Overview

Dataset statistics

Number of variables11
Number of observations2291
Missing cells3337
Missing cells (%)13.2%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory201.5 KiB
Average record size in memory90.1 B

Variable types

Categorical3
DateTime2
Text4
Numeric2

Dataset

Description경기도 관내 이사화물업체 현황
Author가평군
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=EA69CRY94ONV9GLH1Q6P31279845&infSeq=1

Alerts

Dataset has 3 (0.1%) duplicate rowsDuplicates
정제WGS84위도 is highly overall correlated with 시군명High correlation
정제WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 정제WGS84위도 and 3 other fieldsHigh correlation
영업상태구분 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
업종구분 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
업종구분 is highly imbalanced (52.9%)Imbalance
도로명주소 has 57 (2.5%) missing valuesMissing
지번주소 has 969 (42.3%) missing valuesMissing
연락처 has 1477 (64.5%) missing valuesMissing
정제WGS84위도 has 417 (18.2%) missing valuesMissing
정제WGS84경도 has 417 (18.2%) missing valuesMissing

Reproduction

Analysis started2024-04-29 13:15:53.094181
Analysis finished2024-04-29 13:15:56.640536
Duration3.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
이천시
397 
안산시
241 
남양주시
205 
수원시
151 
구리시
129 
Other values (24)
1168 

Length

Max length4
Median length3
Mean length3.1492798
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남양주시
2nd row남양주시
3rd row남양주시
4th row남양주시
5th row구리시

Common Values

ValueCountFrequency (%)
이천시 397
17.3%
안산시 241
 
10.5%
남양주시 205
 
8.9%
수원시 151
 
6.6%
구리시 129
 
5.6%
의정부시 128
 
5.6%
용인시 105
 
4.6%
평택시 95
 
4.1%
부천시 91
 
4.0%
시흥시 89
 
3.9%
Other values (19) 660
28.8%

Length

2024-04-29T22:15:56.705923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천시 397
17.3%
안산시 241
 
10.5%
남양주시 205
 
8.9%
수원시 151
 
6.6%
구리시 129
 
5.6%
의정부시 128
 
5.6%
용인시 105
 
4.6%
평택시 95
 
4.1%
부천시 91
 
4.0%
시흥시 89
 
3.9%
Other values (19) 660
28.8%

인허가일자
Date

Distinct960
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
Minimum1979-11-20 00:00:00
Maximum2023-07-31 00:00:00
2024-04-29T22:15:56.845231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:15:56.972960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태구분
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
운영중
838 
영업중
641 
영업 중
241 
신규
218 
정상
129 
Other values (6)
224 

Length

Max length4
Median length3
Mean length2.9170668
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row영업중
2nd row영업중
3rd row영업중
4th row영업중
5th row정상

Common Values

ValueCountFrequency (%)
운영중 838
36.6%
영업중 641
28.0%
영업 중 241
 
10.5%
신규 218
 
9.5%
정상 129
 
5.6%
영업 127
 
5.5%
<NA> 68
 
3.0%
O 16
 
0.7%
폐업 등 10
 
0.4%
폐업 2
 
0.1%

Length

2024-04-29T22:15:57.104851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
운영중 838
33.0%
영업중 641
25.2%
영업 368
14.5%
중 241
 
9.5%
신규 218
 
8.6%
정상 129
 
5.1%
na 68
 
2.7%
o 16
 
0.6%
폐업 12
 
0.5%
등 10
 
0.4%

업종구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
이사화물
1350 
화물운송주선
451 
주선업
205 
이사
 
128
이사주선
 
47
Other values (12)
 
110

Length

Max length12
Median length4
Mean length4.3234395
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row주선업
2nd row주선업
3rd row주선업
4th row주선업
5th row이사화물

Common Values

ValueCountFrequency (%)
이사화물 1350
58.9%
화물운송주선 451
 
19.7%
주선업 205
 
8.9%
이사 128
 
5.6%
이사주선 47
 
2.1%
화물운송주선업 23
 
1.0%
일반화물 22
 
1.0%
일반.이사화물 19
 
0.8%
주선(이사화물) 16
 
0.7%
일반,이사 8
 
0.3%
Other values (7) 22
 
1.0%

Length

2024-04-29T22:15:57.223260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이사화물 1350
58.9%
화물운송주선 451
 
19.7%
주선업 205
 
8.9%
이사 128
 
5.6%
이사주선 47
 
2.1%
화물운송주선업 23
 
1.0%
일반화물 22
 
1.0%
일반.이사화물 19
 
0.8%
주선(이사화물 16
 
0.7%
일반,이사 8
 
0.3%
Other values (7) 22
 
1.0%

업체명
Text

Distinct1026
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2024-04-29T22:15:57.442562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length6.8171104
Min length2

Characters and Unicode

Total characters15618
Distinct characters437
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)10.1%

Sample

1st row봉성통운
2nd row대진화물
3rd row한국칠천퀵화물
4th row제일종합물류
5th row가람영구이사
ValueCountFrequency (%)
주식회사 33
 
1.3%
익스프레스 32
 
1.3%
통인익스프레스 15
 
0.6%
한솔익스프레스 12
 
0.5%
삼성익스프레스 10
 
0.4%
행운익스프레스 10
 
0.4%
한일익스프레스 9
 
0.4%
가나익스프레스 9
 
0.4%
현대익스프레스 9
 
0.4%
개미익스프레스 9
 
0.4%
Other values (1046) 2305
94.0%
2024-04-29T22:15:57.821815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 2108
 
13.5%
프 872
 
5.6%
익 863
 
5.5%
레 862
 
5.5%
이 628
 
4.0%
물 480
 
3.1%
사 480
 
3.1%
㈜ 305
 
2.0%
화 279
 
1.8%
로 240
 
1.5%
Other values (427) 8501
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14017
89.7%
Uppercase Letter 322
 
2.1%
Decimal Number 317
 
2.0%
Other Symbol 305
 
2.0%
Open Punctuation 217
 
1.4%
Close Punctuation 217
 
1.4%
Space Separator 162
 
1.0%
Other Punctuation 26
 
0.2%
Lowercase Letter 23
 
0.1%
Dash Punctuation 6
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 2108
 
15.0%
프 872
 
6.2%
익 863
 
6.2%
레 862
 
6.1%
이 628
 
4.5%
물 480
 
3.4%
사 480
 
3.4%
화 279
 
2.0%
로 240
 
1.7%
주 236
 
1.7%
Other values (381) 6969
49.7%
Uppercase Letter
ValueCountFrequency (%)
K 84
26.1%
G 58
18.0%
B 43
13.4%
S 28
 
8.7%
O 26
 
8.1%
C 13
 
4.0%
M 12
 
3.7%
T 11
 
3.4%
Y 9
 
2.8%
L 9
 
2.8%
Other values (8) 29
 
9.0%
Decimal Number
ValueCountFrequency (%)
2 118
37.2%
4 88
27.8%
1 29
 
9.1%
6 21
 
6.6%
0 19
 
6.0%
8 11
 
3.5%
9 10
 
3.2%
3 9
 
2.8%
7 6
 
1.9%
5 6
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
e 9
39.1%
s 6
26.1%
k 3
 
13.0%
c 2
 
8.7%
o 2
 
8.7%
n 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
& 11
42.3%
. 7
26.9%
, 3
 
11.5%
? 3
 
11.5%
; 2
 
7.7%
Other Symbol
ValueCountFrequency (%)
㈜ 305
100.0%
Open Punctuation
ValueCountFrequency (%)
( 217
100.0%
Close Punctuation
ValueCountFrequency (%)
) 217
100.0%
Space Separator
ValueCountFrequency (%)
162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Math Symbol
ValueCountFrequency (%)
> 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14320
91.7%
Common 951
 
6.1%
Latin 345
 
2.2%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 2108
 
14.7%
프 872
 
6.1%
익 863
 
6.0%
레 862
 
6.0%
이 628
 
4.4%
물 480
 
3.4%
사 480
 
3.4%
㈜ 305
 
2.1%
화 279
 
1.9%
로 240
 
1.7%
Other values (380) 7203
50.3%
Latin
ValueCountFrequency (%)
K 84
24.3%
G 58
16.8%
B 43
12.5%
S 28
 
8.1%
O 26
 
7.5%
C 13
 
3.8%
M 12
 
3.5%
T 11
 
3.2%
Y 9
 
2.6%
e 9
 
2.6%
Other values (14) 52
15.1%
Common
ValueCountFrequency (%)
( 217
22.8%
) 217
22.8%
162
17.0%
2 118
12.4%
4 88
9.3%
1 29
 
3.0%
6 21
 
2.2%
0 19
 
2.0%
8 11
 
1.2%
& 11
 
1.2%
Other values (11) 58
 
6.1%
Han
ValueCountFrequency (%)
華 1
50.0%
信 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14015
89.7%
ASCII 1296
 
8.3%
None 305
 
2.0%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 2108
 
15.0%
프 872
 
6.2%
익 863
 
6.2%
레 862
 
6.2%
이 628
 
4.5%
물 480
 
3.4%
사 480
 
3.4%
화 279
 
2.0%
로 240
 
1.7%
주 236
 
1.7%
Other values (379) 6967
49.7%
None
ValueCountFrequency (%)
㈜ 305
100.0%
ASCII
ValueCountFrequency (%)
( 217
16.7%
) 217
16.7%
162
12.5%
2 118
9.1%
4 88
 
6.8%
K 84
 
6.5%
G 58
 
4.5%
B 43
 
3.3%
1 29
 
2.2%
S 28
 
2.2%
Other values (35) 252
19.4%
CJK
ValueCountFrequency (%)
華 1
50.0%
信 1
50.0%

도로명주소
Text

MISSING 

Distinct1164
Distinct (%)52.1%
Missing57
Missing (%)2.5%
Memory size18.0 KiB
2024-04-29T22:15:58.095013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length27.072516
Min length13

Characters and Unicode

Total characters60480
Distinct characters418
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique363 ?
Unique (%)16.2%

Sample

1st row경기도 남양주시 화도읍 녹촌리 495-24번지
2nd row경기도 남양주시 화도읍 경춘로 1995, 2층
3rd row경기도 남양주시 진접읍 금강로 985
4th row경기도 남양주시 홍유릉로248번길 52, 102호(금곡동)
5th row경기도 구리시 아차산로500번길 16(교문동)
ValueCountFrequency (%)
경기도 2237
 
17.6%
이천시 399
 
3.1%
안산시 235
 
1.9%
남양주시 205
 
1.6%
상록구 137
 
1.1%
구리시 129
 
1.0%
의정부시 128
 
1.0%
수원시 118
 
0.9%
2층 116
 
0.9%
1층 108
 
0.9%
Other values (2286) 8872
69.9%
2024-04-29T22:15:58.482399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10466
 
17.3%
1 2522
 
4.2%
시 2373
 
3.9%
경 2344
 
3.9%
도 2327
 
3.8%
기 2305
 
3.8%
로 1948
 
3.2%
동 1730
 
2.9%
2 1611
 
2.7%
) 1364
 
2.3%
Other values (408) 31490
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34717
57.4%
Decimal Number 10795
 
17.8%
Space Separator 10466
 
17.3%
Close Punctuation 1364
 
2.3%
Open Punctuation 1362
 
2.3%
Other Punctuation 1028
 
1.7%
Dash Punctuation 658
 
1.1%
Uppercase Letter 86
 
0.1%
Lowercase Letter 2
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2373
 
6.8%
경 2344
 
6.8%
도 2327
 
6.7%
기 2305
 
6.6%
로 1948
 
5.6%
동 1730
 
5.0%
길 1088
 
3.1%
구 880
 
2.5%
번 832
 
2.4%
호 729
 
2.1%
Other values (369) 18161
52.3%
Uppercase Letter
ValueCountFrequency (%)
B 19
22.1%
T 10
11.6%
C 8
9.3%
A 8
9.3%
I 7
 
8.1%
O 7
 
8.1%
L 5
 
5.8%
E 4
 
4.7%
G 3
 
3.5%
U 3
 
3.5%
Other values (8) 12
14.0%
Decimal Number
ValueCountFrequency (%)
1 2522
23.4%
2 1611
14.9%
3 1251
11.6%
0 1072
9.9%
4 952
 
8.8%
5 766
 
7.1%
6 754
 
7.0%
7 684
 
6.3%
8 621
 
5.8%
9 562
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 1021
99.3%
. 4
 
0.4%
? 1
 
0.1%
: 1
 
0.1%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
10466
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1364
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1362
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 658
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34719
57.4%
Common 25673
42.4%
Latin 88
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2373
 
6.8%
경 2344
 
6.8%
도 2327
 
6.7%
기 2305
 
6.6%
로 1948
 
5.6%
동 1730
 
5.0%
길 1088
 
3.1%
구 880
 
2.5%
번 832
 
2.4%
호 729
 
2.1%
Other values (370) 18163
52.3%
Common
ValueCountFrequency (%)
10466
40.8%
1 2522
 
9.8%
2 1611
 
6.3%
) 1364
 
5.3%
( 1362
 
5.3%
3 1251
 
4.9%
0 1072
 
4.2%
, 1021
 
4.0%
4 952
 
3.7%
5 766
 
3.0%
Other values (9) 3286
 
12.8%
Latin
ValueCountFrequency (%)
B 19
21.6%
T 10
11.4%
C 8
9.1%
A 8
9.1%
I 7
 
8.0%
O 7
 
8.0%
L 5
 
5.7%
E 4
 
4.5%
G 3
 
3.4%
U 3
 
3.4%
Other values (9) 14
15.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34717
57.4%
ASCII 25761
42.6%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10466
40.6%
1 2522
 
9.8%
2 1611
 
6.3%
) 1364
 
5.3%
( 1362
 
5.3%
3 1251
 
4.9%
0 1072
 
4.2%
, 1021
 
4.0%
4 952
 
3.7%
5 766
 
3.0%
Other values (28) 3374
 
13.1%
Hangul
ValueCountFrequency (%)
시 2373
 
6.8%
경 2344
 
6.8%
도 2327
 
6.7%
기 2305
 
6.6%
로 1948
 
5.6%
동 1730
 
5.0%
길 1088
 
3.1%
구 880
 
2.5%
번 832
 
2.4%
호 729
 
2.1%
Other values (369) 18161
52.3%
None
ValueCountFrequency (%)
㈜ 2
100.0%

지번주소
Text

MISSING 

Distinct602
Distinct (%)45.5%
Missing969
Missing (%)42.3%
Memory size18.0 KiB
2024-04-29T22:15:58.735392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length38
Mean length21.864599
Min length11

Characters and Unicode

Total characters28905
Distinct characters291
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)9.6%

Sample

1st row경기도 이천시 부발읍 무촌리 65-2
2nd row경기도 이천시 율현동 345-1
3rd row경기도 이천시 마장면 이평리 157-5
4th row경기도 이천시 호법면 안평리 99-29
5th row경기도 이천시 송정동 100-14
ValueCountFrequency (%)
경기도 1325
 
19.6%
이천시 396
 
5.9%
안산시 233
 
3.5%
상록구 136
 
2.0%
수원시 136
 
2.0%
용인시 102
 
1.5%
단원구 97
 
1.4%
평택시 92
 
1.4%
마장면 89
 
1.3%
권선구 77
 
1.1%
Other values (1067) 4065
60.2%
2024-04-29T22:15:59.108065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5428
 
18.8%
시 1383
 
4.8%
기 1368
 
4.7%
경 1335
 
4.6%
도 1326
 
4.6%
1 1117
 
3.9%
- 1021
 
3.5%
동 933
 
3.2%
2 739
 
2.6%
4 678
 
2.3%
Other values (281) 13577
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16352
56.6%
Decimal Number 5925
 
20.5%
Space Separator 5428
 
18.8%
Dash Punctuation 1021
 
3.5%
Other Punctuation 63
 
0.2%
Uppercase Letter 44
 
0.2%
Open Punctuation 35
 
0.1%
Close Punctuation 35
 
0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1383
 
8.5%
기 1368
 
8.4%
경 1335
 
8.2%
도 1326
 
8.1%
동 933
 
5.7%
구 489
 
3.0%
천 489
 
3.0%
이 473
 
2.9%
리 472
 
2.9%
산 342
 
2.1%
Other values (252) 7742
47.3%
Uppercase Letter
ValueCountFrequency (%)
B 9
20.5%
T 8
18.2%
E 4
9.1%
I 4
9.1%
C 3
 
6.8%
U 2
 
4.5%
N 2
 
4.5%
R 2
 
4.5%
G 2
 
4.5%
K 2
 
4.5%
Other values (3) 6
13.6%
Decimal Number
ValueCountFrequency (%)
1 1117
18.9%
2 739
12.5%
4 678
11.4%
6 554
9.4%
3 538
9.1%
5 529
8.9%
7 523
8.8%
0 466
7.9%
9 403
 
6.8%
8 378
 
6.4%
Space Separator
ValueCountFrequency (%)
5428
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1021
100.0%
Other Punctuation
ValueCountFrequency (%)
, 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16354
56.6%
Common 12507
43.3%
Latin 44
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1383
 
8.5%
기 1368
 
8.4%
경 1335
 
8.2%
도 1326
 
8.1%
동 933
 
5.7%
구 489
 
3.0%
천 489
 
3.0%
이 473
 
2.9%
리 472
 
2.9%
산 342
 
2.1%
Other values (253) 7744
47.4%
Common
ValueCountFrequency (%)
5428
43.4%
1 1117
 
8.9%
- 1021
 
8.2%
2 739
 
5.9%
4 678
 
5.4%
6 554
 
4.4%
3 538
 
4.3%
5 529
 
4.2%
7 523
 
4.2%
0 466
 
3.7%
Other values (5) 914
 
7.3%
Latin
ValueCountFrequency (%)
B 9
20.5%
T 8
18.2%
E 4
9.1%
I 4
9.1%
C 3
 
6.8%
U 2
 
4.5%
N 2
 
4.5%
R 2
 
4.5%
G 2
 
4.5%
K 2
 
4.5%
Other values (3) 6
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16352
56.6%
ASCII 12551
43.4%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5428
43.2%
1 1117
 
8.9%
- 1021
 
8.1%
2 739
 
5.9%
4 678
 
5.4%
6 554
 
4.4%
3 538
 
4.3%
5 529
 
4.2%
7 523
 
4.2%
0 466
 
3.7%
Other values (18) 958
 
7.6%
Hangul
ValueCountFrequency (%)
시 1383
 
8.5%
기 1368
 
8.4%
경 1335
 
8.2%
도 1326
 
8.1%
동 933
 
5.7%
구 489
 
3.0%
천 489
 
3.0%
이 473
 
2.9%
리 472
 
2.9%
산 342
 
2.1%
Other values (252) 7742
47.3%
None
ValueCountFrequency (%)
㈜ 2
100.0%

연락처
Text

MISSING 

Distinct408
Distinct (%)50.1%
Missing1477
Missing (%)64.5%
Memory size18.0 KiB
2024-04-29T22:15:59.338044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.864865
Min length9

Characters and Unicode

Total characters9658
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)19.8%

Sample

1st row031-631-7827
2nd row070-4281-4301
3rd row031-637-2677
4th row031-637-0004
5th row031-632-3163
ValueCountFrequency (%)
000-0000-0000 13
 
1.6%
031-766-7293 5
 
0.6%
02-898-2424 4
 
0.5%
031-386-1004 4
 
0.5%
000-000-0000 4
 
0.5%
031-431-8222 4
 
0.5%
031-799-3280 4
 
0.5%
031-473-3000 4
 
0.5%
031-426-2224 4
 
0.5%
031-422-8804 4
 
0.5%
Other values (398) 764
93.9%
2024-04-29T22:15:59.685995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1587
16.4%
0 1481
15.3%
3 1304
13.5%
1 1215
12.6%
2 948
9.8%
4 895
9.3%
6 532
 
5.5%
8 500
 
5.2%
7 484
 
5.0%
5 358
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8071
83.6%
Dash Punctuation 1587
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1481
18.3%
3 1304
16.2%
1 1215
15.1%
2 948
11.7%
4 895
11.1%
6 532
 
6.6%
8 500
 
6.2%
7 484
 
6.0%
5 358
 
4.4%
9 354
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 1587
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1587
16.4%
0 1481
15.3%
3 1304
13.5%
1 1215
12.6%
2 948
9.8%
4 895
9.3%
6 532
 
5.5%
8 500
 
5.2%
7 484
 
5.0%
5 358
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1587
16.4%
0 1481
15.3%
3 1304
13.5%
1 1215
12.6%
2 948
9.8%
4 895
9.3%
6 532
 
5.5%
8 500
 
5.2%
7 484
 
5.0%
5 358
 
3.7%

데이터기준일자
Date

Distinct51
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
Minimum2021-04-12 00:00:00
Maximum2024-04-23 00:00:00
2024-04-29T22:15:59.839986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:15:59.972311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1054
Distinct (%)56.2%
Missing417
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean37.397201
Minimum36.94446
Maximum38.021434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.3 KiB
2024-04-29T22:16:00.111120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.94446
5-th percentile37.105265
Q137.26091
median37.342019
Q337.526421
95-th percentile37.750868
Maximum38.021434
Range1.0769746
Interquartile range (IQR)0.26551139

Descriptive statistics

Standard deviation0.19749221
Coefficient of variation (CV)0.0052809356
Kurtosis-0.31901504
Mean37.397201
Median Absolute Deviation (MAD)0.096506171
Skewness0.49305953
Sum70082.356
Variance0.039003175
MonotonicityNot monotonic
2024-04-29T22:16:00.260346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2504801858 12
 
0.5%
37.2494957611 9
 
0.4%
37.2518142072 7
 
0.3%
37.116765862 6
 
0.3%
37.136969471 6
 
0.3%
37.5535037868 6
 
0.3%
37.2976640128 6
 
0.3%
36.9895835292 6
 
0.3%
37.2521927214 6
 
0.3%
37.1629748519 6
 
0.3%
Other values (1044) 1804
78.7%
(Missing) 417
 
18.2%
ValueCountFrequency (%)
36.9444596016 1
 
< 0.1%
36.9623944216 3
0.1%
36.9665957898 3
0.1%
36.9708236626 3
0.1%
36.9728324566 3
0.1%
36.9877985756 3
0.1%
36.9895835292 6
0.3%
36.9919006082 3
0.1%
36.9931529075 3
0.1%
36.998312727 1
 
< 0.1%
ValueCountFrequency (%)
38.0214342489 2
0.1%
38.0172740382 1
< 0.1%
37.9367483942 1
< 0.1%
37.9119283558 1
< 0.1%
37.9116473309 1
< 0.1%
37.9009653061 2
0.1%
37.8987895384 1
< 0.1%
37.8955140221 2
0.1%
37.8916516111 1
< 0.1%
37.8704383914 1
< 0.1%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1054
Distinct (%)56.2%
Missing417
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean127.07591
Minimum126.57976
Maximum127.67579
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.3 KiB
2024-04-29T22:16:00.419840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.57976
5-th percentile126.76609
Q1126.86091
median127.04228
Q3127.2367
95-th percentile127.49455
Maximum127.67579
Range1.0960269
Interquartile range (IQR)0.37579378

Descriptive statistics

Standard deviation0.24226056
Coefficient of variation (CV)0.0019064241
Kurtosis-0.97132599
Mean127.07591
Median Absolute Deviation (MAD)0.18512936
Skewness0.40532031
Sum238140.25
Variance0.058690181
MonotonicityNot monotonic
2024-04-29T22:16:00.555885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3918850573 12
 
0.5%
127.4242821894 9
 
0.4%
127.4437539877 7
 
0.3%
127.6160515641 6
 
0.3%
127.4988178134 6
 
0.3%
127.1946802895 6
 
0.3%
127.407873425 6
 
0.3%
127.0780799972 6
 
0.3%
127.0056361699 6
 
0.3%
127.5118825598 6
 
0.3%
Other values (1044) 1804
78.7%
(Missing) 417
 
18.2%
ValueCountFrequency (%)
126.5797648292 1
< 0.1%
126.5893434471 1
< 0.1%
126.5976123825 1
< 0.1%
126.6009126245 1
< 0.1%
126.6111562458 1
< 0.1%
126.621303491 1
< 0.1%
126.6506200976 1
< 0.1%
126.6595870726 1
< 0.1%
126.666298046 1
< 0.1%
126.6711671504 1
< 0.1%
ValueCountFrequency (%)
127.6757917358 1
 
< 0.1%
127.6395956569 2
 
0.1%
127.6160515641 6
0.3%
127.6160159938 2
 
0.1%
127.6066044077 3
0.1%
127.569610519 3
0.1%
127.5445372999 3
0.1%
127.5429243926 2
 
0.1%
127.5398395619 3
0.1%
127.5326225397 1
 
< 0.1%

Interactions

2024-04-29T22:15:56.100183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:15:55.868585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:15:56.188829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:15:56.019473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T22:16:00.641139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태구분업종구분데이터기준일자정제WGS84위도정제WGS84경도
시군명1.0000.9780.9620.9980.9610.941
영업상태구분0.9781.0000.8440.9790.8490.792
업종구분0.9620.8441.0000.9760.6700.753
데이터기준일자0.9980.9790.9761.0000.9550.942
정제WGS84위도0.9610.8490.6700.9551.0000.793
정제WGS84경도0.9410.7920.7530.9420.7931.000
2024-04-29T22:16:00.730846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태구분시군명업종구분
영업상태구분1.0000.8520.534
시군명0.8521.0000.705
업종구분0.5340.7051.000
2024-04-29T22:16:01.019815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정제WGS84위도정제WGS84경도시군명영업상태구분업종구분
정제WGS84위도1.000-0.3840.7730.4200.337
정제WGS84경도-0.3841.0000.7040.3570.416
시군명0.7730.7041.0000.8520.705
영업상태구분0.4200.3570.8521.0000.534
업종구분0.3370.4160.7050.5341.000

Missing values

2024-04-29T22:15:56.303148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:15:56.439942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-29T22:15:56.565448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명인허가일자영업상태구분업종구분업체명도로명주소지번주소연락처데이터기준일자정제WGS84위도정제WGS84경도
0남양주시1993-07-24영업중주선업봉성통운경기도 남양주시 화도읍 녹촌리 495-24번지<NA><NA>2021-04-1437.65436127.282333
1남양주시2004-04-07영업중주선업대진화물경기도 남양주시 화도읍 경춘로 1995, 2층<NA><NA>2021-04-1437.651326127.309979
2남양주시2003-03-20영업중주선업한국칠천퀵화물경기도 남양주시 진접읍 금강로 985<NA><NA>2021-04-1437.688409127.161826
3남양주시2003-11-13영업중주선업제일종합물류경기도 남양주시 홍유릉로248번길 52, 102호(금곡동)<NA><NA>2021-04-1437.627986127.203295
4구리시2001-07-31정상이사화물가람영구이사경기도 구리시 아차산로500번길 16(교문동)<NA><NA>2021-04-1837.599581127.131517
5구리시1999-01-07정상이사화물영구크린299호경기도 구리시 건원대로34번길 9,지하층 비103호(인창동)<NA><NA>2021-04-1837.604378127.140851
6구리시1991-11-25정상이사화물영우익스프레스경기도 구리시 경춘로276번길 67-14 1층(수택동)<NA><NA>2021-04-1837.60038127.148189
7구리시1992-06-05정상이사화물주식회사 이사의명가경기도 구리시 이문안로99번길 20, 2층 (수택동)<NA><NA>2021-04-1837.592711127.142618
8구리시2002-11-28정상이사화물KGB기업이전3경기도 구리시 동구릉로514(사노동)<NA><NA>2021-04-1837.642849127.141358
9구리시2004-01-28정상이사화물영구익스프레스경기도 구리시 경춘북로251번길43 (갈매동)구길-재개발지역<NA><NA>2021-04-1837.634621127.113187
시군명인허가일자영업상태구분업종구분업체명도로명주소지번주소연락처데이터기준일자정제WGS84위도정제WGS84경도
2281의정부시1996-07-16<NA>이사화물연합통운 예스이사경기도 의정부시 녹양로62번길 98, 동원아파트 상가동 제지하층 5호<NA><NA>2023-04-0337.756651127.037877
2282용인시2020-04-10<NA>이사화물(주)무버경기도 용인시 기흥구 덕영대로1814번길 3 (하갈동)<NA><NA>2023-04-2537.24926127.089615
2283용인시2000-05-09<NA>이사화물사이버익스프레스경기도 용인시 처인구 남사읍 원암로 481, 델리후레쉬 2층 1호경기도 용인시 처인구 남사읍 방아리 980 델리후레쉬031-236-24792023-04-2537.102539127.174475
2284용인시2008-07-28<NA>이사화물TY로지스경기도 용인시 처인구 경안천로358번길 30-9 (고림동)경기도 용인시 처인구 고림동 519-29<NA>2023-04-2537.261413127.220426
2285용인시2003-02-12<NA>이사화물하하이사(용인점)경기도 용인시 기흥구 신갈로 31 (상갈동)경기도 용인시 기흥구 상갈동 165<NA>2023-04-2537.269468127.105386
2286용인시2003-11-08<NA>이사화물신안전화물경기도 용인시 처인구 양지면 주북로5번길 96경기도 용인시 처인구 양지면 주북리 769-2<NA>2023-04-2537.25489127.241831
2287용인시2002-10-22<NA>이사화물희망운수(주)경기도 용인시 처인구 지삼로 517 (삼가동)경기도 용인시 처인구 삼가동 320-4<NA>2023-04-25<NA><NA>
2288용인시2002-01-11<NA>이사화물특급익스프레스경기도 용인시 수지구 풍덕천로190번길 24, 201호 (풍덕천동)경기도 용인시 수지구 풍덕천동 69-97 201호<NA>2023-04-2537.324469127.10204
2289용인시2002-03-13<NA>이사화물분당삼호익스프레스경기도 용인시 기흥구 상갈로23번길 8, 230호 (상갈동, 신갈종합시장)경기도 용인시 기흥구 상갈동 121-2 203호<NA>2023-04-2537.268807127.107013
2290용인시2000-10-11<NA>이사화물KGB용인 기흥점경기도 용인시 수지구 광교중앙로 314, 791호 (상현동)경기도 용인시 수지구 상현동 1131-2<NA>2023-04-2537.297901127.069809

Duplicate rows

Most frequently occurring

시군명인허가일자영업상태구분업종구분업체명도로명주소지번주소연락처데이터기준일자정제WGS84위도정제WGS84경도# duplicates
0가평군2000-10-05신규화물운송주선유신화물경기도 가평군 청평면 강변나루로 40번길 22, 102호(대성주택)경기도 가평군 청평면 청평리 688-5 대성주택<NA>2023-08-0737.731162127.4139442
1가평군2014-08-08신규화물운송주선㈜태영지엘에스경기도 가평군 조종면 조종희망로 5, 3층(태영빌딩)경기도 가평군 조종면 현리 412-7031-584-92352023-08-0737.818354127.349082
2포천시2007-04-03폐업 등이사화물질성종합화물경기도 포천시 가산면 우현호 123경기도 포천시 가산면 금현리 278-7번지<NA>2021-04-13<NA><NA>2