frappe-reports

Name: frappe-reports
Author: lubusIN

44installs11forks

How to install frappe-reports?

lubusin/frappe-skills/frappe-reports

$npx -y skills add lubusIN/frappe-skills --skill frappe-reports

Installs into the current project.

Summary

Create reports in Frappe including Report Builder, Query Reports (SQL), and Script Reports (Python + JS). Use when building data analysis views, dashboards, or custom reporting features.

Files · 1

View on GitHub

SKILL.md

1	# Frappe Reports
2
3	Build reports using Report Builder, Query Reports (SQL), or Script Reports (Python + JS).
4
5	## When to use
6
7	- Creating data analysis or summary reports
8	- Building SQL-based query reports
9	- Implementing complex reports with Python logic and JS UI
10	- Adding custom filters, formatters, and charts to reports
11	- Creating printable report formats
12
13	## Inputs required
14
15	- Report purpose and data requirements
16	- Source DocType(s) for the report
17	- Filter requirements
18	- Column definitions (fields, types, formatting)
19	- Whether report is standard (app-bundled) or custom (site-specific)
20
21	## Procedure
22
23	### 0) Choose report type
24
25	\| Type \| Complexity \| Code Required \| Best For \|
26	\|------\|-----------\|---------------\|----------\|
27	\| Report Builder \| Low \| None \| Simple field selection, grouping, sorting \|
28	\| Query Report \| Medium \| SQL only \| Direct SQL queries, joins, aggregations \|
29	\| Script Report \| High \| Python + JS \| Complex logic, computed fields, dynamic filters \|
30
31	### 1) Report Builder
32
33	Create via UI with no code:
34	1. Navigate to the Report list → New Report
35	2. Select Reference DocType
36	3. Choose Report Type = "Report Builder"
37	4. Add columns, filters, sorting, and grouping via the builder UI
38
39	### 2) Query Report
40
41	Reports using raw SQL queries:
42
43	1. Create Report → Type = "Query Report"
44	2. Set Reference DocType (controls permissions)
45	3. Write SQL query
46
47	```sql
48	SELECT
49	`tabSales Order`.name AS "Sales Order:Link/Sales Order:200",
50	`tabSales Order`.customer AS "Customer:Link/Customer:200",
51	`tabSales Order`.transaction_date AS "Date:Date:120",
52	`tabSales Order`.grand_total AS "Grand Total:Currency:150",
53	`tabSales Order`.status AS "Status:Data:100"
54	FROM `tabSales Order`
55	WHERE `tabSales Order`.docstatus = 1
56	{% if filters.company %}
57	AND `tabSales Order`.company = %(company)s
58	{% endif %}
59	{% if filters.from_date %}
60	AND `tabSales Order`.transaction_date >= %(from_date)s
61	{% endif %}
62	ORDER BY `tabSales Order`.transaction_date DESC
63	```
64
65	Column format in SELECT: `"Label:Fieldtype/Options:Width"`
66
67	\| Fieldtype \| Example \|
68	\|-----------\|---------\|
69	\| Link \| `"Customer:Link/Customer:200"` \|
70	\| Currency \| `"Amount:Currency:150"` \|
71	\| Date \| `"Date:Date:120"` \|
72	\| Int \| `"Quantity:Int:100"` \|
73	\| Data \| `"Status:Data:100"` \|
74
75	Filter variables: Use `%(filter_name)s` for parameterized queries.
76
77	### 3) Script Report (standard)
78
79	For app-bundled reports with full Python + JS control:
80
81	Create the report structure:
82	```
83	my_app/
84	└── my_module/
85	└── report/
86	└── sales_summary/
87	├── sales_summary.json # Report metadata
88	├── sales_summary.py # Python data logic
89	└── sales_summary.js # JS filters and UI
90	```
91
92	Python script (`sales_summary.py`):
93
94	```python
95	import frappe
96	from frappe import _
97
98	def execute(filters=None):
99	columns = get_columns()
100	data = get_data(filters)
101	chart = get_chart(data)
102	return columns, data, None, chart
103
104	def get_columns():
105	return [
106	{
107	"label": _("Customer"),
108	"fieldname": "customer",
109	"fieldtype": "Link",
110	"options": "Customer",
111	"width": 200
112	},
113	{
114	"label": _("Total Orders"),
115	"fieldname": "total_orders",
116	"fieldtype": "Int",
117	"width": 120
118	},
119	{
120	"label": _("Total Amount"),
121	"fieldname": "total_amount",
122	"fieldtype": "Currency",
123	"width": 150
124	},
125	{
126	"label": _("Average Order"),
127	"fieldname": "avg_order",
128	"fieldtype": "Currency",
129	"width": 150
130	}
131	]
132
133	def get_data(filters):
134	conditions = get_conditions(filters)
135
136	data = frappe.db.sql("""
137	SELECT
138	customer,
139	COUNT(name) as total_orders,
140	SUM(grand_total) as total_amount,
141	AVG(grand_total) as avg_order
142	FROM `tabSales Order`
143	WHERE docstatus = 1 {conditions}
144	GROUP BY customer
145	ORDER BY total_amount DESC
146	""".format(conditions=conditions), filters, as_dict=True)
147
148	return data
149
150	def get_conditions(filters):
151	conditions = ""
152	if filters.get("company"):
153	conditions += " AND company = %(company)s"
154	if filters.get("from_date"):
155	conditions += " AND transaction_date >= %(from_date)s"
156	if filters.get("to_date"):
157	conditions += " AND transaction_date <= %(to_date)s"
158	return conditions
159
160	def get_chart(data):
161	if not data:
162	return None
163
164	return {
165	"data": {
166	"labels": [d.customer for d in data[:10]],
167	"datasets": [{
168	"name": _("Total Amount"),
169	"values": [d.total_amount for d in data[:10]]

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frappe-reports

44installs11forks

View on GitHub

How to install frappe-reports?

lubusin/frappe-skills/frappe-reports

$npx -y skills add lubusIN/frappe-skills --skill frappe-reports

Installs into the current project.

Summary

Create reports in Frappe including Report Builder, Query Reports (SQL), and Script Reports (Python + JS). Use when building data analysis views, dashboards, or custom reporting features.

Files · 1

View on GitHub

SKILL.md

1	# Frappe Reports
2
3	Build reports using Report Builder, Query Reports (SQL), or Script Reports (Python + JS).
4
5	## When to use
6
7	- Creating data analysis or summary reports
8	- Building SQL-based query reports
9	- Implementing complex reports with Python logic and JS UI
10	- Adding custom filters, formatters, and charts to reports
11	- Creating printable report formats
12
13	## Inputs required
14
15	- Report purpose and data requirements
16	- Source DocType(s) for the report
17	- Filter requirements
18	- Column definitions (fields, types, formatting)
19	- Whether report is standard (app-bundled) or custom (site-specific)
20
21	## Procedure
22
23	### 0) Choose report type
24
25	\| Type \| Complexity \| Code Required \| Best For \|
26	\|------\|-----------\|---------------\|----------\|
27	\| Report Builder \| Low \| None \| Simple field selection, grouping, sorting \|
28	\| Query Report \| Medium \| SQL only \| Direct SQL queries, joins, aggregations \|
29	\| Script Report \| High \| Python + JS \| Complex logic, computed fields, dynamic filters \|
30
31	### 1) Report Builder
32
33	Create via UI with no code:
34	1. Navigate to the Report list → New Report
35	2. Select Reference DocType
36	3. Choose Report Type = "Report Builder"
37	4. Add columns, filters, sorting, and grouping via the builder UI
38
39	### 2) Query Report
40
41	Reports using raw SQL queries:
42
43	1. Create Report → Type = "Query Report"
44	2. Set Reference DocType (controls permissions)
45	3. Write SQL query
46
47	```sql
48	SELECT
49	`tabSales Order`.name AS "Sales Order:Link/Sales Order:200",
50	`tabSales Order`.customer AS "Customer:Link/Customer:200",
51	`tabSales Order`.transaction_date AS "Date:Date:120",
52	`tabSales Order`.grand_total AS "Grand Total:Currency:150",
53	`tabSales Order`.status AS "Status:Data:100"
54	FROM `tabSales Order`
55	WHERE `tabSales Order`.docstatus = 1
56	{% if filters.company %}
57	AND `tabSales Order`.company = %(company)s
58	{% endif %}
59	{% if filters.from_date %}
60	AND `tabSales Order`.transaction_date >= %(from_date)s
61	{% endif %}
62	ORDER BY `tabSales Order`.transaction_date DESC
63	```
64
65	Column format in SELECT: `"Label:Fieldtype/Options:Width"`
66
67	\| Fieldtype \| Example \|
68	\|-----------\|---------\|
69	\| Link \| `"Customer:Link/Customer:200"` \|
70	\| Currency \| `"Amount:Currency:150"` \|
71	\| Date \| `"Date:Date:120"` \|
72	\| Int \| `"Quantity:Int:100"` \|
73	\| Data \| `"Status:Data:100"` \|
74
75	Filter variables: Use `%(filter_name)s` for parameterized queries.
76
77	### 3) Script Report (standard)
78
79	For app-bundled reports with full Python + JS control:
80
81	Create the report structure:
82	```
83	my_app/
84	└── my_module/
85	└── report/
86	└── sales_summary/
87	├── sales_summary.json # Report metadata
88	├── sales_summary.py # Python data logic
89	└── sales_summary.js # JS filters and UI
90	```
91
92	Python script (`sales_summary.py`):
93
94	```python
95	import frappe
96	from frappe import _
97
98	def execute(filters=None):
99	columns = get_columns()
100	data = get_data(filters)
101	chart = get_chart(data)
102	return columns, data, None, chart
103
104	def get_columns():
105	return [
106	{
107	"label": _("Customer"),
108	"fieldname": "customer",
109	"fieldtype": "Link",
110	"options": "Customer",
111	"width": 200
112	},
113	{
114	"label": _("Total Orders"),
115	"fieldname": "total_orders",
116	"fieldtype": "Int",
117	"width": 120
118	},
119	{
120	"label": _("Total Amount"),
121	"fieldname": "total_amount",
122	"fieldtype": "Currency",
123	"width": 150
124	},
125	{
126	"label": _("Average Order"),
127	"fieldname": "avg_order",
128	"fieldtype": "Currency",
129	"width": 150
130	}
131	]
132
133	def get_data(filters):
134	conditions = get_conditions(filters)
135
136	data = frappe.db.sql("""
137	SELECT
138	customer,
139	COUNT(name) as total_orders,
140	SUM(grand_total) as total_amount,
141	AVG(grand_total) as avg_order
142	FROM `tabSales Order`
143	WHERE docstatus = 1 {conditions}
144	GROUP BY customer
145	ORDER BY total_amount DESC
146	""".format(conditions=conditions), filters, as_dict=True)
147
148	return data
149
150	def get_conditions(filters):
151	conditions = ""
152	if filters.get("company"):
153	conditions += " AND company = %(company)s"
154	if filters.get("from_date"):
155	conditions += " AND transaction_date >= %(from_date)s"
156	if filters.get("to_date"):
157	conditions += " AND transaction_date <= %(to_date)s"
158	return conditions
159
160	def get_chart(data):
161	if not data:
162	return None
163
164	return {
165	"data": {
166	"labels": [d.customer for d in data[:10]],
167	"datasets": [{
168	"name": _("Total Amount"),
169	"values": [d.total_amount for d in data[:10]]

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