Overview

Dataset statistics

Number of variables30
Number of observations3482
Missing cells43896
Missing cells (%)42.0%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory840.0 KiB
Average record size in memory247.0 B

Variable types

Categorical8
Text15
Numeric7

Dataset

Description국토안전관리원에서 시설물을 대상으로 철근탐사 시험을 한 데이터입니다. 시설물 구분은 교량, 터널, 댐, 항만, 하천, 상하수도 입니다. 주철근, 배력철근, 수직근, 수평근, 띠철근 평가결과 데이터를 제공드립니다. 데이터 값 공란사유 : 데이터 미집계(시험결과 및 보고서 입력방법에 따라 데이터 미집계로 인한 공란발생)
Author국토안전관리원
URLhttps://www.data.go.kr/data/15111008/fileData.do

Alerts

종별 has constant value ""Constant
Dataset has 4 (0.1%) duplicate rowsDuplicates
수직근_설계도면_피복두께(mm) has a high cardinality: 51 distinct valuesHigh cardinality
주철근_설계도면_배근간격(mm) is highly imbalanced (53.0%)Imbalance
배력철근_설계도면_배근간격(mm) is highly imbalanced (56.7%)Imbalance
수직근_설계도면_피복두께(mm) is highly imbalanced (57.6%)Imbalance
수평근_설계도면_배근간격(mm) is highly imbalanced (55.9%)Imbalance
띠철근_설계도면_배근간격(mm) is highly imbalanced (81.5%)Imbalance
주철근_측정결과_피복두께(mm) has 1941 (55.7%) missing valuesMissing
주철근_측정결과_배근간격부터(mm) has 1949 (56.0%) missing valuesMissing
주철근_측정결과_배근간격까지(mm) has 1948 (55.9%) missing valuesMissing
주철근_설계도면_피복두께(mm) has 2000 (57.4%) missing valuesMissing
배력철근_측정결과_피복두께(mm) has 2168 (62.3%) missing valuesMissing
배력철근_측정결과_배근간격부터(mm) has 2185 (62.8%) missing valuesMissing
배력철근_측정결과_배근간격까지(mm) has 2187 (62.8%) missing valuesMissing
배력철근_설계도면_피복두께(mm) has 2234 (64.2%) missing valuesMissing
수직근_측정결과_피복두께(mm) has 1581 (45.4%) missing valuesMissing
수직근_측정결과_배근간격부터(mm) has 1581 (45.4%) missing valuesMissing
수직근_측정결과_배근간격까지(mm) has 1579 (45.3%) missing valuesMissing
수직근_설계도면_배근간격(mm) has 2142 (61.5%) missing valuesMissing
수평근_측정결과_피복두께(mm) has 1769 (50.8%) missing valuesMissing
수평근_측정결과_배근간격부터(mm) has 1783 (51.2%) missing valuesMissing
수평근_측정결과_배근간격까지(mm) has 1803 (51.8%) missing valuesMissing
수평근_설계도면_피복두께(mm) has 2340 (67.2%) missing valuesMissing
띠철근_측정결과_피복두께(mm) has 3170 (91.0%) missing valuesMissing
띠철근_측정결과_배근간격부터(mm) has 3170 (91.0%) missing valuesMissing
띠철근_측정결과_배근간격까지(mm) has 3170 (91.0%) missing valuesMissing
띠철근_설계도면_피복두께(mm) has 3196 (91.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:20:49.634031
Analysis finished2023-12-12 19:20:50.498459
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설물구분
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
교량
1535 
상하수도
1050 
터널
625 
160 
하천
 
64

Length

Max length4
Median length2
Mean length2.5571511
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교량 1535
44.1%
상하수도 1050
30.2%
터널 625
17.9%
160
 
4.6%
하천 64
 
1.8%
항만 48
 
1.4%

Length

2023-12-13T04:20:50.565887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:20:50.689938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교량 1535
44.1%
상하수도 1050
30.2%
터널 625
17.9%
160
 
4.6%
하천 64
 
1.8%
항만 48
 
1.4%

시설물종류
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
도로교량
1279 
광역상수도
1050 
철도터널
356 
도로터널
269 
철도교량
256 
Other values (5)
272 

Length

Max length5
Median length4
Mean length4.2952326
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로교량 1279
36.7%
광역상수도 1050
30.2%
철도터널 356
 
10.2%
도로터널 269
 
7.7%
철도교량 256
 
7.4%
다목적댐 82
 
2.4%
하구둑 64
 
1.8%
계류시설 48
 
1.4%
용수전용댐 42
 
1.2%
발전용댐 36
 
1.0%

Length

2023-12-13T04:20:50.828763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:20:50.958380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로교량 1279
36.7%
광역상수도 1050
30.2%
철도터널 356
 
10.2%
도로터널 269
 
7.7%
철도교량 256
 
7.4%
다목적댐 82
 
2.4%
하구둑 64
 
1.8%
계류시설 48
 
1.4%
용수전용댐 42
 
1.2%
발전용댐 36
 
1.0%

종별
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
1종
3482 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1종
2nd row1종
3rd row1종
4th row1종
5th row1종

Common Values

ValueCountFrequency (%)
1종 3482
100.0%

Length

2023-12-13T04:20:51.096055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:20:51.197433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1종 3482
100.0%
Distinct75
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
2023-12-13T04:20:51.438221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters20892
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTU0002
2nd rowTU0002
3rd rowTU0002
4th rowTU0002
5th rowTU0002
ValueCountFrequency (%)
br0009 293
 
8.4%
ws0001 255
 
7.3%
ws0009 210
 
6.0%
ws0002 180
 
5.2%
br0005 151
 
4.3%
br0016 136
 
3.9%
ws0005 121
 
3.5%
ws0003 116
 
3.3%
br0007 114
 
3.3%
br0015 111
 
3.2%
Other values (65) 1795
51.6%
2023-12-13T04:20:51.815945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9331
44.7%
B 1550
 
7.4%
R 1535
 
7.3%
1 1404
 
6.7%
W 1041
 
5.0%
S 1041
 
5.0%
T 625
 
3.0%
U 625
 
3.0%
2 621
 
3.0%
9 610
 
2.9%
Other values (11) 2509
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13928
66.7%
Uppercase Letter 6964
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1550
22.3%
R 1535
22.0%
W 1041
14.9%
S 1041
14.9%
T 625
9.0%
U 625
9.0%
D 218
 
3.1%
A 169
 
2.4%
E 64
 
0.9%
H 48
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 9331
67.0%
1 1404
 
10.1%
2 621
 
4.5%
9 610
 
4.4%
5 475
 
3.4%
3 442
 
3.2%
6 326
 
2.3%
7 315
 
2.3%
8 210
 
1.5%
4 194
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13928
66.7%
Latin 6964
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1550
22.3%
R 1535
22.0%
W 1041
14.9%
S 1041
14.9%
T 625
9.0%
U 625
9.0%
D 218
 
3.1%
A 169
 
2.4%
E 64
 
0.9%
H 48
 
0.7%
Common
ValueCountFrequency (%)
0 9331
67.0%
1 1404
 
10.1%
2 621
 
4.5%
9 610
 
4.4%
5 475
 
3.4%
3 442
 
3.2%
6 326
 
2.3%
7 315
 
2.3%
8 210
 
1.5%
4 194
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9331
44.7%
B 1550
 
7.4%
R 1535
 
7.3%
1 1404
 
6.7%
W 1041
 
5.0%
S 1041
 
5.0%
T 625
 
3.0%
U 625
 
3.0%
2 621
 
3.0%
9 610
 
2.9%
Other values (11) 2509
 
12.0%
Distinct3215
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
2023-12-13T04:20:52.176162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length36
Mean length17.250718
Min length2

Characters and Unicode

Total characters60067
Distinct characters307
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3126 ?
Unique (%)89.8%

Sample

1st row17k575 RW
2nd row17k580 LW
3rd row18k060 LW
4th row18k065 LW
5th row18k070 LW
ValueCountFrequency (%)
상부구조 226
 
2.0%
하부구조 203
 
1.8%
좌측벽체 187
 
1.6%
여과지 187
 
1.6%
전면벽체 183
 
1.6%
본선 177
 
1.5%
rw 175
 
1.5%
청주정수장 166
 
1.4%
lw 161
 
1.4%
우측벽체 154
 
1.3%
Other values (2863) 9685
84.2%
2023-12-13T04:20:52.978729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8110
 
13.5%
1 2310
 
3.8%
- 2210
 
3.7%
2 1651
 
2.7%
( 1498
 
2.5%
) 1497
 
2.5%
0 1402
 
2.3%
1386
 
2.3%
3 1145
 
1.9%
1135
 
1.9%
Other values (297) 37723
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28186
46.9%
Decimal Number 10534
 
17.5%
Space Separator 8110
 
13.5%
Uppercase Letter 5733
 
9.5%
Dash Punctuation 2210
 
3.7%
Open Punctuation 1498
 
2.5%
Close Punctuation 1497
 
2.5%
Lowercase Letter 1028
 
1.7%
Math Symbol 656
 
1.1%
Other Punctuation 586
 
1.0%
Other values (4) 29
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1386
 
4.9%
1135
 
4.0%
1061
 
3.8%
940
 
3.3%
918
 
3.3%
903
 
3.2%
900
 
3.2%
832
 
3.0%
788
 
2.8%
746
 
2.6%
Other values (222) 18577
65.9%
Uppercase Letter
ValueCountFrequency (%)
S 989
17.3%
R 800
14.0%
W 662
11.5%
P 653
11.4%
L 464
8.1%
C 391
 
6.8%
A 325
 
5.7%
B 311
 
5.4%
F 202
 
3.5%
M 197
 
3.4%
Other values (13) 739
12.9%
Lowercase Letter
ValueCountFrequency (%)
k 219
21.3%
a 197
19.2%
m 197
19.2%
p 118
11.5%
t 102
9.9%
e 38
 
3.7%
b 38
 
3.7%
g 35
 
3.4%
n 35
 
3.4%
i 10
 
1.0%
Other values (9) 39
 
3.8%
Decimal Number
ValueCountFrequency (%)
1 2310
21.9%
2 1651
15.7%
0 1402
13.3%
3 1145
10.9%
5 955
9.1%
4 888
 
8.4%
6 648
 
6.2%
8 551
 
5.2%
7 547
 
5.2%
9 437
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 401
68.4%
# 67
 
11.4%
· 44
 
7.5%
, 43
 
7.3%
' 21
 
3.6%
/ 9
 
1.5%
& 1
 
0.2%
Other Number
ValueCountFrequency (%)
4
36.4%
3
27.3%
2
18.2%
1
 
9.1%
1
 
9.1%
Math Symbol
ValueCountFrequency (%)
+ 418
63.7%
~ 151
 
23.0%
86
 
13.1%
| 1
 
0.2%
Space Separator
ValueCountFrequency (%)
8110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2210
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1498
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1497
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 4
100.0%
Other Symbol
ValueCountFrequency (%)
° 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28186
46.9%
Common 25120
41.8%
Latin 6761
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1386
 
4.9%
1135
 
4.0%
1061
 
3.8%
940
 
3.3%
918
 
3.3%
903
 
3.2%
900
 
3.2%
832
 
3.0%
788
 
2.8%
746
 
2.6%
Other values (222) 18577
65.9%
Latin
ValueCountFrequency (%)
S 989
14.6%
R 800
11.8%
W 662
 
9.8%
P 653
 
9.7%
L 464
 
6.9%
C 391
 
5.8%
A 325
 
4.8%
B 311
 
4.6%
k 219
 
3.2%
F 202
 
3.0%
Other values (32) 1745
25.8%
Common
ValueCountFrequency (%)
8110
32.3%
1 2310
 
9.2%
- 2210
 
8.8%
2 1651
 
6.6%
( 1498
 
6.0%
) 1497
 
6.0%
0 1402
 
5.6%
3 1145
 
4.6%
5 955
 
3.8%
4 888
 
3.5%
Other values (23) 3454
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31737
52.8%
Hangul 28186
46.9%
Math Operators 86
 
0.1%
None 47
 
0.1%
Enclosed Alphanum 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8110
25.6%
1 2310
 
7.3%
- 2210
 
7.0%
2 1651
 
5.2%
( 1498
 
4.7%
) 1497
 
4.7%
0 1402
 
4.4%
3 1145
 
3.6%
S 989
 
3.1%
5 955
 
3.0%
Other values (57) 9970
31.4%
Hangul
ValueCountFrequency (%)
1386
 
4.9%
1135
 
4.0%
1061
 
3.8%
940
 
3.3%
918
 
3.3%
903
 
3.2%
900
 
3.2%
832
 
3.0%
788
 
2.8%
746
 
2.6%
Other values (222) 18577
65.9%
Math Operators
ValueCountFrequency (%)
86
100.0%
None
ValueCountFrequency (%)
· 44
93.6%
° 3
 
6.4%
Enclosed Alphanum
ValueCountFrequency (%)
4
36.4%
3
27.3%
2
18.2%
1
 
9.1%
1
 
9.1%
Distinct718
Distinct (%)46.6%
Missing1941
Missing (%)55.7%
Memory size27.3 KiB
2023-12-13T04:20:53.296529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7514601
Min length1

Characters and Unicode

Total characters5781
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)32.8%

Sample

1st row67
2nd row43
3rd row50
4th row45
5th row64
ValueCountFrequency (%)
41 35
 
2.3%
28 33
 
2.1%
30 32
 
2.1%
49 25
 
1.6%
32 24
 
1.6%
46 24
 
1.6%
44 22
 
1.4%
42 21
 
1.4%
43 21
 
1.4%
48 20
 
1.3%
Other values (707) 1286
83.3%
2023-12-13T04:20:53.746486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 987
17.1%
4 651
11.3%
3 591
10.2%
~ 567
9.8%
5 479
8.3%
2 456
7.9%
8 421
7.3%
6 397
6.9%
0 393
 
6.8%
7 393
 
6.8%
Other values (4) 446
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5091
88.1%
Math Symbol 609
 
10.5%
Other Punctuation 79
 
1.4%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 987
19.4%
4 651
12.8%
3 591
11.6%
5 479
9.4%
2 456
9.0%
8 421
8.3%
6 397
7.8%
0 393
 
7.7%
7 393
 
7.7%
9 323
 
6.3%
Math Symbol
ValueCountFrequency (%)
~ 567
93.1%
42
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 79
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 987
17.1%
4 651
11.3%
3 591
10.2%
~ 567
9.8%
5 479
8.3%
2 456
7.9%
8 421
7.3%
6 397
6.9%
0 393
 
6.8%
7 393
 
6.8%
Other values (4) 446
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5739
99.3%
Math Operators 42
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 987
17.2%
4 651
11.3%
3 591
10.3%
~ 567
9.9%
5 479
8.3%
2 456
7.9%
8 421
7.3%
6 397
6.9%
0 393
 
6.8%
7 393
 
6.8%
Other values (3) 404
7.0%
Math Operators
ValueCountFrequency (%)
42
100.0%
Distinct249
Distinct (%)16.2%
Missing1949
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean150.26595
Minimum50
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:20:53.887578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile87
Q1105
median136
Q3178
95-th percentile255
Maximum500
Range450
Interquartile range (IQR)73

Descriptive statistics

Standard deviation62.299758
Coefficient of variation (CV)0.41459664
Kurtosis5.6475883
Mean150.26595
Median Absolute Deviation (MAD)34
Skewness1.9543266
Sum230357.7
Variance3881.2599
MonotonicityNot monotonic
2023-12-13T04:20:54.021788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0 61
 
1.8%
100.0 46
 
1.3%
170.0 45
 
1.3%
125.0 44
 
1.3%
140.0 39
 
1.1%
120.0 33
 
0.9%
95.0 32
 
0.9%
150.0 31
 
0.9%
90.0 29
 
0.8%
190.0 27
 
0.8%
Other values (239) 1146
32.9%
(Missing) 1949
56.0%
ValueCountFrequency (%)
50.0 1
 
< 0.1%
60.0 1
 
< 0.1%
65.0 2
 
0.1%
66.0 1
 
< 0.1%
67.0 2
 
0.1%
70.0 5
0.1%
71.0 1
 
< 0.1%
72.0 2
 
0.1%
75.0 12
0.3%
76.0 1
 
< 0.1%
ValueCountFrequency (%)
500.0 2
 
0.1%
492.0 1
 
< 0.1%
490.0 2
 
0.1%
480.0 1
 
< 0.1%
470.0 1
 
< 0.1%
400.0 5
0.1%
398.0 2
 
0.1%
395.0 3
0.1%
394.0 2
 
0.1%
393.0 1
 
< 0.1%
Distinct252
Distinct (%)16.4%
Missing1948
Missing (%)55.9%
Infinite0
Infinite (%)0.0%
Mean165.40495
Minimum70
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:20:54.176871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile98
Q1123
median150
Q3200
95-th percentile266.7
Maximum510
Range440
Interquartile range (IQR)77

Descriptive statistics

Standard deviation63.167623
Coefficient of variation (CV)0.3818968
Kurtosis4.8772859
Mean165.40495
Median Absolute Deviation (MAD)35
Skewness1.8017374
Sum253731.2
Variance3990.1485
MonotonicityNot monotonic
2023-12-13T04:20:54.312226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210.0 53
 
1.5%
150.0 53
 
1.5%
125.0 51
 
1.5%
200.0 44
 
1.3%
140.0 43
 
1.2%
120.0 35
 
1.0%
145.0 33
 
0.9%
130.0 33
 
0.9%
160.0 32
 
0.9%
220.0 31
 
0.9%
Other values (242) 1126
32.3%
(Missing) 1948
55.9%
ValueCountFrequency (%)
70.0 1
 
< 0.1%
82.0 1
 
< 0.1%
85.0 2
 
0.1%
87.0 1
 
< 0.1%
88.0 1
 
< 0.1%
89.0 1
 
< 0.1%
90.0 3
0.1%
91.0 6
0.2%
91.7 1
 
< 0.1%
92.0 7
0.2%
ValueCountFrequency (%)
510.0 1
 
< 0.1%
501.0 1
 
< 0.1%
500.0 1
 
< 0.1%
490.0 4
0.1%
420.0 1
 
< 0.1%
418.0 1
 
< 0.1%
415.0 1
 
< 0.1%
409.0 2
0.1%
408.0 1
 
< 0.1%
405.0 1
 
< 0.1%
Distinct68
Distinct (%)4.6%
Missing2000
Missing (%)57.4%
Memory size27.3 KiB
2023-12-13T04:20:54.560167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.9696356
Min length2

Characters and Unicode

Total characters4401
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.6%

Sample

1st row42
2nd row42
3rd row42
4th row42
5th row42
ValueCountFrequency (%)
42 206
 
13.9%
40.5 115
 
7.8%
50 113
 
7.6%
40 109
 
7.3%
25 86
 
5.8%
100 78
 
5.3%
60 71
 
4.8%
108 63
 
4.2%
37.5 62
 
4.2%
39 56
 
3.8%
Other values (59) 524
35.3%
2023-12-13T04:20:54.926595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 834
19.0%
5 697
15.8%
4 629
14.3%
. 490
11.1%
2 478
10.9%
1 417
9.5%
3 272
 
6.2%
8 163
 
3.7%
9 134
 
3.0%
6 116
 
2.6%
Other values (12) 171
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3855
87.6%
Other Punctuation 490
 
11.1%
Math Symbol 46
 
1.0%
Other Letter 9
 
0.2%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 834
21.6%
5 697
18.1%
4 629
16.3%
2 478
12.4%
1 417
10.8%
3 272
 
7.1%
8 163
 
4.2%
9 134
 
3.5%
6 116
 
3.0%
7 115
 
3.0%
Other Letter
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 490
100.0%
Math Symbol
ValueCountFrequency (%)
~ 46
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4392
99.8%
Hangul 9
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 834
19.0%
5 697
15.9%
4 629
14.3%
. 490
11.2%
2 478
10.9%
1 417
9.5%
3 272
 
6.2%
8 163
 
3.7%
9 134
 
3.1%
6 116
 
2.6%
Other values (3) 162
 
3.7%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4392
99.8%
Hangul 9
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 834
19.0%
5 697
15.9%
4 629
14.3%
. 490
11.2%
2 478
10.9%
1 417
9.5%
3 272
 
6.2%
8 163
 
3.7%
9 134
 
3.1%
6 116
 
2.6%
Other values (3) 162
 
3.7%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Distinct29
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
<NA>
1948 
100
434 
150
355 
125
238 
200
 
145
Other values (24)
362 

Length

Max length10
Median length4
Mean length3.5738082
Min length2

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
<NA> 1948
55.9%
100 434
 
12.5%
150 355
 
10.2%
125 238
 
6.8%
200 145
 
4.2%
250 115
 
3.3%
190 103
 
3.0%
300 42
 
1.2%
400 24
 
0.7%
154 12
 
0.3%
Other values (19) 66
 
1.9%

Length

2023-12-13T04:20:55.090446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1948
55.9%
100 434
 
12.5%
150 355
 
10.2%
125 238
 
6.8%
200 145
 
4.2%
250 115
 
3.3%
190 103
 
3.0%
300 42
 
1.2%
400 24
 
0.7%
154 12
 
0.3%
Other values (20) 67
 
1.9%
Distinct632
Distinct (%)48.1%
Missing2168
Missing (%)62.3%
Memory size27.3 KiB
2023-12-13T04:20:55.451608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7161339
Min length1

Characters and Unicode

Total characters4883
Distinct characters15
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique459 ?
Unique (%)34.9%

Sample

1st row67
2nd row43
3rd row50
4th row45
5th row64
ValueCountFrequency (%)
48 42
 
3.2%
46 28
 
2.1%
44 22
 
1.7%
52 21
 
1.6%
63 20
 
1.5%
50 19
 
1.4%
58 17
 
1.3%
64 17
 
1.3%
56 16
 
1.2%
55 16
 
1.2%
Other values (610) 1109
83.6%
2023-12-13T04:20:56.026273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 814
16.7%
5 537
11.0%
~ 474
9.7%
6 435
8.9%
4 408
8.4%
3 380
7.8%
2 371
7.6%
7 358
7.3%
8 354
7.2%
0 334
6.8%
Other values (5) 418
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4302
88.1%
Math Symbol 516
 
10.6%
Other Punctuation 52
 
1.1%
Space Separator 13
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 814
18.9%
5 537
12.5%
6 435
10.1%
4 408
9.5%
3 380
8.8%
2 371
8.6%
7 358
8.3%
8 354
8.2%
0 334
7.8%
9 311
 
7.2%
Math Symbol
ValueCountFrequency (%)
~ 474
91.9%
42
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 51
98.1%
, 1
 
1.9%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 814
16.7%
5 537
11.0%
~ 474
9.7%
6 435
8.9%
4 408
8.4%
3 380
7.8%
2 371
7.6%
7 358
7.3%
8 354
7.2%
0 334
6.8%
Other values (5) 418
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4841
99.1%
Math Operators 42
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 814
16.8%
5 537
11.1%
~ 474
9.8%
6 435
9.0%
4 408
8.4%
3 380
7.8%
2 371
7.7%
7 358
7.4%
8 354
7.3%
0 334
6.9%
Other values (4) 376
7.8%
Math Operators
ValueCountFrequency (%)
42
100.0%
Distinct258
Distinct (%)19.9%
Missing2185
Missing (%)62.8%
Memory size27.3 KiB
2023-12-13T04:20:56.556845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length2.9838088
Min length1

Characters and Unicode

Total characters3870
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)6.3%

Sample

1st row147
2nd row292
3rd row296
4th row293
5th row287
ValueCountFrequency (%)
180 59
 
4.5%
200 39
 
3.0%
170 33
 
2.5%
160 33
 
2.5%
190 32
 
2.5%
150 29
 
2.2%
147 21
 
1.6%
140 20
 
1.5%
153 19
 
1.5%
148 16
 
1.2%
Other values (248) 997
76.8%
2023-12-13T04:20:57.160801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 909
23.5%
2 570
14.7%
0 545
14.1%
5 386
10.0%
8 285
 
7.4%
9 277
 
7.2%
3 262
 
6.8%
4 242
 
6.3%
7 222
 
5.7%
6 148
 
3.8%
Other values (3) 24
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3846
99.4%
Other Punctuation 23
 
0.6%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 909
23.6%
2 570
14.8%
0 545
14.2%
5 386
10.0%
8 285
 
7.4%
9 277
 
7.2%
3 262
 
6.8%
4 242
 
6.3%
7 222
 
5.8%
6 148
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 22
95.7%
, 1
 
4.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 909
23.5%
2 570
14.7%
0 545
14.1%
5 386
10.0%
8 285
 
7.4%
9 277
 
7.2%
3 262
 
6.8%
4 242
 
6.3%
7 222
 
5.7%
6 148
 
3.8%
Other values (3) 24
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 909
23.5%
2 570
14.7%
0 545
14.1%
5 386
10.0%
8 285
 
7.4%
9 277
 
7.2%
3 262
 
6.8%
4 242
 
6.3%
7 222
 
5.7%
6 148
 
3.8%
Other values (3) 24
 
0.6%
Distinct258
Distinct (%)19.9%
Missing2187
Missing (%)62.8%
Memory size27.3 KiB
2023-12-13T04:20:57.628586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.034749
Min length2

Characters and Unicode

Total characters3930
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)5.4%

Sample

1st row147
2nd row292
3rd row296
4th row293
5th row287
ValueCountFrequency (%)
200 81
 
6.2%
210 46
 
3.5%
220 32
 
2.5%
150 25
 
1.9%
125 20
 
1.5%
155 20
 
1.5%
140 17
 
1.3%
153 16
 
1.2%
190 15
 
1.2%
250 15
 
1.2%
Other values (248) 1009
77.9%
2023-12-13T04:20:58.277829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 836
21.3%
2 797
20.3%
0 633
16.1%
5 399
10.2%
3 313
 
8.0%
9 221
 
5.6%
4 220
 
5.6%
8 182
 
4.6%
7 176
 
4.5%
6 126
 
3.2%
Other values (3) 27
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3903
99.3%
Other Punctuation 26
 
0.7%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 836
21.4%
2 797
20.4%
0 633
16.2%
5 399
10.2%
3 313
 
8.0%
9 221
 
5.7%
4 220
 
5.6%
8 182
 
4.7%
7 176
 
4.5%
6 126
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 25
96.2%
, 1
 
3.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 836
21.3%
2 797
20.3%
0 633
16.1%
5 399
10.2%
3 313
 
8.0%
9 221
 
5.6%
4 220
 
5.6%
8 182
 
4.6%
7 176
 
4.5%
6 126
 
3.2%
Other values (3) 27
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 836
21.3%
2 797
20.3%
0 633
16.1%
5 399
10.2%
3 313
 
8.0%
9 221
 
5.6%
4 220
 
5.6%
8 182
 
4.6%
7 176
 
4.5%
6 126
 
3.2%
Other values (3) 27
 
0.7%
Distinct63
Distinct (%)5.0%
Missing2234
Missing (%)64.2%
Memory size27.3 KiB
2023-12-13T04:20:58.533894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.9270833
Min length2

Characters and Unicode

Total characters3653
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.0%

Sample

1st row42
2nd row42
3rd row42
4th row42
5th row42
ValueCountFrequency (%)
58 120
 
9.6%
50 117
 
9.4%
42 100
 
8.0%
44 68
 
5.4%
59.5 51
 
4.1%
92 51
 
4.1%
60 51
 
4.1%
40.5 50
 
4.0%
48 49
 
3.9%
80 46
 
3.7%
Other values (53) 547
43.8%
2023-12-13T04:20:58.956676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 842
23.0%
. 430
11.8%
4 423
11.6%
0 406
11.1%
9 292
 
8.0%
8 253
 
6.9%
2 243
 
6.7%
1 236
 
6.5%
6 225
 
6.2%
7 125
 
3.4%
Other values (13) 178
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3165
86.6%
Other Punctuation 431
 
11.8%
Math Symbol 46
 
1.3%
Other Letter 9
 
0.2%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 842
26.6%
4 423
13.4%
0 406
12.8%
9 292
 
9.2%
8 253
 
8.0%
2 243
 
7.7%
1 236
 
7.5%
6 225
 
7.1%
7 125
 
3.9%
3 120
 
3.8%
Other Letter
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 430
99.8%
, 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 46
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3644
99.8%
Hangul 9
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 842
23.1%
. 430
11.8%
4 423
11.6%
0 406
11.1%
9 292
 
8.0%
8 253
 
6.9%
2 243
 
6.7%
1 236
 
6.5%
6 225
 
6.2%
7 125
 
3.4%
Other values (4) 169
 
4.6%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3644
99.8%
Hangul 9
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 842
23.1%
. 430
11.8%
4 423
11.6%
0 406
11.1%
9 292
 
8.0%
8 253
 
6.9%
2 243
 
6.7%
1 236
 
6.5%
6 225
 
6.2%
7 125
 
3.4%
Other values (4) 169
 
4.6%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Distinct36
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
<NA>
2175 
200
371 
150
267 
300
 
153
250
 
88
Other values (31)
428 

Length

Max length10
Median length4
Mean length3.7050546
Min length2

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row150
2nd row300
3rd row300
4th row300
5th row300

Common Values

ValueCountFrequency (%)
<NA> 2175
62.5%
200 371
 
10.7%
150 267
 
7.7%
300 153
 
4.4%
250 88
 
2.5%
100 84
 
2.4%
125 69
 
2.0%
275 47
 
1.3%
290 40
 
1.1%
125_250 24
 
0.7%
Other values (26) 164
 
4.7%

Length

2023-12-13T04:20:59.141818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2175
62.4%
200 371
 
10.7%
150 267
 
7.7%
300 153
 
4.4%
250 88
 
2.5%
100 84
 
2.4%
125 69
 
2.0%
275 47
 
1.3%
290 40
 
1.1%
125_250 24
 
0.7%
Other values (27) 165
 
4.7%
Distinct393
Distinct (%)20.7%
Missing1581
Missing (%)45.4%
Memory size27.3 KiB
2023-12-13T04:20:59.507550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.8437664
Min length2

Characters and Unicode

Total characters5406
Distinct characters15
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)12.2%

Sample

1st row74
2nd row70
3rd row53
4th row94
5th row57
ValueCountFrequency (%)
79 38
 
2.0%
73 37
 
1.9%
72 31
 
1.6%
71 30
 
1.6%
82 30
 
1.6%
74 30
 
1.6%
55 27
 
1.4%
56 26
 
1.4%
75 26
 
1.4%
89 25
 
1.3%
Other values (380) 1601
84.2%
2023-12-13T04:21:00.010445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1041
19.3%
5 534
9.9%
0 533
9.9%
7 518
9.6%
8 477
8.8%
6 433
8.0%
9 428
7.9%
4 416
 
7.7%
2 387
 
7.2%
3 328
 
6.1%
Other values (5) 311
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5095
94.2%
Math Symbol 302
 
5.6%
Other Punctuation 6
 
0.1%
Dash Punctuation 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1041
20.4%
5 534
10.5%
0 533
10.5%
7 518
10.2%
8 477
9.4%
6 433
8.5%
9 428
8.4%
4 416
 
8.2%
2 387
 
7.6%
3 328
 
6.4%
Math Symbol
ValueCountFrequency (%)
~ 283
93.7%
19
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1041
19.3%
5 534
9.9%
0 533
9.9%
7 518
9.6%
8 477
8.8%
6 433
8.0%
9 428
7.9%
4 416
 
7.7%
2 387
 
7.2%
3 328
 
6.1%
Other values (5) 311
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5387
99.6%
Math Operators 19
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1041
19.3%
5 534
9.9%
0 533
9.9%
7 518
9.6%
8 477
8.9%
6 433
8.0%
9 428
7.9%
4 416
 
7.7%
2 387
 
7.2%
3 328
 
6.1%
Other values (4) 292
 
5.4%
Math Operators
ValueCountFrequency (%)
19
100.0%
Distinct266
Distinct (%)14.0%
Missing1581
Missing (%)45.4%
Memory size27.3 KiB
2023-12-13T04:21:00.415322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9300368
Min length2

Characters and Unicode

Total characters5570
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)4.1%

Sample

1st row176
2nd row215
3rd row174
4th row128
5th row201
ValueCountFrequency (%)
200 72
 
3.8%
180 67
 
3.5%
150 57
 
3.0%
190 55
 
2.9%
100 51
 
2.7%
130 50
 
2.6%
140 45
 
2.4%
170 41
 
2.2%
120 38
 
2.0%
160 31
 
1.6%
Other values (256) 1394
73.3%
2023-12-13T04:21:00.965024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1412
25.4%
0 1055
18.9%
2 772
13.9%
9 396
 
7.1%
5 391
 
7.0%
8 380
 
6.8%
3 356
 
6.4%
4 290
 
5.2%
7 277
 
5.0%
6 229
 
4.1%
Other values (3) 12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5558
99.8%
Other Letter 8
 
0.1%
Other Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1412
25.4%
0 1055
19.0%
2 772
13.9%
9 396
 
7.1%
5 391
 
7.0%
8 380
 
6.8%
3 356
 
6.4%
4 290
 
5.2%
7 277
 
5.0%
6 229
 
4.1%
Other Letter
ValueCountFrequency (%)
4
50.0%
4
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5562
99.9%
Hangul 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1412
25.4%
0 1055
19.0%
2 772
13.9%
9 396
 
7.1%
5 391
 
7.0%
8 380
 
6.8%
3 356
 
6.4%
4 290
 
5.2%
7 277
 
5.0%
6 229
 
4.1%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5562
99.9%
Hangul 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1412
25.4%
0 1055
19.0%
2 772
13.9%
9 396
 
7.1%
5 391
 
7.0%
8 380
 
6.8%
3 356
 
6.4%
4 290
 
5.2%
7 277
 
5.0%
6 229
 
4.1%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%
Distinct286
Distinct (%)15.0%
Missing1579
Missing (%)45.3%
Memory size27.3 KiB
2023-12-13T04:21:01.400864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.982659
Min length2

Characters and Unicode

Total characters5676
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)4.4%

Sample

1st row176
2nd row215
3rd row174
4th row128
5th row201
ValueCountFrequency (%)
200 75
 
3.9%
210 74
 
3.9%
150 50
 
2.6%
220 43
 
2.3%
160 41
 
2.2%
120 40
 
2.1%
130 36
 
1.9%
170 34
 
1.8%
190 34
 
1.8%
140 31
 
1.6%
Other values (276) 1445
75.9%
2023-12-13T04:21:01.977381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1313
23.1%
0 1071
18.9%
2 1061
18.7%
3 468
 
8.2%
5 393
 
6.9%
9 327
 
5.8%
4 293
 
5.2%
8 260
 
4.6%
6 240
 
4.2%
7 238
 
4.2%
Other values (3) 12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5664
99.8%
Other Letter 8
 
0.1%
Other Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1313
23.2%
0 1071
18.9%
2 1061
18.7%
3 468
 
8.3%
5 393
 
6.9%
9 327
 
5.8%
4 293
 
5.2%
8 260
 
4.6%
6 240
 
4.2%
7 238
 
4.2%
Other Letter
ValueCountFrequency (%)
4
50.0%
4
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5668
99.9%
Hangul 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1313
23.2%
0 1071
18.9%
2 1061
18.7%
3 468
 
8.3%
5 393
 
6.9%
9 327
 
5.8%
4 293
 
5.2%
8 260
 
4.6%
6 240
 
4.2%
7 238
 
4.2%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5668
99.9%
Hangul 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1313
23.2%
0 1071
18.9%
2 1061
18.7%
3 468
 
8.3%
5 393
 
6.9%
9 327
 
5.8%
4 293
 
5.2%
8 260
 
4.6%
6 240
 
4.2%
7 238
 
4.2%
Hangul
ValueCountFrequency (%)
4
50.0%
4
50.0%

수직근_설계도면_피복두께(mm)
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
<NA>
2214 
50
250 
100
238 
80
 
134
108
 
110
Other values (46)
536 

Length

Max length11
Median length4
Mean length3.4698449
Min length2

Unique

Unique7 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2214
63.6%
50 250
 
7.2%
100 238
 
6.8%
80 134
 
3.8%
108 110
 
3.2%
75 80
 
2.3%
84 59
 
1.7%
116 56
 
1.6%
42 44
 
1.3%
58 32
 
0.9%
Other values (41) 265
 
7.6%

Length

2023-12-13T04:21:02.130997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2214
63.6%
50 250
 
7.2%
100 238
 
6.8%
80 134
 
3.8%
108 110
 
3.2%
75 80
 
2.3%
84 59
 
1.7%
116 56
 
1.6%
42 44
 
1.3%
58 32
 
0.9%
Other values (41) 265
 
7.6%

수직근_설계도면_배근간격(mm)
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)1.7%
Missing2142
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean191.50448
Minimum100
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:21:02.254978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1150
median200
Q3200
95-th percentile300
Maximum500
Range400
Interquartile range (IQR)50

Descriptive statistics

Standard deviation71.412543
Coefficient of variation (CV)0.37290273
Kurtosis3.7771734
Mean191.50448
Median Absolute Deviation (MAD)50
Skewness1.4762485
Sum256616
Variance5099.7513
MonotonicityNot monotonic
2023-12-13T04:21:02.408413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
200 580
 
16.7%
150 223
 
6.4%
100 149
 
4.3%
300 144
 
4.1%
125 102
 
2.9%
250 42
 
1.2%
400 19
 
0.5%
500 18
 
0.5%
194 12
 
0.3%
102 12
 
0.3%
Other values (13) 39
 
1.1%
(Missing) 2142
61.5%
ValueCountFrequency (%)
100 149
4.3%
102 12
 
0.3%
105 11
 
0.3%
110 3
 
0.1%
120 2
 
0.1%
124 3
 
0.1%
125 102
2.9%
132 9
 
0.3%
133 1
 
< 0.1%
139 2
 
0.1%
ValueCountFrequency (%)
500 18
 
0.5%
400 19
 
0.5%
350 1
 
< 0.1%
300 144
 
4.1%
285 3
 
0.1%
280 1
 
< 0.1%
250 42
 
1.2%
200 580
16.7%
194 12
 
0.3%
186 1
 
< 0.1%
Distinct344
Distinct (%)20.1%
Missing1769
Missing (%)50.8%
Memory size27.3 KiB
2023-12-13T04:21:02.773427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.8674839
Min length2

Characters and Unicode

Total characters4912
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)11.7%

Sample

1st row66
2nd row65
3rd row47
4th row85
5th row52
ValueCountFrequency (%)
92 28
 
1.6%
107 28
 
1.6%
96 27
 
1.6%
87 27
 
1.6%
73 26
 
1.5%
94 24
 
1.4%
100 24
 
1.4%
50 23
 
1.3%
85 22
 
1.3%
88 22
 
1.3%
Other values (334) 1462
85.3%
2023-12-13T04:21:03.312259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 949
19.3%
0 541
11.0%
9 460
9.4%
5 458
9.3%
8 413
8.4%
7 412
8.4%
4 410
8.3%
6 388
7.9%
2 316
 
6.4%
3 278
 
5.7%
Other values (3) 287
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4625
94.2%
Math Symbol 282
 
5.7%
Other Punctuation 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 949
20.5%
0 541
11.7%
9 460
9.9%
5 458
9.9%
8 413
8.9%
7 412
8.9%
4 410
8.9%
6 388
8.4%
2 316
 
6.8%
3 278
 
6.0%
Math Symbol
ValueCountFrequency (%)
~ 262
92.9%
20
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4912
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 949
19.3%
0 541
11.0%
9 460
9.4%
5 458
9.3%
8 413
8.4%
7 412
8.4%
4 410
8.3%
6 388
7.9%
2 316
 
6.4%
3 278
 
5.7%
Other values (3) 287
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4892
99.6%
Math Operators 20
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 949
19.4%
0 541
11.1%
9 460
9.4%
5 458
9.4%
8 413
8.4%
7 412
8.4%
4 410
8.4%
6 388
7.9%
2 316
 
6.5%
3 278
 
5.7%
Other values (2) 267
 
5.5%
Math Operators
ValueCountFrequency (%)
20
100.0%
Distinct278
Distinct (%)16.4%
Missing1783
Missing (%)51.2%
Memory size27.3 KiB
2023-12-13T04:21:03.722774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9693938
Min length1

Characters and Unicode

Total characters5045
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)4.8%

Sample

1st row158
2nd row148
3rd row174
4th row137
5th row189
ValueCountFrequency (%)
180 48
 
2.8%
150 45
 
2.7%
170 44
 
2.6%
200 39
 
2.3%
160 39
 
2.3%
140 37
 
2.2%
300 32
 
1.9%
130 32
 
1.9%
190 31
 
1.8%
100 27
 
1.6%
Other values (267) 1324
78.0%
2023-12-13T04:21:04.289640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1116
22.1%
0 942
18.7%
2 780
15.5%
3 391
 
7.8%
5 381
 
7.6%
9 320
 
6.3%
8 310
 
6.1%
7 278
 
5.5%
4 277
 
5.5%
6 247
 
4.9%
Other values (2) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5042
99.9%
Other Punctuation 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1116
22.1%
0 942
18.7%
2 780
15.5%
3 391
 
7.8%
5 381
 
7.6%
9 320
 
6.3%
8 310
 
6.1%
7 278
 
5.5%
4 277
 
5.5%
6 247
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5045
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1116
22.1%
0 942
18.7%
2 780
15.5%
3 391
 
7.8%
5 381
 
7.6%
9 320
 
6.3%
8 310
 
6.1%
7 278
 
5.5%
4 277
 
5.5%
6 247
 
4.9%
Other values (2) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1116
22.1%
0 942
18.7%
2 780
15.5%
3 391
 
7.8%
5 381
 
7.6%
9 320
 
6.3%
8 310
 
6.1%
7 278
 
5.5%
4 277
 
5.5%
6 247
 
4.9%
Other values (2) 3
 
0.1%
Distinct291
Distinct (%)17.3%
Missing1803
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean225.7975
Minimum60
Maximum803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:21:04.445539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile120
Q1161.5
median207
Q3280
95-th percentile370
Maximum803
Range743
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation88.551688
Coefficient of variation (CV)0.39217303
Kurtosis5.3419594
Mean225.7975
Median Absolute Deviation (MAD)49
Skewness1.6945344
Sum379114
Variance7841.4015
MonotonicityNot monotonic
2023-12-13T04:21:04.634290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200.0 60
 
1.7%
210.0 45
 
1.3%
160.0 43
 
1.2%
300.0 36
 
1.0%
150.0 33
 
0.9%
220.0 33
 
0.9%
130.0 32
 
0.9%
170.0 31
 
0.9%
305.0 23
 
0.7%
190.0 23
 
0.7%
Other values (281) 1320
37.9%
(Missing) 1803
51.8%
ValueCountFrequency (%)
60.0 1
 
< 0.1%
69.0 1
 
< 0.1%
70.0 1
 
< 0.1%
71.0 1
 
< 0.1%
73.0 1
 
< 0.1%
80.0 3
0.1%
85.0 1
 
< 0.1%
88.0 1
 
< 0.1%
90.0 4
0.1%
96.0 1
 
< 0.1%
ValueCountFrequency (%)
803.0 1
< 0.1%
777.0 1
< 0.1%
776.0 1
< 0.1%
761.0 1
< 0.1%
650.0 1
< 0.1%
635.0 1
< 0.1%
608.0 2
0.1%
590.0 1
< 0.1%
570.0 1
< 0.1%
560.0 1
< 0.1%
Distinct56
Distinct (%)4.9%
Missing2340
Missing (%)67.2%
Memory size27.3 KiB
2023-12-13T04:21:04.876182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.4781086
Min length2

Characters and Unicode

Total characters2830
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50
ValueCountFrequency (%)
100 228
20.0%
50 215
18.8%
80 134
11.7%
75 87
 
7.6%
94 56
 
4.9%
58 44
 
3.9%
42 32
 
2.8%
40 27
 
2.4%
116 25
 
2.2%
92 25
 
2.2%
Other values (46) 269
23.6%
2023-12-13T04:21:05.284683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 930
32.9%
5 497
17.6%
1 413
14.6%
8 195
 
6.9%
9 170
 
6.0%
7 155
 
5.5%
4 155
 
5.5%
. 94
 
3.3%
2 91
 
3.2%
6 88
 
3.1%
Other values (2) 42
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2726
96.3%
Other Punctuation 94
 
3.3%
Math Symbol 10
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 930
34.1%
5 497
18.2%
1 413
15.2%
8 195
 
7.2%
9 170
 
6.2%
7 155
 
5.7%
4 155
 
5.7%
2 91
 
3.3%
6 88
 
3.2%
3 32
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 94
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2830
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 930
32.9%
5 497
17.6%
1 413
14.6%
8 195
 
6.9%
9 170
 
6.0%
7 155
 
5.5%
4 155
 
5.5%
. 94
 
3.3%
2 91
 
3.2%
6 88
 
3.1%
Other values (2) 42
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 930
32.9%
5 497
17.6%
1 413
14.6%
8 195
 
6.9%
9 170
 
6.0%
7 155
 
5.5%
4 155
 
5.5%
. 94
 
3.3%
2 91
 
3.2%
6 88
 
3.1%
Other values (2) 42
 
1.5%
Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
<NA>
2267 
200
539 
300
234 
150
 
179
400
 
67
Other values (12)
 
196

Length

Max length7
Median length4
Mean length3.6858128
Min length3

Unique

Unique4 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2267
65.1%
200 539
 
15.5%
300 234
 
6.7%
150 179
 
5.1%
400 67
 
1.9%
250 62
 
1.8%
350 34
 
1.0%
150_300 24
 
0.7%
125 24
 
0.7%
500 17
 
0.5%
Other values (7) 35
 
1.0%

Length

2023-12-13T04:21:05.520858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2267
65.1%
200 539
 
15.5%
300 234
 
6.7%
150 179
 
5.1%
400 67
 
1.9%
250 62
 
1.8%
350 34
 
1.0%
125 24
 
0.7%
150_300 24
 
0.7%
500 17
 
0.5%
Other values (7) 35
 
1.0%
Distinct195
Distinct (%)62.5%
Missing3170
Missing (%)91.0%
Memory size27.3 KiB
2023-12-13T04:21:06.003493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.4358974
Min length2

Characters and Unicode

Total characters1072
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)46.5%

Sample

1st row52
2nd row56
3rd row52
4th row30
5th row56
ValueCountFrequency (%)
96 8
 
2.6%
88 7
 
2.2%
78 7
 
2.2%
89 7
 
2.2%
56 7
 
2.2%
53 7
 
2.2%
82 6
 
1.9%
70 6
 
1.9%
52 6
 
1.9%
76 6
 
1.9%
Other values (185) 245
78.5%
2023-12-13T04:21:06.664687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 161
15.0%
8 125
11.7%
6 100
9.3%
7 98
9.1%
5 94
8.8%
~ 93
8.7%
9 90
8.4%
0 85
7.9%
4 81
7.6%
3 58
 
5.4%
Other values (2) 87
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 946
88.2%
Math Symbol 93
 
8.7%
Other Punctuation 33
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 161
17.0%
8 125
13.2%
6 100
10.6%
7 98
10.4%
5 94
9.9%
9 90
9.5%
0 85
9.0%
4 81
8.6%
3 58
 
6.1%
2 54
 
5.7%
Math Symbol
ValueCountFrequency (%)
~ 93
100.0%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 161
15.0%
8 125
11.7%
6 100
9.3%
7 98
9.1%
5 94
8.8%
~ 93
8.7%
9 90
8.4%
0 85
7.9%
4 81
7.6%
3 58
 
5.4%
Other values (2) 87
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 161
15.0%
8 125
11.7%
6 100
9.3%
7 98
9.1%
5 94
8.8%
~ 93
8.7%
9 90
8.4%
0 85
7.9%
4 81
7.6%
3 58
 
5.4%
Other values (2) 87
8.1%
Distinct144
Distinct (%)46.2%
Missing3170
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean183.54391
Minimum43
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:21:06.856091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile85
Q1130
median155
Q3253.475
95-th percentile310.45
Maximum400
Range357
Interquartile range (IQR)123.475

Descriptive statistics

Standard deviation75.251837
Coefficient of variation (CV)0.40999365
Kurtosis-0.90346679
Mean183.54391
Median Absolute Deviation (MAD)50
Skewness0.43271873
Sum57265.7
Variance5662.839
MonotonicityNot monotonic
2023-12-13T04:21:07.009055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150.0 18
 
0.5%
130.0 11
 
0.3%
140.0 10
 
0.3%
100.0 8
 
0.2%
203.0 8
 
0.2%
145.0 7
 
0.2%
135.0 7
 
0.2%
290.0 7
 
0.2%
125.0 6
 
0.2%
260.0 6
 
0.2%
Other values (134) 224
 
6.4%
(Missing) 3170
91.0%
ValueCountFrequency (%)
43.0 1
 
< 0.1%
49.0 1
 
< 0.1%
55.0 1
 
< 0.1%
60.0 1
 
< 0.1%
64.0 1
 
< 0.1%
67.0 1
 
< 0.1%
70.0 2
 
0.1%
80.0 5
0.1%
84.0 1
 
< 0.1%
85.0 5
0.1%
ValueCountFrequency (%)
400.0 1
< 0.1%
373.0 1
< 0.1%
339.0 1
< 0.1%
330.0 1
< 0.1%
324.0 1
< 0.1%
320.0 1
< 0.1%
318.0 1
< 0.1%
316.0 1
< 0.1%
315.0 1
< 0.1%
314.0 2
0.1%
Distinct147
Distinct (%)47.1%
Missing3170
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean197.94455
Minimum24
Maximum436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:21:07.169799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile100
Q1151.7
median179.5
Q3260
95-th percentile315
Maximum436
Range412
Interquartile range (IQR)108.3

Descriptive statistics

Standard deviation71.821596
Coefficient of variation (CV)0.36283695
Kurtosis-0.50303736
Mean197.94455
Median Absolute Deviation (MAD)50.5
Skewness0.48713525
Sum61758.7
Variance5158.3417
MonotonicityNot monotonic
2023-12-13T04:21:07.333273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.0 21
 
0.6%
170.0 16
 
0.5%
204.0 7
 
0.2%
100.0 7
 
0.2%
165.0 7
 
0.2%
110.0 7
 
0.2%
300.0 7
 
0.2%
102.0 6
 
0.2%
145.0 5
 
0.1%
162.0 5
 
0.1%
Other values (137) 224
 
6.4%
(Missing) 3170
91.0%
ValueCountFrequency (%)
24.0 1
 
< 0.1%
96.0 2
 
0.1%
97.0 2
 
0.1%
98.0 3
0.1%
99.0 4
0.1%
100.0 7
0.2%
101.0 4
0.1%
102.0 6
0.2%
103.0 1
 
< 0.1%
105.0 3
0.1%
ValueCountFrequency (%)
436.0 1
< 0.1%
400.0 1
< 0.1%
373.0 1
< 0.1%
357.0 1
< 0.1%
346.0 1
< 0.1%
339.0 1
< 0.1%
337.0 1
< 0.1%
335.0 1
< 0.1%
330.0 1
< 0.1%
320.0 1
< 0.1%

띠철근_설계도면_피복두께(mm)
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)9.1%
Missing3196
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean86.494755
Minimum37.5
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.7 KiB
2023-12-13T04:21:07.471304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.5
5-th percentile50
Q174.5
median92
Q392
95-th percentile115
Maximum142
Range104.5
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation17.758985
Coefficient of variation (CV)0.20531864
Kurtosis1.3168478
Mean86.494755
Median Absolute Deviation (MAD)6.5
Skewness-0.083961532
Sum24737.5
Variance315.38155
MonotonicityNot monotonic
2023-12-13T04:21:07.945470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
92.0 113
 
3.2%
100.0 30
 
0.9%
50.0 21
 
0.6%
71.0 17
 
0.5%
74.5 13
 
0.4%
115.0 12
 
0.3%
90.5 12
 
0.3%
69.5 10
 
0.3%
81.0 8
 
0.2%
89.0 8
 
0.2%
Other values (16) 42
 
1.2%
(Missing) 3196
91.8%
ValueCountFrequency (%)
37.5 1
 
< 0.1%
48.0 2
 
0.1%
50.0 21
0.6%
59.0 4
 
0.1%
60.0 2
 
0.1%
65.0 1
 
< 0.1%
67.0 2
 
0.1%
68.0 1
 
< 0.1%
68.5 1
 
< 0.1%
69.5 10
0.3%
ValueCountFrequency (%)
142.0 6
 
0.2%
115.0 12
 
0.3%
110.0 2
 
0.1%
105.0 2
 
0.1%
100.0 30
 
0.9%
97.0 1
 
< 0.1%
92.0 113
3.2%
91.0 6
 
0.2%
90.5 12
 
0.3%
90.0 3
 
0.1%
Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size27.3 KiB
<NA>
3196 
150
 
86
100
 
61
200
 
58
250
 
35
Other values (5)
 
46

Length

Max length7
Median length4
Mean length3.9316485
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3196
91.8%
150 86
 
2.5%
100 61
 
1.8%
200 58
 
1.7%
250 35
 
1.0%
300 27
 
0.8%
150_300 12
 
0.3%
125 4
 
0.1%
400 2
 
0.1%
140 1
 
< 0.1%

Length

2023-12-13T04:21:08.101369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:21:08.230373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3196
91.8%
150 86
 
2.5%
100 61
 
1.8%
200 58
 
1.7%
250 35
 
1.0%
300 27
 
0.8%
150_300 12
 
0.3%
125 4
 
0.1%
400 2
 
0.1%
140 1
 
< 0.1%

Sample

시설물구분시설물종류종별시설물번호세부위치주철근_측정결과_피복두께(mm)주철근_측정결과_배근간격부터(mm)주철근_측정결과_배근간격까지(mm)주철근_설계도면_피복두께(mm)주철근_설계도면_배근간격(mm)배력철근_측정결과_피복두께(mm)배력철근_측정결과_배근간격부터(mm)배력철근_측정결과_배근간격까지(mm)배력철근_설계도면_피복두께(mm)배력철근_설계도면_배근간격(mm)수직근_측정결과_피복두께(mm)수직근_측정결과_배근간격부터(mm)수직근_측정결과_배근간격까지(mm)수직근_설계도면_피복두께(mm)수직근_설계도면_배근간격(mm)수평근_측정결과_피복두께(mm)수평근_측정결과_배근간격부터(mm)수평근_측정결과_배근간격까지(mm)수평근_설계도면_피복두께(mm)수평근_설계도면_배근간격(mm)띠철근_측정결과_피복두께(mm)띠철근_측정결과_배근간격부터(mm)띠철근_측정결과_배근간격까지(mm)띠철근_설계도면_피복두께(mm)띠철근_설계도면_배근간격(mm)
0터널철도터널1종TU000217k575 RW67104.0104.0421006714714742150<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1터널철도터널1종TU000217k580 LW43106.0106.0421004329229242300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2터널철도터널1종TU000218k060 LW5095.095.0421005029629642300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3터널철도터널1종TU000218k065 LW45105.0105.0421004529329342300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4터널철도터널1종TU000218k070 LW6494.094.0421006428728742300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5터널철도터널1종TU000218k080 LW4498.098.0421004430730742300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6터널철도터널1종TU000217k545 RW54105.0105.0421005428728742300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7터널철도터널1종TU000218k030 LW4695.095.0421004621021042300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8터널철도터널1종TU000218k030 RW49109.0109.0421004928128142300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9터널철도터널1종TU000218k040 RW57101.0101.0421005728628642300<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
시설물구분시설물종류종별시설물번호세부위치주철근_측정결과_피복두께(mm)주철근_측정결과_배근간격부터(mm)주철근_측정결과_배근간격까지(mm)주철근_설계도면_피복두께(mm)주철근_설계도면_배근간격(mm)배력철근_측정결과_피복두께(mm)배력철근_측정결과_배근간격부터(mm)배력철근_측정결과_배근간격까지(mm)배력철근_설계도면_피복두께(mm)배력철근_설계도면_배근간격(mm)수직근_측정결과_피복두께(mm)수직근_측정결과_배근간격부터(mm)수직근_측정결과_배근간격까지(mm)수직근_설계도면_피복두께(mm)수직근_설계도면_배근간격(mm)수평근_측정결과_피복두께(mm)수평근_측정결과_배근간격부터(mm)수평근_측정결과_배근간격까지(mm)수평근_설계도면_피복두께(mm)수평근_설계도면_배근간격(mm)띠철근_측정결과_피복두께(mm)띠철근_측정결과_배근간격부터(mm)띠철근_측정결과_배근간격까지(mm)띠철근_설계도면_피복두께(mm)띠철근_설계도면_배근간격(mm)
3472교량도로교량1종BR0016고정대교(부산) 하부구조 P3기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>901201208412571145<NA>65150_300<NA><NA><NA><NA><NA>
3473교량도로교량1종BR0016고정대교(부산) 하부구조 P4기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>871281288412569290<NA>65150_300<NA><NA><NA><NA><NA>
3474교량도로교량1종BR0016고정대교(부산) 하부구조 P5기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>881151158412574148<NA>65150_300<NA><NA><NA><NA><NA>
3475교량도로교량1종BR0016고정대교(부산) 하부구조 P6기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>871281288412561145<NA>65150_300<NA><NA><NA><NA><NA>
3476교량도로교량1종BR0016고정대교(부산) 하부구조 P7기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>931251258412559153<NA>65150_300<NA><NA><NA><NA><NA>
3477교량도로교량1종BR0016고정대교(부산) 하부구조 P8기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>831301308412572140<NA>65150_300<NA><NA><NA><NA><NA>
3478교량도로교량1종BR0016고정대교(부산) 하부구조 P9기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>751151158412556145<NA>65150_300<NA><NA><NA><NA><NA>
3479교량도로교량1종BR0016고정대교(부산) 하부구조 P10기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>811201208412569298<NA>65150_300<NA><NA><NA><NA><NA>
3480교량도로교량1종BR0016고정대교(부산) 하부구조 P11기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>791251258412560140<NA>65150_300<NA><NA><NA><NA><NA>
3481교량도로교량1종BR0016고정대교(부산) 하부구조 P12기둥<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>851271278412564293<NA>65150_300<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시설물구분시설물종류종별시설물번호세부위치주철근_측정결과_피복두께(mm)주철근_측정결과_배근간격부터(mm)주철근_측정결과_배근간격까지(mm)주철근_설계도면_피복두께(mm)주철근_설계도면_배근간격(mm)배력철근_측정결과_피복두께(mm)배력철근_측정결과_배근간격부터(mm)배력철근_측정결과_배근간격까지(mm)배력철근_설계도면_피복두께(mm)배력철근_설계도면_배근간격(mm)수직근_측정결과_피복두께(mm)수직근_측정결과_배근간격부터(mm)수직근_측정결과_배근간격까지(mm)수직근_설계도면_피복두께(mm)수직근_설계도면_배근간격(mm)수평근_측정결과_피복두께(mm)수평근_측정결과_배근간격부터(mm)수평근_측정결과_배근간격까지(mm)수평근_설계도면_피복두께(mm)수평근_설계도면_배근간격(mm)띠철근_측정결과_피복두께(mm)띠철근_측정결과_배근간격부터(mm)띠철근_측정결과_배근간격까지(mm)띠철근_설계도면_피복두께(mm)띠철근_설계도면_배근간격(mm)# duplicates
0상하수도광역상수도1종WS00082단계 광주 D-2 10+7.90 M002 전면벽체<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>732162165020051213213.050200<NA><NA><NA><NA><NA>2
1상하수도광역상수도1종WS00082단계 광주 D-2 106+26.20 M007 전면벽체<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>702102105020052206206.050200<NA><NA><NA><NA><NA>2
2상하수도광역상수도1종WS00082단계 광주 D-2 130+22.80 M004 우측벽체<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>482052055020050206206.050200<NA><NA><NA><NA><NA>2
3상하수도광역상수도1종WS00082단계 광주 D-2 136+36.60 A005 전면벽체<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>542102105020052217217.050200<NA><NA><NA><NA><NA>2