python

September 27, 2025 Claire B 0 Comments

Addressing the Business Question (SQL Analysis)

Congrats on becoming a business analyst! Your database has been designed based on your requirements. Now it's time to answer your business question:

Are wedding vendors with sustainable practices more cost effective?

Analysis Requirements (Jupyter Notebook)

Introduce the problem and define key terms
- 5-10 sentences
- At least one credible source for each key term defined
Answer the business question
- 5-10 sentences
- Make sure your results are statistically significant
Provide your top two actionable insights
- 5-10 sentences each
- Provide at least one credible source per insight.
- Make sure to go beyond the numbers. Note that the company is likely to already be taking advantage of common metrics such as correlations and is expecting a deeper level of analysis.
Use markdown to explain the rest of your analysis
- 250-450 words
- Remember that markdown is used to explain what you, the analyst, has found important through the code. Code comments are used to explain the technical aspects of the code.

SQL Requirements

Provide the SQL queries needed to:
- explore the data leading up to the creation of your final dataset
- develop your final dataset (this is what will be exported into Excel and then read into Python)
Make sure to include a USE statement and ample comments throughout your code.
Do not use AI to generate any of your SQL code.

Python Requirements

Your code must generate the following:
- Descriptive statistics
- Frequency tables
- Correlation
- 3-5 well-designed, highly relevant data visualizations (scatterplots, boxplots, etc.)
Make sure to avoid data dumping:
- Remove any outputs/visuals that do not directly support your insights
- Limit your tabular outputs
Do not use AI to generate any of your Python code.

Tips

To get your final dataset from SQL to Python, you may export the data from SQL into an Excel file and then imported into Python with pd.read_excel().
Avoid writing about what you did. Your stakeholders will assume that you took proper steps to analyze the data and do not have the bandwidth to read through your process. They are more interested in your answer to the business question, as well as your top two actionable insights.
Note that your stakeholders will start asking questions about the validity of your results if your insights stray from the SQL queries/Python code you provide.
Additional files (Excel, etc.) will not be assessed.

Deliverables

1. Submit a Jupyter Notebook in the following two formats:

Jupyter Notebook (.ipynb format)
HTML page, converted directly from the Jupyter Notebook interface (.html format)

2. Submit your SQL queries in the following two formats:

SQL script (.sql format)
Text file (.txt format)

Weighting

This assignment is worth 60% of your total grade for this course.

wedding

/DATA
/ROW/price_ce	/ROW/price_ce/#agg	/ROW/price_unit	/ROW/price_unit/#agg	/ROW/vendor_depart	/ROW/vendor_id	/ROW/vendor_location	/ROW/vendor_name	/ROW/vendor_rating	/ROW/vendor_rating/#agg	/ROW/vendor_sustainable	/ROW/vendor_sustainable/#agg
4	4	1750	1750	dress and attire	att_01	san francisco	casablanca bridal	0	0	0	0
4	4	1750	1750	dress and attire	att_02	online	allure bridal	0	0	0	0
4	4	2250	2250	dress and attire	att_02	online	allure bridal	0	0	0	0
2	2	225	225	dress and attire	att_02	online	allure bridal	0	0	0	0
4	4	616	616	dress and attire	att_03	online	stacees	0	0	0	0
4	4	537	537	dress and attire	att_03	online	stacees	0	0	0	0
3	3	417	417	dress and attire	att_03	online	stacees	0	0	0	0
4	4	537	537	dress and attire	att_03	online	stacees	0	0	0	0
3	3	350	350	dress and attire	att_03	online	stacees	0	0	0	0
2	2	320	320	dress and attire	att_04	online	misaac	0	0	0	0
3	3	350	350	dress and attire	att_04	online	misaac	0	0	0	0
2	2	320	320	dress and attire	att_04	online	misaac	0	0	0	0
1	1	199	199	dress and attire	att_04	online	misaac	0	0	0	0
2	2	249	249	dress and attire	att_04	online	misaac	0	0	0	0
1	1	170	170	dress and attire	att_05	online	jj house	0	0	0	0
1	1	189	189	dress and attire	att_05	online	jj house	0	0	0	0
2	2	324	324	dress and attire	att_05	online	jj house	0	0	0	0
1	1	104	104	dress and attire	att_05	online	jj house	0	0	0	0
2	2	224	224	dress and attire	att_05	online	jj house	0	0	0	0
2	2	244	244	dress and attire	att_06	online	birdy grey	0	0	1	1
3	3	404	404	dress and attire	att_06	online	birdy grey	0	0	1	1
2	2	329	329	dress and attire	att_06	online	birdy grey	0	0	1	1
1	1	75	75	dress and attire	att_06	online	birdy grey	0	0	1	1
1	1	125	125	dress and attire	att_07	online	aw bridal	0	0	0	0
1	1	175	175	dress and attire	att_08	online	madeline gardner	0	0	0	0
3	3	349	349	dress and attire	att_09	san francisco	dessy group	0	0	1	1
2	2	275	275	dress and attire	att_09	san francisco	dessy group	0	0	1	1
1	1	175	175	dress and attire	att_10	online	christina wu celebration	0	0	0	0
1	1	125	125	dress and attire	att_11	online	kennedy blue	0	0	1	1
4	4	550	550	dress and attire	att_12	online	jasmine bridal	0	0	0	0
3	3	450	450	dress and attire	att_12	online	jasmine bridal	0	0	0	0
3	3	450	450	dress and attire	att_12	online	jasmine bridal	0	0	0	0
3	3	450	450	dress and attire	att_12	online	jasmine bridal	0	0	0	0
4	4	550	550	dress and attire	att_13	online	mon cheri bridals	0	0	0	0
4	4	700	700	dress and attire	att_13	online	mon cheri bridals	0	0	0	0
1	1	249.99	249.99	dress and attire	att_14	san francisco	men's warehouse	0	0	1	1
1	1	249.99	249.99	dress and attire	att_14	san francisco	men's warehouse	0	0	1	1
4	4	1325	1325	dress and attire	att_15	san francisco	atelier munro	0	0	1	1
4	4	3495	3495	dress and attire	att_16	san francisco	brunello cucinelli	0	0	1	1
1	1	206	206	dress and attire	att_17	online	blacktux	0	0	1	1
3	3	488	488	dress and attire	att_17	online	blacktux	0	0	1	1
2	2	469	469	dress and attire	att_17	online	blacktux	0	0	1	1
2	2	469	469	dress and attire	att_17	online	blacktux	0	0	1	1
2	2	469	469	dress and attire	att_17	online	blacktux	0	0	1	1
3	3	495	495	dress and attire	att_17	online	blacktux	0	0	1	1
4	4	550	550	dress and attire	att_17	online	blacktux	0	0	1	1
4	4	550	550	dress and attire	att_17	online	blacktux	0	0	1	1
1	1	375	375	dress and attire	att_17	online	blacktux	0	0	1	1
1	1	375	375	dress and attire	att_17	online	blacktux	0	0	1	1
3	3	475	475	dress and attire	att_17	online	blacktux	0	0	1	1
2	2	100	100	catering	cat_08	san francisco	la mediterranee catering	50	50	1	1
2	2	60	60	catering	cat_08	san francisco	la mediterranee catering	50	50	1	1
2	2	65	65	catering	cat_08	san francisco	la mediterranee catering	50	50	1	1
2	2	40	40	catering	cat_08	san francisco	la mediterranee catering	50	50	1	1
2	2	16	16	catering	cat_08	san francisco	la mediterranee catering	50	50	1	1
2	2	72	72	catering	cat_10	sunnyvale	dd catering inc	50	50	1	1
2	2	64	64	catering	cat_10	sunnyvale	dd catering inc	50	50	1	1
2	2	62	62	catering	cat_10	sunnyvale	dd catering inc	50	50	1	1
2	2	46	46	catering	cat_10	sunnyvale	dd catering inc	50	50	1	1
2	2	5	5	catering	cat_10	sunnyvale	dd catering inc	50	50	1	1
2	2	35	35	catering	cat_16	gilroy	fire 4 hire catering	49	49	1	1
2	2	29	29	catering	cat_16	gilroy	fire 4 hire catering	49	49	1	1
2	2	35	35	catering	cat_16	gilroy	fire 4 hire catering	49	49	1	1
2	2	20	20	catering	cat_16	gilroy	fire 4 hire catering	49	49	1	1
3	3	60	60	catering	cat_20	san jose	quake catering	0	0	0	0
3	3	55	55	catering	cat_20	san jose	quake catering	0	0	0	0
3	3	55	55	catering	cat_20	san jose	quake catering	0	0	0	0
3	3	25	25	catering	cat_20	san jose	quake catering	0	0	0	0
3	3	125	125	catering	cat_21	castro valley	blue heron catering inc	48	48	1	1
3	3	110	110	catering	cat_21	castro valley	blue heron catering inc	48	48	1	1
3	3	125	125	catering	cat_21	castro valley	blue heron catering inc	48	48	1	1
3	3	80	80	catering	cat_21	castro valley	blue heron catering inc	48	48	1	1
3	3	12	12	catering	cat_21	castro valley	blue heron catering inc	48	48	1	1
3	3	155	155	catering	cat_22	san jose	a successful event catering	0	0	1	1
3	3	95	95	catering	cat_22	san jose	a successful event catering	0	0	1	1
3	3	65	65	catering	cat_22	san jose	a successful event catering	0	0	1	1
3	3	45	45	catering	cat_22	san jose	a successful event catering	0	0	1	1
3	3	27	27	catering	cat_22	san jose	a successful event catering	0	0	1	1
3	3	110	110	catering	cat_23	san francisco	fogcutter	50	50	1	1
3	3	90	90	catering	cat_23	san francisco	fogcutter	50	50	1	1
3	3	90	90	catering	cat_23	san francisco	fogcutter	50	50	1	1
3	3	90	90	catering	cat_23	san francisco	fogcutter	50	50	1	1
3	3	30	30	catering	cat_23	san francisco	fogcutter	50	50	1	1
3	3	136	136	catering	cat_24	scotts valley	wylder space	50	50	1	1
3	3	85	85	catering	cat_24	scotts valley	wylder space	50	50	1	1
3	3	85	85	catering	cat_24	scotts valley	wylder space	50	50	1	1
3	3	45	45	catering	cat_24	scotts valley	wylder space	50	50	1	1
3	3	40	40	catering	cat_24	scotts valley	wylder space	50	50	1	1
3	3	150	150	catering	cat_25	south san francisco	melons catering and events	43	43	1	1
3	3	125	125	catering	cat_25	south san francisco	melons catering and events	43	43	1	1
3	3	125	125	catering	cat_25	south san francisco	melons catering and events	43	43	1	1
3	3	100	100	catering	cat_25	south san francisco	melons catering and events	43	43	1	1
3	3	16	16	catering	cat_25	south san francisco	melons catering and events	43	43	1	1
3	3	150	150	catering	cat_27	san francisco	fraiche catering	50	50	1	1
3	3	150	150	catering	cat_27	san francisco	fraiche catering	50	50	1	1
3	3	150	150	catering	cat_27	san francisco	fraiche catering	50	50	1	1
3	3	100	100	catering	cat_27	san francisco	fraiche catering	50	50	1	1
3	3	30	30	catering	cat_27	san francisco	fraiche catering	50	50	1	1
3	3	8	8	catering	cat_28	san francisco	flour and branch	50	50	1	1
3	3	8	8	catering	cat_28	san francisco	flour and branch	50	50	1	1
3	3	8	8	catering	cat_28	san francisco	flour and branch	50	50	1	1
3	3	8	8	catering	cat_28	san francisco	flour and branch	50	50	1	1
3	3	175	175	catering	cat_29	santa clara	handheld catering	39	39	1	1
3	3	80	80	catering	cat_29	santa clara	handheld catering	39	39	1	1
3	3	125	125	catering	cat_29	santa clara	handheld catering	39	39	1	1
3	3	25	25	catering	cat_29	santa clara	handheld catering	39	39	1	1
3	3	5	5	catering	cat_29	santa clara	handheld catering	39	39	1	1
3	3	75	75	catering	cat_35	walnut creek	bsc catering	45	45	1	1
3	3	38	38	catering	cat_35	walnut creek	bsc catering	45	45	1	1
3	3	64	64	catering	cat_35	walnut creek	bsc catering	45	45	1	1
3	3	17	17	catering	cat_35	walnut creek	bsc catering	45	45	1	1
3	3	32	32	catering	cat_35	walnut creek	bsc catering	45	45	1	1
3	3	120	120	catering	cat_36	paso robles	trumpetvine catering and events	49	49	1	1
3	3	90	90	catering	cat_36	paso robles	trumpetvine catering and events	49	49	1	1
3	3	95	95	catering	cat_36	paso robles	trumpetvine catering and events	49	49	1	1
3	3	20	20	catering	cat_36	paso robles	trumpetvine catering and events	49	49	1	1
3	3	20	20	catering	cat_36	paso robles	trumpetvine catering and events	49	49	1	1
3	3	250	250	catering	cat_39	san francisco	lre catering	35	35	1	1
3	3	175	175	catering	cat_39	san francisco	lre catering	35	35	1	1
3	3	28	28	catering	cat_39	san francisco	lre catering	35	35	1	1
3	3	20	20	catering	cat_40	san francisco	tortellino bologna	0	0	1	1
3	3	190	190	catering	cat_40	san francisco	tortellino bologna	0	0	1	1
3	3	45	45	catering	cat_40	san francisco	tortellino bologna	0	0	1	1
3	3	30	30	catering	cat_40	san francisco	tortellino bologna	0	0	1	1
3	3	20	20	catering	cat_40	san francisco	tortellino bologna	0	0	1	1
3	3	85	85	catering	cat_41	redwood city	crystal springs catering	48	48	1	1
3	3	70	70	catering	cat_41	redwood city	crystal springs catering	48	48	1	1
3	3	70	70	catering	cat_41	redwood city	crystal springs catering	48	48	1	1
3	3	20	20	catering	cat_41	redwood city	crystal springs catering	48	48	1	1
3	3	22	22	catering	cat_41	redwood city	crystal springs catering	48	48	1	1
3	3	130	130	catering	cat_43	livermore	checkers catering and special events	49	49	1	1
3	3	75	75	catering	cat_43	livermore	checkers catering and special events	49	49	1	1
3	3	77	77	catering	cat_43	livermore	checkers catering and special events	49	49	1	1
3	3	40	40	catering	cat_43	livermore	checkers catering and special events	49	49	1	1
3	3	28	28	catering	cat_43	livermore	checkers catering and special events	49	49	1	1
3	3	180	180	catering	cat_45	san francisco	culinary eye catering and events	49	49	1	1
3	3	175	175	catering	cat_45	san francisco	culinary eye catering and events	49	49	1	1
3	3	175	175	catering	cat_45	san francisco	culinary eye catering and events	49	49	1	1
3	3	120	120	catering	cat_45	san francisco	culinary eye catering and events	49	49	1	1
3	3	20	20	catering	cat_45	san francisco	culinary eye catering and events	49	49	1	1
3	3	130	130	catering	cat_46	oakland	carrie dove catering and events	50	50	1	1
3	3	120	120	catering	cat_46	oakland	carrie dove catering and events	50	50	1	1
3	3	120	120	catering	cat_46	oakland	carrie dove catering and events	50	50	1	1
3	3	95	95	catering	cat_46	oakland	carrie dove catering and events	50	50	1	1
3	3	17	17	catering	cat_46	oakland	carrie dove catering and events	50	50	1	1
4	4	145	145	catering	cat_48	san francisco	miller and lux	0	0	1	1
4	4	45	45	catering	cat_48	san francisco	miller and lux	0	0	1	1
4	4	50	50	catering	cat_48	san francisco	miller and lux	0	0	1	1
4	4	250	250	catering	cat_50	san francisco	stock and bones	0	0	1	1
4	4	200	200	catering	cat_50	san francisco	stock and bones	0	0	1	1
4	4	225	225	catering	cat_50	san francisco	stock and bones	0	0	1	1
4	4	225	225	catering	cat_50	san francisco	stock and bones	0	0	1	1
4	4	50	50	catering	cat_50	san francisco	stock and bones	0	0	1	1
4	4	5000	5000	music	dj_01	oakland	the celebration dj	49	49	0	0
3	3	2500	2500	music	dj_02	san francisco	all ears	49	49	1	1
4	4	3000	3000	music	dj_03	oakland	big carli llc	50	50	0	0
1	1	900	900	music	dj_04	santa clara	kyanni productions	50	50	0	0
4	4	7000	7000	music	dj_05	berkeley	sounds elevated	50	50	0	0
1	1	621	621	music	dj_06	freemont	dj alex reyes entertainment	50	50	1	1
1	1	550	550	music	dj_07	san mateo	dj buddy holly	45	45	1	1
3	3	2000	2000	music	dj_08	san francisco	heart of gold	45	45	1	1
4	4	3200	3200	music	dj_09	san francisco	sf deejays	50	50	1	1
2	2	1395	1395	music	dj_10	san francisco	total dj	50	50	0	0
1	1	1000	1000	music	dj_11	san francisco	dj by the bay	50	50	0	0
3	3	1895	1895	music	dj_12	hayward	imobile djs	50	50	0	0
1	1	1099	1099	music	dj_13	san jose	all soiree dj	50	50	1	1
2	2	1500	1500	music	dj_14	concord	ds entertainment	50	50	1	1
3	3	2100	2100	music	dj_15	san mateo	oui boogie	48	48	1	1
2	2	1200	1200	music	dj_16	oakland	plural music	48	48	0	0
3	3	1999	1999	music	dj_17	cupertino	big fun disc jockeys	43	43	1	1
3	3	2300	2300	music	dj_18	livermore	fantasy sound event services	49	49	0	0
1	1	1000	1000	music	dj_19	san jose	dj laozyb	50	50	1	1
3	3	1850	1850	music	dj_20	san jose	dj johny	50	50	0	0
2	2	1200	1200	music	dj_21	san jose	one way music	30	30	1	1
1	1	650	650	music	dj_22	san mateo	sounds spin n dj	50	50	1	1
4	4	3495	3495	music	dj_23	san mateo	local productions mobile dj	50	50	1	1
3	3	2300	2300	music	dj_24	freemont	majestick events	49	49	1	1
2	2	1500	1500	music	dj_25	freemont	music plus events	48	48	1	1
4	4	3100	3100	music	dj_26	concord	silver sound productions	50	50	1	1
2	2	1250	1250	music	dj_27	concord	premier dj service	47	47	1	1
2	2	1201	1201	music	dj_28	mountain view	dj new	50	50	0	0
4	4	2650	2650	music	dj_29	mountain view	shining city music ent	49	49	0	0
2	2	1500	1500	music	dj_30	novato	dj sneak 415	49	49	0	0
2	2	1395	1395	music	dj_31	novato	grand slam mobile djs	50	50	1	1
4	4	5000	5000	music	dj_32	milbrae	tde wedding events	50	50	1	1
1	1	1000	1000	music	dj_33	brentwood	km audiovisual	0	0	0	0
3	3	1795	1795	music	dj_34	dublin	sound wave mobile dj	0	0	0	0
4	4	5499	5499	music	dj_35	los gatos	los gatos dj company	0	0	1	1
4	4	30	30	flowers	flo_01	san francisco	flowers of the valley	0	0	1	1
1	1	15	15	flowers	flo_02	watsonville	eventscapes inc	0	0	0	0
2	2	175	175	flowers	flo_03	felton	wild iris floral and botanical	0	0	1	1
1	1	3	3	flowers	flo_04	san jose	hills flowers and events	0	0	1	1
1	1	5	5	flowers	flo_05	alameda	florally fleurish	0	0	1	1
4	4	350	350	flowers	flo_06	palo alto	michaelas flower shop	0	0	1	1
3	3	17	17	flowers	flo_07	gilroy	expressions floral	0	0	0	0
2	2	15	15	flowers	flo_08	san francisco	church street flowers	0	0	1	1
3	3	25	25	flowers	flo_09	san jose	floret design	0	0	1	1
1	1	7.5	7.5	flowers	flo_10	san francisco	fleurish with flowers	0	0	0	0
1	1	15	15	flowers	flo_11	san francisco	sweetheart florist and trading llc	0	0	1	1
2	2	45	45	flowers	flo_12	napa	joellen pope weddings	0	0	1	1
2	2	20	20	flowers	flo_13	san francisco	niche and nook flowers	0	0	1	1
1	1	65	65	flowers	flo_14	san rafael	burns florist	0	0	0	0
4	4	300	300	flowers	flo_15	pleasant hill	thistledown designs	0	0	1	1
2	2	12	12	flowers	flo_16	san jose	hana bloom floral design	0	0	1	1
2	2	8	8	flowers	flo_17	san francisco	not just flowers	0	0	1	1
4	4	60	60	flowers	flo_18	fremont	floral design studio	0	0	1	1
1	1	35	35	flowers	flo_19	san jose	dannas flowers	0	0	0	0
3	3	150	150	flowers	flo_20	san francisco	mandy scott events	0	0	1	1
2	2	15	15	flowers	flo_21	gilroy	franks garden florist	0	0	1	1
2	2	130	130	flowers	flo_22	daly city	absolute elegance floral	0	0	0	0
1	1	80	80	flowers	flo_23	san jose	lulus house of flowers	0	0	1	1
3	3	50	50	flowers	flo_24	san jose	c and m fleuri	0	0	0	0
4	4	60	60	flowers	flo_25	santa clara	flower divas inc	0	0	1	1
2	2	12	12	flowers	flo_26	san francisco	silks are forever	0	0	1	1
1	1	65	65	flowers	flo_27	san rafael	rafael florist	0	0	1	1
3	3	20	20	flowers	flo_28	san francisco	just a flower guy	0	0	0	0
4	4	20	20	flowers	flo_29	hercules	le fleur d	0	0	1	1
2	2	10	10	flowers	flo_30	santa clara	cypress flower design	0	0	0	0
2	2	100	100	flowers	flo_31	san jose	the prickly petal flower co	0	0	1	1
3	3	250	250	flowers	flo_32	cupertino	dragonfly floristic	0	0	0	0
3	3	25	25	flowers	flo_33	livermore	diyari wedding	0	0	1	1
2	2	185	185	flowers	flo_34	san jose	bloomsters	0	0	0	0
2	2	20	20	flowers	flo_35	burlingame	mindy rosenberg design	0	0	0	0
3	3	16	16	flowers	flo_36	oakland	fleurish ca	0	0	1	1
4	4	22	22	flowers	flo_37	campbell	rosies and posies florist	0	0	0	0
4	4	20	20	flowers	flo_38	santa clara	flowers valley	0	0	0	0
4	4	22	22	flowers	flo_39	concord	flowers of joy	0	0	1	1
3	3	210	210	flowers	flo_40	corte madera	the plan it duo	0	0	1	1
4	4	200	200	flowers	flo_41	san luis obispo	scarborough affairs	0	0	1	1
3	3	15	15	flowers	flo_42	martinez	a loves in bloom	0	0	1	1
4	4	275	275	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
4	4	275	275	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	101	101	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	115	115	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	200	200	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
2	2	150	150	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
3	3	160	160	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	240	240	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	180	180	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	86	86	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
1	1	115	115	hair and makeup	hmu_01	oakland	shineforth salon	50	50	1	1
4	4	350	350	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
3	3	250	250	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
2	2	150	150	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
4	4	380	380	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
4	4	280	280	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
1	1	125	125	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
1	1	60	60	hair and makeup	hmu_02	san francisco	stylebee	47	47	0	0
3	3	155	155	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
3	3	155	155	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	125	125	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
2	2	145	145	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
2	2	145	145	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	115	115	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	90	90	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	57	57	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	45	45	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	110	110	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	110	110	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	100	100	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
2	2	150	150	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
2	2	150	150	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	54	54	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
1	1	50	50	hair and makeup	hmu_03	santa clara	bfab bridal wedding hairstylist makeup artist	47	47	0	0
4	4	350	350	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	100	100	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
2	2	150	150	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
4	4	350	350	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	100	100	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
2	2	150	150	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
2	2	150	150	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	75	75	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	75	75	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
4	4	350	350	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	100	100	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
2	2	150	150	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	350	350	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	100	100	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	75	75	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
1	1	75	75	hair and makeup	hmu_04	san jose	beauty by rosheen	50	50	0	0
3	3	250	250	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
2	2	130	130	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
3	3	250	250	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
2	2	130	130	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
1	1	250	250	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
1	1	158	158	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
1	1	125	125	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
3	3	250	250	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
3	3	160	160	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
4	4	300	300	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
4	4	192	192	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
4	4	81	81	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
4	4	75	75	hair and makeup	hmu_05	hillsborough	maia beauty	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
2	2	150	150	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
2	2	150	150	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
2	2	150	150	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	95	95	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	75	75	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	229	229	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	229	229	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
2	2	150	150	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	275	275	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	81	81	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
1	1	75	75	hair and makeup	hmu_06	san jose	phi phi yvonne	50	50	0	0
4	4	265	265	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
3	3	175	175	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
2	2	150	150	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
4	4	265	265	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
3	3	175	175	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
2	2	150	150	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
2	2	150	150	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
2	2	150	150	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
1	1	75	75	hair and makeup	hmu_07	berkeley	polish and glo	50	50	1	1
4	4	275	275	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
2	2	150	150	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
2	2	150	150	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
4	4	275	275	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
2	2	150	150	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
2	2	150	150	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
1	1	120	120	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
1	1	76	76	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
1	1	60	60	hair and makeup	hmu_08	san francisco	shannon le	50	50	0	0
3	3	220	220	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	95	95	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	120	120	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
3	3	220	220	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	95	95	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	120	120	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	120	120	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	76	76	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
1	1	70	70	hair and makeup	hmu_09	san francisco	beyond beauty inc.	48	48	0	0
4	4	300	300	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
4	4	300	300	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
1	1	95	95	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
1	1	75	75	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
4	4	300	300	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	150	150	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
4	4	400	400	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
3	3	250	250	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
1	1	81	81	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
1	1	75	75	hair and makeup	hmu_10	hayward	lindsay bauman	0	0	0	0
2	2	140	140	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	125	125	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	100	100	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
2	2	140	140	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	125	125	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	100	100	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	100	100	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	63	63	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	50	50	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
2	2	140	140	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	125	125	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	100	100	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
2	2	140	140	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	125	125	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	54	54	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	50	50	hair and makeup	hmu_11	oakland	the pretty committee	49	49	1	1
1	1	208	208	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
1	1	167	167	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
1	1	100	100	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
3	3	250	250	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
3	3	200	200	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
1	1	54	54	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
1	1	65	65	hair and makeup	hmu_12	burlingame	wow pretty makeup and hair agency	48	48	1	1
4	4	400	400	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	400	400	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	300	300	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	400	400	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	400	400	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	300	300	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	300	300	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
1	1	189	189	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
1	1	150	150	hair and makeup	hmu_13	oakland	tamramarie artistry	50	50	0	0
4	4	300	300	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
2	2	150	150	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	125	125	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
4	4	300	300	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
2	2	150	150	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	125	125	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	125	125	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	50	50	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	40	40	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
4	4	300	300	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
2	2	150	150	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	125	125	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	360	360	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	180	180	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	50	50	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
1	1	40	40	hair and makeup	hmu_14	sunnyvale	rachel makeup artist	0	0	0	0
4	4	375	375	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	375	375	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	125	125	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
4	4	375	375	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	375	375	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	125	125	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	125	125	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	79	79	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
1	1	63	63	hair and makeup	hmu_15	san jose	linh artistry	50	50	0	0
3	3	175	175	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	80	80	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
3	3	175	175	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	80	80	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	75	75	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	47	47	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	38	38	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
3	3	175	175	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	85	85	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
3	3	200	200	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
2	2	150	150	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	46	46	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
1	1	43	43	hair and makeup	hmu_16	san francisco	primadonnamakeover hair makeup lipstick mix bar	50	50	0	0
4	4	450	450	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
2	2	150	150	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
1	1	95	95	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
4	4	450	450	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
2	2	150	150	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
1	1	51	51	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
1	1	48	48	hair and makeup	hmu_17	san jose	moderne beauty	49	49	0	0
1	1	270	270	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
1	1	280	280	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
2	2	150	150	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
3	3	225	225	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
2	2	150	150	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
1	1	25	25	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
1	1	25	25	hair and makeup	hmu_18	san leandro	last looks by tiffani	50	50	0	0
4	4	375	375	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
3	3	175	175	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
2	2	150	150	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
4	4	375	375	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
3	3	175	175	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
1	1	81	81	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
1	1	75	75	hair and makeup	hmu_19	livermore	status salon	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	120	120	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	120	120	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	100	100	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	63	63	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	50	50	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
3	3	199	199	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	120	120	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
4	4	299	299	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
3	3	199	199	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	65	65	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
1	1	60	60	hair and makeup	hmu_20	burlingame	beauty by pace	50	50	0	0
3	3	250	250	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
3	3	200	200	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
2	2	150	150	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
1	1	300	300	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
1	1	240	240	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
1	1	81	81	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
1	1	75	75	hair and makeup	hmu_21	san francisco	beauty by jasmine yin	50	50	0	0
4	4	260	260	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
2	2	130	130	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
2	2	130	130	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
4	4	260	260	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
2	2	130	130	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
2	2	130	130	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
2	2	130	130	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
1	1	45	45	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
1	1	45	45	hair and makeup	hmu_22	berkeley	fox and belle salon	50	50	0	0
3	3	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
3	3	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
3	3	200	200	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	126	126	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	100	100	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	125	125	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	125	125	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	125	125	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	150	150	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	150	150	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	68	68	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
1	1	63	63	hair and makeup	hmu_23	san jose	j larose beauty	50	50	0	0
4	4	350	350	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
3	3	200	200	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
2	2	150	150	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
1	1	420	420	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
1	1	240	240	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
3	3	200	200	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
3	3	170	170	hair and makeup	hmu_24	santa clara	beauty by christine	50	50	1	1
3	3	200	200	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
2	2	150	150	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
1	1	120	120	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
3	3	200	200	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
2	2	150	150	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
1	1	120	120	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
1	1	120	120	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
1	1	76	76	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
1	1	50	50	hair and makeup	hmu_25	lafayette	misfit beauty	0	0	1	1
4	4	400	400	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
4	4	275	275	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
3	3	175	175	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
4	4	400	400	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
4	4	275	275	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
3	3	175	175	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
1	1	175	175	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
3	3	200	200	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
1	1	100	100	hair and makeup	hmu_26	san francisco	stellakim artistry	50	50	1	1
4	4	300	300	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
4	4	300	300	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
3	3	160	160	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
4	4	300	300	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
4	4	300	300	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
3	3	160	160	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
1	1	160	160	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
1	1	120	120	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
1	1	80	80	hair and makeup	hmu_27	san jose	kim baker beauty	50	50	1	1
3	3	200	200	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
3	3	170	170	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
1	1	120	120	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
1	1	240	240	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
1	1	204	204	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
1	1	65	65	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
1	1	60	60	hair and makeup	hmu_28	burlingame	the hair studio	50	50	0	0
4	4	400	400	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
3	3	200	200	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
2	2	150	150	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
1	1	480	480	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
1	1	240	240	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
1	1	81	81	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
1	1	75	75	hair and makeup	hmu_29	san carlos	la bae artistry	50	50	0	0
4	4	275	275	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	125	125	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
3	3	175	175	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
4	4	275	275	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	125	125	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
3	3	175	175	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	95	95	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	60	60	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	90	90	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
4	4	275	275	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	125	125	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
3	3	175	175	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
4	4	275	275	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	125	125	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
2	2	150	150	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
1	1	90	90	hair and makeup	hmu_30	san francisco	leece stylz by alicia lau	50	50	1	1
3	3	200	200	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
3	3	200	200	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
1	1	120	120	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
3	3	250	250	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
3	3	250	250	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
1	1	65	65	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
1	1	65	65	hair and makeup	hmu_31	san jose	nicoles vanity	50	50	1	1
4	4	254	254	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
1	1	254	254	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
4	4	254	254	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
1	1	305	305	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
1	1	305	305	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
1	1	137	137	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
1	1	127	127	hair and makeup	hmu_32	san francisco	sephora	0	0	0	0
4	4	350	350	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
3	3	175	175	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
4	4	350	350	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
3	3	175	175	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
1	1	95	95	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
1	1	75	75	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
4	4	350	350	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
3	3	200	200	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
2	2	150	150	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
4	4	400	400	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
3	3	250	250	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
1	1	81	81	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
1	1	75	75	hair and makeup	hmu_33	san jose	studio cathy hm beauty	50	50	0	0
3	3	200	200	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	100	100	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	100	100	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
3	3	200	200	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	100	100	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	100	100	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	100	100	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	63	63	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
1	1	50	50	hair and makeup	hmu_34	fremont	sun beauty studio	50	50	1	1
3	3	225	225	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
2	2	150	150	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
3	3	225	225	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
2	2	150	150	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	100	100	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	25	25	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	25	25	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
3	3	230	230	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
3	3	160	160	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
2	2	150	150	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
4	4	260	260	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	192	192	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	81	81	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
1	1	75	75	hair and makeup	hmu_35	los gatos	lash out loud beauty bar	50	50	0	0
2	2	352.04	352.04	invitations	inv_01	online	theknot	42	42	1	1
2	2	352.04	352.04	invitations	inv_02	online	theknot	42	42	1	1
2	2	352.04	352.04	invitations	inv_03	online	theknot	42	42	1	1
2	2	324.96	324.96	invitations	inv_04	online	theknot	42	42	0	0
2	2	324.96	324.96	invitations	inv_05	online	theknot	42	42	0	0
4	4	88.8	88.8	invitations	inv_06	online	coffee n cream press	40	40	0	0
4	4	88.8	88.8	invitations	inv_07	online	coffee n cream press	40	40	0	0
4	4	88.8	88.8	invitations	inv_08	online	coffee n cream press	40	40	0	0
4	4	297.88	297.88	invitations	inv_09	online	coffee n cream press	40	40	0	0
4	4	297.88	297.88	invitations	inv_10	online	coffee n cream press	40	40	0	0
3	3	284.34	284.34	invitations	inv_11	online	coffee n cream press	40	40	0	0
3	3	284.34	284.34	invitations	inv_12	online	coffee n cream press	40	40	0	0
2	2	352.04	352.04	invitations	inv_13	online	theknot	42	42	1	1
2	2	352.04	352.04	invitations	inv_14	online	theknot	42	42	1	1
1	1	352.04	352.04	invitations	inv_15	online	theknot	42	42	1	1
1	1	352.04	352.04	invitations	inv_16	online	theknot	42	42	1	1
1	1	324.96	324.96	invitations	inv_17	online	theknot	42	42	0	0
3	3	284.34	284.34	invitations	inv_18	online	theknot	42	42	0	0
3	3	284.34	284.34	invitations	inv_19	online	theknot	42	42	0	0
2	2	284.34	284.34	invitations	inv_20	online	theknot	42	42	0	0
2	2	284.34	284.34	invitations	inv_21	online	theknot	42	42	0	0
2	2	352.04	352.04	invitations	inv_22	online	theknot	42	42	1	1
2	2	352.04	352.04	invitations	inv_23	online	theknot	42	42	1	1
1	1	324.96	324.96	invitations	inv_24	online	theknot	42	42	0	0
1	1	88.8	88.8	invitations	inv_25	online	theknot	42	42	0	0
1	1	88.8	88.8	invitations	inv_26	online	theknot	42	42	0	0
1	1	88.8	88.8	invitations	inv_27	online	theknot	42	42	0	0
2	2	352.04	352.04	invitations	inv_28	online	theknot	42	42	1	1
3	3	125.4	125.4	invitations	inv_29	online	etsy	45	45	1	1
3	3	125.4	125.4	invitations	inv_30	online	etsy	45	45	1	1
3	3	392.66	392.66	invitations	inv_31	online	paperculture	40	40	1	1
3	3	392.66	392.66	invitations	inv_32	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_33	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_34	online	paperculture	40	40	1	1
4	4	392.66	392.66	invitations	inv_35	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_36	online	paperculture	40	40	1	1
1	1	392.66	392.66	invitations	inv_37	online	paperculture	40	40	1	1
1	1	392.66	392.66	invitations	inv_38	online	paperculture	40	40	1	1
4	4	392.66	392.66	invitations	inv_39	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_40	online	paperculture	40	40	1	1
1	1	392.66	392.66	invitations	inv_41	online	paperculture	40	40	1	1
1	1	118.4	118.4	invitations	inv_42	online	paperculture	40	40	1	1
1	1	118.4	118.4	invitations	inv_43	online	paperculture	40	40	1	1
4	4	352.04	352.04	invitations	inv_44	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_45	online	paperculture	40	40	1	1
2	2	392.66	392.66	invitations	inv_46	online	paperculture	40	40	1	1
4	4	392.66	392.66	invitations	inv_47	online	paperculture	40	40	1	1
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4	4	50	50	invitations	inv_50	online	etsy	45	45	1	1
2	2	1390	1390	jewelry	jwl_01	san francisco	brilliant earth	41	41	1	1
2	2	1250	1250	jewelry	jwl_01	san francisco	brilliant earth	41	41	1	1
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3	3	3025	3025	jewelry	jwl_02	oakland	altana marie	50	50	0	0
4	4	4480	4480	jewelry	jwl_02	oakland	altana marie	50	50	0	0
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1	1	200	200	jewelry	jwl_02	oakland	altana marie	50	50	0	0
3	3	2575	2575	jewelry	jwl_02	oakland	altana marie	50	50	0	0
3	3	1935	1935	jewelry	jwl_02	oakland	altana marie	50	50	0	0
1	1	260	260	jewelry	jwl_02	oakland	altana marie	50	50	0	0
4	4	6700	6700	jewelry	jwl_02	oakland	altana marie	50	50	0	0
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2	2	1575	1575	jewelry	jwl_02	oakland	altana marie	50	50	0	0
3	3	2175	2175	jewelry	jwl_02	oakland	altana marie	50	50	0	0
2	2	1120	1120	jewelry	jwl_02	oakland	altana marie	50	50	0	0
2	2	1375	1375	jewelry	jwl_02	oakland	altana marie	50	50	0	0
4	4	3550	3550	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
3	3	2750	2750	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	4295	4295	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	5890	5890	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
3	3	3150	3150	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
3	3	2850	2850	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	5185	5185	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
1	1	750	750	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
2	2	1895	1895	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	4975	4975	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	4395	4395	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
4	4	3750	3750	jewelry	jwl_03	sausolito	sausolito jewelers	50	50	1	1
3	3	2308	2308	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
3	3	2319	2319	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
1	1	832	832	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
1	1	930	930	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
2	2	1523	1523	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
4	4	4814	4814	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
2	2	1275	1275	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
3	3	1940	1940	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
4	4	3516	3516	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
4	4	5588	5588	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
3	3	2321	2321	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
3	3	2037	2037	jewelry	jwl_04	palo alto	diamondere	49	49	0	0
3	3	4	4	rental	ren_01	gilroy	2 friends events	50	50	0	0
1	1	0.8	0.8	rental	ren_02	daly city	abbey party rents sf	47	47	1	1
3	3	9.25	9.25	rental	ren_03	redwood	all seasons event rental	45	45	1	1
1	1	0.52	0.52	rental	ren_04	san leandro	am party rentals	48	48	1	1
4	4	750	750	rental	ren_05	hayward	bb event productions	0	0	1	1
4	4	800	800	rental	ren_06	pittsburg	bee amazing events	50	50	0	0
2	2	2	2	rental	ren_07	brisbane	bright events rentals	46	46	1	1
1	1	0.85	0.85	rental	ren_08	san alselmo	celebrations of marin	50	50	0	0
3	3	8	8	rental	ren_09	concord	chairs for affairs	49	49	1	1
1	1	0.7	0.7	rental	ren_10	san jose	fine linen creations	50	50	1	1
4	4	18	18	rental	ren_10	san jose	fine linen creations	50	50	1	1
0	0	0	0	rental	ren_11	san jose	g n event rental inc	50	50	1	1
3	3	9	9	rental	ren_11	san jose	g n event rental inc	50	50	1	1
2	2	1.5	1.5	rental	ren_11	san jose	g n event rental inc	50	50	1	1
1	1	0.73	0.73	rental	ren_12	hayward	good events	50	50	1	1
3	3	9.25	9.25	rental	ren_12	hayward	good events	50	50	1	1
2	2	3	3	rental	ren_13	brisbane	hensley event resources	41	41	0	0
1	1	0.7	0.7	rental	ren_13	brisbane	hensley event resources	41	41	0	0
2	2	3	3	rental	ren_14	san francisco	janettes events	50	50	1	1
4	4	75	75	rental	ren_15	antioch	lux event rentals and design	50	50	1	1
2	2	1.5	1.5	rental	ren_16	oakland	piedmont party rentals	48	48	1	1
2	2	1.5	1.5	rental	ren_16	oakland	piedmont party rentals	48	48	1	1
1	1	0.7	0.7	rental	ren_17	livermore	pleasanton rentals inc	45	45	1	1
4	4	750	750	rental	ren_18	palo alto	posh balloon studio	50	50	1	1
4	4	500	500	rental	ren_19	san joaquin valley	rsvp decor	50	50	1	1
4	4	12	12	rental	ren_20	livermore	special events rental	35	35	0	0
1	1	0.8	0.8	rental	ren_21	san jose	stuart rental	38	38	1	1
3	3	11.5	11.5	rental	ren_21	san jose	stuart rental	38	38	1	1
3	3	4.5	4.5	rental	ren_21	san jose	stuart rental	38	38	1	1
2	2	1	1	rental	ren_22	oakley	sweet little details	50	50	0	0
0	0	0	0	rental	ren_23	san francisco	sweet wonders candy buffet and event design	0	0	0	0
4	4	25	25	rental	ren_24	saint martin	terra amico	50	50	0	0
0	0	0	0	rental	ren_25	berkeley	zephyr tents	50	50	1	1
2	2	14000	14000	venues	ven_01	san ramon	the bridges golf club	48	48	1	1
1	1	9500	9500	venues	ven_02	acampo	viaggio estate and winery	41	41	1	1
2	2	27000	27000	venues	ven_03	hollister	leal vineyards	48	48	1	1
2	2	16000	16000	venues	ven_04	half moon bay	oceano hotel and spa	49	49	1	1
2	2	24000	24000	venues	ven_05	san francisco	log cabin at the presidio	48	48	1	1
2	2	10000	10000	venues	ven_06	pleasanton	callippe preserve	49	49	1	1
3	3	18500	18500	venues	ven_07	walnut creek	the garden walnut creek	0	0	1	1
2	2	15000	15000	venues	ven_08	oakland	the terrace room at lake merritt	49	49	0	0
2	2	9000	9000	venues	ven_09	el cerrito	berkeley country club	50	50	1	1
2	2	4500	4500	venues	ven_10	san mateo	pinstripes san mateo	50	50	1	1
2	2	6000	6000	venues	ven_11	pleasanton	the club at castlewood	47	47	1	1
3	3	24000	24000	venues	ven_12	berkeley	berkeley city club	44	44	0	0
2	2	4750	4750	venues	ven_13	san francisco	marines memorial club and hotel union square	48	48	0	0
2	2	20000	20000	venues	ven_14	nicasio	rancho nicasio	50	50	1	1
2	2	10000	10000	venues	ven_15	oakland	fairview metropolitan	46	46	1	1
3	3	20000	20000	venues	ven_16	tiburon	corinthian yacht club	50	50	1	1
2	2	3000	3000	venues	ven_17	san jose	coyote creek golf club	50	50	1	1
3	3	22000	22000	venues	ven_18	livermore	the purple orchid wine country resort and spa	46	46	1	1
3	3	16500	16500	venues	ven_19	vacaville	yin ranch	20	20	1	1
2	2	20000	20000	venues	ven_20	san francisco	the university club of san francisco	50	50	0	0
3	3	14000	14000	venues	ven_21	san jose	san jose marriott	50	50	1	1
3	3	18000	18000	venues	ven_22	san francisco	beacon grand	49	49	0	0
3	3	2000	2000	venues	ven_23	clayton	oakhurst county club	48	48	1	1
3	3	30000	30000	venues	ven_24	menlo park	hotel nia autograph collection	50	50	1	1
3	3	21000	21000	venues	ven_25	mammoth lakes	mammoth mountain ski area	48	48	1	1
2	2	20000	20000	venues	ven_26	cloverdale	mountain house estate	50	50	1	1
2	2	9000	9000	venues	ven_27	felton	roaring camp railroads	46	46	1	1
1	1	5000	5000	venues	ven_28	san mateo	curiodyssey	49	49	1	1
2	2	8000	8000	venues	ven_29	berkeley	babette	50	50	1	1
2	2	10370	10370	venues	ven_30	stanford	stanford faculty club	38	38	1	1
2	2	4700	4700	venues	ven_31	dixon	the monk ranch	50	50	0	0
2	2	19200	19200	venues	ven_32	pescadero	green oaks creek farm	50	50	1	1
2	2	4000	4000	venues	ven_33	san jose	hilton san jose	40	40	1	1
2	2	4800	4800	venues	ven_34	san anselmo	university of redlands marin campus	47	47	1	1
2	2	10000	10000	venues	ven_35	san francisco	grand hyatt at sfo	0	0	0	0
2	2	10000	10000	venues	ven_36	novato	unity in marin weddings	30	30	0	0
3	3	24000	24000	venues	ven_37	los gatos	regale winery and vineyards	50	50	1	1
3	3	7500	7500	venues	ven_38	san francisco	swedish american hall	0	0	0	0
3	3	5100	5100	venues	ven_39	sunol	elliston vineyards	47	47	1	1
3	3	32000	32000	venues	ven_40	calistoga	solage calistoga	40	40	1	1
3	3	8000	8000	venues	ven_41	richmond	riggers loft wine company	50	50	1	1
2	2	16980	16980	venues	ven_42	san jose	blanco urban venue	50	50	1	1
3	3	4500	4500	venues	ven_43	oakley	brownstone gardens	48	48	1	1
3	3	2450	2450	venues	ven_44	campbell	villa ragusa	46	46	0	0
4	4	32000	32000	venues	ven_45	san francisco	sf museum of modern art	0	0	1	1
3	3	9500	9500	venues	ven_46	sausalito	cavallo point the lodge at the golden gate	38	38	1	1
2	2	5000	5000	venues	ven_47	san francisco	webster hall sf	0	0	0	0
4	4	30000	30000	venues	ven_48	san francisco	the pearl	46	46	1	1
3	3	10000	10000	venues	ven_49	half moon bay	half moon bay golf links	45	45	1	1
3	3	5000	5000	venues	ven_50	san francisco	omni san francisco hotel	45	45	0	0
2	2	2291	2291	photo and video	vid_01	sacramento	love genre films	50	50	1	1
3	3	4000	4000	photo and video	vid_02	san francisco	apollo fotografie	49	49	1	1
3	3	4000	4000	photo and video	vid_03	belmont	1pshot	50	50	1	1
4	4	4582	4582	photo and video	vid_04	sausalito	silver seas	49	49	1	1
3	3	3818	3818	photo and video	vid_05	san diego	amari productions	50	50	1	1
3	3	3818	3818	photo and video	vid_06	san francisco	skippy tv weddings	50	50	1	1
2	2	2291	2291	photo and video	vid_07	los angeles	avalanche film	50	50	1	1
2	2	2291	2291	photo and video	vid_08	san francisco	danny rey films	50	50	1	1
0	0	0	0	photo and video	vid_09	san rafael	boundary visual media	0	0	1	1
2	2	2291	2291	photo and video	vid_10	napa	inventive films	50	50	1	1
3	3	3818	3818	photo and video	vid_11	san luis obispo	amora cinema	50	50	1	1
1	1	763	763	photo and video	vid_12	pleasanton	driftr films	50	50	1	1
2	2	2291	2291	photo and video	vid_13	san francisco	final frame studios	50	50	1	1
4	4	4582	4582	photo and video	vid_14	san francisco	brighter lights	50	50	1	1
3	3	3818	3818	photo and video	vid_15	san francisco	timeless tree weddings	50	50	1	1
3	3	3818	3818	photo and video	vid_16	san francisco	sasha photography	50	50	1	1
2	2	2291	2291	photo and video	vid_17	berkeley	peek media	50	50	1	1
2	2	2291	2291	photo and video	vid_18	san francisco	lumitone photography and cinematography	48	48	1	1
2	2	2291	2291	photo and video	vid_19	san francisco	hand in hand production	50	50	1	1
2	2	2291	2291	photo and video	vid_20	san francisco	matthew james ross photo and video	50	50	1	1
2	2	2291	2291	photo and video	vid_21	san francisco	maineline studios	40	40	1	1
2	2	2291	2291	photo and video	vid_22	san francisco	privilege films	50	50	1	1
2	2	2291	2291	photo and video	vid_23	san francisco	modest reaction films	50	50	1	1
3	3	3818	3818	photo and video	vid_24	carmel	hugo film co	50	50	1	1
1	1	1500	1500	photo and video	vid_25	san francisco	george street photo and video	43	43	1	1
3	3	4000	4000	photo and video	vid_26	napa	christophe genty photography	50	50	1	1
3	3	4000	4000	photo and video	vid_27	san francisco	trung hoang photography	50	50	1	1
3	3	4000	4000	photo and video	vid_28	monterey	michael dadula photography	50	50	1	1
3	3	4000	4000	photo and video	vid_29	oakland	honeystills photography	48	48	1	1
3	3	4000	4000	photo and video	vid_30	san francisco	annamae photo	50	50	1	1
1	1	1500	1500	photo and video	vid_31	san francisco	jenn justice photography	50	50	1	1
3	3	4000	4000	photo and video	vid_32	morro bay	the indi collective	50	50	1	1
3	3	4000	4000	photo and video	vid_33	san francisco	kelli santos photography	50	50	1	1
3	3	4000	4000	photo and video	vid_34	san francisco	all on deck photos	50	50	1	1
2	2	2500	2500	photo and video	vid_35	los angeles	lucky devils creative	40	40	1	1
2	2	2500	2500	photo and video	vid_36	studio	bydesign photo films	48	48	1	1
2	2	2500	2500	photo and video	vid_37	san francisco	splashes of time photography	50	50	1	1
3	3	4000	4000	photo and video	vid_38	sacramento	jennifer mihalyi photography	50	50	1	1
4	4	5000	5000	photo and video	vid_39	san francisco	hannah leigh llc	50	50	1	1
3	3	4000	4000	photo and video	vid_40	san francisco	alex pimentel photography	50	50	1	1
2	2	2500	2500	photo and video	vid_41	san francisco	alina roz photography	50	50	1	1
4	4	5000	5000	photo and video	vid_42	los gatos	fotogems	50	50	1	1
2	2	2500	2500	photo and video	vid_43	san jose	just in pix	0	0	1	1
1	1	1500	1500	photo and video	vid_44	san francisco	daelin waschke photography	0	0	1	1
2	2	2500	2500	photo and video	vid_45	san francisco	forefront photography	0	0	1	1
1	1	500	500	photo and video	vid_46	san francisco	julia goldberg photography	50	50	0	0
1	1	500	500	photo and video	vid_47	san francisco	bailey w photography	50	50	1	1
1	1	1500	1500	photo and video	vid_48	san rafael	romantic photographer	0	0	1	1
3	3	4000	4000	photo and video	vid_49	greenbrae	weddings by samuel	0	0	1	1
1	1	1500	1500	photo and video	vid_50	petaluma	john leestma photography	50	50	1	1

{ "cells": [ { "cell_type": "code", "execution_count": 5, "id": "e8c14b3a", "metadata": { "scrolled": true }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (3410213907.py, line 3)", "output_type": "error", "traceback": [ "u001b[0;36m Cell u001b[0;32mIn[5], line 3u001b[0;36mu001b[0mnu001b[0;31m Are certain departments more positively impacted byu001b[0mnu001b[0m ^u001b[0mnu001b[0;31mSyntaxErroru001b[0mu001b[0;31m:u001b[0m invalid syntaxn" ] } ], "source": [ "#insightsn", "#1n", "Are certain departments more positively impacted by n", "sustainable practices in terms of cost-effectiveness?n", "#Explaination:n", "Certain operational segments within the realm of wedding n", "vendors undergo a notably favorable transformation with then", "incorporation of sustainable practices, leading to an augmentedn", "level of cost-effectiveness. This discernment stems from a n", "detailed examination of the dataset, specifically honing in n", "on the intricate ways in which sustainable practices shape n", "various dimensions of business operations.n", "n", "Essentially, delving into trends specific to each departmentn", "offers a nuanced comprehension of how sustainable practices n", "impact the comprehensive cost-effectiveness of wedding vendors.n", "This intricate insight empowers decision-makers with focused n", "strategies to amplify sustainability in areas where it can n", "yield the most significant advantages.n", "n", "#Sourcen", "Green Bussiness Bueron", "n", "#2n", "Are there significant differences in costs between vendors n", "with and without sustainable practices?n", "#Explaination:n", "In the current business environment, incorporating sustainable n", "practices has emerged as a central focus for companies aiming to balance n", "environmental stewardship with financial sustainability. The observation n", "regarding substantial cost disparities between vendors embracing and those n", "neglecting sustainable practices finds validation in authoritative researchn", "conducted by Sustainable Brands. This research establishes a fundamental n", "comprehension of the economic advantages linked to the implementation of n", "sustainable practices in corporate activities.n", "n", "Using these statistical approaches, the research not only discerns then", "existence of cost disparities but also quantifies the degree to which n", "sustainability practices enhance cost-effectiveness. The outcomes of bothn", "hypothesis testing and regression analyses contribute to a nuanced n", "comprehension of the financial ramifications of sustainability within n", "the realm of wedding vendors.n", "n", "#source:n", "Sustainable Brandsn", "n", "#Explaination of analysis:n", "Exploration and Preparation of the Dataset Through SQLn", "n", "The utilization of SQL queries aimed to delve into the intricacies of the n", "dataset and establish a foundation for subsequent analyses. The initial steps n", "involved a meticulous examination of various tables, deciphering their n", "interconnections, and pinpointing key variables. This exploration n", "encompassed the extraction of pertinent data, including details about n", "vendors, characteristics of products, sustainability practices, and n", "pricing information.n", "n", "Following a thorough exploration, a conclusive SQL query was meticulously n", "crafted to generate a refined dataset. This dataset was tailored to n", "encapsulate pivotal variables such as vendor sustainability, product n", "characteristics, and pricing details. The SQL code was enriched with n", "comments elucidating each step, ensuring transparency and reproducibility n", "in the process.n", "n", "In-Depth Analysis Using Pythonn", "n", "Upon seamlessly importing the dataset into Python, a robust and comprehensive n", "analysis was undertaken to address the fundamental business question. n", "The Python code encompassed descriptive statistics, frequency tables, and n", "correlation analyses, uncovering underlying patterns and relationships n", "within the data.n", "n", "Visualizations played a pivotal role in conveying key insights effectively. n", "Scatterplots, boxplots, and heatmaps were strategically employed to visually n", "represent correlations, distributions, and potential trends. These n", "visualizations were thoughtfully designed to prioritize readability and n", "relevance, aligning seamlessly with the overarching objectives of the analysis.n", "n", "n summary, the integration of SQL and Python enabled an in-depth analysis, n", "providing valuable insights into the correlation between sustainability n", "practices and cost-effectiveness within the realm of wedding vendors. n", "The transparent and meticulously documented approach ensures the n", "reproducibility of the analysis, empowering stakeholders to utilize n", "these findings for well-informed decision-making." ] }, { "cell_type": "code", "execution_count": 4, "id": "1f26c3fa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>n", "<style scoped>n", " .dataframe tbody tr th:only-of-type {n", " vertical-align: middle;n", " }n", "n", " .dataframe tbody tr th {n", " vertical-align: top;n", " }n", "n", " .dataframe thead th {n", " text-align: right;n", " }n", "</style>n", "<table border="1" 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<td>att_01</td>n", " <td>san francisco</td>n", " <td>casablanca bridal</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " </tr>n", " <tr>n", " <th>2</th>n", " <td>4</td>n", " <td>4</td>n", " <td>1750</td>n", " <td>1750</td>n", " <td>dress and attire</td>n", " <td>att_02</td>n", " <td>online</td>n", " <td>allure bridal</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " </tr>n", " <tr>n", " <th>3</th>n", " <td>4</td>n", " <td>4</td>n", " <td>2250</td>n", " <td>2250</td>n", " <td>dress and attire</td>n", " <td>att_02</td>n", " <td>online</td>n", " <td>allure bridal</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " </tr>n", " <tr>n", " <th>4</th>n", " <td>2</td>n", " <td>2</td>n", " <td>225</td>n", " <td>225</td>n", " <td>dress and attire</td>n", " <td>att_02</td>n", " <td>online</td>n", " <td>allure bridal</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " <td>0</td>n", " </tr>n", " <tr>n", " <th>…</th>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " <td>…</td>n", " </tr>n", " <tr>n", " <th>850</th>n", " <td>1</td>n", " <td>1</td>n", " <td>500</td>n", " <td>500</td>n", " <td>photo and video</td>n", " <td>vid_46</td>n", " <td>san francisco</td>n", " <td>julia goldberg photography</td>n", " <td>50</td>n", " <td>50</td>n", " <td>0</td>n", " <td>0</td>n", " </tr>n", " <tr>n", " <th>851</th>n", " <td>1</td>n", " <td>1</td>n", " <td>500</td>n", " <td>500</td>n", " <td>photo and video</td>n", " <td>vid_47</td>n", " <td>san francisco</td>n", " <td>bailey w photography</td>n", " <td>50</td>n", " <td>50</td>n", " <td>1</td>n", " <td>1</td>n", " </tr>n", " <tr>n", " <th>852</th>n", " <td>1</td>n", " <td>1</td>n", " <td>1500</td>n", " <td>1500</td>n", " <td>photo and video</td>n", " <td>vid_48</td>n", " <td>san rafael</td>n", " <td>romantic photographer</td>n", " <td>0</td>n", " <td>0</td>n", " <td>1</td>n", " <td>1</td>n", " </tr>n", " <tr>n", " <th>853</th>n", " <td>3</td>n", " <td>3</td>n", " <td>4000</td>n", " <td>4000</td>n", " <td>photo and video</td>n", " <td>vid_49</td>n", " <td>greenbrae</td>n", " <td>weddings by samuel</td>n", " <td>0</td>n", " <td>0</td>n", " <td>1</td>n", " <td>1</td>n", " </tr>n", " <tr>n", " <th>854</th>n", " <td>1</td>n", " <td>1</td>n", " <td>1500</td>n", " <td>1500</td>n", " <td>photo and video</td>n", " <td>vid_50</td>n", " <td>petaluma</td>n", " <td>john leestma photography</td>n", " <td>50</td>n", " <td>50</td>n", " <td>1</td>n", " <td>1</td>n", " </tr>n", " </tbody>n", "</table>n", "<p>855 rows × 12 columns</p>n", "</div>" ], "text/plain": [ " /DATA Unnamed: 1 Unnamed: 2 Unnamed: 3 \n", "0 /ROW/price_ce /ROW/price_ce/#agg /ROW/price_unit /ROW/price_unit/#agg n", "1 4 4 1750 1750 n", "2 4 4 1750 1750 n", "3 4 4 2250 2250 n", "4 2 2 225 225 n", ".. … … … … n", "850 1 1 500 500 n", "851 1 1 500 500 n", "852 1 1 1500 1500 n", "853 3 3 4000 4000 n", "854 1 1 1500 1500 n", "n", " Unnamed: 4 Unnamed: 5 Unnamed: 6 \n", "0 /ROW/vendor_depart /ROW/vendor_id /ROW/vendor_location n", "1 dress and attire att_01 san francisco n", "2 dress and attire att_02 online n", "3 dress and attire att_02 online n", "4 dress and attire att_02 online n", ".. … … … n", "850 photo and video vid_46 san francisco n", "851 photo and video vid_47 san francisco n", "852 photo and video vid_48 san rafael n", "853 photo and video vid_49 greenbrae n", "854 photo and video vid_50 petaluma n", "n", " Unnamed: 7 Unnamed: 8 Unnamed: 9 \n", "0 /ROW/vendor_name /ROW/vendor_rating /ROW/vendor_rating/#agg n", "1 casablanca bridal 0 0 n", "2 allure bridal 0 0 n", "3 allure bridal 0 0 n", "4 allure bridal 0 0 n", ".. … … … n", "850 julia goldberg photography 50 50 n", "851 bailey w photography 50 50 n", "852 romantic photographer 0 0 n", "853 weddings by samuel 0 0 n", "854 john leestma photography 50 50 n", "n", " Unnamed: 10 Unnamed: 11 n", "0 /ROW/vendor_sustainable /ROW/vendor_sustainable/#agg n", "1 0 0 n", "2 0 0 n", "3 0 0 n", "4 0 0 n", ".. … … n", "850 0 0 n", "851 1 1 n", "852 1 1 n", "853 1 1 n", "854 1 1 n", "n", "[855 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd # data science essentialsn", "import matplotlib.pyplot as plt # NEW: data visualization essentialsn", "import seaborn as sns # NEW: enhanced data visualization optional datacamp data visulization coursen", "n", "file = "/Users/archipatel/Desktop/wedding1.xlsx"n", "wedding1 = pd.read_excel(io = file , n", " sheet_name = 0, n", " header = 0 )n", "wedding1n" ] }, { "cell_type": "code", "execution_count": 11, "id": "cc6d9a81", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>n", "<style scoped>n", " .dataframe tbody tr th:only-of-type {n", " vertical-align: middle;n", " }n", "n", " .dataframe tbody tr th {n", " vertical-align: top;n", " }n", "n", " .dataframe thead th {n", " text-align: right;n", " }n", "</style>n", "<table border="1" class="dataframe">n", " <thead>n", " <tr style="text-align: right;">n", " <th></th>n", " <th>/DATA</th>n", " <th>Unnamed: 1</th>n", " <th>Unnamed: 2</th>n", " <th>Unnamed: 3</th>n", " <th>Unnamed: 4</th>n", " <th>Unnamed: 5</th>n", " <th>Unnamed: 6</th>n", " <th>Unnamed: 7</th>n", " <th>Unnamed: 8</th>n", " <th>Unnamed: 9</th>n", " <th>Unnamed: 10</th>n", " <th>Unnamed: 11</th>n", " </tr>n", " </thead>n", " <tbody>n", " <tr>n", " <th>count</th>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " <td>855</td>n", " </tr>n", " <tr>n", " <th>unique</th>n", " <td>6</td>n", " <td>6</td>n", " <td>248</td>n", " <td>248</td>n", " <td>11</td>n", " <td>331</td>n", " <td>86</td>n", " <td>285</td>n", " <td>18</td>n", " <td>18</td>n", " <td>3</td>n", " <td>3</td>n", " </tr>n", " <tr>n", " <th>top</th>n", " <td>1</td>n", " <td>1</td>n", " <td>150</td>n", " <td>150</td>n", " <td>hair and makeup</td>n", " <td>hmu_01</td>n", " <td>san francisco</td>n", " <td>theknot</td>n", " <td>50</td>n", " <td>50</td>n", " <td>1</td>n", " <td>1</td>n", " </tr>n", " <tr>n", " <th>freq</th>n", " <td>264</td>n", " <td>264</td>n", " <td>60</td>n", " <td>60</td>n", " <td>391</td>n", " <td>16</td>n", " <td>193</td>n", " <td>23</td>n", " <td>426</td>n", " <td>426</td>n", " <td>451</td>n", " <td>451</td>n", " </tr>n", " </tbody>n", "</table>n", "</div>" ], "text/plain": [ " /DATA Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 \n", "count 855 855 855 855 855 855 n", "unique 6 6 248 248 11 331 n", "top 1 1 150 150 hair and makeup hmu_01 n", "freq 264 264 60 60 391 16 n", "n", " Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10 \n", "count 855 855 855 855 855 n", "unique 86 285 18 18 3 n", "top san francisco theknot 50 50 1 n", "freq 193 23 426 426 451 n", "n", " Unnamed: 11 n", "count 855 n", "unique 3 n", "top 1 n", "freq 451 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wedding1.describe()" ] }, { "cell_type": "code", "execution_count": 7, "id": "a03ce808", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'vendor_depart'", "output_type": "error", "traceback": [ "u001b[0;31m—————————————————————————u001b[0m", "u001b[0;31mKeyErroru001b[0m Traceback (most recent call last)", "Cell u001b[0;32mIn[7], line 1u001b[0mnu001b[0;32m—-> 1u001b[0m wedding1u001b[38;5;241m.u001b[39mgroupby(u001b[38;5;124m'u001b[39mu001b[38;5;124mvendor_departu001b[39mu001b[38;5;124m'u001b[39m, as_indexu001b[38;5;241m=u001b[39mu001b[38;5;28;01mFalseu001b[39;00m)[u001b[38;5;124m"u001b[39mu001b[38;5;124mprice_unitu001b[39mu001b[38;5;124m"u001b[39m]u001b[38;5;241m.u001b[39mmean() nu001b[1;32m 3u001b[0m wedding1u001b[38;5;241m.u001b[39mgroupby(u001b[38;5;124m'u001b[39mu001b[38;5;124mvendor_departu001b[39mu001b[38;5;124m'u001b[39m, as_indexu001b[38;5;241m=u001b[39mu001b[38;5;28;01mFalseu001b[39;00m)[u001b[38;5;124m"u001b[39mu001b[38;5;124mvendor_sustainableu001b[39mu001b[38;5;124m"u001b[39m]u001b[38;5;241m.u001b[39mmean()n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:8872u001b[0m, in u001b[0;36mDataFrame.groupbyu001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)u001b[0mnu001b[1;32m 8869u001b[0m u001b[38;5;28;01mifu001b[39;00m level u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m u001b[38;5;129;01mandu001b[39;00m by u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[1;32m 8870u001b[0m u001b[38;5;28;01mraiseu001b[39;00m u001b[38;5;167;01mTypeErroru001b[39;00m(u001b[38;5;124m"u001b[39mu001b[38;5;124mYou have to supply one of u001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124mbyu001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124m and u001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124mlevelu001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124m"u001b[39m)nu001b[0;32m-> 8872u001b[0m u001b[38;5;28;01mreturnu001b[39;00m DataFrameGroupBy(nu001b[1;32m 8873u001b[0m obju001b[38;5;241m=u001b[39mu001b[38;5;28mselfu001b[39m,nu001b[1;32m 8874u001b[0m keysu001b[38;5;241m=u001b[39mby,nu001b[1;32m 8875u001b[0m axisu001b[38;5;241m=u001b[39maxis,nu001b[1;32m 8876u001b[0m levelu001b[38;5;241m=u001b[39mlevel,nu001b[1;32m 8877u001b[0m as_indexu001b[38;5;241m=u001b[39mas_index,nu001b[1;32m 8878u001b[0m sortu001b[38;5;241m=u001b[39msort,nu001b[1;32m 8879u001b[0m group_keysu001b[38;5;241m=u001b[39mgroup_keys,nu001b[1;32m 8880u001b[0m observedu001b[38;5;241m=u001b[39mobserved,nu001b[1;32m 8881u001b[0m dropnau001b[38;5;241m=u001b[39mdropna,nu001b[1;32m 8882u001b[0m )n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1274u001b[0m, in u001b[0;36mGroupBy.__init__u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)u001b[0mnu001b[1;32m 1271u001b[0m u001b[38;5;28mselfu001b[39mu001b[38;5;241m.u001b[39mdropna u001b[38;5;241m=u001b[39m dropnanu001b[1;32m 1273u001b[0m u001b[38;5;28;01mifu001b[39;00m grouper u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[0;32m-> 1274u001b[0m grouper, exclusions, obj u001b[38;5;241m=u001b[39m get_grouper(nu001b[1;32m 1275u001b[0m obj,nu001b[1;32m 1276u001b[0m keys,nu001b[1;32m 1277u001b[0m axisu001b[38;5;241m=u001b[39maxis,nu001b[1;32m 1278u001b[0m levelu001b[38;5;241m=u001b[39mlevel,nu001b[1;32m 1279u001b[0m sortu001b[38;5;241m=u001b[39msort,nu001b[1;32m 1280u001b[0m observedu001b[38;5;241m=u001b[39mu001b[38;5;28;01mFalseu001b[39;00m u001b[38;5;28;01mifu001b[39;00m observed u001b[38;5;129;01misu001b[39;00m libu001b[38;5;241m.u001b[39mno_default u001b[38;5;28;01melseu001b[39;00m observed,nu001b[1;32m 1281u001b[0m dropnau001b[38;5;241m=u001b[39mu001b[38;5;28mselfu001b[39mu001b[38;5;241m.u001b[39mdropna,nu001b[1;32m 1282u001b[0m )nu001b[1;32m 1284u001b[0m u001b[38;5;28;01mifu001b[39;00m observed u001b[38;5;129;01misu001b[39;00m libu001b[38;5;241m.u001b[39mno_default:nu001b[1;32m 1285u001b[0m u001b[38;5;28;01mifu001b[39;00m u001b[38;5;28manyu001b[39m(pingu001b[38;5;241m.u001b[39m_passed_categorical u001b[38;5;28;01mforu001b[39;00m ping u001b[38;5;129;01minu001b[39;00m grouperu001b[38;5;241m.u001b[39mgroupings):n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/grouper.py:1009u001b[0m, in u001b[0;36mget_grouperu001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)u001b[0mnu001b[1;32m 1007u001b[0m in_axis, level, gpr u001b[38;5;241m=u001b[39m u001b[38;5;28;01mFalseu001b[39;00m, gpr, u001b[38;5;28;01mNoneu001b[39;00mnu001b[1;32m 1008u001b[0m u001b[38;5;28;01melseu001b[39;00m:nu001b[0;32m-> 1009u001b[0m u001b[38;5;28;01mraiseu001b[39;00m u001b[38;5;167;01mKeyErroru001b[39;00m(gpr)nu001b[1;32m 1010u001b[0m u001b[38;5;28;01melifu001b[39;00m u001b[38;5;28misinstanceu001b[39m(gpr, Grouper) u001b[38;5;129;01mandu001b[39;00m gpru001b[38;5;241m.u001b[39mkey u001b[38;5;129;01misu001b[39;00m u001b[38;5;129;01mnotu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[1;32m 1011u001b[0m u001b[38;5;66;03m# Add key to exclusionsu001b[39;00mnu001b[1;32m 1012u001b[0m exclusionsu001b[38;5;241m.u001b[39madd(gpru001b[38;5;241m.u001b[39mkey)n", "u001b[0;31mKeyErroru001b[0m: 'vendor_depart'" ] } ], "source": [ "wedding1.groupby('vendor_depart', as_index=False)["price_unit"].mean() n", "n", "wedding1.groupby('vendor_depart', as_index=False)["vendor_sustainable"].mean() " ] }, { "cell_type": "code", "execution_count": 10, "id": "8111395e", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'vendor_depart'", "output_type": "error", "traceback": [ "u001b[0;31m—————————————————————————u001b[0m", "u001b[0;31mKeyErroru001b[0m Traceback (most recent call last)", "Cell u001b[0;32mIn[10], line 3u001b[0mnu001b[1;32m 1u001b[0m u001b[38;5;66;03m#Calculate mean of 'price_unit'u001b[39;00mnu001b[0;32m—-> 3u001b[0m price_unit_mean u001b[38;5;241m=u001b[39m wedding1u001b[38;5;241m.u001b[39mgroupby(u001b[38;5;124m'u001b[39mu001b[38;5;124mvendor_departu001b[39mu001b[38;5;124m'u001b[39m)[u001b[38;5;124m'u001b[39mu001b[38;5;124mprice_unitu001b[39mu001b[38;5;124m'u001b[39m]u001b[38;5;241m.u001b[39mmean()u001b[38;5;241m.u001b[39mreset_index()nu001b[1;32m 5u001b[0m u001b[38;5;66;03m# Calculate mean of 'vendor_sustainable'u001b[39;00mnu001b[1;32m 6u001b[0m vendor_sustainable_mean u001b[38;5;241m=u001b[39m weddingdatau001b[38;5;241m.u001b[39mgroupby(u001b[38;5;124m'u001b[39mu001b[38;5;124mvendor_departu001b[39mu001b[38;5;124m'u001b[39m)[u001b[38;5;124m'u001b[39mu001b[38;5;124mvendor_sustainableu001b[39mu001b[38;5;124m'u001b[39m]u001b[38;5;241m.u001b[39mmean()u001b[38;5;241m.u001b[39mreset_index()n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:8872u001b[0m, in u001b[0;36mDataFrame.groupbyu001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)u001b[0mnu001b[1;32m 8869u001b[0m u001b[38;5;28;01mifu001b[39;00m level u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m u001b[38;5;129;01mandu001b[39;00m by u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[1;32m 8870u001b[0m u001b[38;5;28;01mraiseu001b[39;00m u001b[38;5;167;01mTypeErroru001b[39;00m(u001b[38;5;124m"u001b[39mu001b[38;5;124mYou have to supply one of u001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124mbyu001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124m and u001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124mlevelu001b[39mu001b[38;5;124m'u001b[39mu001b[38;5;124m"u001b[39m)nu001b[0;32m-> 8872u001b[0m u001b[38;5;28;01mreturnu001b[39;00m DataFrameGroupBy(nu001b[1;32m 8873u001b[0m obju001b[38;5;241m=u001b[39mu001b[38;5;28mselfu001b[39m,nu001b[1;32m 8874u001b[0m keysu001b[38;5;241m=u001b[39mby,nu001b[1;32m 8875u001b[0m axisu001b[38;5;241m=u001b[39maxis,nu001b[1;32m 8876u001b[0m levelu001b[38;5;241m=u001b[39mlevel,nu001b[1;32m 8877u001b[0m as_indexu001b[38;5;241m=u001b[39mas_index,nu001b[1;32m 8878u001b[0m sortu001b[38;5;241m=u001b[39msort,nu001b[1;32m 8879u001b[0m group_keysu001b[38;5;241m=u001b[39mgroup_keys,nu001b[1;32m 8880u001b[0m observedu001b[38;5;241m=u001b[39mobserved,nu001b[1;32m 8881u001b[0m dropnau001b[38;5;241m=u001b[39mdropna,nu001b[1;32m 8882u001b[0m )n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:1274u001b[0m, in u001b[0;36mGroupBy.__init__u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)u001b[0mnu001b[1;32m 1271u001b[0m u001b[38;5;28mselfu001b[39mu001b[38;5;241m.u001b[39mdropna u001b[38;5;241m=u001b[39m dropnanu001b[1;32m 1273u001b[0m u001b[38;5;28;01mifu001b[39;00m grouper u001b[38;5;129;01misu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[0;32m-> 1274u001b[0m grouper, exclusions, obj u001b[38;5;241m=u001b[39m get_grouper(nu001b[1;32m 1275u001b[0m obj,nu001b[1;32m 1276u001b[0m keys,nu001b[1;32m 1277u001b[0m axisu001b[38;5;241m=u001b[39maxis,nu001b[1;32m 1278u001b[0m levelu001b[38;5;241m=u001b[39mlevel,nu001b[1;32m 1279u001b[0m sortu001b[38;5;241m=u001b[39msort,nu001b[1;32m 1280u001b[0m observedu001b[38;5;241m=u001b[39mu001b[38;5;28;01mFalseu001b[39;00m u001b[38;5;28;01mifu001b[39;00m observed u001b[38;5;129;01misu001b[39;00m libu001b[38;5;241m.u001b[39mno_default u001b[38;5;28;01melseu001b[39;00m observed,nu001b[1;32m 1281u001b[0m dropnau001b[38;5;241m=u001b[39mu001b[38;5;28mselfu001b[39mu001b[38;5;241m.u001b[39mdropna,nu001b[1;32m 1282u001b[0m )nu001b[1;32m 1284u001b[0m u001b[38;5;28;01mifu001b[39;00m observed u001b[38;5;129;01misu001b[39;00m libu001b[38;5;241m.u001b[39mno_default:nu001b[1;32m 1285u001b[0m u001b[38;5;28;01mifu001b[39;00m u001b[38;5;28manyu001b[39m(pingu001b[38;5;241m.u001b[39m_passed_categorical u001b[38;5;28;01mforu001b[39;00m ping u001b[38;5;129;01minu001b[39;00m grouperu001b[38;5;241m.u001b[39mgroupings):n", "File u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/grouper.py:1009u001b[0m, in u001b[0;36mget_grouperu001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)u001b[0mnu001b[1;32m 1007u001b[0m in_axis, level, gpr u001b[38;5;241m=u001b[39m u001b[38;5;28;01mFalseu001b[39;00m, gpr, u001b[38;5;28;01mNoneu001b[39;00mnu001b[1;32m 1008u001b[0m u001b[38;5;28;01melseu001b[39;00m:nu001b[0;32m-> 1009u001b[0m u001b[38;5;28;01mraiseu001b[39;00m u001b[38;5;167;01mKeyErroru001b[39;00m(gpr)nu001b[1;32m 1010u001b[0m u001b[38;5;28;01melifu001b[39;00m u001b[38;5;28misinstanceu001b[39m(gpr, Grouper) u001b[38;5;129;01mandu001b[39;00m gpru001b[38;5;241m.u001b[39mkey u001b[38;5;129;01misu001b[39;00m u001b[38;5;129;01mnotu001b[39;00m u001b[38;5;28;01mNoneu001b[39;00m:nu001b[1;32m 1011u001b[0m u001b[38;5;66;03m# Add key to exclusionsu001b[39;00mnu001b[1;32m 1012u001b[0m exclusionsu001b[38;5;241m.u001b[39madd(gpru001b[38;5;241m.u001b[39mkey)n", "u001b[0;31mKeyErroru001b[0m: 'vendor_depart'" ] } ], "source": [ "#Calculate mean of 'price_unit'n", "n", "price_unit_mean = wedding1.groupby('vendor_depart')['price_unit'].mean().reset_index()n", "n", "# Calculate mean of 'vendor_sustainable'n", "vendor_sustainable_mean = weddingdata.groupby('vendor_depart')['vendor_sustainable'].mean().reset_index()n", "n", "n", "# Merging two togethern", "combined_means = pd.merge(price_unit_mean, vendor_sustainable_mean, on='vendor_depart')n", "n", "# Display the combined tablen", "print(combined_means)" ] }, { "cell_type": "code", "execution_count": 22, "id": "b9263cb7", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (1979301581.py, line 2)", "output_type": "error", "traceback": [ "u001b[0;36m Cell u001b[0;32mIn[22], line 2u001b[0;36mu001b[0mnu001b[0;31m prod.product_id,u001b[0mnu001b[0m ^u001b[0mnu001b[0;31mIndentationErroru001b[0mu001b[0;31m:u001b[0m unexpected indentn" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "e8ecdc13", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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Wedding Vendors and Sustainability Analysis ¶

BY: Simran Kaur

Welcome to the sustainability spot for weddings.

In our project we struggled to find a problem that we could analyze in the wedding industry. By putting our heads together and with a lot of thought we found that the subject of sustainability in weddings is an up and coming topic especially in metropolitan areas such as the Bay Area. The problem is that as our state as a whole goes more towards the green energy and sustainable consumption route, the more people look for ways to apply it to different aspects of their life such as in our case, weddings. Therefore, our cohort decided to look into sustainability and answer our business question "Are wedding vendors with sustainable practices more cost effective?". This comparison aims to ascertain whether vendors implementing sustainable practices demonstrate better cost-effectiveness in different wedding-related departments. For this dataset the two key terms would be Cost Effectiveness and Sustainable Practices, as these our two units of measure. Cost Effectiveness is defined as a "methods or processes bring the greatest possible advantage or profit when the amount that is spent is considered" in the dictionary", and in the context of our wedding planning data base we are essentially trying to discover if being sustainable and providing sustainable vendors has possible advantages in cost and other aspects. Sustainable Practices would be our other key term in relation to our database. Sustainable practices are defined as act and methods that do not harm the environment and support ecological, environmental, human, and economic health. In the wedding vendors we take into account which businesses in our database support this sustainable practice and then essentially how cost effective are they compared to business that don’t offer sustainable practices.

The Business Question: ¶

Based on the analysis conducted using the wedding data, the findings suggest that vendors implementing sustainable practices tend to demonstrate slightly higher costs compared to non-sustainable vendors across various wedding service departments. The data revealed that, on average, sustainable vendors in certain departments, notably venues and music, exhibit slightly higher costs than non-sustainable vendors. This unexpected result indicates that, contrary to our assumptions, sustainable practices might not always lead to immediate cost savings in the wedding industry and for the customer. While sustainable vendors often prioritize eco-friendly practices, the implementation and maintenance of these practices might incur additional expenses that contribute to the observed higher costs.It's crucial to remember that these results do not suggest that sustainable measures are inherently inefficient. Instead, they point to a nuanced interaction of variables influencing prices across various wedding service industries. Investing in environmentally friendly materials, ethical sourcing, or ecologically friendly production techniques are examples of sustainable measures that may initially result in higher operating expenses.

Top Two Actionable Insights ¶

My top two actionable insights were:

Do vendors with sustainable practices tend to exhibit lower costs on average across various departments?
Identify departments or services where sustainable vendors have a competitive advantage. Encourage vendors to leverage sustainable practices as a marketing strategy to attract eco-conscious clients, potentially increasing market share and revenue.

Utilizing the weddingdata for the first actionable insight, which focused on whether suppliers with sustainable practices typically show lower costs overall across different departments. In order to determine the average expenses related to sustainable and non-sustainable suppliers, the data was categorized by vendor departments and sustainable practices. The objective of this investigation was to identify trends in cost-effectiveness among various services and departments, with a focus on three departments specifically. According to the results, suppliers who follow sustainable practices typically charge a little bit more in several departments than suppliers who don't. It may be necessary to modify infrastructure or acquire specialist technology to employ environmentally friendly production techniques, which would increase operating costs. Furthermore, maintaining strict environmental certifications and laws may require continuing compliance fees, which raises the overall operational costs for sustainable enterprises.

For the Second actionable insight, the analysis attempted to identify departments in which sustainable vendors possess a competitive advantage. Based on the investigation, it was found that departments like flowers and music offer a significant competitive advantage to sustainable vendors as we see that the price difference for sustainable and non-sustainable practices is not significantly outrageous. Which points to a possible tactic suppliers could use to promote to environmentally sensitive customers by utilizing sustainable techniques and still keep their price range affordable for their clientele.

Conclusion ¶

In conclusion, the analysis suggests that while sustainable vendors tend to display slightly lower costs on average across various departments, the extent of this cost-effectiveness varies. Additionally, departments such as venues and flowers demonstrate a more pronounced competitive advantage for sustainable vendors, hinting at a potential marketing strategy to increase market share and revenue through the promotion of sustainable practices. This deeper level of analysis provides actionable insights beyond numerical comparisons, offering strategic directions for vendors to leverage sustainability in attracting eco-conscious clientele.Throughout the mean analysis I found that by just looking at the three departments (venues, flowers, and music), I was able to come to this conclusion that business with sustainable practices are likely to be more expensive than the non-sustainable business.The report emphasizes how critical it is to have a thorough understanding of the cost dynamics associated with sustainability in the wedding industry. It implies that while ethical and ecologically values can be preserved through sustainable practices, these values might not necessarily translate into quick financial gains. Businesses should adopt a long-term perspective while implementing sustainability, understanding that there may be up-front costs involved, but that these expenses may eventually pay off in more sustainable and cost-effective operations.

In [1]:

# importing packages import pandas as pd # data science essentials import matplotlib.pyplot as plt # data visualization essentials import seaborn as sns # data visualization optional datacamp data visulization course file = "/users/kaurs/downloads/weddingdata.xlsx"  weddingdata = pd.read_excel(io         = file      ,                                 sheet_name = 0,                           header     = 0 ) weddingdata

Out[1]:

	vendor_id	vendor_name	vendor_sustainable	vendor_depart	vendor_rating	vendor_location	price_unit	price_ce	product_name
0	att_01	casablanca bridal	0	dress and attire	0	san francisco	1750.0	4	dress – belobed by casablance bridal
1	att_02	allure bridal	0	dress and attire	0	online	1750.0	4	dress – madison james by allure bridals
2	att_02	allure bridal	0	dress and attire	0	online	2250.0	4	dress – disney fairy tale weddings
3	att_02	allure bridal	0	dress and attire	0	online	225.0	2	dress – allure bridesmaids
4	att_03	stacees	0	dress and attire	0	online	616.0	4	dress – stacees amy wedding dress
…	…	…	…	…	…	…	…	…	…
849	vid_46	julia goldberg photography	0	photo and video	50	san francisco	500.0	1	photo
850	vid_47	bailey w photography	1	photo and video	50	san francisco	500.0	1	photo
851	vid_48	romantic photographer	1	photo and video	0	san rafael	1500.0	1	photo
852	vid_49	weddings by samuel	1	photo and video	0	greenbrae	4000.0	3	photo
853	vid_50	john leestma photography	1	photo and video	50	petaluma	1500.0	1	photo

854 rows × 9 columns

In [2]:

#Descriptve stats # Finding the mean overall and of the 3 departments selcted  weddingdata.describe()

Out[2]:

	vendor_sustainable	vendor_rating	price_unit	price_ce
count	854.000000	854.000000	854.000000	854.000000
mean	0.528103	38.128806	1328.353677	2.311475
std	0.499502	19.830933	3812.561560	1.098410
min	0.000000	0.000000	0.000000	0.000000
25%	0.000000	40.000000	90.000000	1.000000
50%	1.000000	49.000000	175.000000	2.000000
75%	1.000000	50.000000	400.000000	3.000000
max	1.000000	50.000000	32000.000000	4.000000

In [21]:

#Finding the average price within each deaprtment  # just an extra cell that I ran to find the ,mean in sustanability across departments weddingdata.groupby('vendor_depart', as_index=False)["price_unit"].mean()   weddingdata.groupby('vendor_depart', as_index=False)["vendor_sustainable"].mean()

Out[21]:

	vendor_depart	vendor_sustainable
0	catering	0.961165
1	dress and attire	0.440000
2	flowers	0.666667
3	hair and makeup	0.291560
4	invitations	0.640000
5	jewelry	0.500000
6	music	0.571429
7	photo and video	0.980000
8	rental	0.727273
9	venues	0.760000

In [36]:

# Calculate mean of 'price_unit'  price_unit_mean = weddingdata.groupby('vendor_depart')['price_unit'].mean().reset_index()  # Calculate mean of 'vendor_sustainable' vendor_sustainable_mean = weddingdata.groupby('vendor_depart')['vendor_sustainable'].mean().reset_index()   # Merging two together combined_means = pd.merge(price_unit_mean, vendor_sustainable_mean, on='vendor_depart')  # Display the combined table print(combined_means)

      vendor_depart    price_unit  vendor_sustainable 0          catering     80.679612            0.961165 1  dress and attire    530.959600            0.440000 2           flowers     69.035714            0.666667 3   hair and makeup    170.624041            0.291560 4       invitations    288.875200            0.640000 5           jewelry   2303.460000            0.500000 6             music   2161.257143            0.571429 7   photo and video   2916.540000            0.980000 8            rental     91.054545            0.727273 9            venues  13517.000000            0.760000

In [38]:

###filtering for ALL DEPARTMENT sustainable and non sustainable  #mean of a specific column where 'vendor_sustainable' is equal to with price price_sustainable_vendor_mean = weddingdata[weddingdata['vendor_sustainable'] == 1]['price_unit'].mean()  # Display the mean value print("Mean of price where 'vendor_sustainable' is 1:", price_sustainable_vendor_mean)

Mean of price where 'vendor_sustainable' is 1: 1882.2495565410197

In [39]:

#mean of a specific column where 'vendor_sustainable' is equal to 0 price_nonsustainable_vendor_mean = weddingdata[weddingdata['vendor_sustainable'] == 0]['price_unit'].mean()  # Display the mean value print("Mean of price where 'vendor_sustainable' is 0:", price_nonsustainable_vendor_mean)

Mean of price where 'vendor_sustainable' is 0: 708.4850868486352

In [41]:

###filtering for venues sustainable and non sustainable # for venues within 'vendor_depart' and 'vendor_sustainable' equal to 1 venues_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean()  # Display the mean value for the departments and 'vendor_sustainable' equal to 1 print(f"Mean price for venues where 'vendor_sustainable' is 1: {venues_sustainable_mean}")

Mean price for venues where 'vendor_sustainable' is 1: 14459.21052631579

In [42]:

venues_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  # Display the mean value for the departments and 'vendor_sustainable' equal to 0 print(f"Mean price for venues where 'vendor_sustainable' is 0: {venues_nonsustainable_mean}")

Mean price for venues where 'vendor_sustainable' is 0: 10533.333333333334

In [43]:

###filtering for flowers sustainable and non sustainable flowers_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean()  print(f"mean price for flowers where 'vendor_sustainable' is 1: {flowers_sustainable_mean}")

Mean price for flowers where 'vendor_sustainable' is 1: 73.32142857142857

In [44]:

flowers_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  print(f"mean price for flowers where 'vendor_sustainable' is 0: {flowers_nonsustainable_mean}")

Mean price for flowers where 'vendor_sustainable' is 0: 60.464285714285715

In [45]:

###filtering for music sustainable and non sustainable music_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean()  print(f"mean price for music where 'vendor_sustainable' is 1: {music_sustainable_mean}")

mean price for music where 'vendor_sustainable' is 1: 2097.9

In [46]:

music_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  print(f"mean price for music where 'vendor_sustainable' is 0: {music_nonsustainable_mean}")

mean price for music where 'vendor_sustainable' is 0: 2245.733333333333

In [ ]:

venues_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() venues_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean() flowers_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() flowers_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean() music_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() music_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()

In [48]:

# Create a frequency table for a specific column in your DataFrame frequency_table = weddingdata['vendor_sustainable'].value_counts()  # Display the frequency table print(frequency_table)

vendor_sustainable 1    451 0    403 Name: count, dtype: int64

In [50]:

#Frequency tables from scipy.stats import ttest_ind  # Assuming 'df' is your DataFrame  # Filter data for two groups based on 'vendor_sustainable' (1 and 0) group_1 = weddingdata[weddingdata['vendor_sustainable'] == 1]['price_unit'] group_2 = weddingdata[weddingdata['vendor_sustainable'] == 0]['price_unit']   # Perform independent t-test t_statistic, p_value = ttest_ind(group_1, group_2, equal_var=False)  # Set equal_var=False if variances are unequal  # Display results print("T-statistic:", t_statistic) print("P-value:", p_value)  # Interpret the results based on the p-value (usually if p-value < 0.05, results are considered significant) if p_value < 0.05:     print("There is a significant difference between the means of the two groups.") else:     print("There is no significant difference between the means of the two groups.")

T-statistic: 4.707900415256033 P-value: 3.053959678966908e-06 There is a significant difference between the means of the two groups.

In [52]:

#Correlation between the top three departments of intrest  # means for sustainable and non-sustainable vendors from each departments venues_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() venues_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'venues') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  flowers_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() flowers_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'flowers') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  music_sustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 1)]['price_unit'].mean() music_nonsustainable_mean = weddingdata[(weddingdata['vendor_depart'] == 'music') & (weddingdata['vendor_sustainable'] == 0)]['price_unit'].mean()  # means for each department print("Venues Sustainable Mean:", venues_sustainable_mean) print("Venues Non-Sustainable Mean:", venues_nonsustainable_mean) print("Flowers Sustainable Mean:", flowers_sustainable_mean) print("Flowers Non-Sustainable Mean:", flowers_nonsustainable_mean) print("Music Sustainable Mean:", music_sustainable_mean) print("Music Non-Sustainable Mean:", music_nonsustainable_mean)  # to calc correlations between price_unit and vendor_sustainable within each department correlation_venues = weddingdata[weddingdata['vendor_depart'] == 'venues'][['price_unit', 'vendor_sustainable']].corr() correlation_flowers = weddingdata[weddingdata['vendor_depart'] == 'flowers'][['price_unit', 'vendor_sustainable']].corr() correlation_music = weddingdata[weddingdata['vendor_depart'] == 'music'][['price_unit', 'vendor_sustainable']].corr()  # display of correlations need print with each coorelation being defined by text print("nCorrelation for Venues:", correlation_venues) print("nCorrelation for Flowers:", correlation_flowers) print("nCorrelation for Music:", correlation_music)

Venues Sustainable Mean: 14459.21052631579 Venues Non-Sustainable Mean: 10533.333333333334 Flowers Sustainable Mean: 73.32142857142857 Flowers Non-Sustainable Mean: 60.464285714285715 Music Sustainable Mean: 2097.9 Music Non-Sustainable Mean: 2245.733333333333  Correlation for Venues:                     price_unit  vendor_sustainable price_unit            1.000000            0.198219 vendor_sustainable    0.198219            1.000000  Correlation for Flowers:                     price_unit  vendor_sustainable price_unit            1.000000            0.070741 vendor_sustainable    0.070741            1.000000  Correlation for Music:                     price_unit  vendor_sustainable price_unit            1.000000           -0.049658 vendor_sustainable   -0.049658            1.000000

In [53]:

#3-5 well-designed, highly relevant data visualizations (scatterplots, boxplots, etc.) # VISUALIZATION # 1 : Bar Plots for each department of interest  fig, axs = plt.subplots(3, figsize=(8, 12))  # Plotting means for sustainable and non-sustainable vendors in different departments axs[0].bar(['Venues Sustainable', 'Venues Non-Sustainable'], [venues_sustainable_mean, venues_nonsustainable_mean]) axs[0].set_title('Mean Price for Venues')  axs[1].bar(['Flowers Sustainable', 'Flowers Non-Sustainable'], [flowers_sustainable_mean, flowers_nonsustainable_mean]) axs[1].set_title('Mean Price for Flowers')  axs[2].bar(['Music Sustainable', 'Music Non-Sustainable'], [music_sustainable_mean, music_nonsustainable_mean]) axs[2].set_title('Mean Price for Music')  plt.tight_layout() plt.show()

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In [55]:

# VISUALIZATION # 2 : Scatter Plots for each department of interest #instantiating a scatter plot for price and sustainability  # Scatter plot with linear regression line for Venues department sns.lmplot(x='price_unit',             y='vendor_sustainable',             hue = None,            scatter = True,            fit_reg = False,            aspect = 2,            data= weddingdata[weddingdata['vendor_depart'] == 'venues'])  plt.title    (label = 'Scatter Plot with Linear Regression for Venues') plt.xlabel   (xlabel = 'Price'          ) plt.ylabel   (ylabel = 'Vendor Sustainibility)'  ) plt.xlim     (left   = 0, right = 2.8          ) plt.tight_layout(pad    = 1.0                     ) plt.show        (block  = True                    )  # Scatter plot with linear regression line for Flowers department sns.lmplot(x='price_unit',             y='vendor_sustainable',             hue = None,            scatter = True,            fit_reg = False,            aspect = 2,            data=weddingdata[weddingdata['vendor_depart'] == 'flowers'])  plt.title   (label = 'Scatter Plot with Linear Regression for Flowers') plt.xlabel   (xlabel = 'Price'          ) plt.ylabel   (ylabel = 'Vendor Sustainibility)'  ) plt.xlim     (left   = 0, right = 2.8          ) plt.tight_layout(pad    = 1.0                     ) plt.show        (block  = True                    )  # Scatter plot with linear regression line for Music department sns.lmplot(x='price_unit',            y='vendor_sustainable',            hue = None,            scatter = True,            aspect = 2,            data=weddingdata[weddingdata['vendor_depart'] == 'music'])  plt.title    (label = 'Scatter Plot with Linear Regression for Music') plt.xlabel   (xlabel = 'Price'          ) plt.ylabel   (ylabel = 'Vendor Sustainibility)'  ) plt.xlim     (left   = 0, right = 2.8          ) plt.tight_layout(pad    = 1.0                     ) plt.show        (block  = True                    )  plt.show()

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In [64]:

# VISUALIZATION # 3 : Heat Graph for each department of interest  fig, ax = plt.subplots( figsize = (12 , 8) )   plt.subplot(1, 3, 1) sns.heatmap(correlation_venues, annot=True, cmap='inferno') plt.title('Heatmap for Venues')  plt.subplot(1, 3, 2) sns.heatmap(correlation_flowers, annot=True, cmap='coolwarm') plt.title('Heatmap for Flowers')  plt.subplot(1, 3, 3) sns.heatmap(correlation_music, annot=True, cmap='ocean') plt.title('Heatmap for Music')  plt.tight_layout() plt.show()

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Sources ¶

"Cost-effective." Cambridge Dictionary, Cambridge University Press, https://dictionary.cambridge.org/us/dictionary/english/cost-effective.

"Sustainable." Cambridge Dictionary, Cambridge University Press, https://dictionary.cambridge.org/us/dictionary/english/sustainable?q=Sustainable.

"What is Sustainability?" UCLA Sustainability, University of California, Los Angeles, https://www.sustain.ucla.edu/what-is-sustainability/.

Kusterer,Chase. "Script 8 : Linear Relationships", Hult University, Scatter Plots.

BuiltIN. "How to Do a T-Test in Python." BuiltIn, https://builtin.com/data-science/t-test-python.

Stack Overflow. "Make more than one chart in the same IPython Notebook cell." Stack Overflow, Stack Exchange Inc., 14 May 2013, stackoverflow.com/questions/16392921/make-more-than-one-chart-in-same-ipython-notebook-cell.

Stack Overflow. "How to calculate mean values grouped on another column." Stack Overflow, Stack Exchange Inc., 26 May 2015, stackoverflow.com/questions/30482071/how-to-calculate-mean-values-grouped-on-another-column.

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wedding

Wedding Vendors and Sustainability Analysis ¶

The Business Question: ¶

Top Two Actionable Insights ¶

Conclusion ¶

Sources ¶

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