Overview

Dataset statistics

Number of variables10
Number of observations18
Missing cells3
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory87.1 B

Variable types

Numeric10

Alerts

Batch is highly correlated with Qty_Applicants and 3 other fieldsHigh correlation
Qty_Applicants is highly correlated with Batch and 3 other fieldsHigh correlation
Qty_Candidates is highly correlated with Batch and 3 other fieldsHigh correlation
Qty_Attended is highly correlated with Qty_Applicants and 2 other fieldsHigh correlation
Passed_T1 is highly correlated with Qty_Applicants and 3 other fieldsHigh correlation
Passed_T2 is highly correlated with Passed_T1High correlation
Passed_T3 is highly correlated with Batch and 3 other fieldsHigh correlation
Selection_Passed is highly correlated with Batch and 3 other fieldsHigh correlation
Participants is highly correlated with Passed_T3 and 2 other fieldsHigh correlation
Graduated is highly correlated with Passed_T3 and 2 other fieldsHigh correlation
Batch is highly correlated with Qty_Applicants and 3 other fieldsHigh correlation
Qty_Applicants is highly correlated with Batch and 4 other fieldsHigh correlation
Qty_Candidates is highly correlated with Qty_Applicants and 3 other fieldsHigh correlation
Qty_Attended is highly correlated with Batch and 5 other fieldsHigh correlation
Passed_T1 is highly correlated with Qty_Applicants and 4 other fieldsHigh correlation
Passed_T2 is highly correlated with Passed_T1 and 2 other fieldsHigh correlation
Passed_T3 is highly correlated with Batch and 4 other fieldsHigh correlation
Selection_Passed is highly correlated with Batch and 6 other fieldsHigh correlation
Participants is highly correlated with Passed_T1 and 4 other fieldsHigh correlation
Graduated is highly correlated with Passed_T2 and 3 other fieldsHigh correlation
Batch is highly correlated with Passed_T3High correlation
Qty_Applicants is highly correlated with Qty_Candidates and 2 other fieldsHigh correlation
Qty_Candidates is highly correlated with Qty_Applicants and 2 other fieldsHigh correlation
Qty_Attended is highly correlated with Qty_Applicants and 2 other fieldsHigh correlation
Passed_T1 is highly correlated with Qty_Applicants and 3 other fieldsHigh correlation
Passed_T2 is highly correlated with Passed_T1High correlation
Passed_T3 is highly correlated with Batch and 3 other fieldsHigh correlation
Selection_Passed is highly correlated with Passed_T3 and 2 other fieldsHigh correlation
Participants is highly correlated with Passed_T3 and 2 other fieldsHigh correlation
Graduated is highly correlated with Passed_T3 and 2 other fieldsHigh correlation
Batch is highly correlated with Qty_Applicants and 6 other fieldsHigh correlation
Qty_Applicants is highly correlated with Batch and 6 other fieldsHigh correlation
Qty_Candidates is highly correlated with Batch and 5 other fieldsHigh correlation
Qty_Attended is highly correlated with Batch and 3 other fieldsHigh correlation
Passed_T1 is highly correlated with Batch and 5 other fieldsHigh correlation
Passed_T2 is highly correlated with Qty_Applicants and 4 other fieldsHigh correlation
Passed_T3 is highly correlated with Passed_T2 and 1 other fieldsHigh correlation
Selection_Passed is highly correlated with Batch and 4 other fieldsHigh correlation
Participants is highly correlated with Batch and 3 other fieldsHigh correlation
Graduated is highly correlated with Batch and 3 other fieldsHigh correlation
Participants has 1 (5.6%) missing values Missing
Graduated has 2 (11.1%) missing values Missing
Batch is uniformly distributed Uniform
Batch has unique values Unique
Qty_Applicants has unique values Unique
Qty_Candidates has unique values Unique
Qty_Attended has unique values Unique

Reproduction

Analysis started2026-09-09 04:10:42.106548
Analysis finished2026-09-09 04:10:57.291528
Duration15.18 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Batch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:57.347338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.85
Q15.25
median9.5
Q313.75
95-th percentile17.15
Maximum18
Range17
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.338539126
Coefficient of variation (CV)0.5619514869
Kurtosis-1.2
Mean9.5
Median Absolute Deviation (MAD)4.5
Skewness0
Sum171
Variance28.5
MonotonicityStrictly increasing
2026-09-09T11:10:57.473175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
181
 
5.6%
171
 
5.6%
21
 
5.6%
31
 
5.6%
41
 
5.6%
51
 
5.6%
61
 
5.6%
71
 
5.6%
81
 
5.6%
91
 
5.6%
Other values (8)8
44.4%
ValueCountFrequency (%)
11
5.6%
21
5.6%
31
5.6%
41
5.6%
51
5.6%
61
5.6%
71
5.6%
81
5.6%
91
5.6%
101
5.6%
ValueCountFrequency (%)
181
5.6%
171
5.6%
161
5.6%
151
5.6%
141
5.6%
131
5.6%
121
5.6%
111
5.6%
101
5.6%
91
5.6%

Qty_Applicants
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.1666667
Minimum141
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:57.613933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile195.4
Q1426.25
median505
Q3717.75
95-th percentile890.15
Maximum891
Range750
Interquartile range (IQR)291.5

Descriptive statistics

Standard deviation225.7190471
Coefficient of variation (CV)0.4186442914
Kurtosis-0.8248972387
Mean539.1666667
Median Absolute Deviation (MAD)170
Skewness-0.001784644116
Sum9705
Variance50949.08824
MonotonicityNot monotonic
2026-09-09T11:10:57.746482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4471
 
5.6%
2051
 
5.6%
7371
 
5.6%
8351
 
5.6%
4201
 
5.6%
4551
 
5.6%
7441
 
5.6%
5231
 
5.6%
4871
 
5.6%
1411
 
5.6%
Other values (8)8
44.4%
ValueCountFrequency (%)
1411
5.6%
2051
5.6%
2831
5.6%
3201
5.6%
4201
5.6%
4451
5.6%
4471
5.6%
4551
5.6%
4871
5.6%
5231
5.6%
ValueCountFrequency (%)
8911
5.6%
8901
5.6%
8351
5.6%
7441
5.6%
7371
5.6%
6601
5.6%
6221
5.6%
6001
5.6%
5231
5.6%
4871
5.6%

Qty_Candidates
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.1111111
Minimum126
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:57.892263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile156.6
Q1382.5
median498.5
Q3717
95-th percentile890.15
Maximum891
Range765
Interquartile range (IQR)334.5

Descriptive statistics

Standard deviation233.8450691
Coefficient of variation (CV)0.4402940631
Kurtosis-0.8438569946
Mean531.1111111
Median Absolute Deviation (MAD)173
Skewness-0.0387961971
Sum9560
Variance54683.51634
MonotonicityNot monotonic
2026-09-09T11:10:58.027328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4471
 
5.6%
5101
 
5.6%
1621
 
5.6%
8351
 
5.6%
4551
 
5.6%
3621
 
5.6%
4871
 
5.6%
6221
 
5.6%
6571
 
5.6%
7431
 
5.6%
Other values (8)8
44.4%
ValueCountFrequency (%)
1261
5.6%
1621
5.6%
2811
5.6%
3111
5.6%
3621
5.6%
4441
5.6%
4471
5.6%
4551
5.6%
4871
5.6%
5101
5.6%
ValueCountFrequency (%)
8911
5.6%
8901
5.6%
8351
5.6%
7431
5.6%
7371
5.6%
6571
5.6%
6221
5.6%
6001
5.6%
5101
5.6%
4871
5.6%

Qty_Attended
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.2222222
Minimum126
Maximum874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:58.329705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile156.6
Q1359
median448.5
Q3592.75
95-th percentile792.4
Maximum874
Range748
Interquartile range (IQR)233.75

Descriptive statistics

Standard deviation210.8025437
Coefficient of variation (CV)0.4511826143
Kurtosis-0.5511474045
Mean467.2222222
Median Absolute Deviation (MAD)136
Skewness0.2258018591
Sum8410
Variance44437.71242
MonotonicityNot monotonic
2026-09-09T11:10:58.465552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6531
 
5.6%
2801
 
5.6%
5471
 
5.6%
4211
 
5.6%
3581
 
5.6%
3621
 
5.6%
7391
 
5.6%
8741
 
5.6%
1621
 
5.6%
3651
 
5.6%
Other values (8)8
44.4%
ValueCountFrequency (%)
1261
5.6%
1621
5.6%
2091
5.6%
2801
5.6%
3581
5.6%
3621
5.6%
3651
5.6%
4091
5.6%
4211
5.6%
4761
5.6%
ValueCountFrequency (%)
8741
5.6%
7781
5.6%
7391
5.6%
6531
5.6%
6011
5.6%
5681
5.6%
5471
5.6%
4821
5.6%
4761
5.6%
4211
5.6%

Passed_T1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.0555556
Minimum66
Maximum229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:58.610717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile77.9
Q1115.25
median131.5
Q3184
95-th percentile223.05
Maximum229
Range163
Interquartile range (IQR)68.75

Descriptive statistics

Standard deviation47.88278854
Coefficient of variation (CV)0.3370708618
Kurtosis-0.7451698771
Mean142.0555556
Median Absolute Deviation (MAD)40
Skewness0.312494748
Sum2557
Variance2292.761438
MonotonicityNot monotonic
2026-09-09T11:10:58.744897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1843
16.7%
1271
 
5.6%
2221
 
5.6%
661
 
5.6%
2291
 
5.6%
1161
 
5.6%
1151
 
5.6%
1461
 
5.6%
801
 
5.6%
821
 
5.6%
Other values (6)6
33.3%
ValueCountFrequency (%)
661
5.6%
801
5.6%
821
5.6%
1011
5.6%
1151
5.6%
1161
5.6%
1271
5.6%
1281
5.6%
1291
5.6%
1341
5.6%
ValueCountFrequency (%)
2291
 
5.6%
2221
 
5.6%
1941
 
5.6%
1843
16.7%
1461
 
5.6%
1361
 
5.6%
1341
 
5.6%
1291
 
5.6%
1281
 
5.6%
1271
 
5.6%

Passed_T2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.27777778
Minimum47
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:58.892931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile60.6
Q168.5
median78
Q391
95-th percentile101.1
Maximum113
Range66
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation15.62353261
Coefficient of variation (CV)0.1970732915
Kurtosis0.3282968322
Mean79.27777778
Median Absolute Deviation (MAD)10.5
Skewness0.1689212232
Sum1427
Variance244.0947712
MonotonicityNot monotonic
2026-09-09T11:10:59.017586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
932
 
11.1%
732
 
11.1%
631
 
5.6%
941
 
5.6%
991
 
5.6%
851
 
5.6%
841
 
5.6%
831
 
5.6%
821
 
5.6%
1131
 
5.6%
Other values (6)6
33.3%
ValueCountFrequency (%)
471
5.6%
631
5.6%
661
5.6%
671
5.6%
681
5.6%
701
5.6%
732
11.1%
741
5.6%
821
5.6%
831
5.6%
ValueCountFrequency (%)
1131
5.6%
991
5.6%
941
5.6%
932
11.1%
851
5.6%
841
5.6%
831
5.6%
821
5.6%
741
5.6%
732
11.1%

Passed_T3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.38888889
Minimum30
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:59.156280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile31.7
Q137.75
median43.5
Q350
95-th percentile60
Maximum60
Range30
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.062780309
Coefficient of variation (CV)0.2041677667
Kurtosis-0.8446509174
Mean44.38888889
Median Absolute Deviation (MAD)6.5
Skewness0.175757985
Sum799
Variance82.13398693
MonotonicityNot monotonic
2026-09-09T11:10:59.281264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
413
16.7%
602
11.1%
502
11.1%
301
 
5.6%
541
 
5.6%
521
 
5.6%
491
 
5.6%
471
 
5.6%
461
 
5.6%
401
 
5.6%
Other values (4)4
22.2%
ValueCountFrequency (%)
301
 
5.6%
321
 
5.6%
341
 
5.6%
351
 
5.6%
371
 
5.6%
401
 
5.6%
413
16.7%
461
 
5.6%
471
 
5.6%
491
 
5.6%
ValueCountFrequency (%)
602
11.1%
541
 
5.6%
521
 
5.6%
502
11.1%
491
 
5.6%
471
 
5.6%
461
 
5.6%
413
16.7%
401
 
5.6%
371
 
5.6%

Selection_Passed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.94444444
Minimum30
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:59.416878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile31.7
Q135.5
median40.5
Q350
95-th percentile60
Maximum60
Range30
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation9.458716629
Coefficient of variation (CV)0.220254721
Kurtosis-0.8398060158
Mean42.94444444
Median Absolute Deviation (MAD)7.5
Skewness0.5006752522
Sum773
Variance89.46732026
MonotonicityNot monotonic
2026-09-09T11:10:59.545554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
602
11.1%
502
11.1%
412
11.1%
402
11.1%
322
11.1%
301
 
5.6%
541
 
5.6%
521
 
5.6%
461
 
5.6%
391
 
5.6%
Other values (3)3
16.7%
ValueCountFrequency (%)
301
5.6%
322
11.1%
341
5.6%
351
5.6%
371
5.6%
391
5.6%
402
11.1%
412
11.1%
461
5.6%
502
11.1%
ValueCountFrequency (%)
602
11.1%
541
5.6%
521
5.6%
502
11.1%
461
5.6%
412
11.1%
402
11.1%
391
5.6%
371
5.6%
351
5.6%

Participants
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)70.6%
Missing1
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean37.94117647
Minimum27
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:59.681314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile29.4
Q135
median38
Q341
95-th percentile50.4
Maximum52
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.693939313
Coefficient of variation (CV)0.1764294083
Kurtosis0.3077601426
Mean37.94117647
Median Absolute Deviation (MAD)3
Skewness0.4447983606
Sum645
Variance44.80882353
MonotonicityNot monotonic
2026-09-09T11:10:59.806437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
303
16.7%
412
11.1%
372
11.1%
392
11.1%
271
 
5.6%
501
 
5.6%
401
 
5.6%
381
 
5.6%
361
 
5.6%
351
 
5.6%
Other values (2)2
11.1%
ValueCountFrequency (%)
271
 
5.6%
303
16.7%
351
 
5.6%
361
 
5.6%
372
11.1%
381
 
5.6%
392
11.1%
401
 
5.6%
412
11.1%
431
 
5.6%
ValueCountFrequency (%)
521
5.6%
501
5.6%
431
5.6%
412
11.1%
401
5.6%
392
11.1%
381
5.6%
372
11.1%
361
5.6%
351
5.6%

Graduated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)62.5%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean37.125
Minimum28
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2026-09-09T11:10:59.935503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile28
Q133.75
median38.5
Q340
95-th percentile47.5
Maximum49
Range21
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation5.942782738
Coefficient of variation (CV)0.1600749559
Kurtosis0.01071059761
Mean37.125
Median Absolute Deviation (MAD)3.5
Skewness0.2596730881
Sum594
Variance35.31666667
MonotonicityNot monotonic
2026-09-09T11:11:00.053205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
403
16.7%
393
16.7%
282
11.1%
342
11.1%
361
 
5.6%
301
 
5.6%
471
 
5.6%
381
 
5.6%
331
 
5.6%
491
 
5.6%
(Missing)2
11.1%
ValueCountFrequency (%)
282
11.1%
301
 
5.6%
331
 
5.6%
342
11.1%
361
 
5.6%
381
 
5.6%
393
16.7%
403
16.7%
471
 
5.6%
491
 
5.6%
ValueCountFrequency (%)
491
 
5.6%
471
 
5.6%
403
16.7%
393
16.7%
381
 
5.6%
361
 
5.6%
342
11.1%
331
 
5.6%
301
 
5.6%
282
11.1%

Interactions

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Correlations

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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.
2026-09-09T11:11:00.434787image/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.
2026-09-09T11:11:00.686918image/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.
2026-09-09T11:11:00.941660image/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

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A simple visualization of nullity by column.
2026-09-09T11:10:56.902770image/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.
2026-09-09T11:10:57.080330image/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.
2026-09-09T11:10:57.187304image/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

BatchQty_ApplicantsQty_CandidatesQty_AttendedPassed_T1Passed_T2Passed_T3Selection_PassedParticipantsGraduated
0189089087412967525239.039.0
1289189177818493606043.039.0
23523510476222113606052.049.0
3462262256818499413535.033.0
4542036236210170494637.034.0
5632031128012774474036.034.0
6766065754713684545441.040.0
7845545535811673464141.040.0
8944744736514685413938.038.0
91048748742112863373730.028.0

Last rows

BatchQty_ApplicantsQty_CandidatesQty_AttendedPassed_T1Passed_T2Passed_T3Selection_PassedParticipantsGraduated
8944744736514685413938.038.0
91048748742112863373730.028.0
101173773765319483505040.040.0
111283583573922994505050.047.0
121374474360118493303030.030.0
131444544440911573414139.039.0
141560060048213447343430.028.0
15161411261268068404037.036.0
16172051621628282323227.0NaN
171828328120966663532NaNNaN