Overview

Dataset statistics

Number of variables7
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory64.4 B

Variable types

Text1
Numeric6

Dataset

Description근로복지공단 지사별 약제비 지급현황입니다.(2012년 4분기)
Author근로복지공단
URLhttps://www.data.go.kr/data/15051519/fileData.do

Alerts

청구건수 is highly overall correlated with 청구액 and 3 other fieldsHigh correlation
청구액 is highly overall correlated with 청구건수 and 3 other fieldsHigh correlation
수급자 is highly overall correlated with 청구건수 and 3 other fieldsHigh correlation
지급건 is highly overall correlated with 청구건수 and 3 other fieldsHigh correlation
지급액 is highly overall correlated with 청구건수 and 3 other fieldsHigh correlation
지사명 has unique valuesUnique
청구건수 has unique valuesUnique
청구액 has unique valuesUnique
지급건 has unique valuesUnique
지급액 has unique valuesUnique
1인평균약제비 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:23:30.836595
Analysis finished2023-12-12 08:23:35.547140
Duration4.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T17:23:35.759419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.7454545
Min length4

Characters and Unicode

Total characters261
Distinct characters56
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

Unique55 ?
Unique (%)100.0%

Sample

1st row서울지역본부
2nd row서울강남지사
3rd row서울서초지사
4th row서울동부지사
5th row서울성동지사
ValueCountFrequency (%)
서울지역본부 1
 
1.8%
포항지사 1
 
1.8%
영주지사 1
 
1.8%
안동지사 1
 
1.8%
경인지역본부 1
 
1.8%
인천북부지사 1
 
1.8%
수원지사 1
 
1.8%
부천지사 1
 
1.8%
안양지사 1
 
1.8%
안산지사 1
 
1.8%
Other values (45) 45
81.8%
2023-12-12T17:23:36.208145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 261
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 261
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 55
21.1%
사 49
18.8%
부 22
 
8.4%
서 12
 
4.6%
산 11
 
4.2%
울 10
 
3.8%
주 8
 
3.1%
역 6
 
2.3%
본 6
 
2.3%
북 4
 
1.5%
Other values (46) 78
29.9%

청구건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5960.3273
Minimum423
Maximum17600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:36.372501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum423
5-th percentile1160.2
Q13000
median4757
Q38213
95-th percentile13827.6
Maximum17600
Range17177
Interquartile range (IQR)5213

Descriptive statistics

Standard deviation4137.6104
Coefficient of variation (CV)0.69419181
Kurtosis0.55692972
Mean5960.3273
Median Absolute Deviation (MAD)2544
Skewness1.0217358
Sum327818
Variance17119819
MonotonicityNot monotonic
2023-12-12T17:23:36.557057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3222 1
 
1.8%
1219 1
 
1.8%
1884 1
 
1.8%
1023 1
 
1.8%
8021 1
 
1.8%
12256 1
 
1.8%
7275 1
 
1.8%
7918 1
 
1.8%
5194 1
 
1.8%
10887 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
423 1
1.8%
808 1
1.8%
1023 1
1.8%
1219 1
1.8%
1465 1
1.8%
1599 1
1.8%
1884 1
1.8%
2015 1
1.8%
2055 1
1.8%
2118 1
1.8%
ValueCountFrequency (%)
17600 1
1.8%
16856 1
1.8%
15012 1
1.8%
13320 1
1.8%
12256 1
1.8%
11911 1
1.8%
11209 1
1.8%
10912 1
1.8%
10887 1
1.8%
10090 1
1.8%

청구액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0083336 × 108
Minimum19925090
Maximum9.7328779 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:37.044013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19925090
5-th percentile35054558
Q189286965
median1.4484155 × 108
Q32.7925767 × 108
95-th percentile4.3765 × 108
Maximum9.7328779 × 108
Range9.533627 × 108
Interquartile range (IQR)1.899707 × 108

Descriptive statistics

Standard deviation1.6825412 × 108
Coefficient of variation (CV)0.83777969
Kurtosis7.0766445
Mean2.0083336 × 108
Median Absolute Deviation (MAD)84972040
Skewness2.1231686
Sum1.1045835 × 1010
Variance2.8309447 × 1016
MonotonicityNot monotonic
2023-12-12T17:23:37.235809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436439670 1
 
1.8%
154232220 1
 
1.8%
73509840 1
 
1.8%
37225100 1
 
1.8%
250959500 1
 
1.8%
384612170 1
 
1.8%
171854070 1
 
1.8%
284938510 1
 
1.8%
121804900 1
 
1.8%
273576830 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
19925090 1
1.8%
27908150 1
1.8%
29989960 1
1.8%
37225100 1
1.8%
44906510 1
1.8%
46557050 1
1.8%
54856740 1
1.8%
56694740 1
1.8%
59869510 1
1.8%
63381440 1
1.8%
ValueCountFrequency (%)
973287790 1
1.8%
566572240 1
1.8%
440474100 1
1.8%
436439670 1
1.8%
425117510 1
1.8%
415864680 1
1.8%
384612170 1
1.8%
383827570 1
1.8%
348456210 1
1.8%
345256190 1
1.8%

수급자
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1104.9455
Minimum92
Maximum2613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:37.440561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile257.5
Q1521
median998
Q31684
95-th percentile2381
Maximum2613
Range2521
Interquartile range (IQR)1163

Descriptive statistics

Standard deviation683.03802
Coefficient of variation (CV)0.61816447
Kurtosis-0.76731222
Mean1104.9455
Median Absolute Deviation (MAD)544
Skewness0.51728504
Sum60772
Variance466540.94
MonotonicityNot monotonic
2023-12-12T17:23:37.629775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
287 2
 
3.6%
454 2
 
3.6%
649 1
 
1.8%
811 1
 
1.8%
340 1
 
1.8%
1662 1
 
1.8%
2613 1
 
1.8%
1575 1
 
1.8%
1776 1
 
1.8%
1132 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
92 1
1.8%
225 1
1.8%
247 1
1.8%
262 1
1.8%
287 2
3.6%
340 1
1.8%
379 1
1.8%
382 1
1.8%
445 1
1.8%
454 2
3.6%
ValueCountFrequency (%)
2613 1
1.8%
2554 1
1.8%
2493 1
1.8%
2333 1
1.8%
2056 1
1.8%
2043 1
1.8%
1947 1
1.8%
1874 1
1.8%
1866 1
1.8%
1798 1
1.8%

지급건
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5960.3273
Minimum423
Maximum17600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:37.794727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum423
5-th percentile1160.2
Q13000
median4757
Q38213
95-th percentile13827.6
Maximum17600
Range17177
Interquartile range (IQR)5213

Descriptive statistics

Standard deviation4137.6104
Coefficient of variation (CV)0.69419181
Kurtosis0.55692972
Mean5960.3273
Median Absolute Deviation (MAD)2544
Skewness1.0217358
Sum327818
Variance17119819
MonotonicityNot monotonic
2023-12-12T17:23:37.936106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3222 1
 
1.8%
1219 1
 
1.8%
1884 1
 
1.8%
1023 1
 
1.8%
8021 1
 
1.8%
12256 1
 
1.8%
7275 1
 
1.8%
7918 1
 
1.8%
5194 1
 
1.8%
10887 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
423 1
1.8%
808 1
1.8%
1023 1
1.8%
1219 1
1.8%
1465 1
1.8%
1599 1
1.8%
1884 1
1.8%
2015 1
1.8%
2055 1
1.8%
2118 1
1.8%
ValueCountFrequency (%)
17600 1
1.8%
16856 1
1.8%
15012 1
1.8%
13320 1
1.8%
12256 1
1.8%
11911 1
1.8%
11209 1
1.8%
10912 1
1.8%
10887 1
1.8%
10090 1
1.8%

지급액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0078131 × 108
Minimum19925090
Maximum9.7327647 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:38.127056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19925090
5-th percentile35054558
Q189263865
median1.4484106 × 108
Q32.7925097 × 108
95-th percentile4.3757156 × 108
Maximum9.7327647 × 108
Range9.5335138 × 108
Interquartile range (IQR)1.899871 × 108

Descriptive statistics

Standard deviation1.6822732 × 108
Coefficient of variation (CV)0.83786344
Kurtosis7.082923
Mean2.0078131 × 108
Median Absolute Deviation (MAD)84979990
Skewness2.1239451
Sum1.1042972 × 1010
Variance2.8300432 × 1016
MonotonicityNot monotonic
2023-12-12T17:23:38.283396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436334980 1
 
1.8%
154232220 1
 
1.8%
73509840 1
 
1.8%
37225100 1
 
1.8%
250914340 1
 
1.8%
384472700 1
 
1.8%
171799230 1
 
1.8%
284938160 1
 
1.8%
121804840 1
 
1.8%
273563780 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
19925090 1
1.8%
27908150 1
1.8%
29989960 1
1.8%
37225100 1
1.8%
44890280 1
1.8%
46485150 1
1.8%
54854270 1
1.8%
56694740 1
1.8%
59861070 1
1.8%
63381440 1
1.8%
ValueCountFrequency (%)
973276470 1
1.8%
566158860 1
1.8%
440456910 1
1.8%
436334980 1
1.8%
425044480 1
1.8%
415848230 1
1.8%
384472700 1
1.8%
383619980 1
1.8%
348455200 1
1.8%
345090520 1
1.8%

1인평균약제비
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191045.96
Minimum78730
Maximum809354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T17:23:38.442282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78730
5-th percentile97681.8
Q1123348
median153890
Q3202707.5
95-th percentile432589.8
Maximum809354
Range730624
Interquartile range (IQR)79359.5

Descriptive statistics

Standard deviation131977.23
Coefficient of variation (CV)0.69081404
Kurtosis11.324182
Mean191045.96
Median Absolute Deviation (MAD)38898
Skewness3.1791181
Sum10507528
Variance1.741799 × 1010
MonotonicityNot monotonic
2023-12-12T17:23:38.684852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
672319 1
 
1.8%
403749 1
 
1.8%
216205 1
 
1.8%
129704 1
 
1.8%
150971 1
 
1.8%
147138 1
 
1.8%
109079 1
 
1.8%
160438 1
 
1.8%
107601 1
 
1.8%
109733 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
78730 1
1.8%
95233 1
1.8%
96048 1
1.8%
98382 1
1.8%
104461 1
1.8%
107601 1
1.8%
109079 1
1.8%
109472 1
1.8%
109733 1
1.8%
112988 1
1.8%
ValueCountFrequency (%)
809354 1
1.8%
672319 1
1.8%
499885 1
1.8%
403749 1
1.8%
349154 1
1.8%
249425 1
1.8%
246877 1
1.8%
242674 1
1.8%
235177 1
1.8%
222855 1
1.8%

Interactions

2023-12-12T17:23:34.578016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.127436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.876807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.578299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.270960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.878644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.704229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.275089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.999965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.713264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.372159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.005011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.819584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.403107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.112921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.811600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.464985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.111425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.927594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.517822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.234649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.933731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.551615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.194405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:35.068244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.644038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.344193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.064013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.670852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.297392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:35.204848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:31.759123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:32.465628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.170356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:33.767304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:34.436142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:23:38.819600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사명청구건수청구액수급자지급건지급액1인평균약제비
지사명1.0001.0001.0001.0001.0001.0001.000
청구건수1.0001.0000.6840.8851.0000.6840.000
청구액1.0000.6841.0000.7800.6841.0000.719
수급자1.0000.8850.7801.0000.8850.7800.317
지급건1.0001.0000.6840.8851.0000.6840.000
지급액1.0000.6841.0000.7800.6841.0000.719
1인평균약제비1.0000.0000.7190.3170.0000.7191.000
2023-12-12T17:23:38.971028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
청구건수청구액수급자지급건지급액1인평균약제비
청구건수1.0000.8420.9721.0000.8420.085
청구액0.8421.0000.8340.8421.0000.482
수급자0.9720.8341.0000.9720.8340.017
지급건1.0000.8420.9721.0000.8420.085
지급액0.8421.0000.8340.8421.0000.482
1인평균약제비0.0850.4820.0170.0850.4821.000

Missing values

2023-12-12T17:23:35.347528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:23:35.491146image/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

지사명청구건수청구액수급자지급건지급액1인평균약제비
0서울지역본부32224364396706493222436334980672319
1서울강남지사12191542322203821219154232220403749
2서울서초지사20552323379702872055232284690809354
3서울동부지사461925756046010424619257245760246877
4서울성동지사8082998996022580829989960133289
5서울서부지사55563484562109985556348455200349154
6서울남부지사889041586468018668890415848230222855
7서울북부지사11209566572240233311209566158860242674
8서울관악지사659815579241013616598155786930114465
9의정부지사16856973287790194716856973276470499885
지사명청구건수청구액수급자지급건지급액1인평균약제비
45군산지사338363381440497338363381440127528
46목포지사26515485674057626515485427095233
47여수지사32191050826406563219105082640160187
48제주지사146544906510379146544890280118444
49대전지역본부10912341423200187410912341419890182188
50유성지사32091132389004543209113238900249425
51청주지사730119254418013627301192544180141369
52충주지사48251229504708004825122950470153688
53천안지사475713693091010184757136899610134479
54보령지사30941088038405453094108280920198681