Overview

Dataset statistics

Number of variables53
Number of observations1500476
Missing cells49375701
Missing cells (%)62.1%
Total size in memory606.7 MiB
Average record size in memory424.0 B

Variable types

Numeric37
Text16

Alerts

formonth_118L has constant value ""Constant
forquarter_462L has constant value ""Constant
forweek_601L has constant value ""Constant
assignmentdate_238D has 1363480 (90.9%) missing valuesMissing
assignmentdate_4527235D has 1385498 (92.3%) missing valuesMissing
assignmentdate_4955616D has 1428843 (95.2%) missing valuesMissing
birthdate_574D has 892605 (59.5%) missing valuesMissing
contractssum_5085716L has 1343147 (89.5%) missing valuesMissing
dateofbirth_337D has 114785 (7.6%) missing valuesMissing
dateofbirth_342D has 1463976 (97.6%) missing valuesMissing
days120_123L has 114785 (7.6%) missing valuesMissing
days180_256L has 114785 (7.6%) missing valuesMissing
days30_165L has 114785 (7.6%) missing valuesMissing
days360_512L has 114785 (7.6%) missing valuesMissing
days90_310L has 114785 (7.6%) missing valuesMissing
firstquarter_103L has 114785 (7.6%) missing valuesMissing
for3years_128L has 1463962 (97.6%) missing valuesMissing
for3years_504L has 1463962 (97.6%) missing valuesMissing
for3years_584L has 1463962 (97.6%) missing valuesMissing
formonth_118L has 1463962 (97.6%) missing valuesMissing
formonth_206L has 1463962 (97.6%) missing valuesMissing
formonth_535L has 1463962 (97.6%) missing valuesMissing
forquarter_1017L has 1463962 (97.6%) missing valuesMissing
forquarter_462L has 1463962 (97.6%) missing valuesMissing
forquarter_634L has 1463962 (97.6%) missing valuesMissing
fortoday_1092L has 1463962 (97.6%) missing valuesMissing
forweek_1077L has 1463962 (97.6%) missing valuesMissing
forweek_528L has 1463962 (97.6%) missing valuesMissing
forweek_601L has 1463962 (97.6%) missing valuesMissing
foryear_618L has 1463962 (97.6%) missing valuesMissing
foryear_818L has 1463962 (97.6%) missing valuesMissing
foryear_850L has 1463962 (97.6%) missing valuesMissing
fourthquarter_440L has 114785 (7.6%) missing valuesMissing
numberofqueries_373L has 114785 (7.6%) missing valuesMissing
pmtaverage_3A has 1356887 (90.4%) missing valuesMissing
pmtaverage_4527227A has 1385498 (92.3%) missing valuesMissing
pmtaverage_4955615A has 1428631 (95.2%) missing valuesMissing
pmtcount_4527229L has 1385498 (92.3%) missing valuesMissing
pmtcount_4955617L has 1428631 (95.2%) missing valuesMissing
pmtcount_693L has 1354070 (90.2%) missing valuesMissing
pmtscount_423L has 927838 (61.8%) missing valuesMissing
pmtssum_45A has 927838 (61.8%) missing valuesMissing
requesttype_4525192L has 827212 (55.1%) missing valuesMissing
responsedate_1012D has 780476 (52.0%) missing valuesMissing
responsedate_4527233D has 840149 (56.0%) missing valuesMissing
responsedate_4917613D has 1275564 (85.0%) missing valuesMissing
riskassesment_302T has 1446917 (96.4%) missing valuesMissing
riskassesment_940T has 1446916 (96.4%) missing valuesMissing
secondquarter_766L has 114785 (7.6%) missing valuesMissing
thirdquarter_1082L has 114785 (7.6%) missing valuesMissing
for3years_128L is highly skewed (γ1 = 110.3147009)Skewed
formonth_206L is highly skewed (γ1 = 52.97169175)Skewed
forquarter_1017L is highly skewed (γ1 = 38.54022557)Skewed
forweek_1077L is highly skewed (γ1 = 135.1129154)Skewed
foryear_618L is highly skewed (γ1 = 135.1129154)Skewed
foryear_818L is highly skewed (γ1 = 25.86903552)Skewed
case_id has unique valuesUnique
contractssum_5085716L has 17954 (1.2%) zerosZeros
days120_123L has 517780 (34.5%) zerosZeros
days180_256L has 379798 (25.3%) zerosZeros
days30_165L has 917530 (61.1%) zerosZeros
days360_512L has 189973 (12.7%) zerosZeros
days90_310L has 622576 (41.5%) zerosZeros
firstquarter_103L has 403359 (26.9%) zerosZeros
for3years_128L has 36511 (2.4%) zerosZeros
for3years_584L has 36245 (2.4%) zerosZeros
formonth_118L has 36514 (2.4%) zerosZeros
formonth_206L has 36501 (2.4%) zerosZeros
formonth_535L has 29492 (2.0%) zerosZeros
forquarter_1017L has 36482 (2.4%) zerosZeros
forquarter_462L has 36514 (2.4%) zerosZeros
forquarter_634L has 24664 (1.6%) zerosZeros
fortoday_1092L has 35169 (2.3%) zerosZeros
forweek_1077L has 36512 (2.4%) zerosZeros
forweek_528L has 33271 (2.2%) zerosZeros
forweek_601L has 36514 (2.4%) zerosZeros
foryear_618L has 36512 (2.4%) zerosZeros
foryear_818L has 36444 (2.4%) zerosZeros
fourthquarter_440L has 377862 (25.2%) zerosZeros
numberofqueries_373L has 189973 (12.7%) zerosZeros
pmtscount_423L has 90373 (6.0%) zerosZeros
pmtssum_45A has 90374 (6.0%) zerosZeros
secondquarter_766L has 396787 (26.4%) zerosZeros
thirdquarter_1082L has 356399 (23.8%) zerosZeros

Reproduction

Analysis started2024-02-13 19:57:21.912378
Analysis finished2024-02-13 19:57:32.729027
Duration10.82 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

case_id
Real number (ℝ)

UNIQUE 

Distinct1500476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1284031.723
Minimum357
Maximum2703454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:32.849743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum357
5-th percentile122491.75
Q1768508.75
median1361878.5
Q31737010.25
95-th percentile2628388.25
Maximum2703454
Range2703097
Interquartile range (IQR)968501.5

Descriptive statistics

Standard deviation716088.1235
Coefficient of variation (CV)0.5576872524
Kurtosis-0.5729526962
Mean1284031.723
Median Absolute Deviation (MAD)484201
Skewness0.1289008248
Sum1.926658783 × 1012
Variance5.127822006 × 1011
MonotonicityStrictly increasing
2024-02-13T20:57:33.019697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
357 1
 
< 0.1%
1611979 1
 
< 0.1%
1611977 1
 
< 0.1%
1611976 1
 
< 0.1%
1611975 1
 
< 0.1%
1611974 1
 
< 0.1%
1611973 1
 
< 0.1%
1611972 1
 
< 0.1%
1611971 1
 
< 0.1%
1611970 1
 
< 0.1%
Other values (1500466) 1500466
> 99.9%
ValueCountFrequency (%)
357 1
< 0.1%
381 1
< 0.1%
388 1
< 0.1%
405 1
< 0.1%
409 1
< 0.1%
ValueCountFrequency (%)
2703454 1
< 0.1%
2703453 1
< 0.1%
2703452 1
< 0.1%
2703451 1
< 0.1%
2703450 1
< 0.1%

assignmentdate_238D
Text

MISSING 

Distinct8887
Distinct (%)6.5%
Missing1363480
Missing (%)90.9%
Memory size11.4 MiB
2024-02-13T20:57:33.471471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1369960
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1096 ?
Unique (%)0.8%

Sample

1st row2017-07-06
2nd row2013-12-25
3rd row2014-07-30
4th row2018-09-21
5th row2017-02-25
ValueCountFrequency (%)
2019-09-30 811
 
0.6%
2019-09-25 764
 
0.6%
2019-09-27 728
 
0.5%
2019-09-23 719
 
0.5%
2019-10-02 709
 
0.5%
2019-10-07 705
 
0.5%
2019-09-24 705
 
0.5%
2019-10-01 684
 
0.5%
2019-09-26 683
 
0.5%
2019-10-03 676
 
0.5%
Other values (8877) 129812
94.8%
2024-02-13T20:57:34.074167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 329902
24.1%
- 273992
20.0%
1 242010
17.7%
2 213544
15.6%
9 78234
 
5.7%
3 42650
 
3.1%
5 40415
 
3.0%
7 39328
 
2.9%
6 37302
 
2.7%
8 37296
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1095968
80.0%
Dash Punctuation 273992
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329902
30.1%
1 242010
22.1%
2 213544
19.5%
9 78234
 
7.1%
3 42650
 
3.9%
5 40415
 
3.7%
7 39328
 
3.6%
6 37302
 
3.4%
8 37296
 
3.4%
4 35287
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 273992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1369960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 329902
24.1%
- 273992
20.0%
1 242010
17.7%
2 213544
15.6%
9 78234
 
5.7%
3 42650
 
3.1%
5 40415
 
3.0%
7 39328
 
2.9%
6 37302
 
2.7%
8 37296
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1369960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 329902
24.1%
- 273992
20.0%
1 242010
17.7%
2 213544
15.6%
9 78234
 
5.7%
3 42650
 
3.1%
5 40415
 
3.0%
7 39328
 
2.9%
6 37302
 
2.7%
8 37296
 
2.7%

assignmentdate_4527235D
Text

MISSING 

Distinct372
Distinct (%)0.3%
Missing1385498
Missing (%)92.3%
Memory size11.4 MiB
2024-02-13T20:57:34.516788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1149780
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row2019-09-13
2nd row2019-09-13
3rd row2019-09-14
4th row2019-09-14
5th row2019-09-14
ValueCountFrequency (%)
2019-12-13 967
 
0.8%
2020-01-01 939
 
0.8%
2019-11-29 860
 
0.7%
2020-01-10 851
 
0.7%
2019-12-27 840
 
0.7%
2019-09-30 811
 
0.7%
2020-01-06 797
 
0.7%
2020-03-30 788
 
0.7%
2020-01-13 786
 
0.7%
2020-01-03 783
 
0.7%
Other values (362) 106556
92.7%
2024-02-13T20:57:35.137660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 293771
25.6%
2 251762
21.9%
- 229956
20.0%
1 196497
17.1%
9 82904
 
7.2%
3 34278
 
3.0%
4 16803
 
1.5%
7 11533
 
1.0%
8 11397
 
1.0%
6 11103
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 919824
80.0%
Dash Punctuation 229956
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 293771
31.9%
2 251762
27.4%
1 196497
21.4%
9 82904
 
9.0%
3 34278
 
3.7%
4 16803
 
1.8%
7 11533
 
1.3%
8 11397
 
1.2%
6 11103
 
1.2%
5 9776
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 229956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1149780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 293771
25.6%
2 251762
21.9%
- 229956
20.0%
1 196497
17.1%
9 82904
 
7.2%
3 34278
 
3.0%
4 16803
 
1.5%
7 11533
 
1.0%
8 11397
 
1.0%
6 11103
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1149780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 293771
25.6%
2 251762
21.9%
- 229956
20.0%
1 196497
17.1%
9 82904
 
7.2%
3 34278
 
3.0%
4 16803
 
1.5%
7 11533
 
1.0%
8 11397
 
1.0%
6 11103
 
1.0%

assignmentdate_4955616D
Text

MISSING 

Distinct7565
Distinct (%)10.6%
Missing1428843
Missing (%)95.2%
Memory size11.4 MiB
2024-02-13T20:57:35.539880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters716330
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1385 ?
Unique (%)1.9%

Sample

1st row2015-02-25
2nd row2014-01-15
3rd row2013-03-04
4th row2017-09-08
5th row2018-08-03
ValueCountFrequency (%)
2005-06-15 3924
 
5.5%
2020-03-30 319
 
0.4%
2017-01-15 105
 
0.1%
2013-07-30 101
 
0.1%
2013-08-01 97
 
0.1%
2013-07-26 91
 
0.1%
2013-07-31 85
 
0.1%
2013-08-02 83
 
0.1%
2016-01-15 82
 
0.1%
2013-07-29 79
 
0.1%
Other values (7555) 66667
93.1%
2024-02-13T20:57:36.088043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 177439
24.8%
- 143266
20.0%
1 120379
16.8%
2 114691
16.0%
5 28808
 
4.0%
9 25194
 
3.5%
6 23970
 
3.3%
3 23035
 
3.2%
7 20986
 
2.9%
8 20349
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 573064
80.0%
Dash Punctuation 143266
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 177439
31.0%
1 120379
21.0%
2 114691
20.0%
5 28808
 
5.0%
9 25194
 
4.4%
6 23970
 
4.2%
3 23035
 
4.0%
7 20986
 
3.7%
8 20349
 
3.6%
4 18213
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 143266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 716330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 177439
24.8%
- 143266
20.0%
1 120379
16.8%
2 114691
16.0%
5 28808
 
4.0%
9 25194
 
3.5%
6 23970
 
3.3%
3 23035
 
3.2%
7 20986
 
2.9%
8 20349
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 716330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 177439
24.8%
- 143266
20.0%
1 120379
16.8%
2 114691
16.0%
5 28808
 
4.0%
9 25194
 
3.5%
6 23970
 
3.3%
3 23035
 
3.2%
7 20986
 
2.9%
8 20349
 
2.8%

birthdate_574D
Text

MISSING 

Distinct667
Distinct (%)0.1%
Missing892605
Missing (%)59.5%
Memory size11.4 MiB
2024-02-13T20:57:36.562549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6078710
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row1988-04-01
2nd row1973-11-01
3rd row1989-04-01
4th row1974-03-01
5th row1993-06-01
ValueCountFrequency (%)
1987-05-01 1531
 
0.3%
1989-03-01 1510
 
0.2%
1987-06-01 1499
 
0.2%
1988-07-01 1495
 
0.2%
1987-03-01 1474
 
0.2%
1986-08-01 1472
 
0.2%
1989-10-01 1468
 
0.2%
1989-08-01 1466
 
0.2%
1988-08-01 1460
 
0.2%
1988-10-01 1452
 
0.2%
Other values (657) 593044
97.6%
2024-02-13T20:57:37.188581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1523498
25.1%
- 1215742
20.0%
0 1178408
19.4%
9 822523
13.5%
8 266264
 
4.4%
7 243786
 
4.0%
6 213426
 
3.5%
5 207954
 
3.4%
2 157196
 
2.6%
4 133850
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862968
80.0%
Dash Punctuation 1215742
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1523498
31.3%
0 1178408
24.2%
9 822523
16.9%
8 266264
 
5.5%
7 243786
 
5.0%
6 213426
 
4.4%
5 207954
 
4.3%
2 157196
 
3.2%
4 133850
 
2.8%
3 116063
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1215742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6078710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1523498
25.1%
- 1215742
20.0%
0 1178408
19.4%
9 822523
13.5%
8 266264
 
4.4%
7 243786
 
4.0%
6 213426
 
3.5%
5 207954
 
3.4%
2 157196
 
2.6%
4 133850
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6078710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1523498
25.1%
- 1215742
20.0%
0 1178408
19.4%
9 822523
13.5%
8 266264
 
4.4%
7 243786
 
4.0%
6 213426
 
3.5%
5 207954
 
3.4%
2 157196
 
2.6%
4 133850
 
2.2%

contractssum_5085716L
Real number (ℝ)

MISSING  ZEROS 

Distinct136290
Distinct (%)86.6%
Missing1343147
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean641604.4177
Minimum0
Maximum31296759.11
Zeros17954
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:37.356959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178531.95
median307282.4
Q3802114.08
95-th percentile2442502.296
Maximum31296759.11
Range31296759.11
Interquartile range (IQR)723582.13

Descriptive statistics

Standard deviation980327.2971
Coefficient of variation (CV)1.527931028
Kurtosis34.3061796
Mean641604.4177
Median Absolute Deviation (MAD)275139.21
Skewness4.143339012
Sum1.009429814 × 1011
Variance9.610416095 × 1011
MonotonicityNot monotonic
2024-02-13T20:57:37.513966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17954
 
1.2%
1000000 39
 
< 0.1%
500000 30
 
< 0.1%
30000 20
 
< 0.1%
200000 19
 
< 0.1%
50000 18
 
< 0.1%
49990 18
 
< 0.1%
100000 18
 
< 0.1%
1 17
 
< 0.1%
150000 17
 
< 0.1%
Other values (136280) 139179
 
9.3%
(Missing) 1343147
89.5%
ValueCountFrequency (%)
0 17954
1.2%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.2 1
 
< 0.1%
0.28 1
 
< 0.1%
ValueCountFrequency (%)
31296759.11 1
< 0.1%
23243713.04 1
< 0.1%
20884000 1
< 0.1%
19731398.06 1
< 0.1%
19028770.9 1
< 0.1%

dateofbirth_337D
Text

MISSING 

Distinct724
Distinct (%)0.1%
Missing114785
Missing (%)7.6%
Memory size11.4 MiB
2024-02-13T20:57:37.934587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13856910
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)< 0.1%

Sample

1st row1989-04-01
2nd row1974-03-01
3rd row1993-06-01
4th row1978-06-01
5th row1959-07-01
ValueCountFrequency (%)
1988-07-01 3444
 
0.2%
1986-07-01 3417
 
0.2%
1987-05-01 3387
 
0.2%
1986-08-01 3358
 
0.2%
1988-08-01 3354
 
0.2%
1988-05-01 3314
 
0.2%
1987-06-01 3306
 
0.2%
1987-07-01 3305
 
0.2%
1989-03-01 3298
 
0.2%
1987-08-01 3294
 
0.2%
Other values (714) 1352214
97.6%
2024-02-13T20:57:38.477489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3475733
25.1%
- 2771382
20.0%
0 2688189
19.4%
9 1860943
13.4%
8 616113
 
4.4%
7 562667
 
4.1%
6 486397
 
3.5%
5 474564
 
3.4%
2 358620
 
2.6%
4 299387
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11085528
80.0%
Dash Punctuation 2771382
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3475733
31.4%
0 2688189
24.2%
9 1860943
16.8%
8 616113
 
5.6%
7 562667
 
5.1%
6 486397
 
4.4%
5 474564
 
4.3%
2 358620
 
3.2%
4 299387
 
2.7%
3 262915
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 2771382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13856910
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3475733
25.1%
- 2771382
20.0%
0 2688189
19.4%
9 1860943
13.4%
8 616113
 
4.4%
7 562667
 
4.1%
6 486397
 
3.5%
5 474564
 
3.4%
2 358620
 
2.6%
4 299387
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13856910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3475733
25.1%
- 2771382
20.0%
0 2688189
19.4%
9 1860943
13.4%
8 616113
 
4.4%
7 562667
 
4.1%
6 486397
 
3.5%
5 474564
 
3.4%
2 358620
 
2.6%
4 299387
 
2.2%

dateofbirth_342D
Text

MISSING 

Distinct664
Distinct (%)1.8%
Missing1463976
Missing (%)97.6%
Memory size11.4 MiB
2024-02-13T20:57:38.890879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters365000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row1953-07-01
2nd row1972-04-01
3rd row1954-07-01
4th row1955-11-01
5th row1956-07-01
ValueCountFrequency (%)
1986-10-01 120
 
0.3%
1986-05-01 115
 
0.3%
1990-08-01 110
 
0.3%
1986-07-01 104
 
0.3%
1991-10-01 104
 
0.3%
1987-10-01 104
 
0.3%
1987-07-01 102
 
0.3%
1979-03-01 102
 
0.3%
1988-07-01 101
 
0.3%
1985-05-01 101
 
0.3%
Other values (654) 35437
97.1%
2024-02-13T20:57:39.472150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 91364
25.0%
- 73000
20.0%
0 70652
19.4%
9 50487
13.8%
8 17032
 
4.7%
7 15064
 
4.1%
6 12583
 
3.4%
5 11133
 
3.1%
2 9386
 
2.6%
4 7439
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292000
80.0%
Dash Punctuation 73000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91364
31.3%
0 70652
24.2%
9 50487
17.3%
8 17032
 
5.8%
7 15064
 
5.2%
6 12583
 
4.3%
5 11133
 
3.8%
2 9386
 
3.2%
4 7439
 
2.5%
3 6860
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 73000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 365000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 91364
25.0%
- 73000
20.0%
0 70652
19.4%
9 50487
13.8%
8 17032
 
4.7%
7 15064
 
4.1%
6 12583
 
3.4%
5 11133
 
3.1%
2 9386
 
2.6%
4 7439
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 91364
25.0%
- 73000
20.0%
0 70652
19.4%
9 50487
13.8%
8 17032
 
4.7%
7 15064
 
4.1%
6 12583
 
3.4%
5 11133
 
3.1%
2 9386
 
2.6%
4 7439
 
2.0%

days120_123L
Real number (ℝ)

MISSING  ZEROS 

Distinct45
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.607714851
Minimum0
Maximum109
Zeros517780
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:39.643382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum109
Range109
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.08300315
Coefficient of variation (CV)1.295629725
Kurtosis15.53315023
Mean1.607714851
Median Absolute Deviation (MAD)1
Skewness2.484813952
Sum2227796
Variance4.338902125
MonotonicityNot monotonic
2024-02-13T20:57:39.793319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 517780
34.5%
1 347306
23.1%
2 208553
13.9%
3 119837
 
8.0%
4 71046
 
4.7%
5 44026
 
2.9%
6 27537
 
1.8%
7 18011
 
1.2%
8 11756
 
0.8%
9 7764
 
0.5%
Other values (35) 12075
 
0.8%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 517780
34.5%
1 347306
23.1%
2 208553
13.9%
3 119837
 
8.0%
4 71046
 
4.7%
ValueCountFrequency (%)
109 1
< 0.1%
47 1
< 0.1%
44 1
< 0.1%
43 2
< 0.1%
42 2
< 0.1%

days180_256L
Real number (ℝ)

MISSING  ZEROS 

Distinct59
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean2.388655912
Minimum0
Maximum110
Zeros379798
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:39.943190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile8
Maximum110
Range110
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.891114764
Coefficient of variation (CV)1.210352127
Kurtosis12.26772997
Mean2.388655912
Median Absolute Deviation (MAD)2
Skewness2.500049067
Sum3309939
Variance8.358544579
MonotonicityNot monotonic
2024-02-13T20:57:40.098085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379798
25.3%
1 310654
20.7%
2 221734
14.8%
3 147802
 
9.9%
4 99130
 
6.6%
5 66687
 
4.4%
6 45698
 
3.0%
7 31825
 
2.1%
8 22632
 
1.5%
9 16451
 
1.1%
Other values (49) 43280
 
2.9%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 379798
25.3%
1 310654
20.7%
2 221734
14.8%
3 147802
 
9.9%
4 99130
 
6.6%
ValueCountFrequency (%)
110 1
< 0.1%
68 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%

days30_165L
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean0.5177077718
Minimum0
Maximum22
Zeros917530
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:40.232047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8992376603
Coefficient of variation (CV)1.73695994
Kurtosis10.09062294
Mean0.5177077718
Median Absolute Deviation (MAD)0
Skewness2.466261642
Sum717383
Variance0.8086283697
MonotonicityNot monotonic
2024-02-13T20:57:40.352640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 917530
61.1%
1 306466
 
20.4%
2 105214
 
7.0%
3 36112
 
2.4%
4 13652
 
0.9%
5 4720
 
0.3%
6 1139
 
0.1%
7 391
 
< 0.1%
8 223
 
< 0.1%
9 104
 
< 0.1%
Other values (12) 140
 
< 0.1%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 917530
61.1%
1 306466
 
20.4%
2 105214
 
7.0%
3 36112
 
2.4%
4 13652
 
0.9%
ValueCountFrequency (%)
22 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
18 1
< 0.1%

days360_512L
Real number (ℝ)

MISSING  ZEROS 

Distinct92
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean4.77706646
Minimum0
Maximum115
Zeros189973
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:40.488667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36.5
95-th percentile15
Maximum115
Range115
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.168856074
Coefficient of variation (CV)1.082014688
Kurtosis10.70523526
Mean4.77706646
Median Absolute Deviation (MAD)2
Skewness2.456375433
Sum6619538
Variance26.71707311
MonotonicityNot monotonic
2024-02-13T20:57:40.641782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 193331
12.9%
0 189973
12.7%
2 184588
12.3%
3 156023
10.4%
4 129468
8.6%
5 103161
6.9%
6 82724
5.5%
7 66235
 
4.4%
8 52195
 
3.5%
9 41347
 
2.8%
Other values (82) 186646
12.4%
(Missing) 114785
7.6%
ValueCountFrequency (%)
0 189973
12.7%
1 193331
12.9%
2 184588
12.3%
3 156023
10.4%
4 129468
8.6%
ValueCountFrequency (%)
115 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%
96 1
< 0.1%
93 1
< 0.1%

days90_310L
Real number (ℝ)

MISSING  ZEROS 

Distinct37
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.211420151
Minimum0
Maximum41
Zeros622576
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:40.789940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum41
Range41
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.655931394
Coefficient of variation (CV)1.366934002
Kurtosis10.01035904
Mean1.211420151
Median Absolute Deviation (MAD)1
Skewness2.310145676
Sum1678654
Variance2.742108782
MonotonicityNot monotonic
2024-02-13T20:57:40.925061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 622576
41.5%
1 354388
23.6%
2 187525
 
12.5%
3 96345
 
6.4%
4 53151
 
3.5%
5 30848
 
2.1%
6 17911
 
1.2%
7 10726
 
0.7%
8 6859
 
0.5%
9 3517
 
0.2%
Other values (27) 1845
 
0.1%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 622576
41.5%
1 354388
23.6%
2 187525
 
12.5%
3 96345
 
6.4%
4 53151
 
3.5%
ValueCountFrequency (%)
41 1
< 0.1%
38 1
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
32 1
< 0.1%

description_5085714M
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:41.074778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12003808
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1316125
87.7%
2fc785b2 184351
 
12.3%
2024-02-13T20:57:41.382657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4132726
34.4%
7 1500476
 
12.5%
b 1500476
 
12.5%
a 1316125
 
11.0%
4 1316125
 
11.0%
1 1316125
 
11.0%
2 368702
 
3.1%
f 184351
 
1.5%
c 184351
 
1.5%
8 184351
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8818505
73.5%
Lowercase Letter 3185303
 
26.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4132726
46.9%
7 1500476
 
17.0%
4 1316125
 
14.9%
1 1316125
 
14.9%
2 368702
 
4.2%
8 184351
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
b 1500476
47.1%
a 1316125
41.3%
f 184351
 
5.8%
c 184351
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8818505
73.5%
Latin 3185303
 
26.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4132726
46.9%
7 1500476
 
17.0%
4 1316125
 
14.9%
1 1316125
 
14.9%
2 368702
 
4.2%
8 184351
 
2.1%
Latin
ValueCountFrequency (%)
b 1500476
47.1%
a 1316125
41.3%
f 184351
 
5.8%
c 184351
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12003808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4132726
34.4%
7 1500476
 
12.5%
b 1500476
 
12.5%
a 1316125
 
11.0%
4 1316125
 
11.0%
1 1316125
 
11.0%
2 368702
 
3.1%
f 184351
 
1.5%
c 184351
 
1.5%
8 184351
 
1.5%

education_1103M
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:41.554943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12003808
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th row717ddd49
ValueCountFrequency (%)
a55475b1 859962
57.3%
6b2ae0fa 452449
30.2%
717ddd49 135342
 
9.0%
39a0853f 47140
 
3.1%
c8e1a1d0 5583
 
0.4%
2024-02-13T20:57:41.846940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2627026
21.9%
a 1817583
15.1%
b 1312411
10.9%
7 1130646
9.4%
1 1006470
 
8.4%
4 995304
 
8.3%
0 505172
 
4.2%
f 499589
 
4.2%
e 458032
 
3.8%
6 452449
 
3.8%
Other values (6) 1199126
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7499001
62.5%
Lowercase Letter 4504807
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2627026
35.0%
7 1130646
15.1%
1 1006470
 
13.4%
4 995304
 
13.3%
0 505172
 
6.7%
6 452449
 
6.0%
2 452449
 
6.0%
9 182482
 
2.4%
3 94280
 
1.3%
8 52723
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a 1817583
40.3%
b 1312411
29.1%
f 499589
 
11.1%
e 458032
 
10.2%
d 411609
 
9.1%
c 5583
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7499001
62.5%
Latin 4504807
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2627026
35.0%
7 1130646
15.1%
1 1006470
 
13.4%
4 995304
 
13.3%
0 505172
 
6.7%
6 452449
 
6.0%
2 452449
 
6.0%
9 182482
 
2.4%
3 94280
 
1.3%
8 52723
 
0.7%
Latin
ValueCountFrequency (%)
a 1817583
40.3%
b 1312411
29.1%
f 499589
 
11.1%
e 458032
 
10.2%
d 411609
 
9.1%
c 5583
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12003808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2627026
21.9%
a 1817583
15.1%
b 1312411
10.9%
7 1130646
9.4%
1 1006470
 
8.4%
4 995304
 
8.3%
0 505172
 
4.2%
f 499589
 
4.2%
e 458032
 
3.8%
6 452449
 
3.8%
Other values (6) 1199126
10.0%

education_88M
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:42.014586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12003808
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1484960
99.0%
6b2ae0fa 11673
 
0.8%
717ddd49 3280
 
0.2%
a34a13c8 437
 
< 0.1%
c8e1a1d0 126
 
< 0.1%
2024-02-13T20:57:42.309494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4454880
37.1%
a 1509306
 
12.6%
b 1496633
 
12.5%
7 1491520
 
12.4%
1 1488929
 
12.4%
4 1488677
 
12.4%
e 11799
 
0.1%
0 11799
 
0.1%
6 11673
 
0.1%
2 11673
 
0.1%
Other values (6) 26919
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8963868
74.7%
Lowercase Letter 3039940
 
25.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4454880
49.7%
7 1491520
 
16.6%
1 1488929
 
16.6%
4 1488677
 
16.6%
0 11799
 
0.1%
6 11673
 
0.1%
2 11673
 
0.1%
9 3280
 
< 0.1%
3 874
 
< 0.1%
8 563
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 1509306
49.6%
b 1496633
49.2%
e 11799
 
0.4%
f 11673
 
0.4%
d 9966
 
0.3%
c 563
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8963868
74.7%
Latin 3039940
 
25.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4454880
49.7%
7 1491520
 
16.6%
1 1488929
 
16.6%
4 1488677
 
16.6%
0 11799
 
0.1%
6 11673
 
0.1%
2 11673
 
0.1%
9 3280
 
< 0.1%
3 874
 
< 0.1%
8 563
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1509306
49.6%
b 1496633
49.2%
e 11799
 
0.4%
f 11673
 
0.4%
d 9966
 
0.3%
c 563
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12003808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4454880
37.1%
a 1509306
 
12.6%
b 1496633
 
12.5%
7 1491520
 
12.4%
1 1488929
 
12.4%
4 1488677
 
12.4%
e 11799
 
0.1%
0 11799
 
0.1%
6 11673
 
0.1%
2 11673
 
0.1%
Other values (6) 26919
 
0.2%

firstquarter_103L
Real number (ℝ)

MISSING  ZEROS 

Distinct61
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean2.860590132
Minimum0
Maximum76
Zeros403359
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:42.457493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum76
Range76
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.610965948
Coefficient of variation (CV)1.26231504
Kurtosis10.44295565
Mean2.860590132
Median Absolute Deviation (MAD)2
Skewness2.500315762
Sum3963894
Variance13.03907508
MonotonicityNot monotonic
2024-02-13T20:57:42.617144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 403359
26.9%
1 238611
15.9%
2 198237
13.2%
3 138264
 
9.2%
4 104739
 
7.0%
5 75825
 
5.1%
6 56018
 
3.7%
7 40567
 
2.7%
8 30679
 
2.0%
9 23057
 
1.5%
Other values (51) 76335
 
5.1%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 403359
26.9%
1 238611
15.9%
2 198237
13.2%
3 138264
 
9.2%
4 104739
 
7.0%
ValueCountFrequency (%)
76 1
 
< 0.1%
61 1
 
< 0.1%
59 1
 
< 0.1%
57 3
< 0.1%
56 2
< 0.1%

for3years_128L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean8.216026729 × 10-5
Minimum0
Maximum1
Zeros36511
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:42.747056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.009063981849
Coefficient of variation (CV)110.3207444
Kurtosis12167.99973
Mean8.216026729 × 10-5
Median Absolute Deviation (MAD)0
Skewness110.3147009
Sum3
Variance8.215576697 × 10-5
MonotonicityNot monotonic
2024-02-13T20:57:42.860628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 36511
 
2.4%
1 3
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36511
2.4%
1 3
 
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0 36511
2.4%

for3years_504L
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean4.382346497
Minimum0
Maximum57
Zeros10443
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:42.992240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile16
Maximum57
Range57
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.815513965
Coefficient of variation (CV)1.327031984
Kurtosis6.988933267
Mean4.382346497
Median Absolute Deviation (MAD)2
Skewness2.242979798
Sum160017
Variance33.82020268
MonotonicityNot monotonic
2024-02-13T20:57:43.140873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10443
 
0.7%
1 5484
 
0.4%
2 3701
 
0.2%
3 2459
 
0.2%
4 2111
 
0.1%
5 1769
 
0.1%
6 1571
 
0.1%
7 1398
 
0.1%
8 1145
 
0.1%
9 1001
 
0.1%
Other values (44) 5432
 
0.4%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 10443
0.7%
1 5484
0.4%
2 3701
 
0.2%
3 2459
 
0.2%
4 2111
 
0.1%
ValueCountFrequency (%)
57 2
< 0.1%
54 1
 
< 0.1%
53 3
< 0.1%
50 2
< 0.1%
49 1
 
< 0.1%

for3years_584L
Real number (ℝ)

MISSING  ZEROS 

Distinct3
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.00769567837
Minimum0
Maximum2
Zeros36245
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:43.263401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09107120394
Coefficient of variation (CV)11.83407096
Kurtosis171.5069065
Mean0.00769567837
Median Absolute Deviation (MAD)0
Skewness12.54581173
Sum281
Variance0.008293964187
MonotonicityNot monotonic
2024-02-13T20:57:43.393230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 36245
 
2.4%
1 257
 
< 0.1%
2 12
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36245
2.4%
1 257
 
< 0.1%
2 12
 
< 0.1%
ValueCountFrequency (%)
2 12
 
< 0.1%
1 257
 
< 0.1%
0 36245
2.4%

formonth_118L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros36514
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:43.527195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:57:43.640159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 36514
 
2.4%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36514
2.4%
ValueCountFrequency (%)
0 36514
2.4%

formonth_206L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.0003560278249
Minimum0
Maximum1
Zeros36501
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:43.772176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01886559875
Coefficient of variation (CV)52.9891133
Kurtosis2804.15372
Mean0.0003560278249
Median Absolute Deviation (MAD)0
Skewness52.97169175
Sum13
Variance0.0003559108164
MonotonicityNot monotonic
2024-02-13T20:57:43.901192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 36501
 
2.4%
1 13
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36501
2.4%
1 13
 
< 0.1%
ValueCountFrequency (%)
1 13
 
< 0.1%
0 36501
2.4%

formonth_535L
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.2367585036
Minimum0
Maximum11
Zeros29492
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:44.025204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5354934977
Coefficient of variation (CV)2.261770917
Kurtosis12.52351402
Mean0.2367585036
Median Absolute Deviation (MAD)0
Skewness2.752137772
Sum8645
Variance0.2867532861
MonotonicityNot monotonic
2024-02-13T20:57:44.146194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 29492
 
2.0%
1 5616
 
0.4%
2 1240
 
0.1%
3 135
 
< 0.1%
4 20
 
< 0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 1
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 29492
2.0%
1 5616
 
0.4%
2 1240
 
0.1%
3 135
 
< 0.1%
4 20
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 8
 
< 0.1%
4 20
< 0.1%

forquarter_1017L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.0009585364518
Minimum0
Maximum2
Zeros36482
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:44.266741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03349580453
Coefficient of variation (CV)34.94473733
Kurtosis1668.115536
Mean0.0009585364518
Median Absolute Deviation (MAD)0
Skewness38.54022557
Sum35
Variance0.001121968921
MonotonicityNot monotonic
2024-02-13T20:57:44.409742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 36482
 
2.4%
1 29
 
< 0.1%
2 3
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36482
2.4%
1 29
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
2 3
 
< 0.1%
1 29
 
< 0.1%
0 36482
2.4%

forquarter_462L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros36514
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:44.555741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:57:44.686823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 36514
 
2.4%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36514
2.4%
ValueCountFrequency (%)
0 36514
2.4%

forquarter_634L
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.613928904
Minimum0
Maximum19
Zeros24664
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:44.832828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.150001349
Coefficient of variation (CV)1.873183265
Kurtosis10.69957259
Mean0.613928904
Median Absolute Deviation (MAD)0
Skewness2.681859676
Sum22417
Variance1.322503102
MonotonicityNot monotonic
2024-02-13T20:57:44.975827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 24664
 
1.6%
1 6320
 
0.4%
2 2795
 
0.2%
3 1441
 
0.1%
4 717
 
< 0.1%
5 346
 
< 0.1%
6 134
 
< 0.1%
7 56
 
< 0.1%
8 18
 
< 0.1%
9 7
 
< 0.1%
Other values (5) 16
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 24664
1.6%
1 6320
 
0.4%
2 2795
 
0.2%
3 1441
 
0.1%
4 717
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 1
 
< 0.1%
12 3
< 0.1%
11 4
< 0.1%
10 7
< 0.1%

fortoday_1092L
Real number (ℝ)

MISSING  ZEROS 

Distinct3
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.03694473353
Minimum0
Maximum2
Zeros35169
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:45.100874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.189208601
Coefficient of variation (CV)5.121395742
Kurtosis22.92507915
Mean0.03694473353
Median Absolute Deviation (MAD)0
Skewness4.958421926
Sum1349
Variance0.03579989468
MonotonicityNot monotonic
2024-02-13T20:57:45.222872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 35169
 
2.3%
1 1341
 
0.1%
2 4
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 35169
2.3%
1 1341
 
0.1%
2 4
 
< 0.1%
ValueCountFrequency (%)
2 4
 
< 0.1%
1 1341
 
0.1%
0 35169
2.3%

forweek_1077L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean5.477351153 × 10-5
Minimum0
Maximum1
Zeros36512
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:45.346889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.007400811538
Coefficient of variation (CV)135.1166162
Kurtosis18254.49978
Mean5.477351153 × 10-5
Median Absolute Deviation (MAD)0
Skewness135.1129154
Sum2
Variance5.477201142 × 10-5
MonotonicityNot monotonic
2024-02-13T20:57:45.465148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 36512
 
2.4%
1 2
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36512
2.4%
1 2
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0 36512
2.4%

forweek_528L
Real number (ℝ)

MISSING  ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.09012981322
Minimum0
Maximum4
Zeros33271
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:45.607942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2913022083
Coefficient of variation (CV)3.232029424
Kurtosis8.683878297
Mean0.09012981322
Median Absolute Deviation (MAD)0
Skewness3.067530114
Sum3291
Variance0.08485697655
MonotonicityNot monotonic
2024-02-13T20:57:45.731916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 33271
 
2.2%
1 3198
 
0.2%
2 43
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 33271
2.2%
1 3198
 
0.2%
2 43
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 1
 
< 0.1%
2 43
 
< 0.1%
1 3198
 
0.2%
0 33271
2.2%

forweek_601L
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros36514
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:45.860069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-02-13T20:57:45.972830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 36514
 
2.4%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36514
2.4%
ValueCountFrequency (%)
0 36514
2.4%

foryear_618L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean5.477351153 × 10-5
Minimum0
Maximum1
Zeros36512
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:46.087457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.007400811538
Coefficient of variation (CV)135.1166162
Kurtosis18254.49978
Mean5.477351153 × 10-5
Median Absolute Deviation (MAD)0
Skewness135.1129154
Sum2
Variance5.477201142 × 10-5
MonotonicityNot monotonic
2024-02-13T20:57:46.204392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 36512
 
2.4%
1 2
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36512
2.4%
1 2
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0 36512
2.4%

foryear_818L
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3
Distinct (%)< 0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean0.002081393438
Minimum0
Maximum2
Zeros36444
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:46.322350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04904863092
Coefficient of variation (CV)23.56528565
Kurtosis749.8421748
Mean0.002081393438
Median Absolute Deviation (MAD)0
Skewness25.86903552
Sum76
Variance0.002405768195
MonotonicityNot monotonic
2024-02-13T20:57:46.461355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 36444
 
2.4%
1 64
 
< 0.1%
2 6
 
< 0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 36444
2.4%
1 64
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
2 6
 
< 0.1%
1 64
 
< 0.1%
0 36444
2.4%

foryear_850L
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)0.1%
Missing1463962
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean2.420715342
Minimum0
Maximum41
Zeros14884
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:46.614355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum41
Range41
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.552459736
Coefficient of variation (CV)1.467524774
Kurtosis8.384687421
Mean2.420715342
Median Absolute Deviation (MAD)1
Skewness2.409795428
Sum88390
Variance12.61997017
MonotonicityNot monotonic
2024-02-13T20:57:46.761360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 14884
 
1.0%
1 6356
 
0.4%
2 3779
 
0.3%
3 2478
 
0.2%
4 1938
 
0.1%
5 1602
 
0.1%
6 1265
 
0.1%
7 937
 
0.1%
8 753
 
0.1%
9 574
 
< 0.1%
Other values (27) 1948
 
0.1%
(Missing) 1463962
97.6%
ValueCountFrequency (%)
0 14884
1.0%
1 6356
0.4%
2 3779
 
0.3%
3 2478
 
0.2%
4 1938
 
0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
36 2
< 0.1%
35 4
< 0.1%

fourthquarter_440L
Real number (ℝ)

MISSING  ZEROS 

Distinct54
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean2.851213582
Minimum0
Maximum66
Zeros377862
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:46.905959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum66
Range66
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.431652285
Coefficient of variation (CV)1.203576016
Kurtosis8.678046246
Mean2.851213582
Median Absolute Deviation (MAD)2
Skewness2.305697724
Sum3950901
Variance11.7762374
MonotonicityNot monotonic
2024-02-13T20:57:47.069943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 377862
25.2%
1 243994
16.3%
2 206643
13.8%
3 145649
 
9.7%
4 109754
 
7.3%
5 78577
 
5.2%
6 57394
 
3.8%
7 41993
 
2.8%
8 30622
 
2.0%
9 23084
 
1.5%
Other values (44) 70119
 
4.7%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 377862
25.2%
1 243994
16.3%
2 206643
13.8%
3 145649
 
9.7%
4 109754
 
7.3%
ValueCountFrequency (%)
66 1
< 0.1%
60 1
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
50 1
< 0.1%

maritalst_385M
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:47.250099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12003808
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa7fcb6e5
ValueCountFrequency (%)
a55475b1 661592
44.1%
3439d993 550336
36.7%
a7fcb6e5 201050
 
13.4%
b6cabe76 55900
 
3.7%
38c061ee 26851
 
1.8%
ecd83604 4747
 
0.3%
2024-02-13T20:57:47.542835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2185826
18.2%
3 1682606
14.0%
9 1651008
13.8%
4 1216675
10.1%
b 974442
8.1%
a 918542
7.7%
7 918542
7.7%
1 688443
 
5.7%
d 555083
 
4.6%
6 344448
 
2.9%
Other values (5) 868193
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8750744
72.9%
Lowercase Letter 3253064
 
27.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2185826
25.0%
3 1682606
19.2%
9 1651008
18.9%
4 1216675
13.9%
7 918542
10.5%
1 688443
 
7.9%
6 344448
 
3.9%
8 31598
 
0.4%
0 31598
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
b 974442
30.0%
a 918542
28.2%
d 555083
17.1%
e 315399
 
9.7%
c 288548
 
8.9%
f 201050
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8750744
72.9%
Latin 3253064
 
27.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2185826
25.0%
3 1682606
19.2%
9 1651008
18.9%
4 1216675
13.9%
7 918542
10.5%
1 688443
 
7.9%
6 344448
 
3.9%
8 31598
 
0.4%
0 31598
 
0.4%
Latin
ValueCountFrequency (%)
b 974442
30.0%
a 918542
28.2%
d 555083
17.1%
e 315399
 
9.7%
c 288548
 
8.9%
f 201050
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12003808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2185826
18.2%
3 1682606
14.0%
9 1651008
13.8%
4 1216675
10.1%
b 974442
8.1%
a 918542
7.7%
7 918542
7.7%
1 688443
 
5.7%
d 555083
 
4.6%
6 344448
 
2.9%
Other values (5) 868193
 
7.2%

maritalst_893M
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:47.809848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters12003808
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowa55475b1
2nd rowa55475b1
3rd rowa55475b1
4th rowa55475b1
5th rowa55475b1
ValueCountFrequency (%)
a55475b1 1479303
98.6%
46b968c3 13889
 
0.9%
1a19667c 5321
 
0.4%
977b2a70 1196
 
0.1%
e18430ff 678
 
< 0.1%
ecd83604 89
 
< 0.1%
2024-02-13T20:57:48.113516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4437909
37.0%
b 1494388
 
12.4%
4 1493959
 
12.4%
1 1490623
 
12.4%
7 1488212
 
12.4%
a 1485820
 
12.4%
6 38509
 
0.3%
9 20406
 
0.2%
c 19299
 
0.2%
8 14656
 
0.1%
Other values (6) 20027
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9002089
75.0%
Lowercase Letter 3001719
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4437909
49.3%
4 1493959
 
16.6%
1 1490623
 
16.6%
7 1488212
 
16.5%
6 38509
 
0.4%
9 20406
 
0.2%
8 14656
 
0.2%
3 14656
 
0.2%
0 1963
 
< 0.1%
2 1196
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
b 1494388
49.8%
a 1485820
49.5%
c 19299
 
0.6%
f 1356
 
< 0.1%
e 767
 
< 0.1%
d 89
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9002089
75.0%
Latin 3001719
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4437909
49.3%
4 1493959
 
16.6%
1 1490623
 
16.6%
7 1488212
 
16.5%
6 38509
 
0.4%
9 20406
 
0.2%
8 14656
 
0.2%
3 14656
 
0.2%
0 1963
 
< 0.1%
2 1196
 
< 0.1%
Latin
ValueCountFrequency (%)
b 1494388
49.8%
a 1485820
49.5%
c 19299
 
0.6%
f 1356
 
< 0.1%
e 767
 
< 0.1%
d 89
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12003808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4437909
37.0%
b 1494388
 
12.4%
4 1493959
 
12.4%
1 1490623
 
12.4%
7 1488212
 
12.4%
a 1485820
 
12.4%
6 38509
 
0.3%
9 20406
 
0.2%
c 19299
 
0.2%
8 14656
 
0.1%
Other values (6) 20027
 
0.2%

numberofqueries_373L
Real number (ℝ)

MISSING  ZEROS 

Distinct92
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean4.77706646
Minimum0
Maximum115
Zeros189973
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:48.270543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36.5
95-th percentile15
Maximum115
Range115
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.168856074
Coefficient of variation (CV)1.082014688
Kurtosis10.70523526
Mean4.77706646
Median Absolute Deviation (MAD)2
Skewness2.456375433
Sum6619538
Variance26.71707311
MonotonicityNot monotonic
2024-02-13T20:57:48.703513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 193331
12.9%
0 189973
12.7%
2 184588
12.3%
3 156023
10.4%
4 129468
8.6%
5 103161
6.9%
6 82724
5.5%
7 66235
 
4.4%
8 52195
 
3.5%
9 41347
 
2.8%
Other values (82) 186646
12.4%
(Missing) 114785
7.6%
ValueCountFrequency (%)
0 189973
12.7%
1 193331
12.9%
2 184588
12.3%
3 156023
10.4%
4 129468
8.6%
ValueCountFrequency (%)
115 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%
96 1
< 0.1%
93 1
< 0.1%

pmtaverage_3A
Real number (ℝ)

MISSING 

Distinct40002
Distinct (%)27.9%
Missing1356887
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean9303.1717
Minimum0
Maximum145257.4
Zeros6595
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:48.873511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile722.1667
Q16590.6
median7305.9
Q313023.9
95-th percentile18047.834
Maximum145257.4
Range145257.4
Interquartile range (IQR)6433.3

Descriptive statistics

Standard deviation5562.386995
Coefficient of variation (CV)0.5979022181
Kurtosis15.65350706
Mean9303.1717
Median Absolute Deviation (MAD)1840.8
Skewness2.096106409
Sum1335833121
Variance30940149.08
MonotonicityNot monotonic
2024-02-13T20:57:49.047522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6595
 
0.4%
7222.2 4887
 
0.3%
7512 4863
 
0.3%
7223.4 3254
 
0.2%
7553 2750
 
0.2%
7222.6 1869
 
0.1%
7221.6 1541
 
0.1%
9087.601 1417
 
0.1%
7222 1266
 
0.1%
7221.8003 1139
 
0.1%
Other values (39992) 114008
 
7.6%
(Missing) 1356887
90.4%
ValueCountFrequency (%)
0 6595
0.4%
2.333334 1
 
< 0.1%
3.166666 1
 
< 0.1%
5.666666 1
 
< 0.1%
6.8 1
 
< 0.1%
ValueCountFrequency (%)
145257.4 1
< 0.1%
127760.2 1
< 0.1%
116732 1
< 0.1%
85133.805 1
< 0.1%
82177.805 1
< 0.1%

pmtaverage_4527227A
Real number (ℝ)

MISSING 

Distinct30526
Distinct (%)26.5%
Missing1385498
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean10033.55609
Minimum4.2000003
Maximum205848.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:49.219515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.2000003
5-th percentile4544.2
Q17192
median7553
Q313464.4
95-th percentile18466.4
Maximum205848.61
Range205844.41
Interquartile range (IQR)6272.4

Descriptive statistics

Standard deviation5455.843604
Coefficient of variation (CV)0.5437597152
Kurtosis68.17206584
Mean10033.55609
Median Absolute Deviation (MAD)1648.2
Skewness4.194182381
Sum1153638213
Variance29766229.43
MonotonicityNot monotonic
2024-02-13T20:57:49.381491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7222.2 6173
 
0.4%
7512 5844
 
0.4%
7223.4 3927
 
0.3%
7553 3395
 
0.2%
7222.6 2341
 
0.2%
7221.6 2023
 
0.1%
9087.601 1794
 
0.1%
7222 1592
 
0.1%
7221.8003 1390
 
0.1%
7306.4 1123
 
0.1%
Other values (30516) 85376
 
5.7%
(Missing) 1385498
92.3%
ValueCountFrequency (%)
4.2000003 1
< 0.1%
5 1
< 0.1%
5.6 1
< 0.1%
6.6 1
< 0.1%
6.8 1
< 0.1%
ValueCountFrequency (%)
205848.61 1
< 0.1%
199301.61 1
< 0.1%
179814.4 1
< 0.1%
153810.4 1
< 0.1%
145257.4 1
< 0.1%

pmtaverage_4955615A
Real number (ℝ)

MISSING 

Distinct32267
Distinct (%)44.9%
Missing1428631
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean17651.73249
Minimum4.4
Maximum99085.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:49.545520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile8932.4
Q113664.601
median15765.2
Q321840
95-th percentile29501.56
Maximum99085.4
Range99081
Interquartile range (IQR)8175.399

Descriptive statistics

Standard deviation6871.642301
Coefficient of variation (CV)0.3892899638
Kurtosis8.310976977
Mean17651.73249
Median Absolute Deviation (MAD)2864
Skewness1.560202362
Sum1268188721
Variance47219467.92
MonotonicityNot monotonic
2024-02-13T20:57:49.721255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2833.4001 171
 
< 0.1%
1416.6 147
 
< 0.1%
14905.4 119
 
< 0.1%
16861.4 115
 
< 0.1%
32800.8 111
 
< 0.1%
15055.8 108
 
< 0.1%
15356.8 103
 
< 0.1%
16975.201 99
 
< 0.1%
15206.4 98
 
< 0.1%
15581.2 98
 
< 0.1%
Other values (32257) 70676
 
4.7%
(Missing) 1428631
95.2%
ValueCountFrequency (%)
4.4 1
< 0.1%
6 1
< 0.1%
31.800001 1
< 0.1%
39.2 1
< 0.1%
43 1
< 0.1%
ValueCountFrequency (%)
99085.4 1
< 0.1%
99012.805 1
< 0.1%
98373.4 1
< 0.1%
98012.805 1
< 0.1%
96997.4 1
< 0.1%

pmtcount_4527229L
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing1385498
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean6.598027449
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:49.847727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q16
median6
Q36
95-th percentile13
Maximum15
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.188991909
Coefficient of variation (CV)0.3317645957
Kurtosis4.223109939
Mean6.598027449
Median Absolute Deviation (MAD)0
Skewness2.060441071
Sum758628
Variance4.791685577
MonotonicityNot monotonic
2024-02-13T20:57:50.000814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 98483
 
6.6%
13 9967
 
0.7%
11 2141
 
0.1%
2 939
 
0.1%
1 900
 
0.1%
3 756
 
0.1%
4 706
 
< 0.1%
5 606
 
< 0.1%
7 346
 
< 0.1%
9 93
 
< 0.1%
Other values (5) 41
 
< 0.1%
(Missing) 1385498
92.3%
ValueCountFrequency (%)
1 900
0.1%
2 939
0.1%
3 756
0.1%
4 706
< 0.1%
5 606
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 7
 
< 0.1%
13 9967
0.7%
12 14
 
< 0.1%
11 2141
 
0.1%

pmtcount_4955617L
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)< 0.1%
Missing1428631
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean13.06111768
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:50.128974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q112
median14
Q314
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.85521621
Coefficient of variation (CV)0.1420411526
Kurtosis14.84949257
Mean13.06111768
Median Absolute Deviation (MAD)0
Skewness-3.508800255
Sum938376
Variance3.441827185
MonotonicityNot monotonic
2024-02-13T20:57:50.255936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14 44107
 
2.9%
12 13601
 
0.9%
13 9381
 
0.6%
11 1438
 
0.1%
7 1021
 
0.1%
6 886
 
0.1%
2 368
 
< 0.1%
10 215
 
< 0.1%
8 214
 
< 0.1%
4 206
 
< 0.1%
Other values (6) 408
 
< 0.1%
(Missing) 1428631
95.2%
ValueCountFrequency (%)
1 178
< 0.1%
2 368
< 0.1%
3 37
 
< 0.1%
4 206
< 0.1%
5 73
 
< 0.1%
ValueCountFrequency (%)
16 6
 
< 0.1%
15 39
 
< 0.1%
14 44107
2.9%
13 9381
 
0.6%
12 13601
 
0.9%

pmtcount_693L
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)< 0.1%
Missing1354070
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean5.714991189
Minimum0
Maximum66
Zeros6593
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:50.398974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum66
Range66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.758117361
Coefficient of variation (CV)0.3076325585
Kurtosis35.62586569
Mean5.714991189
Median Absolute Deviation (MAD)0
Skewness1.080429597
Sum836709
Variance3.090976656
MonotonicityNot monotonic
2024-02-13T20:57:50.547975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6 118818
 
7.9%
5 12658
 
0.8%
0 6593
 
0.4%
7 1108
 
0.1%
12 1105
 
0.1%
4 1024
 
0.1%
2 843
 
0.1%
3 817
 
0.1%
1 782
 
0.1%
8 664
 
< 0.1%
Other values (29) 1994
 
0.1%
(Missing) 1354070
90.2%
ValueCountFrequency (%)
0 6593
0.4%
1 782
 
0.1%
2 843
 
0.1%
3 817
 
0.1%
4 1024
 
0.1%
ValueCountFrequency (%)
66 1
< 0.1%
48 1
< 0.1%
44 1
< 0.1%
43 1
< 0.1%
36 2
< 0.1%

pmtscount_423L
Real number (ℝ)

MISSING  ZEROS 

Distinct66
Distinct (%)< 0.1%
Missing927838
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean5.83929114
Minimum0
Maximum121
Zeros90373
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:50.714974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q37
95-th percentile13
Maximum121
Range121
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.148263756
Coefficient of variation (CV)0.7104053653
Kurtosis7.370254054
Mean5.83929114
Median Absolute Deviation (MAD)2
Skewness1.171195449
Sum3343800
Variance17.20809219
MonotonicityNot monotonic
2024-02-13T20:57:50.883976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 179791
 
12.0%
0 90373
 
6.0%
5 50434
 
3.4%
7 46852
 
3.1%
8 24683
 
1.6%
12 22422
 
1.5%
4 21425
 
1.4%
1 19089
 
1.3%
3 18623
 
1.2%
9 17604
 
1.2%
Other values (56) 81342
 
5.4%
(Missing) 927838
61.8%
ValueCountFrequency (%)
0 90373
6.0%
1 19089
 
1.3%
2 15843
 
1.1%
3 18623
 
1.2%
4 21425
 
1.4%
ValueCountFrequency (%)
121 2
< 0.1%
111 1
< 0.1%
99 1
< 0.1%
69 1
< 0.1%
64 1
< 0.1%

pmtssum_45A
Real number (ℝ)

MISSING  ZEROS 

Distinct265229
Distinct (%)46.3%
Missing927838
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean13199.93597
Minimum0
Maximum476843.4
Zeros90374
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:51.054973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13156.4001
median8391.9
Q316992
95-th percentile41383.75
Maximum476843.4
Range476843.4
Interquartile range (IQR)13835.5999

Descriptive statistics

Standard deviation18117.21831
Coefficient of variation (CV)1.372523197
Kurtosis37.68050675
Mean13199.93597
Median Absolute Deviation (MAD)6632.9
Skewness4.612176696
Sum7558784934
Variance328233599.4
MonotonicityNot monotonic
2024-02-13T20:57:51.247538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90374
 
6.0%
5100 7001
 
0.5%
6000 3235
 
0.2%
2550 2525
 
0.2%
850 1747
 
0.1%
4250 1722
 
0.1%
3400 1633
 
0.1%
1700 1603
 
0.1%
3600 1571
 
0.1%
20 1486
 
0.1%
Other values (265219) 459741
30.6%
(Missing) 927838
61.8%
ValueCountFrequency (%)
0 90374
6.0%
0.002 2
 
< 0.1%
0.020000001 1
 
< 0.1%
0.036000002 1
 
< 0.1%
0.068 1
 
< 0.1%
ValueCountFrequency (%)
476843.4 1
< 0.1%
459898.25 1
< 0.1%
441035.6 1
< 0.1%
439822.8 1
< 0.1%
401034.6 1
< 0.1%

requesttype_4525192L
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing827212
Missing (%)55.1%
Memory size11.4 MiB
2024-02-13T20:57:51.438577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.62632192
Min length8

Characters and Unicode

Total characters7154320
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowDEDUCTION_6
2nd rowDEDUCTION_6
3rd rowPENSION_6
4th rowDEDUCTION_6
5th rowDEDUCTION_6
ValueCountFrequency (%)
deduction_6 550387
81.7%
pension_6 117047
 
17.4%
social_6 5830
 
0.9%
2024-02-13T20:57:51.768887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1100774
15.4%
N 784481
11.0%
I 673264
9.4%
O 673264
9.4%
_ 673264
9.4%
6 673264
9.4%
E 667434
9.3%
C 556217
7.8%
U 550387
7.7%
T 550387
7.7%
Other values (4) 251584
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5807792
81.2%
Connector Punctuation 673264
 
9.4%
Decimal Number 673264
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1100774
19.0%
N 784481
13.5%
I 673264
11.6%
O 673264
11.6%
E 667434
11.5%
C 556217
9.6%
U 550387
9.5%
T 550387
9.5%
S 122877
 
2.1%
P 117047
 
2.0%
Other values (2) 11660
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 673264
100.0%
Decimal Number
ValueCountFrequency (%)
6 673264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5807792
81.2%
Common 1346528
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1100774
19.0%
N 784481
13.5%
I 673264
11.6%
O 673264
11.6%
E 667434
11.5%
C 556217
9.6%
U 550387
9.5%
T 550387
9.5%
S 122877
 
2.1%
P 117047
 
2.0%
Other values (2) 11660
 
0.2%
Common
ValueCountFrequency (%)
_ 673264
50.0%
6 673264
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7154320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1100774
15.4%
N 784481
11.0%
I 673264
9.4%
O 673264
9.4%
_ 673264
9.4%
6 673264
9.4%
E 667434
9.3%
C 556217
7.8%
U 550387
7.7%
T 550387
7.7%
Other values (4) 251584
 
3.5%

responsedate_1012D
Text

MISSING 

Distinct292
Distinct (%)< 0.1%
Missing780476
Missing (%)52.0%
Memory size11.4 MiB
2024-02-13T20:57:52.196679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7200000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2019-01-25
2nd row2019-01-25
3rd row2019-01-28
4th row2019-01-21
5th row2019-01-21
ValueCountFrequency (%)
2019-06-28 5252
 
0.7%
2019-06-29 5222
 
0.7%
2019-09-21 4663
 
0.6%
2019-06-22 4598
 
0.6%
2019-06-21 4483
 
0.6%
2019-08-30 4330
 
0.6%
2019-06-30 4080
 
0.6%
2019-06-23 4065
 
0.6%
2019-09-22 4018
 
0.6%
2019-09-20 4009
 
0.6%
Other values (282) 675280
93.8%
2024-02-13T20:57:52.785753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1725051
24.0%
- 1440000
20.0%
1 1118867
15.5%
2 1098496
15.3%
9 882858
12.3%
3 177651
 
2.5%
8 169084
 
2.3%
7 159452
 
2.2%
6 155077
 
2.2%
4 138898
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5760000
80.0%
Dash Punctuation 1440000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1725051
29.9%
1 1118867
19.4%
2 1098496
19.1%
9 882858
15.3%
3 177651
 
3.1%
8 169084
 
2.9%
7 159452
 
2.8%
6 155077
 
2.7%
4 138898
 
2.4%
5 134566
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1440000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7200000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1725051
24.0%
- 1440000
20.0%
1 1118867
15.5%
2 1098496
15.3%
9 882858
12.3%
3 177651
 
2.5%
8 169084
 
2.3%
7 159452
 
2.2%
6 155077
 
2.2%
4 138898
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7200000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1725051
24.0%
- 1440000
20.0%
1 1118867
15.5%
2 1098496
15.3%
9 882858
12.3%
3 177651
 
2.5%
8 169084
 
2.3%
7 159452
 
2.2%
6 155077
 
2.2%
4 138898
 
1.9%

responsedate_4527233D
Text

MISSING 

Distinct397
Distinct (%)0.1%
Missing840149
Missing (%)56.0%
Memory size11.4 MiB
2024-02-13T20:57:53.266326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6603270
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2019-09-13
2nd row2019-09-13
3rd row2019-09-13
4th row2019-09-13
5th row2019-09-13
ValueCountFrequency (%)
2019-12-13 8735
 
1.3%
2019-12-14 8550
 
1.3%
2020-01-11 6856
 
1.0%
2020-01-12 6579
 
1.0%
2019-12-01 6371
 
1.0%
2020-01-13 6235
 
0.9%
2019-11-30 5850
 
0.9%
2019-12-28 5810
 
0.9%
2019-12-16 5741
 
0.9%
2020-01-10 5479
 
0.8%
Other values (387) 594121
90.0%
2024-02-13T20:57:53.844367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1638927
24.8%
2 1430186
21.7%
- 1320654
20.0%
1 1197519
18.1%
9 491994
 
7.5%
3 179315
 
2.7%
4 97422
 
1.5%
6 64453
 
1.0%
8 64169
 
1.0%
7 63599
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5282616
80.0%
Dash Punctuation 1320654
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1638927
31.0%
2 1430186
27.1%
1 1197519
22.7%
9 491994
 
9.3%
3 179315
 
3.4%
4 97422
 
1.8%
6 64453
 
1.2%
8 64169
 
1.2%
7 63599
 
1.2%
5 55032
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 1320654
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6603270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1638927
24.8%
2 1430186
21.7%
- 1320654
20.0%
1 1197519
18.1%
9 491994
 
7.5%
3 179315
 
2.7%
4 97422
 
1.5%
6 64453
 
1.0%
8 64169
 
1.0%
7 63599
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6603270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1638927
24.8%
2 1430186
21.7%
- 1320654
20.0%
1 1197519
18.1%
9 491994
 
7.5%
3 179315
 
2.7%
4 97422
 
1.5%
6 64453
 
1.0%
8 64169
 
1.0%
7 63599
 
1.0%

responsedate_4917613D
Text

MISSING 

Distinct207
Distinct (%)0.1%
Missing1275564
Missing (%)85.0%
Memory size11.4 MiB
2024-02-13T20:57:54.271327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2249120
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row2020-03-26
2nd row2020-03-26
3rd row2020-03-26
4th row2020-03-27
5th row2020-03-27
ValueCountFrequency (%)
2020-09-15 4266
 
1.9%
2020-09-07 3513
 
1.6%
2020-09-08 3307
 
1.5%
2020-09-11 3198
 
1.4%
2020-09-16 3177
 
1.4%
2020-09-04 3176
 
1.4%
2020-09-09 3105
 
1.4%
2020-08-31 3008
 
1.3%
2020-09-10 2907
 
1.3%
2020-09-28 2844
 
1.3%
Other values (197) 192411
85.5%
2024-02-13T20:57:54.862087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 764747
34.0%
2 535735
23.8%
- 449824
20.0%
1 136924
 
6.1%
9 88716
 
3.9%
6 64108
 
2.9%
7 58160
 
2.6%
8 57458
 
2.6%
5 36395
 
1.6%
3 31654
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1799296
80.0%
Dash Punctuation 449824
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 764747
42.5%
2 535735
29.8%
1 136924
 
7.6%
9 88716
 
4.9%
6 64108
 
3.6%
7 58160
 
3.2%
8 57458
 
3.2%
5 36395
 
2.0%
3 31654
 
1.8%
4 25399
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 449824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2249120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 764747
34.0%
2 535735
23.8%
- 449824
20.0%
1 136924
 
6.1%
9 88716
 
3.9%
6 64108
 
2.9%
7 58160
 
2.6%
8 57458
 
2.6%
5 36395
 
1.6%
3 31654
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2249120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 764747
34.0%
2 535735
23.8%
- 449824
20.0%
1 136924
 
6.1%
9 88716
 
3.9%
6 64108
 
2.9%
7 58160
 
2.6%
8 57458
 
2.6%
5 36395
 
1.6%
3 31654
 
1.4%

riskassesment_302T
Text

MISSING 

Distinct16
Distinct (%)< 0.1%
Missing1446917
Missing (%)96.4%
Memory size11.4 MiB
2024-02-13T20:57:55.007049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.821057152
Min length7

Characters and Unicode

Total characters418888
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row59% - 66%
2nd row6% - 8%
3rd row1% - 1%
4th row2% - 3%
5th row67% - 100%
ValueCountFrequency (%)
53559
33.3%
2 16043
 
10.0%
3 12592
 
7.8%
4 12126
 
7.5%
6 10242
 
6.4%
8 8483
 
5.3%
1 8206
 
5.1%
11 6911
 
4.3%
15 5233
 
3.3%
67 4549
 
2.8%
Other values (12) 22733
14.1%
2024-02-13T20:57:55.293050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 107118
25.6%
107118
25.6%
- 53559
12.8%
1 36980
 
8.8%
2 20676
 
4.9%
3 18694
 
4.5%
6 17872
 
4.3%
4 16422
 
3.9%
0 11854
 
2.8%
5 9969
 
2.4%
Other values (3) 18626
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 151093
36.1%
Other Punctuation 107118
25.6%
Space Separator 107118
25.6%
Dash Punctuation 53559
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36980
24.5%
2 20676
13.7%
3 18694
12.4%
6 17872
11.8%
4 16422
10.9%
0 11854
 
7.8%
5 9969
 
6.6%
8 9670
 
6.4%
7 4549
 
3.0%
9 4407
 
2.9%
Other Punctuation
ValueCountFrequency (%)
% 107118
100.0%
Space Separator
ValueCountFrequency (%)
107118
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 418888
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 107118
25.6%
107118
25.6%
- 53559
12.8%
1 36980
 
8.8%
2 20676
 
4.9%
3 18694
 
4.5%
6 17872
 
4.3%
4 16422
 
3.9%
0 11854
 
2.8%
5 9969
 
2.4%
Other values (3) 18626
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 107118
25.6%
107118
25.6%
- 53559
12.8%
1 36980
 
8.8%
2 20676
 
4.9%
3 18694
 
4.5%
6 17872
 
4.3%
4 16422
 
3.9%
0 11854
 
2.8%
5 9969
 
2.4%
Other values (3) 18626
 
4.4%

riskassesment_940T
Real number (ℝ)

MISSING 

Distinct203
Distinct (%)0.4%
Missing1446916
Missing (%)96.4%
Infinite0
Infinite (%)0.0%
Mean0.2259680301
Minimum-3.6704228
Maximum2.1191318
Zeros3623
Zeros (%)0.2%
Negative16410
Negative (%)1.1%
Memory size11.4 MiB
2024-02-13T20:57:55.462172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.6704228
5-th percentile-1.7666501
Q1-0.22798485
median0.37183386
Q30.9716525
95-th percentile1.5193131
Maximum2.1191318
Range5.7895546
Interquartile range (IQR)1.19963735

Descriptive statistics

Standard deviation0.9761695041
Coefficient of variation (CV)4.319945186
Kurtosis0.2526638604
Mean0.2259680301
Median Absolute Deviation (MAD)0.59981864
Skewness-0.8260753376
Sum12102.84769
Variance0.9529069007
MonotonicityNot monotonic
2024-02-13T20:57:55.632402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3623
 
0.2%
1.2063642 857
 
0.1%
0.8673362 815
 
0.1%
1.2846014 773
 
0.1%
1.128127 771
 
0.1%
0.94557345 751
 
0.1%
0.7369409 746
 
< 0.1%
0.71086186 724
 
< 0.1%
1.5453922 722
 
< 0.1%
0.6326246 712
 
< 0.1%
Other values (193) 43066
 
2.9%
(Missing) 1446916
96.4%
ValueCountFrequency (%)
-3.6704228 1
< 0.1%
-3.3313944 1
< 0.1%
-3.2270782 1
< 0.1%
-3.2009993 1
< 0.1%
-3.122762 2
< 0.1%
ValueCountFrequency (%)
2.1191318 2
 
< 0.1%
2.0408947 80
< 0.1%
1.9626575 53
< 0.1%
1.9365784 27
 
< 0.1%
1.9104993 11
 
< 0.1%

secondquarter_766L
Real number (ℝ)

MISSING  ZEROS 

Distinct55
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean2.688481776
Minimum0
Maximum109
Zeros396787
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:55.797566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum109
Range109
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.324545761
Coefficient of variation (CV)1.236588543
Kurtosis10.29886228
Mean2.688481776
Median Absolute Deviation (MAD)2
Skewness2.42836245
Sum3725405
Variance11.05260452
MonotonicityNot monotonic
2024-02-13T20:57:55.974575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 396787
26.4%
1 260175
17.3%
2 202270
13.5%
3 143694
 
9.6%
4 104672
 
7.0%
5 74298
 
5.0%
6 53622
 
3.6%
7 38399
 
2.6%
8 28172
 
1.9%
9 20788
 
1.4%
Other values (45) 62814
 
4.2%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 396787
26.4%
1 260175
17.3%
2 202270
13.5%
3 143694
 
9.6%
4 104672
 
7.0%
ValueCountFrequency (%)
109 1
< 0.1%
66 1
< 0.1%
61 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%

thirdquarter_1082L
Real number (ℝ)

MISSING  ZEROS 

Distinct52
Distinct (%)< 0.1%
Missing114785
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean2.918341824
Minimum0
Maximum62
Zeros356399
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size11.4 MiB
2024-02-13T20:57:56.146564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum62
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.423862364
Coefficient of variation (CV)1.17322184
Kurtosis8.401173033
Mean2.918341824
Median Absolute Deviation (MAD)2
Skewness2.249997324
Sum4043920
Variance11.72283349
MonotonicityNot monotonic
2024-02-13T20:57:56.315586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 356399
23.8%
1 248492
16.6%
2 206260
13.7%
3 149995
10.0%
4 112894
 
7.5%
5 81511
 
5.4%
6 59886
 
4.0%
7 43355
 
2.9%
8 32184
 
2.1%
9 23698
 
1.6%
Other values (42) 71017
 
4.7%
(Missing) 114785
 
7.6%
ValueCountFrequency (%)
0 356399
23.8%
1 248492
16.6%
2 206260
13.7%
3 149995
10.0%
4 112894
 
7.5%
ValueCountFrequency (%)
62 1
< 0.1%
58 2
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%