Overview

Dataset statistics

Number of variables19
Number of observations32
Missing cells2
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory174.1 B

Variable types

Text1
Numeric18

Dataset

Description서울특별시 경찰관서별(연도별) 성매매 단속현황 (2004년 ~ 2021년)에 대한 데이터를 제공하니 참고하시기 바랍니다.
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/3075765/fileData.do

Alerts

2005년 is highly overall correlated with 2006년 and 2 other fieldsHigh correlation
2006년 is highly overall correlated with 2005년 and 2 other fieldsHigh correlation
2007년 is highly overall correlated with 2005년 and 2 other fieldsHigh correlation
2008년 is highly overall correlated with 2005년 and 2 other fieldsHigh correlation
2009년 is highly overall correlated with 2011년 and 1 other fieldsHigh correlation
2010년 is highly overall correlated with 2011년High correlation
2011년 is highly overall correlated with 2009년 and 6 other fieldsHigh correlation
2012년 is highly overall correlated with 2011년 and 8 other fieldsHigh correlation
2013년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2014년 is highly overall correlated with 2009년 and 10 other fieldsHigh correlation
2015년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2016년 is highly overall correlated with 2012년 and 8 other fieldsHigh correlation
2017년 is highly overall correlated with 2012년 and 7 other fieldsHigh correlation
2018년 is highly overall correlated with 2012년 and 8 other fieldsHigh correlation
2019년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2020년 is highly overall correlated with 2012년 and 6 other fieldsHigh correlation
2021년 is highly overall correlated with 2013년 and 6 other fieldsHigh correlation
2004년 has 2 (6.2%) missing valuesMissing
관서명 has unique valuesUnique
2004년 has 8 (25.0%) zerosZeros
2005년 has 1 (3.1%) zerosZeros
2006년 has 1 (3.1%) zerosZeros
2007년 has 1 (3.1%) zerosZeros
2008년 has 1 (3.1%) zerosZeros
2010년 has 1 (3.1%) zerosZeros
2011년 has 1 (3.1%) zerosZeros
2018년 has 2 (6.2%) zerosZeros
2019년 has 1 (3.1%) zerosZeros
2020년 has 2 (6.2%) zerosZeros
2021년 has 2 (6.2%) zerosZeros

Reproduction

Analysis started2023-12-12 07:18:58.477567
Analysis finished2023-12-12 07:19:34.401274
Duration35.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관서명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T16:19:34.558022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.15625
Min length2

Characters and Unicode

Total characters69
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row서울청
2nd row중부
3rd row종로
4th row남대문
5th row서대문
ValueCountFrequency (%)
서울청 1
 
3.1%
중부 1
 
3.1%
강북 1
 
3.1%
광진 1
 
3.1%
동작 1
 
3.1%
동대문 1
 
3.1%
혜화 1
 
3.1%
수서 1
 
3.1%
도봉 1
 
3.1%
은평 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T16:19:34.941701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
8.7%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (35) 39
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
8.7%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (35) 39
56.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
8.7%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (35) 39
56.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
8.7%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (35) 39
56.5%

2004년
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)56.7%
Missing2
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean23.2
Minimum0
Maximum456
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:35.111242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median5
Q315.25
95-th percentile36.8
Maximum456
Range456
Interquartile range (IQR)15

Descriptive statistics

Standard deviation82.371907
Coefficient of variation (CV)3.5505132
Kurtosis28.995788
Mean23.2
Median Absolute Deviation (MAD)5
Skewness5.3473399
Sum696
Variance6785.131
MonotonicityNot monotonic
2023-12-12T16:19:35.249779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
25.0%
4 3
 
9.4%
6 2
 
6.2%
5 2
 
6.2%
19 2
 
6.2%
1 2
 
6.2%
13 1
 
3.1%
44 1
 
3.1%
2 1
 
3.1%
7 1
 
3.1%
Other values (7) 7
21.9%
(Missing) 2
 
6.2%
ValueCountFrequency (%)
0 8
25.0%
1 2
 
6.2%
2 1
 
3.1%
4 3
 
9.4%
5 2
 
6.2%
6 2
 
6.2%
7 1
 
3.1%
8 1
 
3.1%
10 1
 
3.1%
13 1
 
3.1%
ValueCountFrequency (%)
456 1
3.1%
44 1
3.1%
28 1
3.1%
20 1
3.1%
19 2
6.2%
18 1
3.1%
16 1
3.1%
13 1
3.1%
10 1
3.1%
8 1
3.1%

2005년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.4375
Minimum0
Maximum646
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:35.389982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.75
Q125.5
median43
Q365.5
95-th percentile119.8
Maximum646
Range646
Interquartile range (IQR)40

Descriptive statistics

Standard deviation110.06712
Coefficient of variation (CV)1.682019
Kurtosis27.020022
Mean65.4375
Median Absolute Deviation (MAD)20
Skewness5.0182007
Sum2094
Variance12114.77
MonotonicityNot monotonic
2023-12-12T16:19:35.540353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
26 2
 
6.2%
0 1
 
3.1%
16 1
 
3.1%
40 1
 
3.1%
77 1
 
3.1%
7 1
 
3.1%
78 1
 
3.1%
31 1
 
3.1%
39 1
 
3.1%
2 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
0 1
3.1%
2 1
3.1%
7 1
3.1%
13 1
3.1%
16 1
3.1%
17 1
3.1%
22 1
3.1%
24 1
3.1%
26 2
6.2%
31 1
3.1%
ValueCountFrequency (%)
646 1
3.1%
122 1
3.1%
118 1
3.1%
83 1
3.1%
78 1
3.1%
77 1
3.1%
76 1
3.1%
70 1
3.1%
64 1
3.1%
62 1
3.1%

2006년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.5
Minimum0
Maximum291
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:35.664048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.55
Q125
median43
Q378.75
95-th percentile221
Maximum291
Range291
Interquartile range (IQR)53.75

Descriptive statistics

Standard deviation68.237252
Coefficient of variation (CV)1.0417901
Kurtosis3.9858795
Mean65.5
Median Absolute Deviation (MAD)26.5
Skewness1.9926233
Sum2096
Variance4656.3226
MonotonicityNot monotonic
2023-12-12T16:19:35.797112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
40 2
 
6.2%
46 2
 
6.2%
25 2
 
6.2%
0 1
 
3.1%
203 1
 
3.1%
29 1
 
3.1%
34 1
 
3.1%
18 1
 
3.1%
61 1
 
3.1%
12 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
0 1
3.1%
6 1
3.1%
7 1
3.1%
11 1
3.1%
12 1
3.1%
14 1
3.1%
18 1
3.1%
25 2
6.2%
26 1
3.1%
27 1
3.1%
ValueCountFrequency (%)
291 1
3.1%
243 1
3.1%
203 1
3.1%
120 1
3.1%
111 1
3.1%
108 1
3.1%
106 1
3.1%
87 1
3.1%
76 1
3.1%
71 1
3.1%

2007년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.3125
Minimum0
Maximum350
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:35.921075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q118.5
median34
Q364.75
95-th percentile165.15
Maximum350
Range350
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation73.04459
Coefficient of variation (CV)1.211102
Kurtosis7.1052838
Mean60.3125
Median Absolute Deviation (MAD)22
Skewness2.4111667
Sum1930
Variance5335.5121
MonotonicityNot monotonic
2023-12-12T16:19:36.092887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 2
 
6.2%
19 2
 
6.2%
64 2
 
6.2%
20 2
 
6.2%
31 2
 
6.2%
0 1
 
3.1%
72 1
 
3.1%
43 1
 
3.1%
67 1
 
3.1%
17 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
0 1
3.1%
1 1
3.1%
5 1
3.1%
8 2
6.2%
14 1
3.1%
15 1
3.1%
17 1
3.1%
19 2
6.2%
20 2
6.2%
23 1
3.1%
ValueCountFrequency (%)
350 1
3.1%
169 1
3.1%
162 1
3.1%
158 1
3.1%
151 1
3.1%
144 1
3.1%
72 1
3.1%
67 1
3.1%
64 2
6.2%
58 1
3.1%

2008년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.3125
Minimum0
Maximum350
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:36.212453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q118.5
median34
Q364.75
95-th percentile165.15
Maximum350
Range350
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation73.04459
Coefficient of variation (CV)1.211102
Kurtosis7.1052838
Mean60.3125
Median Absolute Deviation (MAD)22
Skewness2.4111667
Sum1930
Variance5335.5121
MonotonicityNot monotonic
2023-12-12T16:19:36.382527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 2
 
6.2%
19 2
 
6.2%
64 2
 
6.2%
20 2
 
6.2%
31 2
 
6.2%
0 1
 
3.1%
72 1
 
3.1%
43 1
 
3.1%
67 1
 
3.1%
17 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
0 1
3.1%
1 1
3.1%
5 1
3.1%
8 2
6.2%
14 1
3.1%
15 1
3.1%
17 1
3.1%
19 2
6.2%
20 2
6.2%
23 1
3.1%
ValueCountFrequency (%)
350 1
3.1%
169 1
3.1%
162 1
3.1%
158 1
3.1%
151 1
3.1%
144 1
3.1%
72 1
3.1%
67 1
3.1%
64 2
6.2%
58 1
3.1%

2009년
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.09375
Minimum15
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:36.530909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.1
Q126
median37
Q349.5
95-th percentile104.75
Maximum144
Range129
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation29.207113
Coefficient of variation (CV)0.64769758
Kurtosis3.8717067
Mean45.09375
Median Absolute Deviation (MAD)12
Skewness1.9480878
Sum1443
Variance853.05544
MonotonicityNot monotonic
2023-12-12T16:19:36.969762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
38 2
 
6.2%
26 2
 
6.2%
49 2
 
6.2%
32 2
 
6.2%
35 2
 
6.2%
20 2
 
6.2%
39 2
 
6.2%
28 1
 
3.1%
23 1
 
3.1%
44 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
15 1
3.1%
18 1
3.1%
20 2
6.2%
22 1
3.1%
23 1
3.1%
25 1
3.1%
26 2
6.2%
28 1
3.1%
32 2
6.2%
34 1
3.1%
ValueCountFrequency (%)
144 1
3.1%
113 1
3.1%
98 1
3.1%
93 1
3.1%
65 1
3.1%
57 1
3.1%
54 1
3.1%
51 1
3.1%
49 2
6.2%
45 1
3.1%

2010년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.9375
Minimum0
Maximum102
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:37.115241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1
Q16
median14.5
Q326.75
95-th percentile50.15
Maximum102
Range102
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation20.028911
Coefficient of variation (CV)1.0045849
Kurtosis8.3875497
Mean19.9375
Median Absolute Deviation (MAD)8.5
Skewness2.4958008
Sum638
Variance401.15726
MonotonicityNot monotonic
2023-12-12T16:19:37.291227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6 4
 
12.5%
9 2
 
6.2%
30 2
 
6.2%
5 2
 
6.2%
0 1
 
3.1%
25 1
 
3.1%
2 1
 
3.1%
12 1
 
3.1%
17 1
 
3.1%
10 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0 1
 
3.1%
2 1
 
3.1%
4 1
 
3.1%
5 2
6.2%
6 4
12.5%
8 1
 
3.1%
9 2
6.2%
10 1
 
3.1%
11 1
 
3.1%
12 1
 
3.1%
ValueCountFrequency (%)
102 1
3.1%
54 1
3.1%
47 1
3.1%
40 1
3.1%
32 1
3.1%
30 2
6.2%
29 1
3.1%
26 1
3.1%
25 1
3.1%
23 1
3.1%

2011년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum0
Maximum121
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:37.415110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.55
Q18
median18
Q334
95-th percentile81.9
Maximum121
Range121
Interquartile range (IQR)26

Descriptive statistics

Standard deviation28.563455
Coefficient of variation (CV)1.0201234
Kurtosis2.6294237
Mean28
Median Absolute Deviation (MAD)12
Skewness1.6629812
Sum896
Variance815.87097
MonotonicityNot monotonic
2023-12-12T16:19:37.572990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8 2
 
6.2%
34 2
 
6.2%
7 2
 
6.2%
9 2
 
6.2%
6 2
 
6.2%
18 2
 
6.2%
0 1
 
3.1%
70 1
 
3.1%
13 1
 
3.1%
24 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0 1
3.1%
1 1
3.1%
2 1
3.1%
6 2
6.2%
7 2
6.2%
8 2
6.2%
9 2
6.2%
11 1
3.1%
13 1
3.1%
14 1
3.1%
ValueCountFrequency (%)
121 1
3.1%
83 1
3.1%
81 1
3.1%
70 1
3.1%
68 1
3.1%
50 1
3.1%
40 1
3.1%
34 2
6.2%
32 1
3.1%
31 1
3.1%

2012년
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.3125
Minimum3
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:37.726269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.85
Q113.75
median26.5
Q354.5
95-th percentile116.8
Maximum250
Range247
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation48.814618
Coefficient of variation (CV)1.1015993
Kurtosis9.5784711
Mean44.3125
Median Absolute Deviation (MAD)16.5
Skewness2.6943337
Sum1418
Variance2382.8669
MonotonicityNot monotonic
2023-12-12T16:19:37.860080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 3
 
9.4%
14 2
 
6.2%
13 2
 
6.2%
15 2
 
6.2%
3 2
 
6.2%
53 2
 
6.2%
52 2
 
6.2%
23 2
 
6.2%
250 1
 
3.1%
41 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
3 2
6.2%
10 3
9.4%
11 1
 
3.1%
13 2
6.2%
14 2
6.2%
15 2
6.2%
18 1
 
3.1%
19 1
 
3.1%
23 2
6.2%
30 1
 
3.1%
ValueCountFrequency (%)
250 1
3.1%
119 1
3.1%
115 1
3.1%
88 1
3.1%
79 1
3.1%
71 1
3.1%
67 1
3.1%
59 1
3.1%
53 2
6.2%
52 2
6.2%

2013년
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45
Minimum4
Maximum297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:37.998371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.55
Q112
median29.5
Q354.75
95-th percentile134.25
Maximum297
Range293
Interquartile range (IQR)42.75

Descriptive statistics

Standard deviation57.160216
Coefficient of variation (CV)1.270227
Kurtosis12.051776
Mean45
Median Absolute Deviation (MAD)18
Skewness3.1309774
Sum1440
Variance3267.2903
MonotonicityNot monotonic
2023-12-12T16:19:38.137344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
25 2
 
6.2%
9 2
 
6.2%
67 2
 
6.2%
35 2
 
6.2%
4 2
 
6.2%
12 2
 
6.2%
297 1
 
3.1%
40 1
 
3.1%
43 1
 
3.1%
37 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
4 2
6.2%
5 1
3.1%
8 1
3.1%
9 2
6.2%
11 1
3.1%
12 2
6.2%
13 1
3.1%
14 1
3.1%
15 1
3.1%
16 1
3.1%
ValueCountFrequency (%)
297 1
3.1%
137 1
3.1%
132 1
3.1%
94 1
3.1%
67 2
6.2%
66 1
3.1%
60 1
3.1%
53 1
3.1%
43 1
3.1%
40 1
3.1%

2014년
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.78125
Minimum1
Maximum392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:38.273997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.55
Q118
median29.5
Q356.5
95-th percentile131.05
Maximum392
Range391
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation70.540647
Coefficient of variation (CV)1.4170124
Kurtosis18.658744
Mean49.78125
Median Absolute Deviation (MAD)17.5
Skewness4.0034539
Sum1593
Variance4975.9829
MonotonicityNot monotonic
2023-12-12T16:19:38.402282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
53 2
 
6.2%
12 2
 
6.2%
29 2
 
6.2%
61 2
 
6.2%
21 2
 
6.2%
30 2
 
6.2%
20 2
 
6.2%
392 1
 
3.1%
68 1
 
3.1%
46 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
1 1
3.1%
4 1
3.1%
5 1
3.1%
9 1
3.1%
12 2
6.2%
13 1
3.1%
15 1
3.1%
19 1
3.1%
20 2
6.2%
21 2
6.2%
ValueCountFrequency (%)
392 1
3.1%
158 1
3.1%
109 1
3.1%
78 1
3.1%
68 1
3.1%
62 1
3.1%
61 2
6.2%
55 1
3.1%
53 2
6.2%
46 1
3.1%

2015년
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.0625
Minimum4
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:38.575718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.55
Q118.75
median36.5
Q360.25
95-th percentile122.4
Maximum440
Range436
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation77.088109
Coefficient of variation (CV)1.4259072
Kurtosis21.426426
Mean54.0625
Median Absolute Deviation (MAD)20.5
Skewness4.3325061
Sum1730
Variance5942.5766
MonotonicityNot monotonic
2023-12-12T16:19:38.714171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9 2
 
6.2%
26 2
 
6.2%
32 2
 
6.2%
39 2
 
6.2%
440 1
 
3.1%
99 1
 
3.1%
45 1
 
3.1%
58 1
 
3.1%
47 1
 
3.1%
13 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
4 1
3.1%
8 1
3.1%
9 2
6.2%
11 1
3.1%
12 1
3.1%
13 1
3.1%
18 1
3.1%
19 1
3.1%
22 1
3.1%
26 2
6.2%
ValueCountFrequency (%)
440 1
3.1%
151 1
3.1%
99 1
3.1%
80 1
3.1%
76 1
3.1%
73 1
3.1%
71 1
3.1%
67 1
3.1%
58 1
3.1%
56 1
3.1%

2016년
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.3125
Minimum9
Maximum881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:38.866752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10.55
Q125
median50
Q392
95-th percentile138.65
Maximum881
Range872
Interquartile range (IQR)67

Descriptive statistics

Standard deviation150.86448
Coefficient of variation (CV)1.8108265
Kurtosis27.309273
Mean83.3125
Median Absolute Deviation (MAD)33.5
Skewness5.0537189
Sum2666
Variance22760.093
MonotonicityNot monotonic
2023-12-12T16:19:39.005800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
50 2
 
6.2%
83 2
 
6.2%
25 2
 
6.2%
27 2
 
6.2%
881 1
 
3.1%
114 1
 
3.1%
67 1
 
3.1%
37 1
 
3.1%
13 1
 
3.1%
131 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
9 1
3.1%
10 1
3.1%
11 1
3.1%
12 1
3.1%
13 1
3.1%
16 1
3.1%
21 1
3.1%
25 2
6.2%
27 2
6.2%
31 1
3.1%
ValueCountFrequency (%)
881 1
3.1%
148 1
3.1%
131 1
3.1%
115 1
3.1%
114 1
3.1%
105 1
3.1%
99 1
3.1%
95 1
3.1%
91 1
3.1%
86 1
3.1%

2017년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.71875
Minimum1
Maximum472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:39.146705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.55
Q122.75
median42.5
Q361
95-th percentile89.15
Maximum472
Range471
Interquartile range (IQR)38.25

Descriptive statistics

Standard deviation80.254126
Coefficient of variation (CV)1.4939686
Kurtosis25.618898
Mean53.71875
Median Absolute Deviation (MAD)20
Skewness4.8273556
Sum1719
Variance6440.7248
MonotonicityNot monotonic
2023-12-12T16:19:39.286545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
43 2
 
6.2%
24 2
 
6.2%
472 1
 
3.1%
23 1
 
3.1%
64 1
 
3.1%
22 1
 
3.1%
25 1
 
3.1%
59 1
 
3.1%
14 1
 
3.1%
60 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
1 1
3.1%
4 1
3.1%
5 1
3.1%
9 1
3.1%
12 1
3.1%
14 1
3.1%
18 1
3.1%
22 1
3.1%
23 1
3.1%
24 2
6.2%
ValueCountFrequency (%)
472 1
3.1%
104 1
3.1%
77 1
3.1%
76 1
3.1%
69 1
3.1%
66 1
3.1%
65 1
3.1%
64 1
3.1%
60 1
3.1%
59 1
3.1%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.3125
Minimum0
Maximum319
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:39.409199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q115.75
median28.5
Q347
95-th percentile92.3
Maximum319
Range319
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation56.364847
Coefficient of variation (CV)1.3643533
Kurtosis19.911772
Mean41.3125
Median Absolute Deviation (MAD)15
Skewness4.1076056
Sum1322
Variance3176.996
MonotonicityNot monotonic
2023-12-12T16:19:39.524172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.2%
40 2
 
6.2%
17 2
 
6.2%
319 1
 
3.1%
77 1
 
3.1%
43 1
 
3.1%
50 1
 
3.1%
60 1
 
3.1%
44 1
 
3.1%
19 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
0 2
6.2%
2 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
14 1
3.1%
15 1
3.1%
16 1
3.1%
17 2
6.2%
19 1
3.1%
ValueCountFrequency (%)
319 1
3.1%
111 1
3.1%
77 1
3.1%
66 1
3.1%
60 1
3.1%
57 1
3.1%
51 1
3.1%
50 1
3.1%
46 1
3.1%
44 1
3.1%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.78125
Minimum0
Maximum225
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:39.685076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.65
Q19.5
median22.5
Q340.25
95-th percentile57.35
Maximum225
Range225
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation39.463118
Coefficient of variation (CV)1.2820505
Kurtosis19.754574
Mean30.78125
Median Absolute Deviation (MAD)15
Skewness4.0429451
Sum985
Variance1557.3377
MonotonicityNot monotonic
2023-12-12T16:19:39.838715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 2
 
6.2%
31 2
 
6.2%
6 2
 
6.2%
225 1
 
3.1%
20 1
 
3.1%
17 1
 
3.1%
25 1
 
3.1%
21 1
 
3.1%
48 1
 
3.1%
11 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
0 1
3.1%
2 1
3.1%
5 2
6.2%
6 2
6.2%
7 1
3.1%
8 1
3.1%
10 1
3.1%
11 1
3.1%
14 1
3.1%
15 1
3.1%
ValueCountFrequency (%)
225 1
3.1%
59 1
3.1%
56 1
3.1%
52 1
3.1%
51 1
3.1%
50 1
3.1%
48 1
3.1%
41 1
3.1%
40 1
3.1%
31 2
6.2%

2020년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.34375
Minimum0
Maximum168
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:39.977348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q15.75
median14.5
Q321.25
95-th percentile34.9
Maximum168
Range168
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation28.926614
Coefficient of variation (CV)1.4953985
Kurtosis24.048186
Mean19.34375
Median Absolute Deviation (MAD)8
Skewness4.6218349
Sum619
Variance836.74899
MonotonicityNot monotonic
2023-12-12T16:19:40.103177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11 3
 
9.4%
18 3
 
9.4%
16 2
 
6.2%
2 2
 
6.2%
0 2
 
6.2%
4 2
 
6.2%
13 2
 
6.2%
168 1
 
3.1%
5 1
 
3.1%
22 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
0 2
6.2%
2 2
6.2%
3 1
 
3.1%
4 2
6.2%
5 1
 
3.1%
6 1
 
3.1%
8 1
 
3.1%
11 3
9.4%
13 2
6.2%
14 1
 
3.1%
ValueCountFrequency (%)
168 1
 
3.1%
36 1
 
3.1%
34 1
 
3.1%
33 1
 
3.1%
29 1
 
3.1%
28 1
 
3.1%
23 1
 
3.1%
22 1
 
3.1%
21 1
 
3.1%
18 3
9.4%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.96875
Minimum0
Maximum74
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T16:19:40.254925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q17
median11
Q315.25
95-th percentile38.6
Maximum74
Range74
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation14.202418
Coefficient of variation (CV)1.0167279
Kurtosis10.214015
Mean13.96875
Median Absolute Deviation (MAD)4
Skewness2.85285
Sum447
Variance201.70867
MonotonicityNot monotonic
2023-12-12T16:19:40.415269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14 3
 
9.4%
10 3
 
9.4%
11 3
 
9.4%
13 2
 
6.2%
0 2
 
6.2%
7 2
 
6.2%
21 2
 
6.2%
8 2
 
6.2%
74 1
 
3.1%
5 1
 
3.1%
Other values (11) 11
34.4%
ValueCountFrequency (%)
0 2
6.2%
1 1
 
3.1%
2 1
 
3.1%
3 1
 
3.1%
5 1
 
3.1%
6 1
 
3.1%
7 2
6.2%
8 2
6.2%
9 1
 
3.1%
10 3
9.4%
ValueCountFrequency (%)
74 1
 
3.1%
43 1
 
3.1%
35 1
 
3.1%
21 2
6.2%
18 1
 
3.1%
17 1
 
3.1%
16 1
 
3.1%
15 1
 
3.1%
14 3
9.4%
13 2
6.2%

Interactions

2023-12-12T16:19:32.087257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:18:59.339146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:02.087223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:05.228302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.756852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.472793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:10.309510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:12.314240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:14.015583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.736477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.755597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.516288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.433252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:23.049778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:25.010638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.670056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.386256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.969739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:32.187007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:18:59.488035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:02.219231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:05.305265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.835699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.573528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:10.385538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:12.390156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:14.102507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.812800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.842824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.613132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.548311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:23.134045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:25.085330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.769865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.470111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:30.354535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:32.303756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:18:59.647288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:02.373024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:05.400844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.922629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.681960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:19:13.573139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.308958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.317323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:18.997910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:20.922937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:22.602342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:24.578028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.231788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:27.943657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.475795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:31.565291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:33.589279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:01.468505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:04.805525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.412203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.042926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:09.886564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:11.942805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:13.646111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.400403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.400227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.092883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.005427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:22.679032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:24.647372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.298419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.026801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.575484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:31.654145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:33.682964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:01.599911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:04.908240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.508234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.147926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:09.999724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:12.028068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:13.759540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.484771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.484129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.218376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.139715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:22.770769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:24.726463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.384715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.110835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.701001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:31.761189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:33.774916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:01.760767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:05.009329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.577076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.249385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:10.102194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:12.108451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:13.837335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.561768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.573665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.314834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.227561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:22.855055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:24.828417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.478575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.187274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.807525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:31.848279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:33.864883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:01.920634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:05.118648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:06.674169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:08.356771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:10.218491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:12.218761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:13.930842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.646429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:17.660823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:19.415865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:21.315886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:22.947392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:24.921742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:26.568169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:28.269570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:29.888069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:31.971045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:19:40.562684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관서명2004년2005년2006년2007년2008년2009년2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
관서명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2004년1.0001.0001.0001.0000.1740.1740.8120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.115
2005년1.0001.0001.0000.7870.2250.2250.4770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
2006년1.0001.0000.7871.0000.2780.2780.4480.6780.0000.0000.0000.0000.0000.0000.0000.0000.0000.6350.000
2007년1.0000.1740.2250.2781.0001.0000.7430.6720.4920.4680.2080.0000.8060.0000.0000.0000.0000.0000.000
2008년1.0000.1740.2250.2781.0001.0000.7430.6720.4920.4680.2080.0000.8060.0000.0000.0000.0000.0000.000
2009년1.0000.8120.4770.4480.7430.7431.0000.8680.7940.5770.4260.6910.3370.0000.0000.0000.2540.1150.000
2010년1.0000.0000.0000.6780.6720.6720.8681.0000.7720.6930.5150.6510.6140.0000.0000.0000.3880.4680.000
2011년1.0000.0000.0000.0000.4920.4920.7940.7721.0000.7350.6910.8490.7790.0000.0000.6970.8180.5200.000
2012년1.0000.0000.0000.0000.4680.4680.5770.6930.7351.0000.9080.8030.8000.9730.8560.8130.8090.7490.815
2013년1.0000.0000.0000.0000.2080.2080.4260.5150.6910.9081.0000.8490.8140.9510.8110.8780.7940.7400.942
2014년1.0000.0000.0000.0000.0000.0000.6910.6510.8490.8030.8491.0000.9780.7250.6370.9700.7470.6640.628
2015년1.0000.0000.0000.0000.8060.8060.3370.6140.7790.8000.8140.9781.0000.7640.6410.9670.7140.6420.685
2016년1.0000.0000.0000.0000.0000.0000.0000.0000.0000.9730.9510.7250.7641.0000.6880.7370.6800.6600.941
2017년1.0000.0000.0000.0000.0000.0000.0000.0000.0000.8560.8110.6370.6410.6881.0000.6850.9150.8960.925
2018년1.0000.0000.0000.0000.0000.0000.0000.0000.6970.8130.8780.9700.9670.7370.6851.0000.7680.6670.732
2019년1.0000.0000.0000.0000.0000.0000.2540.3880.8180.8090.7940.7470.7140.6800.9150.7681.0000.8890.831
2020년1.0000.0000.0000.6350.0000.0000.1150.4680.5200.7490.7400.6640.6420.6600.8960.6670.8891.0000.702
2021년1.0000.1150.0000.0000.0000.0000.0000.0000.0000.8150.9420.6280.6850.9410.9250.7320.8310.7021.000
2023-12-12T16:19:40.776373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2004년2005년2006년2007년2008년2009년2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
2004년1.0000.4120.3500.4870.4870.0330.4110.208-0.114-0.129-0.041-0.109-0.266-0.224-0.226-0.2680.074-0.158
2005년0.4121.0000.5200.5190.5190.1330.2680.108-0.115-0.031-0.077-0.156-0.1320.002-0.042-0.182-0.0200.065
2006년0.3500.5201.0000.5440.5440.1060.2750.038-0.259-0.185-0.181-0.359-0.258-0.342-0.365-0.3170.056-0.173
2007년0.4870.5190.5441.0001.0000.2130.3060.039-0.191-0.183-0.245-0.233-0.294-0.286-0.211-0.3550.024-0.247
2008년0.4870.5190.5441.0001.0000.2130.3060.039-0.191-0.183-0.245-0.233-0.294-0.286-0.211-0.3550.024-0.247
2009년0.0330.1330.1060.2130.2131.0000.4440.5270.4110.4510.5290.3960.3490.1600.2520.3150.3890.235
2010년0.4110.2680.2750.3060.3060.4441.0000.8040.3490.4460.4150.3920.1330.0660.1290.2800.4900.100
2011년0.2080.1080.0380.0390.0390.5270.8041.0000.5640.5880.5560.5450.2780.3050.3530.5240.4830.225
2012년-0.114-0.115-0.259-0.191-0.1910.4110.3490.5641.0000.8430.7480.7970.7190.6770.7780.7010.6390.477
2013년-0.129-0.031-0.185-0.183-0.1830.4510.4460.5880.8431.0000.8830.8830.7380.6860.8200.8320.6850.598
2014년-0.041-0.077-0.181-0.245-0.2450.5290.4150.5560.7480.8831.0000.9140.7700.6820.7380.8240.6470.699
2015년-0.109-0.156-0.359-0.233-0.2330.3960.3920.5450.7970.8830.9141.0000.8160.7070.7860.8140.5630.618
2016년-0.266-0.132-0.258-0.294-0.2940.3490.1330.2780.7190.7380.7700.8161.0000.7140.6850.6670.5310.534
2017년-0.2240.002-0.342-0.286-0.2860.1600.0660.3050.6770.6860.6820.7070.7141.0000.7340.7510.3850.646
2018년-0.226-0.042-0.365-0.211-0.2110.2520.1290.3530.7780.8200.7380.7860.6850.7341.0000.8400.5790.722
2019년-0.268-0.182-0.317-0.355-0.3550.3150.2800.5240.7010.8320.8240.8140.6670.7510.8401.0000.7010.700
2020년0.074-0.0200.0560.0240.0240.3890.4900.4830.6390.6850.6470.5630.5310.3850.5790.7011.0000.498
2021년-0.1580.065-0.173-0.247-0.2470.2350.1000.2250.4770.5980.6990.6180.5340.6460.7220.7000.4981.000

Missing values

2023-12-12T16:19:34.041182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:19:34.316517image/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

관서명2004년2005년2006년2007년2008년2009년2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
0서울청00000380025029739244088147231922516874
1중부6161081691692066231215193824178188
2종로0131488654141425533731482327213
3남대문4387619192314814912916180200
4서대문1122111646445611231229261156625191714
5용산19702911441449347341327198101210347
6성북1762720202288105591156532
7마포<NA>57106191928405067676267836532311821
8영등포1383715858572668119606140716957412814
9성동652616216225157151112262542205613
관서명2004년2005년2006년2007년2008년2009년2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
22방배4452434040266110814940003
23은평18262555205934911271276411
24도봉0267115169521320359935171488
25수서0212884917408813215815113160111501618
26혜화19396135035014430834215213213141911117
27동대문43118171718128153322139255944481510
28동작078466464322215351313372560211311
29광진0734313144302430354547672250312214
30강북877406767359131837465850434325517
31금천540294343399185343304511464401741