Overview

Dataset statistics

Number of variables12
Number of observations149
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory105.9 B

Variable types

Text2
Numeric7
Categorical3

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=130

Alerts

signgu_nm has constant value ""Constant
signgu_oid has constant value ""Constant
nt_pnt_no has constant value ""Constant
phrm_area is highly overall correlated with phrm_ar_5 and 2 other fieldsHigh correlation
phrm_cn_5 is highly overall correlated with phrm_cn_10 and 1 other fieldsHigh correlation
phrm_cn_10 is highly overall correlated with phrm_cn_5 and 1 other fieldsHigh correlation
phrm_cn_15 is highly overall correlated with phrm_cn_5 and 1 other fieldsHigh correlation
phrm_ar_5 is highly overall correlated with phrm_area and 2 other fieldsHigh correlation
phrm_ar_10 is highly overall correlated with phrm_area and 2 other fieldsHigh correlation
phrm_ar_15 is highly overall correlated with phrm_area and 2 other fieldsHigh correlation
mnnmb has unique valuesUnique
bplc_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:26:24.829227
Analysis finished2023-12-10 13:26:37.965556
Duration13.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

mnnmb
Text

UNIQUE 

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T22:26:38.213414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters3725
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)100.0%

Sample

1st rowPHMD120093230034084000008
2nd rowPHMD120093230034084000009
3rd rowPHMD120093230034084000014
4th rowPHMD120003230034084000012
5th rowPHMD120093230034084000023
ValueCountFrequency (%)
phmd120093230034084000008 1
 
0.7%
phmd120193230034084000034 1
 
0.7%
phmd120203230034084000006 1
 
0.7%
phmd120203230034084000014 1
 
0.7%
phmd120203230034084000007 1
 
0.7%
phmd120203230034084000004 1
 
0.7%
phmd120203230034084000009 1
 
0.7%
phmd120203230034084000010 1
 
0.7%
phmd120203230034084000011 1
 
0.7%
phmd120203230034084000012 1
 
0.7%
Other values (139) 139
93.3%
2023-12-10T22:26:38.789006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1279
34.3%
3 482
 
12.9%
2 374
 
10.0%
4 342
 
9.2%
1 320
 
8.6%
8 202
 
5.4%
P 149
 
4.0%
H 149
 
4.0%
M 149
 
4.0%
D 149
 
4.0%
Other values (4) 130
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3129
84.0%
Uppercase Letter 596
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1279
40.9%
3 482
 
15.4%
2 374
 
12.0%
4 342
 
10.9%
1 320
 
10.2%
8 202
 
6.5%
9 71
 
2.3%
5 25
 
0.8%
6 20
 
0.6%
7 14
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
P 149
25.0%
H 149
25.0%
M 149
25.0%
D 149
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3129
84.0%
Latin 596
 
16.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1279
40.9%
3 482
 
15.4%
2 374
 
12.0%
4 342
 
10.9%
1 320
 
10.2%
8 202
 
6.5%
9 71
 
2.3%
5 25
 
0.8%
6 20
 
0.6%
7 14
 
0.4%
Latin
ValueCountFrequency (%)
P 149
25.0%
H 149
25.0%
M 149
25.0%
D 149
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1279
34.3%
3 482
 
12.9%
2 374
 
10.0%
4 342
 
9.2%
1 320
 
8.6%
8 202
 
5.4%
P 149
 
4.0%
H 149
 
4.0%
M 149
 
4.0%
D 149
 
4.0%
Other values (4) 130
 
3.5%

bplc_nm
Text

UNIQUE 

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T22:26:39.196519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.3691275
Min length3

Characters and Unicode

Total characters800
Distinct characters189
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)100.0%

Sample

1st row스마일약국
2nd row천명약국
3rd row정다운약국
4th row봄약국
5th row하늘약국
ValueCountFrequency (%)
스마일약국 1
 
0.7%
나무약국 1
 
0.7%
가장빠른천사약국 1
 
0.7%
굿데이약국 1
 
0.7%
오약국 1
 
0.7%
송파샘약국 1
 
0.7%
석촌사랑약국 1
 
0.7%
민약국 1
 
0.7%
참소망약국 1
 
0.7%
건강약국 1
 
0.7%
Other values (140) 140
93.3%
2023-12-10T22:26:39.925885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
약 149
 
18.6%
국 149
 
18.6%
리 23
 
2.9%
한 12
 
1.5%
오 10
 
1.2%
사 10
 
1.2%
파 9
 
1.1%
하 8
 
1.0%
라 8
 
1.0%
누 8
 
1.0%
Other values (179) 414
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 789
98.6%
Decimal Number 9
 
1.1%
Uppercase Letter 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
약 149
 
18.9%
국 149
 
18.9%
리 23
 
2.9%
한 12
 
1.5%
오 10
 
1.3%
사 10
 
1.3%
파 9
 
1.1%
하 8
 
1.0%
라 8
 
1.0%
누 8
 
1.0%
Other values (172) 403
51.1%
Decimal Number
ValueCountFrequency (%)
3 3
33.3%
6 2
22.2%
5 2
22.2%
2 1
 
11.1%
4 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 789
98.6%
Common 10
 
1.2%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
약 149
 
18.9%
국 149
 
18.9%
리 23
 
2.9%
한 12
 
1.5%
오 10
 
1.3%
사 10
 
1.3%
파 9
 
1.1%
하 8
 
1.0%
라 8
 
1.0%
누 8
 
1.0%
Other values (172) 403
51.1%
Common
ValueCountFrequency (%)
3 3
30.0%
6 2
20.0%
5 2
20.0%
1
 
10.0%
2 1
 
10.0%
4 1
 
10.0%
Latin
ValueCountFrequency (%)
S 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 789
98.6%
ASCII 11
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
약 149
 
18.9%
국 149
 
18.9%
리 23
 
2.9%
한 12
 
1.5%
오 10
 
1.3%
사 10
 
1.3%
파 9
 
1.1%
하 8
 
1.0%
라 8
 
1.0%
누 8
 
1.0%
Other values (172) 403
51.1%
ASCII
ValueCountFrequency (%)
3 3
27.3%
6 2
18.2%
5 2
18.2%
S 1
 
9.1%
1
 
9.1%
2 1
 
9.1%
4 1
 
9.1%

phrm_area
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.850403
Minimum11.52
Maximum269.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:40.168979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.52
5-th percentile17.066
Q127.2
median35.65
Q351.6
95-th percentile103.306
Maximum269.6
Range258.08
Interquartile range (IQR)24.4

Descriptive statistics

Standard deviation32.271067
Coefficient of variation (CV)0.70383389
Kurtosis15.628298
Mean45.850403
Median Absolute Deviation (MAD)10.45
Skewness3.0997729
Sum6831.71
Variance1041.4218
MonotonicityNot monotonic
2023-12-10T22:26:40.469404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.2 3
 
2.0%
25.35 2
 
1.3%
41.58 2
 
1.3%
33.0 2
 
1.3%
35.0 2
 
1.3%
30.24 2
 
1.3%
19.44 2
 
1.3%
60.0 2
 
1.3%
117.28 1
 
0.7%
55.96 1
 
0.7%
Other values (130) 130
87.2%
ValueCountFrequency (%)
11.52 1
0.7%
12.32 1
0.7%
13.95 1
0.7%
14.44 1
0.7%
15.66 1
0.7%
16.29 1
0.7%
16.32 1
0.7%
16.91 1
0.7%
17.3 1
0.7%
17.39 1
0.7%
ValueCountFrequency (%)
269.6 1
0.7%
141.0 1
0.7%
135.82 1
0.7%
134.26 1
0.7%
121.68 1
0.7%
117.28 1
0.7%
115.6 1
0.7%
105.21 1
0.7%
100.45 1
0.7%
99.63 1
0.7%

phrm_cn_5
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7986577
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:40.681160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.2170501
Coefficient of variation (CV)1.1101422
Kurtosis2.3400765
Mean3.7986577
Median Absolute Deviation (MAD)1
Skewness1.8493844
Sum566
Variance17.783512
MonotonicityNot monotonic
2023-12-10T22:26:40.897030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 59
39.6%
2 30
20.1%
6 17
 
11.4%
3 15
 
10.1%
15 15
 
10.1%
5 5
 
3.4%
4 4
 
2.7%
9 3
 
2.0%
7 1
 
0.7%
ValueCountFrequency (%)
1 59
39.6%
2 30
20.1%
3 15
 
10.1%
4 4
 
2.7%
5 5
 
3.4%
6 17
 
11.4%
7 1
 
0.7%
9 3
 
2.0%
15 15
 
10.1%
ValueCountFrequency (%)
15 15
 
10.1%
9 3
 
2.0%
7 1
 
0.7%
6 17
 
11.4%
5 5
 
3.4%
4 4
 
2.7%
3 15
 
10.1%
2 30
20.1%
1 59
39.6%

phrm_cn_10
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8389262
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:41.102823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.229988
Coefficient of variation (CV)0.87415842
Kurtosis0.8358872
Mean4.8389262
Median Absolute Deviation (MAD)2
Skewness1.3482909
Sum721
Variance17.892799
MonotonicityNot monotonic
2023-12-10T22:26:41.334034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 30
20.1%
2 27
18.1%
3 25
16.8%
6 16
10.7%
15 15
10.1%
4 10
 
6.7%
9 8
 
5.4%
5 7
 
4.7%
8 4
 
2.7%
7 4
 
2.7%
Other values (2) 3
 
2.0%
ValueCountFrequency (%)
1 30
20.1%
2 27
18.1%
3 25
16.8%
4 10
 
6.7%
5 7
 
4.7%
6 16
10.7%
7 4
 
2.7%
8 4
 
2.7%
9 8
 
5.4%
10 1
 
0.7%
ValueCountFrequency (%)
15 15
10.1%
12 2
 
1.3%
10 1
 
0.7%
9 8
 
5.4%
8 4
 
2.7%
7 4
 
2.7%
6 16
10.7%
5 7
 
4.7%
4 10
 
6.7%
3 25
16.8%

phrm_cn_15
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7718121
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:41.493591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile15
Maximum18
Range17
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5248873
Coefficient of variation (CV)0.78396304
Kurtosis-0.2731033
Mean5.7718121
Median Absolute Deviation (MAD)2
Skewness0.92627653
Sum860
Variance20.474605
MonotonicityNot monotonic
2023-12-10T22:26:41.666120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 25
16.8%
1 23
15.4%
2 20
13.4%
6 17
11.4%
15 15
10.1%
12 10
 
6.7%
7 8
 
5.4%
4 8
 
5.4%
5 8
 
5.4%
9 7
 
4.7%
Other values (3) 8
 
5.4%
ValueCountFrequency (%)
1 23
15.4%
2 20
13.4%
3 25
16.8%
4 8
 
5.4%
5 8
 
5.4%
6 17
11.4%
7 8
 
5.4%
8 2
 
1.3%
9 7
 
4.7%
10 5
 
3.4%
ValueCountFrequency (%)
18 1
 
0.7%
15 15
10.1%
12 10
6.7%
10 5
 
3.4%
9 7
4.7%
8 2
 
1.3%
7 8
5.4%
6 17
11.4%
5 8
5.4%
4 8
5.4%

phrm_ar_5
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.975588
Minimum17.1
Maximum224.01667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:41.919546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile20.803167
Q129.44
median35.872
Q350.62
95-th percentile120.14583
Maximum224.01667
Range206.91667
Interquartile range (IQR)21.18

Descriptive statistics

Standard deviation35.64366
Coefficient of variation (CV)0.74295411
Kurtosis9.4457096
Mean47.975588
Median Absolute Deviation (MAD)8.628
Skewness2.8561577
Sum7148.3626
Variance1270.4705
MonotonicityNot monotonic
2023-12-10T22:26:42.197129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.872 15
 
10.1%
53.825 4
 
2.7%
25.09833333 4
 
2.7%
36.58 3
 
2.0%
22.28666667 3
 
2.0%
29.93 3
 
2.0%
19.44 2
 
1.3%
36.16 2
 
1.3%
39.38 2
 
1.3%
30.24 2
 
1.3%
Other values (99) 109
73.2%
ValueCountFrequency (%)
17.1 2
1.3%
17.39 1
 
0.7%
19.11 1
 
0.7%
19.44 2
1.3%
20.28 1
 
0.7%
20.6875 1
 
0.7%
20.97666667 2
1.3%
22.0 1
 
0.7%
22.01 1
 
0.7%
22.28666667 3
2.0%
ValueCountFrequency (%)
224.0166667 2
1.3%
182.93 2
1.3%
134.26 1
0.7%
131.9583333 1
0.7%
127.4571429 1
0.7%
120.5575 1
0.7%
119.5283333 1
0.7%
116.025 1
0.7%
115.6 1
0.7%
98.19333333 1
0.7%

phrm_ar_10
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.676244
Minimum17.39
Maximum213.45333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:42.436573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.39
5-th percentile21.476
Q131.67
median36.58
Q351.46
95-th percentile115.07583
Maximum213.45333
Range196.06333
Interquartile range (IQR)19.79

Descriptive statistics

Standard deviation32.631475
Coefficient of variation (CV)0.68443888
Kurtosis10.615773
Mean47.676244
Median Absolute Deviation (MAD)8.2133333
Skewness3.008594
Sum7103.7603
Variance1064.8131
MonotonicityNot monotonic
2023-12-10T22:26:42.741358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.872 15
 
10.1%
53.825 4
 
2.7%
25.09833333 4
 
2.7%
36.58 3
 
2.0%
22.28666667 3
 
2.0%
46.48714286 3
 
2.0%
38.65 2
 
1.3%
213.4533333 2
 
1.3%
44.67 2
 
1.3%
115.0758333 2
 
1.3%
Other values (96) 109
73.2%
ValueCountFrequency (%)
17.39 1
 
0.7%
19.44 1
 
0.7%
19.455 1
 
0.7%
19.755 1
 
0.7%
20.28 1
 
0.7%
20.97666667 2
1.3%
21.12 1
 
0.7%
22.01 1
 
0.7%
22.28666667 3
2.0%
22.56 1
 
0.7%
ValueCountFrequency (%)
213.4533333 2
1.3%
172.358 2
1.3%
131.28125 1
0.7%
125.3055556 2
1.3%
115.0758333 2
1.3%
112.335 1
0.7%
102.681 1
0.7%
89.32 1
0.7%
80.9 1
0.7%
80.25 1
0.7%

phrm_ar_15
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.350474
Minimum17.39
Maximum213.45333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:26:42.989426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.39
5-th percentile22.286667
Q131.67
median38.498
Q353.825
95-th percentile115.07583
Maximum213.45333
Range196.06333
Interquartile range (IQR)22.155

Descriptive statistics

Standard deviation32.128346
Coefficient of variation (CV)0.66448874
Kurtosis10.932447
Mean48.350474
Median Absolute Deviation (MAD)10.118
Skewness3.016274
Sum7204.2206
Variance1032.2306
MonotonicityNot monotonic
2023-12-10T22:26:43.211412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.872 15
 
10.1%
115.0758333 7
 
4.7%
53.825 4
 
2.7%
48.74555556 4
 
2.7%
38.498 4
 
2.7%
25.09833333 4
 
2.7%
22.28666667 3
 
2.0%
35.59166667 3
 
2.0%
50.36 2
 
1.3%
31.67 2
 
1.3%
Other values (89) 101
67.8%
ValueCountFrequency (%)
17.39 1
 
0.7%
19.455 1
 
0.7%
19.755 1
 
0.7%
20.28 1
 
0.7%
20.97666667 2
1.3%
22.01 1
 
0.7%
22.28666667 3
2.0%
22.56 1
 
0.7%
24.275 1
 
0.7%
24.98 1
 
0.7%
ValueCountFrequency (%)
213.4533333 2
 
1.3%
172.358 2
 
1.3%
115.0758333 7
4.7%
89.32 1
 
0.7%
84.505 1
 
0.7%
80.9 1
 
0.7%
80.25 2
 
1.3%
74.98 1
 
0.7%
67.875 1
 
0.7%
65.55833333 1
 
0.7%

signgu_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
송파구
149 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 149
100.0%

Length

2023-12-10T22:26:43.465788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:26:43.667928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
송파구 149
100.0%

signgu_oid
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1969
149 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1969
2nd row1969
3rd row1969
4th row1969
5th row1969

Common Values

ValueCountFrequency (%)
1969 149
100.0%

Length

2023-12-10T22:26:43.942684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:26:44.127989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1969 149
100.0%

nt_pnt_no
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
128
149 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row128
2nd row128
3rd row128
4th row128
5th row128

Common Values

ValueCountFrequency (%)
128 149
100.0%

Length

2023-12-10T22:26:44.282800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:26:44.463371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128 149
100.0%

Interactions

2023-12-10T22:26:35.667346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:28.219971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.373903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.451571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:31.796051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.034048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:34.119488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:35.846407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:28.477075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.517529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.702609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.038011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.229784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:34.321731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:36.027354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:28.661559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.715270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.894609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.231036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.364303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:34.545431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:36.615482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:28.811668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.877033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:31.063811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.422617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.523104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:34.804130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:36.792523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:28.959345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.008906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:31.239701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.551246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.719284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:35.113896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:36.998285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.072357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.130140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:31.374388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.669510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.834787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:35.261803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:37.181239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:29.225055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:30.281397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:31.579014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:32.814653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:33.968314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:26:35.478144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:26:44.582868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
phrm_areaphrm_cn_5phrm_cn_10phrm_cn_15phrm_ar_5phrm_ar_10phrm_ar_15
phrm_area1.0000.0000.3310.0000.8220.7460.889
phrm_cn_50.0001.0000.8270.7880.4430.5180.630
phrm_cn_100.3310.8271.0000.9530.5830.6600.574
phrm_cn_150.0000.7880.9531.0000.4420.5660.602
phrm_ar_50.8220.4430.5830.4421.0000.9700.892
phrm_ar_100.7460.5180.6600.5660.9701.0000.948
phrm_ar_150.8890.6300.5740.6020.8920.9481.000
2023-12-10T22:26:44.762393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
phrm_areaphrm_cn_5phrm_cn_10phrm_cn_15phrm_ar_5phrm_ar_10phrm_ar_15
phrm_area1.000-0.0010.0290.0060.8120.7220.686
phrm_cn_5-0.0011.0000.8570.7910.0650.0570.038
phrm_cn_100.0290.8571.0000.9210.0940.1390.097
phrm_cn_150.0060.7910.9211.0000.0590.0940.084
phrm_ar_50.8120.0650.0940.0591.0000.8920.837
phrm_ar_100.7220.0570.1390.0940.8921.0000.947
phrm_ar_150.6860.0380.0970.0840.8370.9471.000

Missing values

2023-12-10T22:26:37.507033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:26:37.851832image/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

mnnmbbplc_nmphrm_areaphrm_cn_5phrm_cn_10phrm_cn_15phrm_ar_5phrm_ar_10phrm_ar_15signgu_nmsigngu_oidnt_pnt_no
0PHMD120093230034084000008스마일약국60.8991236.5836.5835.591667송파구1969128
1PHMD120093230034084000009천명약국60.022236.1636.1636.16송파구1969128
2PHMD120093230034084000014정다운약국49.9612249.9636.036.0송파구1969128
3PHMD120003230034084000012봄약국38.9522829.00529.00540.025송파구1969128
4PHMD120093230034084000023하늘약국27.013327.028.3828.38송파구1969128
5PHMD120143230034084000005하나메디칼약국49.1759934.46638.6538.65송파구1969128
6PHMD120143230034084000007녹십자약국32.3221836.936.940.900556송파구1969128
7PHMD120143230034084000008키즈플러스약국58.7811158.7858.7858.78송파구1969128
8PHMD120143230034084000011크게열린약국105.214812120.5575131.28125115.075833송파구1969128
9PHMD120003230034084000018미래약국37.1313737.1342.42666740.215714송파구1969128
mnnmbbplc_nmphrm_areaphrm_cn_5phrm_cn_10phrm_cn_15phrm_ar_5phrm_ar_10phrm_ar_15signgu_nmsigngu_oidnt_pnt_no
139PHMD120163230034084000029기쁨약국62.2723351.23541.46333341.463333송파구1969128
140PHMD120103230034084000018그랜드약국39.914439.943.9943.99송파구1969128
141PHMD120163230034084000018남일온누리약국89.3211189.3289.3289.32송파구1969128
142PHMD120013230034084000042나루역사거리현대약국35.566653.82553.82553.825송파구1969128
143PHMD120023230034084000019우리약국90.013590.051.4659.036송파구1969128
144PHMD120023230034084000020옵티마하나약국19.4412219.4419.45519.455송파구1969128
145PHMD120023230034084000029온누리옥산약국32.1314432.1362.152562.1525송파구1969128
146PHMD120113230034084000019유한약국18.5426717.125.70333329.071429송파구1969128
147PHMD120113230034084000022아산메디칼약국56.486912131.958333125.305556115.075833송파구1969128
148PHMD120163230034084000051문정이화약국49.3622253.6653.6653.66송파구1969128