Overview

Dataset statistics

Number of variables5
Number of observations4
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory288.0 B
Average record size in memory72.0 B

Variable types

Categorical5

Alerts

9/25 is highly overall correlated with 9/26 and 3 other fieldsHigh correlation
9/26 is highly overall correlated with 9/25 and 3 other fieldsHigh correlation
9/27 is highly overall correlated with 9/25 and 3 other fieldsHigh correlation
9/28 is highly overall correlated with 9/25 and 3 other fieldsHigh correlation
日期 is highly overall correlated with 9/25 and 3 other fieldsHigh correlation
9/26 is uniformly distributedUniform
9/27 is uniformly distributedUniform
日期 is uniformly distributedUniform
9/26 has unique valuesUnique
9/27 has unique valuesUnique
日期 has unique valuesUnique

Reproduction

Analysis started2023-10-06 11:10:08.466930
Analysis finished2023-10-06 11:10:09.289943
Duration0.82 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

9/25
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
5
1000
7

Length

Max length4
Median length1
Mean length1.75
Min length1

Characters and Unicode

Total characters7
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row1000
2nd row7
3rd row5
4th row5

Common Values

ValueCountFrequency (%)
5 2
50.0%
1000 1
25.0%
7 1
25.0%

Length

2023-10-06T19:10:09.340996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T19:10:09.435405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5 2
50.0%
1000 1
25.0%
7 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 3
42.9%
5 2
28.6%
1 1
 
14.3%
7 1
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
42.9%
5 2
28.6%
1 1
 
14.3%
7 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
42.9%
5 2
28.6%
1 1
 
14.3%
7 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
42.9%
5 2
28.6%
1 1
 
14.3%
7 1
 
14.3%

9/26
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
1500
9
7
6

Length

Max length4
Median length1
Mean length1.75
Min length1

Characters and Unicode

Total characters7
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1500
2nd row9
3rd row7
4th row6

Common Values

ValueCountFrequency (%)
1500 1
25.0%
9 1
25.0%
7 1
25.0%
6 1
25.0%

Length

2023-10-06T19:10:09.514596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T19:10:09.614315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1500 1
25.0%
9 1
25.0%
7 1
25.0%
6 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%
7 1
14.3%
6 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%
7 1
14.3%
6 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%
7 1
14.3%
6 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%
7 1
14.3%
6 1
14.3%

9/27
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
2000
8
7
5

Length

Max length4
Median length1
Mean length1.75
Min length1

Characters and Unicode

Total characters7
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row2000
2nd row8
3rd row7
4th row5

Common Values

ValueCountFrequency (%)
2000 1
25.0%
8 1
25.0%
7 1
25.0%
5 1
25.0%

Length

2023-10-06T19:10:09.708785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T19:10:09.895155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2000 1
25.0%
8 1
25.0%
7 1
25.0%
5 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 3
42.9%
2 1
 
14.3%
8 1
 
14.3%
7 1
 
14.3%
5 1
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
42.9%
2 1
 
14.3%
8 1
 
14.3%
7 1
 
14.3%
5 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
42.9%
2 1
 
14.3%
8 1
 
14.3%
7 1
 
14.3%
5 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
42.9%
2 1
 
14.3%
8 1
 
14.3%
7 1
 
14.3%
5 1
 
14.3%

9/28
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
6
1500
9

Length

Max length4
Median length1
Mean length1.75
Min length1

Characters and Unicode

Total characters7
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row1500
2nd row9
3rd row6
4th row6

Common Values

ValueCountFrequency (%)
6 2
50.0%
1500 1
25.0%
9 1
25.0%

Length

2023-10-06T19:10:09.976886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T19:10:10.069807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
6 2
50.0%
1500 1
25.0%
9 1
25.0%

Most occurring characters

ValueCountFrequency (%)
6 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2
28.6%
0 2
28.6%
1 1
14.3%
5 1
14.3%
9 1
14.3%

日期
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
照度
湿度
肥力
PH

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row照度
2nd row湿度
3rd row肥力
4th rowPH

Common Values

ValueCountFrequency (%)
照度 1
25.0%
湿度 1
25.0%
肥力 1
25.0%
PH 1
25.0%

Length

2023-10-06T19:10:10.144286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T19:10:10.229179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
照度 1
25.0%
湿度 1
25.0%
肥力 1
25.0%
ph 1
25.0%

Most occurring characters

ValueCountFrequency (%)
2
25.0%
1
12.5%
湿 1
12.5%
1
12.5%
1
12.5%
P 1
12.5%
H 1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
75.0%
Uppercase Letter 2
 
25.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
33.3%
1
16.7%
湿 1
16.7%
1
16.7%
1
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
H 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Han 6
75.0%
Latin 2
 
25.0%

Most frequent character per script

Han
ValueCountFrequency (%)
2
33.3%
1
16.7%
湿 1
16.7%
1
16.7%
1
16.7%
Latin
ValueCountFrequency (%)
P 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 6
75.0%
ASCII 2
 
25.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
2
33.3%
1
16.7%
湿 1
16.7%
1
16.7%
1
16.7%
ASCII
ValueCountFrequency (%)
P 1
50.0%
H 1
50.0%

Correlations

2023-10-06T19:10:10.298956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
9/259/269/279/28日期
9/251.0001.0001.0001.0001.000
9/261.0001.0001.0001.0001.000
9/271.0001.0001.0001.0001.000
9/281.0001.0001.0001.0001.000
日期1.0001.0001.0001.0001.000

Missing values

2023-10-06T19:10:09.168641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-06T19:10:09.250721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

9/259/269/279/28日期
01000150020001500照度
17989湿度
25776肥力
35656PH
9/259/269/279/28日期
01000150020001500照度
17989湿度
25776肥力
35656PH