Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory70.3 B

Variable types

Categorical3
Text1
Numeric4

Alerts

FILE_NAME has constant value ""Constant
base_ymd has constant value ""Constant
adstrd_cd is highly overall correlated with ctprvn_nmHigh correlation
silver_residnt_cnt is highly overall correlated with silver_rateHigh correlation
silver_rate is highly overall correlated with silver_residnt_cntHigh correlation
ctprvn_nm is highly overall correlated with adstrd_cdHigh correlation
adstrd_cd has unique valuesUnique
silver_residnt_cnt has unique valuesUnique
silver_rate has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:49:17.322917
Analysis finished2023-12-10 09:49:21.164021
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
24 
서울특별시
12 
부산광역시
9 
충청남도
7 
강원도
7 
Other values (10)
41 

Length

Max length7
Median length5
Mean length4.04
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row부산광역시
2nd row부산광역시
3rd row대구광역시
4th row경기도
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 24
24.0%
서울특별시 12
12.0%
부산광역시 9
 
9.0%
충청남도 7
 
7.0%
강원도 7
 
7.0%
경상남도 7
 
7.0%
인천광역시 6
 
6.0%
전라남도 6
 
6.0%
경상북도 6
 
6.0%
전라북도 5
 
5.0%
Other values (5) 11
11.0%

Length

2023-12-10T18:49:21.380000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 24
24.0%
서울특별시 12
12.0%
부산광역시 9
 
9.0%
충청남도 7
 
7.0%
강원도 7
 
7.0%
경상남도 7
 
7.0%
인천광역시 6
 
6.0%
전라남도 6
 
6.0%
경상북도 6
 
6.0%
전라북도 5
 
5.0%
Other values (5) 11
11.0%

signgu_nm
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:22.084393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.73
Min length2

Characters and Unicode

Total characters373
Distinct characters91
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)92.0%

Sample

1st row해운대구
2nd row기장군
3rd row동구
4th row안산시 단원구
5th row구로구
ValueCountFrequency (%)
동구 4
 
3.4%
창원시 4
 
3.4%
성남시 3
 
2.5%
서구 2
 
1.7%
안산시 2
 
1.7%
고성군 2
 
1.7%
고양시 2
 
1.7%
남구 2
 
1.7%
천안시 2
 
1.7%
고흥군 1
 
0.8%
Other values (95) 95
79.8%
2023-12-10T18:49:22.953878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 50
 
13.4%
시 47
 
12.6%
군 25
 
6.7%
19
 
5.1%
성 13
 
3.5%
천 11
 
2.9%
동 9
 
2.4%
안 9
 
2.4%
산 8
 
2.1%
양 8
 
2.1%
Other values (81) 174
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 354
94.9%
Space Separator 19
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 50
 
14.1%
시 47
 
13.3%
군 25
 
7.1%
성 13
 
3.7%
천 11
 
3.1%
동 9
 
2.5%
안 9
 
2.5%
산 8
 
2.3%
양 8
 
2.3%
원 8
 
2.3%
Other values (80) 166
46.9%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 354
94.9%
Common 19
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 50
 
14.1%
시 47
 
13.3%
군 25
 
7.1%
성 13
 
3.7%
천 11
 
3.1%
동 9
 
2.5%
안 9
 
2.5%
산 8
 
2.3%
양 8
 
2.3%
원 8
 
2.3%
Other values (80) 166
46.9%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 354
94.9%
ASCII 19
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 50
 
14.1%
시 47
 
13.3%
군 25
 
7.1%
성 13
 
3.7%
천 11
 
3.1%
동 9
 
2.5%
안 9
 
2.5%
산 8
 
2.3%
양 8
 
2.3%
원 8
 
2.3%
Other values (80) 166
46.9%
ASCII
ValueCountFrequency (%)
19
100.0%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36833.86
Minimum11110
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:23.226044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11408.5
Q128132.5
median41505.5
Q344897
95-th percentile48125.2
Maximum50110
Range39000
Interquartile range (IQR)16764.5

Descriptive statistics

Standard deviation11719.54
Coefficient of variation (CV)0.318173
Kurtosis0.047998169
Mean36833.86
Median Absolute Deviation (MAD)4954.5
Skewness-1.1320618
Sum3683386
Variance1.3734761 × 108
MonotonicityNot monotonic
2023-12-10T18:49:23.509606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26350 1
 
1.0%
41281 1
 
1.0%
48240 1
 
1.0%
46770 1
 
1.0%
41133 1
 
1.0%
11410 1
 
1.0%
45113 1
 
1.0%
29140 1
 
1.0%
41570 1
 
1.0%
48125 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
11110 1
1.0%
11215 1
1.0%
11290 1
1.0%
11305 1
1.0%
11380 1
1.0%
11410 1
1.0%
11530 1
1.0%
11545 1
1.0%
11560 1
1.0%
11620 1
1.0%
ValueCountFrequency (%)
50110 1
1.0%
48880 1
1.0%
48820 1
1.0%
48240 1
1.0%
48129 1
1.0%
48125 1
1.0%
48123 1
1.0%
48121 1
1.0%
47920 1
1.0%
47900 1
1.0%

silver_fclt_cnt
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.15
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:23.766620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6291226
Coefficient of variation (CV)0.75773144
Kurtosis15.418776
Mean2.15
Median Absolute Deviation (MAD)1
Skewness3.2716441
Sum215
Variance2.6540404
MonotonicityNot monotonic
2023-12-10T18:49:23.971901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 41
41.0%
2 30
30.0%
3 20
20.0%
4 3
 
3.0%
5 3
 
3.0%
12 1
 
1.0%
6 1
 
1.0%
9 1
 
1.0%
ValueCountFrequency (%)
1 41
41.0%
2 30
30.0%
3 20
20.0%
4 3
 
3.0%
5 3
 
3.0%
6 1
 
1.0%
9 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
12 1
 
1.0%
9 1
 
1.0%
6 1
 
1.0%
5 3
 
3.0%
4 3
 
3.0%
3 20
20.0%
2 30
30.0%
1 41
41.0%

silver_residnt_cnt
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27591.04
Minimum648
Maximum87667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:24.215044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum648
5-th percentile3224.35
Q18376.5
median23788
Q342294.75
95-th percentile59530.25
Maximum87667
Range87019
Interquartile range (IQR)33918.25

Descriptive statistics

Standard deviation20324.308
Coefficient of variation (CV)0.73662712
Kurtosis-0.46083384
Mean27591.04
Median Absolute Deviation (MAD)17008
Skewness0.565345
Sum2759104
Variance4.130775 × 108
MonotonicityNot monotonic
2023-12-10T18:49:24.553169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59179 1
 
1.0%
42160 1
 
1.0%
13125 1
 
1.0%
1047 1
 
1.0%
22248 1
 
1.0%
43965 1
 
1.0%
49642 1
 
1.0%
32430 1
 
1.0%
41483 1
 
1.0%
22553 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
648 1
1.0%
670 1
1.0%
802 1
1.0%
1047 1
1.0%
3041 1
1.0%
3234 1
1.0%
3539 1
1.0%
3634 1
1.0%
3657 1
1.0%
3698 1
1.0%
ValueCountFrequency (%)
87667 1
1.0%
74271 1
1.0%
71954 1
1.0%
64253 1
1.0%
61150 1
1.0%
59445 1
1.0%
59179 1
1.0%
57971 1
1.0%
57502 1
1.0%
55672 1
1.0%

silver_rate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15508.33
Minimum648
Maximum71954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:24.810773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum648
5-th percentile1612.15
Q16057.75
median11706
Q320046.5
95-th percentile42166.35
Maximum71954
Range71306
Interquartile range (IQR)13988.75

Descriptive statistics

Standard deviation13844.299
Coefficient of variation (CV)0.89270084
Kurtosis2.7258396
Mean15508.33
Median Absolute Deviation (MAD)6573
Skewness1.5763333
Sum1550833
Variance1.9166462 × 108
MonotonicityNot monotonic
2023-12-10T18:49:25.413160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29589 1
 
1.0%
42160 1
 
1.0%
13125 1
 
1.0%
1047 1
 
1.0%
11124 1
 
1.0%
8793 1
 
1.0%
8273 1
 
1.0%
16215 1
 
1.0%
20741 1
 
1.0%
11276 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
648 1
1.0%
670 1
1.0%
802 1
1.0%
1047 1
1.0%
1520 1
1.0%
1617 1
1.0%
1626 1
1.0%
1669 1
1.0%
1769 1
1.0%
1828 1
1.0%
ValueCountFrequency (%)
71954 1
1.0%
55344 1
1.0%
55111 1
1.0%
49359 1
1.0%
42287 1
1.0%
42160 1
1.0%
40188 1
1.0%
39402 1
1.0%
37809 1
1.0%
36433 1
1.0%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_598_SLV_CLT_STATN_BIZAEA_2019
100 

Length

Max length32
Median length32
Mean length32
Min length32

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_598_SLV_CLT_STATN_BIZAEA_2019
2nd rowKC_598_SLV_CLT_STATN_BIZAEA_2019
3rd rowKC_598_SLV_CLT_STATN_BIZAEA_2019
4th rowKC_598_SLV_CLT_STATN_BIZAEA_2019
5th rowKC_598_SLV_CLT_STATN_BIZAEA_2019

Common Values

ValueCountFrequency (%)
KC_598_SLV_CLT_STATN_BIZAEA_2019 100
100.0%

Length

2023-12-10T18:49:25.656814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:25.813824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_598_slv_clt_statn_bizaea_2019 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200220
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200220
2nd row20200220
3rd row20200220
4th row20200220
5th row20200220

Common Values

ValueCountFrequency (%)
20200220 100
100.0%

Length

2023-12-10T18:49:25.986845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:26.147527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200220 100
100.0%

Interactions

2023-12-10T18:49:19.954122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:17.741467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.493620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:19.242429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:20.142270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:17.909190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.705470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:19.429492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:20.303146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.088323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.848484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:19.601492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:20.499741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.242644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.994460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:19.768789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:49:26.243655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_nmsigngu_nmadstrd_cdsilver_fclt_cntsilver_residnt_cntsilver_rate
ctprvn_nm1.0000.0000.9980.6980.3390.000
signgu_nm0.0001.0000.0000.9900.9490.986
adstrd_cd0.9980.0001.0000.2870.4220.414
silver_fclt_cnt0.6980.9900.2871.0000.3800.000
silver_residnt_cnt0.3390.9490.4220.3801.0000.768
silver_rate0.0000.9860.4140.0000.7681.000
2023-12-10T18:49:26.413072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
adstrd_cdsilver_fclt_cntsilver_residnt_cntsilver_ratectprvn_nm
adstrd_cd1.000-0.269-0.488-0.3700.922
silver_fclt_cnt-0.2691.0000.392-0.1890.388
silver_residnt_cnt-0.4880.3921.0000.7750.137
silver_rate-0.370-0.1890.7751.0000.000
ctprvn_nm0.9220.3880.1370.0001.000

Missing values

2023-12-10T18:49:20.788279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:21.060035image/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

ctprvn_nmsigngu_nmadstrd_cdsilver_fclt_cntsilver_residnt_cntsilver_rateFILE_NAMEbase_ymd
0부산광역시해운대구2635025917929589KC_598_SLV_CLT_STATN_BIZAEA_201920200220
1부산광역시기장군267103207566918KC_598_SLV_CLT_STATN_BIZAEA_201920200220
2대구광역시동구2714034231814106KC_598_SLV_CLT_STATN_BIZAEA_201920200220
3경기도안산시 단원구4127314228742287KC_598_SLV_CLT_STATN_BIZAEA_201920200220
4서울특별시구로구1153026115030575KC_598_SLV_CLT_STATN_BIZAEA_201920200220
5부산광역시동구261703123244108KC_598_SLV_CLT_STATN_BIZAEA_201920200220
6부산광역시연제구2647022901014505KC_598_SLV_CLT_STATN_BIZAEA_201920200220
7인천광역시중구2811011312113121KC_598_SLV_CLT_STATN_BIZAEA_201920200220
8경기도광명시4121033234410781KC_598_SLV_CLT_STATN_BIZAEA_201920200220
9전라북도무주군45730232341617KC_598_SLV_CLT_STATN_BIZAEA_201920200220
ctprvn_nmsigngu_nmadstrd_cdsilver_fclt_cntsilver_residnt_cntsilver_rateFILE_NAMEbase_ymd
90경기도안양시 동안구4117313235332353KC_598_SLV_CLT_STATN_BIZAEA_201920200220
91서울특별시은평구113809575026389KC_598_SLV_CLT_STATN_BIZAEA_201920200220
92인천광역시연수구281854372409310KC_598_SLV_CLT_STATN_BIZAEA_201920200220
93경기도화성시4159057427114854KC_598_SLV_CLT_STATN_BIZAEA_201920200220
94부산광역시영도구262002162908145KC_598_SLV_CLT_STATN_BIZAEA_201920200220
95대구광역시북구2723015511155111KC_598_SLV_CLT_STATN_BIZAEA_201920200220
96강원도동해시421702130676533KC_598_SLV_CLT_STATN_BIZAEA_201920200220
97경상남도거창군48880170497049KC_598_SLV_CLT_STATN_BIZAEA_201920200220
98전라북도순창군45770236571828KC_598_SLV_CLT_STATN_BIZAEA_201920200220
99부산광역시수영구2650012591725917KC_598_SLV_CLT_STATN_BIZAEA_201920200220