Overview

Dataset statistics

Number of variables11
Number of observations4541
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory417.0 KiB
Average record size in memory94.0 B

Variable types

Categorical4
Numeric6
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/A/1/datasetView.do

Alerts

2019-01-01 has constant value ""Constant
Unnamed: 4 is highly overall correlated with 정기High correlation
정기 is highly overall correlated with Unnamed: 4High correlation
2 is highly overall correlated with 95.64 and 3 other fieldsHigh correlation
95.64 is highly overall correlated with 2 and 3 other fieldsHigh correlation
0.83 is highly overall correlated with 2 and 3 other fieldsHigh correlation
3580.00 is highly overall correlated with 2 and 3 other fieldsHigh correlation
27 is highly overall correlated with 2 and 3 other fieldsHigh correlation
정기 is highly imbalanced (61.7%)Imbalance

Reproduction

Analysis started2024-05-18 05:06:02.845063
Analysis finished2024-05-18 05:06:17.504259
Duration14.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2019-01-01
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
2019-01-01
4541 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01-01
2nd row2019-01-01
3rd row2019-01-01
4th row2019-01-01
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-01-01 4541
100.0%

Length

2024-05-18T14:06:17.710827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:06:18.007302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01-01 4541
100.0%

00108
Real number (ℝ)

Distinct1385
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1171.4263
Minimum101
Maximum3542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:18.335865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile158
Q1451
median1108
Q31691
95-th percentile2407
Maximum3542
Range3441
Interquartile range (IQR)1240

Descriptive statistics

Standard deviation819.95837
Coefficient of variation (CV)0.6999658
Kurtosis0.10823461
Mean1171.4263
Median Absolute Deviation (MAD)608
Skewness0.75674907
Sum5319447
Variance672331.73
MonotonicityNot monotonic
2024-05-18T14:06:18.770370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1673 10
 
0.2%
347 9
 
0.2%
113 9
 
0.2%
907 9
 
0.2%
913 9
 
0.2%
3533 9
 
0.2%
240 8
 
0.2%
505 8
 
0.2%
207 8
 
0.2%
1166 8
 
0.2%
Other values (1375) 4454
98.1%
ValueCountFrequency (%)
101 2
 
< 0.1%
102 4
0.1%
103 4
0.1%
104 5
0.1%
105 3
0.1%
106 7
0.2%
107 6
0.1%
108 4
0.1%
109 2
 
< 0.1%
110 4
0.1%
ValueCountFrequency (%)
3542 3
 
0.1%
3541 4
0.1%
3539 1
 
< 0.1%
3538 2
 
< 0.1%
3537 4
0.1%
3536 3
 
0.1%
3535 3
 
0.1%
3534 7
0.2%
3533 9
0.2%
3532 2
 
< 0.1%

108. 서교동 사거리
Text

Distinct1385
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
2024-05-18T14:06:19.315922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.352345
Min length8

Characters and Unicode

Total characters69715
Distinct characters504
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique244 ?
Unique (%)5.4%

Sample

1st row503. 더샵스타시티 C동 앞
2nd row729. 서부식자재마트 건너편
3rd row731. 서울시 도로환경관리센터
4th row733. 신정이펜하우스314동
5th row734. 신트리공원 입구
ValueCountFrequency (%)
앞 1201
 
8.3%
옆 274
 
1.9%
출구 212
 
1.5%
1번출구 172
 
1.2%
사거리 137
 
0.9%
뒤 135
 
0.9%
4번출구 117
 
0.8%
2번출구 114
 
0.8%
3번출구 109
 
0.8%
교차로 105
 
0.7%
Other values (3046) 11861
82.2%
2024-05-18T14:06:20.157558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9960
 
14.3%
. 4559
 
6.5%
1 4108
 
5.9%
2 2939
 
4.2%
3 2178
 
3.1%
구 1775
 
2.5%
5 1701
 
2.4%
0 1507
 
2.2%
4 1485
 
2.1%
앞 1444
 
2.1%
Other values (494) 38059
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35535
51.0%
Decimal Number 18298
26.2%
Space Separator 9970
 
14.3%
Other Punctuation 4585
 
6.6%
Uppercase Letter 551
 
0.8%
Close Punctuation 344
 
0.5%
Open Punctuation 344
 
0.5%
Lowercase Letter 44
 
0.1%
Dash Punctuation 29
 
< 0.1%
Math Symbol 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1775
 
5.0%
앞 1444
 
4.1%
역 1410
 
4.0%
번 1257
 
3.5%
출 1232
 
3.5%
동 833
 
2.3%
교 721
 
2.0%
리 666
 
1.9%
원 542
 
1.5%
사 527
 
1.5%
Other values (439) 25128
70.7%
Uppercase Letter
ValueCountFrequency (%)
K 81
14.7%
S 65
11.8%
C 59
10.7%
L 40
 
7.3%
G 38
 
6.9%
T 33
 
6.0%
B 31
 
5.6%
I 30
 
5.4%
A 27
 
4.9%
M 22
 
4.0%
Other values (12) 125
22.7%
Decimal Number
ValueCountFrequency (%)
1 4108
22.5%
2 2939
16.1%
3 2178
11.9%
5 1701
9.3%
0 1507
 
8.2%
4 1485
 
8.1%
6 1390
 
7.6%
7 1054
 
5.8%
9 974
 
5.3%
8 962
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 15
34.1%
l 5
 
11.4%
t 4
 
9.1%
n 4
 
9.1%
k 3
 
6.8%
c 3
 
6.8%
o 3
 
6.8%
m 3
 
6.8%
s 2
 
4.5%
y 2
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 4559
99.4%
, 21
 
0.5%
& 3
 
0.1%
@ 1
 
< 0.1%
․ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9960
99.9%
  10
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 10
90.9%
+ 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 344
100.0%
Open Punctuation
ValueCountFrequency (%)
( 344
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35535
51.0%
Common 33585
48.2%
Latin 595
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1775
 
5.0%
앞 1444
 
4.1%
역 1410
 
4.0%
번 1257
 
3.5%
출 1232
 
3.5%
동 833
 
2.3%
교 721
 
2.0%
리 666
 
1.9%
원 542
 
1.5%
사 527
 
1.5%
Other values (439) 25128
70.7%
Latin
ValueCountFrequency (%)
K 81
13.6%
S 65
 
10.9%
C 59
 
9.9%
L 40
 
6.7%
G 38
 
6.4%
T 33
 
5.5%
B 31
 
5.2%
I 30
 
5.0%
A 27
 
4.5%
M 22
 
3.7%
Other values (22) 169
28.4%
Common
ValueCountFrequency (%)
9960
29.7%
. 4559
13.6%
1 4108
12.2%
2 2939
 
8.8%
3 2178
 
6.5%
5 1701
 
5.1%
0 1507
 
4.5%
4 1485
 
4.4%
6 1390
 
4.1%
7 1054
 
3.1%
Other values (13) 2704
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35535
51.0%
ASCII 34169
49.0%
None 10
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9960
29.1%
. 4559
13.3%
1 4108
12.0%
2 2939
 
8.6%
3 2178
 
6.4%
5 1701
 
5.0%
0 1507
 
4.4%
4 1485
 
4.3%
6 1390
 
4.1%
7 1054
 
3.1%
Other values (43) 3288
 
9.6%
Hangul
ValueCountFrequency (%)
구 1775
 
5.0%
앞 1444
 
4.1%
역 1410
 
4.0%
번 1257
 
3.5%
출 1232
 
3.5%
동 833
 
2.3%
교 721
 
2.0%
리 666
 
1.9%
원 542
 
1.5%
사 527
 
1.5%
Other values (439) 25128
70.7%
None
ValueCountFrequency (%)
  10
100.0%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%

정기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
정기
4202 
일일(회원)
 
339

Length

Max length6
Median length2
Mean length2.2986126
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 4202
92.5%
일일(회원) 339
 
7.5%

Length

2024-05-18T14:06:20.610227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:06:20.928418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 4202
92.5%
일일(회원 339
 
7.5%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
M
2275 
<NA>
1328 
F
938 

Length

Max length4
Median length1
Mean length1.8773398
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 (%)
M 2275
50.1%
<NA> 1328
29.2%
F 938
20.7%

Length

2024-05-18T14:06:21.264488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:06:21.613811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 2275
50.1%
na 1328
29.2%
f 938
20.7%

\N
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
\N
1329 
20대
1071 
30대
893 
40대
636 
50대
407 
Other values (3)
205 

Length

Max length4
Median length3
Mean length2.7227483
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N

Common Values

ValueCountFrequency (%)
\N 1329
29.3%
20대 1071
23.6%
30대 893
19.7%
40대 636
14.0%
50대 407
 
9.0%
60대 135
 
3.0%
70대~ 41
 
0.9%
~10대 29
 
0.6%

Length

2024-05-18T14:06:22.021130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:06:22.358190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 1329
29.3%
20대 1071
23.6%
30대 893
19.7%
40대 636
14.0%
50대 407
 
9.0%
60대 135
 
3.0%
70대 41
 
0.9%
10대 29
 
0.6%

2
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.648976
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:22.685372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1635495
Coefficient of variation (CV)0.70561945
Kurtosis11.407527
Mean1.648976
Median Absolute Deviation (MAD)0
Skewness2.8485533
Sum7488
Variance1.3538475
MonotonicityNot monotonic
2024-05-18T14:06:23.038981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2885
63.5%
2 985
 
21.7%
3 364
 
8.0%
4 157
 
3.5%
5 73
 
1.6%
6 36
 
0.8%
7 21
 
0.5%
9 8
 
0.2%
8 5
 
0.1%
10 4
 
0.1%
ValueCountFrequency (%)
1 2885
63.5%
2 985
 
21.7%
3 364
 
8.0%
4 157
 
3.5%
5 73
 
1.6%
6 36
 
0.8%
7 21
 
0.5%
8 5
 
0.1%
9 8
 
0.2%
10 4
 
0.1%
ValueCountFrequency (%)
11 3
 
0.1%
10 4
 
0.1%
9 8
 
0.2%
8 5
 
0.1%
7 21
 
0.5%
6 36
 
0.8%
5 73
 
1.6%
4 157
 
3.5%
3 364
 
8.0%
2 985
21.7%

95.64
Real number (ℝ)

HIGH CORRELATION 

Distinct3563
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.50437
Minimum0
Maximum8658.62
Zeros30
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:23.575413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.22
Q138.35
median76.61
Q3161.88
95-th percentile487.4
Maximum8658.62
Range8658.62
Interquartile range (IQR)123.53

Descriptive statistics

Standard deviation377.17169
Coefficient of variation (CV)2.3795666
Kurtosis224.22723
Mean158.50437
Median Absolute Deviation (MAD)46.67
Skewness12.713145
Sum719768.34
Variance142258.49
MonotonicityNot monotonic
2024-05-18T14:06:24.005500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
 
0.7%
42.77 8
 
0.2%
27.8 8
 
0.2%
32.43 7
 
0.2%
41.18 7
 
0.2%
38.1 7
 
0.2%
20.33 6
 
0.1%
24.97 6
 
0.1%
19.01 6
 
0.1%
36.04 6
 
0.1%
Other values (3553) 4450
98.0%
ValueCountFrequency (%)
0.0 30
0.7%
0.51 1
 
< 0.1%
0.6 1
 
< 0.1%
2.7 1
 
< 0.1%
3.21 1
 
< 0.1%
5.66 1
 
< 0.1%
5.92 1
 
< 0.1%
6.65 1
 
< 0.1%
6.83 1
 
< 0.1%
6.93 1
 
< 0.1%
ValueCountFrequency (%)
8658.62 1
< 0.1%
8327.95 1
< 0.1%
8068.36 1
< 0.1%
7058.9 1
< 0.1%
6630.68 1
< 0.1%
5725.53 1
< 0.1%
5623.94 1
< 0.1%
4431.14 1
< 0.1%
4114.04 1
< 0.1%
3673.73 1
< 0.1%

0.83
Real number (ℝ)

HIGH CORRELATION 

Distinct554
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3217727
Minimum0
Maximum59.08
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:24.483750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14
Q10.34
median0.66
Q31.35
95-th percentile4
Maximum59.08
Range59.08
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation2.9279789
Coefficient of variation (CV)2.2151908
Kurtosis159.64008
Mean1.3217727
Median Absolute Deviation (MAD)0.4
Skewness10.798967
Sum6002.17
Variance8.5730602
MonotonicityNot monotonic
2024-05-18T14:06:24.924150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 63
 
1.4%
0.26 62
 
1.4%
0.23 58
 
1.3%
0.19 57
 
1.3%
0.35 56
 
1.2%
0.45 55
 
1.2%
0.2 54
 
1.2%
0.29 52
 
1.1%
0.32 50
 
1.1%
0.34 48
 
1.1%
Other values (544) 3986
87.8%
ValueCountFrequency (%)
0.0 32
0.7%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.05 2
 
< 0.1%
0.06 8
 
0.2%
0.07 6
 
0.1%
0.08 13
0.3%
0.09 19
0.4%
0.1 29
0.6%
0.11 18
0.4%
ValueCountFrequency (%)
59.08 1
< 0.1%
57.58 1
< 0.1%
49.92 2
< 0.1%
49.76 1
< 0.1%
45.7 1
< 0.1%
42.27 1
< 0.1%
41.93 1
< 0.1%
39.94 1
< 0.1%
33.24 1
< 0.1%
29.83 1
< 0.1%

3580.00
Real number (ℝ)

HIGH CORRELATION 

Distinct1331
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5697.2231
Minimum0
Maximum254650
Zeros30
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:25.268579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile620
Q11450
median2820
Q35830
95-th percentile17230
Maximum254650
Range254650
Interquartile range (IQR)4380

Descriptive statistics

Standard deviation12620.408
Coefficient of variation (CV)2.2151858
Kurtosis159.62515
Mean5697.2231
Median Absolute Deviation (MAD)1680
Skewness10.798214
Sum25871090
Variance1.5927469 × 108
MonotonicityNot monotonic
2024-05-18T14:06:25.711198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
0.7%
1070 23
 
0.5%
970 18
 
0.4%
880 18
 
0.4%
1150 17
 
0.4%
1080 17
 
0.4%
1670 16
 
0.4%
1480 16
 
0.4%
1110 16
 
0.4%
1980 16
 
0.4%
Other values (1321) 4354
95.9%
ValueCountFrequency (%)
0 30
0.7%
20 2
 
< 0.1%
90 1
 
< 0.1%
110 1
 
< 0.1%
230 2
 
< 0.1%
240 1
 
< 0.1%
250 1
 
< 0.1%
260 2
 
< 0.1%
270 1
 
< 0.1%
280 3
 
0.1%
ValueCountFrequency (%)
254650 1
< 0.1%
248180 1
< 0.1%
215180 1
< 0.1%
215150 1
< 0.1%
214470 1
< 0.1%
196990 1
< 0.1%
182210 1
< 0.1%
180730 1
< 0.1%
172150 1
< 0.1%
143210 1
< 0.1%

27
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.533803
Minimum0
Maximum412
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size40.0 KiB
2024-05-18T14:06:26.140831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median18
Q340
95-th percentile103
Maximum412
Range412
Interquartile range (IQR)32

Descriptive statistics

Standard deviation37.03337
Coefficient of variation (CV)1.1744023
Kurtosis15.726263
Mean31.533803
Median Absolute Deviation (MAD)12
Skewness3.0993268
Sum143195
Variance1371.4705
MonotonicityNot monotonic
2024-05-18T14:06:26.555959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 199
 
4.4%
5 190
 
4.2%
8 184
 
4.1%
7 173
 
3.8%
9 147
 
3.2%
11 141
 
3.1%
3 138
 
3.0%
13 136
 
3.0%
10 135
 
3.0%
4 132
 
2.9%
Other values (193) 2966
65.3%
ValueCountFrequency (%)
0 3
 
0.1%
1 30
 
0.7%
2 89
2.0%
3 138
3.0%
4 132
2.9%
5 190
4.2%
6 199
4.4%
7 173
3.8%
8 184
4.1%
9 147
3.2%
ValueCountFrequency (%)
412 1
< 0.1%
395 1
< 0.1%
363 1
< 0.1%
347 1
< 0.1%
345 1
< 0.1%
312 1
< 0.1%
310 1
< 0.1%
295 1
< 0.1%
291 1
< 0.1%
289 1
< 0.1%

Interactions

2024-05-18T14:06:15.220190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:05.718272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:07.606190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:09.733462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:11.477817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:13.302577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:15.475409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:06.043502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:07.923476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:10.121578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:11.801458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:13.583418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:15.691112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:06.321488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:08.174374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:10.390959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:12.121201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:14.106558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:15.941099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:06.591021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:08.510354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:10.645282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:12.408989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:14.407190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:16.230609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:06.912855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:08.906829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:10.921218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:12.700935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:14.677653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:16.503215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:07.267847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:09.274664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:11.239937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:12.976404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:06:14.938240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:06:26.806433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
00108정기Unnamed: 4\N295.640.833580.0027
001081.0000.0610.0000.0580.0000.0720.0480.0400.035
정기0.0611.000NaN0.5820.0000.0510.0760.0770.193
Unnamed: 40.000NaN1.0000.2020.1460.0130.0110.0110.041
\N0.0580.5820.2021.0000.2200.0850.0800.0790.138
20.0000.0000.1460.2201.0000.2030.2960.2960.607
95.640.0720.0510.0130.0850.2031.0000.9740.9740.399
0.830.0480.0760.0110.0800.2960.9741.0001.0000.527
3580.000.0400.0770.0110.0790.2960.9741.0001.0000.522
270.0350.1930.0410.1380.6070.3990.5270.5221.000
2024-05-18T14:06:27.087734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4\N정기
Unnamed: 41.0000.1521.000
\N0.1521.0000.440
정기1.0000.4401.000
2024-05-18T14:06:27.505742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
00108295.640.833580.0027정기Unnamed: 4\N
001081.000-0.039-0.001-0.007-0.007-0.0150.0460.0000.028
2-0.0391.0000.5580.5560.5560.5230.0000.0910.089
95.64-0.0010.5581.0000.9870.9870.8410.0390.0130.041
0.83-0.0070.5560.9871.0001.0000.8550.0580.0110.038
3580.00-0.0070.5560.9871.0001.0000.8550.0590.0110.038
27-0.0150.5230.8410.8550.8551.0000.1480.0410.066
정기0.0460.0000.0390.0580.0590.1481.0001.0000.440
Unnamed: 40.0000.0910.0130.0110.0110.0411.0001.0000.152
\N0.0280.0890.0410.0380.0380.0660.4400.1521.000

Missing values

2024-05-18T14:06:16.863704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:06:17.328036image/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

2019-01-0100108108. 서교동 사거리정기Unnamed: 4\N295.640.833580.0027
02019-01-01503503. 더샵스타시티 C동 앞정기<NA>\N275.240.7302034
12019-01-01729729. 서부식자재마트 건너편정기<NA>\N2268.762.22956040
22019-01-01731731. 서울시 도로환경관리센터정기<NA>\N2101.350.73315018
32019-01-01733733. 신정이펜하우스314동정기<NA>\N2233.982.26976043
42019-01-01734734. 신트리공원 입구정기<NA>\N1160.621.45624034
52019-01-01735735. 영도초등학교정기<NA>\N3152.911.24537050
62019-01-01736736. 오솔길공원정기<NA>\N2263.241.84793044
72019-01-01737737. 장수공원정기<NA>\N185.080.61262017
82019-01-01504504. 신자초교입구교차로정기<NA>\N5239.322.51074073
92019-01-01739739. 신월사거리정기<NA>\N240.150.36156017
2019-01-0100108108. 서교동 사거리정기Unnamed: 4\N295.640.833580.0027
45312019-01-01104104. 합정역 1번출구 앞일일(회원)<NA>\N254.930.37159012
45322019-01-0116011601. 석계역 문화광장 내 자전거 보관소일일(회원)<NA>\N1197.071.48638050
45332019-01-0116031603. 롯데캐슬 102동 코너(월계주유소건너)일일(회원)<NA>\N140.510.3615508
45342019-01-0116101610. 화랑대역 2번출구 앞일일(회원)<NA>\N2201.861.77765036
45352019-01-0116111611. 과기대 입구(우)일일(회원)<NA>\N92442.7119.5584300412
45362019-01-0116161616. 하계2동 공항버스정류장 옆일일(회원)<NA>\N187.670.63270015
45372019-01-0116361636. 백병원 사거리 농협은행 앞일일(회원)<NA>\N164.090.58249027
45382019-01-0116371637. KT 노원점 건물 앞일일(회원)<NA>\N139.20.3113205
45392019-01-0117121712. 창동역공영주차장앞일일(회원)<NA>\N1689.045.382320014
45402019-01-0118241824. 독산근린공원 입구일일(회원)<NA>\N2381.234.0717570170