Overview

Dataset statistics

Number of variables11
Number of observations3684
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory338.3 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/F/1/datasetView.do

Alerts

25.34 is highly overall correlated with 0.19 and 2 other fieldsHigh correlation
0.19 is highly overall correlated with 25.34 and 2 other fieldsHigh correlation
800.00 is highly overall correlated with 25.34 and 2 other fieldsHigh correlation
12 is highly overall correlated with 25.34 and 2 other fieldsHigh correlation
25.34 has 73 (2.0%) zerosZeros
0.19 has 79 (2.1%) zerosZeros
800.00 has 73 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-13 16:26:30.779664
Analysis finished2024-03-13 16:26:34.726523
Duration3.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2018-07-01
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2018-07-01
3220 
2018-07-02
464 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-07-01
2nd row2018-07-01
3rd row2018-07-01
4th row2018-07-01
5th row2018-07-01

Common Values

ValueCountFrequency (%)
2018-07-01 3220
87.4%
2018-07-02 464
 
12.6%

Length

2024-03-14T01:26:34.792670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:34.870951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-07-01 3220
87.4%
2018-07-02 464
 
12.6%

00108
Real number (ℝ)

Distinct1083
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1130.2655
Minimum5
Maximum3523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:34.978775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile135.15
Q1413.75
median1048
Q31674
95-th percentile2327
Maximum3523
Range3518
Interquartile range (IQR)1260.25

Descriptive statistics

Standard deviation784.2208
Coefficient of variation (CV)0.69383771
Kurtosis-0.12314008
Mean1130.2655
Median Absolute Deviation (MAD)633
Skewness0.62766284
Sum4163898
Variance615002.27
MonotonicityNot monotonic
2024-03-14T01:26:35.087527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 14
 
0.4%
113 12
 
0.3%
2102 12
 
0.3%
1158 11
 
0.3%
1503 11
 
0.3%
1608 11
 
0.3%
347 11
 
0.3%
2032 11
 
0.3%
247 11
 
0.3%
1535 11
 
0.3%
Other values (1073) 3569
96.9%
ValueCountFrequency (%)
5 1
 
< 0.1%
101 4
 
0.1%
102 6
0.2%
103 7
0.2%
104 3
 
0.1%
105 2
 
0.1%
106 9
0.2%
107 5
0.1%
108 6
0.2%
109 10
0.3%
ValueCountFrequency (%)
3523 1
 
< 0.1%
3522 3
0.1%
3520 2
 
0.1%
3519 4
0.1%
3518 5
0.1%
3517 4
0.1%
3516 3
0.1%
3515 1
 
< 0.1%
3514 3
0.1%
3513 7
0.2%

108. 서교동 사거리
Text

Distinct1083
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2024-03-14T01:26:35.288974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length29
Mean length15.285831
Min length8

Characters and Unicode

Total characters56313
Distinct characters462
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique237 ?
Unique (%)6.4%

Sample

1st row503. 더샵스타시티 C동 앞
2nd row732. 신월동 이마트
3rd row736. 오솔길공원
4th row740. 으뜸공원
5th row746. 목동2단지 상가
ValueCountFrequency (%)
앞 1021
 
8.7%
옆 204
 
1.7%
1번출구 180
 
1.5%
출구 164
 
1.4%
2번출구 147
 
1.2%
사거리 142
 
1.2%
뒤 128
 
1.1%
3번출구 94
 
0.8%
교차로 92
 
0.8%
4번출구 80
 
0.7%
Other values (2395) 9515
80.9%
2024-03-14T01:26:35.612400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8151
 
14.5%
. 3689
 
6.6%
1 3523
 
6.3%
2 2429
 
4.3%
3 1778
 
3.2%
구 1607
 
2.9%
5 1372
 
2.4%
역 1343
 
2.4%
0 1255
 
2.2%
앞 1224
 
2.2%
Other values (452) 29942
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28618
50.8%
Decimal Number 14927
26.5%
Space Separator 8158
 
14.5%
Other Punctuation 3712
 
6.6%
Uppercase Letter 413
 
0.7%
Close Punctuation 212
 
0.4%
Open Punctuation 212
 
0.4%
Dash Punctuation 26
 
< 0.1%
Lowercase Letter 26
 
< 0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1607
 
5.6%
역 1343
 
4.7%
앞 1224
 
4.3%
번 1222
 
4.3%
출 1184
 
4.1%
동 637
 
2.2%
리 556
 
1.9%
교 545
 
1.9%
사 460
 
1.6%
원 453
 
1.6%
Other values (400) 19387
67.7%
Uppercase Letter
ValueCountFrequency (%)
K 61
14.8%
S 48
11.6%
C 44
10.7%
G 34
8.2%
L 33
8.0%
A 30
7.3%
T 28
 
6.8%
I 24
 
5.8%
B 20
 
4.8%
J 19
 
4.6%
Other values (13) 72
17.4%
Decimal Number
ValueCountFrequency (%)
1 3523
23.6%
2 2429
16.3%
3 1778
11.9%
5 1372
 
9.2%
0 1255
 
8.4%
4 1136
 
7.6%
6 1088
 
7.3%
7 853
 
5.7%
8 752
 
5.0%
9 741
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
e 11
42.3%
t 5
19.2%
k 5
19.2%
s 1
 
3.8%
m 1
 
3.8%
o 1
 
3.8%
c 1
 
3.8%
l 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 3689
99.4%
, 22
 
0.6%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
8151
99.9%
  7
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 3
50.0%
+ 3
50.0%
Close Punctuation
ValueCountFrequency (%)
) 212
100.0%
Open Punctuation
ValueCountFrequency (%)
( 212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28618
50.8%
Common 27256
48.4%
Latin 439
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1607
 
5.6%
역 1343
 
4.7%
앞 1224
 
4.3%
번 1222
 
4.3%
출 1184
 
4.1%
동 637
 
2.2%
리 556
 
1.9%
교 545
 
1.9%
사 460
 
1.6%
원 453
 
1.6%
Other values (400) 19387
67.7%
Latin
ValueCountFrequency (%)
K 61
13.9%
S 48
10.9%
C 44
10.0%
G 34
 
7.7%
L 33
 
7.5%
A 30
 
6.8%
T 28
 
6.4%
I 24
 
5.5%
B 20
 
4.6%
J 19
 
4.3%
Other values (21) 98
22.3%
Common
ValueCountFrequency (%)
8151
29.9%
. 3689
13.5%
1 3523
12.9%
2 2429
 
8.9%
3 1778
 
6.5%
5 1372
 
5.0%
0 1255
 
4.6%
4 1136
 
4.2%
6 1088
 
4.0%
7 853
 
3.1%
Other values (11) 1982
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28618
50.8%
ASCII 27688
49.2%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8151
29.4%
. 3689
13.3%
1 3523
12.7%
2 2429
 
8.8%
3 1778
 
6.4%
5 1372
 
5.0%
0 1255
 
4.5%
4 1136
 
4.1%
6 1088
 
3.9%
7 853
 
3.1%
Other values (41) 2414
 
8.7%
Hangul
ValueCountFrequency (%)
구 1607
 
5.6%
역 1343
 
4.7%
앞 1224
 
4.3%
번 1222
 
4.3%
출 1184
 
4.1%
동 637
 
2.2%
리 556
 
1.9%
교 545
 
1.9%
사 460
 
1.6%
원 453
 
1.6%
Other values (400) 19387
67.7%
None
ValueCountFrequency (%)
  7
100.0%

정기
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
정기
2629 
일일(회원)
904 
일일(비회원)
 
122
단체
 
29

Length

Max length7
Median length2
Mean length3.1471227
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 2629
71.4%
일일(회원) 904
 
24.5%
일일(비회원) 122
 
3.3%
단체 29
 
0.8%

Length

2024-03-14T01:26:35.716584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:35.815545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 2629
71.4%
일일(회원 904
 
24.5%
일일(비회원 122
 
3.3%
단체 29
 
0.8%

Unnamed: 4
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
M
2013 
F
886 
<NA>
785 

Length

Max length4
Median length1
Mean length1.6392508
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 2013
54.6%
F 886
24.0%
<NA> 785
 
21.3%

Length

2024-03-14T01:26:35.903893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:35.993926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 2013
54.6%
f 886
24.0%
na 785
 
21.3%

\N
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
20대
1422 
\N
793 
30대
583 
40대
375 
50대
290 
Other values (3)
221 

Length

Max length4
Median length3
Mean length2.8205755
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 (%)
20대 1422
38.6%
\N 793
21.5%
30대 583
15.8%
40대 375
 
10.2%
50대 290
 
7.9%
~10대 112
 
3.0%
60대 89
 
2.4%
70대~ 20
 
0.5%

Length

2024-03-14T01:26:36.089201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:36.189544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1422
38.6%
n 793
21.5%
30대 583
15.8%
40대 375
 
10.2%
50대 290
 
7.9%
10대 112
 
3.0%
60대 89
 
2.4%
70대 20
 
0.5%

1
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3629207
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:36.281594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82319335
Coefficient of variation (CV)0.60399209
Kurtosis21.297336
Mean1.3629207
Median Absolute Deviation (MAD)0
Skewness3.677624
Sum5021
Variance0.67764729
MonotonicityNot monotonic
2024-03-14T01:26:36.365922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2808
76.2%
2 609
 
16.5%
3 159
 
4.3%
4 61
 
1.7%
5 27
 
0.7%
6 12
 
0.3%
7 3
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1 2808
76.2%
2 609
 
16.5%
3 159
 
4.3%
4 61
 
1.7%
5 27
 
0.7%
6 12
 
0.3%
7 3
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
9 2
 
0.1%
8 2
 
0.1%
7 3
 
0.1%
6 12
 
0.3%
5 27
 
0.7%
4 61
 
1.7%
3 159
 
4.3%
2 609
 
16.5%
1 2808
76.2%

25.34
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2939
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.33531
Minimum0
Maximum5345.7
Zeros73
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:36.465594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.003
Q142.2475
median80.865
Q3163.0425
95-th percentile457.288
Maximum5345.7
Range5345.7
Interquartile range (IQR)120.795

Descriptive statistics

Standard deviation294.9841
Coefficient of variation (CV)1.9364131
Kurtosis122.23763
Mean152.33531
Median Absolute Deviation (MAD)47.635
Skewness9.2827484
Sum561203.29
Variance87015.617
MonotonicityNot monotonic
2024-03-14T01:26:36.588318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 73
 
2.0%
30.89 6
 
0.2%
33.98 6
 
0.2%
39.38 6
 
0.2%
45.3 6
 
0.2%
81.08 5
 
0.1%
42.47 5
 
0.1%
21.62 5
 
0.1%
42.77 5
 
0.1%
37.07 5
 
0.1%
Other values (2929) 3562
96.7%
ValueCountFrequency (%)
0.0 73
2.0%
0.26 2
 
0.1%
0.37 1
 
< 0.1%
0.43 1
 
< 0.1%
0.48 1
 
< 0.1%
0.51 1
 
< 0.1%
1.03 1
 
< 0.1%
1.61 1
 
< 0.1%
3.35 1
 
< 0.1%
4.63 1
 
< 0.1%
ValueCountFrequency (%)
5345.7 1
< 0.1%
5132.86 1
< 0.1%
4659.62 1
< 0.1%
4625.74 1
< 0.1%
4579.03 1
< 0.1%
4370.43 1
< 0.1%
3663.51 1
< 0.1%
3520.02 1
< 0.1%
3256.7 1
< 0.1%
2841.46 1
< 0.1%

0.19
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct511
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2987758
Minimum0
Maximum41.76
Zeros79
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:36.692240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14
Q10.37
median0.7
Q31.43
95-th percentile3.8955
Maximum41.76
Range41.76
Interquartile range (IQR)1.06

Descriptive statistics

Standard deviation2.4380262
Coefficient of variation (CV)1.8771725
Kurtosis118.41588
Mean1.2987758
Median Absolute Deviation (MAD)0.41
Skewness9.1030073
Sum4784.69
Variance5.9439715
MonotonicityNot monotonic
2024-03-14T01:26:36.817238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 79
 
2.1%
0.26 51
 
1.4%
0.42 44
 
1.2%
0.28 42
 
1.1%
0.21 40
 
1.1%
0.32 40
 
1.1%
0.35 39
 
1.1%
0.45 39
 
1.1%
0.44 37
 
1.0%
0.58 37
 
1.0%
Other values (501) 3236
87.8%
ValueCountFrequency (%)
0.0 79
2.1%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
0.1%
0.05 2
 
0.1%
0.06 7
 
0.2%
0.07 9
 
0.2%
0.08 8
 
0.2%
0.09 8
 
0.2%
ValueCountFrequency (%)
41.76 1
< 0.1%
41.69 1
< 0.1%
40.65 1
< 0.1%
40.15 1
< 0.1%
37.84 1
< 0.1%
34.22 1
< 0.1%
29.81 1
< 0.1%
29.55 1
< 0.1%
26.14 1
< 0.1%
19.12 1
< 0.1%

800.00
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1242
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5598.0673
Minimum0
Maximum179990
Zeros73
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:36.955346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile611.5
Q11580
median3025
Q36150
95-th percentile16776.5
Maximum179990
Range179990
Interquartile range (IQR)4570

Descriptive statistics

Standard deviation10508.314
Coefficient of variation (CV)1.8771325
Kurtosis118.40685
Mean5598.0673
Median Absolute Deviation (MAD)1785
Skewness9.1027261
Sum20623280
Variance1.1042467 × 108
MonotonicityNot monotonic
2024-03-14T01:26:37.083057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
2.0%
1890 14
 
0.4%
1400 13
 
0.4%
1970 13
 
0.4%
1190 13
 
0.4%
1200 12
 
0.3%
1100 12
 
0.3%
1220 12
 
0.3%
730 12
 
0.3%
2970 12
 
0.3%
Other values (1232) 3498
95.0%
ValueCountFrequency (%)
0 73
2.0%
10 2
 
0.1%
20 4
 
0.1%
40 1
 
< 0.1%
70 1
 
< 0.1%
130 1
 
< 0.1%
180 2
 
0.1%
210 1
 
< 0.1%
230 1
 
< 0.1%
240 3
 
0.1%
ValueCountFrequency (%)
179990 1
< 0.1%
179710 1
< 0.1%
175200 1
< 0.1%
173040 1
< 0.1%
163070 1
< 0.1%
147500 1
< 0.1%
128490 1
< 0.1%
127350 1
< 0.1%
112670 1
< 0.1%
82450 1
< 0.1%

12
Real number (ℝ)

HIGH CORRELATION 

Distinct188
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.557003
Minimum0
Maximum469
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size32.5 KiB
2024-03-14T01:26:37.194160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median21
Q340
95-th percentile96.85
Maximum469
Range469
Interquartile range (IQR)30

Descriptive statistics

Standard deviation35.618056
Coefficient of variation (CV)1.1286894
Kurtosis25.632691
Mean31.557003
Median Absolute Deviation (MAD)13
Skewness3.8097358
Sum116256
Variance1268.6459
MonotonicityNot monotonic
2024-03-14T01:26:37.295827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 150
 
4.1%
6 134
 
3.6%
9 121
 
3.3%
10 116
 
3.1%
7 113
 
3.1%
15 108
 
2.9%
5 107
 
2.9%
12 105
 
2.9%
13 102
 
2.8%
11 98
 
2.7%
Other values (178) 2530
68.7%
ValueCountFrequency (%)
0 6
 
0.2%
1 10
 
0.3%
2 40
 
1.1%
3 79
2.1%
4 93
2.5%
5 107
2.9%
6 134
3.6%
7 113
3.1%
8 150
4.1%
9 121
3.3%
ValueCountFrequency (%)
469 1
< 0.1%
453 1
< 0.1%
375 1
< 0.1%
371 1
< 0.1%
331 1
< 0.1%
322 1
< 0.1%
319 1
< 0.1%
266 1
< 0.1%
264 1
< 0.1%
262 2
0.1%

Interactions

2024-03-14T01:26:34.081416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.581349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.025030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.467894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.952459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.405826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.177655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.656455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.105163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.554001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.036941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.486962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.243385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.727637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.173828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.638224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.132110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.557484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.322608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.796479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.242753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.711992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.208617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.859760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.389033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.868464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.306446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.781031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.270185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.927699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.464859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:31.949085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.393985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:32.865485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:33.342022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:34.005044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:26:37.367393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2018-07-0100108정기Unnamed: 4\N125.340.19800.0012
2018-07-011.0000.0810.3570.6620.4190.0320.0470.0180.0180.000
001080.0811.0000.0000.0670.0910.0000.0370.0190.0190.077
정기0.3570.0001.0000.0940.5110.0260.1150.1100.1100.256
Unnamed: 40.6620.0670.0941.0000.3620.0310.0670.0700.0700.029
\N0.4190.0910.5110.3621.0000.0940.0310.0000.0000.107
10.0320.0000.0260.0310.0941.0000.4520.4490.4490.650
25.340.0470.0370.1150.0670.0310.4521.0000.9790.9790.687
0.190.0180.0190.1100.0700.0000.4490.9791.0001.0000.708
800.000.0180.0190.1100.0700.0000.4490.9791.0001.0000.708
120.0000.0770.2560.0290.1070.6500.6870.7080.7081.000
2024-03-14T01:26:37.488564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
\NUnnamed: 42018-07-01정기
\N1.0000.2720.3140.248
Unnamed: 40.2721.0000.4600.062
2018-07-010.3140.4601.0000.239
정기0.2480.0620.2391.000
2024-03-14T01:26:37.588546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
00108125.340.19800.00122018-07-01정기Unnamed: 4\N
001081.000-0.075-0.052-0.059-0.059-0.0660.0800.0000.0670.044
1-0.0751.0000.4530.4650.4650.4740.0320.0170.0310.046
25.34-0.0520.4531.0000.9850.9850.8210.0470.0740.0720.015
0.19-0.0590.4650.9851.0001.0000.8420.0180.0700.0530.000
800.00-0.0590.4650.9851.0001.0000.8420.0180.0700.0530.000
12-0.0660.4740.8210.8420.8421.0000.0000.1660.0290.052
2018-07-010.0800.0320.0470.0180.0180.0001.0000.2390.4600.314
정기0.0000.0170.0740.0700.0700.1660.2391.0000.0620.248
Unnamed: 40.0670.0310.0720.0530.0530.0290.4600.0621.0000.272
\N0.0440.0460.0150.0000.0000.0520.3140.2480.2721.000

Missing values

2024-03-14T01:26:34.556663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:26:34.674155image/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

2018-07-0100108108. 서교동 사거리정기Unnamed: 4\N125.340.19800.0012
02018-07-01503503. 더샵스타시티 C동 앞정기<NA>\N141.410.39166011
12018-07-01732732. 신월동 이마트정기<NA>\N283.140.75323013
22018-07-01736736. 오솔길공원정기<NA>\N113.40.114703
32018-07-01740740. 으뜸공원정기<NA>\N1385.613.321432091
42018-07-01746746. 목동2단지 상가정기<NA>\N133.980.3615609
52018-07-01505505. 자양사거리 광진아크로텔 앞정기<NA>\N1104.780.88378014
62018-07-01946946. 독바위역정기<NA>\N125.850.2410203
72018-07-0110251025. 상일초등학교정기<NA>\N1105.850.69297023
82018-07-0110311031. 암사동 CBIS정기<NA>\N18.240.073203
92018-07-0110441044. 굽은다리역정기<NA>\N2216.31.52653036
2018-07-0100108108. 서교동 사거리정기Unnamed: 4\N125.340.19800.0012
36742018-07-02130130. 신촌역(2호선) 7번출구 앞정기F20대1274.13.031306071
36752018-07-02805805. 문배어린이공원 앞정기F20대123.30.2611107
36762018-07-02806806. 전자랜드 본관 앞정기F20대1201.762.361019072
36772018-07-02807807. 서울역 12번 출구 앞정기F20대3210.72.411037072
36782018-07-02813813. 삼각지역 3번출구정기F20대1133.061.3560045
36792018-07-02132132. 창천문화공원정기F20대152.510.47204017
36802018-07-02183183. 하늘채코오롱아파트 건너편정기F20대5142.751.52650072
36812018-07-02192192. 연서어린이공원정기F20대1146.81.56674062
36822018-07-02178178. 증산3교 앞정기F20대164.630.63272019
36832018-07-02194194. 증산교 앞정기F20대272.770.9391067