Overview

Dataset statistics

Number of variables3
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory912.0 B
Average record size in memory30.4 B

Variable types

Categorical1
Numeric2

Dataset

Description대구광역시 시험 회차별 공인중개사 자격현황
Author국토교통부
URLhttps://www.data.go.kr/data/15063459/fileData.do

Alerts

시도명 has constant value ""Constant
시험회차 has unique valuesUnique
관리대상자수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:50:18.534265
Analysis finished2023-12-12 15:50:19.187715
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
대구광역시
30 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 30
100.0%

Length

2023-12-13T00:50:19.261842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:50:19.394578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 30
100.0%

시험회차
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:50:19.607354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-13T00:50:19.934192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

관리대상자수
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.4
Minimum79
Maximum3490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:50:20.204796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile100.95
Q1354.25
median623.5
Q3951.75
95-th percentile1478.9
Maximum3490
Range3411
Interquartile range (IQR)597.5

Descriptive statistics

Standard deviation667.52118
Coefficient of variation (CV)0.8931244
Kurtosis9.1689404
Mean747.4
Median Absolute Deviation (MAD)314
Skewness2.4953931
Sum22422
Variance445584.52
MonotonicityNot monotonic
2023-12-13T00:50:20.364710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3490 1
 
3.3%
659 1
 
3.3%
1425 1
 
3.3%
894 1
 
3.3%
1400 1
 
3.3%
1523 1
 
3.3%
1031 1
 
3.3%
568 1
 
3.3%
494 1
 
3.3%
434 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
79 1
3.3%
87 1
3.3%
118 1
3.3%
122 1
3.3%
163 1
3.3%
190 1
3.3%
191 1
3.3%
343 1
3.3%
388 1
3.3%
434 1
3.3%
ValueCountFrequency (%)
3490 1
3.3%
1523 1
3.3%
1425 1
3.3%
1400 1
3.3%
1312 1
3.3%
1064 1
3.3%
1031 1
3.3%
971 1
3.3%
894 1
3.3%
832 1
3.3%

Interactions

2023-12-13T00:50:18.862172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:18.613902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:18.955365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:50:18.735733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:50:20.458626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.713
관리대상자수0.7131.000
2023-12-13T00:50:20.561249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.496
관리대상자수0.4961.000

Missing values

2023-12-13T00:50:19.074058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:50:19.154083image/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

시도명시험회차관리대상자수
0대구광역시13490
1대구광역시2191
2대구광역시387
3대구광역시4343
4대구광역시5163
5대구광역시679
6대구광역시7122
7대구광역시8118
8대구광역시9190
9대구광역시10745
시도명시험회차관리대상자수
20대구광역시21388
21대구광역시22435
22대구광역시23434
23대구광역시24494
24대구광역시25568
25대구광역시261031
26대구광역시271523
27대구광역시281400
28대구광역시29894
29대구광역시301425