Overview

Dataset statistics

Number of variables16
Number of observations2942
Missing cells4728
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory385.1 KiB
Average record size in memory134.0 B

Variable types

Numeric5
Text7
Categorical1
DateTime2
Unsupported1

Alerts

last_load_dttm has constant value ""Constant
skey is highly overall correlated with gugunHigh correlation
instt_code is highly overall correlated with gugunHigh correlation
gugun is highly overall correlated with skey and 1 other fieldsHigh correlation
seq has 54 (1.8%) missing valuesMissing
addr_road has 75 (2.5%) missing valuesMissing
addr_jibun has 1451 (49.3%) missing valuesMissing
lat has 101 (3.4%) missing valuesMissing
lng has 101 (3.4%) missing valuesMissing
apr_at has 2942 (100.0%) missing valuesMissing
lat is highly skewed (γ1 = 27.50124152)Skewed
skey has unique valuesUnique
apr_at is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 16:04:32.932224
Analysis finished2024-04-17 16:04:36.467907
Duration3.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2942
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49619.491
Minimum48148
Maximum51143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2024-04-18T01:04:36.526051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48148
5-th percentile48295.05
Q148883.25
median49618.5
Q350353.75
95-th percentile50941.95
Maximum51143
Range2995
Interquartile range (IQR)1470.5

Descriptive statistics

Standard deviation851.1413
Coefficient of variation (CV)0.017153366
Kurtosis-1.1904075
Mean49619.491
Median Absolute Deviation (MAD)735.5
Skewness0.0069519429
Sum1.4598054 × 108
Variance724441.51
MonotonicityNot monotonic
2024-04-18T01:04:36.634964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51102 1
 
< 0.1%
49150 1
 
< 0.1%
49152 1
 
< 0.1%
49153 1
 
< 0.1%
49154 1
 
< 0.1%
49155 1
 
< 0.1%
49156 1
 
< 0.1%
49157 1
 
< 0.1%
49158 1
 
< 0.1%
49159 1
 
< 0.1%
Other values (2932) 2932
99.7%
ValueCountFrequency (%)
48148 1
< 0.1%
48149 1
< 0.1%
48150 1
< 0.1%
48151 1
< 0.1%
48152 1
< 0.1%
48153 1
< 0.1%
48154 1
< 0.1%
48155 1
< 0.1%
48156 1
< 0.1%
48157 1
< 0.1%
ValueCountFrequency (%)
51143 1
< 0.1%
51142 1
< 0.1%
51141 1
< 0.1%
51140 1
< 0.1%
51139 1
< 0.1%
51138 1
< 0.1%
51137 1
< 0.1%
51136 1
< 0.1%
51135 1
< 0.1%
51134 1
< 0.1%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3319857.2
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2024-04-18T01:04:36.729545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13280000
median3320000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)70000

Descriptive statistics

Standard deviation42542.217
Coefficient of variation (CV)0.012814472
Kurtosis-1.1758134
Mean3319857.2
Median Absolute Deviation (MAD)30000
Skewness0.20517125
Sum9.76702 × 109
Variance1.8098402 × 109
MonotonicityNot monotonic
2024-04-18T01:04:36.818135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3270000 500
17.0%
3290000 330
11.2%
3330000 304
10.3%
3390000 279
9.5%
3350000 232
7.9%
3370000 198
 
6.7%
3340000 187
 
6.4%
3310000 167
 
5.7%
3300000 154
 
5.2%
3320000 132
 
4.5%
Other values (6) 459
15.6%
ValueCountFrequency (%)
3250000 78
 
2.7%
3260000 90
 
3.1%
3270000 500
17.0%
3280000 100
 
3.4%
3290000 330
11.2%
3300000 154
 
5.2%
3310000 167
 
5.7%
3320000 132
 
4.5%
3330000 304
10.3%
3340000 187
 
6.4%
ValueCountFrequency (%)
3400000 27
 
0.9%
3390000 279
9.5%
3380000 110
 
3.7%
3370000 198
6.7%
3360000 54
 
1.8%
3350000 232
7.9%
3340000 187
6.4%
3330000 304
10.3%
3320000 132
4.5%
3310000 167
5.7%

admns_nm
Text

Distinct189
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
2024-04-18T01:04:37.062833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7882393
Min length3

Characters and Unicode

Total characters11145
Distinct characters105
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

Unique9 ?
Unique (%)0.3%

Sample

1st row지사동
2nd row지사동
3rd row지사동
4th row지사동
5th row지사동
ValueCountFrequency (%)
범일1동 98
 
3.3%
중2동 57
 
1.9%
주례2동 57
 
1.9%
재송2동 54
 
1.8%
초량6동 50
 
1.7%
개금3동 50
 
1.7%
좌천동 50
 
1.7%
엄궁동 49
 
1.7%
수정2동 44
 
1.5%
연산9동 44
 
1.5%
Other values (177) 2389
81.2%
2024-04-18T01:04:37.423921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2984
26.8%
2 820
 
7.4%
1 728
 
6.5%
3 311
 
2.8%
정 252
 
2.3%
천 216
 
1.9%
연 198
 
1.8%
초 191
 
1.7%
수 177
 
1.6%
4 173
 
1.6%
Other values (95) 5095
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8869
79.6%
Decimal Number 2217
 
19.9%
Space Separator 59
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2984
33.6%
정 252
 
2.8%
천 216
 
2.4%
연 198
 
2.2%
초 191
 
2.2%
수 177
 
2.0%
범 169
 
1.9%
산 166
 
1.9%
송 162
 
1.8%
량 158
 
1.8%
Other values (86) 4196
47.3%
Decimal Number
ValueCountFrequency (%)
2 820
37.0%
1 728
32.8%
3 311
 
14.0%
4 173
 
7.8%
5 68
 
3.1%
6 64
 
2.9%
9 44
 
2.0%
8 9
 
0.4%
Space Separator
ValueCountFrequency (%)
59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8869
79.6%
Common 2276
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2984
33.6%
정 252
 
2.8%
천 216
 
2.4%
연 198
 
2.2%
초 191
 
2.2%
수 177
 
2.0%
범 169
 
1.9%
산 166
 
1.9%
송 162
 
1.8%
량 158
 
1.8%
Other values (86) 4196
47.3%
Common
ValueCountFrequency (%)
2 820
36.0%
1 728
32.0%
3 311
 
13.7%
4 173
 
7.6%
5 68
 
3.0%
6 64
 
2.8%
59
 
2.6%
9 44
 
1.9%
8 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8869
79.6%
ASCII 2276
 
20.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2984
33.6%
정 252
 
2.8%
천 216
 
2.4%
연 198
 
2.2%
초 191
 
2.2%
수 177
 
2.0%
범 169
 
1.9%
산 166
 
1.9%
송 162
 
1.8%
량 158
 
1.8%
Other values (86) 4196
47.3%
ASCII
ValueCountFrequency (%)
2 820
36.0%
1 728
32.0%
3 311
 
13.7%
4 173
 
7.6%
5 68
 
3.0%
6 64
 
2.8%
59
 
2.6%
9 44
 
1.9%
8 9
 
0.4%

seq
Text

MISSING 

Distinct2227
Distinct (%)77.1%
Missing54
Missing (%)1.8%
Memory size23.1 KiB
2024-04-18T01:04:37.665035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4141274
Min length1

Characters and Unicode

Total characters15636
Distinct characters79
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1838 ?
Unique (%)63.6%

Sample

1st row장림2동-4
2nd row장림2동-5
3rd row장림2동-6,7
4th row장림2동-8
5th row장림2동-9
ValueCountFrequency (%)
3 17
 
0.6%
1 17
 
0.6%
4 17
 
0.6%
2 17
 
0.6%
5 16
 
0.6%
6 14
 
0.5%
7 13
 
0.5%
8 11
 
0.4%
9 10
 
0.3%
11 10
 
0.3%
Other values (2217) 2746
95.1%
2024-04-18T01:04:38.017637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2407
15.4%
1 1882
 
12.0%
동 1466
 
9.4%
2 1241
 
7.9%
0 826
 
5.3%
3 681
 
4.4%
4 575
 
3.7%
5 477
 
3.1%
6 420
 
2.7%
구 386
 
2.5%
Other values (69) 5275
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7074
45.2%
Other Letter 5869
37.5%
Dash Punctuation 2407
 
15.4%
Uppercase Letter 209
 
1.3%
Other Punctuation 54
 
0.3%
Lowercase Letter 23
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1466
25.0%
구 386
 
6.6%
정 252
 
4.3%
연 209
 
3.6%
남 198
 
3.4%
제 198
 
3.4%
초 188
 
3.2%
범 169
 
2.9%
송 162
 
2.8%
량 158
 
2.7%
Other values (54) 2483
42.3%
Decimal Number
ValueCountFrequency (%)
1 1882
26.6%
2 1241
17.5%
0 826
11.7%
3 681
 
9.6%
4 575
 
8.1%
5 477
 
6.7%
6 420
 
5.9%
7 348
 
4.9%
8 320
 
4.5%
9 304
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 106
50.7%
A 103
49.3%
Dash Punctuation
ValueCountFrequency (%)
- 2407
100.0%
Other Punctuation
ValueCountFrequency (%)
, 54
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9535
61.0%
Hangul 5869
37.5%
Latin 232
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1466
25.0%
구 386
 
6.6%
정 252
 
4.3%
연 209
 
3.6%
남 198
 
3.4%
제 198
 
3.4%
초 188
 
3.2%
범 169
 
2.9%
송 162
 
2.8%
량 158
 
2.7%
Other values (54) 2483
42.3%
Common
ValueCountFrequency (%)
- 2407
25.2%
1 1882
19.7%
2 1241
13.0%
0 826
 
8.7%
3 681
 
7.1%
4 575
 
6.0%
5 477
 
5.0%
6 420
 
4.4%
7 348
 
3.6%
8 320
 
3.4%
Other values (2) 358
 
3.8%
Latin
ValueCountFrequency (%)
B 106
45.7%
A 103
44.4%
a 23
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9767
62.5%
Hangul 5869
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2407
24.6%
1 1882
19.3%
2 1241
12.7%
0 826
 
8.5%
3 681
 
7.0%
4 575
 
5.9%
5 477
 
4.9%
6 420
 
4.3%
7 348
 
3.6%
8 320
 
3.3%
Other values (5) 590
 
6.0%
Hangul
ValueCountFrequency (%)
동 1466
25.0%
구 386
 
6.6%
정 252
 
4.3%
연 209
 
3.6%
남 198
 
3.4%
제 198
 
3.4%
초 188
 
3.2%
범 169
 
2.9%
송 162
 
2.8%
량 158
 
2.7%
Other values (54) 2483
42.3%

spot
Text

Distinct2265
Distinct (%)77.1%
Missing4
Missing (%)0.1%
Memory size23.1 KiB
2024-04-18T01:04:38.213220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length35
Mean length13.474132
Min length2

Characters and Unicode

Total characters39587
Distinct characters614
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1962 ?
Unique (%)66.8%

Sample

1st row부산광역시 강서구 s&t 앞
2nd row부산광역시 강서구 대명테크 뒤
3rd row부산광역시 강서구 동남유공앞 건너편
4th row부산광역시 강서구 한일정공 앞
5th row부산광역시 강서구 신세기SFS 앞
ValueCountFrequency (%)
앞 601
 
7.6%
입구 203
 
2.6%
옆 173
 
2.2%
맞은편 156
 
2.0%
정문 72
 
0.9%
주례2동 57
 
0.7%
강서구 54
 
0.7%
부산광역시 54
 
0.7%
개금3동 50
 
0.6%
아래 49
 
0.6%
Other values (3323) 6467
81.5%
2024-04-18T01:04:38.549979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5167
 
13.1%
로 1378
 
3.5%
동 1306
 
3.3%
1 1035
 
2.6%
) 952
 
2.4%
( 951
 
2.4%
앞 849
 
2.1%
2 757
 
1.9%
구 601
 
1.5%
3 541
 
1.4%
Other values (604) 26050
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27461
69.4%
Space Separator 5167
 
13.1%
Decimal Number 4500
 
11.4%
Close Punctuation 952
 
2.4%
Open Punctuation 951
 
2.4%
Dash Punctuation 218
 
0.6%
Uppercase Letter 164
 
0.4%
Other Punctuation 117
 
0.3%
Math Symbol 34
 
0.1%
Lowercase Letter 22
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1378
 
5.0%
동 1306
 
4.8%
앞 849
 
3.1%
구 601
 
2.2%
길 504
 
1.8%
아 479
 
1.7%
정 475
 
1.7%
대 473
 
1.7%
번 442
 
1.6%
입 417
 
1.5%
Other values (548) 20537
74.8%
Uppercase Letter
ValueCountFrequency (%)
S 27
16.5%
C 25
15.2%
G 13
7.9%
K 13
7.9%
I 12
 
7.3%
A 11
 
6.7%
L 9
 
5.5%
B 8
 
4.9%
R 7
 
4.3%
U 7
 
4.3%
Other values (10) 32
19.5%
Decimal Number
ValueCountFrequency (%)
1 1035
23.0%
2 757
16.8%
3 541
12.0%
4 391
 
8.7%
5 330
 
7.3%
0 327
 
7.3%
7 292
 
6.5%
6 281
 
6.2%
8 278
 
6.2%
9 268
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
m 5
22.7%
e 4
18.2%
t 3
13.6%
k 3
13.6%
s 3
13.6%
g 1
 
4.5%
i 1
 
4.5%
c 1
 
4.5%
a 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
@ 59
50.4%
, 46
39.3%
: 5
 
4.3%
. 2
 
1.7%
? 2
 
1.7%
& 1
 
0.9%
/ 1
 
0.9%
· 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 27
79.4%
→ 4
 
11.8%
> 2
 
5.9%
～ 1
 
2.9%
Space Separator
ValueCountFrequency (%)
5167
100.0%
Close Punctuation
ValueCountFrequency (%)
) 952
100.0%
Open Punctuation
ValueCountFrequency (%)
( 951
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 218
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27461
69.4%
Common 11939
30.2%
Latin 186
 
0.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1378
 
5.0%
동 1306
 
4.8%
앞 849
 
3.1%
구 601
 
2.2%
길 504
 
1.8%
아 479
 
1.7%
정 475
 
1.7%
대 473
 
1.7%
번 442
 
1.6%
입 417
 
1.5%
Other values (548) 20537
74.8%
Latin
ValueCountFrequency (%)
S 27
14.5%
C 25
13.4%
G 13
 
7.0%
K 13
 
7.0%
I 12
 
6.5%
A 11
 
5.9%
L 9
 
4.8%
B 8
 
4.3%
R 7
 
3.8%
U 7
 
3.8%
Other values (19) 54
29.0%
Common
ValueCountFrequency (%)
5167
43.3%
1 1035
 
8.7%
) 952
 
8.0%
( 951
 
8.0%
2 757
 
6.3%
3 541
 
4.5%
4 391
 
3.3%
5 330
 
2.8%
0 327
 
2.7%
7 292
 
2.4%
Other values (16) 1196
 
10.0%
Han
ValueCountFrequency (%)
舊 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27460
69.4%
ASCII 12119
30.6%
Arrows 4
 
< 0.1%
None 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5167
42.6%
1 1035
 
8.5%
) 952
 
7.9%
( 951
 
7.8%
2 757
 
6.2%
3 541
 
4.5%
4 391
 
3.2%
5 330
 
2.7%
0 327
 
2.7%
7 292
 
2.4%
Other values (42) 1376
 
11.4%
Hangul
ValueCountFrequency (%)
로 1378
 
5.0%
동 1306
 
4.8%
앞 849
 
3.1%
구 601
 
2.2%
길 504
 
1.8%
아 479
 
1.7%
정 475
 
1.7%
대 473
 
1.7%
번 442
 
1.6%
입 417
 
1.5%
Other values (547) 20536
74.8%
Arrows
ValueCountFrequency (%)
→ 4
100.0%
None
ValueCountFrequency (%)
㈜ 1
33.3%
～ 1
33.3%
· 1
33.3%
CJK
ValueCountFrequency (%)
舊 1
100.0%

addr_road
Text

MISSING 

Distinct2259
Distinct (%)78.8%
Missing75
Missing (%)2.5%
Memory size23.1 KiB
2024-04-18T01:04:38.855021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length28
Mean length16.537147
Min length2

Characters and Unicode

Total characters47412
Distinct characters276
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

Unique1858 ?
Unique (%)64.8%

Sample

1st row부산광역시 사하구 장평로 50(장림동)
2nd row부산광역시 사하구 다대로320번길 9(장림동)
3rd row부산광역시 사하구 다대로277번길 100(장림동)
4th row부산광역시 사하구 다대로277번길 56(장림동)
5th row부산광역시 사하구 다대로277번길 56(장림동)
ValueCountFrequency (%)
부산광역시 2100
 
21.1%
동구 494
 
5.0%
사상구 279
 
2.8%
금정구 232
 
2.3%
연제구 195
 
2.0%
사하구 187
 
1.9%
남구 167
 
1.7%
동래구 149
 
1.5%
북구 133
 
1.3%
수영구 110
 
1.1%
Other values (2163) 5910
59.4%
2024-04-18T01:04:39.267374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7174
 
15.1%
로 2520
 
5.3%
산 2457
 
5.2%
구 2186
 
4.6%
광 2170
 
4.6%
시 2145
 
4.5%
부 2116
 
4.5%
역 2101
 
4.4%
1 1881
 
4.0%
동 1438
 
3.0%
Other values (266) 21224
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29508
62.2%
Decimal Number 9521
 
20.1%
Space Separator 7174
 
15.1%
Close Punctuation 398
 
0.8%
Open Punctuation 398
 
0.8%
Dash Punctuation 391
 
0.8%
Other Punctuation 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 2520
 
8.5%
산 2457
 
8.3%
구 2186
 
7.4%
광 2170
 
7.4%
시 2145
 
7.3%
부 2116
 
7.2%
역 2101
 
7.1%
동 1438
 
4.9%
길 1150
 
3.9%
번 1048
 
3.6%
Other values (249) 10177
34.5%
Decimal Number
ValueCountFrequency (%)
1 1881
19.8%
2 1268
13.3%
3 1071
11.2%
4 909
9.5%
5 858
9.0%
7 800
8.4%
6 766
8.0%
9 679
 
7.1%
8 674
 
7.1%
0 615
 
6.5%
Close Punctuation
ValueCountFrequency (%)
) 397
99.7%
] 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 397
99.7%
[ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
7174
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 391
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29508
62.2%
Common 17904
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 2520
 
8.5%
산 2457
 
8.3%
구 2186
 
7.4%
광 2170
 
7.4%
시 2145
 
7.3%
부 2116
 
7.2%
역 2101
 
7.1%
동 1438
 
4.9%
길 1150
 
3.9%
번 1048
 
3.6%
Other values (249) 10177
34.5%
Common
ValueCountFrequency (%)
7174
40.1%
1 1881
 
10.5%
2 1268
 
7.1%
3 1071
 
6.0%
4 909
 
5.1%
5 858
 
4.8%
7 800
 
4.5%
6 766
 
4.3%
9 679
 
3.8%
8 674
 
3.8%
Other values (7) 1824
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29508
62.2%
ASCII 17904
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7174
40.1%
1 1881
 
10.5%
2 1268
 
7.1%
3 1071
 
6.0%
4 909
 
5.1%
5 858
 
4.8%
7 800
 
4.5%
6 766
 
4.3%
9 679
 
3.8%
8 674
 
3.8%
Other values (7) 1824
 
10.2%
Hangul
ValueCountFrequency (%)
로 2520
 
8.5%
산 2457
 
8.3%
구 2186
 
7.4%
광 2170
 
7.4%
시 2145
 
7.3%
부 2116
 
7.2%
역 2101
 
7.1%
동 1438
 
4.9%
길 1150
 
3.9%
번 1048
 
3.6%
Other values (249) 10177
34.5%

addr_jibun
Text

MISSING 

Distinct1093
Distinct (%)73.3%
Missing1451
Missing (%)49.3%
Memory size23.1 KiB
2024-04-18T01:04:39.510265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length17.128773
Min length2

Characters and Unicode

Total characters25539
Distinct characters124
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique787 ?
Unique (%)52.8%

Sample

1st row부산광역시 사하구 장림동 541
2nd row부산광역시 사하구 장림동 437-1
3rd row부산광역시 사하구 장림동 3
4th row부산광역시 사하구 장림동 25
5th row부산광역시 사하구 장림동 25
ValueCountFrequency (%)
부산광역시 1184
21.8%
동구 498
 
9.2%
사하구 187
 
3.4%
초량동 154
 
2.8%
수정동 150
 
2.8%
동래구 132
 
2.4%
북구 131
 
2.4%
범일동 118
 
2.2%
수영구 110
 
2.0%
중구 77
 
1.4%
Other values (1197) 2683
49.5%
2024-04-18T01:04:39.862135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4105
16.1%
동 1992
 
7.8%
1 1475
 
5.8%
산 1299
 
5.1%
광 1235
 
4.8%
구 1224
 
4.8%
부 1188
 
4.7%
시 1184
 
4.6%
역 1184
 
4.6%
- 1150
 
4.5%
Other values (114) 9503
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13605
53.3%
Decimal Number 6659
26.1%
Space Separator 4105
 
16.1%
Dash Punctuation 1150
 
4.5%
Math Symbol 20
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1992
14.6%
산 1299
 
9.5%
광 1235
 
9.1%
구 1224
 
9.0%
부 1188
 
8.7%
시 1184
 
8.7%
역 1184
 
8.7%
수 292
 
2.1%
사 240
 
1.8%
정 215
 
1.6%
Other values (101) 3552
26.1%
Decimal Number
ValueCountFrequency (%)
1 1475
22.2%
2 771
11.6%
4 647
9.7%
3 643
9.7%
5 607
9.1%
6 569
 
8.5%
8 525
 
7.9%
0 509
 
7.6%
7 495
 
7.4%
9 418
 
6.3%
Space Separator
ValueCountFrequency (%)
4105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1150
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13605
53.3%
Common 11934
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1992
14.6%
산 1299
 
9.5%
광 1235
 
9.1%
구 1224
 
9.0%
부 1188
 
8.7%
시 1184
 
8.7%
역 1184
 
8.7%
수 292
 
2.1%
사 240
 
1.8%
정 215
 
1.6%
Other values (101) 3552
26.1%
Common
ValueCountFrequency (%)
4105
34.4%
1 1475
 
12.4%
- 1150
 
9.6%
2 771
 
6.5%
4 647
 
5.4%
3 643
 
5.4%
5 607
 
5.1%
6 569
 
4.8%
8 525
 
4.4%
0 509
 
4.3%
Other values (3) 933
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13605
53.3%
ASCII 11934
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4105
34.4%
1 1475
 
12.4%
- 1150
 
9.6%
2 771
 
6.5%
4 647
 
5.4%
3 643
 
5.4%
5 607
 
5.1%
6 569
 
4.8%
8 525
 
4.4%
0 509
 
4.3%
Other values (3) 933
 
7.8%
Hangul
ValueCountFrequency (%)
동 1992
14.6%
산 1299
 
9.5%
광 1235
 
9.1%
구 1224
 
9.0%
부 1188
 
8.7%
시 1184
 
8.7%
역 1184
 
8.7%
수 292
 
2.1%
사 240
 
1.8%
정 215
 
1.6%
Other values (101) 3552
26.1%

qty
Real number (ℝ)

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2641061
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2024-04-18T01:04:39.971787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum52
Range51
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1032341
Coefficient of variation (CV)1.6638115
Kurtosis307.43184
Mean1.2641061
Median Absolute Deviation (MAD)0
Skewness15.785635
Sum3719
Variance4.4235939
MonotonicityNot monotonic
2024-04-18T01:04:40.067072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 2740
93.1%
2 113
 
3.8%
3 31
 
1.1%
4 11
 
0.4%
5 11
 
0.4%
10 7
 
0.2%
6 6
 
0.2%
12 3
 
0.1%
8 2
 
0.1%
14 2
 
0.1%
Other values (15) 16
 
0.5%
ValueCountFrequency (%)
1 2740
93.1%
2 113
 
3.8%
3 31
 
1.1%
4 11
 
0.4%
5 11
 
0.4%
6 6
 
0.2%
7 2
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
10 7
 
0.2%
ValueCountFrequency (%)
52 1
< 0.1%
51 1
< 0.1%
39 1
< 0.1%
34 1
< 0.1%
32 1
< 0.1%
24 1
< 0.1%
23 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
18 1
< 0.1%

manager
Text

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
2024-04-18T01:04:40.244236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length9.5707002
Min length3

Characters and Unicode

Total characters28157
Distinct characters75
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 강서구 산단관리사업소
2nd row부산광역시 강서구 산단관리사업소
3rd row부산광역시 강서구 산단관리사업소
4th row부산광역시 강서구 산단관리사업소
5th row부산광역시 강서구 산단관리사업소
ValueCountFrequency (%)
부산광역시 768
15.3%
주민센터 500
 
10.0%
안전총괄과 473
 
9.4%
해운대구청(안전총괄과 304
 
6.1%
사상구청 279
 
5.6%
금정구(도시안전과 232
 
4.6%
연제구청 198
 
4.0%
도시안전과 198
 
4.0%
건설과 187
 
3.7%
북구 132
 
2.6%
Other values (53) 1739
34.7%
2024-04-18T01:04:40.547370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2068
 
7.3%
과 1855
 
6.6%
구 1667
 
5.9%
전 1544
 
5.5%
안 1519
 
5.4%
시 1459
 
5.2%
청 1086
 
3.9%
동 984
 
3.5%
부 806
 
2.9%
산 806
 
2.9%
Other values (65) 14363
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23770
84.4%
Space Separator 2068
 
7.3%
Decimal Number 867
 
3.1%
Close Punctuation 726
 
2.6%
Open Punctuation 726
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 1855
 
7.8%
구 1667
 
7.0%
전 1544
 
6.5%
안 1519
 
6.4%
시 1459
 
6.1%
청 1086
 
4.6%
동 984
 
4.1%
부 806
 
3.4%
산 806
 
3.4%
도 791
 
3.3%
Other values (56) 11253
47.3%
Decimal Number
ValueCountFrequency (%)
1 312
36.0%
2 261
30.1%
3 126
14.5%
4 60
 
6.9%
5 58
 
6.7%
6 50
 
5.8%
Space Separator
ValueCountFrequency (%)
2068
100.0%
Close Punctuation
ValueCountFrequency (%)
) 726
100.0%
Open Punctuation
ValueCountFrequency (%)
( 726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23770
84.4%
Common 4387
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 1855
 
7.8%
구 1667
 
7.0%
전 1544
 
6.5%
안 1519
 
6.4%
시 1459
 
6.1%
청 1086
 
4.6%
동 984
 
4.1%
부 806
 
3.4%
산 806
 
3.4%
도 791
 
3.3%
Other values (56) 11253
47.3%
Common
ValueCountFrequency (%)
2068
47.1%
) 726
 
16.5%
( 726
 
16.5%
1 312
 
7.1%
2 261
 
5.9%
3 126
 
2.9%
4 60
 
1.4%
5 58
 
1.3%
6 50
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23770
84.4%
ASCII 4387
 
15.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2068
47.1%
) 726
 
16.5%
( 726
 
16.5%
1 312
 
7.1%
2 261
 
5.9%
3 126
 
2.9%
4 60
 
1.4%
5 58
 
1.3%
6 50
 
1.1%
Hangul
ValueCountFrequency (%)
과 1855
 
7.8%
구 1667
 
7.0%
전 1544
 
6.5%
안 1519
 
6.4%
시 1459
 
6.1%
청 1086
 
4.6%
동 984
 
4.1%
부 806
 
3.4%
산 806
 
3.4%
도 791
 
3.3%
Other values (56) 11253
47.3%

tel
Text

Distinct51
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
2024-04-18T01:04:40.750432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters35304
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

Unique0 ?
Unique (%)0.0%

Sample

1st row051-970-4292
2nd row051-970-4292
3rd row051-970-4292
4th row051-970-4292
5th row051-970-4292
ValueCountFrequency (%)
051-605-4124 330
 
11.2%
051-749-6166 304
 
10.3%
051-310-4636 279
 
9.5%
051-519-4654 232
 
7.9%
051-220-4665 187
 
6.4%
051-607-4654 167
 
5.7%
051-309-4712 132
 
4.5%
051-610-4642 110
 
3.7%
051-419-4642 100
 
3.4%
051-440-6352 98
 
3.3%
Other values (41) 1003
34.1%
2024-04-18T01:04:41.027446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5884
16.7%
0 5179
14.7%
5 5139
14.6%
4 4753
13.5%
1 4741
13.4%
6 4628
13.1%
2 1818
 
5.1%
7 1075
 
3.0%
9 962
 
2.7%
3 956
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29420
83.3%
Dash Punctuation 5884
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5179
17.6%
5 5139
17.5%
4 4753
16.2%
1 4741
16.1%
6 4628
15.7%
2 1818
 
6.2%
7 1075
 
3.7%
9 962
 
3.3%
3 956
 
3.2%
8 169
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 5884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5884
16.7%
0 5179
14.7%
5 5139
14.6%
4 4753
13.5%
1 4741
13.4%
6 4628
13.1%
2 1818
 
5.1%
7 1075
 
3.0%
9 962
 
2.7%
3 956
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5884
16.7%
0 5179
14.7%
5 5139
14.6%
4 4753
13.5%
1 4741
13.4%
6 4628
13.1%
2 1818
 
5.1%
7 1075
 
3.0%
9 962
 
2.7%
3 956
 
2.7%

gugun
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
부산광역시 동구
500 
부산광역시 부산진구
330 
부산광역시 해운대구
304 
부산광역시 사상구
279 
부산광역시 금정구
232 
Other values (11)
1297 

Length

Max length10
Median length9
Mean length8.6828688
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 동구 500
17.0%
부산광역시 부산진구 330
11.2%
부산광역시 해운대구 304
10.3%
부산광역시 사상구 279
9.5%
부산광역시 금정구 232
7.9%
부산광역시 연제구 198
 
6.7%
부산광역시 사하구 187
 
6.4%
부산광역시 남구 167
 
5.7%
부산광역시 동래구 154
 
5.2%
부산광역시 북구 132
 
4.5%
Other values (6) 459
15.6%

Length

2024-04-18T01:04:41.142613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 2842
49.1%
동구 500
 
8.6%
부산진구 330
 
5.7%
해운대구 304
 
5.3%
사상구 279
 
4.8%
금정구 232
 
4.0%
연제구 198
 
3.4%
사하구 187
 
3.2%
남구 167
 
2.9%
동래구 154
 
2.7%
Other values (7) 591
 
10.2%

data_day
Date

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Minimum2020-12-31 00:00:00
Maximum2021-01-24 00:00:00
2024-04-18T01:04:41.224300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:41.294326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

lat
Real number (ℝ)

MISSING  SKEWED 

Distinct2331
Distinct (%)82.0%
Missing101
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean35.165
Minimum35.019376
Maximum39.193843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2024-04-18T01:04:41.386988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.019376
5-th percentile35.094191
Q135.128138
median35.157664
Q335.191637
95-th percentile35.248049
Maximum39.193843
Range4.174467
Interquartile range (IQR)0.06349877

Descriptive statistics

Standard deviation0.099563641
Coefficient of variation (CV)0.0028313278
Kurtosis1034.0379
Mean35.165
Median Absolute Deviation (MAD)0.031525
Skewness27.501242
Sum99903.766
Variance0.0099129186
MonotonicityNot monotonic
2024-04-18T01:04:41.492511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.23119942 41
 
1.4%
35.12563216 25
 
0.8%
35.28304308 9
 
0.3%
35.2866052 8
 
0.3%
35.18408371 8
 
0.3%
35.23361375 7
 
0.2%
35.251672 6
 
0.2%
35.14533277 6
 
0.2%
35.248 5
 
0.2%
35.25268181 5
 
0.2%
Other values (2321) 2721
92.5%
(Missing) 101
 
3.4%
ValueCountFrequency (%)
35.019376 1
< 0.1%
35.030191 1
< 0.1%
35.04486234 1
< 0.1%
35.04848711 1
< 0.1%
35.04978906 1
< 0.1%
35.05016341 1
< 0.1%
35.05042627 1
< 0.1%
35.05130592 2
0.1%
35.05164894 1
< 0.1%
35.05210411 1
< 0.1%
ValueCountFrequency (%)
39.193843 1
< 0.1%
37.402728 1
< 0.1%
35.844542 1
< 0.1%
35.3492 1
< 0.1%
35.347742 1
< 0.1%
35.33975 1
< 0.1%
35.33782 1
< 0.1%
35.334712 1
< 0.1%
35.332701 1
< 0.1%
35.3316 1
< 0.1%

lng
Real number (ℝ)

MISSING 

Distinct2335
Distinct (%)82.2%
Missing101
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean129.05305
Minimum126.02291
Maximum129.2823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2024-04-18T01:04:41.603027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.02291
5-th percentile128.97407
Q1129.01891
median129.04954
Q3129.0889
95-th percentile129.15965
Maximum129.2823
Range3.259388
Interquartile range (IQR)0.0699874

Descriptive statistics

Standard deviation0.093510521
Coefficient of variation (CV)0.00072458978
Kurtosis541.01946
Mean129.05305
Median Absolute Deviation (MAD)0.0359523
Skewness-18.043329
Sum366639.72
Variance0.0087442175
MonotonicityNot monotonic
2024-04-18T01:04:41.712149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0757629 41
 
1.4%
129.0878605 25
 
0.8%
129.0676212 9
 
0.3%
129.1099323 8
 
0.3%
129.0802187 8
 
0.3%
129.1270674 7
 
0.2%
129.053243 6
 
0.2%
129.0463882 6
 
0.2%
129.0865054 5
 
0.2%
129.0861996 5
 
0.2%
Other values (2325) 2721
92.5%
(Missing) 101
 
3.4%
ValueCountFrequency (%)
126.022912 1
< 0.1%
126.649371 1
< 0.1%
128.61192 1
< 0.1%
128.810957 1
< 0.1%
128.811495 1
< 0.1%
128.812334 1
< 0.1%
128.815115 1
< 0.1%
128.817265 1
< 0.1%
128.819644 1
< 0.1%
128.820495 1
< 0.1%
ValueCountFrequency (%)
129.2823 1
< 0.1%
129.259298 1
< 0.1%
129.2592 1
< 0.1%
129.232025 1
< 0.1%
129.2142 2
0.1%
129.212348 1
< 0.1%
129.209708 1
< 0.1%
129.209138 1
< 0.1%
129.2089 1
< 0.1%
129.2018 1
< 0.1%

apr_at
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2942
Missing (%)100.0%
Memory size26.0 KiB

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Minimum2021-04-01 06:01:03
Maximum2021-04-01 06:01:03
2024-04-18T01:04:41.796954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:41.865581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T01:04:35.471643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:33.960295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.320769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.705795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.097894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.570813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.036303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.398633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.776805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.169589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.653094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.111750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.484326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.860678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.244432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.721065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.180163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.555375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.944544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.312515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.791790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.247781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:34.629215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.013841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:04:35.378765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:04:41.922725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeqtymanagertelgugundata_daylatlng
skey1.0000.9710.1250.9830.9840.9610.8080.0320.478
instt_code0.9711.0000.1451.0001.0001.0000.8950.0000.459
qty0.1250.1451.0000.5490.5510.6100.1720.0000.099
manager0.9831.0000.5491.0000.9981.0001.0000.3940.762
tel0.9841.0000.5510.9981.0001.0001.0000.3860.779
gugun0.9611.0000.6101.0001.0001.0001.0000.0000.800
data_day0.8080.8950.1721.0001.0001.0001.0000.0000.620
lat0.0320.0000.0000.3940.3860.0000.0001.0000.841
lng0.4780.4590.0990.7620.7790.8000.6200.8411.000
2024-04-18T01:04:42.018669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyinstt_codeqtylatlnggugun
skey1.0000.4590.214-0.057-0.4780.824
instt_code0.4591.0000.1280.4110.0760.999
qty0.2140.1281.0000.083-0.1420.259
lat-0.0570.4110.0831.0000.4870.000
lng-0.4780.076-0.1420.4871.0000.473
gugun0.8240.9990.2590.0000.4731.000

Missing values

2024-04-18T01:04:35.904414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:04:36.300085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-18T01:04:36.405830image/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

skeyinstt_codeadmns_nmseqspotaddr_roadaddr_jibunqtymanagertelgugundata_daylatlngapr_atlast_load_dttm
0511023360000지사동<NA>부산광역시 강서구 s&t 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.150809128.830818<NA>2021-04-01 06:01:03
1511033360000지사동<NA>부산광역시 강서구 대명테크 뒤<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.149686128.822504<NA>2021-04-01 06:01:03
2511043360000지사동<NA>부산광역시 강서구 동남유공앞 건너편<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.141752128.824811<NA>2021-04-01 06:01:03
3511053360000지사동<NA>부산광역시 강서구 한일정공 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.148876128.830627<NA>2021-04-01 06:01:03
4511063360000지사동<NA>부산광역시 강서구 신세기SFS 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.143269128.827443<NA>2021-04-01 06:01:03
5511073360000지사동<NA>부산광역시 강서구 SJ회사 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.142561128.826247<NA>2021-04-01 06:01:03
6511083360000지사동<NA>부산광역시 강서구 정우엔텍 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.142546128.823976<NA>2021-04-01 06:01:03
7511093360000지사동<NA>부산광역시 강서구 쌍용 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.151946128.812334<NA>2021-04-01 06:01:03
8511103360000지사동<NA>부산광역시 강서구 DRM 앞<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.149484128.821603<NA>2021-04-01 06:01:03
9511113360000지사동<NA>부산광역시 강서구 도로변<NA><NA>1부산광역시 강서구 산단관리사업소051-970-4292부산광역시 강서구2021-01-2435.150844128.824472<NA>2021-04-01 06:01:03
skeyinstt_codeadmns_nmseqspotaddr_roadaddr_jibunqtymanagertelgugundata_daylatlngapr_atlast_load_dttm
2932481773270000초량2동초량2동-11바로 맞은편 도로변(망양로506)부산광역시 동구 망양로 506부산광역시 동구 초량동 1302-3661초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.119954129.036453<NA>2021-04-01 06:01:03
2933481783270000초량2동초량2동-12초량성당 밑(초량상로79번길 13)부산광역시 동구 초량상로79번길 13부산광역시 동구 초량동 942-11초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.118997129.037658<NA>2021-04-01 06:01:03
2934481793270000초량2동초량2동-13천운사 맞은편(영초길216번길 5)부산광역시 동구 영초길216번길 5부산광역시 동구 초량동 916-81초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.118435129.035896<NA>2021-04-01 06:01:03
2935481803270000초량2동초량2동-14영림아파트 밑(영초길247번길 16)부산광역시 동구 영초길247번길 16부산광역시 동구 초량동 865-421초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.117893129.033947<NA>2021-04-01 06:01:03
2936481813270000초량2동초량2동-15장기려기념관 좌측맞은편(영초윗길 48)부산광역시 동구 영초윗길 48부산광역시 동구 초량동 856-31초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.119055129.032964<NA>2021-04-01 06:01:03
2937481823270000초량2동초량2동-16화신3차 아파트 앞(초량남로 47-2)부산광역시 동구 초량남로 47-2부산광역시 동구 초량동 865-181초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.11842129.033394<NA>2021-04-01 06:01:03
2938481833270000초량2동초량2동-17동일파크맨션 입구(망양로 483)부산광역시 동구 망양로 483부산광역시 동구 초량동 865-311초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.117065129.032686<NA>2021-04-01 06:01:03
2939481843270000초량2동초량2동-18초량2동 공영주차장 위(영초윗길 33)부산광역시 동구 영초윗길 33부산광역시 동구 초량동 865-2381초량2동 주민센터051-440-6136부산광역시 동구2021-01-0635.117551129.033899<NA>2021-04-01 06:01:03
2940481853270000초량3동초량3동-1메리츠타워 앞 (중앙대로 331)부산광역시 동구 중앙대로 331부산광역시 동구 초량동 1143-11초량3동 주민센터051-440-6152부산광역시 동구2021-01-0635.125775129.045714<NA>2021-04-01 06:01:03
2941481863270000초량3동초량3동-2CU 초량중앙대로점 앞 (중앙대로 328)부산광역시 동구 중앙대로 328부산광역시 동구 초량동 1152-11초량3동 주민센터051-440-6152부산광역시 동구2021-01-0635.125524129.045711<NA>2021-04-01 06:01:03