Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.7 KiB
Average record size in memory78.0 B

Variable types

Numeric5
Text2
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15067/S/1/datasetView.do

Alerts

STDR_DE is highly overall correlated with Unnamed: 7High correlation
NODE_ID is highly overall correlated with STTN_NO and 1 other fieldsHigh correlation
STTN_NO is highly overall correlated with NODE_ID and 1 other fieldsHigh correlation
CRDNT_Y is highly overall correlated with NODE_ID and 2 other fieldsHigh correlation
STTN_TY is highly overall correlated with Unnamed: 7High correlation
Unnamed: 7 is highly overall correlated with STDR_DE and 2 other fieldsHigh correlation
Unnamed: 7 is highly imbalanced (99.7%)Imbalance
CRDNT_Y is highly skewed (γ1 = 49.9074197)Skewed
STTN_TY has 5324 (53.2%) zerosZeros

Reproduction

Analysis started2024-05-11 09:37:50.256331
Analysis finished2024-05-11 09:38:01.211418
Duration10.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20230447
Minimum20230101
Maximum20230801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:01.366891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230101
5-th percentile20230101
Q120230201
median20230401
Q320230601
95-th percentile20230801
Maximum20230801
Range700
Interquartile range (IQR)400

Descriptive statistics

Standard deviation229.15318
Coefficient of variation (CV)1.1327144 × 10-5
Kurtosis-1.2510503
Mean20230447
Median Absolute Deviation (MAD)200
Skewness0.024146452
Sum2.0230447 × 1011
Variance52511.181
MonotonicityNot monotonic
2024-05-11T09:38:01.722654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20230301 1343
13.4%
20230601 1290
12.9%
20230101 1276
12.8%
20230201 1273
12.7%
20230701 1255
12.6%
20230401 1209
12.1%
20230801 1190
11.9%
20230501 1164
11.6%
ValueCountFrequency (%)
20230101 1276
12.8%
20230201 1273
12.7%
20230301 1343
13.4%
20230401 1209
12.1%
20230501 1164
11.6%
20230601 1290
12.9%
20230701 1255
12.6%
20230801 1190
11.9%
ValueCountFrequency (%)
20230801 1190
11.9%
20230701 1255
12.6%
20230601 1290
12.9%
20230501 1164
11.6%
20230401 1209
12.1%
20230301 1343
13.4%
20230201 1273
12.7%
20230101 1276
12.8%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct7174
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.131137 × 108
Minimum1 × 108
Maximum1.2900006 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:02.257897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100025 × 108
Q11.0790017 × 108
median1.1300051 × 108
Q31.1900009 × 108
95-th percentile1.2300032 × 108
Maximum1.2900006 × 108
Range29000059
Interquartile range (IQR)11099925

Descriptive statistics

Standard deviation6905742.9
Coefficient of variation (CV)0.061051341
Kurtosis-1.0830351
Mean1.131137 × 108
Median Absolute Deviation (MAD)5899612
Skewness-0.15843559
Sum1.131137 × 1012
Variance4.7689286 × 1013
MonotonicityNot monotonic
2024-05-11T09:38:02.845437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114000127 6
 
0.1%
121000154 5
 
0.1%
108900040 5
 
0.1%
111000919 5
 
0.1%
115000924 5
 
0.1%
115900022 5
 
0.1%
115000562 5
 
0.1%
107900082 5
 
0.1%
108900214 5
 
0.1%
110000660 5
 
0.1%
Other values (7164) 9949
99.5%
ValueCountFrequency (%)
100000001 2
< 0.1%
100000004 2
< 0.1%
100000005 2
< 0.1%
100000006 3
< 0.1%
100000007 1
 
< 0.1%
100000008 2
< 0.1%
100000009 4
< 0.1%
100000011 2
< 0.1%
100000014 1
 
< 0.1%
100000016 1
 
< 0.1%
ValueCountFrequency (%)
129000060 2
< 0.1%
124900135 1
< 0.1%
124900130 1
< 0.1%
124900126 1
< 0.1%
124900124 2
< 0.1%
124900123 1
< 0.1%
124900122 1
< 0.1%
124900121 1
< 0.1%
124900119 1
< 0.1%
124900114 2
< 0.1%

STTN_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct7172
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14232.121
Minimum1001
Maximum25994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:03.576228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2285
Q18771.5
median14535
Q320182.25
95-th percentile24366.05
Maximum25994
Range24993
Interquartile range (IQR)11410.75

Descriptive statistics

Standard deviation6910.2973
Coefficient of variation (CV)0.48554233
Kurtosis-1.083686
Mean14232.121
Median Absolute Deviation (MAD)5697.5
Skewness-0.15144019
Sum1.4232121 × 108
Variance47752209
MonotonicityNot monotonic
2024-05-11T09:38:04.520013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15230 6
 
0.1%
9835 5
 
0.1%
16500 5
 
0.1%
8585 5
 
0.1%
16618 5
 
0.1%
11859 5
 
0.1%
12408 5
 
0.1%
16997 5
 
0.1%
22230 5
 
0.1%
9575 5
 
0.1%
Other values (7162) 9949
99.5%
ValueCountFrequency (%)
1001 2
< 0.1%
1004 2
< 0.1%
1005 2
< 0.1%
1006 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
1011 2
< 0.1%
1012 1
< 0.1%
1013 1
< 0.1%
1015 1
< 0.1%
ValueCountFrequency (%)
25994 1
< 0.1%
25993 1
< 0.1%
25990 2
< 0.1%
25989 1
< 0.1%
25786 1
< 0.1%
25783 1
< 0.1%
25782 2
< 0.1%
25781 2
< 0.1%
25763 1
< 0.1%
25758 2
< 0.1%

STTN_NM
Text

Distinct5349
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:38:05.262139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length7.5633
Min length2

Characters and Unicode

Total characters75633
Distinct characters637
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2669 ?
Unique (%)26.7%

Sample

1st row진관동주민센터.은평우체국
2nd row방범초소
3rd row이마트.은평점
4th row한신동성아파트.시립과학관
5th row성지아파트
ValueCountFrequency (%)
우성아파트 17
 
0.2%
북서울꿈의숲 16
 
0.2%
송정역 12
 
0.1%
당산역 11
 
0.1%
새마을금고 11
 
0.1%
벽산아파트 10
 
0.1%
가산디지털단지역 10
 
0.1%
동교초등학교 10
 
0.1%
녹번역 10
 
0.1%
현대아파트 10
 
0.1%
Other values (5341) 9885
98.8%
2024-05-11T09:38:06.517945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2260
 
3.0%
파 2069
 
2.7%
아 2037
 
2.7%
트 1983
 
2.6%
. 1922
 
2.5%
교 1783
 
2.4%
구 1499
 
2.0%
역 1443
 
1.9%
대 1235
 
1.6%
학 1230
 
1.6%
Other values (627) 58172
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70396
93.1%
Decimal Number 2357
 
3.1%
Other Punctuation 1939
 
2.6%
Uppercase Letter 636
 
0.8%
Close Punctuation 134
 
0.2%
Open Punctuation 131
 
0.2%
Lowercase Letter 28
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2260
 
3.2%
파 2069
 
2.9%
아 2037
 
2.9%
트 1983
 
2.8%
교 1783
 
2.5%
구 1499
 
2.1%
역 1443
 
2.0%
대 1235
 
1.8%
학 1230
 
1.7%
지 1205
 
1.7%
Other values (581) 53652
76.2%
Uppercase Letter
ValueCountFrequency (%)
T 81
12.7%
S 72
11.3%
C 70
11.0%
K 64
10.1%
A 50
7.9%
P 49
7.7%
B 38
 
6.0%
M 34
 
5.3%
G 32
 
5.0%
D 27
 
4.2%
Other values (14) 119
18.7%
Decimal Number
ValueCountFrequency (%)
1 691
29.3%
2 476
20.2%
3 317
13.4%
5 178
 
7.6%
4 173
 
7.3%
0 144
 
6.1%
7 121
 
5.1%
6 110
 
4.7%
9 87
 
3.7%
8 60
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 1922
99.1%
& 9
 
0.5%
· 7
 
0.4%
? 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 22
78.6%
k 3
 
10.7%
t 2
 
7.1%
s 1
 
3.6%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70396
93.1%
Common 4573
 
6.0%
Latin 664
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2260
 
3.2%
파 2069
 
2.9%
아 2037
 
2.9%
트 1983
 
2.8%
교 1783
 
2.5%
구 1499
 
2.1%
역 1443
 
2.0%
대 1235
 
1.8%
학 1230
 
1.7%
지 1205
 
1.7%
Other values (581) 53652
76.2%
Latin
ValueCountFrequency (%)
T 81
12.2%
S 72
10.8%
C 70
10.5%
K 64
9.6%
A 50
 
7.5%
P 49
 
7.4%
B 38
 
5.7%
M 34
 
5.1%
G 32
 
4.8%
D 27
 
4.1%
Other values (18) 147
22.1%
Common
ValueCountFrequency (%)
. 1922
42.0%
1 691
 
15.1%
2 476
 
10.4%
3 317
 
6.9%
5 178
 
3.9%
4 173
 
3.8%
0 144
 
3.1%
) 134
 
2.9%
( 131
 
2.9%
7 121
 
2.6%
Other values (8) 286
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70396
93.1%
ASCII 5230
 
6.9%
None 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2260
 
3.2%
파 2069
 
2.9%
아 2037
 
2.9%
트 1983
 
2.8%
교 1783
 
2.5%
구 1499
 
2.1%
역 1443
 
2.0%
대 1235
 
1.8%
학 1230
 
1.7%
지 1205
 
1.7%
Other values (581) 53652
76.2%
ASCII
ValueCountFrequency (%)
. 1922
36.7%
1 691
 
13.2%
2 476
 
9.1%
3 317
 
6.1%
5 178
 
3.4%
4 173
 
3.3%
0 144
 
2.8%
) 134
 
2.6%
( 131
 
2.5%
7 121
 
2.3%
Other values (35) 943
18.0%
None
ValueCountFrequency (%)
· 7
100.0%

CRDNT_X
Text

Distinct7331
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:38:07.494609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.7859
Min length4

Characters and Unicode

Total characters107859
Distinct characters21
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

Unique5147 ?
Unique (%)51.5%

Sample

1st row126.9196135
2nd row126.9436277
3rd row126.918032
4th row127.0761635
5th row126.8987382
ValueCountFrequency (%)
126.8737556 5
 
< 0.1%
126.8164831 5
 
< 0.1%
126.8584783 5
 
< 0.1%
127.0231116 5
 
< 0.1%
127.0328514 5
 
< 0.1%
126.9943844 5
 
< 0.1%
126.8008097 5
 
< 0.1%
127.0515463 5
 
< 0.1%
127.0122707 5
 
< 0.1%
126.8864762 5
 
< 0.1%
Other values (7321) 9950
99.5%
2024-05-11T09:38:09.284571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16904
15.7%
2 16187
15.0%
6 10951
10.2%
7 10609
9.8%
. 9998
9.3%
9 8793
8.2%
0 8624
8.0%
8 7441
6.9%
3 6251
 
5.8%
4 6121
 
5.7%
Other values (11) 5980
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97841
90.7%
Other Punctuation 9998
 
9.3%
Other Letter 20
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16904
17.3%
2 16187
16.5%
6 10951
11.2%
7 10609
10.8%
9 8793
9.0%
0 8624
8.8%
8 7441
7.6%
3 6251
 
6.4%
4 6121
 
6.3%
5 5960
 
6.1%
Other Letter
ValueCountFrequency (%)
단 2
10.0%
지 2
10.0%
기 2
10.0%
동 2
10.0%
대 2
10.0%
우 2
10.0%
남 2
10.0%
아 2
10.0%
파 2
10.0%
트 2
10.0%
Other Punctuation
ValueCountFrequency (%)
. 9998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107839
> 99.9%
Hangul 20
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16904
15.7%
2 16187
15.0%
6 10951
10.2%
7 10609
9.8%
. 9998
9.3%
9 8793
8.2%
0 8624
8.0%
8 7441
6.9%
3 6251
 
5.8%
4 6121
 
5.7%
Hangul
ValueCountFrequency (%)
단 2
10.0%
지 2
10.0%
기 2
10.0%
동 2
10.0%
대 2
10.0%
우 2
10.0%
남 2
10.0%
아 2
10.0%
파 2
10.0%
트 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107839
> 99.9%
Hangul 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16904
15.7%
2 16187
15.0%
6 10951
10.2%
7 10609
9.8%
. 9998
9.3%
9 8793
8.2%
0 8624
8.0%
8 7441
6.9%
3 6251
 
5.8%
4 6121
 
5.7%
Hangul
ValueCountFrequency (%)
단 2
10.0%
지 2
10.0%
기 2
10.0%
동 2
10.0%
대 2
10.0%
우 2
10.0%
남 2
10.0%
아 2
10.0%
파 2
10.0%
트 2
10.0%

CRDNT_Y
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7342
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.586981
Minimum37.430862
Maximum126.90583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:09.884217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.430862
5-th percentile37.472235
Q137.503592
median37.550305
Q337.591339
95-th percentile37.647671
Maximum126.90583
Range89.474969
Interquartile range (IQR)0.087746703

Descriptive statistics

Standard deviation1.7867662
Coefficient of variation (CV)0.047536836
Kurtosis2491.5824
Mean37.586981
Median Absolute Deviation (MAD)0.04411022
Skewness49.90742
Sum375869.81
Variance3.1925334
MonotonicityNot monotonic
2024-05-11T09:38:10.434006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.62603535 5
 
0.1%
37.54729799 5
 
0.1%
37.62522907 5
 
0.1%
37.5096265 5
 
0.1%
37.56488592 5
 
0.1%
37.5979671 5
 
0.1%
37.59286649 5
 
0.1%
37.48677537 5
 
0.1%
37.63686474 5
 
0.1%
37.54988855 5
 
0.1%
Other values (7332) 9950
99.5%
ValueCountFrequency (%)
37.43086194 2
< 0.1%
37.43498304 2
< 0.1%
37.43500421 2
< 0.1%
37.43552416 1
< 0.1%
37.43732107 1
< 0.1%
37.43795943 1
< 0.1%
37.43808697 2
< 0.1%
37.43948788 2
< 0.1%
37.44016703 1
< 0.1%
37.44019 1
< 0.1%
ValueCountFrequency (%)
126.9058308 2
< 0.1%
126.8173344 1
< 0.1%
126.8158008 1
< 0.1%
37.690177 2
< 0.1%
37.68987622 1
< 0.1%
37.68933105 2
< 0.1%
37.688568 2
< 0.1%
37.68798832 2
< 0.1%
37.68793977 1
< 0.1%
37.68724435 1
< 0.1%

STTN_TY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1754359
Minimum0
Maximum37.618528
Zeros5324
Zeros (%)53.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:10.874203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile5
Maximum37.618528
Range37.618528
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5218063
Coefficient of variation (CV)1.1592189
Kurtosis13.521887
Mean2.1754359
Median Absolute Deviation (MAD)0
Skewness1.3440056
Sum21754.359
Variance6.3595072
MonotonicityNot monotonic
2024-05-11T09:38:11.367119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 5324
53.2%
5.0 3828
38.3%
1.0 318
 
3.2%
4.0 301
 
3.0%
3.0 136
 
1.4%
6.0 89
 
0.9%
37.61852843 2
 
< 0.1%
37.56072469 1
 
< 0.1%
37.56073162 1
 
< 0.1%
ValueCountFrequency (%)
0.0 5324
53.2%
1.0 318
 
3.2%
3.0 136
 
1.4%
4.0 301
 
3.0%
5.0 3828
38.3%
6.0 89
 
0.9%
37.56072469 1
 
< 0.1%
37.56073162 1
 
< 0.1%
37.61852843 2
 
< 0.1%
ValueCountFrequency (%)
37.61852843 2
 
< 0.1%
37.56073162 1
 
< 0.1%
37.56072469 1
 
< 0.1%
6.0 89
 
0.9%
5.0 3828
38.3%
4.0 301
 
3.0%
3.0 136
 
1.4%
1.0 318
 
3.2%
0.0 5324
53.2%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9996 
1
 
2
0
 
2

Length

Max length4
Median length4
Mean length3.9988
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9996
> 99.9%
1 2
 
< 0.1%
0 2
 
< 0.1%

Length

2024-05-11T09:38:11.830278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:38:12.211852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9996
> 99.9%
1 2
 
< 0.1%
0 2
 
< 0.1%

Interactions

2024-05-11T09:37:58.368575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:53.000220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:54.247248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:55.642511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:56.984076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:58.769327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:53.291781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:54.538027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:55.929645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:57.231030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:59.202398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:53.536877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:54.817701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:56.237406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:57.545152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:59.582316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:53.773047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:55.104599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:56.474057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:57.824655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:59.942017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:53.994567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:55.355014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:56.716266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:58.089949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:38:12.471694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TYUnnamed: 7
STDR_DE1.0000.0580.0350.0000.0001.000
NODE_ID0.0581.0000.9850.0200.1630.000
STTN_NO0.0350.9851.0000.0280.3350.000
CRDNT_Y0.0000.0200.0281.0001.000NaN
STTN_TY0.0000.1630.3351.0001.000NaN
Unnamed: 71.0000.0000.000NaNNaN1.000
2024-05-11T09:38:12.880683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TYUnnamed: 7
STDR_DE1.000-0.007-0.006-0.0100.0041.000
NODE_ID-0.0071.0000.997-0.6740.0240.000
STTN_NO-0.0060.9971.000-0.6750.0210.000
CRDNT_Y-0.010-0.674-0.6751.000-0.0121.000
STTN_TY0.0040.0240.021-0.0121.0001.000
Unnamed: 71.0000.0000.0001.0001.0001.000

Missing values

2024-05-11T09:38:00.494071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:38:00.901615image/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

STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
51492023010111100055112458진관동주민센터.은평우체국126.919613537.634523.0<NA>
222372023020111990024020924방범초소126.943627737.4943395.0<NA>
50042023010111100017012260이마트.은평점126.91803237.6002130.0<NA>
45482023010111000032111422한신동성아파트.시립과학관127.076163537.6408360.0<NA>
839372023070111790000818820성지아파트126.898738237.4512635.0<NA>
78071202307011070000338123돈암동삼성아파트입구127.024575337.6018740.0<NA>
205372023020111690003717564은별유아학교126.852083837.490755.0<NA>
51072023010111100112512410은평공영차고지앞126.884144337.5915520.0<NA>
201992023020111500056216997김포공항국제선126.800809737.5648860.0<NA>
2024202301011050000936179종암초등학교앞127.029599237.5816190.0<NA>
STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
751442023060112400044725338상일파출소.고덕아르테온127.165863737.5553180.0<NA>
592482023050111890002519535SK엔카126.892338237.5342365.0<NA>
700672023060111500031116425화곡2동주민센터126.854762637.5311660.0<NA>
236302023020112200008923192봉은사역3번출구.삼성1파출소127.060162937.5151083.0<NA>
222052023020111990010620892현진빌라.고마트126.943002437.4972115.0<NA>
371752023030112300060324653위례포레샤인.송파꿈에그린서문127.137112337.4770280.0<NA>
1788202301011049001285511중곡아파트.기점127.078226837.5669965.0<NA>
850892023070111990021120957동아아파트126.940130737.4966065.0<NA>
15097202302011060002667363망우사거리127.100145537.6005380.0<NA>
14683202302011050001926278세종대왕기념관127.042380237.5898530.0<NA>