Overview

Dataset statistics

Number of variables14
Number of observations352283
Missing cells53813
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.6 MiB
Average record size in memory112.0 B

Variable types

Categorical3
DateTime1
Numeric9
Text1

Alerts

VERSIE has constant value "1.0"Constant
DATUM_BESTAND has constant value "2024-04-15"Constant
PEILDATUM has constant value "2024-04-01"Constant
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with AANTAL_SUBTRAJECT_PER_SPC and 1 other fieldsHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 53813 (15.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.06362552)Skewed

Reproduction

Analysis started2024-05-06 23:57:50.839297
Analysis finished2024-05-06 23:58:09.725254
Duration18.89 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1.0
352283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1056849
Distinct characters3
Distinct categories1 ?
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 (%)
1.0 352283
100.0%

Length

2024-05-06T23:58:09.816511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:58:09.952957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 352283
100.0%

Most occurring characters

ValueCountFrequency (%)
1 352283
33.3%
. 352283
33.3%
0 352283
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1056849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 352283
33.3%
. 352283
33.3%
0 352283
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1056849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 352283
33.3%
. 352283
33.3%
0 352283
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1056849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 352283
33.3%
. 352283
33.3%
0 352283
33.3%

DATUM_BESTAND
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-04-15
352283 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3522830
Distinct characters6
Distinct categories1 ?
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 row2024-04-15
2nd row2024-04-15
3rd row2024-04-15
4th row2024-04-15
5th row2024-04-15

Common Values

ValueCountFrequency (%)
2024-04-15 352283
100.0%

Length

2024-05-06T23:58:10.096743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:58:10.231499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-15 352283
100.0%

Most occurring characters

ValueCountFrequency (%)
2 704566
20.0%
0 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
10.0%
5 352283
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 704566
20.0%
0 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
10.0%
5 352283
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 704566
20.0%
0 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
10.0%
5 352283
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 704566
20.0%
0 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
10.0%
5 352283
10.0%

PEILDATUM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-04-01
352283 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3522830
Distinct characters5
Distinct categories1 ?
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 row2024-04-01
2nd row2024-04-01
3rd row2024-04-01
4th row2024-04-01
5th row2024-04-01

Common Values

ValueCountFrequency (%)
2024-04-01 352283
100.0%

Length

2024-05-06T23:58:10.373751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:58:10.510635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-01 352283
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1056849
30.0%
2 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1056849
30.0%
2 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1056849
30.0%
2 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3522830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1056849
30.0%
2 704566
20.0%
4 704566
20.0%
- 704566
20.0%
1 352283
 
10.0%

JAAR
Date

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2012-01-01 00:00:00
Maximum2024-01-01 00:00:00
2024-05-06T23:58:10.633403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:10.796309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.54837
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:10.970601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1052.1806
Coefficient of variation (CV)2.3147825
Kurtosis53.20448
Mean454.54837
Median Absolute Deviation (MAD)8
Skewness7.4253593
Sum1.6012966 × 108
Variance1107084.1
MonotonicityNot monotonic
2024-05-06T23:58:11.157743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 49543
14.1%
313 45692
13.0%
303 40550
11.5%
330 27660
 
7.9%
316 23910
 
6.8%
308 19242
 
5.5%
306 14785
 
4.2%
324 14454
 
4.1%
301 13995
 
4.0%
304 11400
 
3.2%
Other values (18) 91052
25.8%
ValueCountFrequency (%)
301 13995
 
4.0%
302 7732
 
2.2%
303 40550
11.5%
304 11400
 
3.2%
305 49543
14.1%
306 14785
 
4.2%
307 6185
 
1.8%
308 19242
 
5.5%
310 3868
 
1.1%
313 45692
13.0%
ValueCountFrequency (%)
8418 4819
 
1.4%
8416 1216
 
0.3%
1900 232
 
0.1%
390 960
 
0.3%
389 3701
 
1.1%
362 4513
 
1.3%
361 2567
 
0.7%
335 3535
 
1.0%
330 27660
7.9%
329 912
 
0.3%
Distinct1904
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:11.598494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3534176
Min length2

Characters and Unicode

Total characters1181352
Distinct characters25
Distinct categories1 ?
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 row203
2nd rowB13
3rd row104
4th rowG14
5th row605
ValueCountFrequency (%)
101 1511
 
0.4%
402 1442
 
0.4%
301 1414
 
0.4%
403 1411
 
0.4%
201 1347
 
0.4%
203 1313
 
0.4%
401 1176
 
0.3%
404 1168
 
0.3%
802 1140
 
0.3%
409 1139
 
0.3%
Other values (1894) 339222
96.3%
2024-05-06T23:58:12.253910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 225950
19.1%
0 217116
18.4%
2 156615
13.3%
3 127667
10.8%
5 91217
7.7%
9 84947
 
7.2%
4 83557
 
7.1%
7 69676
 
5.9%
6 61725
 
5.2%
8 50933
 
4.3%
Other values (15) 11949
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1181352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 225950
19.1%
0 217116
18.4%
2 156615
13.3%
3 127667
10.8%
5 91217
7.7%
9 84947
 
7.2%
4 83557
 
7.1%
7 69676
 
5.9%
6 61725
 
5.2%
8 50933
 
4.3%
Other values (15) 11949
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1181352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 225950
19.1%
0 217116
18.4%
2 156615
13.3%
3 127667
10.8%
5 91217
7.7%
9 84947
 
7.2%
4 83557
 
7.1%
7 69676
 
5.9%
6 61725
 
5.2%
8 50933
 
4.3%
Other values (15) 11949
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1181352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 225950
19.1%
0 217116
18.4%
2 156615
13.3%
3 127667
10.8%
5 91217
7.7%
9 84947
 
7.2%
4 83557
 
7.1%
7 69676
 
5.9%
6 61725
 
5.2%
8 50933
 
4.3%
Other values (15) 11949
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6259
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.419992 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:12.477728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899016
median1.49899 × 108
Q39.9000302 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010401 × 108

Descriptive statistics

Standard deviation4.2919922 × 108
Coefficient of variation (CV)0.97104072
Kurtosis-1.742799
Mean4.419992 × 108
Median Absolute Deviation (MAD)1.199 × 108
Skewness0.46192766
Sum1.557088 × 1014
Variance1.8421197 × 1017
MonotonicityNot monotonic
2024-05-06T23:58:12.692819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2539
 
0.7%
990004007 2503
 
0.7%
990003004 2442
 
0.7%
990004006 2054
 
0.6%
990356076 1858
 
0.5%
990356073 1733
 
0.5%
131999228 1714
 
0.5%
131999164 1691
 
0.5%
990003007 1579
 
0.4%
131999194 1542
 
0.4%
Other values (6249) 332628
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 13
< 0.1%
10501011 4
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 179
0.1%
998418080 164
< 0.1%
998418079 40
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 241
0.1%
998418073 241
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10725
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526.4239
Minimum1
Maximum168842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:13.022874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3106
95-th percentile1793.9
Maximum168842
Range168841
Interquartile range (IQR)103

Descriptive statistics

Standard deviation3234.5378
Coefficient of variation (CV)6.1443598
Kurtosis403.58583
Mean526.4239
Median Absolute Deviation (MAD)13
Skewness16.593281
Sum1.8545019 × 108
Variance10462235
MonotonicityNot monotonic
2024-05-06T23:58:13.231354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57984
 
16.5%
2 28312
 
8.0%
3 18527
 
5.3%
4 13563
 
3.9%
5 10624
 
3.0%
6 8942
 
2.5%
7 7465
 
2.1%
8 6272
 
1.8%
9 5728
 
1.6%
10 5170
 
1.5%
Other values (10715) 189696
53.8%
ValueCountFrequency (%)
1 57984
16.5%
2 28312
8.0%
3 18527
 
5.3%
4 13563
 
3.9%
5 10624
 
3.0%
6 8942
 
2.5%
7 7465
 
2.1%
8 6272
 
1.8%
9 5728
 
1.6%
10 5170
 
1.5%
ValueCountFrequency (%)
168842 1
< 0.1%
165184 1
< 0.1%
163743 1
< 0.1%
155869 1
< 0.1%
154640 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
121067 1
< 0.1%
118396 1
< 0.1%
115934 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11514
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.08623
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:13.436564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3116
95-th percentile2052.9
Maximum240002
Range240001
Interquartile range (IQR)113

Descriptive statistics

Standard deviation4181.9167
Coefficient of variation (CV)6.6901436
Kurtosis702.3786
Mean625.08623
Median Absolute Deviation (MAD)14
Skewness21.063626
Sum2.2020725 × 108
Variance17488427
MonotonicityNot monotonic
2024-05-06T23:58:13.644537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55772
 
15.8%
2 27789
 
7.9%
3 18378
 
5.2%
4 13352
 
3.8%
5 10514
 
3.0%
6 8919
 
2.5%
7 7411
 
2.1%
8 6182
 
1.8%
9 5690
 
1.6%
10 5141
 
1.5%
Other values (11504) 193135
54.8%
ValueCountFrequency (%)
1 55772
15.8%
2 27789
7.9%
3 18378
 
5.2%
4 13352
 
3.8%
5 10514
 
3.0%
6 8919
 
2.5%
7 7411
 
2.1%
8 6182
 
1.8%
9 5690
 
1.6%
10 5141
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231945 1
< 0.1%
230943 1
< 0.1%
227921 1
< 0.1%
227409 1
< 0.1%
226679 1
< 0.1%
223889 1
< 0.1%
218673 1
< 0.1%
215400 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9627
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7906.2921
Minimum1
Maximum240850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:13.841128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1417
median1787
Q36613
95-th percentile37539
Maximum240850
Range240849
Interquartile range (IQR)6196

Descriptive statistics

Standard deviation18197.228
Coefficient of variation (CV)2.3016134
Kurtosis33.882692
Mean7906.2921
Median Absolute Deviation (MAD)1627
Skewness5.0292428
Sum2.7852523 × 109
Variance3.3113911 × 108
MonotonicityNot monotonic
2024-05-06T23:58:14.045882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 580
 
0.2%
26 517
 
0.1%
8 507
 
0.1%
25 494
 
0.1%
2 492
 
0.1%
19 486
 
0.1%
9 478
 
0.1%
14 478
 
0.1%
4 475
 
0.1%
17 474
 
0.1%
Other values (9617) 347302
98.6%
ValueCountFrequency (%)
1 471
0.1%
2 492
0.1%
3 422
0.1%
4 475
0.1%
5 472
0.1%
6 470
0.1%
7 438
0.1%
8 507
0.1%
9 478
0.1%
10 410
0.1%
ValueCountFrequency (%)
240850 22
< 0.1%
232879 23
< 0.1%
227997 23
< 0.1%
218546 24
< 0.1%
214503 17
< 0.1%
213515 25
< 0.1%
211576 17
< 0.1%
210414 19
< 0.1%
205336 17
< 0.1%
200599 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10785
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11506.598
Minimum1
Maximum377812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:14.239352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54
Q1558
median2491
Q39452
95-th percentile54027
Maximum377812
Range377811
Interquartile range (IQR)8894

Descriptive statistics

Standard deviation27358.537
Coefficient of variation (CV)2.3776391
Kurtosis37.024565
Mean11506.598
Median Absolute Deviation (MAD)2286
Skewness5.2521101
Sum4.0535788 × 109
Variance7.4848954 × 108
MonotonicityNot monotonic
2024-05-06T23:58:14.446008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 442
 
0.1%
21 399
 
0.1%
6 398
 
0.1%
34 388
 
0.1%
77 383
 
0.1%
4 382
 
0.1%
39 379
 
0.1%
23 379
 
0.1%
1 378
 
0.1%
20 378
 
0.1%
Other values (10775) 348377
98.9%
ValueCountFrequency (%)
1 378
0.1%
2 364
0.1%
3 352
0.1%
4 382
0.1%
5 364
0.1%
6 398
0.1%
7 366
0.1%
8 336
0.1%
9 275
0.1%
10 357
0.1%
ValueCountFrequency (%)
377812 22
< 0.1%
370304 23
< 0.1%
370137 23
< 0.1%
348482 25
< 0.1%
344907 24
< 0.1%
341651 19
< 0.1%
323753 20
< 0.1%
315768 17
< 0.1%
310748 17
< 0.1%
298625 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct344
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686051.01
Minimum1
Maximum1487625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:14.652767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43323
Q1299319
median768371
Q31027867
95-th percentile1332254
Maximum1487625
Range1487624
Interquartile range (IQR)728548

Descriptive statistics

Standard deviation409425.82
Coefficient of variation (CV)0.59678626
Kurtosis-1.0754111
Mean686051.01
Median Absolute Deviation (MAD)312115
Skewness-0.07081738
Sum2.4168411 × 1011
Variance1.676295 × 1011
MonotonicityNot monotonic
2024-05-06T23:58:14.862314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880922 5102
 
1.4%
874080 4354
 
1.2%
843975 4347
 
1.2%
894296 4333
 
1.2%
880447 4273
 
1.2%
897684 4212
 
1.2%
764999 4089
 
1.2%
809510 4042
 
1.1%
804294 4031
 
1.1%
1067371 3925
 
1.1%
Other values (334) 309575
87.9%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 5
 
< 0.1%
7 5
 
< 0.1%
8 7
 
< 0.1%
26 15
< 0.1%
34 13
 
< 0.1%
36 8
 
< 0.1%
43 12
 
< 0.1%
179 17
< 0.1%
252 37
< 0.1%
ValueCountFrequency (%)
1487625 2975
0.8%
1450388 3048
0.9%
1421694 3564
1.0%
1344168 3543
1.0%
1340471 3441
1.0%
1332254 3545
1.0%
1327142 3412
1.0%
1316252 3463
1.0%
1282927 3576
1.0%
1269137 3352
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct345
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1117599.6
Minimum2
Maximum2668791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:15.073198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile46387
Q1517727
median1142111
Q31812960
95-th percentile2592884
Maximum2668791
Range2668789
Interquartile range (IQR)1295233

Descriptive statistics

Standard deviation741121.4
Coefficient of variation (CV)0.66313677
Kurtosis-0.7690712
Mean1117599.6
Median Absolute Deviation (MAD)645559
Skewness0.32289587
Sum3.9371135 × 1011
Variance5.4926094 × 1011
MonotonicityNot monotonic
2024-05-06T23:58:15.287654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.4%
1281474 4354
 
1.2%
1216248 4347
 
1.2%
1315550 4333
 
1.2%
1300408 4273
 
1.2%
1341792 4212
 
1.2%
1155925 4089
 
1.2%
1221152 4042
 
1.1%
1207076 4031
 
1.1%
2645380 3925
 
1.1%
Other values (335) 309575
87.9%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 5
 
< 0.1%
7 5
 
< 0.1%
9 7
< 0.1%
26 10
< 0.1%
27 5
 
< 0.1%
36 8
< 0.1%
41 13
< 0.1%
43 12
< 0.1%
184 17
< 0.1%
ValueCountFrequency (%)
2668791 3796
1.1%
2663609 3866
1.1%
2645380 3925
1.1%
2618102 3788
1.1%
2592884 3843
1.1%
2547611 3890
1.1%
2479456 3851
1.1%
2178171 3757
1.1%
2061888 3811
1.1%
2051811 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3732
Distinct (%)1.3%
Missing53813
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean3651.713
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-05-06T23:58:15.488061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1485
median1275
Q34245
95-th percentile13915
Maximum287220
Range287150
Interquartile range (IQR)3760

Descriptive statistics

Standard deviation6608.5177
Coefficient of variation (CV)1.8097035
Kurtosis130.78242
Mean3651.713
Median Absolute Deviation (MAD)1045
Skewness6.8474728
Sum1.0899268 × 109
Variance43672506
MonotonicityNot monotonic
2024-05-06T23:58:15.811574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2043
 
0.6%
105 1983
 
0.6%
110 1791
 
0.5%
180 1646
 
0.5%
185 1567
 
0.4%
140 1550
 
0.4%
125 1527
 
0.4%
175 1469
 
0.4%
165 1435
 
0.4%
300 1427
 
0.4%
Other values (3722) 282032
80.1%
(Missing) 53813
 
15.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 670
 
0.2%
95 716
 
0.2%
100 1026
0.3%
105 1983
0.6%
110 1791
0.5%
115 1177
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
107390 4
< 0.1%
101270 8
< 0.1%

Interactions

2024-05-06T23:58:06.933111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:54.945630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:56.553867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.007701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.496556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.932515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.483028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.985722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.482024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:07.222412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.125490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:56.726097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.185302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.666254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:01.103416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.659169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.162454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.652120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:07.381014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.289199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:56.880293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.344234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.819631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:01.260202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.823522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.323522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.808037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:07.548862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.462632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.045246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.511955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.982783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:01.422070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.992757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.493315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.972617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:07.705076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.626297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.200868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.670980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.133585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:01.685672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.153807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.654341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:06.128513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:07.862092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.787009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.354839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.828431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.286082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:01.836082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.312906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.813009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:06.283983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:08.030709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:55.962065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.522730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:58.999351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.448264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.001346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.481584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:04.984836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:06.453003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:08.200601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:56.135523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.689111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.170191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.615101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.168523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.656221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.154257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:06.617484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:08.358966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:56.384371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:57.848611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:57:59.333490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:00.772476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:02.324720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:03.820261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:05.315761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T23:58:06.773800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-06T23:58:15.953532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AANTAL_PAT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_DIAGAANTAL_SUBTRAJECT_PER_SPCAANTAL_SUBTRAJECT_PER_ZPDBEHANDELEND_SPECIALISME_CDGEMIDDELDE_VERKOOPPRIJSZORGPRODUCT_CD
AANTAL_PAT_PER_DIAG1.0000.3130.3220.9870.2970.318-0.0620.028-0.179
AANTAL_PAT_PER_SPC0.3131.0000.0690.3260.9610.071-0.560-0.010-0.383
AANTAL_PAT_PER_ZPD0.3220.0691.0000.3200.0780.9960.008-0.304-0.141
AANTAL_SUBTRAJECT_PER_DIAG0.9870.3260.3201.0000.3270.320-0.0560.037-0.212
AANTAL_SUBTRAJECT_PER_SPC0.2970.9610.0780.3271.0000.084-0.481-0.012-0.414
AANTAL_SUBTRAJECT_PER_ZPD0.3180.0710.9960.3200.0841.0000.013-0.307-0.149
BEHANDELEND_SPECIALISME_CD-0.062-0.5600.008-0.056-0.4810.0131.0000.0450.215
GEMIDDELDE_VERKOOPPRIJS0.028-0.010-0.3040.037-0.012-0.3070.0451.0000.029
ZORGPRODUCT_CD-0.179-0.383-0.141-0.212-0.414-0.1490.2150.0291.000

Missing values

2024-05-06T23:58:08.620843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T23:58:09.133814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02024-04-152024-04-012021-01-0131820311949907814213169044413358881560226832680280.0
11.02024-04-152024-04-012020-01-01307B1315989900710210211811867777611289994455.0
21.02024-04-152024-04-012021-01-01361104990061029157420003439515795133142589195.0
31.02024-04-152024-04-012020-01-01307G14990003024676719592208206777761128999150.0
41.02024-04-152024-04-012021-01-0131860511949907552957458317354560226832680705.0
51.02024-04-152024-04-012021-01-013611089900610181971972214271095133142589310.0
61.02024-04-152024-04-012021-01-013184072919920641247919462618560226832680195.0
71.02024-04-152024-04-012021-01-01318605119499076829058317354560226832680720.0
81.02024-04-152024-04-012021-01-013187139900030089920153600560226832680160.0
91.02024-04-152024-04-012021-01-01361201990061072818713651758951331425897390.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3522731.02024-04-152024-04-012014-01-01313762289990241129923971037157206188825040.0
3522741.02024-04-152024-04-012015-01-013221307290990511131544681442134759714NaN
3522751.02024-04-152024-04-012014-01-01313832201090813347201832710371572061888NaN
3522761.02024-04-152024-04-012012-01-0130343499699088229021012148762519395111505.0
3522771.02024-04-152024-04-012016-01-013032691992990871145405248133225418313262575.0
3522781.02024-04-152024-04-012016-01-01303212199299062113906444560133225418313265450.0
3522791.02024-04-152024-04-012014-01-01313263296990061117432262103715720618884865.0
3522801.02024-04-152024-04-012016-01-013032371992990631115001744133225418313263380.0
3522811.02024-04-152024-04-012014-01-01313779979003008117771239103715720618887560.0
3522821.02024-04-152024-04-012015-01-0132211039900110031123601272154421347597143405.0