Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells16498
Missing cells (%)23.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory683.6 KiB
Average record size in memory70.0 B

Variable types

Numeric6
Text1

Alerts

위도 is highly overall correlated with X좌표High correlation
경도 is highly overall correlated with 권역코드 and 1 other fieldsHigh correlation
노드아이디 is highly overall correlated with 권역코드High correlation
권역코드 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
Y좌표 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
X좌표 is highly overall correlated with 위도High correlation
교차로명 has 7504 (75.0%) missing valuesMissing
권역코드 has 8994 (89.9%) missing valuesMissing
노드아이디 has unique valuesUnique
X좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:57:54.573756
Analysis finished2023-12-10 21:57:59.511474
Duration4.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9297
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3710.8696
Minimum3419.113
Maximum3828.554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:57:59.584102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3419.113
5-th percentile3557.6675
Q13711.3908
median3724.171
Q33736.9195
95-th percentile3754.0054
Maximum3828.554
Range409.441
Interquartile range (IQR)25.52875

Descriptive statistics

Standard deviation57.311853
Coefficient of variation (CV)0.015444319
Kurtosis7.0764728
Mean3710.8696
Median Absolute Deviation (MAD)12.7555
Skewness-2.4587446
Sum37108696
Variance3284.6485
MonotonicityNot monotonic
2023-12-11T06:57:59.750037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3739.481 4
 
< 0.1%
3731.444 4
 
< 0.1%
3739.606 3
 
< 0.1%
3745.674 3
 
< 0.1%
3739.707 3
 
< 0.1%
3730.76 3
 
< 0.1%
3736.64 3
 
< 0.1%
3731.209 3
 
< 0.1%
3733.289 3
 
< 0.1%
3721.807 3
 
< 0.1%
Other values (9287) 9968
99.7%
ValueCountFrequency (%)
3419.113 1
< 0.1%
3419.149 1
< 0.1%
3423.513 1
< 0.1%
3423.54 1
< 0.1%
3423.581 1
< 0.1%
3423.586 1
< 0.1%
3428.72 1
< 0.1%
3429.833 1
< 0.1%
3433.883 1
< 0.1%
3434.257 1
< 0.1%
ValueCountFrequency (%)
3828.554 1
< 0.1%
3827.03 1
< 0.1%
3826.883 1
< 0.1%
3826.455 1
< 0.1%
3825.753 2
< 0.1%
3824.511 1
< 0.1%
3823.692 1
< 0.1%
3823.292 1
< 0.1%
3823.074 1
< 0.1%
3823.038 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9078
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12702.665
Minimum12615.789
Maximum12926.587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:57:59.884150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12615.789
5-th percentile12641.542
Q112651.38
median12703.651
Q312719.464
95-th percentile12848.4
Maximum12926.587
Range310.798
Interquartile range (IQR)68.08425

Descriptive statistics

Standard deviation62.48757
Coefficient of variation (CV)0.0049192487
Kurtosis1.7568998
Mean12702.665
Median Absolute Deviation (MAD)45.869
Skewness1.4155597
Sum1.2702665 × 108
Variance3904.6964
MonotonicityNot monotonic
2023-12-11T06:58:00.032512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12704.36 6
 
0.1%
12704.55 4
 
< 0.1%
12702.41 4
 
< 0.1%
12653.63 4
 
< 0.1%
12703.81 4
 
< 0.1%
12655.45 4
 
< 0.1%
12658.76 4
 
< 0.1%
12704.788 4
 
< 0.1%
12657.0 4
 
< 0.1%
12703.69 4
 
< 0.1%
Other values (9068) 9958
99.6%
ValueCountFrequency (%)
12615.789 1
< 0.1%
12618.234 1
< 0.1%
12618.948 1
< 0.1%
12622.722 1
< 0.1%
12624.466 1
< 0.1%
12624.603 1
< 0.1%
12624.806 1
< 0.1%
12625.025 1
< 0.1%
12625.046 1
< 0.1%
12625.095 1
< 0.1%
ValueCountFrequency (%)
12926.587 1
< 0.1%
12926.569 1
< 0.1%
12926.537 1
< 0.1%
12926.528 2
< 0.1%
12925.573 1
< 0.1%
12925.523 1
< 0.1%
12924.388 1
< 0.1%
12924.38 1
< 0.1%
12924.133 1
< 0.1%
12923.834 1
< 0.1%

교차로명
Text

MISSING 

Distinct2411
Distinct (%)96.6%
Missing7504
Missing (%)75.0%
Memory size156.2 KiB
2023-12-11T06:58:00.269089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length23.945513
Min length16

Characters and Unicode

Total characters59768
Distinct characters525
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2341 ?
Unique (%)93.8%

Sample

1st row치과의사회관앞
2nd row오천터널(서)
3rd row조흥아파트삼거리
4th row대방동우편취급소
5th row기흥IC 입구
ValueCountFrequency (%)
입구 10
 
0.4%
본선 9
 
0.3%
어린이보호구역 7
 
0.3%
ic 4
 
0.2%
남측(본선 4
 
0.2%
서측(본선 4
 
0.2%
북측(연결로 4
 
0.2%
서측(연결로 4
 
0.2%
터미널사거리 4
 
0.2%
소방서삼거리 4
 
0.2%
Other values (2441) 2543
97.9%
2023-12-11T06:58:00.608850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42079
70.4%
리 892
 
1.5%
거 762
 
1.3%
교 711
 
1.2%
( 669
 
1.1%
) 655
 
1.1%
사 512
 
0.9%
로 476
 
0.8%
앞 449
 
0.8%
삼 348
 
0.6%
Other values (515) 12215
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 42079
70.4%
Other Letter 15112
 
25.3%
Open Punctuation 669
 
1.1%
Close Punctuation 655
 
1.1%
Uppercase Letter 515
 
0.9%
Decimal Number 504
 
0.8%
Dash Punctuation 115
 
0.2%
Connector Punctuation 105
 
0.2%
Other Punctuation 10
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 892
 
5.9%
거 762
 
5.0%
교 711
 
4.7%
사 512
 
3.4%
로 476
 
3.1%
앞 449
 
3.0%
삼 348
 
2.3%
차 321
 
2.1%
동 281
 
1.9%
구 263
 
1.7%
Other values (478) 10097
66.8%
Uppercase Letter
ValueCountFrequency (%)
C 225
43.7%
I 195
37.9%
J 29
 
5.6%
S 11
 
2.1%
T 9
 
1.7%
G 8
 
1.6%
K 7
 
1.4%
P 7
 
1.4%
B 6
 
1.2%
A 5
 
1.0%
Other values (7) 13
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 145
28.8%
2 101
20.0%
3 84
16.7%
4 49
 
9.7%
6 39
 
7.7%
5 26
 
5.2%
7 25
 
5.0%
8 15
 
3.0%
9 13
 
2.6%
0 7
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 9
90.0%
/ 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
50.0%
i 1
50.0%
Space Separator
ValueCountFrequency (%)
42079
100.0%
Open Punctuation
ValueCountFrequency (%)
( 669
100.0%
Close Punctuation
ValueCountFrequency (%)
) 655
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 105
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44139
73.9%
Hangul 15112
 
25.3%
Latin 517
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 892
 
5.9%
거 762
 
5.0%
교 711
 
4.7%
사 512
 
3.4%
로 476
 
3.1%
앞 449
 
3.0%
삼 348
 
2.3%
차 321
 
2.1%
동 281
 
1.9%
구 263
 
1.7%
Other values (478) 10097
66.8%
Latin
ValueCountFrequency (%)
C 225
43.5%
I 195
37.7%
J 29
 
5.6%
S 11
 
2.1%
T 9
 
1.7%
G 8
 
1.5%
K 7
 
1.4%
P 7
 
1.4%
B 6
 
1.2%
A 5
 
1.0%
Other values (9) 15
 
2.9%
Common
ValueCountFrequency (%)
42079
95.3%
( 669
 
1.5%
) 655
 
1.5%
1 145
 
0.3%
- 115
 
0.3%
_ 105
 
0.2%
2 101
 
0.2%
3 84
 
0.2%
4 49
 
0.1%
6 39
 
0.1%
Other values (8) 98
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44656
74.7%
Hangul 15112
 
25.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42079
94.2%
( 669
 
1.5%
) 655
 
1.5%
C 225
 
0.5%
I 195
 
0.4%
1 145
 
0.3%
- 115
 
0.3%
_ 105
 
0.2%
2 101
 
0.2%
3 84
 
0.2%
Other values (27) 283
 
0.6%
Hangul
ValueCountFrequency (%)
리 892
 
5.9%
거 762
 
5.0%
교 711
 
4.7%
사 512
 
3.4%
로 476
 
3.1%
앞 449
 
3.0%
삼 348
 
2.3%
차 321
 
2.1%
동 281
 
1.9%
구 263
 
1.7%
Other values (478) 10097
66.8%

노드아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2608268 × 109
Minimum1.0000004 × 109
Maximum3.9800005 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:58:00.730916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000004 × 109
5-th percentile1.1500178 × 109
Q12.1400232 × 109
median2.2800992 × 109
Q32.3700847 × 109
95-th percentile3.2700109 × 109
Maximum3.9800005 × 109
Range2.9800001 × 109
Interquartile range (IQR)2.3006152 × 108

Descriptive statistics

Standard deviation5.2056851 × 108
Coefficient of variation (CV)0.23025582
Kurtosis1.8528644
Mean2.2608268 × 109
Median Absolute Deviation (MAD)1.2990475 × 108
Skewness0.1017408
Sum2.2608268 × 1013
Variance2.7099158 × 1017
MonotonicityNot monotonic
2023-12-11T06:58:00.856585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2520041200 1
 
< 0.1%
2030011300 1
 
< 0.1%
2340046500 1
 
< 0.1%
2560034800 1
 
< 0.1%
2330116400 1
 
< 0.1%
1130014700 1
 
< 0.1%
2230012000 1
 
< 0.1%
2400021300 1
 
< 0.1%
1130012800 1
 
< 0.1%
2010004100 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1000000400 1
< 0.1%
1000001700 1
< 0.1%
1000001900 1
< 0.1%
1000002100 1
< 0.1%
1000002200 1
< 0.1%
1000002600 1
< 0.1%
1000002900 1
< 0.1%
1000003100 1
< 0.1%
1000003200 1
< 0.1%
1000003300 1
< 0.1%
ValueCountFrequency (%)
3980000500 1
< 0.1%
3980000300 1
< 0.1%
3980000100 1
< 0.1%
3970001200 1
< 0.1%
3970000900 1
< 0.1%
3970000800 1
< 0.1%
3970000400 1
< 0.1%
3960000500 1
< 0.1%
3960000400 1
< 0.1%
3950003900 1
< 0.1%

권역코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct155
Distinct (%)15.4%
Missing8994
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean248.03777
Minimum103
Maximum398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:58:00.999478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile161
Q1222
median234
Q3265
95-th percentile367
Maximum398
Range295
Interquartile range (IQR)43

Descriptive statistics

Standard deviation56.294221
Coefficient of variation (CV)0.22695826
Kurtosis1.0131466
Mean248.03777
Median Absolute Deviation (MAD)21
Skewness0.50589453
Sum249526
Variance3169.0394
MonotonicityNot monotonic
2023-12-11T06:58:01.120890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233 74
 
0.7%
229 40
 
0.4%
214 33
 
0.3%
234 31
 
0.3%
261 30
 
0.3%
256 27
 
0.3%
232 26
 
0.3%
224 25
 
0.2%
218 25
 
0.2%
265 24
 
0.2%
Other values (145) 671
 
6.7%
(Missing) 8994
89.9%
ValueCountFrequency (%)
103 3
< 0.1%
104 1
 
< 0.1%
105 2
 
< 0.1%
110 1
 
< 0.1%
111 3
< 0.1%
112 1
 
< 0.1%
115 6
0.1%
116 1
 
< 0.1%
118 1
 
< 0.1%
120 3
< 0.1%
ValueCountFrequency (%)
398 2
 
< 0.1%
396 2
 
< 0.1%
395 5
0.1%
394 1
 
< 0.1%
392 8
0.1%
391 1
 
< 0.1%
390 1
 
< 0.1%
388 1
 
< 0.1%
387 6
0.1%
385 2
 
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221225.88
Minimum132171.01
Maximum490679.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:58:01.464194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132171.01
5-th percentile172599.86
Q1187192.64
median205304.14
Q3228545.18
95-th percentile360550.22
Maximum490679.96
Range358508.95
Interquartile range (IQR)41352.536

Descriptive statistics

Standard deviation54665.748
Coefficient of variation (CV)0.24710377
Kurtosis3.6051676
Mean221225.88
Median Absolute Deviation (MAD)19587.572
Skewness1.9540616
Sum2.2122588 × 109
Variance2.988344 × 109
MonotonicityNot monotonic
2023-12-11T06:58:01.593594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220842.602334 2
 
< 0.1%
366744.388887 1
 
< 0.1%
245408.918084 1
 
< 0.1%
181854.364142 1
 
< 0.1%
230191.314393 1
 
< 0.1%
388745.220035 1
 
< 0.1%
206652.972486 1
 
< 0.1%
192963.984345 1
 
< 0.1%
206150.148244 1
 
< 0.1%
191136.931421 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
132171.006117 1
< 0.1%
137761.750604 1
< 0.1%
138835.104189 1
< 0.1%
145142.773793 1
< 0.1%
146401.609986 1
< 0.1%
146611.055413 1
< 0.1%
146636.370527 1
< 0.1%
146698.545394 1
< 0.1%
147568.111286 1
< 0.1%
147653.031048 1
< 0.1%
ValueCountFrequency (%)
490679.959216 1
< 0.1%
486866.499095 1
< 0.1%
486020.377542 1
< 0.1%
485526.331458 1
< 0.1%
484469.045187 1
< 0.1%
484241.674223 1
< 0.1%
483942.227407 1
< 0.1%
483859.96573 1
< 0.1%
482251.774393 1
< 0.1%
482137.731749 1
< 0.1%

X좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421957.56
Minimum91221.448
Maximum553475.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:58:01.729796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91221.448
5-th percentile273550.41
Q1409733.19
median433281.94
Q3456789.9
95-th percentile488661.34
Maximum553475.81
Range462254.36
Interquartile range (IQR)47056.711

Descriptive statistics

Standard deviation63440.647
Coefficient of variation (CV)0.15034841
Kurtosis6.2083538
Mean421957.56
Median Absolute Deviation (MAD)23530.728
Skewness-2.234145
Sum4.2195756 × 109
Variance4.0247156 × 109
MonotonicityNot monotonic
2023-12-11T06:58:01.852807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477714.753584 1
 
< 0.1%
417239.63113 1
 
< 0.1%
441391.578749 1
 
< 0.1%
439998.009163 1
 
< 0.1%
411640.547278 1
 
< 0.1%
451216.279861 1
 
< 0.1%
406419.994345 1
 
< 0.1%
442655.748579 1
 
< 0.1%
452223.438914 1
 
< 0.1%
418633.677786 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
91221.448383 1
< 0.1%
91289.138572 1
< 0.1%
99382.153422 1
< 0.1%
99464.097875 1
< 0.1%
99519.31608 1
< 0.1%
100198.643307 1
< 0.1%
109203.495306 1
< 0.1%
111078.298985 1
< 0.1%
118550.352411 1
< 0.1%
119259.057396 1
< 0.1%
ValueCountFrequency (%)
553475.811537 1
< 0.1%
550630.523586 1
< 0.1%
550445.35818 1
< 0.1%
549637.092039 1
< 0.1%
548344.88731 1
< 0.1%
548343.764694 1
< 0.1%
546004.638809 1
< 0.1%
544511.359266 1
< 0.1%
543789.44751 1
< 0.1%
543395.108671 1
< 0.1%

Interactions

2023-12-11T06:57:58.727099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:55.934235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.510881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.065581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.614443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.170198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.806138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.031784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.602284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.154451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.703094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.273682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.886099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.117999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.687934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.252895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.806923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.375326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.977031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.214798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.773427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.338495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.913770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.468161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:59.063306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.331936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.870010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.428283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.999111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.551193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:59.150548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.430409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:56.965134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:57.528735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.079812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:57:58.644167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:58:01.932001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도노드아이디권역코드Y좌표X좌표
위도1.0000.4900.8530.8470.5270.974
경도0.4901.0000.8310.6690.9540.536
노드아이디0.8530.8311.0000.9990.7990.863
권역코드0.8470.6690.9991.0000.6430.840
Y좌표0.5270.9540.7990.6431.0000.612
X좌표0.9740.5360.8630.8400.6121.000
2023-12-11T06:58:02.036316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도노드아이디권역코드Y좌표X좌표
위도1.000-0.036-0.201-0.380-0.0350.985
경도-0.0361.0000.4360.5321.000-0.047
노드아이디-0.2010.4361.0001.0000.435-0.209
권역코드-0.3800.5321.0001.0000.532-0.375
Y좌표-0.0351.0000.4350.5321.000-0.047
X좌표0.985-0.047-0.209-0.375-0.0471.000

Missing values

2023-12-11T06:57:59.249149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:57:59.367979image/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.
2023-12-11T06:57:59.464434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

위도경도교차로명노드아이디권역코드Y좌표X좌표
200243747.20612853.616<NA>2520041200<NA>366744.388887477714.753584
309903732.92512703.498치과의사회관앞1030000700<NA>205085.401856449613.05413
164163715.26812721.845오천터널(서)2300048700<NA>232233.405304417011.207826
34683806.04712704.877조흥아파트삼거리2380015700<NA>207072.539713510885.214081
219373808.47812815.344<NA>2650011200<NA>310035.88065516143.446267
272943456.8812729.418<NA>3260004200<NA>244713.553235161128.895458
278923738.1612637.365<NA>2320004000<NA>167300.998915457801.197655
126453603.68512654.843<NA>3080034100<NA>192184.818972284557.27967
196563730.81512655.567대방동우편취급소1180005900<NA>193379.334656445675.001708
21053712.20912708.32<NA>2330003800<NA>212252.803345411299.295381
위도경도교차로명노드아이디권역코드Y좌표X좌표
224883715.78512737.88<NA>2370052700<NA>255934.435907418091.405687
130113802.19412704.401<NA>2380028200238206377.330026503762.965895
37413722.27512708.368<NA>2060003800<NA>212287.721613429919.650783
115343712.20712646.204<NA>2330144300<NA>179520.876678411314.387613
207233729.03712646.387남부경찰서입구사거리2110010700<NA>179866.236627442445.13808
237843717.65612657.25<NA>2010034900<NA>195873.087714421371.276642
98503729.91712648.527<NA>2100031800<NA>183024.718064444065.639637
72193723.56612648.24<NA>2240028300<NA>182575.549676432315.786852
299253744.73212809.515<NA>2570014800<NA>302045.845765472082.432307
154093717.39612714.091<NA>2280087300<NA>220758.457523420911.569643