Overview

Dataset statistics

Number of variables19
Number of observations48
Missing cells420
Missing cells (%)46.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory155.8 B

Variable types

Categorical1
DateTime2
Text16

Alerts

ST6요오드131조사결과 has 18 (37.5%) missing valuesMissing
ST7요오드131조사결과 has 18 (37.5%) missing valuesMissing
ST1요오드131조사결과 has 30 (62.5%) missing valuesMissing
ST3요오드131조사결과 has 30 (62.5%) missing valuesMissing
ST6세슘134조사결과 has 18 (37.5%) missing valuesMissing
ST7ST6세슘134조사결과 has 18 (37.5%) missing valuesMissing
ST1ST6세슘134조사결과 has 30 (62.5%) missing valuesMissing
ST3ST6세슘134조사결과 has 30 (62.5%) missing valuesMissing
ST6세슘137조사결과 has 18 (37.5%) missing valuesMissing
ST6세슘137조사결과오차범위 has 29 (60.4%) missing valuesMissing
ST7세슘137조사결과 has 18 (37.5%) missing valuesMissing
ST7세슘137조사결과오차범위 has 31 (64.6%) missing valuesMissing
ST1세슘137조사결과 has 30 (62.5%) missing valuesMissing
ST1세슘137조사결과오차범위 has 36 (75.0%) missing valuesMissing
ST3세슘137조사결과 has 30 (62.5%) missing valuesMissing
ST3세슘137조사결과오차범위 has 36 (75.0%) missing valuesMissing
시료채취일 has unique valuesUnique
결과보고일 has unique valuesUnique

Reproduction

Analysis started2024-04-14 03:16:30.307995
Analysis finished2024-04-14 03:16:32.617493
Duration2.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023
29 
2024
13 
2022

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 (%)
2023 29
60.4%
2024 13
27.1%
2022 6
 
12.5%

Length

2024-04-14T12:16:32.665121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T12:16:32.744688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 29
60.4%
2024 13
27.1%
2022 6
 
12.5%

시료채취일
Date

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum2022-07-06 00:00:00
Maximum2024-03-25 00:00:00
2024-04-14T12:16:32.832552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:16:32.936247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

결과보고일
Date

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum2022-08-29 00:00:00
Maximum2024-04-04 00:00:00
2024-04-14T12:16:33.036860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T12:16:33.135851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
Distinct30
Distinct (%)100.0%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:33.287724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.5333333
Min length7

Characters and Unicode

Total characters256
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

Unique30 ?
Unique (%)100.0%

Sample

1st row<0.00839
2nd row<0.0092
3rd row<0.02607
4th row<0.01584
5th row<0.01123
ValueCountFrequency (%)
0.025689 1
 
3.3%
0.02607 1
 
3.3%
0.043576 1
 
3.3%
0.054706 1
 
3.3%
0.050496 1
 
3.3%
0.026044 1
 
3.3%
0.03576 1
 
3.3%
0.026458 1
 
3.3%
0.034830 1
 
3.3%
0.017562 1
 
3.3%
Other values (20) 20
66.7%
2024-04-14T12:16:33.550262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 72
28.1%
< 30
11.7%
. 30
11.7%
2 22
 
8.6%
6 16
 
6.2%
7 14
 
5.5%
1 14
 
5.5%
4 14
 
5.5%
3 14
 
5.5%
5 12
 
4.7%
Other values (2) 18
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
76.6%
Math Symbol 30
 
11.7%
Other Punctuation 30
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72
36.7%
2 22
 
11.2%
6 16
 
8.2%
7 14
 
7.1%
1 14
 
7.1%
4 14
 
7.1%
3 14
 
7.1%
5 12
 
6.1%
8 9
 
4.6%
9 9
 
4.6%
Math Symbol
ValueCountFrequency (%)
< 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72
28.1%
< 30
11.7%
. 30
11.7%
2 22
 
8.6%
6 16
 
6.2%
7 14
 
5.5%
1 14
 
5.5%
4 14
 
5.5%
3 14
 
5.5%
5 12
 
4.7%
Other values (2) 18
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72
28.1%
< 30
11.7%
. 30
11.7%
2 22
 
8.6%
6 16
 
6.2%
7 14
 
5.5%
1 14
 
5.5%
4 14
 
5.5%
3 14
 
5.5%
5 12
 
4.7%
Other values (2) 18
 
7.0%
Distinct30
Distinct (%)100.0%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:33.711296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.5333333
Min length7

Characters and Unicode

Total characters256
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

Unique30 ?
Unique (%)100.0%

Sample

1st row<0.01235
2nd row<0.01408
3rd row<0.01691
4th row<0.0075
5th row<0.21772
ValueCountFrequency (%)
0.034382 1
 
3.3%
0.01691 1
 
3.3%
0.033808 1
 
3.3%
0.039681 1
 
3.3%
0.047049 1
 
3.3%
0.039168 1
 
3.3%
0.02467 1
 
3.3%
0.018907 1
 
3.3%
0.025316 1
 
3.3%
0.020516 1
 
3.3%
Other values (20) 20
66.7%
2024-04-14T12:16:33.980969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68
26.6%
< 30
11.7%
. 30
11.7%
1 25
 
9.8%
2 18
 
7.0%
8 15
 
5.9%
3 14
 
5.5%
7 14
 
5.5%
6 12
 
4.7%
4 10
 
3.9%
Other values (2) 20
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
76.6%
Math Symbol 30
 
11.7%
Other Punctuation 30
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68
34.7%
1 25
 
12.8%
2 18
 
9.2%
8 15
 
7.7%
3 14
 
7.1%
7 14
 
7.1%
6 12
 
6.1%
4 10
 
5.1%
9 10
 
5.1%
5 10
 
5.1%
Math Symbol
ValueCountFrequency (%)
< 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68
26.6%
< 30
11.7%
. 30
11.7%
1 25
 
9.8%
2 18
 
7.0%
8 15
 
5.9%
3 14
 
5.5%
7 14
 
5.5%
6 12
 
4.7%
4 10
 
3.9%
Other values (2) 20
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68
26.6%
< 30
11.7%
. 30
11.7%
1 25
 
9.8%
2 18
 
7.0%
8 15
 
5.9%
3 14
 
5.5%
7 14
 
5.5%
6 12
 
4.7%
4 10
 
3.9%
Other values (2) 20
 
7.8%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:34.123397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.9444444
Min length8

Characters and Unicode

Total characters161
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

Unique18 ?
Unique (%)100.0%

Sample

1st row<0.023862
2nd row<0.036137
3rd row<0.03299
4th row<0.028036
5th row<0.031507
ValueCountFrequency (%)
0.023862 1
 
5.6%
0.036137 1
 
5.6%
0.054024 1
 
5.6%
0.056366 1
 
5.6%
0.04212 1
 
5.6%
0.027857 1
 
5.6%
0.027765 1
 
5.6%
0.023151 1
 
5.6%
0.016250 1
 
5.6%
0.031552 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:34.378513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45
28.0%
< 18
 
11.2%
. 18
 
11.2%
2 15
 
9.3%
5 12
 
7.5%
3 11
 
6.8%
1 10
 
6.2%
6 9
 
5.6%
7 7
 
4.3%
4 7
 
4.3%
Other values (2) 9
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125
77.6%
Math Symbol 18
 
11.2%
Other Punctuation 18
 
11.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45
36.0%
2 15
 
12.0%
5 12
 
9.6%
3 11
 
8.8%
1 10
 
8.0%
6 9
 
7.2%
7 7
 
5.6%
4 7
 
5.6%
8 6
 
4.8%
9 3
 
2.4%
Math Symbol
ValueCountFrequency (%)
< 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45
28.0%
< 18
 
11.2%
. 18
 
11.2%
2 15
 
9.3%
5 12
 
7.5%
3 11
 
6.8%
1 10
 
6.2%
6 9
 
5.6%
7 7
 
4.3%
4 7
 
4.3%
Other values (2) 9
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45
28.0%
< 18
 
11.2%
. 18
 
11.2%
2 15
 
9.3%
5 12
 
7.5%
3 11
 
6.8%
1 10
 
6.2%
6 9
 
5.6%
7 7
 
4.3%
4 7
 
4.3%
Other values (2) 9
 
5.6%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:34.521610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9444444
Min length8

Characters and Unicode

Total characters161
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

Unique18 ?
Unique (%)100.0%

Sample

1st row<0.031135
2nd row<0.032166
3rd row<0.035208
4th row<0.023164
5th row<0.031107
ValueCountFrequency (%)
0.031135 1
 
5.6%
0.032166 1
 
5.6%
0.069476 1
 
5.6%
0.062455 1
 
5.6%
0.05234 1
 
5.6%
0.025186 1
 
5.6%
0.034135 1
 
5.6%
0.025761 1
 
5.6%
0.027453 1
 
5.6%
0.028184 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:34.755763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41
25.5%
< 18
11.2%
. 18
11.2%
3 16
 
9.9%
1 13
 
8.1%
4 13
 
8.1%
5 10
 
6.2%
6 10
 
6.2%
2 9
 
5.6%
8 5
 
3.1%
Other values (2) 8
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125
77.6%
Math Symbol 18
 
11.2%
Other Punctuation 18
 
11.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
32.8%
3 16
 
12.8%
1 13
 
10.4%
4 13
 
10.4%
5 10
 
8.0%
6 10
 
8.0%
2 9
 
7.2%
8 5
 
4.0%
7 4
 
3.2%
9 4
 
3.2%
Math Symbol
ValueCountFrequency (%)
< 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41
25.5%
< 18
11.2%
. 18
11.2%
3 16
 
9.9%
1 13
 
8.1%
4 13
 
8.1%
5 10
 
6.2%
6 10
 
6.2%
2 9
 
5.6%
8 5
 
3.1%
Other values (2) 8
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41
25.5%
< 18
11.2%
. 18
11.2%
3 16
 
9.9%
1 13
 
8.1%
4 13
 
8.1%
5 10
 
6.2%
6 10
 
6.2%
2 9
 
5.6%
8 5
 
3.1%
Other values (2) 8
 
5.0%
Distinct28
Distinct (%)93.3%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:34.905195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10
Min length7

Characters and Unicode

Total characters300
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

Unique26 ?
Unique (%)86.7%

Sample

1st row<0.00029
2nd row<0.00036
3rd row<0.00045
4th row<0.00038
5th row<0.00043
ValueCountFrequency (%)
0.00036 2
 
6.7%
0.00045 2
 
6.7%
0.00029 1
 
3.3%
0.00041257 1
 
3.3%
0.00042358 1
 
3.3%
0.00062742 1
 
3.3%
0.0005986 1
 
3.3%
0.00040568 1
 
3.3%
0.00037179 1
 
3.3%
0.00040559 1
 
3.3%
Other values (18) 18
60.0%
2024-04-14T12:16:35.153566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 127
42.3%
< 30
 
10.0%
. 30
 
10.0%
3 20
 
6.7%
4 18
 
6.0%
6 13
 
4.3%
5 13
 
4.3%
2 13
 
4.3%
1 10
 
3.3%
8 9
 
3.0%
Other values (2) 17
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
80.0%
Math Symbol 30
 
10.0%
Other Punctuation 30
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
52.9%
3 20
 
8.3%
4 18
 
7.5%
6 13
 
5.4%
5 13
 
5.4%
2 13
 
5.4%
1 10
 
4.2%
8 9
 
3.8%
9 9
 
3.8%
7 8
 
3.3%
Math Symbol
ValueCountFrequency (%)
< 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
42.3%
< 30
 
10.0%
. 30
 
10.0%
3 20
 
6.7%
4 18
 
6.0%
6 13
 
4.3%
5 13
 
4.3%
2 13
 
4.3%
1 10
 
3.3%
8 9
 
3.0%
Other values (2) 17
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
42.3%
< 30
 
10.0%
. 30
 
10.0%
3 20
 
6.7%
4 18
 
6.0%
6 13
 
4.3%
5 13
 
4.3%
2 13
 
4.3%
1 10
 
3.3%
8 9
 
3.0%
Other values (2) 17
 
5.7%
Distinct29
Distinct (%)96.7%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:35.314412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.066667
Min length8

Characters and Unicode

Total characters302
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

Unique28 ?
Unique (%)93.3%

Sample

1st row<0.00033
2nd row<0.00039
3rd row<0.00036
4th row<0.00034
5th row<0.00032
ValueCountFrequency (%)
0.00039 2
 
6.7%
0.00064849 1
 
3.3%
0.00033 1
 
3.3%
0.00039463 1
 
3.3%
0.00043141 1
 
3.3%
0.00047798 1
 
3.3%
0.00038779 1
 
3.3%
0.00041281 1
 
3.3%
0.0003694 1
 
3.3%
0.00041036 1
 
3.3%
Other values (19) 19
63.3%
2024-04-14T12:16:35.563167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 123
40.7%
< 30
 
9.9%
. 30
 
9.9%
3 26
 
8.6%
4 21
 
7.0%
1 16
 
5.3%
9 12
 
4.0%
6 12
 
4.0%
8 9
 
3.0%
5 9
 
3.0%
Other values (2) 14
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
80.1%
Math Symbol 30
 
9.9%
Other Punctuation 30
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
50.8%
3 26
 
10.7%
4 21
 
8.7%
1 16
 
6.6%
9 12
 
5.0%
6 12
 
5.0%
8 9
 
3.7%
5 9
 
3.7%
7 8
 
3.3%
2 6
 
2.5%
Math Symbol
ValueCountFrequency (%)
< 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123
40.7%
< 30
 
9.9%
. 30
 
9.9%
3 26
 
8.6%
4 21
 
7.0%
1 16
 
5.3%
9 12
 
4.0%
6 12
 
4.0%
8 9
 
3.0%
5 9
 
3.0%
Other values (2) 14
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123
40.7%
< 30
 
9.9%
. 30
 
9.9%
3 26
 
8.6%
4 21
 
7.0%
1 16
 
5.3%
9 12
 
4.0%
6 12
 
4.0%
8 9
 
3.0%
5 9
 
3.0%
Other values (2) 14
 
4.6%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:35.718669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.944444
Min length10

Characters and Unicode

Total characters197
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

Unique18 ?
Unique (%)100.0%

Sample

1st row<0.00040443
2nd row<0.0012381
3rd row<0.00040969
4th row<0.00036689
5th row<0.00036759
ValueCountFrequency (%)
0.00040443 1
 
5.6%
0.0012381 1
 
5.6%
0.00058621 1
 
5.6%
0.00034617 1
 
5.6%
0.00069039 1
 
5.6%
0.00034599 1
 
5.6%
0.00040974 1
 
5.6%
0.00044358 1
 
5.6%
0.00036921 1
 
5.6%
0.00036811 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:35.962955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 75
38.1%
< 18
 
9.1%
. 18
 
9.1%
3 17
 
8.6%
4 14
 
7.1%
9 11
 
5.6%
1 10
 
5.1%
6 10
 
5.1%
5 8
 
4.1%
8 6
 
3.0%
Other values (2) 10
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 161
81.7%
Math Symbol 18
 
9.1%
Other Punctuation 18
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75
46.6%
3 17
 
10.6%
4 14
 
8.7%
9 11
 
6.8%
1 10
 
6.2%
6 10
 
6.2%
5 8
 
5.0%
8 6
 
3.7%
7 6
 
3.7%
2 4
 
2.5%
Math Symbol
ValueCountFrequency (%)
< 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75
38.1%
< 18
 
9.1%
. 18
 
9.1%
3 17
 
8.6%
4 14
 
7.1%
9 11
 
5.6%
1 10
 
5.1%
6 10
 
5.1%
5 8
 
4.1%
8 6
 
3.0%
Other values (2) 10
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75
38.1%
< 18
 
9.1%
. 18
 
9.1%
3 17
 
8.6%
4 14
 
7.1%
9 11
 
5.6%
1 10
 
5.1%
6 10
 
5.1%
5 8
 
4.1%
8 6
 
3.0%
Other values (2) 10
 
5.1%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:36.142148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.777778
Min length9

Characters and Unicode

Total characters194
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

Unique18 ?
Unique (%)100.0%

Sample

1st row<0.00035402
2nd row<0.001436
3rd row<0.0003857
4th row<0.00035837
5th row<0.00043387
ValueCountFrequency (%)
0.00035402 1
 
5.6%
0.001436 1
 
5.6%
0.00042358 1
 
5.6%
0.00061465 1
 
5.6%
0.00037113 1
 
5.6%
0.00032591 1
 
5.6%
0.00039678 1
 
5.6%
0.00044686 1
 
5.6%
0.00036311 1
 
5.6%
0.00056377 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:36.403004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 74
38.1%
3 19
 
9.8%
< 18
 
9.3%
. 18
 
9.3%
4 11
 
5.7%
5 10
 
5.2%
6 10
 
5.2%
1 9
 
4.6%
7 9
 
4.6%
8 7
 
3.6%
Other values (2) 9
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158
81.4%
Math Symbol 18
 
9.3%
Other Punctuation 18
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74
46.8%
3 19
 
12.0%
4 11
 
7.0%
5 10
 
6.3%
6 10
 
6.3%
1 9
 
5.7%
7 9
 
5.7%
8 7
 
4.4%
2 5
 
3.2%
9 4
 
2.5%
Math Symbol
ValueCountFrequency (%)
< 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74
38.1%
3 19
 
9.8%
< 18
 
9.3%
. 18
 
9.3%
4 11
 
5.7%
5 10
 
5.2%
6 10
 
5.2%
1 9
 
4.6%
7 9
 
4.6%
8 7
 
3.6%
Other values (2) 9
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74
38.1%
3 19
 
9.8%
< 18
 
9.3%
. 18
 
9.3%
4 11
 
5.7%
5 10
 
5.2%
6 10
 
5.2%
1 9
 
4.6%
7 9
 
4.6%
8 7
 
3.6%
Other values (2) 9
 
4.6%
Distinct30
Distinct (%)100.0%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:36.563543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.8666667
Min length6

Characters and Unicode

Total characters266
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

Unique30 ?
Unique (%)100.0%

Sample

1st row0.0008
2nd row<0.00043
3rd row0.00097
4th row0.00092
5th row0.00113
ValueCountFrequency (%)
0.0009190 1
 
3.3%
0.00097 1
 
3.3%
0.0006183 1
 
3.3%
0.0011071 1
 
3.3%
0.00064640 1
 
3.3%
0.0013965 1
 
3.3%
0.0008949 1
 
3.3%
0.0008151 1
 
3.3%
0.00051393 1
 
3.3%
0.00057133 1
 
3.3%
Other values (20) 20
66.7%
2024-04-14T12:16:36.998268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 121
45.5%
. 30
 
11.3%
1 18
 
6.8%
9 16
 
6.0%
8 15
 
5.6%
3 14
 
5.3%
< 11
 
4.1%
4 10
 
3.8%
7 9
 
3.4%
5 9
 
3.4%
Other values (2) 13
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225
84.6%
Other Punctuation 30
 
11.3%
Math Symbol 11
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121
53.8%
1 18
 
8.0%
9 16
 
7.1%
8 15
 
6.7%
3 14
 
6.2%
4 10
 
4.4%
7 9
 
4.0%
5 9
 
4.0%
6 7
 
3.1%
2 6
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Math Symbol
ValueCountFrequency (%)
< 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 121
45.5%
. 30
 
11.3%
1 18
 
6.8%
9 16
 
6.0%
8 15
 
5.6%
3 14
 
5.3%
< 11
 
4.1%
4 10
 
3.8%
7 9
 
3.4%
5 9
 
3.4%
Other values (2) 13
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 121
45.5%
. 30
 
11.3%
1 18
 
6.8%
9 16
 
6.0%
8 15
 
5.6%
3 14
 
5.3%
< 11
 
4.1%
4 10
 
3.8%
7 9
 
3.4%
5 9
 
3.4%
Other values (2) 13
 
4.9%
Distinct17
Distinct (%)89.5%
Missing29
Missing (%)60.4%
Memory size516.0 B
2024-04-14T12:16:37.147027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.473684
Min length8

Characters and Unicode

Total characters332
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

Unique15 ?
Unique (%)78.9%

Sample

1st row±0.00015
2nd row±0.00023(<0.00053)
3rd row±0.00018(<0.00044)
4th row±0.00021(<0.00047)
5th row±0.00019(<0.00045)
ValueCountFrequency (%)
±0.00019(<0.00045 2
 
10.5%
±0.00021(<0.00047 2
 
10.5%
±0.00024(<0.00052 1
 
5.3%
±0.00015 1
 
5.3%
±0.00020(<0.00046 1
 
5.3%
±0.00026(<0.00083 1
 
5.3%
±0.00014(<0.00036 1
 
5.3%
±0.00020(<0.00047 1
 
5.3%
±0.00020(<0.00072 1
 
5.3%
±0.00017(<0.00040 1
 
5.3%
Other values (7) 7
36.8%
2024-04-14T12:16:37.397835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 154
46.4%
. 37
 
11.1%
± 19
 
5.7%
( 18
 
5.4%
< 18
 
5.4%
) 18
 
5.4%
4 16
 
4.8%
2 15
 
4.5%
1 10
 
3.0%
5 7
 
2.1%
Other values (5) 20
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 222
66.9%
Other Punctuation 37
 
11.1%
Math Symbol 37
 
11.1%
Open Punctuation 18
 
5.4%
Close Punctuation 18
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154
69.4%
4 16
 
7.2%
2 15
 
6.8%
1 10
 
4.5%
5 7
 
3.2%
3 6
 
2.7%
7 5
 
2.3%
8 4
 
1.8%
6 3
 
1.4%
9 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
± 19
51.4%
< 18
48.6%
Other Punctuation
ValueCountFrequency (%)
. 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 154
46.4%
. 37
 
11.1%
± 19
 
5.7%
( 18
 
5.4%
< 18
 
5.4%
) 18
 
5.4%
4 16
 
4.8%
2 15
 
4.5%
1 10
 
3.0%
5 7
 
2.1%
Other values (5) 20
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 313
94.3%
None 19
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 154
49.2%
. 37
 
11.8%
( 18
 
5.8%
< 18
 
5.8%
) 18
 
5.8%
4 16
 
5.1%
2 15
 
4.8%
1 10
 
3.2%
5 7
 
2.2%
3 6
 
1.9%
Other values (4) 14
 
4.5%
None
ValueCountFrequency (%)
± 19
100.0%
Distinct30
Distinct (%)100.0%
Missing18
Missing (%)37.5%
Memory size516.0 B
2024-04-14T12:16:37.559658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9.5
Mean length9.2666667
Min length6

Characters and Unicode

Total characters278
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

Unique30 ?
Unique (%)100.0%

Sample

1st row0.00103
2nd row0.0009
3rd row0.00118
4th row0.00115
5th row<0.00047
ValueCountFrequency (%)
0.0009037 1
 
3.3%
0.00118 1
 
3.3%
0.00053524 1
 
3.3%
0.00090087 1
 
3.3%
0.000857 1
 
3.3%
0.0010064 1
 
3.3%
0.0011632 1
 
3.3%
0.00050519 1
 
3.3%
0.00049021 1
 
3.3%
0.00038726 1
 
3.3%
Other values (20) 20
66.7%
2024-04-14T12:16:37.806473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 127
45.7%
. 30
 
10.8%
1 18
 
6.5%
3 14
 
5.0%
8 13
 
4.7%
5 13
 
4.7%
< 13
 
4.7%
9 12
 
4.3%
7 12
 
4.3%
4 9
 
3.2%
Other values (2) 17
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235
84.5%
Other Punctuation 30
 
10.8%
Math Symbol 13
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
54.0%
1 18
 
7.7%
3 14
 
6.0%
8 13
 
5.5%
5 13
 
5.5%
9 12
 
5.1%
7 12
 
5.1%
4 9
 
3.8%
6 9
 
3.8%
2 8
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Math Symbol
ValueCountFrequency (%)
< 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 278
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
45.7%
. 30
 
10.8%
1 18
 
6.5%
3 14
 
5.0%
8 13
 
4.7%
5 13
 
4.7%
< 13
 
4.7%
9 12
 
4.3%
7 12
 
4.3%
4 9
 
3.2%
Other values (2) 17
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
45.7%
. 30
 
10.8%
1 18
 
6.5%
3 14
 
5.0%
8 13
 
4.7%
5 13
 
4.7%
< 13
 
4.7%
9 12
 
4.3%
7 12
 
4.3%
4 9
 
3.2%
Other values (2) 17
 
6.1%
Distinct15
Distinct (%)88.2%
Missing31
Missing (%)64.6%
Memory size516.0 B
2024-04-14T12:16:37.954205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length16.764706
Min length7

Characters and Unicode

Total characters285
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

Unique13 ?
Unique (%)76.5%

Sample

1st row±0.00015
2nd row±0.0002
3rd row±0.00021(<0.00047)
4th row±0.00019(<0.00043)
5th row±0.00022(<0.00048)
ValueCountFrequency (%)
±0.00022(<0.00050 2
 
11.8%
±0.00022(<0.00051 2
 
11.8%
±0.00015 1
 
5.9%
±0.0002 1
 
5.9%
±0.00021(<0.00047 1
 
5.9%
±0.00019(<0.00043 1
 
5.9%
±0.00022(<0.00048 1
 
5.9%
±0.00023(<0.00049 1
 
5.9%
±0.00017(<0.00042 1
 
5.9%
±0.00020(<0.00046 1
 
5.9%
Other values (5) 5
29.4%
2024-04-14T12:16:38.210499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 132
46.3%
. 32
 
11.2%
2 20
 
7.0%
± 17
 
6.0%
( 15
 
5.3%
< 15
 
5.3%
) 15
 
5.3%
4 10
 
3.5%
1 8
 
2.8%
5 7
 
2.5%
Other values (5) 14
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191
67.0%
Other Punctuation 32
 
11.2%
Math Symbol 32
 
11.2%
Open Punctuation 15
 
5.3%
Close Punctuation 15
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132
69.1%
2 20
 
10.5%
4 10
 
5.2%
1 8
 
4.2%
5 7
 
3.7%
8 4
 
2.1%
7 3
 
1.6%
9 3
 
1.6%
3 3
 
1.6%
6 1
 
0.5%
Math Symbol
ValueCountFrequency (%)
± 17
53.1%
< 15
46.9%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132
46.3%
. 32
 
11.2%
2 20
 
7.0%
± 17
 
6.0%
( 15
 
5.3%
< 15
 
5.3%
) 15
 
5.3%
4 10
 
3.5%
1 8
 
2.8%
5 7
 
2.5%
Other values (5) 14
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 268
94.0%
None 17
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132
49.3%
. 32
 
11.9%
2 20
 
7.5%
( 15
 
5.6%
< 15
 
5.6%
) 15
 
5.6%
4 10
 
3.7%
1 8
 
3.0%
5 7
 
2.6%
8 4
 
1.5%
Other values (4) 10
 
3.7%
None
ValueCountFrequency (%)
± 17
100.0%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:38.355406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.4444444
Min length6

Characters and Unicode

Total characters170
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

Unique18 ?
Unique (%)100.0%

Sample

1st row0.0011
2nd row<0.001324
3rd row0.0008251
4th row0.0012178
5th row0.0008278
ValueCountFrequency (%)
0.0011 1
 
5.6%
0.001324 1
 
5.6%
0.00063196 1
 
5.6%
0.0008958 1
 
5.6%
0.000652245 1
 
5.6%
0.00032159 1
 
5.6%
0.00049655 1
 
5.6%
0.0009563 1
 
5.6%
0.0010231 1
 
5.6%
0.00033768 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:38.594834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 69
40.6%
. 18
 
10.6%
1 14
 
8.2%
2 10
 
5.9%
4 9
 
5.3%
5 9
 
5.3%
3 8
 
4.7%
8 8
 
4.7%
9 8
 
4.7%
6 7
 
4.1%
Other values (2) 10
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146
85.9%
Other Punctuation 18
 
10.6%
Math Symbol 6
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69
47.3%
1 14
 
9.6%
2 10
 
6.8%
4 9
 
6.2%
5 9
 
6.2%
3 8
 
5.5%
8 8
 
5.5%
9 8
 
5.5%
6 7
 
4.8%
7 4
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Math Symbol
ValueCountFrequency (%)
< 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69
40.6%
. 18
 
10.6%
1 14
 
8.2%
2 10
 
5.9%
4 9
 
5.3%
5 9
 
5.3%
3 8
 
4.7%
8 8
 
4.7%
9 8
 
4.7%
6 7
 
4.1%
Other values (2) 10
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69
40.6%
. 18
 
10.6%
1 14
 
8.2%
2 10
 
5.9%
4 9
 
5.3%
5 9
 
5.3%
3 8
 
4.7%
8 8
 
4.7%
9 8
 
4.7%
6 7
 
4.1%
Other values (2) 10
 
5.9%
Distinct12
Distinct (%)100.0%
Missing36
Missing (%)75.0%
Memory size516.0 B
2024-04-14T12:16:38.749371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters216
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

Unique12 ?
Unique (%)100.0%

Sample

1st row±0.00021(<0.00047)
2nd row±0.00019(<0.00046)
3rd row±0.00019(<0.00042)
4th row±0.00020(<0.00046)
5th row±0.00018(<0.00064)
ValueCountFrequency (%)
±0.00021(<0.00047 1
8.3%
±0.00019(<0.00046 1
8.3%
±0.00019(<0.00042 1
8.3%
±0.00020(<0.00046 1
8.3%
±0.00018(<0.00064 1
8.3%
±0.00021(<0.00046 1
8.3%
±0.00018(<0.00044 1
8.3%
±0.00023(<0.00050 1
8.3%
±0.00024(<0.00053 1
8.3%
±0.00024(<0.00051 1
8.3%
Other values (2) 2
16.7%
2024-04-14T12:16:38.978803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98
45.4%
. 24
 
11.1%
4 13
 
6.0%
± 12
 
5.6%
( 12
 
5.6%
< 12
 
5.6%
) 12
 
5.6%
2 9
 
4.2%
1 7
 
3.2%
6 5
 
2.3%
Other values (5) 12
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144
66.7%
Other Punctuation 24
 
11.1%
Math Symbol 24
 
11.1%
Open Punctuation 12
 
5.6%
Close Punctuation 12
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98
68.1%
4 13
 
9.0%
2 9
 
6.2%
1 7
 
4.9%
6 5
 
3.5%
9 3
 
2.1%
3 3
 
2.1%
5 3
 
2.1%
8 2
 
1.4%
7 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
± 12
50.0%
< 12
50.0%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98
45.4%
. 24
 
11.1%
4 13
 
6.0%
± 12
 
5.6%
( 12
 
5.6%
< 12
 
5.6%
) 12
 
5.6%
2 9
 
4.2%
1 7
 
3.2%
6 5
 
2.3%
Other values (5) 12
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204
94.4%
None 12
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98
48.0%
. 24
 
11.8%
4 13
 
6.4%
( 12
 
5.9%
< 12
 
5.9%
) 12
 
5.9%
2 9
 
4.4%
1 7
 
3.4%
6 5
 
2.5%
9 3
 
1.5%
Other values (4) 9
 
4.4%
None
ValueCountFrequency (%)
± 12
100.0%
Distinct18
Distinct (%)100.0%
Missing30
Missing (%)62.5%
Memory size516.0 B
2024-04-14T12:16:39.124535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.8888889
Min length7

Characters and Unicode

Total characters178
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

Unique18 ?
Unique (%)100.0%

Sample

1st row0.00097156
2nd row<0.0015
3rd row0.00097453
4th row0.00097906
5th row0.00092393
ValueCountFrequency (%)
0.00097156 1
 
5.6%
0.0015 1
 
5.6%
0.0013009 1
 
5.6%
0.00060002 1
 
5.6%
0.00088874 1
 
5.6%
0.00042047 1
 
5.6%
0.00085616 1
 
5.6%
0.0005338 1
 
5.6%
0.0010675 1
 
5.6%
0.00061378 1
 
5.6%
Other values (8) 8
44.4%
2024-04-14T12:16:39.371348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 78
43.8%
. 18
 
10.1%
8 11
 
6.2%
6 10
 
5.6%
7 9
 
5.1%
1 9
 
5.1%
3 9
 
5.1%
5 8
 
4.5%
9 7
 
3.9%
< 7
 
3.9%
Other values (2) 12
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 153
86.0%
Other Punctuation 18
 
10.1%
Math Symbol 7
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 78
51.0%
8 11
 
7.2%
6 10
 
6.5%
7 9
 
5.9%
1 9
 
5.9%
3 9
 
5.9%
5 8
 
5.2%
9 7
 
4.6%
2 7
 
4.6%
4 5
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Math Symbol
ValueCountFrequency (%)
< 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 78
43.8%
. 18
 
10.1%
8 11
 
6.2%
6 10
 
5.6%
7 9
 
5.1%
1 9
 
5.1%
3 9
 
5.1%
5 8
 
4.5%
9 7
 
3.9%
< 7
 
3.9%
Other values (2) 12
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 78
43.8%
. 18
 
10.1%
8 11
 
6.2%
6 10
 
5.6%
7 9
 
5.1%
1 9
 
5.1%
3 9
 
5.1%
5 8
 
4.5%
9 7
 
3.9%
< 7
 
3.9%
Other values (2) 12
 
6.7%
Distinct11
Distinct (%)91.7%
Missing36
Missing (%)75.0%
Memory size516.0 B
2024-04-14T12:16:39.503954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.916667
Min length17

Characters and Unicode

Total characters215
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

Unique10 ?
Unique (%)83.3%

Sample

1st row±0.00024(<0.00053)
2nd row±0.00019(<0.00045)
3rd row±0.00019(<0.00044)
4th row±0.00017(<0.00043)
5th row±0.00021(<0.00048)
ValueCountFrequency (%)
±0.00016(<0.00041 2
16.7%
±0.00024(<0.00053 1
8.3%
±0.00019(<0.00045 1
8.3%
±0.00019(<0.00044 1
8.3%
±0.00017(<0.00043 1
8.3%
±0.00021(<0.00048 1
8.3%
±0.00018(<0.00042 1
8.3%
±0.00020(<0.00046 1
8.3%
±0.00019(<0.00047 1
8.3%
±0.00027(<0.00054 1
8.3%
2024-04-14T12:16:39.728874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 97
45.1%
. 24
 
11.2%
4 13
 
6.0%
± 12
 
5.6%
< 12
 
5.6%
) 12
 
5.6%
1 11
 
5.1%
( 11
 
5.1%
2 6
 
2.8%
7 4
 
1.9%
Other values (5) 13
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144
67.0%
Other Punctuation 24
 
11.2%
Math Symbol 24
 
11.2%
Close Punctuation 12
 
5.6%
Open Punctuation 11
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97
67.4%
4 13
 
9.0%
1 11
 
7.6%
2 6
 
4.2%
7 4
 
2.8%
6 3
 
2.1%
5 3
 
2.1%
9 3
 
2.1%
3 2
 
1.4%
8 2
 
1.4%
Math Symbol
ValueCountFrequency (%)
± 12
50.0%
< 12
50.0%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 215
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97
45.1%
. 24
 
11.2%
4 13
 
6.0%
± 12
 
5.6%
< 12
 
5.6%
) 12
 
5.6%
1 11
 
5.1%
( 11
 
5.1%
2 6
 
2.8%
7 4
 
1.9%
Other values (5) 13
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203
94.4%
None 12
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
47.8%
. 24
 
11.8%
4 13
 
6.4%
< 12
 
5.9%
) 12
 
5.9%
1 11
 
5.4%
( 11
 
5.4%
2 6
 
3.0%
7 4
 
2.0%
6 3
 
1.5%
Other values (4) 10
 
4.9%
None
ValueCountFrequency (%)
± 12
100.0%

Correlations

2024-04-14T12:16:39.813753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도시료채취일결과보고일ST6요오드131조사결과ST7요오드131조사결과ST1요오드131조사결과ST3요오드131조사결과ST6세슘134조사결과ST7ST6세슘134조사결과ST1ST6세슘134조사결과ST3ST6세슘134조사결과ST6세슘137조사결과ST6세슘137조사결과오차범위ST7세슘137조사결과ST7세슘137조사결과오차범위ST1세슘137조사결과ST1세슘137조사결과오차범위ST3세슘137조사결과ST3세슘137조사결과오차범위
년도1.0001.0001.0001.0001.0001.0001.0000.8450.6161.0001.0001.0000.0001.0000.9211.0001.0001.0000.000
시료채취일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
결과보고일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ST6요오드131조사결과1.0001.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST7요오드131조사결과1.0001.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST1요오드131조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST3요오드131조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST6세슘134조사결과0.8451.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0000.920NaNNaNNaNNaN
ST7ST6세슘134조사결과0.6161.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0000.920NaNNaNNaNNaN
ST1ST6세슘134조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST3ST6세슘134조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST6세슘137조사결과1.0001.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST6세슘137조사결과오차범위0.0001.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST7세슘137조사결과1.0001.0001.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST7세슘137조사결과오차범위0.9211.0001.0001.0001.000NaNNaN0.9200.920NaNNaN1.0001.0001.0001.000NaNNaNNaNNaN
ST1세슘137조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST1세슘137조사결과오차범위1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST3세슘137조사결과1.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
ST3세슘137조사결과오차범위0.0001.0001.000NaNNaN1.0001.000NaNNaN1.0001.000NaNNaNNaNNaN1.0001.0001.0001.000

Missing values

2024-04-14T12:16:32.087969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T12:16:32.285940image/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.
2024-04-14T12:16:32.456226image/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

년도시료채취일결과보고일ST6요오드131조사결과ST7요오드131조사결과ST1요오드131조사결과ST3요오드131조사결과ST6세슘134조사결과ST7ST6세슘134조사결과ST1ST6세슘134조사결과ST3ST6세슘134조사결과ST6세슘137조사결과ST6세슘137조사결과오차범위ST7세슘137조사결과ST7세슘137조사결과오차범위ST1세슘137조사결과ST1세슘137조사결과오차범위ST3세슘137조사결과ST3세슘137조사결과오차범위
020222022-07-062022-08-29<0.00839<0.01235<NA><NA><0.00029<0.00033<NA><NA>0.0008±0.000150.00103±0.00015<NA><NA><NA><NA>
120222022-08-032022-09-30<0.0092<0.01408<NA><NA><0.00036<0.00039<NA><NA><0.00043<NA>0.0009±0.0002<NA><NA><NA><NA>
220222022-09-212022-10-27<0.02607<0.01691<NA><NA><0.00045<0.00036<NA><NA>0.00097±0.00023(<0.00053)0.00118±0.00021(<0.00047)<NA><NA><NA><NA>
320222022-10-252022-12-05<0.01584<0.0075<NA><NA><0.00038<0.00034<NA><NA>0.00092±0.00018(<0.00044)0.00115±0.00019(<0.00043)<NA><NA><NA><NA>
420222022-11-282023-01-11<0.01123<0.21772<NA><NA><0.00043<0.00032<NA><NA>0.00113±0.00021(<0.00047)<0.00047<NA><NA><NA><NA><NA>
520222022-12-152023-01-26<0.08231<0.13748<NA><NA><0.00045<0.00048<NA><NA><0.00045<NA><0.00061<NA><NA><NA><NA><NA>
620232023-01-102023-02-15<0.04151<0.05099<NA><NA><0.00036<0.00039<NA><NA><0.00034<NA>0.00133±0.00022(<0.00048)<NA><NA><NA><NA>
720232023-02-132023-03-02<0.00925<0.0113<NA><NA><0.00041<0.00042<NA><NA><0.0005<NA><0.00037<NA><NA><NA><NA><NA>
820232023-03-142023-04-07<0.00977<0.0163<NA><NA><0.0004<0.00035<NA><NA>0.00083±0.00019(<0.00045)<0.00035<NA><NA><NA><NA><NA>
920232023-04-142023-05-12<0.019216<0.018877<NA><NA><0.00040356<0.00041011<NA><NA><0.0003631<NA>0.00092487±0.00023(<0.00049)<NA><NA><NA><NA>
년도시료채취일결과보고일ST6요오드131조사결과ST7요오드131조사결과ST1요오드131조사결과ST3요오드131조사결과ST6세슘134조사결과ST7ST6세슘134조사결과ST1ST6세슘134조사결과ST3ST6세슘134조사결과ST6세슘137조사결과ST6세슘137조사결과오차범위ST7세슘137조사결과ST7세슘137조사결과오차범위ST1세슘137조사결과ST1세슘137조사결과오차범위ST3세슘137조사결과ST3세슘137조사결과오차범위
3820242024-01-232024-01-29<NA><NA><0.027857<0.025186<NA><NA><0.00034599<0.00032591<NA><NA><NA><NA><0.00032159<NA><0.00042047<NA>
3920242024-01-292024-02-06<0.03576<0.02467<NA><NA><0.00040568<0.00041281<NA><NA>0.0008949±0.00019(<0.00045)0.0011632±0.00028(<0.00049)<NA><NA><NA><NA>
4020242024-02-262024-02-15<NA><NA><0.04212<0.05234<NA><NA><0.00069039<0.00037113<NA><NA><NA><NA><0.000652245<NA><0.00088874±0.00019(<0.00047)
4120242024-02-132024-02-21<0.026044<0.039168<NA><NA><0.0005986<0.00038779<NA><NA>0.0013965±0.00026(<0.00083)0.0010064±0.00023(<0.00052)<NA><NA><NA><NA>
4220242024-02-202024-02-27<NA><NA><0.056366<0.062455<NA><NA><0.00034617<0.00061465<NA><NA><NA><NA>0.0008958±0.00022(<0.00049)<0.00060002<NA>
4320242024-02-272024-03-06<0.050496<0.047049<NA><NA><0.00062742<0.00047798<NA><NA><0.00064640<NA>0.000857±0.00022(<0.00051)<NA><NA><NA><NA>
4420242024-03-052024-03-11<NA><NA><0.054024<0.069476<NA><NA><0.00058621<0.00042358<NA><NA><NA><NA><0.00063196<NA>0.0013009±0.00027(<0.00054)
4520242024-03-112024-03-19<0.054706<0.039681<NA><NA><0.00042358<0.00043141<NA><NA>0.0011071±0.00023(<0.00051)0.00090087±0.00018(<0.00045)<NA><NA><NA><NA>
4620242024-03-182024-03-26<NA><NA><0.065530<0.044639<NA><NA><0.00034516<0.00041272<NA><NA><NA><NA>0.0012610±0.00034(<0.00064)0.0014871±0.00042<0.00071)
4720242024-03-252024-04-04<0.064193<0.020568<NA><NA><0.0003762<0.00036614<NA><NA><0.00039919<NA><0.00041066<NA><NA><NA><NA><NA>