Overview

Dataset statistics

Number of variables6
Number of observations6190
Missing cells0
Missing cells (%)0.0%
Total size in memory290.3 KiB
Average record size in memory48.0 B

Variable types

Categorical5
Numeric1

Alerts

EconomyName has a high cardinality: 107 distinct values High cardinality
EconomyCode has a high cardinality: 105 distinct values High cardinality
Pillar & Topic has a high cardinality: 73 distinct values High cardinality

Reproduction

Analysis started2026-09-09 04:16:10.816260
Analysis finished2026-09-09 04:16:11.023727
Duration0.21 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EconomyName
Categorical

HIGH CARDINALITY

Distinct107
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size48.5 KiB
Chad
 
83
New Zealand
 
83
Pakistan
 
83
Botswana
 
83
Indonesia
 
83
Other values (102)
5775 

Length

Max length24
Median length8
Mean length9.116801292
Min length4

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 rowBangladesh
2nd rowBangladesh
3rd rowBangladesh
4th rowBangladesh
5th rowBangladesh

Common Values

ValueCountFrequency (%)
Chad83
 
1.3%
New Zealand83
 
1.3%
Pakistan83
 
1.3%
Botswana83
 
1.3%
Indonesia83
 
1.3%
Kyrgyz Republic83
 
1.3%
Madagascar83
 
1.3%
Seychelles83
 
1.3%
Lesotho83
 
1.3%
Georgia83
 
1.3%
Other values (97)5360
86.6%

Length

2026-09-09T11:16:11.116172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic249
 
2.9%
and206
 
2.4%
new123
 
1.5%
china123
 
1.5%
rep120
 
1.4%
côte83
 
1.0%
african83
 
1.0%
estonia83
 
1.0%
bangladesh83
 
1.0%
greece83
 
1.0%
Other values (120)7218
85.4%

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

EconomyCode
Categorical

HIGH CARDINALITY

Distinct105
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size48.5 KiB
RWA
 
83
BRB
 
83
TCD
 
83
TZA
 
83
NPL
 
83
Other values (100)
5775 

Length

Max length3
Median length3
Mean length3
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 rowBGD
2nd rowBGD
3rd rowBGD
4th rowBGD
5th rowBGD

Common Values

ValueCountFrequency (%)
RWA83
 
1.3%
BRB83
 
1.3%
TCD83
 
1.3%
TZA83
 
1.3%
NPL83
 
1.3%
COL83
 
1.3%
PRY83
 
1.3%
EST83
 
1.3%
SLE83
 
1.3%
MDG83
 
1.3%
Other values (95)5360
86.6%

Length

2026-09-09T11:16:11.280257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rwa83
 
1.3%
lso83
 
1.3%
slv83
 
1.3%
cri83
 
1.3%
vut83
 
1.3%
bgd83
 
1.3%
geo83
 
1.3%
bgr83
 
1.3%
mne83
 
1.3%
mkd83
 
1.3%
Other values (95)5360
86.6%

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

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.5 KiB
2025
4040 
2024
2150 

Length

Max length4
Median length4
Mean length4
Min length4

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 row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
20254040
65.3%
20242150
34.7%

Length

2026-09-09T11:16:11.418672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2026-09-09T11:16:11.509480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
20254040
65.3%
20242150
34.7%

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

Main
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.5 KiB
Business Insolvency
604 
Utility Services
604 
Dispute Resolution
604 
Market Competition
604 
Business Location
604 
Other values (6)
3170 

Length

Max length19
Median length17
Mean length15.00129241
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverall
2nd rowOverall
3rd rowOverall
4th rowBusiness Entry
5th rowBusiness Entry

Common Values

ValueCountFrequency (%)
Business Insolvency604
9.8%
Utility Services604
9.8%
Dispute Resolution604
9.8%
Market Competition604
9.8%
Business Location604
9.8%
International Trade604
9.8%
Taxation604
9.8%
Business Entry604
9.8%
Financial Services604
9.8%
Labor604
9.8%

Length

2026-09-09T11:16:11.615171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
business1812
16.4%
services1208
 
11.0%
labor604
 
5.5%
financial604
 
5.5%
entry604
 
5.5%
taxation604
 
5.5%
trade604
 
5.5%
international604
 
5.5%
location604
 
5.5%
competition604
 
5.5%
Other values (6)3170
28.8%

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

Pillar & Topic
Categorical

HIGH CARDINALITY

Distinct73
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size48.5 KiB
Business Location Overall
 
151
Financial Services Overall
 
151
Business Insolvency Overall
 
151
Labor Overall
 
151
Market Competition Overall
 
151
Other values (68)
5435 

Length

Max length102
Median length55
Mean length52.61082391
Min length13

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 rowPillar 1 Regulatory Framework
2nd rowPillar 2 Public Services
3rd rowPillar 3 Operational Efficiency
4th rowBusiness Entry Overall
5th rowPillar I: Quality of Regulations for Business Entry

Common Values

ValueCountFrequency (%)
Business Location Overall151
 
2.4%
Financial Services Overall151
 
2.4%
Business Insolvency Overall151
 
2.4%
Labor Overall151
 
2.4%
Market Competition Overall151
 
2.4%
Utility Services Overall151
 
2.4%
International Trade Overall151
 
2.4%
Dispute Resolution Overall151
 
2.4%
Business Entry Overall151
 
2.4%
Taxation Overall151
 
2.4%
Other values (63)4680
75.6%

Length

2026-09-09T11:16:11.793092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pillar4680
 
10.4%
of4530
 
10.0%
services2315
 
5.1%
quality2114
 
4.7%
for1812
 
4.0%
regulations1661
 
3.7%
overall1510
 
3.3%
business1359
 
3.0%
efficiency1258
 
2.8%
operational1258
 
2.8%
Other values (65)22697
50.2%

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

Score
Real number (ℝ≥0)

Distinct5871
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.97668886
Minimum0
Maximum100
Zeros44
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size48.5 KiB
2026-09-09T11:16:11.977859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.13870281
Q148.16161642
median62.16666659
Q373.9925988
95-th percentile88.57089372
Maximum100
Range100
Interquartile range (IQR)25.83098238

Descriptive statistics

Standard deviation19.60044564
Coefficient of variation (CV)0.3268010624
Kurtosis0.1747513863
Mean59.97668886
Median Absolute Deviation (MAD)12.83333293
Skewness-0.5840304841
Sum371255.704
Variance384.1774691
MonotonicityNot monotonic
2026-09-09T11:16:12.163288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044
 
0.7%
757
 
0.1%
1006
 
0.1%
13.333333336
 
0.1%
106
 
0.1%
82.777777786
 
0.1%
87.55
 
0.1%
79.833333335
 
0.1%
51.255
 
0.1%
46.254
 
0.1%
Other values (5861)6096
98.5%
ValueCountFrequency (%)
044
0.7%
1.2345679011
 
< 0.1%
2.2751322391
 
< 0.1%
2.7777776721
 
< 0.1%
2.8475667091
 
< 0.1%
31
 
< 0.1%
3.1251
 
< 0.1%
3.2000000481
 
< 0.1%
3.2098765431
 
< 0.1%
3.3333332
 
< 0.1%
ValueCountFrequency (%)
1006
0.1%
1001
 
< 0.1%
1001
 
< 0.1%
99.844361341
 
< 0.1%
99.444444421
 
< 0.1%
99.253
< 0.1%
99.204545451
 
< 0.1%
994
0.1%
98.958335881
 
< 0.1%
98.666664121
 
< 0.1%

Sample

First rows

EconomyNameEconomyCodeYearMainPillar & TopicScore
0BangladeshBGD2024OverallPillar 1 Regulatory Framework56.985467
1BangladeshBGD2024OverallPillar 2 Public Services41.639013
2BangladeshBGD2024OverallPillar 3 Operational Efficiency70.494722
3BangladeshBGD2024Business EntryBusiness Entry Overall74.083333
4BangladeshBGD2024Business EntryPillar I: Quality of Regulations for Business Entry79.999999
5BangladeshBGD2024Business EntryPillar II: Digital Public Services and Transparency of Information for Business Entry56.500000
6BangladeshBGD2024Business EntryPillar III: Operational Efficiency of Business Entry85.750000
7BangladeshBGD2024Business LocationBusiness Location Overall66.910332
8BangladeshBGD2024Business LocationPillar I: Quality of Regulations for Business Location61.550439
9BangladeshBGD2024Business LocationPillar II: Quality of Public Services and Transparency of Information for Business Location63.447223

Last rows

EconomyNameEconomyCodeYearMainPillar & TopicScore
6180West Bank and GazaPSE2025Dispute ResolutionPillar 2: Public Services For Dispute Resolution20.601852
6181West Bank and GazaPSE2025Dispute ResolutionPillar 3: Ease Of Resolving A Commercial Dispute26.363601
6182West Bank and GazaPSE2025Market CompetitionMarket Competition Overall28.355615
6183West Bank and GazaPSE2025Market CompetitionPillar 1: Quality Of Regulations That Promote Market Competition27.219907
6184West Bank and GazaPSE2025Market CompetitionPillar 2: Public Services That Promote Market Competition17.407407
6185West Bank and GazaPSE2025Market CompetitionPillar 3: Implementation Of Key Services Promoting Market Competition40.439529
6186West Bank and GazaPSE2025Business InsolvencyBusiness Insolvency Overall11.699074
6187West Bank and GazaPSE2025Business InsolvencyPillar 1: Quality Of Regulations For Judicial Insolvency Proceedings26.763889
6188West Bank and GazaPSE2025Business InsolvencyPillar 2: Quality Of Institutional And Operational Infrastructure For Judicial Insolvency Proceedings8.333333
6189West Bank and GazaPSE2025Business InsolvencyPillar 3: Operational Efficiency Of Resolving Judicial Insolvency Proceedings0.000000