Overview

Dataset statistics

Number of variables19
Number of observations602
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.6 KiB
Average record size in memory159.2 B

Variable types

Numeric5
Categorical9
Text4
Boolean1

Alerts

apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
gugun is highly overall correlated with skey and 10 other fieldsHigh correlation
atchm_limit_date is highly overall correlated with skey and 8 other fieldsHigh correlation
data_day is highly overall correlated with skey and 8 other fieldsHigh correlation
inst_nm is highly overall correlated with skey and 10 other fieldsHigh correlation
tel is highly overall correlated with skey and 10 other fieldsHigh correlation
ftrs is highly overall correlated with skey and 10 other fieldsHigh correlation
skey is highly overall correlated with inst_nm and 6 other fieldsHigh correlation
instt_code is highly overall correlated with inst_nm and 6 other fieldsHigh correlation
atchm_nmbr is highly overall correlated with occ_use_feeHigh correlation
occ_use_fee is highly overall correlated with atchm_nmbr and 5 other fieldsHigh correlation
lng is highly overall correlated with inst_nm and 4 other fieldsHigh correlation
spec is highly overall correlated with skey and 10 other fieldsHigh correlation
civil_svc_fee is highly overall correlated with inst_nm and 6 other fieldsHigh correlation
atchm_limit_date is highly imbalanced (52.2%)Imbalance
civil_svc_fee is highly imbalanced (56.3%)Imbalance
data_day is highly imbalanced (72.6%)Imbalance
skey has unique valuesUnique
occ_use_fee has 83 (13.8%) zerosZeros

Reproduction

Analysis started2024-04-16 23:10:57.158561
Analysis finished2024-04-16 23:11:00.695341
Duration3.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct602
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6462.5
Minimum6162
Maximum6763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-17T08:11:00.752352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6162
5-th percentile6192.05
Q16312.25
median6462.5
Q36612.75
95-th percentile6732.95
Maximum6763
Range601
Interquartile range (IQR)300.5

Descriptive statistics

Standard deviation173.92671
Coefficient of variation (CV)0.026913224
Kurtosis-1.2
Mean6462.5
Median Absolute Deviation (MAD)150.5
Skewness0
Sum3890425
Variance30250.5
MonotonicityNot monotonic
2024-04-17T08:11:00.878844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6686 1
 
0.2%
6430 1
 
0.2%
6710 1
 
0.2%
6711 1
 
0.2%
6712 1
 
0.2%
6713 1
 
0.2%
6714 1
 
0.2%
6715 1
 
0.2%
6716 1
 
0.2%
6717 1
 
0.2%
Other values (592) 592
98.3%
ValueCountFrequency (%)
6162 1
0.2%
6163 1
0.2%
6164 1
0.2%
6165 1
0.2%
6166 1
0.2%
6167 1
0.2%
6168 1
0.2%
6169 1
0.2%
6170 1
0.2%
6171 1
0.2%
ValueCountFrequency (%)
6763 1
0.2%
6762 1
0.2%
6761 1
0.2%
6760 1
0.2%
6759 1
0.2%
6758 1
0.2%
6757 1
0.2%
6756 1
0.2%
6755 1
0.2%
6754 1
0.2%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3336760.8
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-17T08:11:00.981882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13310000
median3340000
Q33367500
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)57500

Descriptive statistics

Standard deviation40487.535
Coefficient of variation (CV)0.012133784
Kurtosis-0.70007358
Mean3336760.8
Median Absolute Deviation (MAD)30000
Skewness-0.26388822
Sum2.00873 × 109
Variance1.6392405 × 109
MonotonicityNot monotonic
2024-04-17T08:11:01.076501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3340000 117
19.4%
3310000 64
10.6%
3400000 56
9.3%
3360000 54
9.0%
3350000 39
 
6.5%
3390000 34
 
5.6%
3270000 32
 
5.3%
3380000 31
 
5.1%
3320000 30
 
5.0%
3370000 30
 
5.0%
Other values (6) 115
19.1%
ValueCountFrequency (%)
3250000 13
 
2.2%
3260000 19
 
3.2%
3270000 32
 
5.3%
3280000 13
 
2.2%
3290000 28
 
4.7%
3300000 19
 
3.2%
3310000 64
10.6%
3320000 30
 
5.0%
3330000 23
 
3.8%
3340000 117
19.4%
ValueCountFrequency (%)
3400000 56
9.3%
3390000 34
 
5.6%
3380000 31
 
5.1%
3370000 30
 
5.0%
3360000 54
9.0%
3350000 39
 
6.5%
3340000 117
19.4%
3330000 23
 
3.8%
3320000 30
 
5.0%
3310000 64
10.6%

inst_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
부산광역시 사하구청
117 
부산광역시 남구청
64 
부산광역시 기장군청
56 
부산광역시 강서구청
54 
부산광역시 사상구청
34 
Other values (12)
277 

Length

Max length11
Median length10
Mean length9.7940199
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사하구청
2nd row부산광역시 사하구청
3rd row부산광역시 사하구청
4th row부산광역시 사하구청
5th row부산광역시 사하구청

Common Values

ValueCountFrequency (%)
부산광역시 사하구청 117
19.4%
부산광역시 남구청 64
10.6%
부산광역시 기장군청 56
9.3%
부산광역시 강서구청 54
9.0%
부산광역시 사상구청 34
 
5.6%
부산광역시 동구청 32
 
5.3%
부산광역시 수영구청 31
 
5.1%
부산광역시 북구청 30
 
5.0%
부산광역시 연제구청 30
 
5.0%
부산광역시 부산진구청 28
 
4.7%
Other values (7) 126
20.9%

Length

2024-04-17T08:11:01.214898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 585
48.6%
사하구청 117
 
9.7%
남구청 64
 
5.3%
기장군청 56
 
4.7%
강서구청 54
 
4.5%
사상구청 34
 
2.8%
동구청 32
 
2.7%
수영구청 31
 
2.6%
연제구청 30
 
2.5%
북구청 30
 
2.5%
Other values (9) 171
 
14.2%

admns_nm
Text

Distinct146
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-17T08:11:01.453474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5431894
Min length3

Characters and Unicode

Total characters2133
Distinct characters106
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)5.6%

Sample

1st row하단동
2nd row신평동
3rd row신평동
4th row신평동
5th row당리동
ValueCountFrequency (%)
녹산동 28
 
4.7%
신평동 26
 
4.3%
정관읍 23
 
3.8%
하단동 23
 
3.8%
다대동 19
 
3.2%
기장읍 18
 
3.0%
괴정동 17
 
2.8%
명지1동 15
 
2.5%
당리동 12
 
2.0%
대연제3동 12
 
2.0%
Other values (136) 409
67.9%
2024-04-17T08:11:01.833225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 558
26.2%
1 94
 
4.4%
제 85
 
4.0%
대 62
 
2.9%
2 61
 
2.9%
산 51
 
2.4%
연 48
 
2.3%
읍 48
 
2.3%
정 46
 
2.2%
장 45
 
2.1%
Other values (96) 1035
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1889
88.6%
Decimal Number 241
 
11.3%
Space Separator 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 558
29.5%
제 85
 
4.5%
대 62
 
3.3%
산 51
 
2.7%
연 48
 
2.5%
읍 48
 
2.5%
정 46
 
2.4%
장 45
 
2.4%
천 40
 
2.1%
신 36
 
1.9%
Other values (87) 870
46.1%
Decimal Number
ValueCountFrequency (%)
1 94
39.0%
2 61
25.3%
3 41
17.0%
4 23
 
9.5%
9 8
 
3.3%
6 8
 
3.3%
5 3
 
1.2%
8 3
 
1.2%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1889
88.6%
Common 244
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 558
29.5%
제 85
 
4.5%
대 62
 
3.3%
산 51
 
2.7%
연 48
 
2.5%
읍 48
 
2.5%
정 46
 
2.4%
장 45
 
2.4%
천 40
 
2.1%
신 36
 
1.9%
Other values (87) 870
46.1%
Common
ValueCountFrequency (%)
1 94
38.5%
2 61
25.0%
3 41
16.8%
4 23
 
9.4%
9 8
 
3.3%
6 8
 
3.3%
3
 
1.2%
5 3
 
1.2%
8 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1889
88.6%
ASCII 244
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 558
29.5%
제 85
 
4.5%
대 62
 
3.3%
산 51
 
2.7%
연 48
 
2.5%
읍 48
 
2.5%
정 46
 
2.4%
장 45
 
2.4%
천 40
 
2.1%
신 36
 
1.9%
Other values (87) 870
46.1%
ASCII
ValueCountFrequency (%)
1 94
38.5%
2 61
25.0%
3 41
16.8%
4 23
 
9.4%
9 8
 
3.3%
6 8
 
3.3%
3
 
1.2%
5 3
 
1.2%
8 3
 
1.2%

spot
Text

Distinct553
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-17T08:11:02.025043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length22
Mean length12.10299
Min length3

Characters and Unicode

Total characters7286
Distinct characters396
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique516 ?
Unique (%)85.7%

Sample

1st row하단강변삼거리 횡단보도(강변쪽)
2nd row새신평 사거리 산쪽
3rd row새신평 사거리 산쪽
4th row새신평 사거리 산쪽
5th row사하구청 앞 횡단보도
ValueCountFrequency (%)
앞 126
 
8.9%
옆 50
 
3.5%
맞은편 32
 
2.3%
입구 29
 
2.1%
사거리 26
 
1.8%
삼거리 17
 
1.2%
쪽 14
 
1.0%
건너편 13
 
0.9%
횡단보도 13
 
0.9%
옹벽 9
 
0.6%
Other values (753) 1081
76.7%
2024-04-17T08:11:02.335900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
860
 
11.8%
) 220
 
3.0%
( 219
 
3.0%
앞 175
 
2.4%
동 171
 
2.3%
리 157
 
2.2%
대 138
 
1.9%
구 137
 
1.9%
거 135
 
1.9%
로 117
 
1.6%
Other values (386) 4957
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5651
77.6%
Space Separator 860
 
11.8%
Decimal Number 227
 
3.1%
Close Punctuation 220
 
3.0%
Open Punctuation 219
 
3.0%
Uppercase Letter 60
 
0.8%
Dash Punctuation 19
 
0.3%
Lowercase Letter 16
 
0.2%
Other Punctuation 14
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 175
 
3.1%
동 171
 
3.0%
리 157
 
2.8%
대 138
 
2.4%
구 137
 
2.4%
거 135
 
2.4%
로 117
 
2.1%
교 112
 
2.0%
사 102
 
1.8%
아 101
 
1.8%
Other values (341) 4306
76.2%
Uppercase Letter
ValueCountFrequency (%)
G 8
13.3%
L 7
11.7%
K 7
11.7%
I 6
10.0%
S 6
10.0%
T 5
8.3%
C 4
6.7%
E 3
 
5.0%
A 3
 
5.0%
P 3
 
5.0%
Other values (6) 8
13.3%
Decimal Number
ValueCountFrequency (%)
2 71
31.3%
1 53
23.3%
3 28
 
12.3%
0 19
 
8.4%
4 13
 
5.7%
6 11
 
4.8%
9 10
 
4.4%
5 9
 
4.0%
7 8
 
3.5%
8 5
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 4
25.0%
t 3
18.8%
m 2
12.5%
p 1
 
6.2%
k 1
 
6.2%
a 1
 
6.2%
d 1
 
6.2%
h 1
 
6.2%
c 1
 
6.2%
n 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 5
35.7%
@ 5
35.7%
, 2
 
14.3%
: 1
 
7.1%
/ 1
 
7.1%
Space Separator
ValueCountFrequency (%)
860
100.0%
Close Punctuation
ValueCountFrequency (%)
) 220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5649
77.5%
Common 1559
 
21.4%
Latin 76
 
1.0%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 175
 
3.1%
동 171
 
3.0%
리 157
 
2.8%
대 138
 
2.4%
구 137
 
2.4%
거 135
 
2.4%
로 117
 
2.1%
교 112
 
2.0%
사 102
 
1.8%
아 101
 
1.8%
Other values (340) 4304
76.2%
Latin
ValueCountFrequency (%)
G 8
 
10.5%
L 7
 
9.2%
K 7
 
9.2%
I 6
 
7.9%
S 6
 
7.9%
T 5
 
6.6%
e 4
 
5.3%
C 4
 
5.3%
t 3
 
3.9%
E 3
 
3.9%
Other values (16) 23
30.3%
Common
ValueCountFrequency (%)
860
55.2%
) 220
 
14.1%
( 219
 
14.0%
2 71
 
4.6%
1 53
 
3.4%
3 28
 
1.8%
0 19
 
1.2%
- 19
 
1.2%
4 13
 
0.8%
6 11
 
0.7%
Other values (9) 46
 
3.0%
Han
ValueCountFrequency (%)
區 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5649
77.5%
ASCII 1635
 
22.4%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
860
52.6%
) 220
 
13.5%
( 219
 
13.4%
2 71
 
4.3%
1 53
 
3.2%
3 28
 
1.7%
0 19
 
1.2%
- 19
 
1.2%
4 13
 
0.8%
6 11
 
0.7%
Other values (35) 122
 
7.5%
Hangul
ValueCountFrequency (%)
앞 175
 
3.1%
동 171
 
3.0%
리 157
 
2.8%
대 138
 
2.4%
구 137
 
2.4%
거 135
 
2.4%
로 117
 
2.1%
교 112
 
2.0%
사 102
 
1.8%
아 101
 
1.8%
Other values (340) 4304
76.2%
CJK
ValueCountFrequency (%)
區 2
100.0%

addr_road
Text

Distinct452
Distinct (%)75.6%
Missing4
Missing (%)0.7%
Memory size4.8 KiB
2024-04-17T08:11:02.602965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length250
Median length247
Mean length75.613712
Min length6

Characters and Unicode

Total characters45217
Distinct characters211
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique352 ?
Unique (%)58.9%

Sample

1st row부산광역시 사하구 하단동 1157-17
2nd row부산광역시 사하구 신평동 870-302
3rd row부산광역시 사하구 신평동 870-302
4th row부산광역시 사하구 신평동 870-302
5th row부산광역시 사하구 당리동 317-82
ValueCountFrequency (%)
부산광역시 584
 
23.1%
사하구 117
 
4.6%
남구 64
 
2.5%
기장군 56
 
2.2%
강서구 54
 
2.1%
사상구 34
 
1.3%
금정구 32
 
1.3%
동구 32
 
1.3%
수영구 31
 
1.2%
북구 30
 
1.2%
Other values (668) 1490
59.0%
2024-04-17T08:11:02.963303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35355
78.2%
산 609
 
1.3%
광 605
 
1.3%
부 599
 
1.3%
시 592
 
1.3%
역 588
 
1.3%
구 556
 
1.2%
1 526
 
1.2%
동 459
 
1.0%
2 344
 
0.8%
Other values (201) 4984
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 35355
78.2%
Other Letter 7155
 
15.8%
Decimal Number 2316
 
5.1%
Dash Punctuation 292
 
0.6%
Open Punctuation 45
 
0.1%
Close Punctuation 45
 
0.1%
Other Punctuation 7
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 609
 
8.5%
광 605
 
8.5%
부 599
 
8.4%
시 592
 
8.3%
역 588
 
8.2%
구 556
 
7.8%
동 459
 
6.4%
로 230
 
3.2%
사 164
 
2.3%
대 164
 
2.3%
Other values (185) 2589
36.2%
Decimal Number
ValueCountFrequency (%)
1 526
22.7%
2 344
14.9%
3 256
11.1%
7 217
9.4%
5 200
 
8.6%
4 198
 
8.5%
8 169
 
7.3%
0 153
 
6.6%
6 136
 
5.9%
9 117
 
5.1%
Space Separator
ValueCountFrequency (%)
35355
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 292
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38062
84.2%
Hangul 7155
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 609
 
8.5%
광 605
 
8.5%
부 599
 
8.4%
시 592
 
8.3%
역 588
 
8.2%
구 556
 
7.8%
동 459
 
6.4%
로 230
 
3.2%
사 164
 
2.3%
대 164
 
2.3%
Other values (185) 2589
36.2%
Common
ValueCountFrequency (%)
35355
92.9%
1 526
 
1.4%
2 344
 
0.9%
- 292
 
0.8%
3 256
 
0.7%
7 217
 
0.6%
5 200
 
0.5%
4 198
 
0.5%
8 169
 
0.4%
0 153
 
0.4%
Other values (6) 352
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38062
84.2%
Hangul 7155
 
15.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35355
92.9%
1 526
 
1.4%
2 344
 
0.9%
- 292
 
0.8%
3 256
 
0.7%
7 217
 
0.6%
5 200
 
0.5%
4 198
 
0.5%
8 169
 
0.4%
0 153
 
0.4%
Other values (6) 352
 
0.9%
Hangul
ValueCountFrequency (%)
산 609
 
8.5%
광 605
 
8.5%
부 599
 
8.4%
시 592
 
8.3%
역 588
 
8.2%
구 556
 
7.8%
동 459
 
6.4%
로 230
 
3.2%
사 164
 
2.3%
대 164
 
2.3%
Other values (185) 2589
36.2%

ftrs
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
탱탱걸이(우측고정)
117 
탱탱이 걸이식(좌측고정)
64 
접철식
56 
탱탱이걸이형(좌측고정)
52 
탱탱이
44 
Other values (19)
269 

Length

Max length13
Median length11
Mean length7.6710963
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row탱탱걸이(우측고정)
2nd row탱탱걸이(우측고정)
3rd row탱탱걸이(우측고정)
4th row탱탱걸이(우측고정)
5th row탱탱걸이(우측고정)

Common Values

ValueCountFrequency (%)
탱탱걸이(우측고정) 117
19.4%
탱탱이 걸이식(좌측고정) 64
10.6%
접철식 56
9.3%
탱탱이걸이형(좌측고정) 52
8.6%
탱탱이 44
 
7.3%
반자동접이형 32
 
5.3%
탱탱걸이방식 31
 
5.1%
탱탱걸이식 31
 
5.1%
탱탱걸이식(우측고정) 30
 
5.0%
탱탱걸이 28
 
4.7%
Other values (14) 117
19.4%

Length

2024-04-17T08:11:03.080997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
탱탱이 121
17.8%
탱탱걸이(우측고정 117
17.2%
걸이식(좌측고정 64
9.4%
접철식 56
8.2%
탱탱이걸이형(좌측고정 52
7.7%
반자동접이형 32
 
4.7%
탱탱걸이방식 31
 
4.6%
탱탱걸이식 31
 
4.6%
탱탱걸이식(우측고정 30
 
4.4%
탱탱걸이 28
 
4.1%
Other values (14) 117
17.2%

spec
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
5.5*0.7
108 
6*0.9
105 
6.0*0.7
59 
6*0.8
56 
6.2*0.9
56 
Other values (32)
218 

Length

Max length17
Median length7
Mean length6.4534884
Min length5

Unique

Unique10 ?
Unique (%)1.7%

Sample

1st row5.5*0.7
2nd row5.5*0.7
3rd row5.5*0.7
4th row5.5*0.7
5th row5.5*0.7

Common Values

ValueCountFrequency (%)
5.5*0.7 108
17.9%
6*0.9 105
17.4%
6.0*0.7 59
9.8%
6*0.8 56
9.3%
6.2*0.9 56
9.3%
6*0.7 32
 
5.3%
7.0*0.9 21
 
3.5%
4.7*0.7 17
 
2.8%
5.9*0.9 17
 
2.8%
5.8*0.9 14
 
2.3%
Other values (27) 117
19.4%

Length

2024-04-17T08:11:03.179669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.5*0.7 108
17.9%
6*0.9 105
17.4%
6.0*0.7 59
9.8%
6*0.8 56
9.3%
6.2*0.9 56
9.3%
6*0.7 32
 
5.3%
7.0*0.9 21
 
3.5%
4.7*0.7 17
 
2.8%
5.9*0.9 17
 
2.8%
5.0*0.7 14
 
2.3%
Other values (28) 118
19.6%

atchm_nmbr
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3471761
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-17T08:11:03.259120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile8.95
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5735246
Coefficient of variation (CV)0.59199915
Kurtosis3.3224712
Mean4.3471761
Median Absolute Deviation (MAD)1
Skewness1.0170759
Sum2617
Variance6.6230286
MonotonicityNot monotonic
2024-04-17T08:11:03.347377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5 222
36.9%
1 135
22.4%
6 115
19.1%
2 34
 
5.6%
3 31
 
5.1%
4 25
 
4.2%
10 19
 
3.2%
15 6
 
1.0%
7 6
 
1.0%
12 4
 
0.7%
Other values (3) 5
 
0.8%
ValueCountFrequency (%)
1 135
22.4%
2 34
 
5.6%
3 31
 
5.1%
4 25
 
4.2%
5 222
36.9%
6 115
19.1%
7 6
 
1.0%
8 3
 
0.5%
9 1
 
0.2%
10 19
 
3.2%
ValueCountFrequency (%)
18 1
 
0.2%
15 6
 
1.0%
12 4
 
0.7%
10 19
 
3.2%
9 1
 
0.2%
8 3
 
0.5%
7 6
 
1.0%
6 115
19.1%
5 222
36.9%
4 25
 
4.2%

atchm_limit_date
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
10
540 
0
62 

Length

Max length2
Median length2
Mean length1.89701
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 540
89.7%
0 62
 
10.3%

Length

2024-04-17T08:11:03.444220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:03.520053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 540
89.7%
0 62
 
10.3%

civil_svc_fee
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
10000
482 
0
73 
5000
 
30
28900
 
10
21500
 
7

Length

Max length5
Median length5
Mean length4.4651163
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000
2nd row10000
3rd row10000
4th row10000
5th row10000

Common Values

ValueCountFrequency (%)
10000 482
80.1%
0 73
 
12.1%
5000 30
 
5.0%
28900 10
 
1.7%
21500 7
 
1.2%

Length

2024-04-17T08:11:03.609959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:03.697435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 482
80.1%
0 73
 
12.1%
5000 30
 
5.0%
28900 10
 
1.7%
21500 7
 
1.2%

occ_use_fee
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2203.2392
Minimum0
Maximum16200
Zeros83
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-17T08:11:03.793383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11050
median1260
Q31620
95-th percentile12600
Maximum16200
Range16200
Interquartile range (IQR)570

Descriptive statistics

Standard deviation3676.4266
Coefficient of variation (CV)1.6686462
Kurtosis8.5826159
Mean2203.2392
Median Absolute Deviation (MAD)360
Skewness3.1612518
Sum1326350
Variance13516113
MonotonicityNot monotonic
2024-04-17T08:11:03.904897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1620 179
29.7%
1150 108
17.9%
0 83
13.8%
720 48
 
8.0%
1260 37
 
6.1%
1560 34
 
5.6%
16200 27
 
4.5%
1890 26
 
4.3%
980 19
 
3.2%
12600 19
 
3.2%
Other values (8) 22
 
3.7%
ValueCountFrequency (%)
0 83
13.8%
720 48
 
8.0%
980 19
 
3.2%
1050 7
 
1.2%
1150 108
17.9%
1260 37
 
6.1%
1470 1
 
0.2%
1560 34
 
5.6%
1590 1
 
0.2%
1620 179
29.7%
ValueCountFrequency (%)
16200 27
 
4.5%
12600 19
 
3.2%
1890 26
 
4.3%
1860 1
 
0.2%
1830 1
 
0.2%
1780 1
 
0.2%
1755 2
 
0.3%
1750 8
 
1.3%
1620 179
29.7%
1590 1
 
0.2%

tel
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
051-220-4715
117 
051-607-4626
64 
051-709-4626
56 
051-970-4281
54 
051-310-4626
34 
Other values (13)
277 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-220-4715
2nd row051-220-4715
3rd row051-220-4715
4th row051-220-4715
5th row051-220-4715

Common Values

ValueCountFrequency (%)
051-220-4715 117
19.4%
051-607-4626 64
10.6%
051-709-4626 56
9.3%
051-970-4281 54
9.0%
051-310-4626 34
 
5.6%
051-440-4625 32
 
5.3%
051-610-4625 31
 
5.1%
051-665-4621 30
 
5.0%
051-309-4625 30
 
5.0%
051-605-4629 28
 
4.7%
Other values (8) 126
20.9%

Length

2024-04-17T08:11:04.009693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-220-4715 117
19.4%
051-607-4626 64
10.6%
051-709-4626 56
9.3%
051-970-4281 54
9.0%
051-310-4626 34
 
5.6%
051-440-4625 32
 
5.3%
051-610-4625 31
 
5.1%
051-665-4621 30
 
5.0%
051-309-4625 30
 
5.0%
051-605-4629 28
 
4.7%
Other values (8) 126
20.9%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
부산광역시 사하구
117 
부산광역시 남구
64 
부산광역시 기장군
56 
부산광역시 강서구
54 
부산광역시 금정구
39 
Other values (11)
272 

Length

Max length10
Median length9
Mean length8.8222591
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사하구
2nd row부산광역시 사하구
3rd row부산광역시 사하구
4th row부산광역시 사하구
5th row부산광역시 사하구

Common Values

ValueCountFrequency (%)
부산광역시 사하구 117
19.4%
부산광역시 남구 64
10.6%
부산광역시 기장군 56
9.3%
부산광역시 강서구 54
9.0%
부산광역시 금정구 39
 
6.5%
부산광역시 사상구 34
 
5.6%
부산광역시 동구 32
 
5.3%
부산광역시 수영구 31
 
5.1%
부산광역시 연제구 30
 
5.0%
부산광역시 북구 30
 
5.0%
Other values (6) 115
19.1%

Length

2024-04-17T08:11:04.103351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 602
50.0%
사하구 117
 
9.7%
남구 64
 
5.3%
기장군 56
 
4.7%
강서구 54
 
4.5%
금정구 39
 
3.2%
사상구 34
 
2.8%
동구 32
 
2.7%
수영구 31
 
2.6%
연제구 30
 
2.5%
Other values (7) 145
 
12.0%

data_day
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2020-12-31
559 
2021-01-08
 
30
2020-07-31
 
13

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 559
92.9%
2021-01-08 30
 
5.0%
2020-07-31 13
 
2.2%

Length

2024-04-17T08:11:04.201520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:04.279107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 559
92.9%
2021-01-08 30
 
5.0%
2020-07-31 13
 
2.2%

lat
Text

Distinct457
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-04-17T08:11:04.464848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length9.2458472
Min length6

Characters and Unicode

Total characters5566
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

Unique354 ?
Unique (%)58.8%

Sample

1st row35.115294
2nd row35.09923
3rd row35.09923
4th row35.09923
5th row35.103451
ValueCountFrequency (%)
35.115393 5
 
0.8%
35.083096 4
 
0.7%
35.057293 4
 
0.7%
35.085483 4
 
0.7%
35.098895 4
 
0.7%
35.094272 4
 
0.7%
35.115294 4
 
0.7%
35.1288896 4
 
0.7%
35.100625 4
 
0.7%
35.103451 4
 
0.7%
Other values (447) 561
93.2%
2024-04-17T08:11:04.787394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 969
17.4%
5 904
16.2%
1 659
11.8%
. 603
10.8%
2 407
7.3%
0 369
 
6.6%
9 349
 
6.3%
8 348
 
6.3%
6 340
 
6.1%
4 314
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4963
89.2%
Other Punctuation 603
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 969
19.5%
5 904
18.2%
1 659
13.3%
2 407
8.2%
0 369
 
7.4%
9 349
 
7.0%
8 348
 
7.0%
6 340
 
6.9%
4 314
 
6.3%
7 304
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5566
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 969
17.4%
5 904
16.2%
1 659
11.8%
. 603
10.8%
2 407
7.3%
0 369
 
6.6%
9 349
 
6.3%
8 348
 
6.3%
6 340
 
6.1%
4 314
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 969
17.4%
5 904
16.2%
1 659
11.8%
. 603
10.8%
2 407
7.3%
0 369
 
6.6%
9 349
 
6.3%
8 348
 
6.3%
6 340
 
6.1%
4 314
 
5.6%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct457
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04232
Minimum127.46873
Maximum129.28274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-04-17T08:11:04.902025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.46873
5-th percentile128.88094
Q1128.9806
median129.04983
Q3129.09843
95-th percentile129.19562
Maximum129.28274
Range1.814011
Interquartile range (IQR)0.11783625

Descriptive statistics

Standard deviation0.10761051
Coefficient of variation (CV)0.00083391639
Kurtosis75.136255
Mean129.04232
Median Absolute Deviation (MAD)0.058036655
Skewness-5.1880505
Sum77683.477
Variance0.011580021
MonotonicityNot monotonic
2024-04-17T08:11:05.026909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.961535 5
 
0.8%
128.959648 4
 
0.7%
128.967647 4
 
0.7%
128.965242 4
 
0.7%
128.963693 4
 
0.7%
129.1143634 4
 
0.7%
128.976001 4
 
0.7%
128.993993 4
 
0.7%
128.974553 4
 
0.7%
128.976554 4
 
0.7%
Other values (447) 561
93.2%
ValueCountFrequency (%)
127.468726 1
0.2%
128.812243 1
0.2%
128.8226696 1
0.2%
128.830716 1
0.2%
128.831874 1
0.2%
128.832948 1
0.2%
128.836211 1
0.2%
128.839459 1
0.2%
128.839491 1
0.2%
128.839518 1
0.2%
ValueCountFrequency (%)
129.282737 1
0.2%
129.282694 1
0.2%
129.282624 1
0.2%
129.258162 1
0.2%
129.257847 1
0.2%
129.257346 1
0.2%
129.240175 1
0.2%
129.240064 1
0.2%
129.235056 1
0.2%
129.232624 1
0.2%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size734.0 B
False
602 
ValueCountFrequency (%)
False 602
100.0%
2024-04-17T08:11:05.116120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2021-02-01 05:43:03
602 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01 05:43:03
2nd row2021-02-01 05:43:03
3rd row2021-02-01 05:43:03
4th row2021-02-01 05:43:03
5th row2021-02-01 05:43:03

Common Values

ValueCountFrequency (%)
2021-02-01 05:43:03 602
100.0%

Length

2024-04-17T08:11:05.205778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T08:11:05.524503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-01 602
50.0%
05:43:03 602
50.0%

Interactions

2024-04-17T08:10:59.965188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.144780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.516585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.928699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.569108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:00.048236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.211042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.589319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.017300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.646614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:00.133181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.284060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.664928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.097639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.725264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:00.210094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.353826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.760100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.171615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.803731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:11:00.291975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.443836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:58.837489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.477817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T08:10:59.882950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T08:11:05.577892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeinst_nmftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylng
skey1.0000.9620.9620.9610.9210.5430.8310.8080.6440.9640.9620.6770.636
instt_code0.9621.0001.0000.9970.9460.6030.8580.8390.7471.0001.0000.7150.718
inst_nm0.9621.0001.0000.9920.9730.7331.0000.9200.9341.0001.0001.0000.877
ftrs0.9610.9970.9921.0000.9680.7371.0000.8640.9130.9940.9980.9740.931
spec0.9210.9460.9730.9681.0000.7860.7100.8540.8990.9750.9710.8760.874
atchm_nmbr0.5430.6030.7330.7370.7861.0000.0610.4630.3950.8040.7080.4200.374
atchm_limit_date0.8310.8581.0001.0000.7100.0611.0000.5910.4481.0001.0000.4330.152
civil_svc_fee0.8080.8390.9200.8640.8540.4630.5911.0000.3500.9230.8620.7200.069
occ_use_fee0.6440.7470.9340.9130.8990.3950.4480.3501.0000.9390.9760.2440.222
tel0.9641.0001.0000.9940.9750.8041.0000.9230.9391.0001.0001.0000.883
gugun0.9621.0001.0000.9980.9710.7081.0000.8620.9761.0001.0001.0000.940
data_day0.6770.7151.0000.9740.8760.4200.4330.7200.2441.0001.0001.0000.057
lng0.6360.7180.8770.9310.8740.3740.1520.0690.2220.8830.9400.0571.000
2024-04-17T08:11:05.683462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugunatchm_limit_datedata_daycivil_svc_feeinst_nmtelspecftrs
gugun1.0000.9880.9890.6550.9990.9980.7330.934
atchm_limit_date0.9881.0000.6750.7110.9870.9870.5940.981
data_day0.9890.6751.0000.7090.9880.9870.6760.817
civil_svc_fee0.6550.7110.7091.0000.7760.7750.5900.627
inst_nm0.9990.9870.9880.7761.0000.9990.7340.910
tel0.9980.9870.9870.7750.9991.0000.7350.909
spec0.7330.5940.6760.5900.7340.7351.0000.649
ftrs0.9340.9810.8170.6270.9100.9090.6491.000
2024-04-17T08:11:05.780532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeatchm_nmbrocc_use_feelnginst_nmftrsspecatchm_limit_datecivil_svc_feetelgugundata_day
skey1.000-0.066-0.450-0.153-0.3680.8230.7860.6300.6620.4650.8220.8240.530
instt_code-0.0661.000-0.004-0.3110.1090.9940.9690.7160.6880.4940.9930.9950.586
atchm_nmbr-0.450-0.0041.0000.5000.1570.3980.3830.4150.0610.2880.4020.3850.204
occ_use_fee-0.153-0.3110.5001.000-0.2970.8140.6610.6810.3020.2920.8130.7960.233
lng-0.3680.1090.157-0.2971.0000.7010.6930.6390.1010.0560.7000.7020.054
inst_nm0.8230.9940.3980.8140.7011.0000.9100.7340.9870.7760.9990.9990.988
ftrs0.7860.9690.3830.6610.6930.9101.0000.6490.9810.6270.9090.9340.817
spec0.6300.7160.4150.6810.6390.7340.6491.0000.5940.5900.7350.7330.676
atchm_limit_date0.6620.6880.0610.3020.1010.9870.9810.5941.0000.7110.9870.9880.675
civil_svc_fee0.4650.4940.2880.2920.0560.7760.6270.5900.7111.0000.7750.6550.709
tel0.8220.9930.4020.8130.7000.9990.9090.7350.9870.7751.0000.9980.987
gugun0.8240.9950.3850.7960.7020.9990.9340.7330.9880.6550.9981.0000.989
data_day0.5300.5860.2040.2330.0540.9880.8170.6760.6750.7090.9870.9891.000

Missing values

2024-04-17T08:11:00.412928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T08:11:00.606875image/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.

Sample

skeyinstt_codeinst_nmadmns_nmspotaddr_roadftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylatlngapr_atlast_load_dttm
066863340000부산광역시 사하구청하단동하단강변삼거리 횡단보도(강변쪽)부산광역시 사하구 하단동 1157-17탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.115294128.959648N2021-02-01 05:43:03
166873340000부산광역시 사하구청신평동새신평 사거리 산쪽부산광역시 사하구 신평동 870-302탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.09923128.961614N2021-02-01 05:43:03
266883340000부산광역시 사하구청신평동새신평 사거리 산쪽부산광역시 사하구 신평동 870-302탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.09923128.961614N2021-02-01 05:43:03
366893340000부산광역시 사하구청신평동새신평 사거리 산쪽부산광역시 사하구 신평동 870-302탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.09923128.961614N2021-02-01 05:43:03
466903340000부산광역시 사하구청당리동사하구청 앞 횡단보도부산광역시 사하구 당리동 317-82탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.103451128.974553N2021-02-01 05:43:03
566913340000부산광역시 사하구청당리동사하구청 앞 횡단보도부산광역시 사하구 당리동 317-82탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.103451128.974553N2021-02-01 05:43:03
666923340000부산광역시 사하구청당리동낙동초교 사거리 양지쌀유통 앞부산광역시 사하구 당리동 313-25탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.104367128.971859N2021-02-01 05:43:03
766933340000부산광역시 사하구청당리동협진태양아파트 횡단보도 건너편부산광역시 사하구 당리동 498-7탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.100625128.976554N2021-02-01 05:43:03
866943340000부산광역시 사하구청당리동협진태양아파트 횡단보도 건너편부산광역시 사하구 당리동 498-7탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.100625128.976554N2021-02-01 05:43:03
966953340000부산광역시 사하구청괴정동동주대 입구부산광역시 사하구 괴정동 250탱탱걸이(우측고정)5.5*0.7110100001150051-220-4715부산광역시 사하구2020-12-3135.10268128.998164N2021-02-01 05:43:03
skeyinstt_codeinst_nmadmns_nmspotaddr_roadftrsspecatchm_nmbratchm_limit_datecivil_svc_feeocc_use_feetelgugundata_daylatlngapr_atlast_load_dttm
59262103290000부산광역시 부산진구청당감동외식1번가 맞은편부산광역시 진구 신천대로 222탱탱걸이6*0.9510100001620051-605-4629부산광역시 부산진구2020-12-3135.161952129.049017N2021-02-01 05:43:03
59362113290000부산광역시 부산진구청연지동정묘사 옆 옹벽부산광역시 진구 동평로 315탱탱걸이6*0.91010100001620051-605-4629부산광역시 부산진구2020-12-3135.173729129.060765N2021-02-01 05:43:03
59464863310000부산광역시 남구청감만제1동감만삼거리부산광역시 남구 감만동 75-56탱탱이 걸이식(좌측고정)7.0*0.9510100001890051-607-4626부산광역시 남구2020-12-3135.124381129.07396N2021-02-01 05:43:03
59567323270000부산광역시 동구청좌천동좌천동 좌천역 2번 출구 옆부산광역시 동구 좌천1동반자동접이형6*0.91000051-440-4625부산광역시 동구2020-12-3135.13357129.053714N2021-02-01 05:43:03
59667333270000부산광역시 동구청초량동초량동 제1지하도로 충장로 인도변부산광역시 동구 초량동 초량제1지하차도 출구반자동접이형6*0.95000051-440-4625부산광역시 동구2020-12-3135.12131129.04762N2021-02-01 05:43:03
59767343270000부산광역시 동구청초량동초량동 제1지하도로 충장로 인도변부산광역시 동구 초량동 초량제1지하차도 출구반자동접이형6*0.76000051-440-4625부산광역시 동구2020-12-3135.12131129.04762N2021-02-01 05:43:03
59867353270000부산광역시 동구청초량동초량3동 부산역 앞부산광역시 동구 초량동 중앙대로 206반자동접이형6*0.91000051-440-4625부산광역시 동구2020-12-3135.11547129.039855N2021-02-01 05:43:03
59967363270000부산광역시 동구청초량동부산역 충장대로변 선상주차장 입구부산광역시 동구 초량동 부산역 선상주차장 출구반자동접이형6*0.9501000016200051-440-4625부산광역시 동구2020-12-3135.11677129.04446N2021-02-01 05:43:03
60067373270000부산광역시 동구청초량동초량 메리츠화재옆부산광역시 동구 초량동 1143-1반자동접이형6*0.9501000016200051-440-4625부산광역시 동구2020-12-3135.12591129.0453N2021-02-01 05:43:03
60167383270000부산광역시 동구청초량동윤흥신석상 대기오염측정소옆공지부산광역시 동구 초량동 1143-1반자동접이형6*0.9501000016200051-440-4625부산광역시 동구2020-12-3135.12591129.0453N2021-02-01 05:43:03