Overview

Dataset statistics

Number of variables9
Number of observations628
Missing cells2330
Missing cells (%)41.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.3 KiB
Average record size in memory72.2 B

Variable types

Categorical5
Numeric3
Unsupported1

Alerts

ชื่อดัชนีย่อย_lv1 has a high cardinality: 74 distinct values High cardinality
No. is highly correlated with ตัวย่อดัชนีหลัก and 3 other fieldsHigh correlation
ตัวย่อดัชนีหลัก is highly correlated with No. and 3 other fieldsHigh correlation
ชื่อดัชนีหลัก is highly correlated with No. and 3 other fieldsHigh correlation
ชื่อดัชนีย่อย_lv1 is highly correlated with No. and 3 other fieldsHigh correlation
รายละเอียดดัชนี้ย่อย1 is highly correlated with No. and 3 other fieldsHigh correlation
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 5 other fieldsHigh correlation
รายละเอียดดัชนี้ย่อย1 is highly correlated with No. and 1 other fieldsHigh correlation
ปี is highly correlated with ชื่อดัชนีหลัก and 1 other fieldsHigh correlation
Score is highly correlated with ชื่อดัชนีหลัก and 3 other fieldsHigh correlation
Rank is highly correlated with ชื่อดัชนีหลัก and 3 other fieldsHigh correlation
ตัวย่อดัชนีหลัก has 138 (22.0%) missing values Missing
No. has 532 (84.7%) missing values Missing
รายละเอียดดัชนี้ย่อย1 has 532 (84.7%) missing values Missing
Score has 56 (8.9%) missing values Missing
Rank has 444 (70.7%) missing values Missing
หมายเหตุ has 628 (100.0%) missing values Missing
No. is uniformly distributed Uniform
รายละเอียดดัชนี้ย่อย1 is uniformly distributed Uniform
หมายเหตุ is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2026-09-09 04:12:48.093346
Analysis finished2026-09-09 04:12:50.722342
Duration2.63 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ชื่อดัชนีหลัก
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Worldwide Governance Indicators
156 
WJP Rule of Law Index
96 
Corruption Perceptions Index
90 
Edelman Trust Barometer
78 
Doing Business
60 
Other values (5)
148 

Length

Max length33
Median length28
Mean length26.07006369
Min length14

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 rowDoing Business
2nd rowDoing Business
3rd rowDoing Business
4th rowDoing Business
5th rowDoing Business

Common Values

ValueCountFrequency (%)
Worldwide Governance Indicators156
24.8%
WJP Rule of Law Index96
15.3%
Corruption Perceptions Index90
14.3%
Edelman Trust Barometer78
12.4%
Doing Business60
 
9.6%
Chandler Good Government Index42
 
6.7%
IMD World Competitiveness40
 
6.4%
E-Government Development Index24
 
3.8%
The Global Competitiveness Report24
 
3.8%
IMD World Digital Competitiveness18
 
2.9%

Length

2026-09-09T11:12:50.805218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:12:50.927374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
index252
 
12.0%
worldwide156
 
7.4%
governance156
 
7.4%
indicators156
 
7.4%
wjp96
 
4.6%
rule96
 
4.6%
of96
 
4.6%
law96
 
4.6%
corruption90
 
4.3%
perceptions90
 
4.3%
Other values (17)816
38.9%

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

ตัวย่อดัชนีหลัก
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)1.6%
Missing138
Missing (%)22.0%
Memory size5.0 KiB
WGI
156 
WJP
96 
CPI
90 
CGGI
42 
IMD
40 
Other values (3)
66 

Length

Max length8
Median length3
Mean length3.318367347
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 (%)
WGI156
24.8%
WJP96
15.3%
CPI90
14.3%
CGGI42
 
6.7%
IMD40
 
6.4%
GCR24
 
3.8%
EGDI24
 
3.8%
IMD WDCR18
 
2.9%
(Missing)138
22.0%

Length

2026-09-09T11:12:51.121640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:12:51.220464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wgi156
30.7%
wjp96
18.9%
cpi90
17.7%
imd58
 
11.4%
cggi42
 
8.3%
egdi24
 
4.7%
gcr24
 
4.7%
wdcr18
 
3.5%

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.
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct8
Distinct (%)8.3%
Missing532
Missing (%)84.7%
Memory size5.0 KiB
Factor 1
12 
Factor 3
12 
Factor 4
12 
Factor 2
12 
Factor 8
12 
Other values (3)
36 

Length

Max length8
Median length8
Mean length8
Min length8

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 rowFactor 1
2nd rowFactor 1
3rd rowFactor 1
4th rowFactor 1
5th rowFactor 1

Common Values

ValueCountFrequency (%)
Factor 112
 
1.9%
Factor 312
 
1.9%
Factor 412
 
1.9%
Factor 212
 
1.9%
Factor 812
 
1.9%
Factor 712
 
1.9%
Factor 612
 
1.9%
Factor 512
 
1.9%
(Missing)532
84.7%

Length

2026-09-09T11:12:51.369363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:12:51.466440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
factor96
50.0%
512
 
6.2%
612
 
6.2%
712
 
6.2%
812
 
6.2%
212
 
6.2%
412
 
6.2%
312
 
6.2%
112
 
6.2%

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

ชื่อดัชนีย่อย_lv1
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Control of Corruption
 
26
Political Stability No Violence
 
26
Rule of Law
 
26
Regulatory Quality
 
26
Voice and Accountability
 
26
Other values (69)
498 

Length

Max length62
Median length21
Mean length22.96178344
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 rowDealing with Construction Permits
2nd rowDealing with Construction Permits
3rd rowDealing with Construction Permits
4th rowDealing with Construction Permits
5th rowDealing with Construction Permits

Common Values

ValueCountFrequency (%)
Control of Corruption26
 
4.1%
Political Stability No Violence26
 
4.1%
Rule of Law26
 
4.1%
Regulatory Quality26
 
4.1%
Voice and Accountability26
 
4.1%
Government Effectiveness26
 
4.1%
Fundamental Rights12
 
1.9%
Order and Security12
 
1.9%
Absence of Corruption12
 
1.9%
Open Government 12
 
1.9%
Other values (64)424
67.5%

Length

2026-09-09T11:12:51.643060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of84
 
4.4%
government66
 
3.5%
trust60
 
3.2%
in60
 
3.2%
index44
 
2.3%
corruption38
 
2.0%
regulatory38
 
2.0%
and38
 
2.0%
world36
 
1.9%
rule36
 
1.9%
Other values (139)1396
73.6%

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

รายละเอียดดัชนี้ย่อย1
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct8
Distinct (%)8.3%
Missing532
Missing (%)84.7%
Memory size5.0 KiB
กระบวนการยุติธรรมทางแพ่ง
12 
การบังคับใช้กฎหมาย
12 
สิทธิขั้นพื้นฐาน
12 
ระเบียบและความมั่นคง
12 
การจำกัดอำนาจของรัฐบาล
12 
Other values (3)
36 

Length

Max length24
Median length21
Mean length20.375
Min length16

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 rowการจำกัดอำนาจของรัฐบาล
2nd rowการจำกัดอำนาจของรัฐบาล
3rd rowการจำกัดอำนาจของรัฐบาล
4th rowการจำกัดอำนาจของรัฐบาล
5th rowการจำกัดอำนาจของรัฐบาล

Common Values

ValueCountFrequency (%)
กระบวนการยุติธรรมทางแพ่ง12
 
1.9%
การบังคับใช้กฎหมาย12
 
1.9%
สิทธิขั้นพื้นฐาน12
 
1.9%
ระเบียบและความมั่นคง12
 
1.9%
การจำกัดอำนาจของรัฐบาล12
 
1.9%
กระบวนการยุติธรรมทางอาญา12
 
1.9%
การปราศจากการคอร์รัปชัน12
 
1.9%
รัฐบาลที่โปร่งใส12
 
1.9%
(Missing)532
84.7%

Length

2026-09-09T11:12:51.813350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:12:51.930878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
รัฐบาลที่โปร่งใส12
12.5%
การปราศจากการคอร์รัปชัน12
12.5%
กระบวนการยุติธรรมทางอาญา12
12.5%
การจำกัดอำนาจของรัฐบาล12
12.5%
ระเบียบและความมั่นคง12
12.5%
สิทธิขั้นพื้นฐาน12
12.5%
การบังคับใช้กฎหมาย12
12.5%
กระบวนการยุติธรรมทางแพ่ง12
12.5%

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

Distinct28
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.187898
Minimum1996
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2026-09-09T11:12:52.092764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2004
Q12016
median2020
Q32023
95-th percentile2025
Maximum2026
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.394753136
Coefficient of variation (CV)0.003168561828
Kurtosis1.778628659
Mean2018.187898
Median Absolute Deviation (MAD)3
Skewness-1.391750428
Sum1267422
Variance40.89286766
MonotonicityNot monotonic
2026-09-09T11:12:52.397468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
202453
 
8.4%
202253
 
8.4%
201852
 
8.3%
202350
 
8.0%
202150
 
8.0%
201949
 
7.8%
202544
 
7.0%
202043
 
6.8%
201636
 
5.7%
201729
 
4.6%
Other values (18)169
26.9%
ValueCountFrequency (%)
19966
1.0%
19986
1.0%
20006
1.0%
20026
1.0%
20036
1.0%
20046
1.0%
20056
1.0%
20066
1.0%
20076
1.0%
20086
1.0%
ValueCountFrequency (%)
202624
3.8%
202544
7.0%
202453
8.4%
202350
8.0%
202253
8.4%
202150
8.0%
202043
6.8%
201949
7.8%
201852
8.3%
201729
4.6%

Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct417
Distinct (%)72.9%
Missing56
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean39.46401959
Minimum0.1746
Maximum98.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2026-09-09T11:12:52.576208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1746
5-th percentile0.4331074056
Q10.72
median43
Q359.3575
95-th percentile83.121
Maximum98.7
Range98.5254
Interquartile range (IQR)58.6375

Descriptive statistics

Standard deviation28.31098052
Coefficient of variation (CV)0.7173871494
Kurtosis-1.173174181
Mean39.46401959
Median Absolute Deviation (MAD)21
Skewness-0.1744246736
Sum22573.41921
Variance801.5116179
MonotonicityNot monotonic
2026-09-09T11:12:52.765716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3718
 
2.9%
3212
 
1.9%
3511
 
1.8%
0.367
 
1.1%
677
 
1.1%
706
 
1.0%
386
 
1.0%
436
 
1.0%
805
 
0.8%
0.495
 
0.8%
Other values (407)489
77.9%
(Missing)56
 
8.9%
ValueCountFrequency (%)
0.17461
 
0.2%
0.236081
 
0.2%
0.284281
 
0.2%
0.31
 
0.2%
0.322
 
0.3%
0.333331
 
0.2%
0.341
 
0.2%
0.367
1.1%
0.3875733051
 
0.2%
0.4015991431
 
0.2%
ValueCountFrequency (%)
98.71
0.2%
98.571
0.2%
92.721
0.2%
92.41
0.2%
92.341
0.2%
91.711
0.2%
90.991
0.2%
90.51
0.2%
901
0.2%
88.771
0.2%

Rank
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct73
Distinct (%)39.7%
Missing444
Missing (%)70.7%
Infinite0
Infinite (%)0.0%
Mean45.08152174
Minimum3
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2026-09-09T11:12:52.957320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q124
median44
Q362
95-th percentile88.4
Maximum109
Range106
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.32916948
Coefficient of variation (CV)0.5174885092
Kurtosis-0.4691363318
Mean45.08152174
Median Absolute Deviation (MAD)19
Skewness0.2997099831
Sum8295
Variance544.2501485
MonotonicityNot monotonic
2026-09-09T11:12:53.145135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679
 
1.4%
447
 
1.1%
457
 
1.1%
236
 
1.0%
245
 
0.8%
205
 
0.8%
425
 
0.8%
564
 
0.6%
634
 
0.6%
754
 
0.6%
Other values (63)128
 
20.4%
(Missing)444
70.7%
ValueCountFrequency (%)
31
 
0.2%
51
 
0.2%
63
0.5%
82
0.3%
104
0.6%
111
 
0.2%
121
 
0.2%
132
0.3%
142
0.3%
153
0.5%
ValueCountFrequency (%)
1091
0.2%
1081
0.2%
971
0.2%
961
0.2%
942
0.3%
921
0.2%
901
0.2%
892
0.3%
851
0.2%
821
0.2%

หมายเหตุ
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing628
Missing (%)100.0%
Memory size5.0 KiB

Interactions

2026-09-09T11:12:49.473206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:48.607097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:49.047880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:49.606086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:48.767644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:49.200639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:49.735065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:48.918144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:12:49.344946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2026-09-09T11:12:53.296961image/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.
2026-09-09T11:12:53.483307image/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.
2026-09-09T11:12:53.669340image/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.
2026-09-09T11:12:53.858522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2026-09-09T11:12:54.053160image/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

2026-09-09T11:12:49.968880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-09-09T11:12:50.241236image/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.
2026-09-09T11:12:50.450820image/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.
2026-09-09T11:12:50.610697image/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_lv1รายละเอียดดัชนี้ย่อย1ปีScoreRankหมายเหตุ
0Doing BusinessNaNNaNDealing with Construction PermitsNaN201588.776.0NaN
1Doing BusinessNaNNaNDealing with Construction PermitsNaN201675.6439.0NaN
2Doing BusinessNaNNaNDealing with Construction PermitsNaN201775.6542.0NaN
3Doing BusinessNaNNaNDealing with Construction PermitsNaN201874.5843.0NaN
4Doing BusinessNaNNaNDealing with Construction PermitsNaN201971.8667.0NaN
5Doing BusinessNaNNaNDealing with Construction PermitsNaN202077.3034.0NaN
6Doing BusinessNaNNaNEnforcing ContractsNaN201570.0525.0NaN
7Doing BusinessNaNNaNEnforcing ContractsNaN201662.6956.0NaN
8Doing BusinessNaNNaNEnforcing ContractsNaN201764.5451.0NaN
9Doing BusinessNaNNaNEnforcing ContractsNaN201867.9134.0NaN

Last rows

ชื่อดัชนีหลักตัวย่อดัชนีหลักNo.ชื่อดัชนีย่อย_lv1รายละเอียดดัชนี้ย่อย1ปีScoreRankหมายเหตุ
618Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN201637.0NaNNaN
619Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN201742.0NaNNaN
620Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN201842.0NaNNaN
621Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN201943.0NaNNaN
622Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202043.0NaNNaN
623Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202142.0NaNNaN
624Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202245.0NaNNaN
625Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202336.0NaNNaN
626Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202434.0NaNNaN
627Corruption Perceptions IndexCPINaNWorld Economic Forum EOSNaN202535.0NaNNaN