Overview

Dataset statistics

Number of variables5
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory46.3 B

Variable types

Categorical2
Numeric3

Dataset

Description샘플 데이터
Author코리아크레딧뷰로 / 장윤상
URLhttps://www.bigdata-transportation.kr/frn/prdt/detail?prdtId=PRDTNUM_000000020199

Alerts

BS_YR_MON has constant value ""Constant

Reproduction

Analysis started2023-12-11 22:43:22.503930
Analysis finished2023-12-11 22:43:23.505145
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BS_YR_MON
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
202112
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202112
2nd row202112
3rd row202112
4th row202112
5th row202112

Common Values

ValueCountFrequency (%)
202112 99
100.0%

Length

2023-12-12T07:43:23.555858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:43:23.635461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202112 99
100.0%

ADM_CD
Real number (ℝ)

Distinct96
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26133802
Minimum11110550
Maximum41800250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:23.725839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110550
5-th percentile11197691
Q111560630
median28177580
Q341271548
95-th percentile41590293
Maximum41800250
Range30689700
Interquartile range (IQR)29710918

Descriptive statistics

Standard deviation14227767
Coefficient of variation (CV)0.54442008
Kurtosis-1.926086
Mean26133802
Median Absolute Deviation (MAD)13452940
Skewness0.022288162
Sum2.5872464 × 109
Variance2.0242934 × 1014
MonotonicityNot monotonic
2023-12-12T07:43:23.887727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11740600 2
 
2.0%
41310580 2
 
2.0%
11350595 2
 
2.0%
11440655 1
 
1.0%
11710532 1
 
1.0%
11170640 1
 
1.0%
11620615 1
 
1.0%
41113650 1
 
1.0%
41390589 1
 
1.0%
11410620 1
 
1.0%
Other values (86) 86
86.9%
ValueCountFrequency (%)
11110550 1
1.0%
11140615 1
1.0%
11140625 1
1.0%
11170640 1
1.0%
11170700 1
1.0%
11200690 1
1.0%
11215830 1
1.0%
11230560 1
1.0%
11260620 1
1.0%
11290630 1
1.0%
ValueCountFrequency (%)
41800250 1
1.0%
41670510 1
1.0%
41650250 1
1.0%
41630520 1
1.0%
41590570 1
1.0%
41590262 1
1.0%
41590253 1
1.0%
41570256 1
1.0%
41550340 1
1.0%
41480560 1
1.0%

GENDER
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
1
50 
2
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.5%
2 49
49.5%

Length

2023-12-12T07:43:23.995043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:43:24.067832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.5%
2 49
49.5%

AGE_CD
Real number (ℝ)

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.959596
Minimum25
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:24.138088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q137.5
median50
Q360
95-th percentile70
Maximum71
Range46
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation12.916466
Coefficient of variation (CV)0.26381888
Kurtosis-1.1359904
Mean48.959596
Median Absolute Deviation (MAD)10
Skewness0.18505016
Sum4847
Variance166.83509
MonotonicityNot monotonic
2023-12-12T07:43:24.225099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
35 16
16.2%
50 15
15.2%
65 13
13.1%
45 13
13.1%
40 10
10.1%
70 8
8.1%
30 8
8.1%
55 8
8.1%
60 5
 
5.1%
71 2
 
2.0%
ValueCountFrequency (%)
25 1
 
1.0%
30 8
8.1%
35 16
16.2%
40 10
10.1%
45 13
13.1%
50 15
15.2%
55 8
8.1%
60 5
 
5.1%
65 13
13.1%
70 8
8.1%
ValueCountFrequency (%)
71 2
 
2.0%
70 8
8.1%
65 13
13.1%
60 5
 
5.1%
55 8
8.1%
50 15
15.2%
45 13
13.1%
40 10
10.1%
35 16
16.2%
30 8
8.1%

POP_CNT
Real number (ℝ)

Distinct32
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.464646
Minimum4
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:24.319151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median10
Q320
95-th percentile47.2
Maximum124
Range120
Interquartile range (IQR)14

Descriptive statistics

Standard deviation16.311579
Coefficient of variation (CV)1.0547657
Kurtosis19.677515
Mean15.464646
Median Absolute Deviation (MAD)5
Skewness3.6462699
Sum1531
Variance266.06761
MonotonicityNot monotonic
2023-12-12T07:43:24.443616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5 12
 
12.1%
6 11
 
11.1%
4 8
 
8.1%
8 8
 
8.1%
7 7
 
7.1%
11 6
 
6.1%
10 5
 
5.1%
16 4
 
4.0%
20 3
 
3.0%
19 3
 
3.0%
Other values (22) 32
32.3%
ValueCountFrequency (%)
4 8
8.1%
5 12
12.1%
6 11
11.1%
7 7
7.1%
8 8
8.1%
9 2
 
2.0%
10 5
5.1%
11 6
6.1%
12 3
 
3.0%
13 2
 
2.0%
ValueCountFrequency (%)
124 1
1.0%
55 1
1.0%
51 2
2.0%
49 1
1.0%
47 1
1.0%
44 1
1.0%
43 1
1.0%
31 1
1.0%
30 2
2.0%
29 1
1.0%

Interactions

2023-12-12T07:43:23.060257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.611838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.855336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:23.171839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.687960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.925590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:23.270272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.751997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:22.986937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:43:24.511036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDGENDERAGE_CDPOP_CNT
ADM_CD1.0000.0000.0000.306
GENDER0.0001.0000.0000.000
AGE_CD0.0000.0001.0000.278
POP_CNT0.3060.0000.2781.000
2023-12-12T07:43:24.577844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDAGE_CDPOP_CNTGENDER
ADM_CD1.000-0.0900.2010.000
AGE_CD-0.0901.000-0.2140.000
POP_CNT0.201-0.2141.0000.000
GENDER0.0000.0000.0001.000

Missing values

2023-12-12T07:43:23.399113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:43:23.474818image/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

BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
0202112115006052507
12021124148056026520
2202112282375701605
3202112112158301706
4202112111707001705
5202112115905502407
62021124167051025016
72021121174066014519
82021121165055015010
9202112415503401455
BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
89202112117405801358
902021121138055115011
91202112412875901257
922021124115057315031
93202112412715602558
94202112281775302358
952021124128152015522
962021121168056513551
97202112112606201309
982021121135059524516