Overview

Dataset statistics

Number of variables14
Number of observations309117
Missing cells49688
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.0 MiB
Average record size in memory112.0 B

Variable types

Categorical4
DateTime1
Numeric9

Alerts

VERSIE has constant value "1.0" Constant
DATUM_BESTAND has constant value "2022-08-26" Constant
PEILDATUM has constant value "2022-08-01" Constant
TYPERENDE_DIAGNOSE_CD has a high cardinality: 1875 distinct values High cardinality
BEHANDELEND_SPECIALISME_CD is highly correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly correlated with AANTAL_SUBTRAJECT_PER_SPCHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly correlated with AANTAL_SUBTRAJECT_PER_SPCHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly correlated with AANTAL_PAT_PER_SPCHigh correlation
VERSIE is highly correlated with DATUM_BESTAND and 1 other fieldsHigh correlation
DATUM_BESTAND is highly correlated with VERSIE and 1 other fieldsHigh correlation
PEILDATUM is highly correlated with VERSIE and 1 other fieldsHigh correlation
JAAR is highly correlated with AANTAL_PAT_PER_SPC and 1 other fieldsHigh correlation
AANTAL_PAT_PER_ZPD is highly correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly correlated with JAAR and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly correlated with JAAR and 1 other fieldsHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 49688 (16.1%) missing values Missing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.41443709) Skewed

Reproduction

Analysis started2022-09-06 19:04:41.717331
Analysis finished2022-09-06 19:05:05.282810
Duration23.57 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
309117 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters927351
Distinct characters3
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0309117
100.0%

Length

2022-09-06T19:05:05.460520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:05.555670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0309117
100.0%

Most occurring characters

ValueCountFrequency (%)
1309117
33.3%
.309117
33.3%
0309117
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number618234
66.7%
Other Punctuation309117
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1309117
50.0%
0309117
50.0%
Other Punctuation
ValueCountFrequency (%)
.309117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common927351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1309117
33.3%
.309117
33.3%
0309117
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII927351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1309117
33.3%
.309117
33.3%
0309117
33.3%

DATUM_BESTAND
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2022-08-26
309117 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3091170
Distinct characters5
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 row2022-08-26
2nd row2022-08-26
3rd row2022-08-26
4th row2022-08-26
5th row2022-08-26

Common Values

ValueCountFrequency (%)
2022-08-26309117
100.0%

Length

2022-09-06T19:05:05.633128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:05.725770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-26309117
100.0%

Most occurring characters

ValueCountFrequency (%)
21236468
40.0%
0618234
20.0%
-618234
20.0%
8309117
 
10.0%
6309117
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2472936
80.0%
Dash Punctuation618234
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21236468
50.0%
0618234
25.0%
8309117
 
12.5%
6309117
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-618234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3091170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21236468
40.0%
0618234
20.0%
-618234
20.0%
8309117
 
10.0%
6309117
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3091170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21236468
40.0%
0618234
20.0%
-618234
20.0%
8309117
 
10.0%
6309117
 
10.0%

PEILDATUM
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2022-08-01
309117 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3091170
Distinct characters5
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 row2022-08-01
2nd row2022-08-01
3rd row2022-08-01
4th row2022-08-01
5th row2022-08-01

Common Values

ValueCountFrequency (%)
2022-08-01309117
100.0%

Length

2022-09-06T19:05:05.803618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:05.896897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-01309117
100.0%

Most occurring characters

ValueCountFrequency (%)
2927351
30.0%
0927351
30.0%
-618234
20.0%
8309117
 
10.0%
1309117
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2472936
80.0%
Dash Punctuation618234
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2927351
37.5%
0927351
37.5%
8309117
 
12.5%
1309117
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-618234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3091170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2927351
30.0%
0927351
30.0%
-618234
20.0%
8309117
 
10.0%
1309117
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3091170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2927351
30.0%
0927351
30.0%
-618234
20.0%
8309117
 
10.0%
1309117
 
10.0%

JAAR
Date

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Minimum2012-01-01 00:00:00
Maximum2022-01-01 00:00:00
2022-09-06T19:05:05.966088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:05:06.048390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.2313299
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:06.155983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile335
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation977.2946637
Coefficient of variation (CV)2.24546028
Kurtosis62.61042729
Mean435.2313299
Median Absolute Deviation (MAD)8
Skewness8.032311663
Sum134537403
Variance955104.8597
MonotonicityNot monotonic
2022-09-06T19:05:06.263680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
30543554
14.1%
31340271
13.0%
30335594
11.5%
33024609
 
8.0%
31620945
 
6.8%
30816266
 
5.3%
30612929
 
4.2%
32412752
 
4.1%
30112529
 
4.1%
30410098
 
3.3%
Other values (18)79570
25.7%
ValueCountFrequency (%)
30112529
 
4.1%
3026807
 
2.2%
30335594
11.5%
30410098
 
3.3%
30543554
14.1%
30612929
 
4.2%
3075414
 
1.8%
30816266
 
5.3%
3103448
 
1.1%
31340271
13.0%
ValueCountFrequency (%)
84184207
 
1.4%
8416349
 
0.1%
1900204
 
0.1%
390843
 
0.3%
3893288
 
1.1%
3624209
 
1.4%
3612220
 
0.7%
3353145
 
1.0%
33024609
8.0%
329826
 
0.3%

TYPERENDE_DIAGNOSE_CD
Categorical

HIGH CARDINALITY

Distinct1875
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
101
 
1315
402
 
1277
301
 
1245
403
 
1244
201
 
1173
Other values (1870)
302863 

Length

Max length4
Median length3
Mean length3.349608724
Min length2

Characters and Unicode

Total characters1035421
Distinct characters25
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

Unique33 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row12
3rd row12
4th row11
5th row13

Common Values

ValueCountFrequency (%)
1011315
 
0.4%
4021277
 
0.4%
3011245
 
0.4%
4031244
 
0.4%
2011173
 
0.4%
2031171
 
0.4%
4011045
 
0.3%
4041035
 
0.3%
4091009
 
0.3%
802996
 
0.3%
Other values (1865)297607
96.3%

Length

2022-09-06T19:05:06.382866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1011315
 
0.4%
4021277
 
0.4%
3011245
 
0.4%
4031244
 
0.4%
2011173
 
0.4%
2031171
 
0.4%
4011045
 
0.3%
4041035
 
0.3%
4091009
 
0.3%
802996
 
0.3%
Other values (1865)297607
96.3%

Most occurring characters

ValueCountFrequency (%)
1198144
19.1%
0189682
18.3%
2137110
13.2%
3112400
10.9%
579637
7.7%
974842
 
7.2%
473733
 
7.1%
760874
 
5.9%
653990
 
5.2%
844516
 
4.3%
Other values (15)10493
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1024928
99.0%
Uppercase Letter10493
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G1976
18.8%
M1746
16.6%
B1267
12.1%
E889
8.5%
Z874
8.3%
D698
 
6.7%
A682
 
6.5%
F662
 
6.3%
C345
 
3.3%
K342
 
3.3%
Other values (5)1012
9.6%
Decimal Number
ValueCountFrequency (%)
1198144
19.3%
0189682
18.5%
2137110
13.4%
3112400
11.0%
579637
7.8%
974842
 
7.3%
473733
 
7.2%
760874
 
5.9%
653990
 
5.3%
844516
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common1024928
99.0%
Latin10493
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1976
18.8%
M1746
16.6%
B1267
12.1%
E889
8.5%
Z874
8.3%
D698
 
6.7%
A682
 
6.5%
F662
 
6.3%
C345
 
3.3%
K342
 
3.3%
Other values (5)1012
9.6%
Common
ValueCountFrequency (%)
1198144
19.3%
0189682
18.5%
2137110
13.4%
3112400
11.0%
579637
7.8%
974842
 
7.3%
473733
 
7.2%
760874
 
5.9%
653990
 
5.3%
844516
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1035421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1198144
19.1%
0189682
18.3%
2137110
13.2%
3112400
10.9%
579637
7.7%
974842
 
7.2%
473733
 
7.1%
760874
 
5.9%
653990
 
5.2%
844516
 
4.3%
Other values (15)10493
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ≥0)

Distinct5987
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439281596
Minimum10501002
Maximum998418081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:06.505454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999037
Q199799028
median149599019
Q3990004004
95-th percentile990516041
Maximum998418081
Range987917079
Interquartile range (IQR)890204976

Descriptive statistics

Standard deviation428715106.4
Coefficient of variation (CV)0.9759459769
Kurtosis-1.730415344
Mean439281596
Median Absolute Deviation (MAD)119600010
Skewness0.4747735254
Sum1.357894091 × 1014
Variance1.837966424 × 1017
MonotonicityNot monotonic
2022-09-06T19:05:06.632907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9900040092295
 
0.7%
9900040072239
 
0.7%
9900030042153
 
0.7%
9900040061782
 
0.6%
9903560761638
 
0.5%
9903560731489
 
0.5%
1319992281458
 
0.5%
1319991641433
 
0.5%
9900030071413
 
0.5%
1992990131299
 
0.4%
Other values (5977)291918
94.4%
ValueCountFrequency (%)
105010028
< 0.1%
1050100311
< 0.1%
1050100411
< 0.1%
1050100511
< 0.1%
105010073
 
< 0.1%
1050100811
< 0.1%
1050101011
< 0.1%
105010113
 
< 0.1%
111010029
< 0.1%
1110100311
< 0.1%
ValueCountFrequency (%)
998418081151
< 0.1%
998418080134
< 0.1%
99841807938
 
< 0.1%
9984180778
 
< 0.1%
9984180768
 
< 0.1%
9984180756
 
< 0.1%
998418074213
0.1%
998418073212
0.1%
9984180728
 
< 0.1%
9984180718
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9757
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.6605363
Minimum1
Maximum163220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:06.759671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q399
95-th percentile1689
Maximum163220
Range163219
Interquartile range (IQR)96

Descriptive statistics

Standard deviation3135.177096
Coefficient of variation (CV)6.237165779
Kurtosis407.1125908
Mean502.6605363
Median Absolute Deviation (MAD)12
Skewness16.7580401
Sum155380917
Variance9829335.421
MonotonicityNot monotonic
2022-09-06T19:05:06.883533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151659
 
16.7%
225244
 
8.2%
316472
 
5.3%
412092
 
3.9%
59420
 
3.0%
67947
 
2.6%
76566
 
2.1%
85589
 
1.8%
95136
 
1.7%
104497
 
1.5%
Other values (9747)164495
53.2%
ValueCountFrequency (%)
151659
16.7%
225244
8.2%
316472
 
5.3%
412092
 
3.9%
59420
 
3.0%
67947
 
2.6%
76566
 
2.1%
85589
 
1.8%
95136
 
1.7%
104497
 
1.5%
ValueCountFrequency (%)
1632201
< 0.1%
1544471
< 0.1%
1529941
< 0.1%
1506701
< 0.1%
1471731
< 0.1%
1437391
< 0.1%
1168941
< 0.1%
1147961
< 0.1%
1095131
< 0.1%
1087201
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct10479
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593.7468337
Minimum1
Maximum239696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:07.015262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3108
95-th percentile1925
Maximum239696
Range239695
Interquartile range (IQR)105

Descriptive statistics

Standard deviation4027.55619
Coefficient of variation (CV)6.783288704
Kurtosis729.5269347
Mean593.7468337
Median Absolute Deviation (MAD)13
Skewness21.41443709
Sum183537240
Variance16221208.87
MonotonicityNot monotonic
2022-09-06T19:05:07.143026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149785
 
16.1%
224836
 
8.0%
316305
 
5.3%
411877
 
3.8%
59347
 
3.0%
67939
 
2.6%
76474
 
2.1%
85593
 
1.8%
95085
 
1.6%
104505
 
1.5%
Other values (10469)167371
54.1%
ValueCountFrequency (%)
149785
16.1%
224836
8.0%
316305
 
5.3%
411877
 
3.8%
59347
 
3.0%
67939
 
2.6%
76474
 
2.1%
85593
 
1.8%
95085
 
1.6%
104505
 
1.5%
ValueCountFrequency (%)
2396961
< 0.1%
2318241
< 0.1%
2308841
< 0.1%
2268811
< 0.1%
2263221
< 0.1%
2244181
< 0.1%
2220251
< 0.1%
2184351
< 0.1%
2125671
< 0.1%
2124421
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8682
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7526.39236
Minimum1
Maximum225597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:07.271608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1375
median1640
Q36109
95-th percentile36289
Maximum225597
Range225596
Interquartile range (IQR)5734

Descriptive statistics

Standard deviation17666.07277
Coefficient of variation (CV)2.347216559
Kurtosis34.39923292
Mean7526.39236
Median Absolute Deviation (MAD)1504
Skewness5.093200147
Sum2326535827
Variance312090127.3
MonotonicityNot monotonic
2022-09-06T19:05:07.389508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21586
 
0.2%
19537
 
0.2%
8517
 
0.2%
9509
 
0.2%
4503
 
0.2%
17481
 
0.2%
2473
 
0.2%
12469
 
0.2%
36466
 
0.2%
7461
 
0.1%
Other values (8672)304115
98.4%
ValueCountFrequency (%)
1444
0.1%
2473
0.2%
3458
0.1%
4503
0.2%
5446
0.1%
6442
0.1%
7461
0.1%
8517
0.2%
9509
0.2%
10353
0.1%
ValueCountFrequency (%)
22559723
< 0.1%
21278124
< 0.1%
21241323
< 0.1%
21162017
< 0.1%
21157325
< 0.1%
20939917
< 0.1%
20874519
< 0.1%
20347617
< 0.1%
19911316
< 0.1%
19718920
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9716
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10841.23361
Minimum1
Maximum365524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:07.619855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1493
median2272
Q38788
95-th percentile51237
Maximum365524
Range365523
Interquartile range (IQR)8295

Descriptive statistics

Standard deviation26296.44824
Coefficient of variation (CV)2.425595572
Kurtosis37.95431952
Mean10841.23361
Median Absolute Deviation (MAD)2096
Skewness5.335213527
Sum3351209610
Variance691503189.9
MonotonicityNot monotonic
2022-09-06T19:05:07.739208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17434
 
0.1%
4415
 
0.1%
19405
 
0.1%
38401
 
0.1%
13399
 
0.1%
3399
 
0.1%
2397
 
0.1%
7395
 
0.1%
23382
 
0.1%
6382
 
0.1%
Other values (9706)305108
98.7%
ValueCountFrequency (%)
1374
0.1%
2397
0.1%
3399
0.1%
4415
0.1%
5380
0.1%
6382
0.1%
7395
0.1%
8355
0.1%
9337
0.1%
10350
0.1%
ValueCountFrequency (%)
36552423
< 0.1%
34546425
< 0.1%
33898319
< 0.1%
33473424
< 0.1%
32707323
< 0.1%
32162520
< 0.1%
31151617
< 0.1%
30754317
< 0.1%
29589817
< 0.1%
28682916
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean656097.5372
Minimum361
Maximum1479231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:07.869521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum361
5-th percentile39316
Q1246823
median736514
Q3991298
95-th percentile1319370
Maximum1479231
Range1478870
Interquartile range (IQR)744475

Descriptive statistics

Standard deviation420363.3094
Coefficient of variation (CV)0.640702465
Kurtosis-1.180010047
Mean656097.5372
Median Absolute Deviation (MAD)318069
Skewness-0.007527202955
Sum2.028109024 × 1011
Variance1.767053119 × 1011
MonotonicityNot monotonic
2022-09-06T19:05:07.992998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8741725096
 
1.6%
8647054344
 
1.4%
8361894341
 
1.4%
8835104317
 
1.4%
8681584254
 
1.4%
8827784203
 
1.4%
7496974079
 
1.3%
7244773918
 
1.3%
10732563886
 
1.3%
10882593860
 
1.2%
Other values (287)266819
86.3%
ValueCountFrequency (%)
36154
 
< 0.1%
1596130
< 0.1%
1673135
< 0.1%
1680212
0.1%
1892131
< 0.1%
197267
 
< 0.1%
2120173
0.1%
236277
 
< 0.1%
2462173
0.1%
2711252
0.1%
ValueCountFrequency (%)
14792312971
1.0%
14417603044
1.0%
14124763561
1.2%
13289753534
1.1%
13193703545
1.1%
13179983432
1.1%
12989633460
1.1%
12719733572
1.2%
12586931177
 
0.4%
12558891201
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1059258.375
Minimum369
Maximum2637767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:08.123046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum369
5-th percentile46355
Q1360758
median1062980
Q31714002
95-th percentile2470320
Maximum2637767
Range2637398
Interquartile range (IQR)1353244

Descriptive statistics

Standard deviation744554.9739
Coefficient of variation (CV)0.7029021353
Kurtosis-0.8817237113
Mean1059258.375
Median Absolute Deviation (MAD)701200
Skewness0.3273051977
Sum3.274347712 × 1011
Variance5.543621092 × 1011
MonotonicityNot monotonic
2022-09-06T19:05:08.252586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12020495096
 
1.6%
12672424344
 
1.4%
12048434341
 
1.4%
12991564317
 
1.4%
12820604254
 
1.4%
13187354203
 
1.4%
11296134079
 
1.3%
10629803918
 
1.3%
25349513886
 
1.3%
26377673860
 
1.2%
Other values (287)266819
86.3%
ValueCountFrequency (%)
36954
 
< 0.1%
1803212
0.1%
1847130
< 0.1%
1937135
< 0.1%
202867
 
< 0.1%
2167131
< 0.1%
236277
 
< 0.1%
2715252
0.1%
2779173
0.1%
2852173
0.1%
ValueCountFrequency (%)
26377673860
1.2%
25759093842
1.2%
25542723768
1.2%
25349513886
1.3%
24703203850
1.2%
23716423713
1.2%
21712343754
1.2%
20568313809
1.2%
20247401169
 
0.4%
19706121165
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ≥0)

MISSING

Distinct3410
Distinct (%)1.3%
Missing49688
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean3519.708918
Minimum70
Maximum287010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2022-09-06T19:05:08.380984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1465
median1215
Q34065
95-th percentile13410
Maximum287010
Range286940
Interquartile range (IQR)3600

Descriptive statistics

Standard deviation6500.98308
Coefficient of variation (CV)1.84702293
Kurtosis154.0289247
Mean3519.708918
Median Absolute Deviation (MAD)995
Skewness7.399902723
Sum913114565
Variance42262781.01
MonotonicityNot monotonic
2022-09-06T19:05:08.505305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1601951
 
0.6%
1051875
 
0.6%
1101786
 
0.6%
1801527
 
0.5%
1451362
 
0.4%
1851312
 
0.4%
3001285
 
0.4%
1251271
 
0.4%
1651253
 
0.4%
1751230
 
0.4%
Other values (3400)244577
79.1%
(Missing)49688
 
16.1%
ValueCountFrequency (%)
70225
 
0.1%
7575
 
< 0.1%
80362
 
0.1%
85918
0.3%
90688
 
0.2%
95691
 
0.2%
100905
0.3%
1051875
0.6%
1101786
0.6%
1151015
0.3%
ValueCountFrequency (%)
2870108
< 0.1%
1489103
 
< 0.1%
1428604
< 0.1%
1221504
< 0.1%
1167653
 
< 0.1%
1097257
< 0.1%
1085707
< 0.1%
1076554
< 0.1%
1012708
< 0.1%
954607
< 0.1%

Interactions

2022-09-06T19:05:02.354304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:04:50.376529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:04:51.869025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:04:53.465348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:04:54.916143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/