Overview

Dataset statistics

Number of variables11
Number of observations179
Missing cells169
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 KiB
Average record size in memory93.7 B

Variable types

Categorical3
Text3
Numeric5

Alerts

영업상태명 is highly overall correlated with 폐업일자 and 1 other fieldsHigh correlation
업체유형명 is highly overall correlated with 인허가일자 and 6 other fieldsHigh correlation
시군명 is highly overall correlated with 폐업일자 and 4 other fieldsHigh correlation
인허가일자 is highly overall correlated with 업체유형명High correlation
폐업일자 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
영업상태명 is highly imbalanced (51.2%)Imbalance
폐업일자 has 160 (89.4%) missing valuesMissing
소재지도로명주소 has 7 (3.9%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:10:34.859724
Analysis finished2023-12-10 23:10:38.723987
Duration3.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
수원시
21 
안양시
20 
의정부시
15 
광명시
11 
평택시
11 
Other values (26)
101 

Length

Max length4
Median length3
Mean length3.1620112
Min length3

Unique

Unique5 ?
Unique (%)2.8%

Sample

1st row가평군
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
수원시 21
 
11.7%
안양시 20
 
11.2%
의정부시 15
 
8.4%
광명시 11
 
6.1%
평택시 11
 
6.1%
파주시 9
 
5.0%
부천시 8
 
4.5%
광주시 8
 
4.5%
동두천시 8
 
4.5%
시흥시 7
 
3.9%
Other values (21) 61
34.1%

Length

2023-12-11T08:10:38.831057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 21
 
11.7%
안양시 20
 
11.2%
의정부시 15
 
8.4%
광명시 11
 
6.1%
평택시 11
 
6.1%
파주시 9
 
5.0%
부천시 8
 
4.5%
광주시 8
 
4.5%
동두천시 8
 
4.5%
시흥시 7
 
3.9%
Other values (21) 61
34.1%

사업장명
Text

Distinct172
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T08:10:39.118038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length6.5586592
Min length3

Characters and Unicode

Total characters1174
Distinct characters150
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)92.2%

Sample

1st row동운택시(주)
2nd row우신교통(주)
3rd row오복운수(합)
4th row문화택시(주)
5th row태현기업(주)
ValueCountFrequency (%)
광주택시(주 3
 
1.6%
금성공사(합 2
 
1.1%
대경운수(주 2
 
1.1%
신창운수(합 2
 
1.1%
동성운수(주 2
 
1.1%
합)신흥교통 2
 
1.1%
충무택시 2
 
1.1%
사용할것 2
 
1.1%
주식회사 2
 
1.1%
양평운수(자 1
 
0.5%
Other values (165) 165
89.2%
2023-12-11T08:10:39.558748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 124
 
10.6%
( 122
 
10.4%
주 98
 
8.3%
운 92
 
7.8%
수 83
 
7.1%
합 38
 
3.2%
시 36
 
3.1%
택 35
 
3.0%
통 34
 
2.9%
교 31
 
2.6%
Other values (140) 481
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 896
76.3%
Close Punctuation 124
 
10.6%
Open Punctuation 122
 
10.4%
Decimal Number 24
 
2.0%
Space Separator 6
 
0.5%
Connector Punctuation 1
 
0.1%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 98
 
10.9%
운 92
 
10.3%
수 83
 
9.3%
합 38
 
4.2%
시 36
 
4.0%
택 35
 
3.9%
통 34
 
3.8%
교 31
 
3.5%
성 23
 
2.6%
진 18
 
2.0%
Other values (128) 408
45.5%
Decimal Number
ValueCountFrequency (%)
9 5
20.8%
2 5
20.8%
0 5
20.8%
5 4
16.7%
1 2
 
8.3%
4 2
 
8.3%
3 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 122
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 897
76.4%
Common 277
 
23.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 98
 
10.9%
운 92
 
10.3%
수 83
 
9.3%
합 38
 
4.2%
시 36
 
4.0%
택 35
 
3.9%
통 34
 
3.8%
교 31
 
3.5%
성 23
 
2.6%
진 18
 
2.0%
Other values (129) 409
45.6%
Common
ValueCountFrequency (%)
) 124
44.8%
( 122
44.0%
6
 
2.2%
9 5
 
1.8%
2 5
 
1.8%
0 5
 
1.8%
5 4
 
1.4%
1 2
 
0.7%
4 2
 
0.7%
_ 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 896
76.3%
ASCII 277
 
23.6%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 124
44.8%
( 122
44.0%
6
 
2.2%
9 5
 
1.8%
2 5
 
1.8%
0 5
 
1.8%
5 4
 
1.4%
1 2
 
0.7%
4 2
 
0.7%
_ 1
 
0.4%
Hangul
ValueCountFrequency (%)
주 98
 
10.9%
운 92
 
10.3%
수 83
 
9.3%
합 38
 
4.2%
시 36
 
4.0%
택 35
 
3.9%
통 34
 
3.8%
교 31
 
3.5%
성 23
 
2.6%
진 18
 
2.0%
Other values (128) 408
45.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct153
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19879392
Minimum19620525
Maximum20170607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T08:10:39.728288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19620525
5-th percentile19701101
Q119791125
median19831128
Q320000572
95-th percentile20110178
Maximum20170607
Range550082
Interquartile range (IQR)209447

Descriptive statistics

Standard deviation126225.13
Coefficient of variation (CV)0.0063495466
Kurtosis-0.55264875
Mean19879392
Median Absolute Deviation (MAD)40218
Skewness0.68668076
Sum3.5584112 × 109
Variance1.5932782 × 1010
MonotonicityNot monotonic
2023-12-11T08:10:39.875151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19791203 5
 
2.8%
19810131 5
 
2.8%
19810307 4
 
2.2%
20090212 3
 
1.7%
20040409 2
 
1.1%
19791002 2
 
1.1%
19791231 2
 
1.1%
19811121 2
 
1.1%
19790924 2
 
1.1%
19791020 2
 
1.1%
Other values (143) 150
83.8%
ValueCountFrequency (%)
19620525 1
0.6%
19650930 1
0.6%
19661018 1
0.6%
19681028 1
0.6%
19690414 1
0.6%
19690526 1
0.6%
19690821 1
0.6%
19700926 1
0.6%
19701022 1
0.6%
19701110 1
0.6%
ValueCountFrequency (%)
20170607 1
0.6%
20160825 1
0.6%
20160711 1
0.6%
20150428 1
0.6%
20140902 1
0.6%
20140901 1
0.6%
20130227 1
0.6%
20110822 1
0.6%
20110715 1
0.6%
20110118 1
0.6%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
운영중
160 
폐업 등
19 

Length

Max length4
Median length3
Mean length3.1061453
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 160
89.4%
폐업 등 19
 
10.6%

Length

2023-12-11T08:10:40.031681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:10:40.158817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 160
80.8%
폐업 19
 
9.6%
등 19
 
9.6%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)73.7%
Missing160
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean20130458
Minimum20100326
Maximum20170605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T08:10:40.251518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100326
5-th percentile20109676
Q120120111
median20120731
Q320140516
95-th percentile20170436
Maximum20170605
Range70279
Interquartile range (IQR)20405

Descriptive statistics

Standard deviation20052.645
Coefficient of variation (CV)0.0009961346
Kurtosis-0.032708065
Mean20130458
Median Absolute Deviation (MAD)9930
Skewness0.8865487
Sum3.824787 × 108
Variance4.0210859 × 108
MonotonicityNot monotonic
2023-12-11T08:10:40.367459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20120111 6
 
3.4%
20140901 1
 
0.6%
20170605 1
 
0.6%
20130227 1
 
0.6%
20110715 1
 
0.6%
20150430 1
 
0.6%
20160711 1
 
0.6%
20100326 1
 
0.6%
20110801 1
 
0.6%
20120731 1
 
0.6%
Other values (4) 4
 
2.2%
(Missing) 160
89.4%
ValueCountFrequency (%)
20100326 1
 
0.6%
20110715 1
 
0.6%
20110801 1
 
0.6%
20120111 6
3.4%
20120731 1
 
0.6%
20120913 1
 
0.6%
20130227 1
 
0.6%
20131125 1
 
0.6%
20140131 1
 
0.6%
20140901 1
 
0.6%
ValueCountFrequency (%)
20170605 1
0.6%
20170417 1
0.6%
20160711 1
0.6%
20150430 1
0.6%
20140901 1
0.6%
20140131 1
0.6%
20131125 1
0.6%
20130227 1
0.6%
20120913 1
0.6%
20120731 1
0.6%

업체유형명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
일반택시
116 
<NA>
63 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row일반택시
3rd row일반택시
4th row일반택시
5th row일반택시

Common Values

ValueCountFrequency (%)
일반택시 116
64.8%
<NA> 63
35.2%

Length

2023-12-11T08:10:40.507500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:10:40.592437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반택시 116
64.8%
na 63
35.2%

소재지도로명주소
Text

MISSING 

Distinct156
Distinct (%)90.7%
Missing7
Missing (%)3.9%
Memory size1.5 KiB
2023-12-11T08:10:40.789265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length29.5
Mean length23
Min length14

Characters and Unicode

Total characters3956
Distinct characters206
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147 ?
Unique (%)85.5%

Sample

1st row경기도 가평군 가평읍 가화로 159-1
2nd row경기도 고양시 일산동구 고봉로678번길 53 (설문동)
3rd row경기도 고양시 일산서구송포로447번길 54(가좌동)
4th row경기도 고양시 일산동구성현로135번길 112(성석동)
5th row경기도 고양시 덕양구 통일로1108번길 165, 2층 (내유동)
ValueCountFrequency (%)
경기도 172
 
21.5%
수원시 20
 
2.5%
안양시 18
 
2.3%
광명시 11
 
1.4%
권선구 11
 
1.4%
파주시 9
 
1.1%
광명동 9
 
1.1%
동두천시 8
 
1.0%
광주시 8
 
1.0%
부천시 8
 
1.0%
Other values (377) 525
65.7%
2023-12-11T08:10:41.139366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
627
 
15.8%
시 182
 
4.6%
경 181
 
4.6%
기 177
 
4.5%
도 173
 
4.4%
로 159
 
4.0%
1 140
 
3.5%
동 136
 
3.4%
) 103
 
2.6%
( 103
 
2.6%
Other values (196) 1975
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2413
61.0%
Decimal Number 646
 
16.3%
Space Separator 627
 
15.8%
Close Punctuation 103
 
2.6%
Open Punctuation 103
 
2.6%
Dash Punctuation 45
 
1.1%
Other Punctuation 19
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 182
 
7.5%
경 181
 
7.5%
기 177
 
7.3%
도 173
 
7.2%
로 159
 
6.6%
동 136
 
5.6%
길 82
 
3.4%
안 68
 
2.8%
번 66
 
2.7%
양 57
 
2.4%
Other values (181) 1132
46.9%
Decimal Number
ValueCountFrequency (%)
1 140
21.7%
2 87
13.5%
4 64
9.9%
3 62
9.6%
7 59
9.1%
5 58
9.0%
9 49
 
7.6%
8 46
 
7.1%
6 43
 
6.7%
0 38
 
5.9%
Space Separator
ValueCountFrequency (%)
627
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2413
61.0%
Common 1543
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 182
 
7.5%
경 181
 
7.5%
기 177
 
7.3%
도 173
 
7.2%
로 159
 
6.6%
동 136
 
5.6%
길 82
 
3.4%
안 68
 
2.8%
번 66
 
2.7%
양 57
 
2.4%
Other values (181) 1132
46.9%
Common
ValueCountFrequency (%)
627
40.6%
1 140
 
9.1%
) 103
 
6.7%
( 103
 
6.7%
2 87
 
5.6%
4 64
 
4.1%
3 62
 
4.0%
7 59
 
3.8%
5 58
 
3.8%
9 49
 
3.2%
Other values (5) 191
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2413
61.0%
ASCII 1543
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
627
40.6%
1 140
 
9.1%
) 103
 
6.7%
( 103
 
6.7%
2 87
 
5.6%
4 64
 
4.1%
3 62
 
4.0%
7 59
 
3.8%
5 58
 
3.8%
9 49
 
3.2%
Other values (5) 191
 
12.4%
Hangul
ValueCountFrequency (%)
시 182
 
7.5%
경 181
 
7.5%
기 177
 
7.3%
도 173
 
7.2%
로 159
 
6.6%
동 136
 
5.6%
길 82
 
3.4%
안 68
 
2.8%
번 66
 
2.7%
양 57
 
2.4%
Other values (181) 1132
46.9%

소재지지번주소
Text

Distinct158
Distinct (%)88.8%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2023-12-11T08:10:41.366933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length21.342697
Min length11

Characters and Unicode

Total characters3799
Distinct characters158
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147 ?
Unique (%)82.6%

Sample

1st row경기도 가평군 가평읍 읍내리 625-7번지
2nd row경기도 고양시 일산동구 설문동 787-9번지
3rd row경기도 고양시 일산서구 가좌동 6-4번지
4th row경기도 고양시 일산동구 성석동 1043-3번지
5th row경기도 고양시 덕양구 내유동 432-13번지
ValueCountFrequency (%)
경기도 178
 
21.8%
수원시 21
 
2.6%
안양시 20
 
2.4%
의정부시 15
 
1.8%
만안구 14
 
1.7%
안양동 13
 
1.6%
권선구 11
 
1.3%
광명시 11
 
1.3%
평택시 11
 
1.3%
광명동 10
 
1.2%
Other values (328) 513
62.8%
2023-12-11T08:10:41.780492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
639
 
16.8%
시 187
 
4.9%
기 182
 
4.8%
도 181
 
4.8%
경 179
 
4.7%
지 173
 
4.6%
동 172
 
4.5%
번 169
 
4.4%
- 148
 
3.9%
1 132
 
3.5%
Other values (148) 1637
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2315
60.9%
Decimal Number 695
 
18.3%
Space Separator 639
 
16.8%
Dash Punctuation 148
 
3.9%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 187
 
8.1%
기 182
 
7.9%
도 181
 
7.8%
경 179
 
7.7%
지 173
 
7.5%
동 172
 
7.4%
번 169
 
7.3%
안 69
 
3.0%
양 61
 
2.6%
구 59
 
2.5%
Other values (134) 883
38.1%
Decimal Number
ValueCountFrequency (%)
1 132
19.0%
2 96
13.8%
3 83
11.9%
4 72
10.4%
6 65
9.4%
8 57
8.2%
7 56
8.1%
5 47
 
6.8%
0 46
 
6.6%
9 41
 
5.9%
Space Separator
ValueCountFrequency (%)
639
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2315
60.9%
Common 1484
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 187
 
8.1%
기 182
 
7.9%
도 181
 
7.8%
경 179
 
7.7%
지 173
 
7.5%
동 172
 
7.4%
번 169
 
7.3%
안 69
 
3.0%
양 61
 
2.6%
구 59
 
2.5%
Other values (134) 883
38.1%
Common
ValueCountFrequency (%)
639
43.1%
- 148
 
10.0%
1 132
 
8.9%
2 96
 
6.5%
3 83
 
5.6%
4 72
 
4.9%
6 65
 
4.4%
8 57
 
3.8%
7 56
 
3.8%
5 47
 
3.2%
Other values (4) 89
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2315
60.9%
ASCII 1484
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
639
43.1%
- 148
 
10.0%
1 132
 
8.9%
2 96
 
6.5%
3 83
 
5.6%
4 72
 
4.9%
6 65
 
4.4%
8 57
 
3.8%
7 56
 
3.8%
5 47
 
3.2%
Other values (4) 89
 
6.0%
Hangul
ValueCountFrequency (%)
시 187
 
8.1%
기 182
 
7.9%
도 181
 
7.8%
경 179
 
7.7%
지 173
 
7.5%
동 172
 
7.4%
번 169
 
7.3%
안 69
 
3.0%
양 61
 
2.6%
구 59
 
2.5%
Other values (134) 883
38.1%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)74.7%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean339329.69
Minimum10017
Maximum487060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T08:10:41.964014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10017
5-th percentile10813
Q1116075.75
median431081
Q3464060
95-th percentile482885.05
Maximum487060
Range477043
Interquartile range (IQR)347984.25

Descriptive statistics

Standard deviation191190.76
Coefficient of variation (CV)0.56343659
Kurtosis-0.71756016
Mean339329.69
Median Absolute Deviation (MAD)32979
Skewness-1.1117804
Sum60400685
Variance3.6553908 × 1010
MonotonicityNot monotonic
2023-12-11T08:10:42.127645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430010 10
 
5.6%
464060 8
 
4.5%
480810 4
 
2.2%
429450 3
 
1.7%
480823 3
 
1.7%
431080 3
 
1.7%
13806 3
 
1.7%
10064 2
 
1.1%
10813 2
 
1.1%
483080 2
 
1.1%
Other values (123) 138
77.1%
ValueCountFrequency (%)
10017 1
0.6%
10064 2
1.1%
10205 1
0.6%
10251 1
0.6%
10257 1
0.6%
10264 1
0.6%
10809 1
0.6%
10813 2
1.1%
10944 1
0.6%
11025 1
0.6%
ValueCountFrequency (%)
487060 2
1.1%
483130 1
 
0.6%
483120 1
 
0.6%
483080 2
1.1%
483031 2
1.1%
483010 1
 
0.6%
482863 2
1.1%
480840 1
 
0.6%
480838 2
1.1%
480823 3
1.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.468475
Minimum36.985241
Maximum38.035145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T08:10:42.300845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.985241
5-th percentile37.032598
Q137.297842
median37.403591
Q337.703499
95-th percentile37.882952
Maximum38.035145
Range1.0499046
Interquartile range (IQR)0.40565721

Descriptive statistics

Standard deviation0.25086249
Coefficient of variation (CV)0.0066952951
Kurtosis-0.74244809
Mean37.468475
Median Absolute Deviation (MAD)0.14765618
Skewness0.19088354
Sum6706.857
Variance0.062931991
MonotonicityNot monotonic
2023-12-11T08:10:42.481421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4035005423 7
 
3.9%
37.4747331262 4
 
2.2%
37.4292721926 3
 
1.7%
37.346440814 3
 
1.7%
37.8579462158 2
 
1.1%
37.7549852776 2
 
1.1%
37.5940370805 2
 
1.1%
37.392805762 2
 
1.1%
37.3847804682 2
 
1.1%
37.8724134693 2
 
1.1%
Other values (147) 150
83.8%
ValueCountFrequency (%)
36.9852406633 1
0.6%
36.9891078574 1
0.6%
36.991747302 1
0.6%
36.9965227736 1
0.6%
37.0086851655 1
0.6%
37.0093779624 1
0.6%
37.0137710256 1
0.6%
37.0292949528 1
0.6%
37.0312992577 1
0.6%
37.0327427476 1
0.6%
ValueCountFrequency (%)
38.0351452763 1
0.6%
37.9351585887 1
0.6%
37.9344372279 1
0.6%
37.9224990861 1
0.6%
37.9117463416 1
0.6%
37.9069357165 1
0.6%
37.9051608222 1
0.6%
37.9025251052 1
0.6%
37.8933577171 1
0.6%
37.8817959525 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.01617
Minimum126.61248
Maximum127.66689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T08:10:42.654458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.61248
5-th percentile126.72689
Q1126.85332
median127.00153
Q3127.08508
95-th percentile127.46632
Maximum127.66689
Range1.0544145
Interquartile range (IQR)0.23176503

Descriptive statistics

Standard deviation0.20465616
Coefficient of variation (CV)0.0016112606
Kurtosis1.117349
Mean127.01617
Median Absolute Deviation (MAD)0.11501405
Skewness0.87082223
Sum22735.895
Variance0.041884142
MonotonicityNot monotonic
2023-12-11T08:10:42.819138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2575339326 7
 
3.9%
126.8533169444 4
 
2.2%
126.987448679 3
 
1.7%
126.6921974753 3
 
1.7%
127.0490167501 2
 
1.1%
127.0170435963 2
 
1.1%
127.1561728628 2
 
1.1%
126.9312299221 2
 
1.1%
126.9358444805 2
 
1.1%
127.1702499562 2
 
1.1%
Other values (147) 150
83.8%
ValueCountFrequency (%)
126.6124752783 1
 
0.6%
126.6427224909 1
 
0.6%
126.6506368728 1
 
0.6%
126.6921974753 3
1.7%
126.7120836672 1
 
0.6%
126.7216082803 1
 
0.6%
126.7253811709 1
 
0.6%
126.7270543953 1
 
0.6%
126.7512487604 1
 
0.6%
126.75508119 1
 
0.6%
ValueCountFrequency (%)
127.6668898192 1
0.6%
127.6448599634 1
0.6%
127.62941904 1
0.6%
127.6266650236 1
0.6%
127.5173932202 1
0.6%
127.5111379812 1
0.6%
127.5006971482 1
0.6%
127.4871753785 1
0.6%
127.4755393701 1
0.6%
127.4652977302 1
0.6%

Interactions

2023-12-11T08:10:37.852313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.426947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.967338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.825726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.354980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.954102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.536357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.093543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.949064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.449954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:38.042555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.641426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.193744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.075424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.566876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:38.140352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.725577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.316178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.163867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.672803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:38.237004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:35.813783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:36.443872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.263555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:10:37.768318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:10:43.215380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자소재지우편번호WGS84위도WGS84경도
시군명1.0000.7410.4970.9220.8910.9760.972
인허가일자0.7411.0000.3260.5740.2160.4500.539
영업상태명0.4970.3261.000NaN0.0720.2640.486
폐업일자0.9220.574NaN1.0000.6950.8030.525
소재지우편번호0.8910.2160.0720.6951.0000.5760.637
WGS84위도0.9760.4500.2640.8030.5761.0000.767
WGS84경도0.9720.5390.4860.5250.6370.7671.000
2023-12-11T08:10:43.349897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명업체유형명시군명
영업상태명1.0001.0000.388
업체유형명1.0001.0001.000
시군명0.3881.0001.000
2023-12-11T08:10:43.462034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명업체유형명
인허가일자1.0000.127-0.032-0.081-0.1280.3390.2461.000
폐업일자0.1271.000-0.3830.204-0.2250.5101.0001.000
소재지우편번호-0.032-0.3831.000-0.0220.5740.6710.1221.000
WGS84위도-0.0810.204-0.0221.000-0.1470.7870.1971.000
WGS84경도-0.128-0.2250.574-0.1471.0000.7680.3651.000
시군명0.3390.5100.6710.7870.7681.0000.3881.000
영업상태명0.2461.0000.1220.1970.3650.3881.0001.000
업체유형명1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T08:10:38.372796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:10:38.529227image/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.
2023-12-11T08:10:38.639631image/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

시군명사업장명인허가일자영업상태명폐업일자업체유형명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군동운택시(주)19751017운영중<NA><NA>경기도 가평군 가평읍 가화로 159-1경기도 가평군 가평읍 읍내리 625-7번지47780537.833371127.511138
1고양시우신교통(주)19831101운영중<NA>일반택시경기도 고양시 일산동구 고봉로678번길 53 (설문동)경기도 고양시 일산동구 설문동 787-9번지1025137.716236126.791622
2고양시오복운수(합)19831010운영중<NA>일반택시경기도 고양시 일산서구송포로447번길 54(가좌동)경기도 고양시 일산서구 가좌동 6-4번지41144037.702764126.725381
3고양시문화택시(주)20041209운영중<NA>일반택시경기도 고양시 일산동구성현로135번길 112(성석동)경기도 고양시 일산동구 성석동 1043-3번지41057037.71276126.797518
4고양시태현기업(주)20000713운영중<NA>일반택시경기도 고양시 덕양구 통일로1108번길 165, 2층 (내유동)경기도 고양시 덕양구 내유동 432-13번지1026437.715177126.853141
5고양시코엑스운수(주)20000520운영중<NA>일반택시경기도 고양시 일산서구 덕산로195번길 107 (가좌동, 코엑스운수주식회사 건물)경기도 고양시 일산서구 가좌동 547-3번지1020537.698926126.721608
6고양시세기상운(주)19751209운영중<NA>일반택시경기도 고양시 일산동구지영로194번길 4(지영동)경기도 고양시 일산동구 지영동 304-6번지41054037.713819126.828681
7고양시신도택시(주)19791127운영중<NA>일반택시경기도 고양시 일산동구 문봉길 44-134 (문봉동)경기도 고양시 일산동구 문봉동 168-63번지1025737.704234126.823737
8과천시안전운수합자회사19840201운영중<NA>일반택시경기도 과천시 관문로 69, 과천시청 (중앙동)경기도 과천시 중앙동 1-3번지 과천시청1380637.429272126.987449
9과천시영강교통19840206운영중<NA>일반택시경기도 과천시 관문로 69, 과천시청 (중앙동)경기도 과천시 중앙동 1-3번지 과천시청1380637.429272126.987449
시군명사업장명인허가일자영업상태명폐업일자업체유형명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
169평택시(합자)평마택시19860830운영중<NA>일반택시경기도 평택시만세로 1519-26(청룡동)경기도 평택시 청룡동 81-25번지45006037.029295127.12517
170평택시해강산업(주)19650930운영중<NA>일반택시경기도 평택시 원평2로20번길 26 (평택동)경기도 평택시 평택동 113-2번지1792136.989108127.082061
171포천시광복택시(합)19790910운영중<NA>일반택시경기도 포천시 신북면 틀못이길 11-9경기도 포천시 신북면 기지리 693-1번지1113937.922499127.223215
172포천시동우운수(주)19870427운영중<NA>일반택시경기도 포천시 자작로4길 12 (자작동)경기도 포천시 자작동 479번지48706037.872413127.17025
173포천시동일교통(주)20030904운영중<NA>일반택시경기도 포천시 자작로4길 12 (자작동)경기도 포천시 자작동 479번지48706037.872413127.17025
174하남시신장택시(유)19790920운영중<NA>일반택시경기도 하남시 대청로21번길 31 (신장동)경기도 하남시 신장동 382-5번지1295037.541899127.213287
175화성시동성운수(주)19831214운영중<NA><NA>경기도 화성시 세자로396번길 32-14 (안녕동)경기도 화성시 안녕동 180-274번지44538037.200305126.987811
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