Overview

Dataset statistics

Number of variables15
Number of observations6204
Missing cells125
Missing cells (%)0.1%
Duplicate rows293
Duplicate rows (%)4.7%
Total size in memory775.6 KiB
Average record size in memory128.0 B

Variable types

Numeric6
Categorical7
Text1
DateTime1

Dataset

Description설계 시 필요한 자재품목 현황(자재약호 식별번호 ERP 식별번호 통제번호 자재명 단위 분야 내외자 조달구분 적상하구분 여입구분 자재단가구분 자재성격 단가 등)
URLhttps://www.data.go.kr/data/3068722/fileData.do

Alerts

Dataset has 293 (4.7%) duplicate rowsDuplicates
수리단가(원) is highly overall correlated with 내외자구분High correlation
분야구분 is highly overall correlated with 자재단가(원) and 2 other fieldsHigh correlation
조달구분 is highly overall correlated with 통제번호 and 3 other fieldsHigh correlation
여입구분 is highly overall correlated with 자재단가(원) and 2 other fieldsHigh correlation
자재단가구분 is highly overall correlated with 내외자구분High correlation
적상하구분 is highly overall correlated with 자재단가(원) and 2 other fieldsHigh correlation
내외자구분 is highly overall correlated with 통제번호 and 11 other fieldsHigh correlation
통제번호 is highly overall correlated with 내외자구분 and 1 other fieldsHigh correlation
자재단가(원) is highly overall correlated with 분야구분 and 4 other fieldsHigh correlation
중량(kg) is highly overall correlated with 환입중량(kg) and 1 other fieldsHigh correlation
환입중량(kg) is highly overall correlated with 중량(kg) and 1 other fieldsHigh correlation
환입금액(원) is highly overall correlated with 내외자구분High correlation
구제금액(원) is highly overall correlated with 분야구분 and 4 other fieldsHigh correlation
자재단가구분 is highly imbalanced (54.3%)Imbalance
수리단가(원) is highly imbalanced (99.7%)Imbalance
자재단가(원) is highly skewed (γ1 = 20.29469357)Skewed
환입중량(kg) is highly skewed (γ1 = 54.32647584)Skewed
환입금액(원) is highly skewed (γ1 = 22.77891622)Skewed
구제금액(원) is highly skewed (γ1 = 32.44089973)Skewed
자재단가(원) has 1448 (23.3%) zerosZeros
중량(kg) has 3021 (48.7%) zerosZeros
환입중량(kg) has 3128 (50.4%) zerosZeros
환입금액(원) has 5493 (88.5%) zerosZeros
구제금액(원) has 4630 (74.6%) zerosZeros

Reproduction

Analysis started2023-12-12 04:08:28.162269
Analysis finished2023-12-12 04:08:35.547826
Duration7.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통제번호
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing39
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5.4035685
Minimum0
Maximum9
Zeros42
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:35.601305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4430528
Coefficient of variation (CV)0.26705551
Kurtosis2.8660656
Mean5.4035685
Median Absolute Deviation (MAD)0
Skewness0.13707819
Sum33313
Variance2.0824013
MonotonicityNot monotonic
2023-12-12T13:08:35.715613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 4900
79.0%
8 904
 
14.6%
1 150
 
2.4%
9 139
 
2.2%
0 42
 
0.7%
6 30
 
0.5%
(Missing) 39
 
0.6%
ValueCountFrequency (%)
0 42
 
0.7%
1 150
 
2.4%
5 4900
79.0%
6 30
 
0.5%
8 904
 
14.6%
9 139
 
2.2%
ValueCountFrequency (%)
9 139
 
2.2%
8 904
 
14.6%
6 30
 
0.5%
5 4900
79.0%
1 150
 
2.4%
0 42
 
0.7%

분야구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
배전
2097 
<NA>
1817 
지중
1165 
송전
641 
내선자재
400 

Length

Max length4
Median length2
Mean length2.7147002
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row기타
3rd row기타
4th row기타
5th row배전

Common Values

ValueCountFrequency (%)
배전 2097
33.8%
<NA> 1817
29.3%
지중 1165
18.8%
송전 641
 
10.3%
내선자재 400
 
6.4%
기타 84
 
1.4%

Length

2023-12-12T13:08:35.862554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:35.993959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
배전 2097
33.8%
na 1817
29.3%
지중 1165
18.8%
송전 641
 
10.3%
내선자재 400
 
6.4%
기타 84
 
1.4%

내외자구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
1
4029 
<NA>
2175 

Length

Max length4
Median length1
Mean length2.0517408
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
1 4029
64.9%
<NA> 2175
35.1%

Length

2023-12-12T13:08:36.142106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:36.264994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4029
64.9%
na 2175
35.1%

조달구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
본사분
2350 
<NA>
2260 
지입자재
924 
사업소분
535 
작업시 부설물
 
135

Length

Max length7
Median length4
Mean length3.6864926
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지입자재
2nd row지입자재
3rd row지입자재
4th row지입자재
5th row<NA>

Common Values

ValueCountFrequency (%)
본사분 2350
37.9%
<NA> 2260
36.4%
지입자재 924
 
14.9%
사업소분 535
 
8.6%
작업시 부설물 135
 
2.2%

Length

2023-12-12T13:08:36.399735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:36.544322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본사분 2350
37.1%
na 2260
35.7%
지입자재 924
 
14.6%
사업소분 535
 
8.4%
작업시 135
 
2.1%
부설물 135
 
2.1%

적상하구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
<NA>
2273 
철재류
1751 
애자류
1352 
전선류
470 
시멘트 및 근가류
253 
Other values (5)
 
105

Length

Max length9
Median length3
Mean length3.6663443
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시멘트 및 근가류
2nd row시멘트 및 근가류
3rd row시멘트 및 근가류
4th row시멘트 및 근가류
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 2273
36.6%
철재류 1751
28.2%
애자류 1352
21.8%
전선류 470
 
7.6%
시멘트 및 근가류 253
 
4.1%
전주11M이상 58
 
0.9%
전주10M이하 23
 
0.4%
폐전선류 12
 
0.2%
비계목류 7
 
0.1%
근가류 5
 
0.1%

Length

2023-12-12T13:08:36.697072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:36.847482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2273
33.9%
철재류 1751
26.1%
애자류 1352
20.1%
전선류 470
 
7.0%
근가류 258
 
3.8%
시멘트 253
 
3.8%
및 253
 
3.8%
전주11m이상 58
 
0.9%
전주10m이하 23
 
0.3%
폐전선류 12
 
0.2%

여입구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
창고여입
2501 
<NA>
2278 
현장폐기
1082 
위탁처리
281 
상태판정후 현장폐기
 
62

Length

Max length10
Median length4
Mean length4.0599613
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row현장폐기
2nd row현장폐기
3rd row현장폐기
4th row현장폐기
5th row<NA>

Common Values

ValueCountFrequency (%)
창고여입 2501
40.3%
<NA> 2278
36.7%
현장폐기 1082
17.4%
위탁처리 281
 
4.5%
상태판정후 현장폐기 62
 
1.0%

Length

2023-12-12T13:08:37.024822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:37.153805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창고여입 2501
39.9%
na 2278
36.4%
현장폐기 1144
18.3%
위탁처리 281
 
4.5%
상태판정후 62
 
1.0%

자재단가구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
한전가격정보
3580 
<NA>
1266 
단가직접입력(주관부서및사업소)
1261 
유사물품준용단가
 
54
월간물자자료(4종)
 
20
Other values (5)
 
23

Length

Max length16
Median length6
Mean length7.6587685
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단가직접입력(주관부서및사업소)
2nd row단가직접입력(주관부서및사업소)
3rd row단가직접입력(주관부서및사업소)
4th row단가직접입력(주관부서및사업소)
5th row<NA>

Common Values

ValueCountFrequency (%)
한전가격정보 3580
57.7%
<NA> 1266
 
20.4%
단가직접입력(주관부서및사업소) 1261
 
20.3%
유사물품준용단가 54
 
0.9%
월간물자자료(4종) 20
 
0.3%
조사반자체조사 8
 
0.1%
중량비례계산단가 6
 
0.1%
중량만조사 4
 
0.1%
조달청가격정보 3
 
< 0.1%
규격별비례계산단가 2
 
< 0.1%

Length

2023-12-12T13:08:37.283616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:37.415697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한전가격정보 3580
57.7%
na 1266
 
20.4%
단가직접입력(주관부서및사업소 1261
 
20.3%
유사물품준용단가 54
 
0.9%
월간물자자료(4종 20
 
0.3%
조사반자체조사 8
 
0.1%
중량비례계산단가 6
 
0.1%
중량만조사 4
 
0.1%
조달청가격정보 3
 
< 0.1%
규격별비례계산단가 2
 
< 0.1%

자재단가(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3659
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099011
Minimum0
Maximum5.4300442 × 109
Zeros1448
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:37.591382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1226.75
median20802.5
Q3793538
95-th percentile21347600
Maximum5.4300442 × 109
Range5.4300442 × 109
Interquartile range (IQR)793311.25

Descriptive statistics

Standard deviation1.6095917 × 108
Coefficient of variation (CV)10.660246
Kurtosis521.98473
Mean15099011
Median Absolute Deviation (MAD)20802.5
Skewness20.294694
Sum9.3674266 × 1010
Variance2.5907855 × 1016
MonotonicityNot monotonic
2023-12-12T13:08:37.745045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1448
 
23.3%
1 21
 
0.3%
4400 18
 
0.3%
5000 14
 
0.2%
12000 12
 
0.2%
30000 12
 
0.2%
150000 11
 
0.2%
4100 11
 
0.2%
3560 11
 
0.2%
3000 11
 
0.2%
Other values (3649) 4635
74.7%
ValueCountFrequency (%)
0 1448
23.3%
1 21
 
0.3%
7 1
 
< 0.1%
10 4
 
0.1%
16 1
 
< 0.1%
19 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 2
 
< 0.1%
30 2
 
< 0.1%
ValueCountFrequency (%)
5430044204 1
< 0.1%
5046692497 1
< 0.1%
4360000000 1
< 0.1%
2793250000 1
< 0.1%
2658632500 1
< 0.1%
2525000000 1
< 0.1%
2370000000 1
< 0.1%
2250000000 1
< 0.1%
2220000000 1
< 0.1%
2080000000 1
< 0.1%

중량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct469
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.05835
Minimum0
Maximum33600
Zeros3021
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:37.882988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile910
Maximum33600
Range33600
Interquartile range (IQR)8

Descriptive statistics

Standard deviation984.96699
Coefficient of variation (CV)5.4702656
Kurtosis527.80949
Mean180.05835
Median Absolute Deviation (MAD)1
Skewness19.738125
Sum1117082
Variance970159.98
MonotonicityNot monotonic
2023-12-12T13:08:38.013614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3021
48.7%
1 785
 
12.7%
2 326
 
5.3%
3 209
 
3.4%
4 131
 
2.1%
5 94
 
1.5%
6 61
 
1.0%
10 36
 
0.6%
350 27
 
0.4%
9 26
 
0.4%
Other values (459) 1488
24.0%
ValueCountFrequency (%)
0 3021
48.7%
1 785
 
12.7%
2 326
 
5.3%
3 209
 
3.4%
4 131
 
2.1%
5 94
 
1.5%
6 61
 
1.0%
7 25
 
0.4%
8 26
 
0.4%
9 26
 
0.4%
ValueCountFrequency (%)
33600 1
< 0.1%
30000 1
< 0.1%
26000 1
< 0.1%
25000 1
< 0.1%
16100 1
< 0.1%
14300 2
< 0.1%
12690 1
< 0.1%
10170 1
< 0.1%
10000 2
< 0.1%
9830 1
< 0.1%

환입중량(kg)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct397
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.44794
Minimum0
Maximum98300
Zeros3128
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:38.190662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile800
Maximum98300
Range98300
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1428.2138
Coefficient of variation (CV)8.5805439
Kurtosis3620.4975
Mean166.44794
Median Absolute Deviation (MAD)0
Skewness54.326476
Sum1032643
Variance2039794.7
MonotonicityNot monotonic
2023-12-12T13:08:38.376941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3128
50.4%
1 731
 
11.8%
2 330
 
5.3%
3 211
 
3.4%
4 139
 
2.2%
5 100
 
1.6%
6 58
 
0.9%
10 30
 
0.5%
8 28
 
0.5%
9 28
 
0.5%
Other values (387) 1421
22.9%
ValueCountFrequency (%)
0 3128
50.4%
1 731
 
11.8%
2 330
 
5.3%
3 211
 
3.4%
4 139
 
2.2%
5 100
 
1.6%
6 58
 
0.9%
7 20
 
0.3%
8 28
 
0.5%
9 28
 
0.5%
ValueCountFrequency (%)
98300 1
< 0.1%
25000 1
< 0.1%
21130 1
< 0.1%
14300 1
< 0.1%
12690 1
< 0.1%
10170 1
< 0.1%
10000 2
< 0.1%
8800 1
< 0.1%
8130 1
< 0.1%
7000 1
< 0.1%

환입금액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct615
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67113.359
Minimum0
Maximum23603900
Zeros5493
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:38.528945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile126181.65
Maximum23603900
Range23603900
Interquartile range (IQR)0

Descriptive statistics

Standard deviation589609.72
Coefficient of variation (CV)8.7852811
Kurtosis694.70717
Mean67113.359
Median Absolute Deviation (MAD)0
Skewness22.778916
Sum4.1637128 × 108
Variance3.4763963 × 1011
MonotonicityNot monotonic
2023-12-12T13:08:38.686295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5493
88.5%
1176000 9
 
0.1%
3556000 9
 
0.1%
923850 4
 
0.1%
1908000 4
 
0.1%
1228950 4
 
0.1%
1153350 4
 
0.1%
4100 4
 
0.1%
938700 4
 
0.1%
781200 4
 
0.1%
Other values (605) 665
 
10.7%
ValueCountFrequency (%)
0 5493
88.5%
60 1
 
< 0.1%
87 3
 
< 0.1%
125 1
 
< 0.1%
128 1
 
< 0.1%
131 2
 
< 0.1%
145 1
 
< 0.1%
159 1
 
< 0.1%
176 1
 
< 0.1%
180 1
 
< 0.1%
ValueCountFrequency (%)
23603900 1
< 0.1%
16931620 1
< 0.1%
14760900 1
< 0.1%
14710000 1
< 0.1%
12590179 1
< 0.1%
10419458 1
< 0.1%
5635337 1
< 0.1%
5224800 1
< 0.1%
4587459 1
< 0.1%
4566164 1
< 0.1%

수리단가(원)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
0
6202 
100
 
1
1152000
 
1

Length

Max length7
Median length1
Mean length1.0012895
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 6202
> 99.9%
100 1
 
< 0.1%
1152000 1
 
< 0.1%

Length

2023-12-12T13:08:38.838577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:08:38.949813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6202
> 99.9%
100 1
 
< 0.1%
1152000 1
 
< 0.1%

구제금액(원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1195
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224348.14
Minimum0
Maximum1.9219091 × 108
Zeros4630
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size54.7 KiB
2023-12-12T13:08:39.407957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile247828.4
Maximum1.9219091 × 108
Range1.9219091 × 108
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3761403
Coefficient of variation (CV)16.76592
Kurtosis1337.7114
Mean224348.14
Median Absolute Deviation (MAD)0
Skewness32.4409
Sum1.3918559 × 109
Variance1.4148153 × 1013
MonotonicityNot monotonic
2023-12-12T13:08:39.553927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4630
74.6%
1 94
 
1.5%
10 22
 
0.4%
121 14
 
0.2%
152 9
 
0.1%
87 9
 
0.1%
25200 9
 
0.1%
11 8
 
0.1%
2 7
 
0.1%
76200 7
 
0.1%
Other values (1185) 1395
 
22.5%
ValueCountFrequency (%)
0 4630
74.6%
1 94
 
1.5%
2 7
 
0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
10 22
 
0.4%
11 8
 
0.1%
12 2
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
192190909 1
< 0.1%
112996106 1
< 0.1%
84153400 1
< 0.1%
76295481 1
< 0.1%
64249500 1
< 0.1%
63545921 1
< 0.1%
53197242 1
< 0.1%
46475700 1
< 0.1%
43794832 1
< 0.1%
43255000 1
< 0.1%

적용일자
Text

Distinct1605
Distinct (%)26.1%
Missing43
Missing (%)0.7%
Memory size48.6 KiB
2023-12-12T13:08:39.907749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters61610
Distinct characters11
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

Unique802 ?
Unique (%)13.0%

Sample

1st row1989-02-01
2nd row1992-06-01
3rd row1992-06-01
4th row2023-01-01
5th row2021-11-04
ValueCountFrequency (%)
2021-11-04 268
 
4.3%
2011-01-20 244
 
4.0%
1985-03-22 195
 
3.2%
2006-10-02 147
 
2.4%
2020-03-11 76
 
1.2%
2011-02-10 61
 
1.0%
2022-12-05 50
 
0.8%
2023-05-31 42
 
0.7%
2002-11-11 41
 
0.7%
2022-11-01 33
 
0.5%
Other values (1595) 5004
81.2%
2023-12-12T13:08:40.430962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14863
24.1%
2 13196
21.4%
- 12322
20.0%
1 9325
15.1%
3 2658
 
4.3%
9 2215
 
3.6%
8 1675
 
2.7%
5 1472
 
2.4%
4 1351
 
2.2%
7 1304
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49288
80.0%
Dash Punctuation 12322
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14863
30.2%
2 13196
26.8%
1 9325
18.9%
3 2658
 
5.4%
9 2215
 
4.5%
8 1675
 
3.4%
5 1472
 
3.0%
4 1351
 
2.7%
7 1304
 
2.6%
6 1229
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 12322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14863
24.1%
2 13196
21.4%
- 12322
20.0%
1 9325
15.1%
3 2658
 
4.3%
9 2215
 
3.6%
8 1675
 
2.7%
5 1472
 
2.4%
4 1351
 
2.2%
7 1304
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14863
24.1%
2 13196
21.4%
- 12322
20.0%
1 9325
15.1%
3 2658
 
4.3%
9 2215
 
3.6%
8 1675
 
2.7%
5 1472
 
2.4%
4 1351
 
2.2%
7 1304
 
2.1%

수정일자
Date

Distinct941
Distinct (%)15.3%
Missing43
Missing (%)0.7%
Memory size48.6 KiB
Minimum1989-09-20 00:00:00
Maximum2023-08-14 00:00:00
2023-12-12T13:08:40.602338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:40.808030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T13:08:34.022595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.256278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.882321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.475014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.052936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.874247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:34.160683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.344775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.983354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.564055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.153939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.994018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:34.304547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.468010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.081943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.656898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.255240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:33.135713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:34.444297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.584814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.176505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.752606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.375161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:33.670182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:34.562368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.679695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.266028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.841957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.547014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:33.795734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:34.677234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:30.768492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.362751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:31.935156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:32.684767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:08:33.908110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:08:40.926542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통제번호분야구분조달구분적상하구분여입구분자재단가구분자재단가(원)중량(kg)환입중량(kg)환입금액(원)수리단가(원)구제금액(원)
통제번호1.0000.4270.7490.4140.2070.4660.0000.0000.0000.0640.0000.000
분야구분0.4271.0000.3430.4970.3090.247NaN0.0480.0000.0000.000NaN
조달구분0.7490.3431.0000.4580.5310.421NaN0.0540.0000.0000.012NaN
적상하구분0.4140.4970.4581.0000.5160.182NaN0.1570.0500.0620.000NaN
여입구분0.2070.3090.5310.5161.0000.251NaN0.2540.1000.0370.000NaN
자재단가구분0.4660.2470.4210.1820.2511.0000.0000.1420.0000.4320.0000.000
자재단가(원)0.000NaNNaNNaNNaN0.0001.0000.0000.0000.0000.0000.498
중량(kg)0.0000.0480.0540.1570.2540.1420.0001.0000.8750.4840.0000.000
환입중량(kg)0.0000.0000.0000.0500.1000.0000.0000.8751.0000.0000.0000.000
환입금액(원)0.0640.0000.0000.0620.0370.4320.0000.4840.0001.0000.0000.000
수리단가(원)0.0000.0000.0120.0000.0000.0000.0000.0000.0000.0001.0000.000
구제금액(원)0.000NaNNaNNaNNaN0.0000.4980.0000.0000.0000.0001.000
2023-12-12T13:08:41.093094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수리단가(원)분야구분조달구분여입구분자재단가구분적상하구분내외자구분
수리단가(원)1.0000.0000.0120.0000.0000.0001.000
분야구분0.0001.0000.2860.2570.1450.3151.000
조달구분0.0120.2861.0000.2300.2820.3101.000
여입구분0.0000.2570.2301.0000.1620.3571.000
자재단가구분0.0000.1450.2820.1621.0000.0901.000
적상하구분0.0000.3150.3100.3570.0901.0001.000
내외자구분1.0001.0001.0001.0001.0001.0001.000
2023-12-12T13:08:41.250610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통제번호자재단가(원)중량(kg)환입중량(kg)환입금액(원)구제금액(원)분야구분내외자구분조달구분적상하구분여입구분자재단가구분수리단가(원)
통제번호1.000-0.2180.0250.0030.1010.0960.3061.0000.5870.2200.1350.2530.000
자재단가(원)-0.2181.0000.3470.3390.0600.2781.0001.0001.0001.0001.0000.0000.000
중량(kg)0.0250.3471.0000.8780.3090.2910.0291.0000.0240.0770.1160.0700.000
환입중량(kg)0.0030.3390.8781.0000.2870.3050.0001.0000.0000.0320.0400.0000.000
환입금액(원)0.1010.0600.3090.2871.0000.3450.0001.0000.0000.0300.0160.2280.000
구제금액(원)0.0960.2780.2910.3050.3451.0001.0001.0001.0001.0001.0000.0000.000
분야구분0.3061.0000.0290.0000.0001.0001.0001.0000.2860.3150.2570.1450.000
내외자구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
조달구분0.5871.0000.0240.0000.0001.0000.2861.0001.0000.3100.2300.2820.012
적상하구분0.2201.0000.0770.0320.0301.0000.3151.0000.3101.0000.3570.0900.000
여입구분0.1351.0000.1160.0400.0161.0000.2571.0000.2300.3571.0000.1620.000
자재단가구분0.2530.0000.0700.0000.2280.0000.1451.0000.2820.0900.1621.0000.000
수리단가(원)0.0000.0000.0000.0000.0000.0000.0001.0000.0120.0000.0000.0001.000

Missing values

2023-12-12T13:08:34.888574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:08:35.168006image/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.
2023-12-12T13:08:35.396526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

통제번호분야구분내외자구분조달구분적상하구분여입구분자재단가구분자재단가(원)중량(kg)환입중량(kg)환입금액(원)수리단가(원)구제금액(원)적용일자수정일자
05기타1지입자재시멘트 및 근가류현장폐기단가직접입력(주관부서및사업소)0000001989-02-011990-08-14
18기타1지입자재시멘트 및 근가류현장폐기단가직접입력(주관부서및사업소)124000001992-06-012007-09-05
28기타1지입자재시멘트 및 근가류현장폐기단가직접입력(주관부서및사업소)102000001992-06-012007-09-05
38기타1지입자재시멘트 및 근가류현장폐기단가직접입력(주관부서및사업소)1579000002023-01-012023-01-09
48배전<NA><NA><NA><NA><NA>0000002021-11-042021-11-04
55배전1지입자재철재류창고여입단가직접입력(주관부서및사업소)01001000046002001-08-242007-10-05
65배전1본사분철재류창고여입단가직접입력(주관부서및사업소)2880002102100001999-08-042007-10-05
78배전1지입자재철재류창고여입한전가격정보531250332332220251002010-10-142013-10-14
88배전1본사분철재류창고여입한전가격정보989000600600408493002009-06-172011-03-07
95배전1본사분철재류창고여입단가직접입력(주관부서및사업소)10450007007000002006-08-022007-10-05
통제번호분야구분내외자구분조달구분적상하구분여입구분자재단가구분자재단가(원)중량(kg)환입중량(kg)환입금액(원)수리단가(원)구제금액(원)적용일자수정일자
6194<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6195<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6196<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6197<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6198<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6199<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6200<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6201<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6202<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>
6203<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>

Duplicate rows

Most frequently occurring

통제번호분야구분내외자구분조달구분적상하구분여입구분자재단가구분자재단가(원)중량(kg)환입중량(kg)환입금액(원)수리단가(원)구제금액(원)적용일자수정일자# duplicates
2195<NA><NA><NA><NA><NA><NA>0000002021-11-042021-11-0473
1635송전<NA><NA><NA><NA><NA>0000002021-11-042021-11-0467
2919<NA>1작업시 부설물철재류창고여입<NA>0000002020-03-112020-03-1167
105내선자재1본사분애자류창고여입<NA>0000001985-03-222007-10-0566
2688배전<NA><NA><NA><NA><NA>0000002021-11-042021-11-0450
292<NA><NA><NA><NA><NA><NA><NA>000000<NA><NA>39
1495송전1<NA><NA><NA><NA>0000002023-05-312023-05-3138
2888<NA><NA><NA><NA><NA><NA>0000002021-11-042021-11-0431
2065<NA>1<NA><NA><NA><NA>0000002022-12-212022-12-2128
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