Overview

Dataset statistics

Number of variables7
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory65.0 B

Variable types

Text2
Numeric5

Dataset

Description매년 공표되는 공인노무사 자격시험 현황- 회차, 접수, 응시(A), 1차 합격자, 2차 합격자, 3차 합격자(B), 합격률(B/A,단위:%)
Author고용노동부
URLhttps://www.data.go.kr/data/15068747/fileData.do

Alerts

접수 is highly overall correlated with 응시(A) and 3 other fieldsHigh correlation
응시(A) is highly overall correlated with 접수 and 3 other fieldsHigh correlation
1차 합격자 is highly overall correlated with 접수 and 3 other fieldsHigh correlation
2차 합격자 is highly overall correlated with 접수 and 3 other fieldsHigh correlation
3차 합격자(B) is highly overall correlated with 접수 and 3 other fieldsHigh correlation
회차 has unique valuesUnique
접수 has unique valuesUnique
응시(A) has unique valuesUnique
1차 합격자 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:17:56.138259
Analysis finished2024-03-14 18:18:03.632834
Duration7.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회차
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-03-15T03:18:04.297055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.71875
Min length7

Characters and Unicode

Total characters247
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row제1회(86)
2nd row제2회(89)
3rd row제3회(91)
4th row제4회(93)
5th row제5회(95)
ValueCountFrequency (%)
제1회(86 1
 
3.1%
제2회(89 1
 
3.1%
제31회(22 1
 
3.1%
제30회(21 1
 
3.1%
제29회(20 1
 
3.1%
제28회(19 1
 
3.1%
제27회(18 1
 
3.1%
제26회(17 1
 
3.1%
제25회(16 1
 
3.1%
제24회(15 1
 
3.1%
Other values (22) 22
68.8%
2024-03-15T03:18:05.543796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 32
13.0%
회 32
13.0%
( 32
13.0%
) 32
13.0%
1 28
11.3%
2 21
8.5%
0 16
6.5%
9 13
5.3%
3 10
 
4.0%
8 8
 
3.2%
Other values (4) 23
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119
48.2%
Other Letter 64
25.9%
Open Punctuation 32
 
13.0%
Close Punctuation 32
 
13.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28
23.5%
2 21
17.6%
0 16
13.4%
9 13
10.9%
3 10
 
8.4%
8 8
 
6.7%
6 6
 
5.0%
5 6
 
5.0%
7 6
 
5.0%
4 5
 
4.2%
Other Letter
ValueCountFrequency (%)
제 32
50.0%
회 32
50.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 183
74.1%
Hangul 64
 
25.9%

Most frequent character per script

Common
ValueCountFrequency (%)
( 32
17.5%
) 32
17.5%
1 28
15.3%
2 21
11.5%
0 16
8.7%
9 13
7.1%
3 10
 
5.5%
8 8
 
4.4%
6 6
 
3.3%
5 6
 
3.3%
Other values (2) 11
 
6.0%
Hangul
ValueCountFrequency (%)
제 32
50.0%
회 32
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183
74.1%
Hangul 64
 
25.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 32
50.0%
회 32
50.0%
ASCII
ValueCountFrequency (%)
( 32
17.5%
) 32
17.5%
1 28
15.3%
2 21
11.5%
0 16
8.7%
9 13
7.1%
3 10
 
5.5%
8 8
 
4.4%
6 6
 
3.3%
5 6
 
3.3%
Other values (2) 11
 
6.0%

접수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5954.625
Minimum622
Maximum71696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:18:05.930231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum622
5-th percentile817.5
Q11703
median3554.5
Q35499.25
95-th percentile9144.8
Maximum71696
Range71074
Interquartile range (IQR)3796.25

Descriptive statistics

Standard deviation12246.001
Coefficient of variation (CV)2.0565529
Kurtosis29.21778
Mean5954.625
Median Absolute Deviation (MAD)1964.5
Skewness5.3014587
Sum190548
Variance1.4996455 × 108
MonotonicityNot monotonic
2024-03-15T03:18:06.346941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
71696 1
 
3.1%
6346 1
 
3.1%
10225 1
 
3.1%
8261 1
 
3.1%
7654 1
 
3.1%
7549 1
 
3.1%
6211 1
 
3.1%
4744 1
 
3.1%
4728 1
 
3.1%
4760 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
622 1
3.1%
812 1
3.1%
822 1
3.1%
908 1
3.1%
1018 1
3.1%
1282 1
3.1%
1283 1
3.1%
1364 1
3.1%
1816 1
3.1%
2188 1
3.1%
ValueCountFrequency (%)
71696 1
3.1%
10225 1
3.1%
8261 1
3.1%
7654 1
3.1%
7549 1
3.1%
6573 1
3.1%
6346 1
3.1%
6211 1
3.1%
5262 1
3.1%
4760 1
3.1%

응시(A)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4370.3125
Minimum409
Maximum45785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:18:06.746439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum409
5-th percentile544.2
Q11312.5
median2861
Q34046.75
95-th percentile7726.05
Maximum45785
Range45376
Interquartile range (IQR)2734.25

Descriptive statistics

Standard deviation7837.1962
Coefficient of variation (CV)1.7932805
Kurtosis27.240109
Mean4370.3125
Median Absolute Deviation (MAD)1333.5
Skewness5.0502973
Sum139850
Variance61421645
MonotonicityNot monotonic
2024-03-15T03:18:07.144915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
45785 1
 
3.1%
4945 1
 
3.1%
8611 1
 
3.1%
7002 1
 
3.1%
6692 1
 
3.1%
6203 1
 
3.1%
5269 1
 
3.1%
4044 1
 
3.1%
4055 1
 
3.1%
4026 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
409 1
3.1%
531 1
3.1%
555 1
3.1%
570 1
3.1%
793 1
3.1%
962 1
3.1%
979 1
3.1%
1035 1
3.1%
1405 1
3.1%
1650 1
3.1%
ValueCountFrequency (%)
45785 1
3.1%
8611 1
3.1%
7002 1
3.1%
6692 1
3.1%
6203 1
3.1%
5269 1
3.1%
4945 1
3.1%
4055 1
3.1%
4044 1
3.1%
4026 1
3.1%

1차 합격자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1795.2812
Minimum124
Maximum15087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:18:07.521008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile142.35
Q1399.25
median1336
Q32228.75
95-th percentile3790.9
Maximum15087
Range14963
Interquartile range (IQR)1829.5

Descriptive statistics

Standard deviation2672.2629
Coefficient of variation (CV)1.4884926
Kurtosis20.742135
Mean1795.2812
Median Absolute Deviation (MAD)941.5
Skewness4.2022539
Sum57449
Variance7140988.8
MonotonicityNot monotonic
2024-03-15T03:18:07.883643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
15087 1
 
3.1%
1480 1
 
3.1%
3019 1
 
3.1%
4221 1
 
3.1%
3413 1
 
3.1%
3439 1
 
3.1%
2494 1
 
3.1%
2420 1
 
3.1%
2165 1
 
3.1%
2652 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
124 1
3.1%
133 1
3.1%
150 1
3.1%
165 1
3.1%
213 1
3.1%
293 1
3.1%
343 1
3.1%
385 1
3.1%
404 1
3.1%
412 1
3.1%
ValueCountFrequency (%)
15087 1
3.1%
4221 1
3.1%
3439 1
3.1%
3413 1
3.1%
3019 1
3.1%
2652 1
3.1%
2494 1
3.1%
2420 1
3.1%
2165 1
3.1%
1786 1
3.1%

2차 합격자
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.3125
Minimum18
Maximum549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:18:08.274153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27.3
Q195
median240
Q3254
95-th percentile366.4
Maximum549
Range531
Interquartile range (IQR)159

Descriptive statistics

Standard deviation124.76105
Coefficient of variation (CV)0.62911337
Kurtosis0.41409831
Mean198.3125
Median Absolute Deviation (MAD)89.5
Skewness0.45022898
Sum6346
Variance15565.319
MonotonicityNot monotonic
2024-03-15T03:18:08.666582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
250 6
 
18.8%
254 3
 
9.4%
118 1
 
3.1%
24 1
 
3.1%
395 1
 
3.1%
549 1
 
3.1%
322 1
 
3.1%
343 1
 
3.1%
303 1
 
3.1%
300 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
18 1
3.1%
24 1
3.1%
30 1
3.1%
37 1
3.1%
43 1
3.1%
44 1
3.1%
61 1
3.1%
71 1
3.1%
103 1
3.1%
118 1
3.1%
ValueCountFrequency (%)
549 1
 
3.1%
395 1
 
3.1%
343 1
 
3.1%
322 1
 
3.1%
303 1
 
3.1%
300 1
 
3.1%
286 1
 
3.1%
254 3
9.4%
250 6
18.8%
230 1
 
3.1%

3차 합격자(B)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.90625
Minimum18
Maximum551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-15T03:18:09.045895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile29.65
Q195
median236.5
Q3254.25
95-th percentile366.4
Maximum551
Range533
Interquartile range (IQR)159.25

Descriptive statistics

Standard deviation124.34768
Coefficient of variation (CV)0.62831609
Kurtosis0.51017261
Mean197.90625
Median Absolute Deviation (MAD)86.5
Skewness0.47704061
Sum6333
Variance15462.346
MonotonicityNot monotonic
2024-03-15T03:18:09.449838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
254 2
 
6.2%
247 2
 
6.2%
111 1
 
3.1%
208 1
 
3.1%
395 1
 
3.1%
551 1
 
3.1%
320 1
 
3.1%
343 1
 
3.1%
303 1
 
3.1%
300 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
18 1
3.1%
28 1
3.1%
31 1
3.1%
37 1
3.1%
42 1
3.1%
43 1
3.1%
61 1
3.1%
71 1
3.1%
103 1
3.1%
111 1
3.1%
ValueCountFrequency (%)
551 1
3.1%
395 1
3.1%
343 1
3.1%
320 1
3.1%
303 1
3.1%
300 1
3.1%
275 1
3.1%
255 1
3.1%
254 2
6.2%
253 1
3.1%

합격률
Text

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-03-15T03:18:10.299209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.15625
Min length5

Characters and Unicode

Total characters165
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)87.5%

Sample

1st row0.20%
2nd row1.40%
3rd row1.30%
4th row3.20%
5th row10.30%
ValueCountFrequency (%)
6.50 2
 
6.2%
8.60 2
 
6.2%
0.20 1
 
3.1%
5.00 1
 
3.1%
7.80 1
 
3.1%
4.80 1
 
3.1%
5.50 1
 
3.1%
5.80 1
 
3.1%
7.40 1
 
3.1%
6.30 1
 
3.1%
Other values (20) 20
62.5%
2024-03-15T03:18:11.509811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
23.6%
. 32
19.4%
% 32
19.4%
5 10
 
6.1%
6 9
 
5.5%
8 8
 
4.8%
4 8
 
4.8%
1 7
 
4.2%
2 6
 
3.6%
3 5
 
3.0%
Other values (2) 9
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101
61.2%
Other Punctuation 64
38.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
38.6%
5 10
 
9.9%
6 9
 
8.9%
8 8
 
7.9%
4 8
 
7.9%
1 7
 
6.9%
2 6
 
5.9%
3 5
 
5.0%
7 5
 
5.0%
9 4
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 32
50.0%
% 32
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39
23.6%
. 32
19.4%
% 32
19.4%
5 10
 
6.1%
6 9
 
5.5%
8 8
 
4.8%
4 8
 
4.8%
1 7
 
4.2%
2 6
 
3.6%
3 5
 
3.0%
Other values (2) 9
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
23.6%
. 32
19.4%
% 32
19.4%
5 10
 
6.1%
6 9
 
5.5%
8 8
 
4.8%
4 8
 
4.8%
1 7
 
4.2%
2 6
 
3.6%
3 5
 
3.0%
Other values (2) 9
 
5.5%

Interactions

2024-03-15T03:18:01.793004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:56.398152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:57.955304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:59.150980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:00.530508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:02.045906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:56.656700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:58.205022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:59.531147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:00.830544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:02.277860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:56.904185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:58.428983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:59.773600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:01.065246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:02.529546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:57.170113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:58.679556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:00.035883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:01.321014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:02.763634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:57.709524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:58.913198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:00.285071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:18:01.555513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:18:11.733970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회차접수응시(A)1차 합격자2차 합격자3차 합격자(B)합격률
회차1.0001.0001.0001.0001.0001.0001.000
접수1.0001.0000.9760.7030.9480.9481.000
응시(A)1.0000.9761.0000.8310.8820.9091.000
1차 합격자1.0000.7030.8311.0000.7200.7450.948
2차 합격자1.0000.9480.8820.7201.0001.0000.954
3차 합격자(B)1.0000.9480.9090.7451.0001.0000.954
합격률1.0001.0001.0000.9480.9540.9541.000
2024-03-15T03:18:12.054015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
접수응시(A)1차 합격자2차 합격자3차 합격자(B)
접수1.0000.9390.8060.5920.588
응시(A)0.9391.0000.9270.7400.737
1차 합격자0.8060.9271.0000.8110.798
2차 합격자0.5920.7400.8111.0000.991
3차 합격자(B)0.5880.7370.7980.9911.000

Missing values

2024-03-15T03:18:03.093004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:18:03.488828image/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

회차접수응시(A)1차 합격자2차 합격자3차 합격자(B)합격률
0제1회(86)7169645785150871181110.20%
1제2회(89)6573205916524281.40%
2제3회(91)3768242634330311.30%
3제4회(93)90855521318183.20%
4제5회(95)622409124444210.30%
5제6회(97)82253115043438.10%
6제7회(98)81257013337376.50%
7제8회(99)128296238510310310.70%
8제9회(00)101879329371719.00%
9제10회(01)128397940420520120.50%
회차접수응시(A)1차 합격자2차 합격자3차 합격자(B)합격률
22제23회(14)2890245214682502478.50%
23제24회(15)3956339416882542547.50%
24제25회(16)4760402626522502496.20%
25제26회(17)4728405521652542546.30%
26제27회(18)4744404424203003007.40%
27제28회(19)6211526924943033035.80%
28제29회(20)7549620334393433435.50%
29제30회(21)7654669234133223204.80%
30제31회(22)8261700242215495517.80%
31제32회(23)10225861130193953954.60%