Overview

Dataset statistics

Number of variables60
Number of observations2004
Missing cells323
Missing cells (%)0.3%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory988.4 KiB
Average record size in memory505.1 B

Variable types

Text34
Categorical1
Numeric25

Dataset

Description전국사업체조사는 우리나라에 소재하는 모든 사업체를 대상으로 사업체의 기본적인 특성을 파악하는 조사통계입니다. 이 조사결과는 정부의 정책입안은 물론 연구소, 민간기업체의 경영계획수립에 필요한 기초자료로 활용되고 있습니다. 아울러 수집된 사업체에 관한 자료는 각종 사업체단위 통계조사의 모집단명부로도 활용됩니다.
Author통계청
URLhttps://www.data.go.kr/data/15087673/fileData.do

Alerts

행정구역 명칭 has constant value ""Constant
Dataset has 3 (0.1%) duplicate rowsDuplicates
자영업자 종사자수 has 53 (2.6%) missing valuesMissing
무급가족 종사자수 has 111 (5.5%) missing valuesMissing
기타 종사자수 has 42 (2.1%) missing valuesMissing
총사업체수 is highly skewed (γ1 = 33.47441034)Skewed
개인사업체수 is highly skewed (γ1 = 31.70500658)Skewed
회사법인사업체수 is highly skewed (γ1 = 34.40258999)Skewed
회사이외법인사업체수 is highly skewed (γ1 = 27.21051277)Skewed
단독사업체 사업체수 is highly skewed (γ1 = 33.22181772)Skewed
공장지사 사업체수 is highly skewed (γ1 = 33.55216951)Skewed
본사본점 사업체수 is highly skewed (γ1 = 35.2513845)Skewed
1-4명 사업체수 is highly skewed (γ1 = 32.61143156)Skewed
5-9명 사업체수 is highly skewed (γ1 = 34.32364817)Skewed
10-19명 사업체수 is highly skewed (γ1 = 35.88638618)Skewed
20-49명 사업체수 is highly skewed (γ1 = 36.23530963)Skewed
50-99명 사업체수 is highly skewed (γ1 = 33.53260177)Skewed
100-299명 사업체수 is highly skewed (γ1 = 35.77955241)Skewed
300-499명 사업체수 is highly skewed (γ1 = 35.10895871)Skewed
500-999명 사업체수 is highly skewed (γ1 = 32.92984596)Skewed
1000명이상 사업체수 is highly skewed (γ1 = 30.1964733)Skewed
대표자남자 사업체수 is highly skewed (γ1 = 33.90556168)Skewed
대표자여자 사업체수 is highly skewed (γ1 = 30.11479436)Skewed
20세미만 대표자 사업체수 is highly skewed (γ1 = 21.84019769)Skewed
20-29세 대표자 사업체수 is highly skewed (γ1 = 27.55039684)Skewed
30-39세 대표자 사업체수 is highly skewed (γ1 = 30.90746972)Skewed
40-49세 대표자 사업체수 is highly skewed (γ1 = 33.62339267)Skewed
50-59세 대표자 사업체수 is highly skewed (γ1 = 33.98971214)Skewed
60세 이상 대표자 사업체수 is highly skewed (γ1 = 32.83127139)Skewed
개인사업체수 has 247 (12.3%) zerosZeros
회사법인사업체수 has 81 (4.0%) zerosZeros
회사이외법인사업체수 has 325 (16.2%) zerosZeros
비법인단체사업체수 has 1282 (64.0%) zerosZeros
단독사업체 사업체수 has 23 (1.1%) zerosZeros
공장지사 사업체수 has 41 (2.0%) zerosZeros
본사본점 사업체수 has 68 (3.4%) zerosZeros
1-4명 사업체수 has 24 (1.2%) zerosZeros
5-9명 사업체수 has 25 (1.2%) zerosZeros
10-19명 사업체수 has 55 (2.7%) zerosZeros
20-49명 사업체수 has 67 (3.3%) zerosZeros
50-99명 사업체수 has 216 (10.8%) zerosZeros
100-299명 사업체수 has 409 (20.4%) zerosZeros
300-499명 사업체수 has 1050 (52.4%) zerosZeros
500-999명 사업체수 has 1320 (65.9%) zerosZeros
1000명이상 사업체수 has 1513 (75.5%) zerosZeros
대표자여자 사업체수 has 27 (1.3%) zerosZeros
20세미만 대표자 사업체수 has 1226 (61.2%) zerosZeros
20-29세 대표자 사업체수 has 192 (9.6%) zerosZeros
30-39세 대표자 사업체수 has 88 (4.4%) zerosZeros
40-49세 대표자 사업체수 has 24 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 02:36:38.782944
Analysis finished2023-12-12 02:36:41.269019
Duration2.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2001
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2023-12-12T11:36:41.450991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.365269
Min length1

Characters and Unicode

Total characters24780
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1998 ?
Unique (%)99.7%

Sample

1st row0
2nd row0000000A
3rd row0000000A01
4th row0000000A011
5th row0000000A0111
ValueCountFrequency (%)
0000000e360 2
 
0.1%
0000000e390 2
 
0.1%
0000000e370 2
 
0.1%
0000000s9539 1
 
< 0.1%
0000000h5293 1
 
< 0.1%
0000000h52921 1
 
< 0.1%
0000000h5292 1
 
< 0.1%
0000000h52919 1
 
< 0.1%
0000000h52915 1
 
< 0.1%
0000000h52914 1
 
< 0.1%
Other values (1991) 1991
99.4%
2023-12-12T11:36:42.209056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14861
60.0%
1 1836
 
7.4%
2 1739
 
7.0%
9 890
 
3.6%
3 833
 
3.4%
4 812
 
3.3%
C 771
 
3.1%
6 555
 
2.2%
5 507
 
2.0%
7 447
 
1.8%
Other values (19) 1529
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22777
91.9%
Uppercase Letter 2003
 
8.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 771
38.5%
G 269
 
13.4%
M 90
 
4.5%
J 84
 
4.2%
H 83
 
4.1%
F 71
 
3.5%
S 71
 
3.5%
N 69
 
3.4%
A 67
 
3.3%
R 67
 
3.3%
Other values (9) 361
18.0%
Decimal Number
ValueCountFrequency (%)
0 14861
65.2%
1 1836
 
8.1%
2 1739
 
7.6%
9 890
 
3.9%
3 833
 
3.7%
4 812
 
3.6%
6 555
 
2.4%
5 507
 
2.2%
7 447
 
2.0%
8 297
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 22777
91.9%
Latin 2003
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 771
38.5%
G 269
 
13.4%
M 90
 
4.5%
J 84
 
4.2%
H 83
 
4.1%
F 71
 
3.5%
S 71
 
3.5%
N 69
 
3.4%
A 67
 
3.3%
R 67
 
3.3%
Other values (9) 361
18.0%
Common
ValueCountFrequency (%)
0 14861
65.2%
1 1836
 
8.1%
2 1739
 
7.6%
9 890
 
3.9%
3 833
 
3.7%
4 812
 
3.6%
6 555
 
2.4%
5 507
 
2.2%
7 447
 
2.0%
8 297
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14861
60.0%
1 1836
 
7.4%
2 1739
 
7.0%
9 890
 
3.6%
3 833
 
3.4%
4 812
 
3.3%
C 771
 
3.1%
6 555
 
2.2%
5 507
 
2.0%
7 447
 
1.8%
Other values (19) 1529
 
6.2%

행정구역 명칭
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
전국
2004 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전국 2004
100.0%

Length

2023-12-12T11:36:42.458073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:36:42.624040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 2004
100.0%
Distinct1736
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2023-12-12T11:36:43.165518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length12.446607
Min length2

Characters and Unicode

Total characters24943
Distinct characters459
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1508 ?
Unique (%)75.2%

Sample

1st row전체산업
2nd row농업 임업 및 어업(0103)
3rd row농업
4th row작물 재배업
5th row곡물 및 기타 식량작물 재배업
ValueCountFrequency (%)
876
 
11.8%
제조업 678
 
9.1%
기타 360
 
4.8%
서비스업 149
 
2.0%
도매업 120
 
1.6%
소매업 106
 
1.4%
89
 
1.2%
86
 
1.2%
운영업 76
 
1.0%
운송업 50
 
0.7%
Other values (1688) 4842
65.2%
2023-12-12T11:36:43.872433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5435
21.8%
1947
 
7.8%
995
 
4.0%
876
 
3.5%
830
 
3.3%
799
 
3.2%
407
 
1.6%
369
 
1.5%
356
 
1.4%
346
 
1.4%
Other values (449) 12583
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19384
77.7%
Space Separator 5435
 
21.8%
Decimal Number 80
 
0.3%
Close Punctuation 22
 
0.1%
Open Punctuation 22
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1947
 
10.0%
995
 
5.1%
876
 
4.5%
830
 
4.3%
799
 
4.1%
407
 
2.1%
369
 
1.9%
356
 
1.8%
346
 
1.8%
295
 
1.5%
Other values (436) 12164
62.8%
Decimal Number
ValueCountFrequency (%)
1 16
20.0%
6 10
12.5%
4 10
12.5%
5 9
11.2%
8 7
8.8%
0 7
8.8%
3 7
8.8%
7 6
 
7.5%
9 6
 
7.5%
2 2
 
2.5%
Space Separator
ValueCountFrequency (%)
5435
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19384
77.7%
Common 5559
 
22.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1947
 
10.0%
995
 
5.1%
876
 
4.5%
830
 
4.3%
799
 
4.1%
407
 
2.1%
369
 
1.9%
356
 
1.8%
346
 
1.8%
295
 
1.5%
Other values (436) 12164
62.8%
Common
ValueCountFrequency (%)
5435
97.8%
) 22
 
0.4%
( 22
 
0.4%
1 16
 
0.3%
6 10
 
0.2%
4 10
 
0.2%
5 9
 
0.2%
8 7
 
0.1%
0 7
 
0.1%
3 7
 
0.1%
Other values (3) 14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19384
77.7%
ASCII 5559
 
22.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5435
97.8%
) 22
 
0.4%
( 22
 
0.4%
1 16
 
0.3%
6 10
 
0.2%
4 10
 
0.2%
5 9
 
0.2%
8 7
 
0.1%
0 7
 
0.1%
3 7
 
0.1%
Other values (3) 14
 
0.3%
Hangul
ValueCountFrequency (%)
1947
 
10.0%
995
 
5.1%
876
 
4.5%
830
 
4.3%
799
 
4.1%
407
 
2.1%
369
 
1.9%
356
 
1.8%
346
 
1.8%
295
 
1.5%
Other values (436) 12164
62.8%

총사업체수
Real number (ℝ)

SKEWED 

Distinct1501
Distinct (%)75.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean18220.885
Minimum2
Maximum6079702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:44.049073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile47.1
Q1414
median1760
Q37037
95-th percentile60450
Maximum6079702
Range6079700
Interquartile range (IQR)6623

Descriptive statistics

Standard deviation150837.82
Coefficient of variation (CV)8.2782928
Kurtosis1311.6626
Mean18220.885
Median Absolute Deviation (MAD)1608
Skewness33.47441
Sum36478212
Variance2.2752048 × 1010
MonotonicityNot monotonic
2023-12-12T11:36:44.210473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 8
 
0.4%
87 7
 
0.3%
11 5
 
0.2%
34 5
 
0.2%
2 5
 
0.2%
16 5
 
0.2%
316 5
 
0.2%
308 5
 
0.2%
152 5
 
0.2%
14 5
 
0.2%
Other values (1491) 1947
97.2%
ValueCountFrequency (%)
2 5
0.2%
3 4
0.2%
4 1
 
< 0.1%
5 4
0.2%
6 1
 
< 0.1%
7 2
 
0.1%
8 1
 
< 0.1%
10 5
0.2%
11 5
0.2%
12 4
0.2%
ValueCountFrequency (%)
6079702 1
< 0.1%
1536229 1
< 0.1%
985818 1
< 0.1%
863009 1
< 0.1%
800648 1
< 0.1%
616484 1
< 0.1%
579050 1
< 0.1%
572550 1
< 0.1%
572257 1
< 0.1%
509783 1
< 0.1%
Distinct1681
Distinct (%)84.0%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:44.624717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.5994006
Min length1

Characters and Unicode

Total characters9208
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1419 ?
Unique (%)70.9%

Sample

1st row24931600
2nd row66163
3rd row48117
4th row25245
5th row8376
ValueCountFrequency (%)
x 5
 
0.2%
470 4
 
0.2%
2146 4
 
0.2%
3925 4
 
0.2%
2603 4
 
0.2%
26338 3
 
0.1%
331 3
 
0.1%
204568 3
 
0.1%
11500 3
 
0.1%
771 3
 
0.1%
Other values (1671) 1966
98.2%
2023-12-12T11:36:45.199150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1383
15.0%
2 1135
12.3%
3 958
10.4%
4 918
10.0%
5 858
9.3%
7 848
9.2%
6 806
8.8%
0 779
8.5%
9 763
8.3%
8 755
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9203
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1383
15.0%
2 1135
12.3%
3 958
10.4%
4 918
10.0%
5 858
9.3%
7 848
9.2%
6 806
8.8%
0 779
8.5%
9 763
8.3%
8 755
8.2%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9203
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1383
15.0%
2 1135
12.3%
3 958
10.4%
4 918
10.0%
5 858
9.3%
7 848
9.2%
6 806
8.8%
0 779
8.5%
9 763
8.3%
8 755
8.2%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1383
15.0%
2 1135
12.3%
3 958
10.4%
4 918
10.0%
5 858
9.3%
7 848
9.2%
6 806
8.8%
0 779
8.5%
9 763
8.3%
8 755
8.2%
Distinct1668
Distinct (%)83.3%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:45.636242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.3796204
Min length1

Characters and Unicode

Total characters8768
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1391 ?
Unique (%)69.5%

Sample

1st row14296681
2nd row47015
3rd row32853
4th row15503
5th row5853
ValueCountFrequency (%)
x 5
 
0.2%
2166 4
 
0.2%
325 4
 
0.2%
9252 4
 
0.2%
6010 3
 
0.1%
6477 3
 
0.1%
3483 3
 
0.1%
96963 3
 
0.1%
20655 3
 
0.1%
7532 3
 
0.1%
Other values (1658) 1967
98.3%
2023-12-12T11:36:46.234317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1325
15.1%
2 1012
11.5%
3 914
10.4%
5 893
10.2%
4 842
9.6%
7 791
9.0%
6 780
8.9%
9 758
8.6%
8 735
8.4%
0 713
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8763
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1325
15.1%
2 1012
11.5%
3 914
10.4%
5 893
10.2%
4 842
9.6%
7 791
9.0%
6 780
8.9%
9 758
8.7%
8 735
8.4%
0 713
8.1%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8763
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1325
15.1%
2 1012
11.5%
3 914
10.4%
5 893
10.2%
4 842
9.6%
7 791
9.0%
6 780
8.9%
9 758
8.7%
8 735
8.4%
0 713
8.1%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1325
15.1%
2 1012
11.5%
3 914
10.4%
5 893
10.2%
4 842
9.6%
7 791
9.0%
6 780
8.9%
9 758
8.6%
8 735
8.4%
0 713
8.1%
Distinct1581
Distinct (%)79.0%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:46.743658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.0504496
Min length1

Characters and Unicode

Total characters8109
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1263 ?
Unique (%)63.1%

Sample

1st row10634919
2nd row19148
3rd row15264
4th row9742
5th row2523
ValueCountFrequency (%)
52 7
 
0.3%
437 7
 
0.3%
67 6
 
0.3%
4 5
 
0.2%
x 5
 
0.2%
285 5
 
0.2%
324 4
 
0.2%
246 4
 
0.2%
1336 4
 
0.2%
942 4
 
0.2%
Other values (1571) 1951
97.5%
2023-12-12T11:36:47.381392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1241
15.3%
2 963
11.9%
3 914
11.3%
4 860
10.6%
5 750
9.2%
6 717
8.8%
7 715
8.8%
8 669
8.3%
9 651
8.0%
0 624
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8104
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1241
15.3%
2 963
11.9%
3 914
11.3%
4 860
10.6%
5 750
9.3%
6 717
8.8%
7 715
8.8%
8 669
8.3%
9 651
8.0%
0 624
7.7%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8104
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1241
15.3%
2 963
11.9%
3 914
11.3%
4 860
10.6%
5 750
9.3%
6 717
8.8%
7 715
8.8%
8 669
8.3%
9 651
8.0%
0 624
7.7%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1241
15.3%
2 963
11.9%
3 914
11.3%
4 860
10.6%
5 750
9.2%
6 717
8.8%
7 715
8.8%
8 669
8.3%
9 651
8.0%
0 624
7.7%

개인사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1249
Distinct (%)62.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14363.622
Minimum0
Maximum4792662
Zeros247
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:47.591576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1107.25
median833.5
Q34326.25
95-th percentile45204.8
Maximum4792662
Range4792662
Interquartile range (IQR)4219

Descriptive statistics

Standard deviation121523.18
Coefficient of variation (CV)8.460483
Kurtosis1205.1519
Mean14363.622
Median Absolute Deviation (MAD)833.5
Skewness31.705007
Sum28755972
Variance1.4767884 × 1010
MonotonicityNot monotonic
2023-12-12T11:36:47.783319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247
 
12.3%
3 14
 
0.7%
9 11
 
0.5%
67 10
 
0.5%
1 9
 
0.4%
12 8
 
0.4%
14 7
 
0.3%
32 6
 
0.3%
109 6
 
0.3%
75 6
 
0.3%
Other values (1239) 1678
83.7%
ValueCountFrequency (%)
0 247
12.3%
1 9
 
0.4%
2 4
 
0.2%
3 14
 
0.7%
4 5
 
0.2%
6 6
 
0.3%
7 2
 
0.1%
8 2
 
0.1%
9 11
 
0.5%
10 5
 
0.2%
ValueCountFrequency (%)
4792662 1
< 0.1%
1285080 1
< 0.1%
895128 1
< 0.1%
819885 1
< 0.1%
761506 1
< 0.1%
577034 1
< 0.1%
556003 1
< 0.1%
544495 1
< 0.1%
410530 1
< 0.1%
375082 1
< 0.1%
Distinct1403
Distinct (%)70.1%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:48.452149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5794206
Min length1

Characters and Unicode

Total characters7166
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)57.1%

Sample

1st row8853049
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 247
 
12.3%
x 13
 
0.6%
111 5
 
0.2%
37 5
 
0.2%
2967 5
 
0.2%
477 5
 
0.2%
16 4
 
0.2%
1765 4
 
0.2%
368 4
 
0.2%
3672 4
 
0.2%
Other values (1393) 1706
85.2%
2023-12-12T11:36:49.061182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1114
15.5%
0 823
11.5%
2 814
11.4%
3 779
10.9%
6 657
9.2%
5 624
8.7%
4 617
8.6%
7 598
8.3%
8 586
8.2%
9 541
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7153
99.8%
Uppercase Letter 13
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1114
15.6%
0 823
11.5%
2 814
11.4%
3 779
10.9%
6 657
9.2%
5 624
8.7%
4 617
8.6%
7 598
8.4%
8 586
8.2%
9 541
7.6%
Uppercase Letter
ValueCountFrequency (%)
X 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7153
99.8%
Latin 13
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1114
15.6%
0 823
11.5%
2 814
11.4%
3 779
10.9%
6 657
9.2%
5 624
8.7%
4 617
8.6%
7 598
8.4%
8 586
8.2%
9 541
7.6%
Latin
ValueCountFrequency (%)
X 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1114
15.5%
0 823
11.5%
2 814
11.4%
3 779
10.9%
6 657
9.2%
5 624
8.7%
4 617
8.6%
7 598
8.3%
8 586
8.2%
9 541
7.5%

회사법인사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1098
Distinct (%)54.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2784.6683
Minimum0
Maximum929151
Zeros81
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:49.232605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1102
median392
Q31380.25
95-th percentile8565.65
Maximum929151
Range929151
Interquartile range (IQR)1278.25

Descriptive statistics

Standard deviation22841.544
Coefficient of variation (CV)8.2026085
Kurtosis1361.3033
Mean2784.6683
Median Absolute Deviation (MAD)360.5
Skewness34.40259
Sum5574906
Variance5.2173614 × 108
MonotonicityNot monotonic
2023-12-12T11:36:49.386327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
4.0%
1 22
 
1.1%
5 17
 
0.8%
7 14
 
0.7%
57 11
 
0.5%
100 10
 
0.5%
8 10
 
0.5%
261 9
 
0.4%
59 9
 
0.4%
21 9
 
0.4%
Other values (1088) 1810
90.3%
ValueCountFrequency (%)
0 81
4.0%
1 22
 
1.1%
2 9
 
0.4%
3 3
 
0.1%
4 7
 
0.3%
5 17
 
0.8%
6 6
 
0.3%
7 14
 
0.7%
8 10
 
0.5%
9 4
 
0.2%
ValueCountFrequency (%)
929151 1
< 0.1%
237447 1
< 0.1%
163049 1
< 0.1%
144397 1
< 0.1%
109500 1
< 0.1%
89661 2
0.1%
83638 1
< 0.1%
79127 1
< 0.1%
69165 1
< 0.1%
62568 1
< 0.1%
Distinct1535
Distinct (%)76.7%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:49.749294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.0304695
Min length1

Characters and Unicode

Total characters8069
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1270 ?
Unique (%)63.4%

Sample

1st row10966322
2nd row33908
3rd row23042
4th row13802
5th row3660
ValueCountFrequency (%)
0 81
 
4.0%
x 31
 
1.5%
1916 5
 
0.2%
97 5
 
0.2%
125 5
 
0.2%
706 5
 
0.2%
354 5
 
0.2%
2069 5
 
0.2%
1563 4
 
0.2%
27 4
 
0.2%
Other values (1525) 1852
92.5%
2023-12-12T11:36:50.317275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1183
14.7%
2 955
11.8%
3 875
10.8%
4 789
9.8%
5 754
9.3%
0 726
9.0%
6 721
8.9%
7 710
8.8%
9 668
8.3%
8 657
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8038
99.6%
Uppercase Letter 31
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1183
14.7%
2 955
11.9%
3 875
10.9%
4 789
9.8%
5 754
9.4%
0 726
9.0%
6 721
9.0%
7 710
8.8%
9 668
8.3%
8 657
8.2%
Uppercase Letter
ValueCountFrequency (%)
X 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8038
99.6%
Latin 31
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1183
14.7%
2 955
11.9%
3 875
10.9%
4 789
9.8%
5 754
9.4%
0 726
9.0%
6 721
9.0%
7 710
8.8%
9 668
8.3%
8 657
8.2%
Latin
ValueCountFrequency (%)
X 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1183
14.7%
2 955
11.8%
3 875
10.8%
4 789
9.8%
5 754
9.3%
0 726
9.0%
6 721
8.9%
7 710
8.8%
9 668
8.3%
8 657
8.1%

회사이외법인사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct459
Distinct (%)22.9%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean765.19181
Minimum0
Maximum255319
Zeros325
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:50.478583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q3115.5
95-th percentile2157.25
Maximum255319
Range255319
Interquartile range (IQR)113.5

Descriptive statistics

Standard deviation6958.5396
Coefficient of variation (CV)9.0938501
Kurtosis922.29777
Mean765.19181
Median Absolute Deviation (MAD)14
Skewness27.210513
Sum1531914
Variance48421274
MonotonicityNot monotonic
2023-12-12T11:36:50.625025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 325
 
16.2%
1 146
 
7.3%
2 104
 
5.2%
3 75
 
3.7%
4 59
 
2.9%
7 48
 
2.4%
5 48
 
2.4%
6 47
 
2.3%
8 38
 
1.9%
11 23
 
1.1%
Other values (449) 1089
54.3%
ValueCountFrequency (%)
0 325
16.2%
1 146
7.3%
2 104
 
5.2%
3 75
 
3.7%
4 59
 
2.9%
5 48
 
2.4%
6 47
 
2.3%
7 48
 
2.4%
8 38
 
1.9%
9 22
 
1.1%
ValueCountFrequency (%)
255319 1
< 0.1%
82807 1
< 0.1%
81119 1
< 0.1%
72770 1
< 0.1%
55355 1
< 0.1%
43610 1
< 0.1%
30673 1
< 0.1%
26861 2
0.1%
24600 1
< 0.1%
24278 2
0.1%
Distinct865
Distinct (%)43.2%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:51.041823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.6133866
Min length1

Characters and Unicode

Total characters5232
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique599 ?
Unique (%)29.9%

Sample

1st row4304032
2nd row30486
3rd row24760
4th row11310
5th row4688
ValueCountFrequency (%)
0 325
 
16.2%
x 250
 
12.5%
14 14
 
0.7%
12 13
 
0.6%
13 11
 
0.5%
23 11
 
0.5%
7 11
 
0.5%
11 11
 
0.5%
22 11
 
0.5%
17 9
 
0.4%
Other values (855) 1336
66.7%
2023-12-12T11:36:51.580174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 808
15.4%
0 649
12.4%
2 590
11.3%
3 517
9.9%
4 455
8.7%
5 426
8.1%
7 407
7.8%
6 391
7.5%
8 376
7.2%
9 363
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4982
95.2%
Uppercase Letter 250
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 808
16.2%
0 649
13.0%
2 590
11.8%
3 517
10.4%
4 455
9.1%
5 426
8.6%
7 407
8.2%
6 391
7.8%
8 376
7.5%
9 363
7.3%
Uppercase Letter
ValueCountFrequency (%)
X 250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4982
95.2%
Latin 250
 
4.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 808
16.2%
0 649
13.0%
2 590
11.8%
3 517
10.4%
4 455
9.1%
5 426
8.6%
7 407
8.2%
6 391
7.8%
8 376
7.5%
9 363
7.3%
Latin
ValueCountFrequency (%)
X 250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 808
15.4%
0 649
12.4%
2 590
11.3%
3 517
9.9%
4 455
8.7%
5 426
8.1%
7 407
7.8%
6 391
7.5%
8 376
7.2%
9 363
6.9%

비법인단체사업체수
Real number (ℝ)

ZEROS 

Distinct162
Distinct (%)8.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean307.4026
Minimum0
Maximum102570
Zeros1282
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:51.773400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile144.6
Maximum102570
Range102570
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3466.4231
Coefficient of variation (CV)11.276493
Kurtosis454.8845
Mean307.4026
Median Absolute Deviation (MAD)0
Skewness19.077165
Sum615420
Variance12016089
MonotonicityNot monotonic
2023-12-12T11:36:51.926708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1282
64.0%
1 201
 
10.0%
2 76
 
3.8%
3 45
 
2.2%
6 25
 
1.2%
4 25
 
1.2%
5 19
 
0.9%
8 17
 
0.8%
7 15
 
0.7%
9 15
 
0.7%
Other values (152) 282
 
14.1%
ValueCountFrequency (%)
0 1282
64.0%
1 201
 
10.0%
2 76
 
3.8%
3 45
 
2.2%
4 25
 
1.2%
5 19
 
0.9%
6 25
 
1.2%
7 15
 
0.7%
8 17
 
0.8%
9 15
 
0.7%
ValueCountFrequency (%)
102570 1
< 0.1%
51508 1
< 0.1%
51453 1
< 0.1%
46476 1
< 0.1%
31621 1
< 0.1%
31195 1
< 0.1%
29523 2
0.1%
29307 1
< 0.1%
16953 1
< 0.1%
15957 1
< 0.1%
Distinct212
Distinct (%)10.6%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:52.245430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.3311688
Min length1

Characters and Unicode

Total characters2665
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)6.6%

Sample

1st row808197
2nd row1769
3rd row315
4th row133
5th row28
ValueCountFrequency (%)
0 1282
64.0%
x 277
 
13.8%
6 16
 
0.8%
14 15
 
0.7%
9 13
 
0.6%
3 12
 
0.6%
13 10
 
0.5%
20 9
 
0.4%
28 8
 
0.4%
8 8
 
0.4%
Other values (202) 352
 
17.6%
2023-12-12T11:36:52.753932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1348
50.6%
X 277
 
10.4%
1 208
 
7.8%
3 137
 
5.1%
2 134
 
5.0%
6 110
 
4.1%
4 100
 
3.8%
5 94
 
3.5%
7 94
 
3.5%
8 93
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2388
89.6%
Uppercase Letter 277
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1348
56.4%
1 208
 
8.7%
3 137
 
5.7%
2 134
 
5.6%
6 110
 
4.6%
4 100
 
4.2%
5 94
 
3.9%
7 94
 
3.9%
8 93
 
3.9%
9 70
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
X 277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2388
89.6%
Latin 277
 
10.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1348
56.4%
1 208
 
8.7%
3 137
 
5.7%
2 134
 
5.6%
6 110
 
4.6%
4 100
 
4.2%
5 94
 
3.9%
7 94
 
3.9%
8 93
 
3.9%
9 70
 
2.9%
Latin
ValueCountFrequency (%)
X 277
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1348
50.6%
X 277
 
10.4%
1 208
 
7.8%
3 137
 
5.1%
2 134
 
5.0%
6 110
 
4.1%
4 100
 
3.8%
5 94
 
3.5%
7 94
 
3.5%
8 93
 
3.5%

단독사업체 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1427
Distinct (%)71.3%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17170.522
Minimum0
Maximum5729231
Zeros23
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:52.911830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.05
Q1321
median1502.5
Q36221
95-th percentile54879
Maximum5729231
Range5729231
Interquartile range (IQR)5900

Descriptive statistics

Standard deviation142542.48
Coefficient of variation (CV)8.3015811
Kurtosis1297.1205
Mean17170.522
Median Absolute Deviation (MAD)1423.5
Skewness33.221818
Sum34375386
Variance2.031836 × 1010
MonotonicityNot monotonic
2023-12-12T11:36:53.066875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
1.1%
10 9
 
0.4%
112 8
 
0.4%
2 7
 
0.3%
5 6
 
0.3%
33 6
 
0.3%
298 6
 
0.3%
31 5
 
0.2%
17 5
 
0.2%
108 5
 
0.2%
Other values (1417) 1922
95.9%
ValueCountFrequency (%)
0 23
1.1%
1 4
 
0.2%
2 7
 
0.3%
3 2
 
0.1%
4 5
 
0.2%
5 6
 
0.3%
6 4
 
0.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 9
 
0.4%
ValueCountFrequency (%)
5729231 1
< 0.1%
1446486 1
< 0.1%
930390 1
< 0.1%
832803 1
< 0.1%
772019 1
< 0.1%
602428 1
< 0.1%
567757 1
< 0.1%
551954 1
< 0.1%
542155 1
< 0.1%
478808 1
< 0.1%
Distinct1638
Distinct (%)81.8%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:53.463272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.3061938
Min length1

Characters and Unicode

Total characters8621
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1358 ?
Unique (%)67.8%

Sample

1st row17538082
2nd row47592
3rd row32506
4th row22456
5th row7612
ValueCountFrequency (%)
0 23
 
1.1%
x 11
 
0.5%
2504 4
 
0.2%
30 4
 
0.2%
604 4
 
0.2%
693 4
 
0.2%
486 4
 
0.2%
520 4
 
0.2%
43907 3
 
0.1%
5165 3
 
0.1%
Other values (1628) 1938
96.8%
2023-12-12T11:36:54.023342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1205
14.0%
2 1043
12.1%
3 898
10.4%
4 879
10.2%
5 809
9.4%
7 804
9.3%
6 802
9.3%
8 767
8.9%
9 723
8.4%
0 680
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8610
99.9%
Uppercase Letter 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1205
14.0%
2 1043
12.1%
3 898
10.4%
4 879
10.2%
5 809
9.4%
7 804
9.3%
6 802
9.3%
8 767
8.9%
9 723
8.4%
0 680
7.9%
Uppercase Letter
ValueCountFrequency (%)
X 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8610
99.9%
Latin 11
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1205
14.0%
2 1043
12.1%
3 898
10.4%
4 879
10.2%
5 809
9.4%
7 804
9.3%
6 802
9.3%
8 767
8.9%
9 723
8.4%
0 680
7.9%
Latin
ValueCountFrequency (%)
X 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1205
14.0%
2 1043
12.1%
3 898
10.4%
4 879
10.2%
5 809
9.4%
7 804
9.3%
6 802
9.3%
8 767
8.9%
9 723
8.4%
0 680
7.9%

공장지사 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct670
Distinct (%)33.5%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean795.995
Minimum0
Maximum265597
Zeros41
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:54.238566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median76
Q3314.75
95-th percentile2645.5
Maximum265597
Range265597
Interquartile range (IQR)291.75

Descriptive statistics

Standard deviation6588.965
Coefficient of variation (CV)8.2776461
Kurtosis1313.459
Mean795.995
Median Absolute Deviation (MAD)69
Skewness33.55217
Sum1593582
Variance43414460
MonotonicityNot monotonic
2023-12-12T11:36:54.408064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
2.0%
1 39
 
1.9%
6 34
 
1.7%
7 33
 
1.6%
5 30
 
1.5%
13 29
 
1.4%
23 28
 
1.4%
15 26
 
1.3%
3 23
 
1.1%
4 21
 
1.0%
Other values (660) 1698
84.7%
ValueCountFrequency (%)
0 41
2.0%
1 39
1.9%
2 19
0.9%
3 23
1.1%
4 21
1.0%
5 30
1.5%
6 34
1.7%
7 33
1.6%
8 18
0.9%
9 13
 
0.6%
ValueCountFrequency (%)
265597 1
< 0.1%
70989 1
< 0.1%
49857 1
< 0.1%
28500 2
0.1%
26883 1
< 0.1%
26529 1
< 0.1%
25685 1
< 0.1%
20521 1
< 0.1%
19700 1
< 0.1%
18456 1
< 0.1%
Distinct1420
Distinct (%)70.9%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:54.815000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5759241
Min length1

Characters and Unicode

Total characters7159
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1076 ?
Unique (%)53.7%

Sample

1st row4184791
2nd row12660
3rd row11327
4th row1510
5th row353
ValueCountFrequency (%)
x 58
 
2.9%
0 41
 
2.0%
57 8
 
0.4%
7 6
 
0.3%
348 6
 
0.3%
116 5
 
0.2%
225 5
 
0.2%
36 5
 
0.2%
95 5
 
0.2%
251 5
 
0.2%
Other values (1410) 1858
92.8%
2023-12-12T11:36:55.439056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1100
15.4%
2 906
12.7%
3 766
10.7%
4 688
9.6%
5 664
9.3%
6 657
9.2%
0 622
8.7%
7 617
8.6%
8 571
8.0%
9 510
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7101
99.2%
Uppercase Letter 58
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1100
15.5%
2 906
12.8%
3 766
10.8%
4 688
9.7%
5 664
9.4%
6 657
9.3%
0 622
8.8%
7 617
8.7%
8 571
8.0%
9 510
7.2%
Uppercase Letter
ValueCountFrequency (%)
X 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7101
99.2%
Latin 58
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1100
15.5%
2 906
12.8%
3 766
10.8%
4 688
9.7%
5 664
9.4%
6 657
9.3%
0 622
8.8%
7 617
8.7%
8 571
8.0%
9 510
7.2%
Latin
ValueCountFrequency (%)
X 58
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1100
15.4%
2 906
12.7%
3 766
10.7%
4 688
9.6%
5 664
9.3%
6 657
9.2%
0 622
8.7%
7 617
8.6%
8 571
8.0%
9 510
7.1%

본사본점 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct461
Distinct (%)23.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean254.36763
Minimum0
Maximum84874
Zeros68
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:55.627106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median41
Q3132.75
95-th percentile800.9
Maximum84874
Range84874
Interquartile range (IQR)119.75

Descriptive statistics

Standard deviation2065.3096
Coefficient of variation (CV)8.1193884
Kurtosis1415.6636
Mean254.36763
Median Absolute Deviation (MAD)36
Skewness35.251384
Sum509244
Variance4265503.7
MonotonicityNot monotonic
2023-12-12T11:36:55.823501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
3.4%
1 64
 
3.2%
2 50
 
2.5%
3 46
 
2.3%
13 41
 
2.0%
4 38
 
1.9%
10 36
 
1.8%
5 34
 
1.7%
8 30
 
1.5%
19 30
 
1.5%
Other values (451) 1565
78.1%
ValueCountFrequency (%)
0 68
3.4%
1 64
3.2%
2 50
2.5%
3 46
2.3%
4 38
1.9%
5 34
1.7%
6 25
 
1.2%
7 13
 
0.6%
8 30
1.5%
9 26
 
1.3%
ValueCountFrequency (%)
84874 1
< 0.1%
18754 1
< 0.1%
16374 1
< 0.1%
12535 1
< 0.1%
9413 1
< 0.1%
6463 1
< 0.1%
6269 2
0.1%
6080 1
< 0.1%
5571 1
< 0.1%
4680 1
< 0.1%
Distinct1369
Distinct (%)68.4%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:56.284456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.457043
Min length1

Characters and Unicode

Total characters6921
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1033 ?
Unique (%)51.6%

Sample

1st row3208727
2nd row5911
3rd row4284
4th row1279
5th row411
ValueCountFrequency (%)
x 114
 
5.7%
0 68
 
3.4%
11 8
 
0.4%
99 6
 
0.3%
118 5
 
0.2%
10 5
 
0.2%
381 4
 
0.2%
2123 4
 
0.2%
78 4
 
0.2%
364 4
 
0.2%
Other values (1359) 1780
88.9%
2023-12-12T11:36:56.894057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1063
15.4%
2 813
11.7%
3 749
10.8%
4 685
9.9%
6 638
9.2%
8 597
8.6%
0 594
8.6%
5 566
8.2%
7 562
8.1%
9 540
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6807
98.4%
Uppercase Letter 114
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1063
15.6%
2 813
11.9%
3 749
11.0%
4 685
10.1%
6 638
9.4%
8 597
8.8%
0 594
8.7%
5 566
8.3%
7 562
8.3%
9 540
7.9%
Uppercase Letter
ValueCountFrequency (%)
X 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6807
98.4%
Latin 114
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1063
15.6%
2 813
11.9%
3 749
11.0%
4 685
10.1%
6 638
9.4%
8 597
8.8%
0 594
8.7%
5 566
8.3%
7 562
8.3%
9 540
7.9%
Latin
ValueCountFrequency (%)
X 114
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1063
15.4%
2 813
11.7%
3 749
10.8%
4 685
9.9%
6 638
9.2%
8 597
8.6%
0 594
8.6%
5 566
8.2%
7 562
8.1%
9 540
7.8%

1-4명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1366
Distinct (%)68.2%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15739.103
Minimum0
Maximum5251614
Zeros24
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:57.046232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1230.5
median1162
Q35544
95-th percentile47644.55
Maximum5251614
Range5251614
Interquartile range (IQR)5313.5

Descriptive statistics

Standard deviation131677.6
Coefficient of variation (CV)8.3662708
Kurtosis1259.1101
Mean15739.103
Median Absolute Deviation (MAD)1115.5
Skewness32.611432
Sum31509684
Variance1.733899 × 1010
MonotonicityNot monotonic
2023-12-12T11:36:57.187389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
1.2%
2 15
 
0.7%
3 14
 
0.7%
1 14
 
0.7%
7 10
 
0.5%
18 10
 
0.5%
4 8
 
0.4%
150 8
 
0.4%
52 7
 
0.3%
26 7
 
0.3%
Other values (1356) 1885
94.1%
ValueCountFrequency (%)
0 24
1.2%
1 14
0.7%
2 15
0.7%
3 14
0.7%
4 8
 
0.4%
5 4
 
0.2%
6 2
 
0.1%
7 10
0.5%
8 6
 
0.3%
9 3
 
0.1%
ValueCountFrequency (%)
5251614 1
< 0.1%
1404426 1
< 0.1%
925901 1
< 0.1%
774661 1
< 0.1%
716273 1
< 0.1%
592063 1
< 0.1%
559261 1
< 0.1%
506286 1
< 0.1%
452392 1
< 0.1%
444835 1
< 0.1%
Distinct1479
Distinct (%)73.9%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:57.524219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7422577
Min length1

Characters and Unicode

Total characters7492
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1144 ?
Unique (%)57.1%

Sample

1st row7779460
2nd row16075
3rd row13881
4th row10117
5th row4597
ValueCountFrequency (%)
x 29
 
1.4%
0 24
 
1.2%
9 8
 
0.4%
116 7
 
0.3%
5 7
 
0.3%
15 7
 
0.3%
54 7
 
0.3%
715 6
 
0.3%
7 6
 
0.3%
78 6
 
0.3%
Other values (1469) 1895
94.7%
2023-12-12T11:36:57.990614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1169
15.6%
2 930
12.4%
3 754
10.1%
6 739
9.9%
4 716
9.6%
5 700
9.3%
7 685
9.1%
8 605
8.1%
0 593
7.9%
9 572
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7463
99.6%
Uppercase Letter 29
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1169
15.7%
2 930
12.5%
3 754
10.1%
6 739
9.9%
4 716
9.6%
5 700
9.4%
7 685
9.2%
8 605
8.1%
0 593
7.9%
9 572
7.7%
Uppercase Letter
ValueCountFrequency (%)
X 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7463
99.6%
Latin 29
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1169
15.7%
2 930
12.5%
3 754
10.1%
6 739
9.9%
4 716
9.6%
5 700
9.4%
7 685
9.2%
8 605
8.1%
0 593
7.9%
9 572
7.7%
Latin
ValueCountFrequency (%)
X 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1169
15.6%
2 930
12.4%
3 754
10.1%
6 739
9.9%
4 716
9.6%
5 700
9.3%
7 685
9.1%
8 605
8.1%
0 593
7.9%
9 572
7.6%

5-9명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct858
Distinct (%)42.9%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1424.991
Minimum0
Maximum475472
Zeros25
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:58.128219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q150
median185
Q3640
95-th percentile4561.05
Maximum475472
Range475472
Interquartile range (IQR)590

Descriptive statistics

Standard deviation11670.153
Coefficient of variation (CV)8.1896329
Kurtosis1366.4011
Mean1424.991
Median Absolute Deviation (MAD)164
Skewness34.323648
Sum2852832
Variance1.3619248 × 108
MonotonicityNot monotonic
2023-12-12T11:36:58.259730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 35
 
1.7%
0 25
 
1.2%
2 20
 
1.0%
5 19
 
0.9%
4 18
 
0.9%
9 15
 
0.7%
7 15
 
0.7%
14 15
 
0.7%
8 15
 
0.7%
36 14
 
0.7%
Other values (848) 1811
90.4%
ValueCountFrequency (%)
0 25
1.2%
1 35
1.7%
2 20
1.0%
3 14
 
0.7%
4 18
0.9%
5 19
0.9%
6 7
 
0.3%
7 15
0.7%
8 15
0.7%
9 15
0.7%
ValueCountFrequency (%)
475472 1
< 0.1%
92195 1
< 0.1%
79962 1
< 0.1%
70696 1
< 0.1%
67993 1
< 0.1%
53485 1
< 0.1%
48356 1
< 0.1%
45035 1
< 0.1%
43397 1
< 0.1%
42352 1
< 0.1%
Distinct1385
Distinct (%)69.2%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:58.590210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.521978
Min length1

Characters and Unicode

Total characters7051
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1022 ?
Unique (%)51.0%

Sample

1st row3020712
2nd row14052
3rd row9022
4th row5698
5th row1743
ValueCountFrequency (%)
x 55
 
2.7%
0 25
 
1.2%
255 6
 
0.3%
243 6
 
0.3%
262 5
 
0.2%
36 5
 
0.2%
507 5
 
0.2%
1026 5
 
0.2%
84 5
 
0.2%
257 5
 
0.2%
Other values (1375) 1880
93.9%
2023-12-12T11:36:59.048753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1114
15.8%
2 918
13.0%
3 728
10.3%
4 726
10.3%
5 647
9.2%
6 635
9.0%
7 604
8.6%
9 567
8.0%
8 545
7.7%
0 512
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6996
99.2%
Uppercase Letter 55
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1114
15.9%
2 918
13.1%
3 728
10.4%
4 726
10.4%
5 647
9.2%
6 635
9.1%
7 604
8.6%
9 567
8.1%
8 545
7.8%
0 512
7.3%
Uppercase Letter
ValueCountFrequency (%)
X 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6996
99.2%
Latin 55
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1114
15.9%
2 918
13.1%
3 728
10.4%
4 726
10.4%
5 647
9.2%
6 635
9.1%
7 604
8.6%
9 567
8.1%
8 545
7.8%
0 512
7.3%
Latin
ValueCountFrequency (%)
X 55
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1114
15.8%
2 918
13.0%
3 728
10.3%
4 726
10.3%
5 647
9.2%
6 635
9.0%
7 604
8.6%
9 567
8.0%
8 545
7.7%
0 512
7.3%

10-19명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct641
Distinct (%)32.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean577.60939
Minimum0
Maximum192729
Zeros55
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:36:59.204239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median84
Q3288.75
95-th percentile1909.35
Maximum192729
Range192729
Interquartile range (IQR)264.75

Descriptive statistics

Standard deviation4650.7448
Coefficient of variation (CV)8.0517126
Kurtosis1461.2157
Mean577.60939
Median Absolute Deviation (MAD)74
Skewness35.886386
Sum1156374
Variance21629428
MonotonicityNot monotonic
2023-12-12T11:36:59.641160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
2.7%
1 37
 
1.8%
4 30
 
1.5%
2 25
 
1.2%
9 24
 
1.2%
7 21
 
1.0%
14 21
 
1.0%
3 20
 
1.0%
12 20
 
1.0%
6 19
 
0.9%
Other values (631) 1730
86.3%
ValueCountFrequency (%)
0 55
2.7%
1 37
1.8%
2 25
1.2%
3 20
 
1.0%
4 30
1.5%
5 18
 
0.9%
6 19
 
0.9%
7 21
 
1.0%
8 15
 
0.7%
9 24
1.2%
ValueCountFrequency (%)
192729 1
< 0.1%
38477 1
< 0.1%
26528 1
< 0.1%
25528 1
< 0.1%
18269 1
< 0.1%
18152 1
< 0.1%
15756 1
< 0.1%
14252 1
< 0.1%
14137 1
< 0.1%
13481 1
< 0.1%
Distinct1363
Distinct (%)68.1%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:36:59.983800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.4795205
Min length1

Characters and Unicode

Total characters6966
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique991 ?
Unique (%)49.5%

Sample

1st row2543068
2nd row11890
3rd row8538
4th row4749
5th row1064
ValueCountFrequency (%)
x 62
 
3.1%
0 55
 
2.7%
192 6
 
0.3%
120 5
 
0.2%
215 5
 
0.2%
201 5
 
0.2%
149 5
 
0.2%
284 5
 
0.2%
62 5
 
0.2%
147 4
 
0.2%
Other values (1353) 1845
92.2%
2023-12-12T11:37:00.687205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1100
15.8%
2 847
12.2%
3 724
10.4%
4 667
9.6%
9 608
8.7%
5 608
8.7%
6 599
8.6%
0 593
8.5%
8 584
8.4%
7 574
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6904
99.1%
Uppercase Letter 62
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1100
15.9%
2 847
12.3%
3 724
10.5%
4 667
9.7%
9 608
8.8%
5 608
8.8%
6 599
8.7%
0 593
8.6%
8 584
8.5%
7 574
8.3%
Uppercase Letter
ValueCountFrequency (%)
X 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6904
99.1%
Latin 62
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1100
15.9%
2 847
12.3%
3 724
10.5%
4 667
9.7%
9 608
8.8%
5 608
8.8%
6 599
8.7%
0 593
8.6%
8 584
8.5%
7 574
8.3%
Latin
ValueCountFrequency (%)
X 62
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1100
15.8%
2 847
12.2%
3 724
10.4%
4 667
9.6%
9 608
8.7%
5 608
8.7%
6 599
8.6%
0 593
8.5%
8 584
8.4%
7 574
8.2%

20-49명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct507
Distinct (%)25.3%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean319.47153
Minimum0
Maximum106597
Zeros67
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:00.870322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median48
Q3159
95-th percentile1219.3
Maximum106597
Range106597
Interquartile range (IQR)145

Descriptive statistics

Standard deviation2565.0496
Coefficient of variation (CV)8.0290397
Kurtosis1478.8524
Mean319.47153
Median Absolute Deviation (MAD)43
Skewness36.23531
Sum639582
Variance6579479.3
MonotonicityNot monotonic
2023-12-12T11:37:01.045706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
3.3%
1 54
 
2.7%
5 44
 
2.2%
2 43
 
2.1%
6 40
 
2.0%
3 35
 
1.7%
7 33
 
1.6%
4 31
 
1.5%
10 31
 
1.5%
18 28
 
1.4%
Other values (497) 1596
79.6%
ValueCountFrequency (%)
0 67
3.3%
1 54
2.7%
2 43
2.1%
3 35
1.7%
4 31
1.5%
5 44
2.2%
6 40
2.0%
7 33
1.6%
8 23
 
1.1%
9 20
 
1.0%
ValueCountFrequency (%)
106597 1
< 0.1%
24153 1
< 0.1%
13865 1
< 0.1%
10705 1
< 0.1%
9997 1
< 0.1%
8845 1
< 0.1%
8362 2
0.1%
8188 1
< 0.1%
6388 1
< 0.1%
5982 1
< 0.1%
Distinct1370
Distinct (%)68.4%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:01.464011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5554446
Min length1

Characters and Unicode

Total characters7118
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1028 ?
Unique (%)51.3%

Sample

1st row3167523
2nd row13669
3rd row10243
4th row3308
5th row533
ValueCountFrequency (%)
x 97
 
4.8%
0 67
 
3.3%
611 6
 
0.3%
1597 5
 
0.2%
747 5
 
0.2%
139 5
 
0.2%
132 5
 
0.2%
173 5
 
0.2%
134 5
 
0.2%
137 5
 
0.2%
Other values (1360) 1797
89.8%
2023-12-12T11:37:02.120017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1132
15.9%
2 786
11.0%
3 751
10.6%
4 690
9.7%
5 676
9.5%
7 660
9.3%
0 634
8.9%
6 593
8.3%
8 559
7.9%
9 540
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7021
98.6%
Uppercase Letter 97
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1132
16.1%
2 786
11.2%
3 751
10.7%
4 690
9.8%
5 676
9.6%
7 660
9.4%
0 634
9.0%
6 593
8.4%
8 559
8.0%
9 540
7.7%
Uppercase Letter
ValueCountFrequency (%)
X 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7021
98.6%
Latin 97
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1132
16.1%
2 786
11.2%
3 751
10.7%
4 690
9.8%
5 676
9.6%
7 660
9.4%
0 634
9.0%
6 593
8.4%
8 559
8.0%
9 540
7.7%
Latin
ValueCountFrequency (%)
X 97
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1132
15.9%
2 786
11.0%
3 751
10.6%
4 690
9.7%
5 676
9.5%
7 660
9.3%
0 634
8.9%
6 593
8.3%
8 559
7.9%
9 540
7.6%

50-99명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct280
Distinct (%)14.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean100.04296
Minimum0
Maximum33381
Zeros216
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:02.279260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q342
95-th percentile293
Maximum33381
Range33381
Interquartile range (IQR)39

Descriptive statistics

Standard deviation827.00475
Coefficient of variation (CV)8.2664965
Kurtosis1317.6159
Mean100.04296
Median Absolute Deviation (MAD)11
Skewness33.532602
Sum200286
Variance683936.86
MonotonicityNot monotonic
2023-12-12T11:37:02.419687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 216
 
10.8%
2 135
 
6.7%
1 118
 
5.9%
3 83
 
4.1%
8 69
 
3.4%
6 65
 
3.2%
4 64
 
3.2%
5 64
 
3.2%
7 53
 
2.6%
9 47
 
2.3%
Other values (270) 1088
54.3%
ValueCountFrequency (%)
0 216
10.8%
1 118
5.9%
2 135
6.7%
3 83
 
4.1%
4 64
 
3.2%
5 64
 
3.2%
6 65
 
3.2%
7 53
 
2.6%
8 69
 
3.4%
9 47
 
2.3%
ValueCountFrequency (%)
33381 1
< 0.1%
6382 2
0.1%
6321 1
< 0.1%
4805 1
< 0.1%
3127 1
< 0.1%
2880 1
< 0.1%
2834 1
< 0.1%
2622 1
< 0.1%
2592 2
0.1%
2293 1
< 0.1%
Distinct1130
Distinct (%)56.4%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:02.843441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.1013986
Min length1

Characters and Unicode

Total characters6209
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique835 ?
Unique (%)41.7%

Sample

1st row2292949
2nd row5430
3rd row3155
4th row460
5th rowX
ValueCountFrequency (%)
x 253
 
12.6%
0 216
 
10.8%
721 7
 
0.3%
419 5
 
0.2%
389 5
 
0.2%
236 5
 
0.2%
362 4
 
0.2%
354 4
 
0.2%
531 4
 
0.2%
753 4
 
0.2%
Other values (1120) 1495
74.7%
2023-12-12T11:37:03.454675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 941
15.2%
2 744
12.0%
0 650
10.5%
3 590
9.5%
5 582
9.4%
4 561
9.0%
7 505
8.1%
9 483
7.8%
8 458
7.4%
6 442
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5956
95.9%
Uppercase Letter 253
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 941
15.8%
2 744
12.5%
0 650
10.9%
3 590
9.9%
5 582
9.8%
4 561
9.4%
7 505
8.5%
9 483
8.1%
8 458
7.7%
6 442
7.4%
Uppercase Letter
ValueCountFrequency (%)
X 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5956
95.9%
Latin 253
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 941
15.8%
2 744
12.5%
0 650
10.9%
3 590
9.9%
5 582
9.8%
4 561
9.4%
7 505
8.5%
9 483
8.1%
8 458
7.7%
6 442
7.4%
Latin
ValueCountFrequency (%)
X 253
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 941
15.2%
2 744
12.0%
0 650
10.5%
3 590
9.5%
5 582
9.4%
4 561
9.0%
7 505
8.1%
9 483
7.8%
8 458
7.4%
6 442
7.1%

100-299명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct209
Distinct (%)10.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean46.468531
Minimum0
Maximum15505
Zeros409
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:03.650662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q321
95-th percentile162
Maximum15505
Range15505
Interquartile range (IQR)20

Descriptive statistics

Standard deviation374.52005
Coefficient of variation (CV)8.059649
Kurtosis1455.6728
Mean46.468531
Median Absolute Deviation (MAD)5
Skewness35.779552
Sum93030
Variance140265.27
MonotonicityNot monotonic
2023-12-12T11:37:03.815956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 409
20.4%
1 197
 
9.8%
2 173
 
8.6%
3 98
 
4.9%
5 84
 
4.2%
4 65
 
3.2%
6 63
 
3.1%
8 47
 
2.3%
9 43
 
2.1%
10 42
 
2.1%
Other values (199) 781
39.0%
ValueCountFrequency (%)
0 409
20.4%
1 197
9.8%
2 173
8.6%
3 98
 
4.9%
4 65
 
3.2%
5 84
 
4.2%
6 63
 
3.1%
7 37
 
1.8%
8 47
 
2.3%
9 43
 
2.1%
ValueCountFrequency (%)
15505 1
< 0.1%
3291 1
< 0.1%
2089 1
< 0.1%
1426 1
< 0.1%
1275 1
< 0.1%
1229 1
< 0.1%
1211 2
0.1%
1122 1
< 0.1%
1027 1
< 0.1%
1012 2
0.1%
Distinct956
Distinct (%)47.8%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:04.199452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.8156843
Min length1

Characters and Unicode

Total characters5637
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique749 ?
Unique (%)37.4%

Sample

1st row2455931
2nd row3746
3rd row2423
4th row913
5th rowX
ValueCountFrequency (%)
0 409
 
20.4%
x 370
 
18.5%
525 9
 
0.4%
905 5
 
0.2%
551 5
 
0.2%
2864 4
 
0.2%
6019 4
 
0.2%
5011 4
 
0.2%
1943 4
 
0.2%
480 4
 
0.2%
Other values (946) 1184
59.1%
2023-12-12T11:37:04.712334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 834
14.8%
1 741
13.1%
2 607
10.8%
3 535
9.5%
4 483
8.6%
5 475
8.4%
6 450
8.0%
7 409
7.3%
X 370
6.6%
8 367
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5267
93.4%
Uppercase Letter 370
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 834
15.8%
1 741
14.1%
2 607
11.5%
3 535
10.2%
4 483
9.2%
5 475
9.0%
6 450
8.5%
7 409
7.8%
8 367
7.0%
9 366
6.9%
Uppercase Letter
ValueCountFrequency (%)
X 370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5267
93.4%
Latin 370
 
6.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 834
15.8%
1 741
14.1%
2 607
11.5%
3 535
10.2%
4 483
9.2%
5 475
9.0%
6 450
8.5%
7 409
7.8%
8 367
7.0%
9 366
6.9%
Latin
ValueCountFrequency (%)
X 370
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 834
14.8%
1 741
13.1%
2 607
10.8%
3 535
9.5%
4 483
8.6%
5 475
8.4%
6 450
8.0%
7 409
7.3%
X 370
6.6%
8 367
6.5%

300-499명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct83
Distinct (%)4.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.7102897
Minimum0
Maximum2239
Zeros1050
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:04.847891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile22
Maximum2239
Range2239
Interquartile range (IQR)3

Descriptive statistics

Standard deviation54.415654
Coefficient of variation (CV)8.1092854
Kurtosis1419.5456
Mean6.7102897
Median Absolute Deviation (MAD)0
Skewness35.108959
Sum13434
Variance2961.0635
MonotonicityNot monotonic
2023-12-12T11:37:04.974511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1050
52.4%
1 283
 
14.1%
2 149
 
7.4%
3 81
 
4.0%
4 62
 
3.1%
5 50
 
2.5%
6 32
 
1.6%
7 31
 
1.5%
8 31
 
1.5%
11 16
 
0.8%
Other values (73) 217
 
10.8%
ValueCountFrequency (%)
0 1050
52.4%
1 283
 
14.1%
2 149
 
7.4%
3 81
 
4.0%
4 62
 
3.1%
5 50
 
2.5%
6 32
 
1.6%
7 31
 
1.5%
8 31
 
1.5%
9 14
 
0.7%
ValueCountFrequency (%)
2239 1
 
< 0.1%
419 1
 
< 0.1%
301 1
 
< 0.1%
224 1
 
< 0.1%
217 1
 
< 0.1%
216 3
0.1%
208 1
 
< 0.1%
161 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
Distinct390
Distinct (%)19.5%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:05.292841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.8176823
Min length1

Characters and Unicode

Total characters3639
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique290 ?
Unique (%)14.5%

Sample

1st row848835
2nd rowX
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1050
52.4%
x 432
21.6%
2341 4
 
0.2%
2517 4
 
0.2%
1119 4
 
0.2%
1755 4
 
0.2%
3154 4
 
0.2%
1740 3
 
0.1%
5525 3
 
0.1%
7834 3
 
0.1%
Other values (380) 491
24.5%
2023-12-12T11:37:05.699814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1229
33.8%
X 432
 
11.9%
1 385
 
10.6%
2 267
 
7.3%
3 216
 
5.9%
5 207
 
5.7%
4 202
 
5.6%
7 200
 
5.5%
9 171
 
4.7%
8 171
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3207
88.1%
Uppercase Letter 432
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1229
38.3%
1 385
 
12.0%
2 267
 
8.3%
3 216
 
6.7%
5 207
 
6.5%
4 202
 
6.3%
7 200
 
6.2%
9 171
 
5.3%
8 171
 
5.3%
6 159
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
X 432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3207
88.1%
Latin 432
 
11.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1229
38.3%
1 385
 
12.0%
2 267
 
8.3%
3 216
 
6.7%
5 207
 
6.5%
4 202
 
6.3%
7 200
 
6.2%
9 171
 
5.3%
8 171
 
5.3%
6 159
 
5.0%
Latin
ValueCountFrequency (%)
X 432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1229
33.8%
X 432
 
11.9%
1 385
 
10.6%
2 267
 
7.3%
3 216
 
5.9%
5 207
 
5.7%
4 202
 
5.6%
7 200
 
5.5%
9 171
 
4.7%
8 171
 
4.7%

500-999명 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct60
Distinct (%)3.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.9350649
Minimum0
Maximum1313
Zeros1320
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:05.828543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14
Maximum1313
Range1313
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.67447
Coefficient of variation (CV)8.3034131
Kurtosis1292.1556
Mean3.9350649
Median Absolute Deviation (MAD)0
Skewness32.929846
Sum7878
Variance1067.621
MonotonicityNot monotonic
2023-12-12T11:37:05.946371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1320
65.9%
1 222
 
11.1%
2 131
 
6.5%
3 69
 
3.4%
4 40
 
2.0%
5 34
 
1.7%
6 26
 
1.3%
7 13
 
0.6%
8 11
 
0.5%
12 8
 
0.4%
Other values (50) 128
 
6.4%
ValueCountFrequency (%)
0 1320
65.9%
1 222
 
11.1%
2 131
 
6.5%
3 69
 
3.4%
4 40
 
2.0%
5 34
 
1.7%
6 26
 
1.3%
7 13
 
0.6%
8 11
 
0.5%
9 6
 
0.3%
ValueCountFrequency (%)
1313 1
< 0.1%
215 1
< 0.1%
200 2
0.1%
191 1
< 0.1%
158 1
< 0.1%
132 1
< 0.1%
122 1
< 0.1%
120 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
Distinct255
Distinct (%)12.7%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:06.215443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.5424575
Min length1

Characters and Unicode

Total characters3088
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)9.7%

Sample

1st row905126
2nd rowX
3rd rowX
4th row0
5th row0
ValueCountFrequency (%)
0 1320
65.9%
x 353
 
17.6%
3555 4
 
0.2%
1773 3
 
0.1%
1980 3
 
0.1%
18723 3
 
0.1%
2493 3
 
0.1%
3293 3
 
0.1%
4095 3
 
0.1%
2003 3
 
0.1%
Other values (245) 304
 
15.2%
2023-12-12T11:37:06.603112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1426
46.2%
X 353
 
11.4%
2 200
 
6.5%
1 194
 
6.3%
3 172
 
5.6%
9 156
 
5.1%
5 122
 
4.0%
8 122
 
4.0%
7 119
 
3.9%
4 117
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2735
88.6%
Uppercase Letter 353
 
11.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1426
52.1%
2 200
 
7.3%
1 194
 
7.1%
3 172
 
6.3%
9 156
 
5.7%
5 122
 
4.5%
8 122
 
4.5%
7 119
 
4.4%
4 117
 
4.3%
6 107
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
X 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2735
88.6%
Latin 353
 
11.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1426
52.1%
2 200
 
7.3%
1 194
 
7.1%
3 172
 
6.3%
9 156
 
5.7%
5 122
 
4.5%
8 122
 
4.5%
7 119
 
4.4%
4 117
 
4.3%
6 107
 
3.9%
Latin
ValueCountFrequency (%)
X 353
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1426
46.2%
X 353
 
11.4%
2 200
 
6.5%
1 194
 
6.3%
3 172
 
5.6%
9 156
 
5.1%
5 122
 
4.0%
8 122
 
4.0%
7 119
 
3.9%
4 117
 
3.8%

1000명이상 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct47
Distinct (%)2.3%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.5534466
Minimum0
Maximum852
Zeros1513
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:06.730249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum852
Range852
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.927858
Coefficient of variation (CV)8.5875533
Kurtosis1131.6301
Mean2.5534466
Median Absolute Deviation (MAD)0
Skewness30.196473
Sum5112
Variance480.83098
MonotonicityNot monotonic
2023-12-12T11:37:06.839644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 1513
75.5%
1 190
 
9.5%
2 71
 
3.5%
3 37
 
1.8%
4 33
 
1.6%
6 21
 
1.0%
5 16
 
0.8%
8 16
 
0.8%
7 15
 
0.7%
9 12
 
0.6%
Other values (37) 78
 
3.9%
ValueCountFrequency (%)
0 1513
75.5%
1 190
 
9.5%
2 71
 
3.5%
3 37
 
1.8%
4 33
 
1.6%
5 16
 
0.8%
6 21
 
1.0%
7 15
 
0.7%
8 16
 
0.8%
9 12
 
0.6%
ValueCountFrequency (%)
852 1
 
< 0.1%
145 2
0.1%
130 1
 
< 0.1%
129 2
0.1%
125 1
 
< 0.1%
121 1
 
< 0.1%
113 1
 
< 0.1%
94 4
0.2%
89 1
 
< 0.1%
74 2
0.1%
Distinct164
Distinct (%)8.2%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:07.105582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.4305694
Min length1

Characters and Unicode

Total characters2864
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)5.6%

Sample

1st row1917996
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 1513
75.6%
x 261
 
13.0%
4012 4
 
0.2%
215342 4
 
0.2%
8087 4
 
0.2%
25035 4
 
0.2%
13225 3
 
0.1%
29885 3
 
0.1%
12241 3
 
0.1%
7402 3
 
0.1%
Other values (154) 200
 
10.0%
2023-12-12T11:37:07.565562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1602
55.9%
X 261
 
9.1%
1 166
 
5.8%
2 137
 
4.8%
4 115
 
4.0%
5 114
 
4.0%
3 113
 
3.9%
7 102
 
3.6%
8 91
 
3.2%
6 86
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2603
90.9%
Uppercase Letter 261
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1602
61.5%
1 166
 
6.4%
2 137
 
5.3%
4 115
 
4.4%
5 114
 
4.4%
3 113
 
4.3%
7 102
 
3.9%
8 91
 
3.5%
6 86
 
3.3%
9 77
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
X 261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2603
90.9%
Latin 261
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1602
61.5%
1 166
 
6.4%
2 137
 
5.3%
4 115
 
4.4%
5 114
 
4.4%
3 113
 
4.3%
7 102
 
3.9%
8 91
 
3.5%
6 86
 
3.3%
9 77
 
3.0%
Latin
ValueCountFrequency (%)
X 261
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1602
55.9%
X 261
 
9.1%
1 166
 
5.8%
2 137
 
4.8%
4 115
 
4.0%
5 114
 
4.0%
3 113
 
3.9%
7 102
 
3.6%
8 91
 
3.2%
6 86
 
3.0%
Distinct1271
Distinct (%)65.1%
Missing53
Missing (%)2.6%
Memory size15.8 KiB
2023-12-12T11:37:07.926403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.3264992
Min length1

Characters and Unicode

Total characters6490
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique968 ?
Unique (%)49.6%

Sample

1st row4816448
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 191
 
9.8%
3 14
 
0.7%
1 9
 
0.5%
9 9
 
0.5%
12 9
 
0.5%
10 7
 
0.4%
49 6
 
0.3%
6 6
 
0.3%
32 6
 
0.3%
106 6
 
0.3%
Other values (1261) 1688
86.5%
2023-12-12T11:37:08.516183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1092
16.8%
2 751
11.6%
0 693
10.7%
3 686
10.6%
4 605
9.3%
5 571
8.8%
6 553
8.5%
9 539
8.3%
7 511
7.9%
8 484
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6485
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1092
16.8%
2 751
11.6%
0 693
10.7%
3 686
10.6%
4 605
9.3%
5 571
8.8%
6 553
8.5%
9 539
8.3%
7 511
7.9%
8 484
7.5%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6485
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1092
16.8%
2 751
11.6%
0 693
10.7%
3 686
10.6%
4 605
9.3%
5 571
8.8%
6 553
8.5%
9 539
8.3%
7 511
7.9%
8 484
7.5%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1092
16.8%
2 751
11.6%
0 693
10.7%
3 686
10.6%
4 605
9.3%
5 571
8.8%
6 553
8.5%
9 539
8.3%
7 511
7.9%
8 484
7.5%
Distinct666
Distinct (%)35.2%
Missing111
Missing (%)5.5%
Memory size15.8 KiB
2023-12-12T11:37:08.984070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3359746
Min length1

Characters and Unicode

Total characters4422
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique428 ?
Unique (%)22.6%

Sample

1st row483437
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 219
 
11.6%
1 50
 
2.6%
3 47
 
2.5%
2 38
 
2.0%
5 33
 
1.7%
4 30
 
1.6%
6 27
 
1.4%
8 23
 
1.2%
10 21
 
1.1%
7 21
 
1.1%
Other values (656) 1384
73.1%
2023-12-12T11:37:09.621351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 827
18.7%
2 530
12.0%
0 493
11.1%
3 469
10.6%
5 412
9.3%
4 405
9.2%
6 353
8.0%
7 325
 
7.3%
9 318
 
7.2%
8 285
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4417
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 827
18.7%
2 530
12.0%
0 493
11.2%
3 469
10.6%
5 412
9.3%
4 405
9.2%
6 353
8.0%
7 325
 
7.4%
9 318
 
7.2%
8 285
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4417
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 827
18.7%
2 530
12.0%
0 493
11.2%
3 469
10.6%
5 412
9.3%
4 405
9.2%
6 353
8.0%
7 325
 
7.4%
9 318
 
7.2%
8 285
 
6.5%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 827
18.7%
2 530
12.0%
0 493
11.1%
3 469
10.6%
5 412
9.3%
4 405
9.2%
6 353
8.0%
7 325
 
7.3%
9 318
 
7.2%
8 285
 
6.4%
Distinct1671
Distinct (%)83.5%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:10.085570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.4080919
Min length1

Characters and Unicode

Total characters8825
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1404 ?
Unique (%)70.1%

Sample

1st row16083945
2nd row49889
3rd row38387
4th row18188
5th row6116
ValueCountFrequency (%)
215 5
 
0.2%
x 5
 
0.2%
2269 4
 
0.2%
2434 4
 
0.2%
110 4
 
0.2%
429 4
 
0.2%
831 4
 
0.2%
1105 4
 
0.2%
10152 3
 
0.1%
5319 3
 
0.1%
Other values (1661) 1962
98.0%
2023-12-12T11:37:10.672067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1347
15.3%
2 1031
11.7%
3 978
11.1%
6 809
9.2%
4 807
9.1%
5 792
9.0%
0 780
8.8%
7 769
8.7%
9 760
8.6%
8 747
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8820
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1347
15.3%
2 1031
11.7%
3 978
11.1%
6 809
9.2%
4 807
9.1%
5 792
9.0%
0 780
8.8%
7 769
8.7%
9 760
8.6%
8 747
8.5%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8820
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1347
15.3%
2 1031
11.7%
3 978
11.1%
6 809
9.2%
4 807
9.1%
5 792
9.0%
0 780
8.8%
7 769
8.7%
9 760
8.6%
8 747
8.5%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1347
15.3%
2 1031
11.7%
3 978
11.1%
6 809
9.2%
4 807
9.1%
5 792
9.0%
0 780
8.8%
7 769
8.7%
9 760
8.6%
8 747
8.5%
Distinct1244
Distinct (%)62.4%
Missing11
Missing (%)0.5%
Memory size15.8 KiB
2023-12-12T11:37:11.203020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.2995484
Min length1

Characters and Unicode

Total characters6576
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique841 ?
Unique (%)42.2%

Sample

1st row2731109
2nd row12855
3rd row8057
4th row5793
5th row1782
ValueCountFrequency (%)
72 10
 
0.5%
38 9
 
0.5%
2 8
 
0.4%
82 8
 
0.4%
40 8
 
0.4%
74 8
 
0.4%
1 8
 
0.4%
95 8
 
0.4%
6 8
 
0.4%
310 7
 
0.4%
Other values (1234) 1911
95.9%
2023-12-12T11:37:11.825027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1053
16.0%
2 879
13.4%
3 757
11.5%
4 650
9.9%
5 608
9.2%
6 608
9.2%
9 547
8.3%
0 494
7.5%
7 492
7.5%
8 483
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6571
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1053
16.0%
2 879
13.4%
3 757
11.5%
4 650
9.9%
5 608
9.3%
6 608
9.3%
9 547
8.3%
0 494
7.5%
7 492
7.5%
8 483
7.4%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6571
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1053
16.0%
2 879
13.4%
3 757
11.5%
4 650
9.9%
5 608
9.3%
6 608
9.3%
9 547
8.3%
0 494
7.5%
7 492
7.5%
8 483
7.4%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1053
16.0%
2 879
13.4%
3 757
11.5%
4 650
9.9%
5 608
9.2%
6 608
9.2%
9 547
8.3%
0 494
7.5%
7 492
7.5%
8 483
7.3%

기타 종사자수
Text

MISSING 

Distinct648
Distinct (%)33.0%
Missing42
Missing (%)2.1%
Memory size15.8 KiB
2023-12-12T11:37:12.267788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3944954
Min length1

Characters and Unicode

Total characters4698
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique385 ?
Unique (%)19.6%

Sample

1st row816661
2nd row3419
3rd row1673
4th row1264
5th row478
ValueCountFrequency (%)
0 121
 
6.2%
1 51
 
2.6%
3 45
 
2.3%
4 41
 
2.1%
6 39
 
2.0%
2 36
 
1.8%
7 35
 
1.8%
10 34
 
1.7%
12 30
 
1.5%
5 23
 
1.2%
Other values (638) 1507
76.8%
2023-12-12T11:37:12.796345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 903
19.2%
2 596
12.7%
3 533
11.3%
0 424
9.0%
5 423
9.0%
4 410
8.7%
6 408
8.7%
8 353
 
7.5%
7 323
 
6.9%
9 320
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4693
99.9%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 903
19.2%
2 596
12.7%
3 533
11.4%
0 424
9.0%
5 423
9.0%
4 410
8.7%
6 408
8.7%
8 353
 
7.5%
7 323
 
6.9%
9 320
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
X 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4693
99.9%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 903
19.2%
2 596
12.7%
3 533
11.4%
0 424
9.0%
5 423
9.0%
4 410
8.7%
6 408
8.7%
8 353
 
7.5%
7 323
 
6.9%
9 320
 
6.8%
Latin
ValueCountFrequency (%)
X 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 903
19.2%
2 596
12.7%
3 533
11.3%
0 424
9.0%
5 423
9.0%
4 410
8.7%
6 408
8.7%
8 353
 
7.5%
7 323
 
6.9%
9 320
 
6.8%

대표자남자 사업체수
Real number (ℝ)

SKEWED 

Distinct1430
Distinct (%)71.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11523.171
Minimum2
Maximum3844898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:12.954682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39.05
Q1340.5
median1268
Q34689.5
95-th percentile36731.65
Maximum3844898
Range3844896
Interquartile range (IQR)4349

Descriptive statistics

Standard deviation94847.125
Coefficient of variation (CV)8.2309919
Kurtosis1340.2421
Mean11523.171
Median Absolute Deviation (MAD)1133.5
Skewness33.905562
Sum23069388
Variance8.9959772 × 109
MonotonicityNot monotonic
2023-12-12T11:37:13.118626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 8
 
0.4%
2 8
 
0.4%
14 7
 
0.3%
10 7
 
0.3%
134 7
 
0.3%
517 6
 
0.3%
12 6
 
0.3%
37 6
 
0.3%
46 6
 
0.3%
184 6
 
0.3%
Other values (1420) 1935
96.6%
ValueCountFrequency (%)
2 8
0.4%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 4
0.2%
6 3
 
0.1%
8 1
 
< 0.1%
9 2
 
0.1%
10 7
0.3%
11 1
 
< 0.1%
12 6
0.3%
ValueCountFrequency (%)
3844898 1
< 0.1%
886940 1
< 0.1%
556499 1
< 0.1%
520979 1
< 0.1%
474616 1
< 0.1%
445047 1
< 0.1%
383701 1
< 0.1%
377793 1
< 0.1%
362959 1
< 0.1%
344273 1
< 0.1%
Distinct1678
Distinct (%)83.8%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:13.531133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.497003
Min length1

Characters and Unicode

Total characters9003
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1418 ?
Unique (%)70.8%

Sample

1st row18979332
2nd row56256
3rd row41719
4th row20760
5th row6998
ValueCountFrequency (%)
x 8
 
0.4%
672 5
 
0.2%
2438 4
 
0.2%
469 4
 
0.2%
2603 4
 
0.2%
21441 4
 
0.2%
706 4
 
0.2%
5021 4
 
0.2%
17048 3
 
0.1%
44 3
 
0.1%
Other values (1668) 1959
97.9%
2023-12-12T11:37:14.085749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1365
15.2%
2 1080
12.0%
3 961
10.7%
4 870
9.7%
5 866
9.6%
6 802
8.9%
9 777
8.6%
8 767
8.5%
7 759
8.4%
0 748
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8995
99.9%
Uppercase Letter 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1365
15.2%
2 1080
12.0%
3 961
10.7%
4 870
9.7%
5 866
9.6%
6 802
8.9%
9 777
8.6%
8 767
8.5%
7 759
8.4%
0 748
8.3%
Uppercase Letter
ValueCountFrequency (%)
X 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8995
99.9%
Latin 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1365
15.2%
2 1080
12.0%
3 961
10.7%
4 870
9.7%
5 866
9.6%
6 802
8.9%
9 777
8.6%
8 767
8.5%
7 759
8.4%
0 748
8.3%
Latin
ValueCountFrequency (%)
X 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1365
15.2%
2 1080
12.0%
3 961
10.7%
4 870
9.7%
5 866
9.6%
6 802
8.9%
9 777
8.6%
8 767
8.5%
7 759
8.4%
0 748
8.3%

대표자여자 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1112
Distinct (%)55.5%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6697.7143
Minimum0
Maximum2234804
Zeros27
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:14.227228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q174.25
median367.5
Q31868.75
95-th percentile19776.2
Maximum2234804
Range2234804
Interquartile range (IQR)1794.5

Descriptive statistics

Standard deviation58037.259
Coefficient of variation (CV)8.6652337
Kurtosis1101.7646
Mean6697.7143
Median Absolute Deviation (MAD)350.5
Skewness30.114794
Sum13408824
Variance3.3683235 × 109
MonotonicityNot monotonic
2023-12-12T11:37:14.591676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34
 
1.7%
0 27
 
1.3%
12 18
 
0.9%
3 17
 
0.8%
7 16
 
0.8%
8 15
 
0.7%
2 14
 
0.7%
9 12
 
0.6%
10 12
 
0.6%
16 11
 
0.5%
Other values (1102) 1826
91.1%
ValueCountFrequency (%)
0 27
1.3%
1 34
1.7%
2 14
0.7%
3 17
0.8%
4 6
 
0.3%
5 2
 
0.1%
6 10
 
0.5%
7 16
0.8%
8 15
0.7%
9 12
 
0.6%
ValueCountFrequency (%)
2234804 1
< 0.1%
649289 1
< 0.1%
511202 1
< 0.1%
500050 1
< 0.1%
469202 1
< 0.1%
325460 1
< 0.1%
228913 1
< 0.1%
203923 1
< 0.1%
187675 1
< 0.1%
159821 1
< 0.1%
Distinct1460
Distinct (%)72.9%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:14.885521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.6638362
Min length1

Characters and Unicode

Total characters7335
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1114 ?
Unique (%)55.6%

Sample

1st row5952268
2nd row9907
3rd row6398
4th row4485
5th row1378
ValueCountFrequency (%)
x 48
 
2.4%
0 27
 
1.3%
232 8
 
0.4%
8 8
 
0.4%
40 6
 
0.3%
1892 5
 
0.2%
18 5
 
0.2%
300 4
 
0.2%
98 4
 
0.2%
681 4
 
0.2%
Other values (1450) 1883
94.1%
2023-12-12T11:37:15.317396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1165
15.9%
2 858
11.7%
3 830
11.3%
4 700
9.5%
8 643
8.8%
5 641
8.7%
6 637
8.7%
7 615
8.4%
0 600
8.2%
9 598
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7287
99.3%
Uppercase Letter 48
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1165
16.0%
2 858
11.8%
3 830
11.4%
4 700
9.6%
8 643
8.8%
5 641
8.8%
6 637
8.7%
7 615
8.4%
0 600
8.2%
9 598
8.2%
Uppercase Letter
ValueCountFrequency (%)
X 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7287
99.3%
Latin 48
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1165
16.0%
2 858
11.8%
3 830
11.4%
4 700
9.6%
8 643
8.8%
5 641
8.8%
6 637
8.7%
7 615
8.4%
0 600
8.2%
9 598
8.2%
Latin
ValueCountFrequency (%)
X 48
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1165
15.9%
2 858
11.7%
3 830
11.3%
4 700
9.5%
8 643
8.8%
5 641
8.7%
6 637
8.7%
7 615
8.4%
0 600
8.2%
9 598
8.2%

20세미만 대표자 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct69
Distinct (%)3.4%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.3816184
Minimum0
Maximum2463
Zeros1226
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:15.449309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile17
Maximum2463
Range2463
Interquartile range (IQR)2

Descriptive statistics

Standard deviation80.289072
Coefficient of variation (CV)10.876893
Kurtosis549.33624
Mean7.3816184
Median Absolute Deviation (MAD)0
Skewness21.840198
Sum14778
Variance6446.3351
MonotonicityNot monotonic
2023-12-12T11:37:15.557448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1226
61.2%
1 240
 
12.0%
2 130
 
6.5%
3 107
 
5.3%
5 31
 
1.5%
4 29
 
1.4%
6 28
 
1.4%
7 26
 
1.3%
9 16
 
0.8%
8 15
 
0.7%
Other values (59) 154
 
7.7%
ValueCountFrequency (%)
0 1226
61.2%
1 240
 
12.0%
2 130
 
6.5%
3 107
 
5.3%
4 29
 
1.4%
5 31
 
1.5%
6 28
 
1.4%
7 26
 
1.3%
8 15
 
0.7%
9 16
 
0.8%
ValueCountFrequency (%)
2463 1
< 0.1%
1443 1
< 0.1%
1196 1
< 0.1%
1057 1
< 0.1%
1044 1
< 0.1%
957 1
< 0.1%
230 1
< 0.1%
140 1
< 0.1%
128 1
< 0.1%
114 1
< 0.1%
Distinct98
Distinct (%)4.9%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:15.723826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.1398601
Min length1

Characters and Unicode

Total characters2282
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)2.2%

Sample

1st row3972
2nd row28
3rd row28
4th row23
5th row23
ValueCountFrequency (%)
0 1226
61.2%
x 370
 
18.5%
4 36
 
1.8%
3 36
 
1.8%
9 27
 
1.3%
5 27
 
1.3%
7 21
 
1.0%
10 14
 
0.7%
11 13
 
0.6%
18 12
 
0.6%
Other values (88) 220
 
11.0%
2023-12-12T11:37:15.999930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1257
55.1%
X 370
 
16.2%
1 159
 
7.0%
3 93
 
4.1%
2 78
 
3.4%
4 67
 
2.9%
5 61
 
2.7%
7 59
 
2.6%
6 48
 
2.1%
8 46
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1912
83.8%
Uppercase Letter 370
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1257
65.7%
1 159
 
8.3%
3 93
 
4.9%
2 78
 
4.1%
4 67
 
3.5%
5 61
 
3.2%
7 59
 
3.1%
6 48
 
2.5%
8 46
 
2.4%
9 44
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
X 370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1912
83.8%
Latin 370
 
16.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1257
65.7%
1 159
 
8.3%
3 93
 
4.9%
2 78
 
4.1%
4 67
 
3.5%
5 61
 
3.2%
7 59
 
3.1%
6 48
 
2.5%
8 46
 
2.4%
9 44
 
2.3%
Latin
ValueCountFrequency (%)
X 370
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1257
55.1%
X 370
 
16.2%
1 159
 
7.0%
3 93
 
4.1%
2 78
 
3.4%
4 67
 
2.9%
5 61
 
2.7%
7 59
 
2.6%
6 48
 
2.1%
8 46
 
2.0%

20-29세 대표자 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct539
Distinct (%)26.9%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean751.08492
Minimum0
Maximum250612
Zeros192
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:16.133733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median32
Q3182.75
95-th percentile2209.95
Maximum250612
Range250612
Interquartile range (IQR)176.75

Descriptive statistics

Standard deviation6790.9828
Coefficient of variation (CV)9.0415647
Kurtosis941.86044
Mean751.08492
Median Absolute Deviation (MAD)32
Skewness27.550397
Sum1503672
Variance46117448
MonotonicityNot monotonic
2023-12-12T11:37:16.275240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192
 
9.6%
1 86
 
4.3%
2 77
 
3.8%
6 48
 
2.4%
5 48
 
2.4%
3 47
 
2.3%
4 40
 
2.0%
8 34
 
1.7%
9 33
 
1.6%
13 27
 
1.3%
Other values (529) 1370
68.4%
ValueCountFrequency (%)
0 192
9.6%
1 86
4.3%
2 77
3.8%
3 47
 
2.3%
4 40
 
2.0%
5 48
 
2.4%
6 48
 
2.4%
7 25
 
1.2%
8 34
 
1.7%
9 33
 
1.6%
ValueCountFrequency (%)
250612 1
< 0.1%
87085 1
< 0.1%
72714 1
< 0.1%
55889 1
< 0.1%
54759 1
< 0.1%
44972 1
< 0.1%
44425 1
< 0.1%
38404 1
< 0.1%
34312 1
< 0.1%
20447 1
< 0.1%
Distinct753
Distinct (%)37.6%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:16.604717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.457043
Min length1

Characters and Unicode

Total characters4919
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)23.0%

Sample

1st row478551
2nd row1018
3rd row720
4th row607
5th row289
ValueCountFrequency (%)
0 192
 
9.6%
x 163
 
8.1%
20 16
 
0.8%
11 15
 
0.7%
14 14
 
0.7%
15 14
 
0.7%
12 13
 
0.6%
6 12
 
0.6%
29 12
 
0.6%
9 12
 
0.6%
Other values (743) 1539
76.9%
2023-12-12T11:37:17.159888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 748
15.2%
2 579
11.8%
0 521
10.6%
3 517
10.5%
4 455
9.2%
5 431
8.8%
7 394
8.0%
6 376
7.6%
9 374
7.6%
8 361
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4756
96.7%
Uppercase Letter 163
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 748
15.7%
2 579
12.2%
0 521
11.0%
3 517
10.9%
4 455
9.6%
5 431
9.1%
7 394
8.3%
6 376
7.9%
9 374
7.9%
8 361
7.6%
Uppercase Letter
ValueCountFrequency (%)
X 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4756
96.7%
Latin 163
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 748
15.7%
2 579
12.2%
0 521
11.0%
3 517
10.9%
4 455
9.6%
5 431
9.1%
7 394
8.3%
6 376
7.9%
9 374
7.9%
8 361
7.6%
Latin
ValueCountFrequency (%)
X 163
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 748
15.2%
2 579
11.8%
0 521
10.6%
3 517
10.5%
4 455
9.2%
5 431
8.8%
7 394
8.0%
6 376
7.6%
9 374
7.6%
8 361
7.3%

30-39세 대표자 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct881
Distinct (%)44.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2544.0929
Minimum0
Maximum848879
Zeros88
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:17.327533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q131
median153
Q3813.75
95-th percentile7548.35
Maximum848879
Range848879
Interquartile range (IQR)782.75

Descriptive statistics

Standard deviation21819.764
Coefficient of variation (CV)8.5766382
Kurtosis1147.5448
Mean2544.0929
Median Absolute Deviation (MAD)148
Skewness30.90747
Sum5093274
Variance4.7610212 × 108
MonotonicityNot monotonic
2023-12-12T11:37:17.493385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88
 
4.4%
1 38
 
1.9%
3 27
 
1.3%
4 24
 
1.2%
8 23
 
1.1%
30 22
 
1.1%
2 19
 
0.9%
6 18
 
0.9%
17 16
 
0.8%
10 15
 
0.7%
Other values (871) 1712
85.4%
ValueCountFrequency (%)
0 88
4.4%
1 38
1.9%
2 19
 
0.9%
3 27
 
1.3%
4 24
 
1.2%
5 11
 
0.5%
6 18
 
0.9%
7 14
 
0.7%
8 23
 
1.1%
9 13
 
0.6%
ValueCountFrequency (%)
848879 1
< 0.1%
263322 1
< 0.1%
195873 1
< 0.1%
150949 1
< 0.1%
145517 1
< 0.1%
97049 1
< 0.1%
91345 1
< 0.1%
88359 1
< 0.1%
72784 1
< 0.1%
62266 1
< 0.1%
Distinct1229
Distinct (%)61.4%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:17.839231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.2097902
Min length1

Characters and Unicode

Total characters6426
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique864 ?
Unique (%)43.2%

Sample

1st row2114418
2nd row4654
3rd row3320
4th row2089
5th row742
ValueCountFrequency (%)
0 88
 
4.4%
x 57
 
2.8%
7 8
 
0.4%
74 8
 
0.4%
51 8
 
0.4%
199 7
 
0.3%
85 6
 
0.3%
10 6
 
0.3%
70 6
 
0.3%
21 6
 
0.3%
Other values (1219) 1802
90.0%
2023-12-12T11:37:18.389550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1030
16.0%
2 777
12.1%
3 682
10.6%
4 643
10.0%
0 618
9.6%
5 531
8.3%
9 531
8.3%
7 526
8.2%
8 526
8.2%
6 505
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6369
99.1%
Uppercase Letter 57
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1030
16.2%
2 777
12.2%
3 682
10.7%
4 643
10.1%
0 618
9.7%
5 531
8.3%
9 531
8.3%
7 526
8.3%
8 526
8.3%
6 505
7.9%
Uppercase Letter
ValueCountFrequency (%)
X 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6369
99.1%
Latin 57
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1030
16.2%
2 777
12.2%
3 682
10.7%
4 643
10.1%
0 618
9.7%
5 531
8.3%
9 531
8.3%
7 526
8.3%
8 526
8.3%
6 505
7.9%
Latin
ValueCountFrequency (%)
X 57
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1030
16.0%
2 777
12.1%
3 682
10.6%
4 643
10.0%
0 618
9.6%
5 531
8.3%
9 531
8.3%
7 526
8.2%
8 526
8.2%
6 505
7.9%

40-49세 대표자 사업체수
Real number (ℝ)

SKEWED  ZEROS 

Distinct1124
Distinct (%)56.1%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4826.9311
Minimum0
Maximum1610586
Zeros24
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:18.553128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q191.25
median404
Q31893.25
95-th percentile14609.4
Maximum1610586
Range1610586
Interquartile range (IQR)1802

Descriptive statistics

Standard deviation39902.293
Coefficient of variation (CV)8.2665968
Kurtosis1319.6379
Mean4826.9311
Median Absolute Deviation (MAD)384
Skewness33.623393
Sum9663516
Variance1.592193 × 109
MonotonicityNot monotonic
2023-12-12T11:37:18.710545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
1.2%
2 20
 
1.0%
3 20
 
1.0%
11 17
 
0.8%
4 16
 
0.8%
8 15
 
0.7%
17 13
 
0.6%
1 12
 
0.6%
6 12
 
0.6%
12 11
 
0.5%
Other values (1114) 1842
91.9%
ValueCountFrequency (%)
0 24
1.2%
1 12
0.6%
2 20
1.0%
3 20
1.0%
4 16
0.8%
5 2
 
0.1%
6 12
0.6%
7 1
 
< 0.1%
8 15
0.7%
9 9
 
0.4%
ValueCountFrequency (%)
1610586 1
< 0.1%
423874 1
< 0.1%
257698 1
< 0.1%
208578 1
< 0.1%
196190 1
< 0.1%
153067 1
< 0.1%
139434 1
< 0.1%
137821 1
< 0.1%
137577 1
< 0.1%
131523 1
< 0.1%
Distinct1524
Distinct (%)76.1%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:19.085758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7872128
Min length1

Characters and Unicode

Total characters7582
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1198 ?
Unique (%)59.8%

Sample

1st row5220375
2nd row12207
3rd row8438
4th row5322
5th row1743
ValueCountFrequency (%)
x 32
 
1.6%
0 24
 
1.2%
267 5
 
0.2%
9 5
 
0.2%
13 5
 
0.2%
1076 5
 
0.2%
1603 4
 
0.2%
41 4
 
0.2%
25 4
 
0.2%
11 4
 
0.2%
Other values (1514) 1910
95.4%
2023-12-12T11:37:19.543338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1241
16.4%
2 894
11.8%
3 791
10.4%
4 763
10.1%
5 678
8.9%
6 670
8.8%
7 665
8.8%
0 656
8.7%
8 606
8.0%
9 586
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7550
99.6%
Uppercase Letter 32
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1241
16.4%
2 894
11.8%
3 791
10.5%
4 763
10.1%
5 678
9.0%
6 670
8.9%
7 665
8.8%
0 656
8.7%
8 606
8.0%
9 586
7.8%
Uppercase Letter
ValueCountFrequency (%)
X 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7550
99.6%
Latin 32
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1241
16.4%
2 894
11.8%
3 791
10.5%
4 763
10.1%
5 678
9.0%
6 670
8.9%
7 665
8.8%
0 656
8.7%
8 606
8.0%
9 586
7.8%
Latin
ValueCountFrequency (%)
X 32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1241
16.4%
2 894
11.8%
3 791
10.4%
4 763
10.1%
5 678
8.9%
6 670
8.8%
7 665
8.8%
0 656
8.7%
8 606
8.0%
9 586
7.7%

50-59세 대표자 사업체수
Real number (ℝ)

SKEWED 

Distinct1237
Distinct (%)61.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5831.2268
Minimum0
Maximum1945686
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:19.684083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q1157.25
median603.5
Q32405.5
95-th percentile19972.2
Maximum1945686
Range1945686
Interquartile range (IQR)2248.25

Descriptive statistics

Standard deviation47947.183
Coefficient of variation (CV)8.2224864
Kurtosis1345.6663
Mean5831.2268
Median Absolute Deviation (MAD)546
Skewness33.989712
Sum11674116
Variance2.2989323 × 109
MonotonicityNot monotonic
2023-12-12T11:37:19.841790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
0.4%
24 9
 
0.4%
8 8
 
0.4%
34 8
 
0.4%
5 8
 
0.4%
16 8
 
0.4%
92 8
 
0.4%
68 8
 
0.4%
55 8
 
0.4%
127 7
 
0.3%
Other values (1227) 1921
95.9%
ValueCountFrequency (%)
0 5
0.2%
1 3
 
0.1%
2 9
0.4%
3 5
0.2%
4 7
0.3%
5 8
0.4%
6 7
0.3%
7 2
 
0.1%
8 8
0.4%
9 4
0.2%
ValueCountFrequency (%)
1945686 1
< 0.1%
449477 1
< 0.1%
262888 1
< 0.1%
261719 1
< 0.1%
245406 1
< 0.1%
219201 1
< 0.1%
193604 1
< 0.1%
187998 1
< 0.1%
178412 1
< 0.1%
177744 1
< 0.1%
Distinct1627
Distinct (%)81.3%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:20.259917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.1588412
Min length1

Characters and Unicode

Total characters8326
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1330 ?
Unique (%)66.4%

Sample

1st row9954918
2nd row27841
3rd row21688
4th row9287
5th row2748
ValueCountFrequency (%)
x 12
 
0.6%
767 6
 
0.3%
146 5
 
0.2%
0 5
 
0.2%
11949 4
 
0.2%
2121 4
 
0.2%
538 4
 
0.2%
1773 4
 
0.2%
1661 4
 
0.2%
62 4
 
0.2%
Other values (1617) 1950
97.4%
2023-12-12T11:37:20.787385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1295
15.6%
2 984
11.8%
3 867
10.4%
4 814
9.8%
6 786
9.4%
5 786
9.4%
7 729
8.8%
0 710
8.5%
9 673
8.1%
8 670
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8314
99.9%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1295
15.6%
2 984
11.8%
3 867
10.4%
4 814
9.8%
6 786
9.5%
5 786
9.5%
7 729
8.8%
0 710
8.5%
9 673
8.1%
8 670
8.1%
Uppercase Letter
ValueCountFrequency (%)
X 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8314
99.9%
Latin 12
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1295
15.6%
2 984
11.8%
3 867
10.4%
4 814
9.8%
6 786
9.5%
5 786
9.5%
7 729
8.8%
0 710
8.5%
9 673
8.1%
8 670
8.1%
Latin
ValueCountFrequency (%)
X 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1295
15.6%
2 984
11.8%
3 867
10.4%
4 814
9.8%
6 786
9.4%
5 786
9.4%
7 729
8.8%
0 710
8.5%
9 673
8.1%
8 670
8.0%

60세 이상 대표자 사업체수
Real number (ℝ)

SKEWED 

Distinct1111
Distinct (%)55.5%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4260.1678
Minimum0
Maximum1421476
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T11:37:20.937255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q1108.25
median402.5
Q31429.75
95-th percentile12474.75
Maximum1421476
Range1421476
Interquartile range (IQR)1321.5

Descriptive statistics

Standard deviation35476.15
Coefficient of variation (CV)8.3274067
Kurtosis1279.3194
Mean4260.1678
Median Absolute Deviation (MAD)359.5
Skewness32.831271
Sum8528856
Variance1.2585572 × 109
MonotonicityNot monotonic
2023-12-12T11:37:21.113852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
1.0%
6 11
 
0.5%
8 11
 
0.5%
11 11
 
0.5%
45 11
 
0.5%
70 10
 
0.5%
3 10
 
0.5%
114 10
 
0.5%
0 10
 
0.5%
39 9
 
0.4%
Other values (1101) 1889
94.3%
ValueCountFrequency (%)
0 10
0.5%
1 20
1.0%
2 5
 
0.2%
3 10
0.5%
4 6
 
0.3%
5 6
 
0.3%
6 11
0.5%
7 7
 
0.3%
8 11
0.5%
9 4
 
0.2%
ValueCountFrequency (%)
1421476 1
< 0.1%
311028 1
< 0.1%
226273 1
< 0.1%
212522 1
< 0.1%
196618 1
< 0.1%
184622 1
< 0.1%
158696 1
< 0.1%
154774 1
< 0.1%
123286 1
< 0.1%
119536 1
< 0.1%
Distinct1567
Distinct (%)78.3%
Missing2
Missing (%)0.1%
Memory size15.8 KiB
2023-12-12T11:37:21.535937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.9925075
Min length1

Characters and Unicode

Total characters7993
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1252 ?
Unique (%)62.5%

Sample

1st row7159366
2nd row20415
3rd row13923
4th row7917
5th row2831
ValueCountFrequency (%)
x 25
 
1.2%
0 10
 
0.5%
461 7
 
0.3%
657 5
 
0.2%
1680 5
 
0.2%
217 5
 
0.2%
1824 5
 
0.2%
1895 5
 
0.2%
5558 4
 
0.2%
971 4
 
0.2%
Other values (1557) 1927
96.3%
2023-12-12T11:37:22.094074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1200
15.0%
2 979
12.2%
3 809
10.1%
4 808
10.1%
6 767
9.6%
5 749
9.4%
7 730
9.1%
8 667
8.3%
0 634
7.9%
9 625
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7968
99.7%
Uppercase Letter 25
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1200
15.1%
2 979
12.3%
3 809
10.2%
4 808
10.1%
6 767
9.6%
5 749
9.4%
7 730
9.2%
8 667
8.4%
0 634
8.0%
9 625
7.8%
Uppercase Letter
ValueCountFrequency (%)
X 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7968
99.7%
Latin 25
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1200
15.1%
2 979
12.3%
3 809
10.2%
4 808
10.1%
6 767
9.6%
5 749
9.4%
7 730
9.2%
8 667
8.4%
0 634
8.0%
9 625
7.8%
Latin
ValueCountFrequency (%)
X 25
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1200
15.0%
2 979
12.2%
3 809
10.1%
4 808
10.1%
6 767
9.6%
5 749
9.4%
7 730
9.1%
8 667
8.3%
0 634
7.9%
9 625
7.8%

Sample

행정구역 및 산업분류 코드행정구역 명칭산업분류명칭총사업체수총종사자수남자종사자수여자종사자수개인사업체수개인종사자수회사법인사업체수회사법인종사자수회사이외법인사업체수회사이외법인종사자수비법인단체사업체수비법인단체종사자수단독사업체 사업체수단독사업체 종사자수공장지사 사업체수공장지사 종사자수본사본점 사업체수본사본점 종사자수1-4명 사업체수1-4명 종사자수5-9명 사업체수5-9명 종사자수10-19명 사업체수10-19명 종사자수20-49명 사업체수20-49명 종사자수50-99명 사업체수50-99명 종사자수100-299명 사업체수100-299명 종사자수300-499명 사업체수300-499명 종사자수500-999명 사업체수500-999명 종사자수1000명이상 사업체수1000명이상 종사자수자영업자 종사자수무급가족 종사자수상용 종사자수임시및일용 종사자수기타 종사자수대표자남자 사업체수대표자남자 종사자수대표자여자 사업체수대표자여자 종사자수20세미만 대표자 사업체수20세미만 대표자 종사자수20-29세 대표자 사업체수20-29세 대표자 종사자수30-39세 대표자 사업체수30-39세 대표자 종사자수40-49세 대표자 사업체수40-49세 대표자 종사자수50-59세 대표자 사업체수50-59세 대표자 종사자수60세 이상 대표자 사업체수60세 이상 대표자 종사자수
00전국전체산업607970224931600142966811063491947926628853049929151109663222553194304032102570808197572923117538082265597418479184874320872752516147779460475472302071219272925430681065973167523333812292949155052455931223984883513139051268521917996481644848343716083945273110981666138448981897933222348045952268246339722506124785518488792114418161058652203751945686995491814214767159366
10000000A전국농업 임업 및 어업(0103)12775661634701519148007257339085370304861481769113784759294912660448591191171607521801405290911890459136698054302837461X1X0000498891285534191038656256238999072328377101812514654279412207460427841372620415
20000000A01전국농업10589481173285315264005793230424707247608931593653250686511327359428480641388114559022653853835010243483155182423001X000038387805716738768417191821639823283217201045332023198438384521688303613923
30000000A011전국작물 재배업756725245155039742004253138023267113104713371732245620815101861279613610117929569836647491223308746079130000000018188579312646131207601436448521232616077752089167953222611928722207917
40000000A0111전국곡물 및 기타 식량작물 재배업35578376585325230015823660196546881028341776127235368411315445972951743841064205332X2X0000000061161782478297269985851378212315428941174282017431149274810022831
50000000A01110전국곡물 및 기타 식량작물 재배업35578376585325230015823660196546881028341776127235368411315445972951743841064205332X2X0000000061161782478297269985851378212315428941174282017431149274810022831
60000000A0112전국채소 화훼작물 및 종묘 재배업181374434230321300120745465922860143716926521695265239613362431299186813117134411421X2X00000000495321213691421614539212980044150157600389149166831225552080
70000000A01121전국채소작물 재배업102633141904141000686216832811241222965302533177281128041395158978546861025500000000000021239662258032677223637002881943412297733501130325989
80000000A01122전국화훼작물 재배업21880651229400152468663380021277732435167314321911621538600000000000<NA>5032614215663862168008451953461987828067230
90000000A01123전국종자 및 묘목 생산업56933231814150900369191019813982X5152719333252127936572210969961812318011X2X000000002327894102462283010749300824442061145202401712163861
행정구역 및 산업분류 코드행정구역 명칭산업분류명칭총사업체수총종사자수남자종사자수여자종사자수개인사업체수개인종사자수회사법인사업체수회사법인종사자수회사이외법인사업체수회사이외법인종사자수비법인단체사업체수비법인단체종사자수단독사업체 사업체수단독사업체 종사자수공장지사 사업체수공장지사 종사자수본사본점 사업체수본사본점 종사자수1-4명 사업체수1-4명 종사자수5-9명 사업체수5-9명 종사자수10-19명 사업체수10-19명 종사자수20-49명 사업체수20-49명 종사자수50-99명 사업체수50-99명 종사자수100-299명 사업체수100-299명 종사자수300-499명 사업체수300-499명 종사자수500-999명 사업체수500-999명 종사자수1000명이상 사업체수1000명이상 종사자수자영업자 종사자수무급가족 종사자수상용 종사자수임시및일용 종사자수기타 종사자수대표자남자 사업체수대표자남자 종사자수대표자여자 사업체수대표자여자 종사자수20세미만 대표자 사업체수20세미만 대표자 종사자수20-29세 대표자 사업체수20-29세 대표자 종사자수30-39세 대표자 사업체수30-39세 대표자 종사자수40-49세 대표자 사업체수40-49세 대표자 종사자수50-59세 대표자 사업체수50-59세 대표자 종사자수60세 이상 대표자 사업체수60세 이상 대표자 종사자수
19940000000S9692전국장례식장 및 관련 서비스업470820790123458445333673758468371517503091442211323638643241013230366256735563679318421414239151611031319011X00003367228124492334241237121716099636300068106363869111541221656779115067902
19950000000S96921전국장례식장 및 장의관련 서비스업3548156228448717426616350723788316213872X31909428286320472299027404211449295823431121032767117551015141X00002691165854218342390277812886770273600558429877787733911276562610425744
19960000000S96922전국화장터 운영 묘지 분양 및 관리업11605168389712716751025123488355364371210313808100112029240922146210772184110239114853483387000000676633907500229344274226894001322659223873138021654642158
19970000000S9699전국그 외 기타 분류 안된 개인 서비스업310637382224571492512843350779150018028740414139087430491656204205113152308929032347387474788664904850114552915922253663311110000285719932612412451568313693356731737038149131919462827554310064950921064867924333537315515
19980000000S96991전국예식장업6861194053206620365347730983651298005958811571812341317269500123846113160311937234832301316441X000037432542059291854599408227253200104652429157253524846922194238
19990000000S96992전국점술 및 유사 서비스업9028971133696342829087704311536843832738889599617658945901796231059001X0000000000829719389616316231713510585762012X135144475518307532673153338821882392
20000000000S96993전국개인 간병 및 유사 서비스업1869216541118205361460132102364957131304942438175818399742250371005878127618112644436056322906738240058742X00001527181352038512738386646914831518500552892151997518585167184144105103
20010000000S96994전국결혼 상담 및 준비 서비스업11693683963272095915232002142101800109627195243321531103313897043237474246902X3592000000965341695438551477185669218270045771906602831421258853393672
20020000000S96995전국애완동물 장묘 및 보호 서비스업69891003335326501663888282679487122013376913970852200241256814871014889723291413500000000006670319218171914424034046458659872X9721311233034082076288411741764435661
20030000000S96999전국그 외 기타 달리 분류되지 않은 개인 서비스업11322168011026965321072114971445150114831881111170163661203293210611021132402151290486243190831864553000000107383972412135119036797103844525641791272996022813052340051063175522217282449

Duplicate rows

Most frequently occurring

행정구역 및 산업분류 코드행정구역 명칭산업분류명칭총사업체수총종사자수남자종사자수여자종사자수개인사업체수개인종사자수회사법인사업체수회사법인종사자수회사이외법인사업체수회사이외법인종사자수비법인단체사업체수비법인단체종사자수단독사업체 사업체수단독사업체 종사자수공장지사 사업체수공장지사 종사자수본사본점 사업체수본사본점 종사자수1-4명 사업체수1-4명 종사자수5-9명 사업체수5-9명 종사자수10-19명 사업체수10-19명 종사자수20-49명 사업체수20-49명 종사자수50-99명 사업체수50-99명 종사자수100-299명 사업체수100-299명 종사자수300-499명 사업체수300-499명 종사자수500-999명 사업체수500-999명 종사자수1000명이상 사업체수1000명이상 종사자수자영업자 종사자수무급가족 종사자수상용 종사자수임시및일용 종사자수기타 종사자수대표자남자 사업체수대표자남자 종사자수대표자여자 사업체수대표자여자 종사자수20세미만 대표자 사업체수20세미만 대표자 종사자수20-29세 대표자 사업체수20-29세 대표자 종사자수30-39세 대표자 사업체수30-39세 대표자 종사자수40-49세 대표자 사업체수40-49세 대표자 종사자수50-59세 대표자 사업체수50-59세 대표자 종사자수60세 이상 대표자 사업체수60세 이상 대표자 종사자수# duplicates
00000000E360전국수도업548202061558946170024335523198691X4131390413343782X150379583866798115652377249144360191X001X001874713768353919998920800008195950543919073426092
10000000E370전국하수 폐수 및 분뇨 처리업2680231782004231361060230811631322945576382X2117114634969109672606168733194172719287397922667004227672033341X00001070108206401306542306215453741633002368152458542388511741318078955872
20000000E390전국환경 정화 및 복원업17417571451306339411311492851400108539538541336493211362362028418537748900000000333158910032144167130860032315764443567767454562