Overview

Dataset statistics

Number of variables18
Number of observations1146
Missing cells12346
Missing cells (%)59.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory161.3 KiB
Average record size in memory144.1 B

Variable types

Categorical6
Numeric4
Unsupported8

Alerts

ชื่อดัชนีย่อย_lv2 has a high cardinality: 224 distinct values High cardinality
ชื่อดัชนีหลัก is highly correlated with ตัวย่อดัชนีหลัก and 4 other fieldsHigh correlation
ตัวย่อดัชนีหลัก is highly correlated with ชื่อดัชนีหลัก and 4 other fieldsHigh correlation
No. is highly correlated with ชื่อดัชนีย่อย_lv1 and 1 other fieldsHigh correlation
ชื่อดัชนีย่อย_lv1 is highly correlated with ชื่อดัชนีหลัก and 7 other fieldsHigh correlation
รายละเอียดดัชนีย่อย2 is highly correlated with No. and 1 other fieldsHigh correlation
ปี is highly correlated with ชื่อดัชนีหลัก and 2 other fieldsHigh correlation
Score is highly correlated with ชื่อดัชนีหลัก and 2 other fieldsHigh correlation
Rank is highly correlated with ชื่อดัชนีหลัก and 2 other fieldsHigh correlation
หมายเหตุ is highly correlated with ชื่อดัชนีย่อย_lv1High correlation
No. has 618 (53.9%) missing values Missing
รายละเอียดดัชนีย่อย2 has 618 (53.9%) missing values Missing
Score has 289 (25.2%) missing values Missing
Rank has 532 (46.4%) missing values Missing
หมายเหตุ has 1117 (97.5%) missing values Missing
Unnamed: 10 has 1146 (100.0%) missing values Missing
Unnamed: 11 has 1146 (100.0%) missing values Missing
Unnamed: 12 has 1146 (100.0%) missing values Missing
Unnamed: 13 has 1146 (100.0%) missing values Missing
Unnamed: 14 has 1146 (100.0%) missing values Missing
Unnamed: 15 has 1146 (100.0%) missing values Missing
Unnamed: 16 has 1146 (100.0%) missing values Missing
Unnamed: 17 has 1146 (100.0%) missing values Missing
รายละเอียดดัชนีย่อย2 is uniformly distributed Uniform
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2026-09-09 04:12:55.610224
Analysis finished2026-09-09 04:12:59.782247
Duration4.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ชื่อดัชนีหลัก
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
WJP Rule of Law Index
528 
Chandler Good Government Index
210 
IMD World Competitiveness
200 
The Global Competitiveness Report
127 
IMD World Digital Competitiveness
81 

Length

Max length33
Median length25
Mean length25.52530541
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
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 rowIMD World Competitiveness
2nd rowIMD World Competitiveness
3rd rowIMD World Competitiveness
4th rowIMD World Competitiveness
5th rowIMD World Competitiveness

Common Values

ValueCountFrequency (%)
WJP Rule of Law Index528
46.1%
Chandler Good Government Index210
 
18.3%
IMD World Competitiveness200
 
17.5%
The Global Competitiveness Report127
 
11.1%
IMD World Digital Competitiveness81
 
7.1%

Length

2026-09-09T11:12:59.849354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:12:59.945045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
index738
15.0%
law528
10.7%
of528
10.7%
rule528
10.7%
wjp528
10.7%
competitiveness408
8.3%
world281
 
5.7%
imd281
 
5.7%
government210
 
4.3%
good210
 
4.3%
Other values (5)672
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ตัวย่อดัชนีหลัก
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
WJP
528 
CGGI
210 
IMD
200 
GCR
127 
IMD WDCR
81 

Length

Max length8
Median length3
Mean length3.536649215
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
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 rowIMD
2nd rowIMD
3rd rowIMD
4th rowIMD
5th rowIMD

Common Values

ValueCountFrequency (%)
WJP528
46.1%
CGGI210
 
18.3%
IMD200
 
17.5%
GCR127
 
11.1%
IMD WDCR81
 
7.1%

Length

2026-09-09T11:13:00.082103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:00.176156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wjp528
43.0%
imd281
22.9%
cggi210
 
17.1%
gcr127
 
10.4%
wdcr81
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

No.
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct44
Distinct (%)8.3%
Missing618
Missing (%)53.9%
Infinite0
Infinite (%)0.0%
Mean5.077272727
Minimum1.1
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2026-09-09T11:13:00.460531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.3
Q13.175
median4.95
Q37.325
95-th percentile8.5
Maximum8.7
Range7.6
Interquartile range (IQR)4.15

Descriptive statistics

Standard deviation2.413327666
Coefficient of variation (CV)0.4753196835
Kurtosis-1.268678952
Mean5.077272727
Median Absolute Deviation (MAD)2.3
Skewness-0.1173099307
Sum2680.8
Variance5.824150423
MonotonicityNot monotonic
2026-09-09T11:13:00.629195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5.112
 
1.0%
7.712
 
1.0%
8.112
 
1.0%
1.312
 
1.0%
1.212
 
1.0%
7.412
 
1.0%
4.212
 
1.0%
5.312
 
1.0%
2.212
 
1.0%
8.712
 
1.0%
Other values (34)408
35.6%
(Missing)618
53.9%
ValueCountFrequency (%)
1.112
1.0%
1.212
1.0%
1.312
1.0%
1.412
1.0%
1.512
1.0%
1.612
1.0%
2.112
1.0%
2.212
1.0%
2.312
1.0%
2.412
1.0%
ValueCountFrequency (%)
8.712
1.0%
8.612
1.0%
8.512
1.0%
8.412
1.0%
8.312
1.0%
8.212
1.0%
8.112
1.0%
7.712
1.0%
7.612
1.0%
7.512
1.0%

ชื่อดัชนีย่อย_lv1
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
Fundamental Rights
96 
Civil Justice
84 
Criminal Justice
84 
Constraints on Government Powers
 
72
Infrastructure
 
64
Other values (28)
746 

Length

Max length32
Median length18
Mean length18.59162304
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
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 rowBusiness efficiency
2nd rowBusiness efficiency
3rd rowBusiness efficiency
4th rowBusiness efficiency
5th rowBusiness efficiency

Common Values

ValueCountFrequency (%)
Fundamental Rights96
 
8.4%
Civil Justice84
 
7.3%
Criminal Justice84
 
7.3%
Constraints on Government Powers72
 
6.3%
Infrastructure64
 
5.6%
Helping People Rise 60
 
5.2%
Regulatory Enforcement60
 
5.2%
Business efficiency50
 
4.4%
Government Efficiency50
 
4.4%
Economic Performance50
 
4.4%
Other values (23)476
41.5%

Length

2026-09-09T11:13:00.814849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
government170
 
6.7%
justice168
 
6.6%
efficiency100
 
3.9%
fundamental96
 
3.8%
rights96
 
3.8%
civil84
 
3.3%
criminal84
 
3.3%
78
 
3.1%
constraints72
 
2.8%
on72
 
2.8%
Other values (50)1525
59.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ชื่อดัชนีย่อย_lv2
Categorical

HIGH CARDINALITY

Distinct224
Distinct (%)19.6%
Missing4
Missing (%)0.3%
Memory size9.1 KiB
Due process of the law and rights of the accused
 
24
Employment
 
16
Education
 
16
International Trade
 
16
Criminal system is free of corruption
 
12
Other values (219)
1058 

Length

Max length93
Median length25
Mean length34.97285464
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)10.3%

Sample

1st rowAllitudes and Values
2nd rowAllitudes and Values
3rd rowAllitudes and Values
4th rowAllitudes and Values
5th rowAllitudes and Values

Common Values

ValueCountFrequency (%)
Due process of the law and rights of the accused24
 
2.1%
Employment16
 
1.4%
Education16
 
1.4%
International Trade16
 
1.4%
Criminal system is free of corruption12
 
1.0%
The government does not expropriate without lawful process and adequate compensation12
 
1.0%
People can access and afford civil justice12
 
1.0%
Administrative proceedings are conducted without unreasonable delay12
 
1.0%
Civil justice is not subject to unreasonable delay12
 
1.0%
Due process is respected in administrative proceedings12
 
1.0%
Other values (214)998
87.1%

Length

2026-09-09T11:13:00.992102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
is240
 
4.4%
of235
 
4.3%
and209
 
3.9%
government189
 
3.5%
the182
 
3.4%
effectively156
 
2.9%
are120
 
2.2%
to88
 
1.6%
civil84
 
1.6%
not84
 
1.6%
Other values (382)3820
70.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

รายละเอียดดัชนีย่อย2
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct44
Distinct (%)8.3%
Missing618
Missing (%)53.9%
Memory size9.1 KiB
มีการจำกัดความขัดแย้งทางการเมืองที่มีประสิทธิภาพ
 
12
มีกระบวนการแก้ปัญหาข้อพิพาทแบบทางเลือกที่สามารถเข้าถึงได้ มีประสิทธิภาพ และเป็นกลาง
 
12
มีการรับประกันเสรีภาพในการคุ้มครองสิทธิส่วนบุคคล
 
12
มีการรับประกันสิทธิในชีวิตและความมั่นคงปลอดภัยของบุคคล
 
12
ระบบการพิพากษาคดีอาชญากรรมมีประสิทธิภาพและเหมาะสมกับเวลา
 
12
Other values (39)
468 

Length

Max length83
Median length48
Mean length47.93181818
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
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 rowอำนาจของรัฐถูกจำกัดโดยฝ่ายนิติบัญญัติ
2nd rowอำนาจของรัฐถูกจำกัดโดยฝ่ายตุลาการ
3rd rowอำนาจของรัฐถูกจำกัดโดยฝ่ายองค์กรตรวจสอบอิสระ
4th rowเจ้าหน้าที่รัฐถูกลงโทษ (sanction) จากการประพฤติมิชอบ (misconduct)
5th rowอำนาจของรัฐอยู่ภายใต้การตรวจสอบจากฝ่ายที่ไม่ใช่รัฐ

Common Values

ValueCountFrequency (%)
มีการจำกัดความขัดแย้งทางการเมืองที่มีประสิทธิภาพ12
 
1.0%
มีกระบวนการแก้ปัญหาข้อพิพาทแบบทางเลือกที่สามารถเข้าถึงได้ มีประสิทธิภาพ และเป็นกลาง12
 
1.0%
มีการรับประกันเสรีภาพในการคุ้มครองสิทธิส่วนบุคคล12
 
1.0%
มีการรับประกันสิทธิในชีวิตและความมั่นคงปลอดภัยของบุคคล12
 
1.0%
ระบบการพิพากษาคดีอาชญากรรมมีประสิทธิภาพและเหมาะสมกับเวลา12
 
1.0%
ประชาชนไม่ใช้อำนาจตามอำเภอใจ (ศาลเตี้ย) เพื่อการแก้แค้นส่วนบุคคล12
 
1.0%
มีกระบวนการออกกฎหมายที่เป็นธรรม12
 
1.0%
กระบวนการทางแพ่งถูกบังคับใช้อย่างมีประสิทธิภาพ12
 
1.0%
ระบบการสืบสวนสอบสวนทางอาญามีประสิทธิภาพ12
 
1.0%
การเปลี่ยนผ่านทางอำนาจที่อยู่ภายใต้กฎหมาย12
 
1.0%
Other values (34)408
35.6%
(Missing)618
53.9%

Length

2026-09-09T11:13:01.172114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ไม่ใช้สาธารณสมบัติเพื่อผลประโยชน์ส่วนตน48
 
7.0%
อำนาจของรัฐถูกจำกัดโดยฝ่ายตุลาการ12
 
1.8%
การเปลี่ยนผ่านทางอำนาจที่อยู่ภายใต้กฎหมาย12
 
1.8%
ประชาชนสามารถมีส่วนร่วม12
 
1.8%
ระบบราชทัณฑ์มีประสิทธิภาพในการลดการกระทำที่เป็นอาชญากรรม12
 
1.8%
ขั้นตอนการทำงานเป็นไปตามกฎเกณฑ์อย่างตรงไปตรงมา12
 
1.8%
กระบวนการทางแพ่งปราศจากการคอร์รัปชัน12
 
1.8%
กระบวนการทางแพ่งปราศจากการแบ่งแยก12
 
1.8%
เจ้าหน้าที่รัฐในฝ่ายบริหาร12
 
1.8%
misconduct12
 
1.8%
Other values (44)528
77.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ปี
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.355148
Minimum2012
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2026-09-09T11:13:01.316170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2014
Q12018
median2021
Q32023
95-th percentile2025
Maximum2026
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.622642841
Coefficient of variation (CV)0.001793072294
Kurtosis-0.5673401932
Mean2020.355148
Median Absolute Deviation (MAD)3
Skewness-0.4137283559
Sum2315327
Variance13.12354116
MonotonicityNot monotonic
2026-09-09T11:13:01.445595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2018171
14.9%
2025108
9.4%
2024108
9.4%
2023108
9.4%
2022108
9.4%
2021108
9.4%
2019102
8.9%
202073
6.4%
202655
 
4.8%
201644
 
3.8%
Other values (4)161
14.0%
ValueCountFrequency (%)
201244
 
3.8%
201444
 
3.8%
201544
 
3.8%
201644
 
3.8%
201729
 
2.5%
2018171
14.9%
2019102
8.9%
202073
6.4%
2021108
9.4%
2022108
9.4%
ValueCountFrequency (%)
202655
 
4.8%
2025108
9.4%
2024108
9.4%
2023108
9.4%
2022108
9.4%
2021108
9.4%
202073
6.4%
2019102
8.9%
2018171
14.9%
201729
 
2.5%

Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct627
Distinct (%)73.2%
Missing289
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean8.909896448
Minimum0.06
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2026-09-09T11:13:01.618171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.2769802
Q10.42
median0.51057834
Q30.68
95-th percentile64.76
Maximum100
Range99.94
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation22.2952199
Coefficient of variation (CV)2.502298431
Kurtosis5.495074953
Mean8.909896448
Median Absolute Deviation (MAD)0.11164366
Skewness2.574315547
Sum7635.781256
Variance497.0768304
MonotonicityNot monotonic
2026-09-09T11:13:01.799957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2513
 
1.1%
0.4712
 
1.0%
0.4910
 
0.9%
0.39
 
0.8%
0.58
 
0.7%
0.537
 
0.6%
0.787
 
0.6%
0.337
 
0.6%
0.586
 
0.5%
0.646
 
0.5%
Other values (617)772
67.4%
(Missing)289
 
25.2%
ValueCountFrequency (%)
0.061
 
0.1%
0.1266231
 
0.1%
0.128105751
 
0.1%
0.196
0.5%
0.2460376621
 
0.1%
0.2490757711
 
0.1%
0.2513
1.1%
0.2519185451
 
0.1%
0.2519291141
 
0.1%
0.2525781
 
0.1%
ValueCountFrequency (%)
1005
0.4%
99.91
 
0.1%
99.81
 
0.1%
98.91
 
0.1%
98.81
 
0.1%
97.71
 
0.1%
96.91
 
0.1%
961
 
0.1%
951
 
0.1%
93.61
 
0.1%

Rank
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct105
Distinct (%)17.1%
Missing532
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean44.74592834
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2026-09-09T11:13:01.981341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q127
median42
Q358
95-th percentile88
Maximum171
Range170
Interquartile range (IQR)31

Descriptive statistics

Standard deviation24.62900467
Coefficient of variation (CV)0.5504189002
Kurtosis0.8773891011
Mean44.74592834
Median Absolute Deviation (MAD)16
Skewness0.6606381611
Sum27474
Variance606.5878708
MonotonicityNot monotonic
2026-09-09T11:13:02.154208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3417
 
1.5%
6816
 
1.4%
5216
 
1.4%
3815
 
1.3%
5314
 
1.2%
5514
 
1.2%
5614
 
1.2%
3714
 
1.2%
3914
 
1.2%
3613
 
1.1%
Other values (95)467
40.8%
(Missing)532
46.4%
ValueCountFrequency (%)
11
 
0.1%
37
0.6%
47
0.6%
54
0.3%
69
0.8%
76
0.5%
86
0.5%
94
0.3%
104
0.3%
112
 
0.2%
ValueCountFrequency (%)
1711
0.1%
1331
0.1%
1281
0.1%
1141
0.1%
1111
0.1%
1101
0.1%
1071
0.1%
1041
0.1%
1021
0.1%
1011
0.1%

หมายเหตุ
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)6.9%
Missing1117
Missing (%)97.5%
Memory size9.1 KiB
เพิ่มดัชนีย่อยระดับที่ 3
25 
ค่า Score ตาม Original file คือ N/A
4 

Length

Max length35
Median length24
Mean length25.51724138
Min length24

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
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 rowค่า Score ตาม Original file คือ N/A
2nd rowค่า Score ตาม Original file คือ N/A
3rd rowค่า Score ตาม Original file คือ N/A
4th rowค่า Score ตาม Original file คือ N/A
5th rowเพิ่มดัชนีย่อยระดับที่ 3

Common Values

ValueCountFrequency (%)
เพิ่มดัชนีย่อยระดับที่ 325
 
2.2%
ค่า Score ตาม Original file คือ N/A4
 
0.3%
(Missing)1117
97.5%

Length

2026-09-09T11:13:02.323427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:13:02.412138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
325
32.1%
เพิ่มดัชนีย่อยระดับที่25
32.1%
n/a4
 
5.1%
คือ4
 
5.1%
file4
 
5.1%
original4
 
5.1%
ตาม4
 
5.1%
score4
 
5.1%
ค่า4
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Unnamed: 10
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 11
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 12
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 13
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 14
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 15
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 16
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Unnamed: 17
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1146
Missing (%)100.0%
Memory size9.1 KiB

Interactions

2026-09-09T11:12:57.968567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.097271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.689064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.347301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:58.084460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.265566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.854478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.498036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:58.235275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.428676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.024931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.667310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:58.377700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:56.573399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.192806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:57.823522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2026-09-09T11:13:02.512669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2026-09-09T11:13:02.785386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2026-09-09T11:13:03.058639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2026-09-09T11:13:03.480262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2026-09-09T11:12:58.687960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-09-09T11:12:59.121292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-09-09T11:12:59.400493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2026-09-09T11:12:59.619490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ชื่อดัชนีหลักตัวย่อดัชนีหลักNo.ชื่อดัชนีย่อย_lv1ชื่อดัชนีย่อย_lv2รายละเอียดดัชนีย่อย2ปีScoreRankหมายเหตุUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17
0IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2017NaN23.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
1IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2018NaN27.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
2IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2019NaN26.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
3IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2020NaN20.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
4IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2021NaN20.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
5IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2022NaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
6IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2023NaN19.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
7IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2024NaN18.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
8IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2025NaN22.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
9IMD World CompetitivenessIMDNaNBusiness efficiencyAllitudes and ValuesNaN2026NaN25.0NaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

ชื่อดัชนีหลักตัวย่อดัชนีหลักNo.ชื่อดัชนีย่อย_lv1ชื่อดัชนีย่อย_lv2รายละเอียดดัชนีย่อย2ปีScoreRankหมายเหตุUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17
1136WJP Rule of Law IndexWJP7.5Civil JusticeCivil justice is not subject to unreasonable delayกระบวนการทางแพ่งไม่เป็นไปด้วยความล่าช้าที่เกิดจากเหตุผลอันไม่สมควร20250.400356NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1137WJP Rule of Law IndexWJP7.6Civil JusticeCivil justice is effectively enforcedกระบวนการทางแพ่งถูกบังคับใช้อย่างมีประสิทธิภาพ20250.348938NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1138WJP Rule of Law IndexWJP7.7Civil JusticeAlternative dispute resolution mechanisms are accessible, impartial, and effectiveมีกระบวนการแก้ปัญหาข้อพิพาทแบบทางเลือกที่สามารถเข้าถึงได้ มีประสิทธิภาพ และเป็นกลาง20250.494247NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1139WJP Rule of Law IndexWJP8.1Criminal JusticeCriminal investigation system is effectiveระบบการสืบสวนสอบสวนทางอาญามีประสิทธิภาพ20250.375438NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1140WJP Rule of Law IndexWJP8.2Criminal JusticeCriminal adjudication system is timely and effectiveระบบการพิพากษาคดีอาชญากรรมมีประสิทธิภาพและเหมาะสมกับเวลา20250.424851NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1141WJP Rule of Law IndexWJP8.3Criminal JusticeCorrectional system is effective in reducing criminal behaviorระบบราชทัณฑ์มีประสิทธิภาพในการลดการกระทำที่เป็นอาชญากรรม20250.272444NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1142WJP Rule of Law IndexWJP8.4Criminal JusticeCriminal system is impartialระบบยุติธรรมทางอาญาปราศจากการแบ่งแยก20250.318437NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1143WJP Rule of Law IndexWJP8.5Criminal JusticeCriminal system is free of corruptionระบบยุติธรรมทางอาญาปราศจากการคอร์รัปชัน20250.575434NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1144WJP Rule of Law IndexWJP8.6Criminal JusticeCriminal system is free of improper government influenceระบบยุติธรรมทางอาญาปราศจากอิทธิพลครอบงำของรัฐบาล20250.541150NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1145WJP Rule of Law IndexWJP8.7Criminal JusticeDue process of the law and rights of the accusedมีกระบวนการที่ชอบด้วยกฎหมายและประกันสิทธิของผู้ต้องหา20250.442854NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN