Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations363659
Missing cells59122
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 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 "2024-06-21"Constant
PEILDATUM has constant value "2024-06-01"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 59122 (16.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.23686011)Skewed

Reproduction

Analysis started2024-07-15 18:23:53.095900
Analysis finished2024-07-15 18:24:11.317119
Duration18.22 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
1.0
363659 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1090977
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 363659
100.0%

Length

2024-07-15T18:24:11.410097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T18:24:11.549051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 363659
100.0%

Most occurring characters

ValueCountFrequency (%)
1 363659
33.3%
. 363659
33.3%
0 363659
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1090977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 363659
33.3%
. 363659
33.3%
0 363659
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1090977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 363659
33.3%
. 363659
33.3%
0 363659
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1090977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 363659
33.3%
. 363659
33.3%
0 363659
33.3%

DATUM_BESTAND
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-06-21
363659 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3636590
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 row2024-06-21
2nd row2024-06-21
3rd row2024-06-21
4th row2024-06-21
5th row2024-06-21

Common Values

ValueCountFrequency (%)
2024-06-21 363659
100.0%

Length

2024-07-15T18:24:11.694470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T18:24:11.831196image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
2024-06-21 363659
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1090977
30.0%
0 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1090977
30.0%
0 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1090977
30.0%
0 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1090977
30.0%
0 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

PEILDATUM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-06-01
363659 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3636590
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 row2024-06-01
2nd row2024-06-01
3rd row2024-06-01
4th row2024-06-01
5th row2024-06-01

Common Values

ValueCountFrequency (%)
2024-06-01 363659
100.0%

Length

2024-07-15T18:24:11.976382image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T18:24:12.113767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
2024-06-01 363659
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1090977
30.0%
2 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1090977
30.0%
2 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1090977
30.0%
2 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3636590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1090977
30.0%
2 727318
20.0%
- 727318
20.0%
4 363659
 
10.0%
6 363659
 
10.0%
1 363659
 
10.0%

JAAR
Date

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2012-01-01 00:00:00
Maximum2024-01-01 00:00:00
2024-07-15T18:24:12.238963image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:12.401321image/svg+xmlMatplotlib v3.9.1, 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%
Mean459.25334
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:12.576143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1069.5272
Coefficient of variation (CV)2.3288393
Kurtosis51.300322
Mean459.25334
Median Absolute Deviation (MAD)8
Skewness7.2962265
Sum1.6701161 × 108
Variance1143888.5
MonotonicityNot monotonic
2024-07-15T18:24:12.764377image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 50894
14.0%
313 47340
13.0%
303 41946
11.5%
330 28525
 
7.8%
316 24589
 
6.8%
308 19645
 
5.4%
306 15255
 
4.2%
324 14776
 
4.1%
301 14568
 
4.0%
304 11842
 
3.3%
Other values (18) 94279
25.9%
ValueCountFrequency (%)
301 14568
 
4.0%
302 8032
 
2.2%
303 41946
11.5%
304 11842
 
3.3%
305 50894
14.0%
306 15255
 
4.2%
307 6460
 
1.8%
308 19645
 
5.4%
310 4029
 
1.1%
313 47340
13.0%
ValueCountFrequency (%)
8418 5016
 
1.4%
8416 1425
 
0.4%
1900 245
 
0.1%
390 1014
 
0.3%
389 3821
 
1.1%
362 4556
 
1.3%
361 2660
 
0.7%
335 3641
 
1.0%
330 28525
7.8%
329 961
 
0.3%
Distinct1904
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:13.201560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3519286
Min length2

Characters and Unicode

Total characters1218959
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

Unique8 ?
Unique (%)< 0.1%

Sample

1st row07
2nd row02
3rd row16
4th row15
5th row12
ValueCountFrequency (%)
101 1565
 
0.4%
402 1508
 
0.4%
301 1473
 
0.4%
403 1466
 
0.4%
201 1404
 
0.4%
203 1370
 
0.4%
401 1229
 
0.3%
404 1222
 
0.3%
409 1195
 
0.3%
302 1177
 
0.3%
Other values (1894) 350050
96.3%
2024-07-15T18:24:13.737456image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 232938
19.1%
0 224357
18.4%
2 161521
13.3%
3 131847
10.8%
5 93986
7.7%
9 87818
 
7.2%
4 86302
 
7.1%
7 71819
 
5.9%
6 63463
 
5.2%
8 52597
 
4.3%
Other values (15) 12311
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1218959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 232938
19.1%
0 224357
18.4%
2 161521
13.3%
3 131847
10.8%
5 93986
7.7%
9 87818
 
7.2%
4 86302
 
7.1%
7 71819
 
5.9%
6 63463
 
5.2%
8 52597
 
4.3%
Other values (15) 12311
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1218959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 232938
19.1%
0 224357
18.4%
2 161521
13.3%
3 131847
10.8%
5 93986
7.7%
9 87818
 
7.2%
4 86302
 
7.1%
7 71819
 
5.9%
6 63463
 
5.2%
8 52597
 
4.3%
Other values (15) 12311
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1218959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 232938
19.1%
0 224357
18.4%
2 161521
13.3%
3 131847
10.8%
5 93986
7.7%
9 87818
 
7.2%
4 86302
 
7.1%
7 71819
 
5.9%
6 63463
 
5.2%
8 52597
 
4.3%
Other values (15) 12311
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6273
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3965861 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:13.892432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799061
median1.4959902 × 108
Q39.9000302 × 108
95-th percentile9.9051606 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020396 × 108

Descriptive statistics

Standard deviation4.2863675 × 108
Coefficient of variation (CV)0.97493088
Kurtosis-1.7317419
Mean4.3965861 × 108
Median Absolute Deviation (MAD)1.1960001 × 108
Skewness0.47334058
Sum1.5988581 × 1014
Variance1.8372947 × 1017
MonotonicityNot monotonic
2024-07-15T18:24:14.161485image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2655
 
0.7%
990004007 2605
 
0.7%
990003004 2505
 
0.7%
990004006 2093
 
0.6%
990356076 1912
 
0.5%
131999228 1850
 
0.5%
131999164 1816
 
0.5%
990356073 1772
 
0.5%
131999194 1606
 
0.4%
990003007 1603
 
0.4%
Other values (6263) 343242
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 13
< 0.1%
10501004 13
< 0.1%
10501005 13
< 0.1%
10501007 3
 
< 0.1%
10501008 13
< 0.1%
10501010 13
< 0.1%
10501011 4
 
< 0.1%
11101002 11
< 0.1%
11101003 13
< 0.1%
ValueCountFrequency (%)
998418081 182
0.1%
998418080 169
< 0.1%
998418079 43
 
< 0.1%
998418077 10
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 256
0.1%
998418073 250
0.1%
998418072 10
 
< 0.1%
998418071 10
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10765
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean518.0186
Minimum1
Maximum170178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:14.308051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3101
95-th percentile1749
Maximum170178
Range170177
Interquartile range (IQR)98

Descriptive statistics

Standard deviation3222.4851
Coefficient of variation (CV)6.2207904
Kurtosis413.24108
Mean518.0186
Median Absolute Deviation (MAD)12
Skewness16.789918
Sum1.8838212 × 108
Variance10384410
MonotonicityNot monotonic
2024-07-15T18:24:14.462036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 61069
 
16.8%
2 29622
 
8.1%
3 19352
 
5.3%
4 14192
 
3.9%
5 11113
 
3.1%
6 9311
 
2.6%
7 7737
 
2.1%
8 6569
 
1.8%
9 5943
 
1.6%
10 5246
 
1.4%
Other values (10755) 193505
53.2%
ValueCountFrequency (%)
1 61069
16.8%
2 29622
8.1%
3 19352
 
5.3%
4 14192
 
3.9%
5 11113
 
3.1%
6 9311
 
2.6%
7 7737
 
2.1%
8 6569
 
1.8%
9 5943
 
1.6%
10 5246
 
1.4%
ValueCountFrequency (%)
170178 1
< 0.1%
165182 1
< 0.1%
163757 1
< 0.1%
155868 1
< 0.1%
154637 1
< 0.1%
154256 1
< 0.1%
148457 1
< 0.1%
144711 1
< 0.1%
118396 1
< 0.1%
115934 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11625
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean616.17669
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:14.615845image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3110
95-th percentile2002.1
Maximum240002
Range240001
Interquartile range (IQR)107

Descriptive statistics

Standard deviation4171.7285
Coefficient of variation (CV)6.7703446
Kurtosis712.16297
Mean616.17669
Median Absolute Deviation (MAD)13
Skewness21.23686
Sum2.240782 × 108
Variance17403319
MonotonicityNot monotonic
2024-07-15T18:24:14.769939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 58799
 
16.2%
2 29112
 
8.0%
3 19168
 
5.3%
4 13949
 
3.8%
5 11046
 
3.0%
6 9269
 
2.5%
7 7689
 
2.1%
8 6487
 
1.8%
9 5876
 
1.6%
10 5264
 
1.4%
Other values (11615) 197000
54.2%
ValueCountFrequency (%)
1 58799
16.2%
2 29112
8.0%
3 19168
 
5.3%
4 13949
 
3.8%
5 11046
 
3.0%
6 9269
 
2.5%
7 7689
 
2.1%
8 6487
 
1.8%
9 5876
 
1.6%
10 5264
 
1.4%
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%
226680 1
< 0.1%
223889 1
< 0.1%
218673 1
< 0.1%
216113 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9727
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7771.8252
Minimum1
Maximum242182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:14.914235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1376
median1688
Q36337
95-th percentile37206
Maximum242182
Range242181
Interquartile range (IQR)5961

Descriptive statistics

Standard deviation18152.681
Coefficient of variation (CV)2.3357037
Kurtosis34.648646
Mean7771.8252
Median Absolute Deviation (MAD)1558
Skewness5.0849733
Sum2.8262942 × 109
Variance3.2951983 × 108
MonotonicityNot monotonic
2024-07-15T18:24:15.070276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 704
 
0.2%
8 649
 
0.2%
21 645
 
0.2%
7 642
 
0.2%
2 641
 
0.2%
5 635
 
0.2%
6 621
 
0.2%
1 612
 
0.2%
9 606
 
0.2%
17 601
 
0.2%
Other values (9717) 357303
98.3%
ValueCountFrequency (%)
1 612
0.2%
2 641
0.2%
3 578
0.2%
4 704
0.2%
5 635
0.2%
6 621
0.2%
7 642
0.2%
8 649
0.2%
9 606
0.2%
10 534
0.1%
ValueCountFrequency (%)
242182 23
< 0.1%
232901 23
< 0.1%
227994 23
< 0.1%
219673 23
< 0.1%
218543 24
< 0.1%
214503 17
< 0.1%
213512 25
< 0.1%
211575 17
< 0.1%
210411 19
< 0.1%
205335 17
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10869
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11354.243
Minimum1
Maximum381466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:15.216956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43
Q1502
median2376
Q39256
95-th percentile53456
Maximum381466
Range381465
Interquartile range (IQR)8754

Descriptive statistics

Standard deviation27376.217
Coefficient of variation (CV)2.4111002
Kurtosis37.647104
Mean11354.243
Median Absolute Deviation (MAD)2207
Skewness5.2984574
Sum4.1290727 × 109
Variance7.4945727 × 108
MonotonicityNot monotonic
2024-07-15T18:24:15.375203image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 608
 
0.2%
7 557
 
0.2%
6 551
 
0.2%
5 526
 
0.1%
2 518
 
0.1%
1 514
 
0.1%
25 501
 
0.1%
3 500
 
0.1%
20 494
 
0.1%
10 491
 
0.1%
Other values (10859) 358399
98.6%
ValueCountFrequency (%)
1 514
0.1%
2 518
0.1%
3 500
0.1%
4 608
0.2%
5 526
0.1%
6 551
0.2%
7 557
0.2%
8 459
0.1%
9 407
0.1%
10 491
0.1%
ValueCountFrequency (%)
381466 23
< 0.1%
370347 23
< 0.1%
370131 23
< 0.1%
348474 25
< 0.1%
344901 24
< 0.1%
341645 19
< 0.1%
326868 23
< 0.1%
323745 20
< 0.1%
315768 17
< 0.1%
310748 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct352
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean674025.51
Minimum285
Maximum1487620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:15.528795image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum285
5-th percentile32812
Q1277886
median764996
Q31029694
95-th percentile1337689
Maximum1487620
Range1487335
Interquartile range (IQR)751808

Descriptive statistics

Standard deviation420331.07
Coefficient of variation (CV)0.623613
Kurtosis-1.1398073
Mean674025.51
Median Absolute Deviation (MAD)316347
Skewness-0.071471056
Sum2.4511544 × 1011
Variance1.7667821 × 1011
MonotonicityNot monotonic
2024-07-15T18:24:15.683963image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880920 5102
 
1.4%
874077 4354
 
1.2%
843969 4347
 
1.2%
894295 4333
 
1.2%
880446 4273
 
1.2%
897681 4212
 
1.2%
764996 4089
 
1.1%
813658 4045
 
1.1%
804363 4031
 
1.1%
1072183 3932
 
1.1%
Other values (342) 320941
88.3%
ValueCountFrequency (%)
285 53
 
< 0.1%
299 76
 
< 0.1%
710 49
 
< 0.1%
809 192
0.1%
853 120
< 0.1%
1015 13
 
< 0.1%
1317 34
 
< 0.1%
1418 95
 
< 0.1%
1610 130
< 0.1%
1683 283
0.1%
ValueCountFrequency (%)
1487620 2975
0.8%
1450382 3048
0.8%
1421693 3564
1.0%
1344144 3543
1.0%
1340452 3441
0.9%
1337689 3416
0.9%
1332329 3545
1.0%
1316224 3463
1.0%
1282921 3576
1.0%
1269157 3352
0.9%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102383.2
Minimum285
Maximum2668531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:15.841204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum285
5-th percentile41738
Q1457234
median1107036
Q31810303
95-th percentile2592589
Maximum2668531
Range2668246
Interquartile range (IQR)1353069

Descriptive statistics

Standard deviation758055.69
Coefficient of variation (CV)0.68765173
Kurtosis-0.82933535
Mean1102383.2
Median Absolute Deviation (MAD)697731
Skewness0.30855564
Sum4.0089156 × 1011
Variance5.7464843 × 1011
MonotonicityNot monotonic
2024-07-15T18:24:15.998410image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.4%
1281473 4354
 
1.2%
1216248 4347
 
1.2%
1315547 4333
 
1.2%
1300405 4273
 
1.2%
1341786 4212
 
1.2%
1155919 4089
 
1.1%
1228790 4045
 
1.1%
1207171 4031
 
1.1%
2664890 3932
 
1.1%
Other values (343) 320941
88.3%
ValueCountFrequency (%)
285 53
 
< 0.1%
299 76
 
< 0.1%
729 49
 
< 0.1%
813 192
0.1%
1003 120
< 0.1%
1016 13
 
< 0.1%
1344 34
 
< 0.1%
1604 95
 
< 0.1%
1714 283
0.1%
1861 130
< 0.1%
ValueCountFrequency (%)
2668531 3796
1.0%
2664890 3932
1.1%
2663293 3866
1.1%
2617736 3788
1.0%
2592589 3843
1.1%
2547364 3890
1.1%
2479229 3851
1.1%
2248123 3852
1.1%
2178002 3757
1.0%
2061793 3810
1.0%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3754
Distinct (%)1.2%
Missing59122
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean3647.0684
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-15T18:24:16.264101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile145
Q1480
median1260
Q34230
95-th percentile13970
Maximum287220
Range287150
Interquartile range (IQR)3750

Descriptive statistics

Standard deviation6598.1992
Coefficient of variation (CV)1.8091789
Kurtosis129.45188
Mean3647.0684
Median Absolute Deviation (MAD)1030
Skewness6.8094561
Sum1.1106673 × 109
Variance43536233
MonotonicityNot monotonic
2024-07-15T18:24:16.411454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2105
 
0.6%
105 2003
 
0.6%
110 1863
 
0.5%
180 1668
 
0.5%
185 1654
 
0.5%
125 1560
 
0.4%
140 1560
 
0.4%
145 1545
 
0.4%
175 1542
 
0.4%
300 1442
 
0.4%
Other values (3744) 287595
79.1%
(Missing) 59122
 
16.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 920
0.3%
90 670
 
0.2%
95 723
 
0.2%
100 1022
0.3%
105 2003
0.6%
110 1863
0.5%
115 1184
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%
107425 4
< 0.1%
101270 8
< 0.1%

Interactions

2024-07-15T18:24:08.530822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:56.948421image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.211205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.781457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.187478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.384325image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.953192image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:05.517462image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.046267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:08.813262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.080311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.383944image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.960442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.313174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.558016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.137366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:05.696330image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.220510image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:08.974231image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.201561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.633091image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.121017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.428024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.716480image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.308324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:05.859303image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.379746image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.143665image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.327026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.801621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.290101image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.545603image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.881364image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.484204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.031924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.547948image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.302785image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.445710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.959100image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.449401image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.656568image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.036744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.650305image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.195742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.707673image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.459190image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.564379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.116623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.609012image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.766014image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.190207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.814370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.357193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:07.864940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.634417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.694568image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.291787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.785617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.896575image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.362425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:04.994771image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.537610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:08.040836image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.805928image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:57.871113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.460708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:00.948916image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.065866image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.633444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:05.173805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.709091image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:08.208946image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:09.966512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:58.038258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:23:59.619609image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:01.066914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:02.224316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:03.793817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:05.345678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:06.875302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-15T18:24:08.367423image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-15T18:24:16.515625image/svg+xmlMatplotlib v3.9.1, 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_DIAG1.0000.3550.3340.9880.3350.331-0.0600.035-0.168
AANTAL_PAT_PER_SPC0.3551.0000.0930.3670.9600.095-0.534-0.001-0.359
AANTAL_PAT_PER_ZPD0.3340.0931.0000.3320.1000.9960.008-0.297-0.136
AANTAL_SUBTRAJECT_PER_DIAG0.9880.3670.3321.0000.3650.333-0.0530.043-0.200
AANTAL_SUBTRAJECT_PER_SPC0.3350.9600.1000.3651.0000.106-0.454-0.003-0.389
AANTAL_SUBTRAJECT_PER_ZPD0.3310.0950.9960.3330.1061.0000.013-0.300-0.144
BEHANDELEND_SPECIALISME_CD-0.060-0.5340.008-0.053-0.4540.0131.0000.0490.213
GEMIDDELDE_VERKOOPPRIJS0.035-0.001-0.2970.043-0.003-0.3000.0491.0000.030
ZORGPRODUCT_CD-0.168-0.359-0.136-0.200-0.389-0.1440.2130.0301.000

Missing values

2024-07-15T18:24:10.230864image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-15T18:24:10.752551image/svg+xmlMatplotlib v3.9.1, 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
01.02024-06-212024-06-012018-01-01329079900290102272341439150021980241661345.0
11.02024-06-212024-06-012018-01-0132902990029002246247487249772198024166205.0
21.02024-06-212024-06-012018-01-0132916990029011546577107711962198024166545.0
31.02024-06-212024-06-012018-01-01329159900290122652721029107521980241661040.0
41.02024-06-212024-06-012018-01-013291299002901147501141202198024166545.0
51.02024-06-212024-06-012018-01-013290399002900258588478632198024166205.0
61.02024-06-212024-06-012018-01-0132907990029011676690143915002198024166545.0
71.02024-06-212024-06-012018-01-013291959899067111215228421980241661440.0
81.02024-06-212024-06-012018-01-0132908990029010131316216721980241661345.0
91.02024-06-212024-06-012018-01-0132913990029010303115615721980241661345.0
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
3636491.02024-06-212024-06-012014-01-0130321499000300722149591634514216931845585105.0
3636501.02024-06-212024-06-012020-01-013131319979902211426010466632617736NaN
3636511.02024-06-212024-06-012020-01-013208069790011881130604419081030921183137634420.0
3636521.02024-06-212024-06-012015-01-013623149900620041114815470900815972900.0
3636531.02024-06-212024-06-012020-01-013207029790012241122273547103092118313763650.0
3636541.02024-06-212024-06-012020-01-01313831201091071141991424410466632617736NaN
3636551.02024-06-212024-06-012015-01-01362197990062012225996517090081597370.0
3636561.02024-06-212024-06-012014-01-013032131992990591179218583142169318455852095.0
3636571.02024-06-212024-06-012020-01-0131383999000300411343120310466632617736120.0
3636581.02024-06-212024-06-012016-01-01307Z20159999020117288701303118559524045.0