Overview

Dataset statistics

Number of variables8
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows18
Duplicate rows (%)20.0%
Total size in memory6.4 KiB
Average record size in memory72.5 B

Variable types

Numeric5
Categorical3

Alerts

기준년도 has constant value ""Constant
Dataset has 18 (20.0%) duplicate rowsDuplicates
호송경비 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
기관 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
시설경비 is highly overall correlated with 신변보호 and 5 other fieldsHigh correlation
신변보호 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
기계경비 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
법인수 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
특수경비 is highly overall correlated with 시설경비 and 5 other fieldsHigh correlation
기계경비 has 10 (11.1%) zerosZeros
특수경비 has 15 (16.7%) zerosZeros

Reproduction

Analysis started2024-01-09 21:51:47.109644
Analysis finished2024-01-09 21:51:49.133812
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설경비
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.05556
Minimum16
Maximum1464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T06:51:49.173396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17.8
Q199
median143.5
Q3208
95-th percentile1110.3
Maximum1464
Range1448
Interquartile range (IQR)109

Descriptive statistics

Standard deviation330.38817
Coefficient of variation (CV)1.3762988
Kurtosis8.43834
Mean240.05556
Median Absolute Deviation (MAD)52.5
Skewness2.9903069
Sum21605
Variance109156.35
MonotonicityNot monotonic
2024-01-10T06:51:49.263301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
135 10
 
11.1%
1464 5
 
5.6%
310 5
 
5.6%
20 5
 
5.6%
160 5
 
5.6%
119 5
 
5.6%
115 5
 
5.6%
78 5
 
5.6%
99 5
 
5.6%
179 5
 
5.6%
Other values (7) 35
38.9%
ValueCountFrequency (%)
16 5
5.6%
20 5
5.6%
78 5
5.6%
83 5
5.6%
99 5
5.6%
115 5
5.6%
119 5
5.6%
135 10
11.1%
152 5
5.6%
158 5
5.6%
ValueCountFrequency (%)
1464 5
5.6%
678 5
5.6%
310 5
5.6%
212 5
5.6%
208 5
5.6%
179 5
5.6%
160 5
5.6%
158 5
5.6%
152 5
5.6%
135 10
11.1%

신변보호
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.722222
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T06:51:49.353907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q18
median13.5
Q330
95-th percentile185.35
Maximum265
Range264
Interquartile range (IQR)22

Descriptive statistics

Standard deviation59.685322
Coefficient of variation (CV)1.7699107
Kurtosis10.509154
Mean33.722222
Median Absolute Deviation (MAD)8
Skewness3.338401
Sum3035
Variance3562.3377
MonotonicityNot monotonic
2024-01-10T06:51:49.438910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9 15
16.7%
7 10
 
11.1%
265 5
 
5.6%
40 5
 
5.6%
23 5
 
5.6%
31 5
 
5.6%
25 5
 
5.6%
1 5
 
5.6%
88 5
 
5.6%
30 5
 
5.6%
Other values (5) 25
27.8%
ValueCountFrequency (%)
1 5
 
5.6%
6 5
 
5.6%
7 10
11.1%
8 5
 
5.6%
9 15
16.7%
12 5
 
5.6%
15 5
 
5.6%
22 5
 
5.6%
23 5
 
5.6%
25 5
 
5.6%
ValueCountFrequency (%)
265 5
5.6%
88 5
5.6%
40 5
5.6%
31 5
5.6%
30 5
5.6%
25 5
5.6%
23 5
5.6%
22 5
5.6%
15 5
5.6%
12 5
5.6%

기계경비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9444444
Minimum0
Maximum46
Zeros10
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T06:51:49.516978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile38.8
Maximum46
Range46
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.379423
Coefficient of variation (CV)1.4323749
Kurtosis5.3222853
Mean7.9444444
Median Absolute Deviation (MAD)3
Skewness2.4730945
Sum715
Variance129.49126
MonotonicityNot monotonic
2024-01-10T06:51:49.591487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 15
16.7%
1 15
16.7%
6 15
16.7%
7 10
11.1%
0 10
11.1%
46 5
 
5.6%
10 5
 
5.6%
8 5
 
5.6%
2 5
 
5.6%
30 5
 
5.6%
ValueCountFrequency (%)
0 10
11.1%
1 15
16.7%
2 5
 
5.6%
4 15
16.7%
6 15
16.7%
7 10
11.1%
8 5
 
5.6%
10 5
 
5.6%
30 5
 
5.6%
46 5
 
5.6%
ValueCountFrequency (%)
46 5
 
5.6%
30 5
 
5.6%
10 5
 
5.6%
8 5
 
5.6%
7 10
11.1%
6 15
16.7%
4 15
16.7%
2 5
 
5.6%
1 15
16.7%
0 10
11.1%

법인수
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.83333
Minimum16
Maximum1501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T06:51:49.665429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17.8
Q1100
median145.5
Q3214
95-th percentile1137.4
Maximum1501
Range1485
Interquartile range (IQR)114

Descriptive statistics

Standard deviation338.91205
Coefficient of variation (CV)1.3842562
Kurtosis8.4595665
Mean244.83333
Median Absolute Deviation (MAD)54
Skewness2.9939328
Sum22035
Variance114861.38
MonotonicityNot monotonic
2024-01-10T06:51:49.747850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1501 5
 
5.6%
100 5
 
5.6%
20 5
 
5.6%
163 5
 
5.6%
136 5
 
5.6%
120 5
 
5.6%
137 5
 
5.6%
115 5
 
5.6%
79 5
 
5.6%
184 5
 
5.6%
Other values (8) 40
44.4%
ValueCountFrequency (%)
16 5
5.6%
20 5
5.6%
79 5
5.6%
83 5
5.6%
100 5
5.6%
115 5
5.6%
120 5
5.6%
136 5
5.6%
137 5
5.6%
154 5
5.6%
ValueCountFrequency (%)
1501 5
5.6%
693 5
5.6%
313 5
5.6%
220 5
5.6%
214 5
5.6%
184 5
5.6%
163 5
5.6%
159 5
5.6%
154 5
5.6%
137 5
5.6%

호송경비
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
0
55 
1
25 
22
 
5
2
 
5

Length

Max length2
Median length1
Mean length1.0555556
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 55
61.1%
1 25
27.8%
22 5
 
5.6%
2 5
 
5.6%

Length

2024-01-10T06:51:49.843501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:49.924940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55
61.1%
1 25
27.8%
22 5
 
5.6%
2 5
 
5.6%

특수경비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1111111
Minimum0
Maximum47
Zeros15
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T06:51:50.003339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile32.15
Maximum47
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.519585
Coefficient of variation (CV)1.4793167
Kurtosis9.3919632
Mean7.1111111
Median Absolute Deviation (MAD)3.5
Skewness3.0605635
Sum640
Variance110.66167
MonotonicityNot monotonic
2024-01-10T06:51:50.078408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 20
22.2%
0 15
16.7%
8 10
11.1%
2 10
11.1%
1 10
11.1%
47 5
 
5.6%
11 5
 
5.6%
14 5
 
5.6%
4 5
 
5.6%
10 5
 
5.6%
ValueCountFrequency (%)
0 15
16.7%
1 10
11.1%
2 10
11.1%
4 5
 
5.6%
5 20
22.2%
8 10
11.1%
10 5
 
5.6%
11 5
 
5.6%
14 5
 
5.6%
47 5
 
5.6%
ValueCountFrequency (%)
47 5
 
5.6%
14 5
 
5.6%
11 5
 
5.6%
10 5
 
5.6%
8 10
11.1%
5 20
22.2%
4 5
 
5.6%
2 10
11.1%
1 10
11.1%
0 15
16.7%

기관
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
서울청
 
5
부산청
 
5
대구청
 
5
인천청
 
5
광주청
 
5
Other values (13)
65 

Length

Max length5
Median length3
Mean length3.2222222
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울청
2nd row부산청
3rd row대구청
4th row인천청
5th row광주청

Common Values

ValueCountFrequency (%)
서울청 5
 
5.6%
부산청 5
 
5.6%
대구청 5
 
5.6%
인천청 5
 
5.6%
광주청 5
 
5.6%
대전청 5
 
5.6%
울산청 5
 
5.6%
세종청 5
 
5.6%
경기남부청 5
 
5.6%
경기북부청 5
 
5.6%
Other values (8) 40
44.4%

Length

2024-01-10T06:51:50.176679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울청 5
 
5.6%
부산청 5
 
5.6%
경남청 5
 
5.6%
경북청 5
 
5.6%
전남청 5
 
5.6%
전북청 5
 
5.6%
충남청 5
 
5.6%
충북청 5
 
5.6%
강원청 5
 
5.6%
경기북부청 5
 
5.6%
Other values (8) 40
44.4%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
2022
90 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 90
100.0%

Length

2024-01-10T06:51:50.267929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:51:50.339850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 90
100.0%

Interactions

2024-01-10T06:51:48.676565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.334746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.656026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.973522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.297076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.750422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.403134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.720535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.046015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.365712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.810909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.464474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.784199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.121927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.433177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.865318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.529680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.846003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.176882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.502061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.928459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.595327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:47.914128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.241608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:51:48.589066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:51:50.393689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설경비신변보호기계경비법인수호송경비특수경비기관
시설경비1.0000.9330.9701.0000.7760.7831.000
신변보호0.9331.0000.8380.9980.9450.9341.000
기계경비0.9700.8381.0000.8650.7250.7471.000
법인수1.0000.9980.8651.0000.9480.9361.000
호송경비0.7760.9450.7250.9481.0000.9171.000
특수경비0.7830.9340.7470.9360.9171.0001.000
기관1.0001.0001.0001.0001.0001.0001.000
2024-01-10T06:51:50.475239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호송경비기관
호송경비1.0000.915
기관0.9151.000
2024-01-10T06:51:50.540351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설경비신변보호기계경비법인수특수경비호송경비기관
시설경비1.0000.8650.7800.9990.7260.7240.920
신변보호0.8651.0000.7260.8650.7110.6870.915
기계경비0.7800.7261.0000.7870.5640.6620.920
법인수0.9990.8650.7871.0000.7270.6940.915
특수경비0.7260.7110.5640.7271.0000.6210.915
호송경비0.7240.6870.6620.6940.6211.0000.915
기관0.9200.9150.9200.9150.9150.9151.000

Missing values

2024-01-10T06:51:49.006781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:51:49.097828image/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

시설경비신변보호기계경비법인수호송경비특수경비기관기준년도
014642654615012247서울청2022
1310404313111부산청2022
2212231022015대구청2022
320831821408인천청2022
415225715410광주청2022
51589715905대전청2022
683928305울산청2022
716101600세종청2022
86788830693114경기남부청2022
917930418422경기북부청2022
시설경비신변보호기계경비법인수호송경비특수경비기관기준년도
806788830693114경기남부청2022
8117930418422경기북부청2022
82998110001강원청2022
8378967901충북청2022
8411512111505충남청2022
851357613714전북청2022
86119154120010전남청2022
871357113602경북청2022
8816022616308경남청2022
8920602000제주청2022

Duplicate rows

Most frequently occurring

시설경비신변보호기계경비법인수호송경비특수경비기관기준년도# duplicates
016101600세종청20225
120602000제주청20225
278967901충북청20225
383928305울산청20225
4998110001강원청20225
511512111505충남청20225
6119154120010전남청20225
71357113602경북청20225
81357613714전북청20225
915225715410광주청20225