Overview

Dataset statistics

Number of variables6
Number of observations47
Missing cells34
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory50.7 B

Variable types

Categorical4
Numeric2

Alerts

จำนวน MiniApps is highly correlated with จำนวนบริการHigh correlation
จำนวนบริการ is highly correlated with จำนวน MiniAppsHigh correlation
จำนวน MiniApps is highly correlated with จำนวนบริการHigh correlation
จำนวนบริการ is highly correlated with จำนวน MiniAppsHigh correlation
จำนวน MiniApps is highly correlated with จำนวนบริการHigh correlation
จำนวนบริการ is highly correlated with จำนวน MiniAppsHigh correlation
Total Transaction is highly correlated with User สะสม and 2 other fieldsHigh correlation
User สะสม is highly correlated with Total Transaction and 2 other fieldsHigh correlation
Date is highly correlated with Total Transaction and 2 other fieldsHigh correlation
DownloadsApps สะสม is highly correlated with Total Transaction and 2 other fieldsHigh correlation
Date is highly correlated with จำนวน MiniApps and 4 other fieldsHigh correlation
จำนวน MiniApps is highly correlated with Date and 4 other fieldsHigh correlation
จำนวนบริการ is highly correlated with Date and 3 other fieldsHigh correlation
User สะสม is highly correlated with Date and 4 other fieldsHigh correlation
DownloadsApps สะสม is highly correlated with Date and 3 other fieldsHigh correlation
Total Transaction is highly correlated with Date and 4 other fieldsHigh correlation
จำนวน MiniApps has 21 (44.7%) missing values Missing
DownloadsApps สะสม has 13 (27.7%) missing values Missing
Date is uniformly distributed Uniform
User สะสม is uniformly distributed Uniform
DownloadsApps สะสม is uniformly distributed Uniform
Total Transaction is uniformly distributed Uniform
Date has unique values Unique
User สะสม has unique values Unique
Total Transaction has unique values Unique

Reproduction

Analysis started2026-09-09 04:20:55.776120
Analysis finished2026-09-09 04:20:57.368304
Duration1.59 second
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size504.0 B
25-03-2024
 
1
25-01-2024
 
1
25-07-2025
 
1
25-09-2023
 
1
25-10-2024
 
1
Other values (42)
42 

Length

Max length10
Median length10
Mean length9.957446809
Min length9

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

Unique47 ?
Unique (%)100.0%

Sample

1st row31-03-2022
2nd row6-06-2022
3rd row25-07-2022
4th row25-08-2022
5th row20-09-2022

Common Values

ValueCountFrequency (%)
25-03-20241
 
2.1%
25-01-20241
 
2.1%
25-07-20251
 
2.1%
25-09-20231
 
2.1%
25-10-20241
 
2.1%
25-07-20231
 
2.1%
25-01-20251
 
2.1%
25-07-20221
 
2.1%
25-08-20251
 
2.1%
25-08-20241
 
2.1%
Other values (37)37
78.7%

Length

2026-09-09T11:20:57.433422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25-05-20251
 
2.1%
30-01-20231
 
2.1%
25-09-20241
 
2.1%
27-12-20221
 
2.1%
30-10-20231
 
2.1%
25-08-20231
 
2.1%
25-03-20261
 
2.1%
25-04-20241
 
2.1%
25-07-20241
 
2.1%
25-08-20221
 
2.1%
Other values (37)37
78.7%

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

จำนวน MiniApps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)80.8%
Missing21
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean61.19230769
Minimum27
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size504.0 B
2026-09-09T11:20:57.558731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34.75
Q150
median59.5
Q376.25
95-th percentile86
Maximum87
Range60
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation17.26040377
Coefficient of variation (CV)0.2820681949
Kurtosis-0.9491871498
Mean61.19230769
Median Absolute Deviation (MAD)14.5
Skewness-0.1347645176
Sum1591
Variance297.9215385
MonotonicityIncreasing
2026-09-09T11:20:57.690342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
862
 
4.3%
792
 
4.3%
402
 
4.3%
742
 
4.3%
502
 
4.3%
541
 
2.1%
331
 
2.1%
441
 
2.1%
491
 
2.1%
521
 
2.1%
Other values (11)11
23.4%
(Missing)21
44.7%
ValueCountFrequency (%)
271
2.1%
331
2.1%
402
4.3%
441
2.1%
491
2.1%
502
4.3%
521
2.1%
531
2.1%
541
2.1%
561
2.1%
ValueCountFrequency (%)
871
2.1%
862
4.3%
821
2.1%
792
4.3%
771
2.1%
742
4.3%
701
2.1%
661
2.1%
641
2.1%
611
2.1%

จำนวนบริการ
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.6170213
Minimum45
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size504.0 B
2026-09-09T11:20:57.854159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile63.6
Q194.5
median150
Q3190.5
95-th percentile470
Maximum479
Range434
Interquartile range (IQR)96

Descriptive statistics

Standard deviation129.4022299
Coefficient of variation (CV)0.7125005628
Kurtosis1.2365046
Mean181.6170213
Median Absolute Deviation (MAD)47
Skewness1.561677761
Sum8536
Variance16744.9371
MonotonicityIncreasing
2026-09-09T11:20:58.018943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1502
 
4.3%
1432
 
4.3%
4702
 
4.3%
1342
 
4.3%
812
 
4.3%
1732
 
4.3%
4662
 
4.3%
851
 
2.1%
841
 
2.1%
831
 
2.1%
Other values (30)30
63.8%
ValueCountFrequency (%)
451
2.1%
531
2.1%
631
2.1%
651
2.1%
691
2.1%
801
2.1%
812
4.3%
831
2.1%
841
2.1%
851
2.1%
ValueCountFrequency (%)
4791
2.1%
4721
2.1%
4702
4.3%
4662
4.3%
4651
2.1%
2261
2.1%
2071
2.1%
1991
2.1%
1951
2.1%
1921
2.1%

User สะสม
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size504.0 B
31,838,686
 
1
32,967,310
 
1
552,081
 
1
118,692
 
1
302,617
 
1
Other values (42)
42 

Length

Max length12
Median length9
Mean length9.744680851
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

Unique47 ?
Unique (%)100.0%

Sample

1st row 85,333
2nd row 102,697
3rd row 118,692
4th row 129,465
5th row 139,386

Common Values

ValueCountFrequency (%)
31,838,686 1
 
2.1%
32,967,3101
 
2.1%
552,081 1
 
2.1%
118,692 1
 
2.1%
302,617 1
 
2.1%
174,855 1
 
2.1%
4,656,464 1
 
2.1%
31,525,574 1
 
2.1%
220,045 1
 
2.1%
155,323 1
 
2.1%
Other values (37)37
78.7%

Length

2026-09-09T11:20:58.191153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
33,672,7751
 
2.1%
388,2291
 
2.1%
33,348,3511
 
2.1%
33,584,4821
 
2.1%
843,0061
 
2.1%
31,717,0231
 
2.1%
33,063,9031
 
2.1%
33,103,3261
 
2.1%
32,804,2891
 
2.1%
33,548,2961
 
2.1%
Other values (37)37
78.7%

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

DownloadsApps สะสม
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct33
Distinct (%)97.1%
Missing13
Missing (%)27.7%
Memory size504.0 B
437,453
 
2
479,151
 
1
352,465
 
1
879,421
 
1
3,699,297
 
1
Other values (28)
28 

Length

Max length12
Median length9
Mean length9.852941176
Min length9

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

Unique32 ?
Unique (%)94.1%

Sample

1st row 185,273
2nd row 239,444
3rd row 269,394
4th row 295,287
5th row 316,252

Common Values

ValueCountFrequency (%)
437,453 2
 
4.3%
479,151 1
 
2.1%
352,465 1
 
2.1%
879,421 1
 
2.1%
3,699,297 1
 
2.1%
295,287 1
 
2.1%
37,138,016 1
 
2.1%
948,769 1
 
2.1%
269,394 1
 
2.1%
38,746,417 1
 
2.1%
Other values (23)23
48.9%
(Missing)13
27.7%

Length

2026-09-09T11:20:58.342586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
437,4532
 
5.9%
185,2731
 
2.9%
479,1511
 
2.9%
352,4651
 
2.9%
879,4211
 
2.9%
3,699,2971
 
2.9%
295,2871
 
2.9%
37,138,0161
 
2.9%
948,7691
 
2.9%
269,3941
 
2.9%
Other values (23)23
67.6%

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

Total Transaction
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size504.0 B
14,161,568
 
1
793,986,047
 
1
590,488,683
 
1
359,893,798
 
1
5,367,526
 
1
Other values (42)
42 

Length

Max length13
Median length11
Mean length11.46808511
Min length11

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

Unique47 ?
Unique (%)100.0%

Sample

1st row 1,349,697
2nd row 1,832,515
3rd row 2,199,741
4th row 2,394,966
5th row 2,663,274

Common Values

ValueCountFrequency (%)
14,161,568 1
 
2.1%
793,986,0471
 
2.1%
590,488,683 1
 
2.1%
359,893,798 1
 
2.1%
5,367,526 1
 
2.1%
8,258,165 1
 
2.1%
15,296,460 1
 
2.1%
471,158,389 1
 
2.1%
3,218,876 1
 
2.1%
12,625,518 1
 
2.1%
Other values (37)37
78.7%

Length

2026-09-09T11:20:58.492723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9,924,9961
 
2.1%
772,489,8411
 
2.1%
742,352,9221
 
2.1%
812,827,0841
 
2.1%
817,993,0551
 
2.1%
11,819,3591
 
2.1%
18,950,5951
 
2.1%
7,442,4431
 
2.1%
49,800,0711
 
2.1%
5,999,9611
 
2.1%
Other values (37)37
78.7%

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

Interactions

2026-09-09T11:20:56.365040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:20:56.106008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:20:56.492898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2026-09-09T11:20:56.237670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2026-09-09T11:20:58.618559image/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.
2026-09-09T11:20:58.776440image/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.
2026-09-09T11:20:58.933941image/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.
2026-09-09T11:20:59.099040image/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.
2026-09-09T11:20:59.271714image/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

2026-09-09T11:20:56.740409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-09-09T11:20:57.015993image/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.
2026-09-09T11:20:57.180264image/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.
2026-09-09T11:20:57.281017image/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

Dateจำนวน MiniAppsจำนวนบริการUser สะสมDownloadsApps สะสมTotal Transaction
031-03-202227.04585,333185,2731,349,697
16-06-202233.053102,697239,4441,832,515
225-07-202240.063118,692269,3942,199,741
325-08-202240.065129,465295,2872,394,966
420-09-202244.069139,386316,2522,663,274
525-10-202249.080147,451335,5572,978,187
625-11-202250.081155,323352,4653,218,876
727-12-202250.081164,380374,3623,493,349
830-01-202352.083174,855397,9803,843,306
98-02-202353.084177,598404,4593,946,427

Last rows

Dateจำนวน MiniAppsจำนวนบริการUser สะสมDownloadsApps สะสมTotal Transaction
3725-06-2025NaN19933,140,402NaN742,352,922
3825-07-2025NaN20733,202,288NaN755,357,698
3925-08-2025NaN22633,348,351NaN764,925,588
4030-09-2025NaN46533,421,849NaN772,489,841
4125-10-2025NaN46633,477,931NaN783,402,575
4225-11-2025NaN46633,154,757NaN788,851,877
4325-12-2025NaN47033,548,296NaN793,986,047
4425-01-2026NaN47033,584,482NaN800,517,084
4525-02-2026NaN47233,643,617NaN812,827,084
4625-03-2026NaN47933,672,775NaN817,993,055