values()), "chartType": "bar"}],
+		}
 		return result
 
 	else: