Overview

Dataset statistics

Number of variables38
Number of observations1191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory374.6 KiB
Average record size in memory322.1 B

Variable types

Numeric14
Text17
Categorical7

Dataset

Description전라북도 군산시 사업체조사(산업분류 코드, 산업분류, 사업체수, 종사자수, 조직형태별, 사업체구분별, 종사자규모별)
Author전라북도 군산시
URLhttps://www.data.go.kr/data/3046083/fileData.do

Alerts

비법인 종자사수 is highly imbalanced (92.5%)Imbalance
종사자규모별 100 - 299명 사업체수 is highly imbalanced (84.4%)Imbalance
종사자규모별 300 - 499명 사업체수 is highly imbalanced (96.1%)Imbalance
종사자규모별 500 - 999명 사업체수 is highly imbalanced (97.8%)Imbalance
종사자규모별 500 - 999명 종사자수 is highly imbalanced (98.3%)Imbalance
종사자규모별 1000명 이상 사업체수 is highly imbalanced (96.8%)Imbalance
종사자규모별 1000명 이상 종사자수 is highly imbalanced (98.1%)Imbalance
회사이외 법인 사업체수 is highly skewed (γ1 = 28.07090078)Skewed
산업분류 코드 has unique valuesUnique
산업분류 has unique valuesUnique
회사법인 사업체수 has 509 (42.7%) zerosZeros
회사이외 법인 사업체수 has 958 (80.4%) zerosZeros
비법인 사업체수 has 1152 (96.7%) zerosZeros
공장_ 지사 사업체수 has 792 (66.5%) zerosZeros
본사_ 본점 사업체수 has 974 (81.8%) zerosZeros
종사자 규모별 5-9명 사업체수 has 696 (58.4%) zerosZeros
종사자 규모별 5-9명 종사자수 has 696 (58.4%) zerosZeros
종사자 규모별 10-19명 사업체수 has 885 (74.3%) zerosZeros
종사자 규모별 10-19명 종사자수 has 885 (74.3%) zerosZeros
종사자규모별 20 - 49명 사업체수 has 976 (81.9%) zerosZeros
종사자규모별 50 - 99명 사업체수 has 1089 (91.4%) zerosZeros
종사자규모별 100 - 299명 종사자수 has 1135 (95.3%) zerosZeros
종사자규모별 300 - 499명 종사자수 has 1186 (99.6%) zerosZeros

Reproduction

Analysis started2023-12-12 13:15:07.050000
Analysis finished2023-12-12 13:15:07.911962
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업분류 코드
Real number (ℝ)

UNIQUE 

Distinct1191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44416.179
Minimum1110
Maximum96999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:07.997383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1110
5-th percentile10402.5
Q124212.5
median45110
Q363111.5
95-th percentile91133.5
Maximum96999
Range95889
Interquartile range (IQR)38899

Descriptive statistics

Standard deviation25560.913
Coefficient of variation (CV)0.57548655
Kurtosis-0.81417215
Mean44416.179
Median Absolute Deviation (MAD)19987
Skewness0.43244045
Sum52899669
Variance6.5336029 × 108
MonotonicityStrictly increasing
2023-12-12T22:15:08.143239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110 1
 
0.1%
50130 1
 
0.1%
50121 1
 
0.1%
50112 1
 
0.1%
50111 1
 
0.1%
49500 1
 
0.1%
49402 1
 
0.1%
49401 1
 
0.1%
49309 1
 
0.1%
49303 1
 
0.1%
Other values (1181) 1181
99.2%
ValueCountFrequency (%)
1110 1
0.1%
1121 1
0.1%
1122 1
0.1%
1123 1
0.1%
1131 1
0.1%
1132 1
0.1%
1140 1
0.1%
1151 1
0.1%
1152 1
0.1%
1159 1
0.1%
ValueCountFrequency (%)
96999 1
0.1%
96995 1
0.1%
96994 1
0.1%
96993 1
0.1%
96992 1
0.1%
96991 1
0.1%
96922 1
0.1%
96921 1
0.1%
96913 1
0.1%
96912 1
0.1%

산업분류
Text

UNIQUE 

Distinct1191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:08.544955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length23
Mean length12.548279
Min length2

Characters and Unicode

Total characters14945
Distinct characters450
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1191 ?
Unique (%)100.0%

Sample

1st row곡물 및 기타 식량작물 재배업
2nd row채소작물 재배업
3rd row화훼작물 재배업
4th row종자 및 묘목 생산업
5th row과실작물 재배업
ValueCountFrequency (%)
483
 
10.9%
제조업 418
 
9.4%
기타 241
 
5.4%
도매업 88
 
2.0%
소매업 68
 
1.5%
64
 
1.4%
서비스업 63
 
1.4%
62
 
1.4%
운영업 56
 
1.3%
운송업 25
 
0.6%
Other values (1530) 2862
64.6%
2023-12-12T22:15:09.145837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3242
21.7%
1148
 
7.7%
597
 
4.0%
504
 
3.4%
490
 
3.3%
483
 
3.2%
249
 
1.7%
239
 
1.6%
223
 
1.5%
180
 
1.2%
Other values (440) 7590
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11562
77.4%
Space Separator 3242
 
21.7%
Connector Punctuation 130
 
0.9%
Decimal Number 5
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1148
 
9.9%
597
 
5.2%
504
 
4.4%
490
 
4.2%
483
 
4.2%
249
 
2.2%
239
 
2.1%
223
 
1.9%
180
 
1.6%
176
 
1.5%
Other values (435) 7273
62.9%
Space Separator
ValueCountFrequency (%)
3242
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 130
100.0%
Decimal Number
ValueCountFrequency (%)
1 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11562
77.4%
Common 3383
 
22.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1148
 
9.9%
597
 
5.2%
504
 
4.4%
490
 
4.2%
483
 
4.2%
249
 
2.2%
239
 
2.1%
223
 
1.9%
180
 
1.6%
176
 
1.5%
Other values (435) 7273
62.9%
Common
ValueCountFrequency (%)
3242
95.8%
_ 130
 
3.8%
1 5
 
0.1%
( 3
 
0.1%
) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11551
77.3%
ASCII 3383
 
22.6%
Compat Jamo 11
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3242
95.8%
_ 130
 
3.8%
1 5
 
0.1%
( 3
 
0.1%
) 3
 
0.1%
Hangul
ValueCountFrequency (%)
1148
 
9.9%
597
 
5.2%
504
 
4.4%
490
 
4.2%
483
 
4.2%
249
 
2.2%
239
 
2.1%
223
 
1.9%
180
 
1.6%
176
 
1.5%
Other values (434) 7262
62.9%
Compat Jamo
ValueCountFrequency (%)
11
100.0%
Distinct139
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:09.510894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4122586
Min length1

Characters and Unicode

Total characters1682
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)5.8%

Sample

1st row60
2nd row8
3rd row0
4th row2
5th row3
ValueCountFrequency (%)
0 223
18.7%
1 152
 
12.8%
2 114
 
9.6%
3 72
 
6.0%
5 52
 
4.4%
4 50
 
4.2%
6 37
 
3.1%
7 30
 
2.5%
8 26
 
2.2%
10 23
 
1.9%
Other values (129) 412
34.6%
2023-12-12T22:15:09.990001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 380
22.6%
0 292
17.4%
2 255
15.2%
3 184
10.9%
4 126
 
7.5%
5 119
 
7.1%
6 101
 
6.0%
7 86
 
5.1%
8 73
 
4.3%
9 62
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1678
99.8%
Other Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 380
22.6%
0 292
17.4%
2 255
15.2%
3 184
11.0%
4 126
 
7.5%
5 119
 
7.1%
6 101
 
6.0%
7 86
 
5.1%
8 73
 
4.4%
9 62
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 380
22.6%
0 292
17.4%
2 255
15.2%
3 184
10.9%
4 126
 
7.5%
5 119
 
7.1%
6 101
 
6.0%
7 86
 
5.1%
8 73
 
4.3%
9 62
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 380
22.6%
0 292
17.4%
2 255
15.2%
3 184
10.9%
4 126
 
7.5%
5 119
 
7.1%
6 101
 
6.0%
7 86
 
5.1%
8 73
 
4.3%
9 62
 
3.7%
Distinct289
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:10.360680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.8463476
Min length1

Characters and Unicode

Total characters2199
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)12.7%

Sample

1st row91
2nd row11
3rd row0
4th row20
5th row3
ValueCountFrequency (%)
0 223
 
18.7%
1 58
 
4.9%
2 39
 
3.3%
3 30
 
2.5%
4 29
 
2.4%
6 25
 
2.1%
5 23
 
1.9%
8 18
 
1.5%
10 18
 
1.5%
12 17
 
1.4%
Other values (279) 711
59.7%
2023-12-12T22:15:10.961931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 436
19.8%
0 343
15.6%
2 262
11.9%
3 243
11.1%
4 171
 
7.8%
5 161
 
7.3%
6 159
 
7.2%
8 150
 
6.8%
7 141
 
6.4%
9 117
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2183
99.3%
Other Punctuation 16
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 436
20.0%
0 343
15.7%
2 262
12.0%
3 243
11.1%
4 171
 
7.8%
5 161
 
7.4%
6 159
 
7.3%
8 150
 
6.9%
7 141
 
6.5%
9 117
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 436
19.8%
0 343
15.6%
2 262
11.9%
3 243
11.1%
4 171
 
7.8%
5 161
 
7.3%
6 159
 
7.2%
8 150
 
6.8%
7 141
 
6.4%
9 117
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 436
19.8%
0 343
15.6%
2 262
11.9%
3 243
11.1%
4 171
 
7.8%
5 161
 
7.3%
6 159
 
7.2%
8 150
 
6.8%
7 141
 
6.4%
9 117
 
5.3%
Distinct220
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:11.366281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.6826196
Min length1

Characters and Unicode

Total characters2004
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)9.0%

Sample

1st row71
2nd row7
3rd row0
4th row9
5th row3
ValueCountFrequency (%)
0 245
 
20.6%
1 71
 
6.0%
3 41
 
3.4%
2 41
 
3.4%
4 36
 
3.0%
6 30
 
2.5%
5 29
 
2.4%
9 25
 
2.1%
7 22
 
1.8%
8 21
 
1.8%
Other values (210) 630
52.9%
2023-12-12T22:15:12.014056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 394
19.7%
0 339
16.9%
2 264
13.2%
3 202
10.1%
5 156
 
7.8%
4 155
 
7.7%
8 127
 
6.3%
6 123
 
6.1%
9 121
 
6.0%
7 117
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1998
99.7%
Other Punctuation 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 394
19.7%
0 339
17.0%
2 264
13.2%
3 202
10.1%
5 156
 
7.8%
4 155
 
7.8%
8 127
 
6.4%
6 123
 
6.2%
9 121
 
6.1%
7 117
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 394
19.7%
0 339
16.9%
2 264
13.2%
3 202
10.1%
5 156
 
7.8%
4 155
 
7.7%
8 127
 
6.3%
6 123
 
6.1%
9 121
 
6.0%
7 117
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 394
19.7%
0 339
16.9%
2 264
13.2%
3 202
10.1%
5 156
 
7.8%
4 155
 
7.7%
8 127
 
6.3%
6 123
 
6.1%
9 121
 
6.0%
7 117
 
5.8%
Distinct159
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:12.367540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4844668
Min length1

Characters and Unicode

Total characters1768
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)6.8%

Sample

1st row20
2nd row4
3rd row0
4th row11
5th row0
ValueCountFrequency (%)
0 316
26.5%
1 109
 
9.2%
2 66
 
5.5%
3 52
 
4.4%
4 40
 
3.4%
7 30
 
2.5%
5 28
 
2.4%
8 26
 
2.2%
9 26
 
2.2%
10 25
 
2.1%
Other values (149) 473
39.7%
2023-12-12T22:15:12.878763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 384
21.7%
1 353
20.0%
2 239
13.5%
3 189
10.7%
4 142
 
8.0%
7 98
 
5.5%
5 94
 
5.3%
6 94
 
5.3%
8 87
 
4.9%
9 84
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1764
99.8%
Other Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 384
21.8%
1 353
20.0%
2 239
13.5%
3 189
10.7%
4 142
 
8.0%
7 98
 
5.6%
5 94
 
5.3%
6 94
 
5.3%
8 87
 
4.9%
9 84
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 384
21.7%
1 353
20.0%
2 239
13.5%
3 189
10.7%
4 142
 
8.0%
7 98
 
5.5%
5 94
 
5.3%
6 94
 
5.3%
8 87
 
4.9%
9 84
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 384
21.7%
1 353
20.0%
2 239
13.5%
3 189
10.7%
4 142
 
8.0%
7 98
 
5.5%
5 94
 
5.3%
6 94
 
5.3%
8 87
 
4.9%
9 84
 
4.8%
Distinct122
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:13.173527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.2989085
Min length1

Characters and Unicode

Total characters1547
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)5.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 449
37.7%
1 154
 
12.9%
2 86
 
7.2%
3 54
 
4.5%
4 39
 
3.3%
5 38
 
3.2%
6 23
 
1.9%
8 20
 
1.7%
7 16
 
1.3%
9 15
 
1.3%
Other values (112) 297
24.9%
2023-12-12T22:15:13.592869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 489
31.6%
1 320
20.7%
2 202
13.1%
3 122
 
7.9%
4 94
 
6.1%
5 92
 
5.9%
6 65
 
4.2%
7 57
 
3.7%
8 52
 
3.4%
9 50
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1543
99.7%
Other Punctuation 4
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 489
31.7%
1 320
20.7%
2 202
13.1%
3 122
 
7.9%
4 94
 
6.1%
5 92
 
6.0%
6 65
 
4.2%
7 57
 
3.7%
8 52
 
3.4%
9 50
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 489
31.6%
1 320
20.7%
2 202
13.1%
3 122
 
7.9%
4 94
 
6.1%
5 92
 
5.9%
6 65
 
4.2%
7 57
 
3.7%
8 52
 
3.4%
9 50
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 489
31.6%
1 320
20.7%
2 202
13.1%
3 122
 
7.9%
4 94
 
6.1%
5 92
 
5.9%
6 65
 
4.2%
7 57
 
3.7%
8 52
 
3.4%
9 50
 
3.2%
Distinct168
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:13.996321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4265323
Min length1

Characters and Unicode

Total characters1699
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)8.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 449
37.7%
1 80
 
6.7%
2 56
 
4.7%
3 38
 
3.2%
4 35
 
2.9%
5 30
 
2.5%
6 27
 
2.3%
7 26
 
2.2%
11 22
 
1.8%
8 21
 
1.8%
Other values (158) 407
34.2%
2023-12-12T22:15:14.518651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 502
29.5%
1 312
18.4%
2 183
 
10.8%
3 131
 
7.7%
4 122
 
7.2%
7 104
 
6.1%
5 101
 
5.9%
6 91
 
5.4%
8 78
 
4.6%
9 70
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1694
99.7%
Other Punctuation 5
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 502
29.6%
1 312
18.4%
2 183
 
10.8%
3 131
 
7.7%
4 122
 
7.2%
7 104
 
6.1%
5 101
 
6.0%
6 91
 
5.4%
8 78
 
4.6%
9 70
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 502
29.5%
1 312
18.4%
2 183
 
10.8%
3 131
 
7.7%
4 122
 
7.2%
7 104
 
6.1%
5 101
 
5.9%
6 91
 
5.4%
8 78
 
4.6%
9 70
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 502
29.5%
1 312
18.4%
2 183
 
10.8%
3 131
 
7.7%
4 122
 
7.2%
7 104
 
6.1%
5 101
 
5.9%
6 91
 
5.4%
8 78
 
4.6%
9 70
 
4.1%

회사법인 사업체수
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7279597
Minimum0
Maximum151
Zeros509
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:14.700360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile17
Maximum151
Range151
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.3308857
Coefficient of variation (CV)2.502947
Kurtosis81.659518
Mean3.7279597
Median Absolute Deviation (MAD)1
Skewness7.3022857
Sum4440
Variance87.065428
MonotonicityNot monotonic
2023-12-12T22:15:15.186444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 509
42.7%
1 198
 
16.6%
2 124
 
10.4%
3 74
 
6.2%
4 49
 
4.1%
5 38
 
3.2%
6 30
 
2.5%
8 19
 
1.6%
7 16
 
1.3%
10 14
 
1.2%
Other values (36) 120
 
10.1%
ValueCountFrequency (%)
0 509
42.7%
1 198
 
16.6%
2 124
 
10.4%
3 74
 
6.2%
4 49
 
4.1%
5 38
 
3.2%
6 30
 
2.5%
7 16
 
1.3%
8 19
 
1.6%
9 11
 
0.9%
ValueCountFrequency (%)
151 1
0.1%
116 1
0.1%
80 1
0.1%
75 1
0.1%
68 1
0.1%
59 1
0.1%
57 1
0.1%
54 1
0.1%
50 2
0.2%
45 1
0.1%
Distinct191
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:15.592450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4693535
Min length1

Characters and Unicode

Total characters1750
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)8.7%

Sample

1st row43
2nd row8
3rd row0
4th row17
5th row0
ValueCountFrequency (%)
0 509
42.7%
1 52
 
4.4%
2 39
 
3.3%
3 28
 
2.4%
4 28
 
2.4%
9 24
 
2.0%
5 21
 
1.8%
7 21
 
1.8%
8 21
 
1.8%
6 19
 
1.6%
Other values (181) 429
36.0%
2023-12-12T22:15:16.168034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 581
33.2%
1 292
16.7%
2 183
 
10.5%
3 139
 
7.9%
4 122
 
7.0%
7 104
 
5.9%
5 95
 
5.4%
6 82
 
4.7%
9 81
 
4.6%
8 68
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1747
99.8%
Other Punctuation 3
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 581
33.3%
1 292
16.7%
2 183
 
10.5%
3 139
 
8.0%
4 122
 
7.0%
7 104
 
6.0%
5 95
 
5.4%
6 82
 
4.7%
9 81
 
4.6%
8 68
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 581
33.2%
1 292
16.7%
2 183
 
10.5%
3 139
 
7.9%
4 122
 
7.0%
7 104
 
5.9%
5 95
 
5.4%
6 82
 
4.7%
9 81
 
4.6%
8 68
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 581
33.2%
1 292
16.7%
2 183
 
10.5%
3 139
 
7.9%
4 122
 
7.0%
7 104
 
5.9%
5 95
 
5.4%
6 82
 
4.7%
9 81
 
4.6%
8 68
 
3.9%

회사이외 법인 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct34
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4794291
Minimum0
Maximum472
Zeros958
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:16.378348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum472
Range472
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.708234
Coefficient of variation (CV)9.9418311
Kurtosis884.01655
Mean1.4794291
Median Absolute Deviation (MAD)0
Skewness28.070901
Sum1762
Variance216.33214
MonotonicityNot monotonic
2023-12-12T22:15:16.512098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 958
80.4%
1 100
 
8.4%
2 43
 
3.6%
3 20
 
1.7%
4 17
 
1.4%
5 10
 
0.8%
6 5
 
0.4%
12 5
 
0.4%
7 3
 
0.3%
10 2
 
0.2%
Other values (24) 28
 
2.4%
ValueCountFrequency (%)
0 958
80.4%
1 100
 
8.4%
2 43
 
3.6%
3 20
 
1.7%
4 17
 
1.4%
5 10
 
0.8%
6 5
 
0.4%
7 3
 
0.3%
8 2
 
0.2%
9 2
 
0.2%
ValueCountFrequency (%)
472 1
0.1%
96 1
0.1%
68 1
0.1%
67 1
0.1%
56 1
0.1%
42 1
0.1%
38 1
0.1%
37 1
0.1%
34 1
0.1%
32 1
0.1%
Distinct87
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:16.705168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.1469353
Min length1

Characters and Unicode

Total characters1366
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)4.4%

Sample

1st row47
2nd row3
3rd row0
4th row3
5th row3
ValueCountFrequency (%)
0 958
80.4%
1 26
 
2.2%
3 15
 
1.3%
7 13
 
1.1%
6 12
 
1.0%
5 11
 
0.9%
2 10
 
0.8%
8 9
 
0.8%
17 8
 
0.7%
4 8
 
0.7%
Other values (77) 121
 
10.2%
2023-12-12T22:15:17.030563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 981
71.8%
1 101
 
7.4%
3 53
 
3.9%
2 50
 
3.7%
6 38
 
2.8%
7 36
 
2.6%
5 31
 
2.3%
8 27
 
2.0%
4 27
 
2.0%
9 18
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1362
99.7%
Other Punctuation 4
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 981
72.0%
1 101
 
7.4%
3 53
 
3.9%
2 50
 
3.7%
6 38
 
2.8%
7 36
 
2.6%
5 31
 
2.3%
8 27
 
2.0%
4 27
 
2.0%
9 18
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1366
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 981
71.8%
1 101
 
7.4%
3 53
 
3.9%
2 50
 
3.7%
6 38
 
2.8%
7 36
 
2.6%
5 31
 
2.3%
8 27
 
2.0%
4 27
 
2.0%
9 18
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 981
71.8%
1 101
 
7.4%
3 53
 
3.9%
2 50
 
3.7%
6 38
 
2.8%
7 36
 
2.6%
5 31
 
2.3%
8 27
 
2.0%
4 27
 
2.0%
9 18
 
1.3%

비법인 사업체수
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53988245
Minimum0
Maximum150
Zeros1152
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:17.141832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.9668999
Coefficient of variation (CV)12.904475
Kurtosis326.23277
Mean0.53988245
Median Absolute Deviation (MAD)0
Skewness17.410323
Sum643
Variance48.537694
MonotonicityNot monotonic
2023-12-12T22:15:17.255882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 1152
96.7%
1 13
 
1.1%
2 8
 
0.7%
4 4
 
0.3%
3 2
 
0.2%
103 1
 
0.1%
6 1
 
0.1%
44 1
 
0.1%
131 1
 
0.1%
7 1
 
0.1%
Other values (7) 7
 
0.6%
ValueCountFrequency (%)
0 1152
96.7%
1 13
 
1.1%
2 8
 
0.7%
3 2
 
0.2%
4 4
 
0.3%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
16 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
150 1
0.1%
131 1
0.1%
103 1
0.1%
63 1
0.1%
44 1
0.1%
26 1
0.1%
20 1
0.1%
18 1
0.1%
16 1
0.1%
8 1
0.1%

비법인 종자사수
Categorical

IMBALANCE 

Distinct24
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1152 
1
 
8
2
 
4
6
 
3
5
 
2
Other values (19)
 
22

Length

Max length5
Median length1
Mean length1.0209908
Min length1

Unique

Unique16 ?
Unique (%)1.3%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1152
96.7%
1 8
 
0.7%
2 4
 
0.3%
6 3
 
0.3%
5 2
 
0.2%
3 2
 
0.2%
8 2
 
0.2%
4 2
 
0.2%
32 1
 
0.1%
330 1
 
0.1%
Other values (14) 14
 
1.2%

Length

2023-12-12T22:15:17.414236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 1152
96.7%
1 8
 
0.7%
2 4
 
0.3%
6 3
 
0.3%
5 2
 
0.2%
3 2
 
0.2%
8 2
 
0.2%
4 2
 
0.2%
1,406 1
 
0.1%
179 1
 
0.1%
Other values (14) 14
 
1.2%
Distinct139
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:17.669496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.3820319
Min length1

Characters and Unicode

Total characters1646
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)5.9%

Sample

1st row59
2nd row6
3rd row0
4th row2
5th row3
ValueCountFrequency (%)
0 277
23.3%
1 163
13.7%
2 99
 
8.3%
3 61
 
5.1%
5 50
 
4.2%
4 46
 
3.9%
6 42
 
3.5%
10 26
 
2.2%
7 25
 
2.1%
8 22
 
1.8%
Other values (129) 380
31.9%
2023-12-12T22:15:18.095255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 374
22.7%
0 343
20.8%
2 229
13.9%
3 164
10.0%
4 122
 
7.4%
5 108
 
6.6%
6 99
 
6.0%
7 72
 
4.4%
8 69
 
4.2%
9 62
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1642
99.8%
Other Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 374
22.8%
0 343
20.9%
2 229
13.9%
3 164
10.0%
4 122
 
7.4%
5 108
 
6.6%
6 99
 
6.0%
7 72
 
4.4%
8 69
 
4.2%
9 62
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1646
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 374
22.7%
0 343
20.8%
2 229
13.9%
3 164
10.0%
4 122
 
7.4%
5 108
 
6.6%
6 99
 
6.0%
7 72
 
4.4%
8 69
 
4.2%
9 62
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 374
22.7%
0 343
20.8%
2 229
13.9%
3 164
10.0%
4 122
 
7.4%
5 108
 
6.6%
6 99
 
6.0%
7 72
 
4.4%
8 69
 
4.2%
9 62
 
3.8%
Distinct248
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:18.538922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.7078086
Min length1

Characters and Unicode

Total characters2034
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)11.0%

Sample

1st row86
2nd row7
3rd row0
4th row20
5th row3
ValueCountFrequency (%)
0 277
23.3%
1 57
 
4.8%
2 48
 
4.0%
4 35
 
2.9%
3 34
 
2.9%
6 28
 
2.4%
8 24
 
2.0%
5 22
 
1.8%
7 20
 
1.7%
16 16
 
1.3%
Other values (238) 630
52.9%
2023-12-12T22:15:19.130575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 382
18.8%
1 355
17.5%
2 240
11.8%
3 223
11.0%
4 167
8.2%
6 158
7.8%
5 138
 
6.8%
7 128
 
6.3%
8 125
 
6.1%
9 107
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2023
99.5%
Other Punctuation 11
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 382
18.9%
1 355
17.5%
2 240
11.9%
3 223
11.0%
4 167
8.3%
6 158
7.8%
5 138
 
6.8%
7 128
 
6.3%
8 125
 
6.2%
9 107
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2034
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 382
18.8%
1 355
17.5%
2 240
11.8%
3 223
11.0%
4 167
8.2%
6 158
7.8%
5 138
 
6.8%
7 128
 
6.3%
8 125
 
6.1%
9 107
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 382
18.8%
1 355
17.5%
2 240
11.8%
3 223
11.0%
4 167
8.2%
6 158
7.8%
5 138
 
6.8%
7 128
 
6.3%
8 125
 
6.1%
9 107
 
5.3%

공장_ 지사 사업체수
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1712846
Minimum0
Maximum66
Zeros792
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:19.307890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum66
Range66
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.8728811
Coefficient of variation (CV)3.3065243
Kurtosis102.72469
Mean1.1712846
Median Absolute Deviation (MAD)0
Skewness8.6145471
Sum1395
Variance14.999208
MonotonicityNot monotonic
2023-12-12T22:15:19.468239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 792
66.5%
1 192
 
16.1%
2 75
 
6.3%
3 40
 
3.4%
4 21
 
1.8%
5 15
 
1.3%
6 11
 
0.9%
9 7
 
0.6%
7 7
 
0.6%
16 4
 
0.3%
Other values (16) 27
 
2.3%
ValueCountFrequency (%)
0 792
66.5%
1 192
 
16.1%
2 75
 
6.3%
3 40
 
3.4%
4 21
 
1.8%
5 15
 
1.3%
6 11
 
0.9%
7 7
 
0.6%
8 3
 
0.3%
9 7
 
0.6%
ValueCountFrequency (%)
66 1
0.1%
43 1
0.1%
40 1
0.1%
39 1
0.1%
34 1
0.1%
28 2
0.2%
23 1
0.1%
20 1
0.1%
18 1
0.1%
17 1
0.1%
Distinct117
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:19.741069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.2325777
Min length1

Characters and Unicode

Total characters1468
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)5.5%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 792
66.5%
1 55
 
4.6%
2 27
 
2.3%
4 20
 
1.7%
5 18
 
1.5%
10 18
 
1.5%
3 12
 
1.0%
7 12
 
1.0%
8 11
 
0.9%
6 11
 
0.9%
Other values (107) 215
 
18.1%
2023-12-12T22:15:20.225211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 840
57.2%
1 167
 
11.4%
2 105
 
7.2%
3 71
 
4.8%
5 66
 
4.5%
4 58
 
4.0%
6 52
 
3.5%
9 40
 
2.7%
7 37
 
2.5%
8 31
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1467
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 840
57.3%
1 167
 
11.4%
2 105
 
7.2%
3 71
 
4.8%
5 66
 
4.5%
4 58
 
4.0%
6 52
 
3.5%
9 40
 
2.7%
7 37
 
2.5%
8 31
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 840
57.2%
1 167
 
11.4%
2 105
 
7.2%
3 71
 
4.8%
5 66
 
4.5%
4 58
 
4.0%
6 52
 
3.5%
9 40
 
2.7%
7 37
 
2.5%
8 31
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 840
57.2%
1 167
 
11.4%
2 105
 
7.2%
3 71
 
4.8%
5 66
 
4.5%
4 58
 
4.0%
6 52
 
3.5%
9 40
 
2.7%
7 37
 
2.5%
8 31
 
2.1%

본사_ 본점 사업체수
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29303107
Minimum0
Maximum19
Zeros974
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:20.385209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9297746
Coefficient of variation (CV)3.1729557
Kurtosis149.55086
Mean0.29303107
Median Absolute Deviation (MAD)0
Skewness9.2646769
Sum349
Variance0.8644808
MonotonicityNot monotonic
2023-12-12T22:15:20.530869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 974
81.8%
1 153
 
12.8%
2 38
 
3.2%
3 14
 
1.2%
4 4
 
0.3%
5 3
 
0.3%
6 2
 
0.2%
8 2
 
0.2%
19 1
 
0.1%
ValueCountFrequency (%)
0 974
81.8%
1 153
 
12.8%
2 38
 
3.2%
3 14
 
1.2%
4 4
 
0.3%
5 3
 
0.3%
6 2
 
0.2%
8 2
 
0.2%
19 1
 
0.1%
ValueCountFrequency (%)
19 1
 
0.1%
8 2
 
0.2%
6 2
 
0.2%
5 3
 
0.3%
4 4
 
0.3%
3 14
 
1.2%
2 38
 
3.2%
1 153
 
12.8%
0 974
81.8%
Distinct73
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:20.733549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.1099916
Min length1

Characters and Unicode

Total characters1322
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)3.4%

Sample

1st row5
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 974
81.8%
1 21
 
1.8%
2 20
 
1.7%
3 14
 
1.2%
5 10
 
0.8%
9 10
 
0.8%
7 9
 
0.8%
4 9
 
0.8%
10 8
 
0.7%
6 7
 
0.6%
Other values (63) 109
 
9.2%
2023-12-12T22:15:21.133989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 997
75.4%
1 79
 
6.0%
2 56
 
4.2%
3 35
 
2.6%
4 33
 
2.5%
5 27
 
2.0%
7 25
 
1.9%
6 25
 
1.9%
8 23
 
1.7%
9 21
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1321
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 997
75.5%
1 79
 
6.0%
2 56
 
4.2%
3 35
 
2.6%
4 33
 
2.5%
5 27
 
2.0%
7 25
 
1.9%
6 25
 
1.9%
8 23
 
1.7%
9 21
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 997
75.4%
1 79
 
6.0%
2 56
 
4.2%
3 35
 
2.6%
4 33
 
2.5%
5 27
 
2.0%
7 25
 
1.9%
6 25
 
1.9%
8 23
 
1.7%
9 21
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 997
75.4%
1 79
 
6.0%
2 56
 
4.2%
3 35
 
2.6%
4 33
 
2.5%
5 27
 
2.0%
7 25
 
1.9%
6 25
 
1.9%
8 23
 
1.7%
9 21
 
1.6%
Distinct134
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:21.437469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.3526448
Min length1

Characters and Unicode

Total characters1611
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)5.7%

Sample

1st row57
2nd row8
3rd row0
4th row1
5th row3
ValueCountFrequency (%)
0 334
28.0%
1 163
13.7%
2 93
 
7.8%
3 72
 
6.0%
4 52
 
4.4%
5 44
 
3.7%
9 21
 
1.8%
6 20
 
1.7%
7 20
 
1.7%
10 18
 
1.5%
Other values (124) 354
29.7%
2023-12-12T22:15:21.916486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 393
24.4%
1 347
21.5%
2 228
14.2%
3 155
 
9.6%
4 128
 
7.9%
5 111
 
6.9%
6 74
 
4.6%
9 61
 
3.8%
7 58
 
3.6%
8 52
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1607
99.8%
Other Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 393
24.5%
1 347
21.6%
2 228
14.2%
3 155
 
9.6%
4 128
 
8.0%
5 111
 
6.9%
6 74
 
4.6%
9 61
 
3.8%
7 58
 
3.6%
8 52
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1611
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 393
24.4%
1 347
21.5%
2 228
14.2%
3 155
 
9.6%
4 128
 
7.9%
5 111
 
6.9%
6 74
 
4.6%
9 61
 
3.8%
7 58
 
3.6%
8 52
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 393
24.4%
1 347
21.5%
2 228
14.2%
3 155
 
9.6%
4 128
 
7.9%
5 111
 
6.9%
6 74
 
4.6%
9 61
 
3.8%
7 58
 
3.6%
8 52
 
3.2%
Distinct161
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:22.289912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4424853
Min length1

Characters and Unicode

Total characters1718
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)7.0%

Sample

1st row76
2nd row11
3rd row0
4th row3
5th row3
ValueCountFrequency (%)
0 334
28.0%
1 93
 
7.8%
2 61
 
5.1%
3 59
 
5.0%
4 54
 
4.5%
6 44
 
3.7%
5 43
 
3.6%
8 26
 
2.2%
10 25
 
2.1%
7 21
 
1.8%
Other values (151) 431
36.2%
2023-12-12T22:15:22.818508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 407
23.7%
1 334
19.4%
2 186
10.8%
3 173
10.1%
4 136
 
7.9%
5 119
 
6.9%
6 114
 
6.6%
7 85
 
4.9%
9 85
 
4.9%
8 75
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1714
99.8%
Other Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 407
23.7%
1 334
19.5%
2 186
10.9%
3 173
10.1%
4 136
 
7.9%
5 119
 
6.9%
6 114
 
6.7%
7 85
 
5.0%
9 85
 
5.0%
8 75
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 407
23.7%
1 334
19.4%
2 186
10.8%
3 173
10.1%
4 136
 
7.9%
5 119
 
6.9%
6 114
 
6.6%
7 85
 
4.9%
9 85
 
4.9%
8 75
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 407
23.7%
1 334
19.4%
2 186
10.8%
3 173
10.1%
4 136
 
7.9%
5 119
 
6.9%
6 114
 
6.6%
7 85
 
4.9%
9 85
 
4.9%
8 75
 
4.4%
Distinct33
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8774139
Minimum0
Maximum92
Zeros696
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:22.981501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8
Maximum92
Range92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.8127723
Coefficient of variation (CV)3.0961591
Kurtosis88.538391
Mean1.8774139
Median Absolute Deviation (MAD)0
Skewness8.0541921
Sum2236
Variance33.788321
MonotonicityNot monotonic
2023-12-12T22:15:23.133676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 696
58.4%
1 199
 
16.7%
2 94
 
7.9%
3 57
 
4.8%
4 26
 
2.2%
5 24
 
2.0%
6 16
 
1.3%
7 14
 
1.2%
8 10
 
0.8%
11 8
 
0.7%
Other values (23) 47
 
3.9%
ValueCountFrequency (%)
0 696
58.4%
1 199
 
16.7%
2 94
 
7.9%
3 57
 
4.8%
4 26
 
2.2%
5 24
 
2.0%
6 16
 
1.3%
7 14
 
1.2%
8 10
 
0.8%
9 4
 
0.3%
ValueCountFrequency (%)
92 1
0.1%
68 1
0.1%
64 1
0.1%
59 1
0.1%
46 1
0.1%
37 2
0.2%
33 2
0.2%
32 1
0.1%
30 2
0.2%
28 1
0.1%
Distinct94
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.082284
Minimum0
Maximum649
Zeros696
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:23.269524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile52
Maximum649
Range649
Interquartile range (IQR)9

Descriptive statistics

Standard deviation38.118765
Coefficient of variation (CV)3.1549305
Kurtosis107.11767
Mean12.082284
Median Absolute Deviation (MAD)0
Skewness8.7701776
Sum14390
Variance1453.0403
MonotonicityNot monotonic
2023-12-12T22:15:23.436665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 696
58.4%
5 57
 
4.8%
6 46
 
3.9%
7 35
 
2.9%
9 31
 
2.6%
8 30
 
2.5%
14 17
 
1.4%
13 17
 
1.4%
11 16
 
1.3%
12 15
 
1.3%
Other values (84) 231
 
19.4%
ValueCountFrequency (%)
0 696
58.4%
5 57
 
4.8%
6 46
 
3.9%
7 35
 
2.9%
8 30
 
2.5%
9 31
 
2.6%
10 10
 
0.8%
11 16
 
1.3%
12 15
 
1.3%
13 17
 
1.4%
ValueCountFrequency (%)
649 1
0.1%
487 1
0.1%
390 1
0.1%
389 1
0.1%
268 1
0.1%
233 1
0.1%
222 1
0.1%
220 2
0.2%
207 1
0.1%
196 1
0.1%
Distinct20
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74811083
Minimum0
Maximum64
Zeros885
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:23.568476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.9608699
Coefficient of variation (CV)3.9577958
Kurtosis221.63635
Mean0.74811083
Median Absolute Deviation (MAD)0
Skewness12.569442
Sum891
Variance8.7667506
MonotonicityNot monotonic
2023-12-12T22:15:23.685980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 885
74.3%
1 158
 
13.3%
2 71
 
6.0%
3 19
 
1.6%
4 17
 
1.4%
6 12
 
1.0%
5 7
 
0.6%
11 4
 
0.3%
8 3
 
0.3%
12 3
 
0.3%
Other values (10) 12
 
1.0%
ValueCountFrequency (%)
0 885
74.3%
1 158
 
13.3%
2 71
 
6.0%
3 19
 
1.6%
4 17
 
1.4%
5 7
 
0.6%
6 12
 
1.0%
7 1
 
0.1%
8 3
 
0.3%
9 1
 
0.1%
ValueCountFrequency (%)
64 1
 
0.1%
44 1
 
0.1%
26 1
 
0.1%
22 1
 
0.1%
17 1
 
0.1%
15 2
0.2%
13 2
0.2%
12 3
0.3%
11 4
0.3%
10 1
 
0.1%
Distinct85
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.041982
Minimum0
Maximum850
Zeros885
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:23.857741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile43.5
Maximum850
Range850
Interquartile range (IQR)10

Descriptive statistics

Standard deviation39.421083
Coefficient of variation (CV)3.9256279
Kurtosis216.7208
Mean10.041982
Median Absolute Deviation (MAD)0
Skewness12.392855
Sum11960
Variance1554.0218
MonotonicityNot monotonic
2023-12-12T22:15:23.997234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 885
74.3%
10 30
 
2.5%
11 18
 
1.5%
13 18
 
1.5%
12 18
 
1.5%
15 18
 
1.5%
14 16
 
1.3%
17 14
 
1.2%
16 13
 
1.1%
27 9
 
0.8%
Other values (75) 152
 
12.8%
ValueCountFrequency (%)
0 885
74.3%
10 30
 
2.5%
11 18
 
1.5%
12 18
 
1.5%
13 18
 
1.5%
14 16
 
1.3%
15 18
 
1.5%
16 13
 
1.1%
17 14
 
1.2%
18 7
 
0.6%
ValueCountFrequency (%)
850 1
0.1%
569 1
0.1%
363 1
0.1%
296 1
0.1%
241 1
0.1%
204 1
0.1%
196 1
0.1%
183 1
0.1%
163 1
0.1%
160 1
0.1%
Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.434089
Minimum0
Maximum35
Zeros976
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:24.116809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7214846
Coefficient of variation (CV)3.9657411
Kurtosis176.97705
Mean0.434089
Median Absolute Deviation (MAD)0
Skewness11.066455
Sum517
Variance2.9635092
MonotonicityNot monotonic
2023-12-12T22:15:24.257113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 976
81.9%
1 118
 
9.9%
2 45
 
3.8%
3 18
 
1.5%
4 13
 
1.1%
5 8
 
0.7%
6 2
 
0.2%
9 2
 
0.2%
7 2
 
0.2%
12 1
 
0.1%
Other values (6) 6
 
0.5%
ValueCountFrequency (%)
0 976
81.9%
1 118
 
9.9%
2 45
 
3.8%
3 18
 
1.5%
4 13
 
1.1%
5 8
 
0.7%
6 2
 
0.2%
7 2
 
0.2%
8 1
 
0.1%
9 2
 
0.2%
ValueCountFrequency (%)
35 1
0.1%
24 1
0.1%
15 1
0.1%
14 1
0.1%
12 1
0.1%
11 1
0.1%
9 2
0.2%
8 1
0.1%
7 2
0.2%
6 2
0.2%
Distinct95
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:24.491998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.2124265
Min length1

Characters and Unicode

Total characters1444
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 976
81.9%
20 11
 
0.9%
45 7
 
0.6%
21 7
 
0.6%
23 7
 
0.6%
24 6
 
0.5%
28 6
 
0.5%
26 6
 
0.5%
22 6
 
0.5%
35 6
 
0.5%
Other values (85) 153
 
12.8%
2023-12-12T22:15:24.953153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1008
69.8%
2 90
 
6.2%
3 63
 
4.4%
4 58
 
4.0%
1 54
 
3.7%
5 46
 
3.2%
6 41
 
2.8%
7 37
 
2.6%
8 30
 
2.1%
9 16
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1443
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1008
69.9%
2 90
 
6.2%
3 63
 
4.4%
4 58
 
4.0%
1 54
 
3.7%
5 46
 
3.2%
6 41
 
2.8%
7 37
 
2.6%
8 30
 
2.1%
9 16
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1444
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1008
69.8%
2 90
 
6.2%
3 63
 
4.4%
4 58
 
4.0%
1 54
 
3.7%
5 46
 
3.2%
6 41
 
2.8%
7 37
 
2.6%
8 30
 
2.1%
9 16
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1008
69.8%
2 90
 
6.2%
3 63
 
4.4%
4 58
 
4.0%
1 54
 
3.7%
5 46
 
3.2%
6 41
 
2.8%
7 37
 
2.6%
8 30
 
2.1%
9 16
 
1.1%
Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15365239
Minimum0
Maximum15
Zeros1089
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:25.099933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81110031
Coefficient of variation (CV)5.2788004
Kurtosis199.64366
Mean0.15365239
Median Absolute Deviation (MAD)0
Skewness12.262891
Sum183
Variance0.65788371
MonotonicityNot monotonic
2023-12-12T22:15:25.244375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1089
91.4%
1 72
 
6.0%
2 15
 
1.3%
3 8
 
0.7%
4 2
 
0.2%
15 2
 
0.2%
6 1
 
0.1%
8 1
 
0.1%
5 1
 
0.1%
ValueCountFrequency (%)
0 1089
91.4%
1 72
 
6.0%
2 15
 
1.3%
3 8
 
0.7%
4 2
 
0.2%
5 1
 
0.1%
6 1
 
0.1%
8 1
 
0.1%
15 2
 
0.2%
ValueCountFrequency (%)
15 2
 
0.2%
8 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
4 2
 
0.2%
3 8
 
0.7%
2 15
 
1.3%
1 72
 
6.0%
0 1089
91.4%
Distinct64
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2023-12-12T22:15:25.472099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.1125105
Min length1

Characters and Unicode

Total characters1325
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)3.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1089
91.4%
60 6
 
0.5%
85 4
 
0.3%
56 3
 
0.3%
53 3
 
0.3%
68 3
 
0.3%
80 3
 
0.3%
64 3
 
0.3%
54 3
 
0.3%
55 3
 
0.3%
Other values (54) 71
 
6.0%
2023-12-12T22:15:25.827220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1109
83.7%
5 37
 
2.8%
6 30
 
2.3%
1 29
 
2.2%
7 27
 
2.0%
8 25
 
1.9%
2 22
 
1.7%
4 16
 
1.2%
9 16
 
1.2%
3 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1324
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1109
83.8%
5 37
 
2.8%
6 30
 
2.3%
1 29
 
2.2%
7 27
 
2.0%
8 25
 
1.9%
2 22
 
1.7%
4 16
 
1.2%
9 16
 
1.2%
3 13
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1325
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1109
83.7%
5 37
 
2.8%
6 30
 
2.3%
1 29
 
2.2%
7 27
 
2.0%
8 25
 
1.9%
2 22
 
1.7%
4 16
 
1.2%
9 16
 
1.2%
3 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1109
83.7%
5 37
 
2.8%
6 30
 
2.3%
1 29
 
2.2%
7 27
 
2.0%
8 25
 
1.9%
2 22
 
1.7%
4 16
 
1.2%
9 16
 
1.2%
3 13
 
1.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1135 
1
 
46
2
 
8
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1135
95.3%
1 46
 
3.9%
2 8
 
0.7%
3 2
 
0.2%

Length

2023-12-12T22:15:25.963068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:26.068955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1135
95.3%
1 46
 
3.9%
2 8
 
0.7%
3 2
 
0.2%
Distinct49
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0033585
Minimum0
Maximum599
Zeros1135
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:26.198904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum599
Range599
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.34099
Coefficient of variation (CV)5.1470782
Kurtosis52.41205
Mean9.0033585
Median Absolute Deviation (MAD)0
Skewness6.5729666
Sum10723
Variance2147.4874
MonotonicityNot monotonic
2023-12-12T22:15:26.380826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 1135
95.3%
104 3
 
0.3%
110 2
 
0.2%
105 2
 
0.2%
116 2
 
0.2%
128 2
 
0.2%
153 2
 
0.2%
162 2
 
0.2%
100 1
 
0.1%
457 1
 
0.1%
Other values (39) 39
 
3.3%
ValueCountFrequency (%)
0 1135
95.3%
100 1
 
0.1%
102 1
 
0.1%
104 3
 
0.3%
105 2
 
0.2%
106 1
 
0.1%
107 1
 
0.1%
108 1
 
0.1%
110 2
 
0.2%
113 1
 
0.1%
ValueCountFrequency (%)
599 1
0.1%
457 1
0.1%
448 1
0.1%
400 1
0.1%
329 1
0.1%
299 1
0.1%
291 1
0.1%
290 1
0.1%
286 1
0.1%
281 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1186 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1186
99.6%
1 5
 
0.4%

Length

2023-12-12T22:15:26.879656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:26.999113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1186
99.6%
1 5
 
0.4%
Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3115029
Minimum0
Maximum336
Zeros1186
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T22:15:27.109091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum336
Range336
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.222739
Coefficient of variation (CV)15.419515
Kurtosis235.71602
Mean1.3115029
Median Absolute Deviation (MAD)0
Skewness15.392214
Sum1562
Variance408.95919
MonotonicityNot monotonic
2023-12-12T22:15:27.227278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1186
99.6%
336 1
 
0.1%
301 1
 
0.1%
310 1
 
0.1%
308 1
 
0.1%
307 1
 
0.1%
ValueCountFrequency (%)
0 1186
99.6%
301 1
 
0.1%
307 1
 
0.1%
308 1
 
0.1%
310 1
 
0.1%
336 1
 
0.1%
ValueCountFrequency (%)
336 1
 
0.1%
310 1
 
0.1%
308 1
 
0.1%
307 1
 
0.1%
301 1
 
0.1%
0 1186
99.6%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1187 
1
 
3
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1187
99.7%
1 3
 
0.3%
2 1
 
0.1%

Length

2023-12-12T22:15:27.369380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:27.480089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1187
99.7%
1 3
 
0.3%
2 1
 
0.1%
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1187 
563
 
1
588
 
1
659
 
1
1,167
 
1

Length

Max length5
Median length1
Mean length1.0083963
Min length1

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1187
99.7%
563 1
 
0.1%
588 1
 
0.1%
659 1
 
0.1%
1,167 1
 
0.1%

Length

2023-12-12T22:15:27.632894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:27.777615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1187
99.7%
563 1
 
0.1%
588 1
 
0.1%
659 1
 
0.1%
1,167 1
 
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1187 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1187
99.7%
1 4
 
0.3%

Length

2023-12-12T22:15:27.906119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:28.017598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1187
99.7%
1 4
 
0.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0
1187 
1,003
 
2
1,537
 
1
1,161
 
1

Length

Max length5
Median length1
Mean length1.0134341
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1187
99.7%
1,003 2
 
0.2%
1,537 1
 
0.1%
1,161 1
 
0.1%

Length

2023-12-12T22:15:28.146283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:15:28.273153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1187
99.7%
1,003 2
 
0.2%
1,537 1
 
0.1%
1,161 1
 
0.1%

Sample

산업분류 코드산업분류사업체수종사자수종사자수남종사자수여개인 사업체수개인 사업체 종사자수회사법인 사업체수회사법인 종사자수회사이외 법인 사업체수회사이외 법인 종사자수비법인 사업체수비법인 종자사수단독 사업체수단독 사업체 종사자수공장_ 지사 사업체수공장_ 지사 사업체 종사자수본사_ 본점 사업체수본사_ 본점 종사자수종사자규모별 1-4명 사업체수종사자규모별 1-4명 종사자수종사자 규모별 5-9명 사업체수종사자 규모별 5-9명 종사자수종사자 규모별 10-19명 사업체수종사자 규모별 10-19명 종사자수종사자규모별 20 - 49명 사업체수종사자규모별 20 - 49명 종사자수종사자규모별 50 - 99명 사업체수종사자규모별 50 - 99명 종사자수종사자규모별 100 - 299명 사업체수종사자규모별 100 - 299명 종사자수종사자규모별 300 - 499명 사업체수종사자규모별 300 - 499명 종사자수종사자규모별 500 - 999명 사업체수종사자규모별 500 - 999명 종사자수종사자규모별 1000명 이상 사업체수종사자규모별 1000명 이상 종사자수
01110곡물 및 기타 식량작물 재배업6091712000254334471159860015577631500000000000000
11121채소작물 재배업81174006823006713118110000000000000000
21122화훼작물 재배업000000000000000000000000000000000000
31123종자 및 묘목 생산업22091100117130022000001300117000000000000
41131과실작물 재배업333000003300330000330000000000000000
51132음료용 및 향신용 작물 재배업111000110000110000110000000000000000
61140기타 작물 재배업000000000000000000000000000000000000
71151콩나물 재배업166000001600160000001600000000000000
81152채소_ 화훼 및 과실작물 시설 재배업4131120041300004130000351800000000000000
91159기타 시설작물 재배업475200470000470000470000000000000000
산업분류 코드산업분류사업체수종사자수종사자수남종사자수여개인 사업체수개인 사업체 종사자수회사법인 사업체수회사법인 종사자수회사이외 법인 사업체수회사이외 법인 종사자수비법인 사업체수비법인 종자사수단독 사업체수단독 사업체 종사자수공장_ 지사 사업체수공장_ 지사 사업체 종사자수본사_ 본점 사업체수본사_ 본점 종사자수종사자규모별 1-4명 사업체수종사자규모별 1-4명 종사자수종사자 규모별 5-9명 사업체수종사자 규모별 5-9명 종사자수종사자 규모별 10-19명 사업체수종사자 규모별 10-19명 종사자수종사자규모별 20 - 49명 사업체수종사자규모별 20 - 49명 종사자수종사자규모별 50 - 99명 사업체수종사자규모별 50 - 99명 종사자수종사자규모별 100 - 299명 사업체수종사자규모별 100 - 299명 종사자수종사자규모별 300 - 499명 사업체수종사자규모별 300 - 499명 종사자수종사자규모별 500 - 999명 사업체수종사자규모별 500 - 999명 종사자수종사자규모별 1000명 이상 사업체수종사자규모별 1000명 이상 종사자수
118196912가정용 세탁업201261129132196255560000197256450019824032100000000000000
118296913세탁물 공급업52112952100000052100003621500000000000000
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