`deft sheet convert`

Change format: --to csv|xlsx|json

5 runs on real files, 19 tests on generated files, not shown. 0 failed.

Real-file tasks

Real files and a plain-language job, from the agent benchmark. These are the reference answers, not an agent run.

Chinese table to ExcelPassed

把销售.csv 转成 Excel 文件销售.xlsx。

deft sheet convert 销售.csv -o 销售.xlsx

Input

销售.csv 39.8 KB

日期	浏览量	访客数	人均浏览量	平均停留时间	跳失率	成交客户数	成交单量	成交金额	客单价	成交商品件数	下单客户数	下单单量	下单金额	下单商品件数
2022/1/1	452364	68954	6.56	97.01	0.4	9593	10144	1031017	107.48	39392	11050	12507	1187654	44142
2022/1/2	834110	152613	5.47	106.95	0.46	15890	19094	1790335	112.67	39395	18843	22576	2123040.81	40220
2022/1/3	417625	65555	6.37	90.59	0.41	8930	10891	1149018	128.67	39412	10170	13339	1308573.9	45885
2022/1/4	1148466	302171	3.8	126.68	0.52	18942	20996	3211457	169.54	39448	19806	21148	3357909.24	43199
2022/1/5	427821	72782	5.88	82.86	0.41	11710	13125	1190114	101.63	39469	13761	16521	1398530.43	50941
2022/1/6	467915	103065	4.54	80.04	0.44	34651	35405	741581	21.4	39485	35895	36994	768153	43858
2022/1/7	523368	104172	5.02	83.69	0.44	15796	17502	1014059	64.2	39561	17626	19615	1131589.2	42278
2022/1/8	604035	99225	6.09	97.95	0.41	12088	12658	968713	80.14	39603	13710	14509	1098719.4	40828
2022/1/9	287369	54278	5.29	66.26	0.46	7845	8653	646868	82.46	39644	8711	10194	718309.06	45187
2022/1/10	362603	60829	5.96	85.33	0.42	8964	10555	916861	102.28	39675	10370	12830	1060643.6	44860
2022/1/11	349943	83562	4.19	50.23	0.61	7279	7945	719425	98.84	39676	8238	9650	814243.92	48064
2022/1/12	310206	60010	5.17	70.27	0.43	8529	9437	822115	96.39	39679	9496	11117	915319.44	41086
2022/1/13	305895	50670	6.04	79.27	0.41	7796	8442	756940	97.09	39814	8788	10256	853226.92	46746
2022/1/14	309745	84177	3.68	58.36	0.57	11445	12539	1046601	91.45	39892	12635	14582	1155470.75	41100
2022/1/15	480358	93865	5.12	81.32	0.44	12210	14380	964297	78.98	39908	13493	15960	1065677.14	46015
2022/1/16	441995	69582	6.35	84.01	0.43	7847	8619	827358	105.44	39937	8981	10700	946956.64	43511
2022/1/17	729683	146559	4.98	79.11	0.48	13482	15932	1157674	85.87	40002	15955	19821	1370055.85	52073
2022/1/18	311125	65396	4.76	58.2	0.53	7066	7761	750296	106.18	40005	7963	9348	845511.34	46013
2022/1/19	406520	86842	4.68	58.19	0.6	8477	10149	799631	94.33	40044	9504	12032	896512.32	43885
2022/1/20	535209	100077	5.35	85.13	0.43	13837	15692	1021893	73.85	40229	16019	19445	1183003.15	40685
2022/1/21	445281	195551	2.28	24.55	0.78	10677	12386	672876	63.02	40282	11536	13986	726998.72	41418
2022/1/22	508329	123059	4.13	53.57	0.6	9439	11704	938855	99.47	40459	10755	13937	1069799.85	46868
2022/1/23	408446	60293	6.77	85.57	0.4	7678	8447	763427	99.43	40461	8866	10549	881546.38	43054

Showing 24 of 401 rows.

Output

销售.xlsx 43.1 KB

Rendered by macOS Quick Look, first page

Automatic checks (1)

xlsx: file = "销售.xlsx", rows = 401, header = ["日期", "浏览量", "访客数", "人均浏览量", "平均停留时间", "跳失率", "成交客户数", "成交单量", "成交金额", "客单价", "成交商品件数", "下单客户数", "下单单量", "下单金额", "下单商品件数"]

Large CSV to ExcelPassed

Convert gdp.csv to an Excel file called gdp.xlsx.

deft sheet convert gdp.csv -o gdp.xlsx

Input

gdp.csv 549.6 KB

Country Name	Country Code	Year	Value
Afghanistan	AFG	2000	3521418059.923445
Afghanistan	AFG	2001	2813571753.8725324
Afghanistan	AFG	2002	3825701438.9996333
Afghanistan	AFG	2003	4520946818.545814
Afghanistan	AFG	2004	5224896718.67782
Afghanistan	AFG	2005	6203256538.709669
Afghanistan	AFG	2006	6971758282.293506
Afghanistan	AFG	2007	9747886187.393929
Afghanistan	AFG	2008	10109297047.543194
Afghanistan	AFG	2009	12416152732.056717
Afghanistan	AFG	2010	15856668555.833595
Afghanistan	AFG	2011	17805098206.314083
Afghanistan	AFG	2012	19907329777.587166
Afghanistan	AFG	2013	20146416757.598675
Afghanistan	AFG	2014	20497128555.69723
Afghanistan	AFG	2015	19134221644.732494
Afghanistan	AFG	2016	18116572395.077213
Afghanistan	AFG	2017	18753456497.815865
Afghanistan	AFG	2018	18053222687.412624
Afghanistan	AFG	2019	18799444490.11278
Afghanistan	AFG	2020	19955929052.149597
Afghanistan	AFG	2021	14266499429.874573
Afghanistan	AFG	2022	14502158192.090395

Showing 24 of 4985 rows.

Output

gdp.xlsx 371.2 KB

Rendered by macOS Quick Look, first page

Automatic checks (1)

xlsx: file = "gdp.xlsx", rows = 13980, columns = 4, header = ["Country Name", "Country Code", "Year", "Value"]

Read LibreOffice and old Excel filesPassed

Convert the first sheet of issues.ods to ods.csv and the first sheet of issues.xls to xls.csv.

deft sheet convert issues.ods --to csv -o ods.csv
deft sheet convert issues.xls --to csv -o xls.csv

Input

issues.ods 4.0 KB

[content.xml] 1 1.5 ab FALSE test 10/20/2016 0 1 a 2 b 3 c 1 1 2 ab FALSE & < > aaa ' aaa " ☺ ֍ àâéêèçöïî«»

issues.xls 38.0 KB

No preview for this format. Open the file to read it.

Output

ods.csv 40 B

1
1.5
ab
false
test
2016-10-20T00:00:00

xls.csv 31 B

1
1.5
ab
false
test
2016-10-20

Automatic checks (2)

csv: file = "ods.csv", rows = 6
csv: file = "xls.csv", rows = 6

Export one sheetPassed

Export the sheet named issue2 from issues.xlsx to issue2.csv.

deft sheet convert issues.xlsx --sheet issue2 -o issue2.csv

Input

issues.xlsx 13.3 KB

Rendered by macOS Quick Look, first page

Output

issue2.csv 12 B

1	a
2	b
3	c

Automatic checks (1)

csv: file = "issue2.csv", rows = 3, columns = 2

Parse a European CSVPassed

ventas.csv came from the Spanish statistics office. Convert it to ventas.json as an array of objects, one per row.

deft sheet convert ventas.csv -o ventas.json

Input

ventas.csv 63.3 KB

Grupo de Venta: Nivel 1;Grupo de Venta: Nivel 2;Índice y tasas;Periodo;Total
General de Grandes Superficies;;Índice;2003M12;173,566
General de Grandes Superficies;;Índice;2003M11;119,516
General de Grandes Superficies;;Índice;2003M10;123,792
General de Grandes Superficies;;Índice;2003M09;116,5
General de Grandes Superficies;;Índice;2003M08;124,11
General de Grandes Superficies;;Índice;2003M07;131,51
General de Grandes Superficies;;Índice;2003M06;113,42
General de Grandes Superficies;;Índice;2003M05;116,84
General de Grandes Superficies;;Índice;2003M04;110
General de Grandes Superficies;;Índice;2003M03;107,29
General de Grandes Superficies;;Índice;2003M02;98,82
General de Grandes Superficies;;Índice;2003M01;124,38
General de Grandes Superficies;;Índice;2002M12;156,24
General de Grandes Superficies;;Índice;2002M11;112,23
General de Grandes Superficies;;Índice;2002M10;111,34
General de Grandes Superficies;;Índice;2002M09;103,84
General de Grandes Superficies;;Índice;2002M08;114,04
General de Grandes Superficies;;Índice;2002M07;119,45
General de Grandes Superficies;;Índice;2002M06;102,78
General de Grandes Superficies;;Índice;2002M05;102,92
General de Grandes Superficies;;Índice;2002M04;98,57
General de Grandes Superficies;;Índice;2002M03;100,07
General de Grandes Superficies;;Índice;2002M02;88,23
General de Grandes Superficies;;Índice;2002M01;109,48
General de Grandes Superficies;;Índice;2001M12;146,19
General de Grandes Superficies;;Índice;2001M11;97,33
General de Grandes Superficies;;Índice;2001M10;97,58
General de Grandes Superficies;;Índice;2001M09;92,73
General de Grandes Superficies;;Índice;2001M08;99,16
General de Grandes Superficies;;Índice;2001M07;104,41
General de Grandes Superficies;;Índice;2001M06;96,08
General de Grandes Superficies;;Índice;2001M05;90,45
General de Grandes Superficies;;Índice;2001M04;86,62
General de Grandes Superficies;;Índice;2001M03;90,5
General de Grandes Superficies;;Índice;2001M02;79,4
General de Grandes Superficies;;Índice;2001M01;102,562
General de Grandes Superficies;;Índice;2000M12;136,442
General de Grandes Superficies;;Índice;2000M11;89,045
General de Grandes Superficies;;Índice;2000M10;88,37
General de Grandes Superficies;;Índice;2000M09;90,595
General de Grandes Superficies;;Índice;2000M08;92,144
General de Grandes Superficies;;Índice;2000M07;100,9
General de Grandes Superficies;;Índice;2000M06;86,2
General de Grandes Superficies;;Índice;2000M05;83,432
General de Grandes Superficies;;Índice;2000M04;82,403
General de Grandes Superficies;;Índice;2000M03;81,55
General de Grandes Superficies;;Índice;2000M02;75,63
General de Grandes Superficies;;Índice;2000M01;97,2
General de Grandes Superficies;;Índice;1999M12;128,562
General de Grandes Superficies;;Índice;1999M11;84,31
General de Grandes Superficies;;Índice;1999M10;86,88
General de Grandes Superficies;;Índice;1999M09;81,41
General de Grandes Superficies;;Índice;1999M08;81,57
General de Grandes Superficies
… 60554 more characters

Output

ventas.json 190.2 KB

[
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M12",
    "Total": 173.566
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M11",
    "Total": 119.516
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M10",
    "Total": 123.792
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M09",
    "Total": 116.5
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M08",
    "Total": 124.11
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M07",
    "Total": 131.51
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M06",
    "Total": 113.42
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M05",
    "Total": 116.84
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M04",
    "Total": 110
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M03",
    "Total": 107.29
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M02",
    "Total": 98.82
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2003M01",
    "Total": 124.38
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2002M12",
    "Total": 156.24
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2002M11",
    "Total": 112.23
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2002M10",
    "Total": 111.34
  },
  {
    "Grupo de Venta: Nivel 1": "General de Grandes Superficies",
    "Grupo de Venta: Nivel 2": null,
    "Índice y tasas": "Índice",
    "Periodo": "2002M09",
 
… 189508 more characters

Automatic checks (2)

json_rows: file = "ventas.json", rows = 972, keys = ["Grupo de Venta: Nivel 1", "Índice y tasas", "Periodo", "Total"]
python: code = "import json\nrows = json.load(open('ventas.json', encoding='utf-8'))\nfirst = rows[0]\nassert not any(k.startswith(chr(0xfeff)) for k in first), list(first)\nassert first['Periodo'] == '2003M12', first\nassert str(first['Total']).replace(',', '.') == '173.566', first\n"