Overview

Dataset statistics

Number of variables20
Number of observations744
Missing cells746
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.1 KiB
Average record size in memory172.2 B

Variable types

Numeric8
Categorical5
Unsupported1
Text5
Boolean1

Dataset

Description경기도_산부인과정보정제기본
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8PS4U4FXJS8LV2WEP3SZ34644311&infSeq=1

Alerts

기준연월 has constant value ""Constant
행정동코드 has constant value ""Constant
마트완료여부 has constant value ""Constant
생성일자 has constant value ""Constant
아이디 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
분석시군코드 is highly overall correlated with 시군명High correlation
소재지우편번호 is highly overall correlated with 정제우편번호 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 정제WGS84경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 정제WGS84위도 and 1 other fieldsHigh correlation
정제우편번호 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 아이디 and 7 other fieldsHigh correlation
종별구분명 is highly overall correlated with 아이디High correlation
종별구분명 is highly imbalanced (59.3%)Imbalance
행정동명 has 744 (100.0%) missing valuesMissing
아이디 has unique valuesUnique
행정동명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-12 23:31:25.161583
Analysis finished2024-03-12 23:31:31.925355
Duration6.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct744
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.5
Minimum1
Maximum744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:31.980141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38.15
Q1186.75
median372.5
Q3558.25
95-th percentile706.85
Maximum744
Range743
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation214.91859
Coefficient of variation (CV)0.57696266
Kurtosis-1.2
Mean372.5
Median Absolute Deviation (MAD)186
Skewness0
Sum277140
Variance46190
MonotonicityNot monotonic
2024-03-13T08:31:32.089501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281 1
 
0.1%
374 1
 
0.1%
361 1
 
0.1%
359 1
 
0.1%
364 1
 
0.1%
368 1
 
0.1%
369 1
 
0.1%
370 1
 
0.1%
371 1
 
0.1%
372 1
 
0.1%
Other values (734) 734
98.7%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
744 1
0.1%
743 1
0.1%
742 1
0.1%
741 1
0.1%
740 1
0.1%
739 1
0.1%
738 1
0.1%
737 1
0.1%
736 1
0.1%
735 1
0.1%

기준연월
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
201712
744 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201712
2nd row201712
3rd row201712
4th row201712
5th row201712

Common Values

ValueCountFrequency (%)
201712 744
100.0%

Length

2024-03-13T08:31:32.196934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:31:32.276048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201712 744
100.0%

분석시군코드
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41305.161
Minimum41110
Maximum41830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:32.370692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41110
5-th percentile41110
Q141165
median41270
Q341450
95-th percentile41610
Maximum41830
Range720
Interquartile range (IQR)285

Descriptive statistics

Standard deviation170.96189
Coefficient of variation (CV)0.0041389959
Kurtosis-0.41864364
Mean41305.161
Median Absolute Deviation (MAD)120
Skewness0.7414911
Sum30731040
Variance29227.969
MonotonicityNot monotonic
2024-03-13T08:31:32.478329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
41110 79
 
10.6%
41130 78
 
10.5%
41190 63
 
8.5%
41280 59
 
7.9%
41460 52
 
7.0%
41270 52
 
7.0%
41170 40
 
5.4%
41590 36
 
4.8%
41360 30
 
4.0%
41150 29
 
3.9%
Other values (20) 226
30.4%
ValueCountFrequency (%)
41110 79
10.6%
41130 78
10.5%
41150 29
 
3.9%
41170 40
5.4%
41190 63
8.5%
41210 18
 
2.4%
41220 26
 
3.5%
41250 6
 
0.8%
41270 52
7.0%
41280 59
7.9%
ValueCountFrequency (%)
41830 3
 
0.4%
41820 4
 
0.5%
41670 8
 
1.1%
41650 9
 
1.2%
41630 8
 
1.1%
41610 12
 
1.6%
41590 36
4.8%
41570 20
2.7%
41550 8
 
1.1%
41500 11
 
1.5%

행정동코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1234567890
744 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1234567890
2nd row1234567890
3rd row1234567890
4th row1234567890
5th row1234567890

Common Values

ValueCountFrequency (%)
1234567890 744
100.0%

Length

2024-03-13T08:31:32.806755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:31:32.881182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1234567890 744
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
수원시
79 
성남시
78 
부천시
63 
고양시
59 
용인시
52 
Other values (25)
413 

Length

Max length4
Median length3
Mean length3.0873656
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row부천시
2nd row부천시
3rd row부천시
4th row부천시
5th row성남시

Common Values

ValueCountFrequency (%)
수원시 79
 
10.6%
성남시 78
 
10.5%
부천시 63
 
8.5%
고양시 59
 
7.9%
용인시 52
 
7.0%
안산시 52
 
7.0%
안양시 40
 
5.4%
화성시 36
 
4.8%
남양주시 30
 
4.0%
의정부시 29
 
3.9%
Other values (20) 226
30.4%

Length

2024-03-13T08:31:32.974406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 79
 
10.6%
성남시 78
 
10.5%
부천시 63
 
8.5%
고양시 59
 
7.9%
용인시 52
 
7.0%
안산시 52
 
7.0%
안양시 40
 
5.4%
화성시 36
 
4.8%
남양주시 30
 
4.0%
의정부시 29
 
3.9%
Other values (20) 226
30.4%

행정동명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing744
Missing (%)100.0%
Memory size6.7 KiB
Distinct693
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2024-03-13T08:31:33.132954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.3024194
Min length3

Characters and Unicode

Total characters6177
Distinct characters342
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique659 ?
Unique (%)88.6%

Sample

1st row이박산부인과의원
2nd row벨라산부인과의원
3rd row미앤미의원
4th row메디슬림의원
5th row지인의원
ValueCountFrequency (%)
의료법인 10
 
1.3%
미앤미의원 6
 
0.8%
로앤산부인과의원 5
 
0.6%
서울산부인과의원 5
 
0.6%
성모의원 4
 
0.5%
하나산부인과의원 4
 
0.5%
서울여성병원 3
 
0.4%
서울가정의학과의원 3
 
0.4%
이화산부인과의원 3
 
0.4%
산부인과의원 3
 
0.4%
Other values (716) 745
94.2%
2024-03-13T08:31:33.412578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
776
 
12.6%
762
 
12.3%
424
 
6.9%
332
 
5.4%
299
 
4.8%
290
 
4.7%
120
 
1.9%
115
 
1.9%
79
 
1.3%
76
 
1.2%
Other values (332) 2904
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6088
98.6%
Space Separator 47
 
0.8%
Decimal Number 21
 
0.3%
Uppercase Letter 10
 
0.2%
Close Punctuation 4
 
0.1%
Open Punctuation 3
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
776
 
12.7%
762
 
12.5%
424
 
7.0%
332
 
5.5%
299
 
4.9%
290
 
4.8%
120
 
2.0%
115
 
1.9%
79
 
1.3%
76
 
1.2%
Other values (313) 2815
46.2%
Decimal Number
ValueCountFrequency (%)
2 5
23.8%
1 4
19.0%
3 4
19.0%
5 3
14.3%
6 3
14.3%
7 1
 
4.8%
4 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
S 3
30.0%
K 2
20.0%
O 2
20.0%
B 1
 
10.0%
M 1
 
10.0%
D 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6088
98.6%
Common 78
 
1.3%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
776
 
12.7%
762
 
12.5%
424
 
7.0%
332
 
5.5%
299
 
4.9%
290
 
4.8%
120
 
2.0%
115
 
1.9%
79
 
1.3%
76
 
1.2%
Other values (313) 2815
46.2%
Common
ValueCountFrequency (%)
47
60.3%
2 5
 
6.4%
1 4
 
5.1%
) 4
 
5.1%
3 4
 
5.1%
( 3
 
3.8%
5 3
 
3.8%
6 3
 
3.8%
. 2
 
2.6%
7 1
 
1.3%
Other values (2) 2
 
2.6%
Latin
ValueCountFrequency (%)
S 3
27.3%
K 2
18.2%
O 2
18.2%
B 1
 
9.1%
M 1
 
9.1%
s 1
 
9.1%
D 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6088
98.6%
ASCII 89
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
776
 
12.7%
762
 
12.5%
424
 
7.0%
332
 
5.5%
299
 
4.9%
290
 
4.8%
120
 
2.0%
115
 
1.9%
79
 
1.3%
76
 
1.2%
Other values (313) 2815
46.2%
ASCII
ValueCountFrequency (%)
47
52.8%
2 5
 
5.6%
1 4
 
4.5%
) 4
 
4.5%
3 4
 
4.5%
( 3
 
3.4%
5 3
 
3.4%
6 3
 
3.4%
S 3
 
3.4%
K 2
 
2.2%
Other values (9) 11
 
12.4%

종별구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
E
628 
D
 
56
B
 
55
F
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
E 628
84.4%
D 56
 
7.5%
B 55
 
7.4%
F 5
 
0.7%

Length

2024-03-13T08:31:33.517898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:31:33.602125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 628
84.4%
d 56
 
7.5%
b 55
 
7.4%
f 5
 
0.7%
Distinct738
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2024-03-13T08:31:33.832149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length49
Mean length27.439516
Min length3

Characters and Unicode

Total characters20415
Distinct characters363
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique733 ?
Unique (%)98.5%

Sample

1st row경기도 부천시 중동 1035-3번지 중동메디칼 302호 301호,302호
2nd row경기도 부천시 송내동 373번지 대우프라자 302호, 401호
3rd row경기도 부천시 상동 535-9번지 시그마타워 601호
4th row경기도 부천시 상동 544-6번지
5th row경기도 성남시 중원구 성남동 3208번지 성남동메디칼센타 503호
ValueCountFrequency (%)
경기도 738
 
16.5%
수원시 79
 
1.8%
1호 78
 
1.7%
성남시 66
 
1.5%
부천시 63
 
1.4%
2층 59
 
1.3%
고양시 57
 
1.3%
안산시 53
 
1.2%
용인시 52
 
1.2%
2호 43
 
1.0%
Other values (1513) 3174
71.1%
2024-03-13T08:31:34.244490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3739
 
18.3%
770
 
3.8%
766
 
3.8%
764
 
3.7%
1 754
 
3.7%
750
 
3.7%
746
 
3.7%
705
 
3.5%
630
 
3.1%
595
 
2.9%
Other values (353) 10196
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11839
58.0%
Decimal Number 4270
 
20.9%
Space Separator 3739
 
18.3%
Dash Punctuation 245
 
1.2%
Other Punctuation 186
 
0.9%
Math Symbol 38
 
0.2%
Uppercase Letter 35
 
0.2%
Close Punctuation 27
 
0.1%
Open Punctuation 27
 
0.1%
Lowercase Letter 7
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (310) 5484
46.3%
Uppercase Letter
ValueCountFrequency (%)
A 7
20.0%
B 4
11.4%
C 3
 
8.6%
H 3
 
8.6%
M 3
 
8.6%
I 2
 
5.7%
T 2
 
5.7%
K 1
 
2.9%
E 1
 
2.9%
U 1
 
2.9%
Other values (8) 8
22.9%
Decimal Number
ValueCountFrequency (%)
1 754
17.7%
2 581
13.6%
3 557
13.0%
0 481
11.3%
4 454
10.6%
5 359
8.4%
6 307
7.2%
7 273
 
6.4%
9 254
 
5.9%
8 250
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 155
83.3%
. 24
 
12.9%
/ 6
 
3.2%
@ 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
l 3
42.9%
o 2
28.6%
n 2
28.6%
Math Symbol
ValueCountFrequency (%)
~ 37
97.4%
1
 
2.6%
Space Separator
ValueCountFrequency (%)
3739
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 245
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11840
58.0%
Common 8532
41.8%
Latin 43
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (311) 5485
46.3%
Latin
ValueCountFrequency (%)
A 7
16.3%
B 4
 
9.3%
C 3
 
7.0%
l 3
 
7.0%
H 3
 
7.0%
M 3
 
7.0%
o 2
 
4.7%
I 2
 
4.7%
n 2
 
4.7%
T 2
 
4.7%
Other values (12) 12
27.9%
Common
ValueCountFrequency (%)
3739
43.8%
1 754
 
8.8%
2 581
 
6.8%
3 557
 
6.5%
0 481
 
5.6%
4 454
 
5.3%
5 359
 
4.2%
6 307
 
3.6%
7 273
 
3.2%
9 254
 
3.0%
Other values (10) 773
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11839
58.0%
ASCII 8573
42.0%
Number Forms 1
 
< 0.1%
Math Operators 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3739
43.6%
1 754
 
8.8%
2 581
 
6.8%
3 557
 
6.5%
0 481
 
5.6%
4 454
 
5.3%
5 359
 
4.2%
6 307
 
3.6%
7 273
 
3.2%
9 254
 
3.0%
Other values (30) 814
 
9.5%
Hangul
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (310) 5484
46.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct741
Distinct (%)99.7%
Missing1
Missing (%)0.1%
Memory size5.9 KiB
2024-03-13T08:31:34.513955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length48
Mean length32.341857
Min length14

Characters and Unicode

Total characters24030
Distinct characters398
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

Unique739 ?
Unique (%)99.5%

Sample

1st row경기도 부천시 길주로 237, 301호,302호 (중동, 중동메디칼)
2nd row경기도 부천시 중동로 48, 302호 401호 (송내동, 대우프라자)
3rd row경기도 부천시 길주로 115, 601호 (상동, 시그마타워)
4th row경기도 부천시 상동로 81, 202호 (상동, 센터프라자)
5th row경기도 성남시 중원구 광명로 5, 503호 (성남동)
ValueCountFrequency (%)
경기도 743
 
14.8%
수원시 79
 
1.6%
성남시 78
 
1.6%
2층 77
 
1.5%
부천시 63
 
1.3%
고양시 59
 
1.2%
용인시 52
 
1.0%
안산시 52
 
1.0%
3층 51
 
1.0%
분당구 48
 
1.0%
Other values (1669) 3717
74.1%
2024-03-13T08:31:34.880979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4279
 
17.8%
, 890
 
3.7%
813
 
3.4%
789
 
3.3%
789
 
3.3%
775
 
3.2%
759
 
3.2%
746
 
3.1%
) 705
 
2.9%
( 705
 
2.9%
Other values (388) 12780
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13358
55.6%
Space Separator 4279
 
17.8%
Decimal Number 3921
 
16.3%
Other Punctuation 901
 
3.7%
Close Punctuation 705
 
2.9%
Open Punctuation 705
 
2.9%
Dash Punctuation 85
 
0.4%
Math Symbol 42
 
0.2%
Uppercase Letter 33
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Uppercase Letter
ValueCountFrequency (%)
A 8
24.2%
B 6
18.2%
C 4
12.1%
M 3
 
9.1%
G 2
 
6.1%
S 2
 
6.1%
H 2
 
6.1%
K 1
 
3.0%
Y 1
 
3.0%
T 1
 
3.0%
Other values (3) 3
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 700
17.9%
2 611
15.6%
3 562
14.3%
0 558
14.2%
4 350
8.9%
5 296
7.5%
6 246
 
6.3%
7 219
 
5.6%
8 212
 
5.4%
9 167
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 890
98.8%
/ 5
 
0.6%
. 5
 
0.6%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4279
100.0%
Close Punctuation
ValueCountFrequency (%)
) 705
100.0%
Open Punctuation
ValueCountFrequency (%)
( 705
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 42
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13358
55.6%
Common 10638
44.3%
Latin 34
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Common
ValueCountFrequency (%)
4279
40.2%
, 890
 
8.4%
) 705
 
6.6%
( 705
 
6.6%
1 700
 
6.6%
2 611
 
5.7%
3 562
 
5.3%
0 558
 
5.2%
4 350
 
3.3%
5 296
 
2.8%
Other values (9) 982
 
9.2%
Latin
ValueCountFrequency (%)
A 8
23.5%
B 6
17.6%
C 4
11.8%
M 3
 
8.8%
G 2
 
5.9%
S 2
 
5.9%
H 2
 
5.9%
K 1
 
2.9%
1
 
2.9%
Y 1
 
2.9%
Other values (4) 4
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13358
55.6%
ASCII 10671
44.4%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4279
40.1%
, 890
 
8.3%
) 705
 
6.6%
( 705
 
6.6%
1 700
 
6.6%
2 611
 
5.7%
3 562
 
5.3%
0 558
 
5.2%
4 350
 
3.3%
5 296
 
2.8%
Other values (22) 1015
 
9.5%
Hangul
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Number Forms
ValueCountFrequency (%)
1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct584
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235380.31
Minimum10071
Maximum487892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:34.996666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10071
5-th percentile10503.6
Q114607.25
median410550.5
Q3445160
95-th percentile480358.25
Maximum487892
Range477821
Interquartile range (IQR)430552.75

Descriptive statistics

Standard deviation216376.63
Coefficient of variation (CV)0.91926392
Kurtosis-1.9831824
Mean235380.31
Median Absolute Deviation (MAD)72474.5
Skewness-0.033062478
Sum1.7512295 × 108
Variance4.6818846 × 1010
MonotonicityNot monotonic
2024-03-13T08:31:35.107479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426815 7
 
0.9%
435040 5
 
0.7%
14072 5
 
0.7%
449924 4
 
0.5%
442081 4
 
0.5%
480848 4
 
0.5%
467800 4
 
0.5%
13591 4
 
0.5%
445893 4
 
0.5%
14544 3
 
0.4%
Other values (574) 700
94.1%
ValueCountFrequency (%)
10071 3
0.4%
10077 1
 
0.1%
10086 1
 
0.1%
10099 1
 
0.1%
10105 1
 
0.1%
10108 1
 
0.1%
10110 1
 
0.1%
10237 1
 
0.1%
10275 1
 
0.1%
10323 1
 
0.1%
ValueCountFrequency (%)
487892 1
0.1%
487851 1
0.1%
487824 1
0.1%
487803 1
0.1%
483801 1
0.1%
483130 2
0.3%
483030 2
0.3%
483020 1
0.1%
482862 1
0.1%
482844 1
0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct712
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.426679
Minimum36.984829
Maximum38.090366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:35.215185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.984829
5-th percentile37.134148
Q137.292675
median37.391919
Q337.592243
95-th percentile37.750646
Maximum38.090366
Range1.1055368
Interquartile range (IQR)0.2995688

Descriptive statistics

Standard deviation0.19813449
Coefficient of variation (CV)0.0052939373
Kurtosis-0.14299965
Mean37.426679
Median Absolute Deviation (MAD)0.114983
Skewness0.29854514
Sum27845.449
Variance0.039257277
MonotonicityNot monotonic
2024-03-13T08:31:35.343434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.43310658 4
 
0.5%
37.30673429 3
 
0.4%
37.3904391 3
 
0.4%
37.71910642 2
 
0.3%
37.2395787 2
 
0.3%
37.31869877 2
 
0.3%
37.5857609 2
 
0.3%
37.39227765 2
 
0.3%
37.44309703 2
 
0.3%
37.6540491 2
 
0.3%
Other values (702) 720
96.8%
ValueCountFrequency (%)
36.98482921 1
0.1%
36.98963256 1
0.1%
36.98988434 1
0.1%
36.98990451 1
0.1%
36.99055379 1
0.1%
36.99141003 1
0.1%
36.99168851 1
0.1%
36.99215885 1
0.1%
36.99304975 1
0.1%
36.9931627 1
0.1%
ValueCountFrequency (%)
38.09036597 1
0.1%
38.03498263 1
0.1%
37.95823805 1
0.1%
37.91647138 1
0.1%
37.90893619 1
0.1%
37.90744107 1
0.1%
37.90535145 1
0.1%
37.90396369 1
0.1%
37.90304856 1
0.1%
37.90071976 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct710
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00108
Minimum126.58704
Maximum127.70269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:35.448473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58704
5-th percentile126.75025
Q1126.83133
median127.02752
Q3127.12003
95-th percentile127.29419
Maximum127.70269
Range1.1156496
Interquartile range (IQR)0.28869875

Descriptive statistics

Standard deviation0.19075326
Coefficient of variation (CV)0.0015019813
Kurtosis0.73333798
Mean127.00108
Median Absolute Deviation (MAD)0.1215942
Skewness0.5821174
Sum94488.806
Variance0.036386805
MonotonicityNot monotonic
2024-03-13T08:31:35.557782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1296537 4
 
0.5%
127.0844687 3
 
0.4%
126.7373884 3
 
0.4%
127.132012 2
 
0.3%
127.2123344 2
 
0.3%
126.9448171 2
 
0.3%
127.1360676 2
 
0.3%
127.0563345 2
 
0.3%
126.8309888 2
 
0.3%
126.847662 2
 
0.3%
Other values (700) 720
96.8%
ValueCountFrequency (%)
126.587038 1
0.1%
126.5989452 1
0.1%
126.6220936 1
0.1%
126.6250568 1
0.1%
126.6268905 2
0.3%
126.6274982 1
0.1%
126.6276368 1
0.1%
126.6599994 1
0.1%
126.6664733 1
0.1%
126.6677224 1
0.1%
ValueCountFrequency (%)
127.7026876 1
0.1%
127.6440018 1
0.1%
127.6399082 1
0.1%
127.6391059 1
0.1%
127.6372475 1
0.1%
127.6368567 2
0.3%
127.6358643 1
0.1%
127.6332211 1
0.1%
127.6298131 1
0.1%
127.624994 1
0.1%

마트완료여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size876.0 B
False
744 
ValueCountFrequency (%)
False 744
100.0%
2024-03-13T08:31:35.645838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

생성일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
20181123
744 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20181123
2nd row20181123
3rd row20181123
4th row20181123
5th row20181123

Common Values

ValueCountFrequency (%)
20181123 744
100.0%

Length

2024-03-13T08:31:35.724177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:31:35.816489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20181123 744
100.0%
Distinct738
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2024-03-13T08:31:36.070288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length49
Mean length27.439516
Min length3

Characters and Unicode

Total characters20415
Distinct characters363
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique733 ?
Unique (%)98.5%

Sample

1st row경기도 부천시 중동 1035-3번지 중동메디칼 302호 301호,302호
2nd row경기도 부천시 송내동 373번지 대우프라자 302호, 401호
3rd row경기도 부천시 상동 535-9번지 시그마타워 601호
4th row경기도 부천시 상동 544-6번지
5th row경기도 성남시 중원구 성남동 3208번지 성남동메디칼센타 503호
ValueCountFrequency (%)
경기도 738
 
16.5%
수원시 79
 
1.8%
1호 78
 
1.7%
성남시 66
 
1.5%
부천시 63
 
1.4%
2층 59
 
1.3%
고양시 57
 
1.3%
안산시 53
 
1.2%
용인시 52
 
1.2%
2호 43
 
1.0%
Other values (1513) 3174
71.1%
2024-03-13T08:31:36.472094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3739
 
18.3%
770
 
3.8%
766
 
3.8%
764
 
3.7%
1 754
 
3.7%
750
 
3.7%
746
 
3.7%
705
 
3.5%
630
 
3.1%
595
 
2.9%
Other values (353) 10196
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11839
58.0%
Decimal Number 4270
 
20.9%
Space Separator 3739
 
18.3%
Dash Punctuation 245
 
1.2%
Other Punctuation 186
 
0.9%
Math Symbol 38
 
0.2%
Uppercase Letter 35
 
0.2%
Close Punctuation 27
 
0.1%
Open Punctuation 27
 
0.1%
Lowercase Letter 7
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (310) 5484
46.3%
Uppercase Letter
ValueCountFrequency (%)
A 7
20.0%
B 4
11.4%
C 3
 
8.6%
H 3
 
8.6%
M 3
 
8.6%
I 2
 
5.7%
T 2
 
5.7%
K 1
 
2.9%
E 1
 
2.9%
U 1
 
2.9%
Other values (8) 8
22.9%
Decimal Number
ValueCountFrequency (%)
1 754
17.7%
2 581
13.6%
3 557
13.0%
0 481
11.3%
4 454
10.6%
5 359
8.4%
6 307
7.2%
7 273
 
6.4%
9 254
 
5.9%
8 250
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 155
83.3%
. 24
 
12.9%
/ 6
 
3.2%
@ 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
l 3
42.9%
o 2
28.6%
n 2
28.6%
Math Symbol
ValueCountFrequency (%)
~ 37
97.4%
1
 
2.6%
Space Separator
ValueCountFrequency (%)
3739
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 245
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11840
58.0%
Common 8532
41.8%
Latin 43
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (311) 5485
46.3%
Latin
ValueCountFrequency (%)
A 7
16.3%
B 4
 
9.3%
C 3
 
7.0%
l 3
 
7.0%
H 3
 
7.0%
M 3
 
7.0%
o 2
 
4.7%
I 2
 
4.7%
n 2
 
4.7%
T 2
 
4.7%
Other values (12) 12
27.9%
Common
ValueCountFrequency (%)
3739
43.8%
1 754
 
8.8%
2 581
 
6.8%
3 557
 
6.5%
0 481
 
5.6%
4 454
 
5.3%
5 359
 
4.2%
6 307
 
3.6%
7 273
 
3.2%
9 254
 
3.0%
Other values (10) 773
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11839
58.0%
ASCII 8573
42.0%
Number Forms 1
 
< 0.1%
Math Operators 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3739
43.6%
1 754
 
8.8%
2 581
 
6.8%
3 557
 
6.5%
0 481
 
5.6%
4 454
 
5.3%
5 359
 
4.2%
6 307
 
3.6%
7 273
 
3.2%
9 254
 
3.0%
Other values (30) 814
 
9.5%
Hangul
ValueCountFrequency (%)
770
 
6.5%
766
 
6.5%
764
 
6.5%
750
 
6.3%
746
 
6.3%
705
 
6.0%
630
 
5.3%
595
 
5.0%
388
 
3.3%
241
 
2.0%
Other values (310) 5484
46.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct741
Distinct (%)99.7%
Missing1
Missing (%)0.1%
Memory size5.9 KiB
2024-03-13T08:31:36.725574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length48
Mean length32.341857
Min length14

Characters and Unicode

Total characters24030
Distinct characters398
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

Unique739 ?
Unique (%)99.5%

Sample

1st row경기도 부천시 길주로 237, 301호,302호 (중동, 중동메디칼)
2nd row경기도 부천시 중동로 48, 302호 401호 (송내동, 대우프라자)
3rd row경기도 부천시 길주로 115, 601호 (상동, 시그마타워)
4th row경기도 부천시 상동로 81, 202호 (상동, 센터프라자)
5th row경기도 성남시 중원구 광명로 5, 503호 (성남동)
ValueCountFrequency (%)
경기도 743
 
14.8%
수원시 79
 
1.6%
성남시 78
 
1.6%
2층 77
 
1.5%
부천시 63
 
1.3%
고양시 59
 
1.2%
용인시 52
 
1.0%
안산시 52
 
1.0%
3층 51
 
1.0%
분당구 48
 
1.0%
Other values (1669) 3717
74.1%
2024-03-13T08:31:37.114861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4279
 
17.8%
, 890
 
3.7%
813
 
3.4%
789
 
3.3%
789
 
3.3%
775
 
3.2%
759
 
3.2%
746
 
3.1%
) 705
 
2.9%
( 705
 
2.9%
Other values (388) 12780
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13358
55.6%
Space Separator 4279
 
17.8%
Decimal Number 3921
 
16.3%
Other Punctuation 901
 
3.7%
Close Punctuation 705
 
2.9%
Open Punctuation 705
 
2.9%
Dash Punctuation 85
 
0.4%
Math Symbol 42
 
0.2%
Uppercase Letter 33
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Uppercase Letter
ValueCountFrequency (%)
A 8
24.2%
B 6
18.2%
C 4
12.1%
M 3
 
9.1%
G 2
 
6.1%
S 2
 
6.1%
H 2
 
6.1%
K 1
 
3.0%
Y 1
 
3.0%
T 1
 
3.0%
Other values (3) 3
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 700
17.9%
2 611
15.6%
3 562
14.3%
0 558
14.2%
4 350
8.9%
5 296
7.5%
6 246
 
6.3%
7 219
 
5.6%
8 212
 
5.4%
9 167
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 890
98.8%
/ 5
 
0.6%
. 5
 
0.6%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4279
100.0%
Close Punctuation
ValueCountFrequency (%)
) 705
100.0%
Open Punctuation
ValueCountFrequency (%)
( 705
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 42
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13358
55.6%
Common 10638
44.3%
Latin 34
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Common
ValueCountFrequency (%)
4279
40.2%
, 890
 
8.4%
) 705
 
6.6%
( 705
 
6.6%
1 700
 
6.6%
2 611
 
5.7%
3 562
 
5.3%
0 558
 
5.2%
4 350
 
3.3%
5 296
 
2.8%
Other values (9) 982
 
9.2%
Latin
ValueCountFrequency (%)
A 8
23.5%
B 6
17.6%
C 4
11.8%
M 3
 
8.8%
G 2
 
5.9%
S 2
 
5.9%
H 2
 
5.9%
K 1
 
2.9%
1
 
2.9%
Y 1
 
2.9%
Other values (4) 4
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13358
55.6%
ASCII 10671
44.4%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4279
40.1%
, 890
 
8.3%
) 705
 
6.6%
( 705
 
6.6%
1 700
 
6.6%
2 611
 
5.7%
3 562
 
5.3%
0 558
 
5.2%
4 350
 
3.3%
5 296
 
2.8%
Other values (22) 1015
 
9.5%
Hangul
ValueCountFrequency (%)
813
 
6.1%
789
 
5.9%
789
 
5.9%
775
 
5.8%
759
 
5.7%
746
 
5.6%
400
 
3.0%
336
 
2.5%
297
 
2.2%
229
 
1.7%
Other values (355) 7425
55.6%
Number Forms
ValueCountFrequency (%)
1
100.0%

정제우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct584
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235380.31
Minimum10071
Maximum487892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:37.235738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10071
5-th percentile10503.6
Q114607.25
median410550.5
Q3445160
95-th percentile480358.25
Maximum487892
Range477821
Interquartile range (IQR)430552.75

Descriptive statistics

Standard deviation216376.63
Coefficient of variation (CV)0.91926392
Kurtosis-1.9831824
Mean235380.31
Median Absolute Deviation (MAD)72474.5
Skewness-0.033062478
Sum1.7512295 × 108
Variance4.6818846 × 1010
MonotonicityNot monotonic
2024-03-13T08:31:37.354812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426815 7
 
0.9%
435040 5
 
0.7%
14072 5
 
0.7%
449924 4
 
0.5%
442081 4
 
0.5%
480848 4
 
0.5%
467800 4
 
0.5%
13591 4
 
0.5%
445893 4
 
0.5%
14544 3
 
0.4%
Other values (574) 700
94.1%
ValueCountFrequency (%)
10071 3
0.4%
10077 1
 
0.1%
10086 1
 
0.1%
10099 1
 
0.1%
10105 1
 
0.1%
10108 1
 
0.1%
10110 1
 
0.1%
10237 1
 
0.1%
10275 1
 
0.1%
10323 1
 
0.1%
ValueCountFrequency (%)
487892 1
0.1%
487851 1
0.1%
487824 1
0.1%
487803 1
0.1%
483801 1
0.1%
483130 2
0.3%
483030 2
0.3%
483020 1
0.1%
482862 1
0.1%
482844 1
0.1%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct712
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.426679
Minimum36.984829
Maximum38.090366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:37.465380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.984829
5-th percentile37.134148
Q137.292675
median37.391919
Q337.592243
95-th percentile37.750646
Maximum38.090366
Range1.1055368
Interquartile range (IQR)0.2995688

Descriptive statistics

Standard deviation0.19813449
Coefficient of variation (CV)0.0052939373
Kurtosis-0.14299965
Mean37.426679
Median Absolute Deviation (MAD)0.114983
Skewness0.29854514
Sum27845.449
Variance0.039257277
MonotonicityNot monotonic
2024-03-13T08:31:37.577996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.43310658 4
 
0.5%
37.30673429 3
 
0.4%
37.3904391 3
 
0.4%
37.71910642 2
 
0.3%
37.2395787 2
 
0.3%
37.31869877 2
 
0.3%
37.5857609 2
 
0.3%
37.39227765 2
 
0.3%
37.44309703 2
 
0.3%
37.6540491 2
 
0.3%
Other values (702) 720
96.8%
ValueCountFrequency (%)
36.98482921 1
0.1%
36.98963256 1
0.1%
36.98988434 1
0.1%
36.98990451 1
0.1%
36.99055379 1
0.1%
36.99141003 1
0.1%
36.99168851 1
0.1%
36.99215885 1
0.1%
36.99304975 1
0.1%
36.9931627 1
0.1%
ValueCountFrequency (%)
38.09036597 1
0.1%
38.03498263 1
0.1%
37.95823805 1
0.1%
37.91647138 1
0.1%
37.90893619 1
0.1%
37.90744107 1
0.1%
37.90535145 1
0.1%
37.90396369 1
0.1%
37.90304856 1
0.1%
37.90071976 1
0.1%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct710
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00108
Minimum126.58704
Maximum127.70269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T08:31:37.694186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58704
5-th percentile126.75025
Q1126.83133
median127.02752
Q3127.12003
95-th percentile127.29419
Maximum127.70269
Range1.1156496
Interquartile range (IQR)0.28869875

Descriptive statistics

Standard deviation0.19075326
Coefficient of variation (CV)0.0015019813
Kurtosis0.73333798
Mean127.00108
Median Absolute Deviation (MAD)0.1215942
Skewness0.5821174
Sum94488.806
Variance0.036386805
MonotonicityNot monotonic
2024-03-13T08:31:37.803819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1296537 4
 
0.5%
127.0844687 3
 
0.4%
126.7373884 3
 
0.4%
127.132012 2
 
0.3%
127.2123344 2
 
0.3%
126.9448171 2
 
0.3%
127.1360676 2
 
0.3%
127.0563345 2
 
0.3%
126.8309888 2
 
0.3%
126.847662 2
 
0.3%
Other values (700) 720
96.8%
ValueCountFrequency (%)
126.587038 1
0.1%
126.5989452 1
0.1%
126.6220936 1
0.1%
126.6250568 1
0.1%
126.6268905 2
0.3%
126.6274982 1
0.1%
126.6276368 1
0.1%
126.6599994 1
0.1%
126.6664733 1
0.1%
126.6677224 1
0.1%
ValueCountFrequency (%)
127.7026876 1
0.1%
127.6440018 1
0.1%
127.6399082 1
0.1%
127.6391059 1
0.1%
127.6372475 1
0.1%
127.6368567 2
0.3%
127.6358643 1
0.1%
127.6332211 1
0.1%
127.6298131 1
0.1%
127.624994 1
0.1%

Interactions

2024-03-13T08:31:30.880661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.342733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.958937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.592583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.395199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.009335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.666839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.257942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.954645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.423479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.035576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.883505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.469208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.101501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.749022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.335661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.029556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.502957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.116926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.961227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.565662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.191020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.826082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.422590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.103867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.582711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.190219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.032215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.637412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.282535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.896567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.491804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.175644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.659602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.269758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.106971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.708646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.355341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.968815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.564525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.246470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.740923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.353920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.180684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.782846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.435106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.041280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.650264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.321564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.814187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.438044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.247421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.862560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.511417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.111498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.739143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:31.418545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:26.885608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:27.515372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.321793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:28.937012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:29.589545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.183179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:31:30.810628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:31:37.878203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디분석시군코드시군명종별구분명소재지우편번호위도경도정제우편번호정제WGS84경도정제WGS84위도
아이디1.0000.7100.9730.8110.4160.7740.7290.4160.7740.729
분석시군코드0.7101.0001.0000.0000.5210.7310.7830.5210.7310.783
시군명0.9731.0001.0000.0000.7960.9820.9780.7960.9820.978
종별구분명0.8110.0000.0001.0000.0390.0000.0000.0390.0000.000
소재지우편번호0.4160.5210.7960.0391.0000.3650.6261.0000.3650.626
위도0.7740.7310.9820.0000.3651.0000.6780.3651.0000.678
경도0.7290.7830.9780.0000.6260.6781.0000.6260.6781.000
정제우편번호0.4160.5210.7960.0391.0000.3650.6261.0000.3650.626
정제WGS84경도0.7740.7310.9820.0000.3651.0000.6780.3651.0000.678
정제WGS84위도0.7290.7830.9780.0000.6260.6781.0000.6260.6781.000
2024-03-13T08:31:38.258551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종별구분명시군명
종별구분명1.0000.000
시군명0.0001.000
2024-03-13T08:31:38.334674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디분석시군코드소재지우편번호위도경도정제우편번호정제WGS84경도정제WGS84위도시군명종별구분명
아이디1.0000.2000.184-0.4060.2000.184-0.4060.2000.7380.636
분석시군코드0.2001.000-0.0420.0650.092-0.0420.0650.0920.9860.000
소재지우편번호0.184-0.0421.000-0.1460.3011.000-0.1460.3010.5260.036
위도-0.4060.065-0.1461.000-0.211-0.1461.000-0.2110.7810.000
경도0.2000.0920.301-0.2111.0000.301-0.2111.0000.7620.000
정제우편번호0.184-0.0421.000-0.1460.3011.000-0.1460.3010.5260.036
정제WGS84경도-0.4060.065-0.1461.000-0.211-0.1461.000-0.2110.7810.000
정제WGS84위도0.2000.0920.301-0.2111.0000.301-0.2111.0000.7620.000
시군명0.7380.9860.5260.7810.7620.5260.7810.7621.0000.000
종별구분명0.6360.0000.0360.0000.0000.0360.0000.0000.0001.000

Missing values

2024-03-13T08:31:31.548475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:31:31.764075image/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.
2024-03-13T08:31:31.885187image/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

아이디기준연월분석시군코드행정동코드시군명행정동명의료기관명종별구분명소재지지번주소소재지도로명주소소재지우편번호위도경도마트완료여부생성일자정제지번주소정제도로명주소정제우편번호정제WGS84경도정제WGS84위도
0281201712411901234567890부천시<NA>이박산부인과의원E경기도 부천시 중동 1035-3번지 중동메디칼 302호 301호,302호경기도 부천시 길주로 237, 301호,302호 (중동, 중동메디칼)1453837.504401126.768855N20181123경기도 부천시 중동 1035-3번지 중동메디칼 302호 301호,302호경기도 부천시 길주로 237, 301호,302호 (중동, 중동메디칼)1453837.504401126.768855
1300201712411901234567890부천시<NA>벨라산부인과의원E경기도 부천시 송내동 373번지 대우프라자 302호, 401호경기도 부천시 중동로 48, 302호 401호 (송내동, 대우프라자)1472237.484263126.764192N20181123경기도 부천시 송내동 373번지 대우프라자 302호, 401호경기도 부천시 중동로 48, 302호 401호 (송내동, 대우프라자)1472237.484263126.764192
2301201712411901234567890부천시<NA>미앤미의원E경기도 부천시 상동 535-9번지 시그마타워 601호경기도 부천시 길주로 115, 601호 (상동, 시그마타워)1454237.506175126.755301N20181123경기도 부천시 상동 535-9번지 시그마타워 601호경기도 부천시 길주로 115, 601호 (상동, 시그마타워)1454237.506175126.755301
3302201712411901234567890부천시<NA>메디슬림의원E경기도 부천시 상동 544-6번지경기도 부천시 상동로 81, 202호 (상동, 센터프라자)42086437.504511126.752332N20181123경기도 부천시 상동 544-6번지경기도 부천시 상동로 81, 202호 (상동, 센터프라자)42086437.504511126.752332
4318201712411301234567890성남시<NA>지인의원E경기도 성남시 중원구 성남동 3208번지 성남동메디칼센타 503호경기도 성남시 중원구 광명로 5, 503호 (성남동)46282737.433107127.129654N20181123경기도 성남시 중원구 성남동 3208번지 성남동메디칼센타 503호경기도 성남시 중원구 광명로 5, 503호 (성남동)46282737.433107127.129654
5313201712411901234567890부천시<NA>비너스의원E경기도 부천시 상동 534-5번지 현해프라자경기도 부천시 상동로 105, 302,303호 (상동, 현해프라자)42086137.506593126.752743N20181123경기도 부천시 상동 534-5번지 현해프라자경기도 부천시 상동로 105, 302,303호 (상동, 현해프라자)42086137.506593126.752743
6332201712411301234567890성남시<NA>의료법인성심의료재단성남성심연합영상의원E성남시 중원구 상대원동1974-2경기도 성남시 중원구 둔촌대로 387, 지하1~5층 (상대원동)46281337.433584127.160037N20181123성남시 중원구 상대원동1974-2경기도 성남시 중원구 둔촌대로 387, 지하1~5층 (상대원동)46281337.433584127.160037
7734201712415901234567890화성시<NA>남양정형외과의원E경기도 화성시 남양읍 남양리 1172-4번지 삼기프라자 2층경기도 화성시 남양읍 남양성지로 123, 2층 (삼기프라자)1826137.208134126.817166N20181123경기도 화성시 남양읍 남양리 1172-4번지 삼기프라자 2층경기도 화성시 남양읍 남양성지로 123, 2층 (삼기프라자)1826137.208134126.817166
8348201712411301234567890성남시<NA>일신의원E경기도 성남시수정구 신흥3동 4211, 2층경기도 성남시 수정구 산성대로249번길 3, 2층 (신흥동)1335337.440244127.144649N20181123경기도 성남시수정구 신흥3동 4211, 2층경기도 성남시 수정구 산성대로249번길 3, 2층 (신흥동)1335337.440244127.144649
9363201712411301234567890성남시<NA>판교성모내과의원E경기도 성남시 분당구 운중동 959번지 502호경기도 성남시 분당구 운중로 115, 502호 (운중동)1346137.392003127.076024N20181123경기도 성남시 분당구 운중동 959번지 502호경기도 성남시 분당구 운중로 115, 502호 (운중동)1346137.392003127.076024
아이디기준연월분석시군코드행정동코드시군명행정동명의료기관명종별구분명소재지지번주소소재지도로명주소소재지우편번호위도경도마트완료여부생성일자정제지번주소정제도로명주소정제우편번호정제WGS84경도정제WGS84위도
734563201712418301234567890양평군<NA>편안한내과통증영상의학과의원E경기도 양평군 양평읍 양근리 180-4번지 (2층,3층,4층)경기도 양평군 양평읍 시민로 31-1 (2층,3층,4층)1255737.490471127.495033N20181123경기도 양평군 양평읍 양근리 180-4번지 (2층,3층,4층)경기도 양평군 양평읍 시민로 31-1 (2층,3층,4층)1255737.490471127.495033
735564201712418301234567890양평군<NA>차빛의원E경기도 양평군 지평면 지평리 137-1번지경기도 양평군 지평면 지평의병로124번길 5-61254137.473949127.639908N20181123경기도 양평군 지평면 지평리 137-1번지경기도 양평군 지평면 지평의병로124번길 5-61254137.473949127.639908
736565201712418301234567890양평군<NA>김란미즈산부인과의원E경기도 양평군 양평읍 양근리 199-1번지 2층경기도 양평군 양평읍 시민로 12, 2층1256237.489217127.493363N20181123경기도 양평군 양평읍 양근리 199-1번지 2층경기도 양평군 양평읍 시민로 12, 2층1256237.489217127.493363
737567201712416701234567890여주시<NA>에덴산부인과의원E경기도 여주시 홍문동110경기도 여주시 세종로 11 (홍문동)1262137.296548127.637248N20181123경기도 여주시 홍문동110경기도 여주시 세종로 11 (홍문동)1262137.296548127.637248
738589201712414601234567890용인시<NA>성모의원E경기도 용인시 처인구 이동면 묵리 457번지 외1필지경기도 용인시 처인구 이동면 이원로 494 (외1필지)44983337.167922127.2384N20181123경기도 용인시 처인구 이동면 묵리 457번지 외1필지경기도 용인시 처인구 이동면 이원로 494 (외1필지)44983337.167922127.2384
739590201712414601234567890용인시<NA>현산부인과의원E경기도 용인시 기흥구 보정동 1265번지 3호 304호경기도 용인시 기흥구 죽전로 52 (보정동, 훼미리프라자)1689837.32018127.114606N20181123경기도 용인시 기흥구 보정동 1265번지 3호 304호경기도 용인시 기흥구 죽전로 52 (보정동, 훼미리프라자)1689837.32018127.114606
740595201712414601234567890용인시<NA>아이앤미가정의학과의원E경기도 용인시 기흥구 청덕동 502번지 1호 일월프라자 302,303호경기도 용인시 기흥구 구성3로28번길 32 (청덕동, 일월프라자 302, 303호)44691537.296289127.152104N20181123경기도 용인시 기흥구 청덕동 502번지 1호 일월프라자 302,303호경기도 용인시 기흥구 구성3로28번길 32 (청덕동, 일월프라자 302, 303호)44691537.296289127.152104
741600201712414601234567890용인시<NA>SOK수지속편한내과의원E경기도 용인시 수지구 동천동 948번지 송란빌딩 308,309,310호경기도 용인시 수지구 손곡로 95, 308,309,310호 (동천동, 송란빌딩)44812037.335895127.091745N20181123경기도 용인시 수지구 동천동 948번지 송란빌딩 308,309,310호경기도 용인시 수지구 손곡로 95, 308,309,310호 (동천동, 송란빌딩)44812037.335895127.091745
742601201712414601234567890용인시<NA>홍종욱내과의원E경기도 용인시 기흥구 보라동 573번지 6호 스카이프라자303호경기도 용인시 기흥구 한보라1로 8 (보라동)44690437.253905127.109357N20181123경기도 용인시 기흥구 보라동 573번지 6호 스카이프라자303호경기도 용인시 기흥구 한보라1로 8 (보라동)44690437.253905127.109357
743602201712414601234567890용인시<NA>연세가정의학과의원E경기도 용인시 수지구 죽전동 484번지 4호 3층경기도 용인시 수지구 대지로 46, 3층 (죽전동)44880337.328208127.11446N20181123경기도 용인시 수지구 죽전동 484번지 4호 3층경기도 용인시 수지구 대지로 46, 3층 (죽전동)44880337.328208127.11446