Overview

Dataset statistics

Number of variables14
Number of observations351331
Missing cells53545
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.5 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""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 53545 (15.2%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.12240234)Skewed

Reproduction

Analysis started2024-03-18 18:22:44.872240
Analysis finished2024-03-18 18:23:03.362406
Duration18.49 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
351331 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1053993
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 351331
100.0%

Length

2024-03-18T18:23:03.453150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T18:23:03.592135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 351331
100.0%

Most occurring characters

ValueCountFrequency (%)
1 351331
33.3%
. 351331
33.3%
0 351331
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1053993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 351331
33.3%
. 351331
33.3%
0 351331
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1053993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 351331
33.3%
. 351331
33.3%
0 351331
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1053993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 351331
33.3%
. 351331
33.3%
0 351331
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2024-03-08 00:00:00
Maximum2024-03-08 00:00:00
2024-03-18T18:23:03.708153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:03.841606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2024-03-01 00:00:00
Maximum2024-03-01 00:00:00
2024-03-18T18:23:03.963968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:04.094836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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
2024-03-18T18:23:04.226080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:04.385970image/svg+xmlMatplotlib v3.8.3, 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%
Mean452.22386
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:04.559296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1043.5078
Coefficient of variation (CV)2.3075028
Kurtosis54.191221
Mean452.22386
Median Absolute Deviation (MAD)8
Skewness7.4913619
Sum1.5888026 × 108
Variance1088908.5
MonotonicityNot monotonic
2024-03-18T18:23:04.747195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 49411
14.1%
313 45595
13.0%
303 40435
11.5%
330 27616
 
7.9%
316 23857
 
6.8%
308 19194
 
5.5%
306 14757
 
4.2%
324 14443
 
4.1%
301 13973
 
4.0%
304 11384
 
3.2%
Other values (18) 90666
25.8%
ValueCountFrequency (%)
301 13973
 
4.0%
302 7710
 
2.2%
303 40435
11.5%
304 11384
 
3.2%
305 49411
14.1%
306 14757
 
4.2%
307 6162
 
1.8%
308 19194
 
5.5%
310 3821
 
1.1%
313 45595
13.0%
ValueCountFrequency (%)
8418 4723
 
1.3%
8416 1195
 
0.3%
1900 231
 
0.1%
390 960
 
0.3%
389 3680
 
1.0%
362 4499
 
1.3%
361 2554
 
0.7%
335 3532
 
1.0%
330 27616
7.9%
329 910
 
0.3%
Distinct1904
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:05.139366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3536209
Min length2

Characters and Unicode

Total characters1178231
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

Unique11 ?
Unique (%)< 0.1%

Sample

1st row203
2nd row501
3rd row309
4th row101
5th row303
ValueCountFrequency (%)
101 1504
 
0.4%
402 1437
 
0.4%
301 1409
 
0.4%
403 1408
 
0.4%
201 1337
 
0.4%
203 1308
 
0.4%
401 1172
 
0.3%
404 1167
 
0.3%
802 1139
 
0.3%
409 1133
 
0.3%
Other values (1894) 338317
96.3%
2024-03-18T18:23:05.748138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 225353
19.1%
0 216500
18.4%
2 156197
13.3%
3 127334
10.8%
5 90956
7.7%
9 84751
 
7.2%
4 83341
 
7.1%
7 69492
 
5.9%
6 61579
 
5.2%
8 50811
 
4.3%
Other values (15) 11917
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1178231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 225353
19.1%
0 216500
18.4%
2 156197
13.3%
3 127334
10.8%
5 90956
7.7%
9 84751
 
7.2%
4 83341
 
7.1%
7 69492
 
5.9%
6 61579
 
5.2%
8 50811
 
4.3%
Other values (15) 11917
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1178231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 225353
19.1%
0 216500
18.4%
2 156197
13.3%
3 127334
10.8%
5 90956
7.7%
9 84751
 
7.2%
4 83341
 
7.1%
7 69492
 
5.9%
6 61579
 
5.2%
8 50811
 
4.3%
Other values (15) 11917
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1178231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 225353
19.1%
0 216500
18.4%
2 156197
13.3%
3 127334
10.8%
5 90956
7.7%
9 84751
 
7.2%
4 83341
 
7.1%
7 69492
 
5.9%
6 61579
 
5.2%
8 50811
 
4.3%
Other values (15) 11917
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6247
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4185276 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:05.974939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899013
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 deviation4.2916647 × 108
Coefficient of variation (CV)0.97128842
Kurtosis-1.7421448
Mean4.4185276 × 108
Median Absolute Deviation (MAD)1.199 × 108
Skewness0.46260833
Sum1.5523657 × 1014
Variance1.8418386 × 1017
MonotonicityNot monotonic
2024-03-18T18:23:06.315117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2539
 
0.7%
990004007 2503
 
0.7%
990003004 2442
 
0.7%
990004006 2052
 
0.6%
990356076 1858
 
0.5%
990356073 1730
 
0.5%
131999228 1711
 
0.5%
131999164 1689
 
0.5%
990003007 1574
 
0.4%
131999194 1541
 
0.4%
Other values (6237) 331692
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 12
< 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 

Distinct10695
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.33049
Minimum1
Maximum168486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:06.515551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3105
95-th percentile1785
Maximum168486
Range168485
Interquartile range (IQR)102

Descriptive statistics

Standard deviation3223.164
Coefficient of variation (CV)6.1471993
Kurtosis405.62933
Mean524.33049
Median Absolute Deviation (MAD)13
Skewness16.628355
Sum1.8421356 × 108
Variance10388786
MonotonicityNot monotonic
2024-03-18T18:23:06.726019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57769
 
16.4%
2 28234
 
8.0%
3 18514
 
5.3%
4 13545
 
3.9%
5 10586
 
3.0%
6 8914
 
2.5%
7 7466
 
2.1%
8 6272
 
1.8%
9 5710
 
1.6%
10 5149
 
1.5%
Other values (10685) 189172
53.8%
ValueCountFrequency (%)
1 57769
16.4%
2 28234
8.0%
3 18514
 
5.3%
4 13545
 
3.9%
5 10586
 
3.0%
6 8914
 
2.5%
7 7466
 
2.1%
8 6272
 
1.8%
9 5710
 
1.6%
10 5149
 
1.5%
ValueCountFrequency (%)
168486 1
< 0.1%
165184 1
< 0.1%
163742 1
< 0.1%
155869 1
< 0.1%
154640 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
118396 1
< 0.1%
115934 1
< 0.1%
113287 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11516
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean622.27717
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:06.933964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3115
95-th percentile2041
Maximum240002
Range240001
Interquartile range (IQR)112

Descriptive statistics

Standard deviation4166.0374
Coefficient of variation (CV)6.6948259
Kurtosis706.94937
Mean622.27717
Median Absolute Deviation (MAD)14
Skewness21.122402
Sum2.1862526 × 108
Variance17355867
MonotonicityNot monotonic
2024-03-18T18:23:07.143201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55586
 
15.8%
2 27712
 
7.9%
3 18353
 
5.2%
4 13324
 
3.8%
5 10485
 
3.0%
6 8898
 
2.5%
7 7413
 
2.1%
8 6183
 
1.8%
9 5663
 
1.6%
10 5139
 
1.5%
Other values (11506) 192575
54.8%
ValueCountFrequency (%)
1 55586
15.8%
2 27712
7.9%
3 18353
 
5.2%
4 13324
 
3.8%
5 10485
 
3.0%
6 8898
 
2.5%
7 7413
 
2.1%
8 6183
 
1.8%
9 5663
 
1.6%
10 5139
 
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%
215390 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9629
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7873.4298
Minimum1
Maximum240482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:07.342077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1417
median1779
Q36583
95-th percentile37461
Maximum240482
Range240481
Interquartile range (IQR)6166

Descriptive statistics

Standard deviation18134.788
Coefficient of variation (CV)2.3032895
Kurtosis34.057348
Mean7873.4298
Median Absolute Deviation (MAD)1619
Skewness5.0404818
Sum2.76618 × 109
Variance3.2887053 × 108
MonotonicityNot monotonic
2024-03-18T18:23:07.551609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 580
 
0.2%
8 513
 
0.1%
19 499
 
0.1%
26 497
 
0.1%
25 483
 
0.1%
17 478
 
0.1%
28 474
 
0.1%
9 467
 
0.1%
12 466
 
0.1%
4 459
 
0.1%
Other values (9619) 346415
98.6%
ValueCountFrequency (%)
1 403
0.1%
2 446
0.1%
3 425
0.1%
4 459
0.1%
5 447
0.1%
6 433
0.1%
7 435
0.1%
8 513
0.1%
9 467
0.1%
10 411
0.1%
ValueCountFrequency (%)
240482 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%
205337 17
< 0.1%
200599 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10722
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11446.614
Minimum1
Maximum376865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:07.748992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile55
Q1557
median2482
Q39410
95-th percentile53436.5
Maximum376865
Range376864
Interquartile range (IQR)8853

Descriptive statistics

Standard deviation27248.092
Coefficient of variation (CV)2.3804499
Kurtosis37.319715
Mean11446.614
Median Absolute Deviation (MAD)2278
Skewness5.2695784
Sum4.0215505 × 109
Variance7.4245852 × 108
MonotonicityNot monotonic
2024-03-18T18:23:07.957307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 427
 
0.1%
77 394
 
0.1%
23 390
 
0.1%
39 388
 
0.1%
6 382
 
0.1%
20 379
 
0.1%
45 379
 
0.1%
18 376
 
0.1%
31 370
 
0.1%
34 369
 
0.1%
Other values (10712) 347477
98.9%
ValueCountFrequency (%)
1 314
0.1%
2 322
0.1%
3 352
0.1%
4 357
0.1%
5 357
0.1%
6 382
0.1%
7 349
0.1%
8 324
0.1%
9 289
0.1%
10 353
0.1%
ValueCountFrequency (%)
376865 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 

Distinct338
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683322.47
Minimum1
Maximum1487625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:08.167384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43684
Q1329924
median764879
Q31026428
95-th percentile1332258
Maximum1487625
Range1487624
Interquartile range (IQR)696504

Descriptive statistics

Standard deviation409165.71
Coefficient of variation (CV)0.59878861
Kurtosis-1.0740483
Mean683322.47
Median Absolute Deviation (MAD)315054
Skewness-0.052451911
Sum2.4007237 × 1011
Variance1.6741658 × 1011
MonotonicityNot monotonic
2024-03-18T18:23:08.380779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880923 5102
 
1.5%
874080 4354
 
1.2%
843975 4347
 
1.2%
894295 4333
 
1.2%
880448 4273
 
1.2%
897684 4212
 
1.2%
764999 4089
 
1.2%
808505 4041
 
1.2%
804285 4031
 
1.1%
1066233 3925
 
1.1%
Other values (328) 308624
87.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
6 2
 
< 0.1%
16 4
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
22 6
 
< 0.1%
34 26
< 0.1%
41 5
 
< 0.1%
54 2
 
< 0.1%
66 3
 
< 0.1%
ValueCountFrequency (%)
1487625 2975
0.8%
1450388 3048
0.9%
1421696 3564
1.0%
1344174 3543
1.0%
1340477 3441
1.0%
1332258 3545
1.0%
1325444 3413
1.0%
1316254 3463
1.0%
1282927 3576
1.0%
1269140 3352
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct339
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1111888.5
Minimum1
Maximum2668851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:08.593717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46632
Q1512469
median1139150
Q31810408
95-th percentile2592938
Maximum2668851
Range2668850
Interquartile range (IQR)1297939

Descriptive statistics

Standard deviation740552.97
Coefficient of variation (CV)0.66603168
Kurtosis-0.75610165
Mean1111888.5
Median Absolute Deviation (MAD)629029
Skewness0.33962784
Sum3.906409 × 1011
Variance5.484187 × 1011
MonotonicityNot monotonic
2024-03-18T18:23:08.810663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211797 5102
 
1.5%
1281474 4354
 
1.2%
1216248 4347
 
1.2%
1315550 4333
 
1.2%
1300409 4273
 
1.2%
1341793 4212
 
1.2%
1155926 4089
 
1.2%
1218770 4041
 
1.2%
1207051 4031
 
1.1%
2640049 3925
 
1.1%
Other values (329) 308624
87.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
6 2
 
< 0.1%
16 3
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
20 2
 
< 0.1%
22 6
 
< 0.1%
34 26
< 0.1%
47 5
 
< 0.1%
54 2
 
< 0.1%
ValueCountFrequency (%)
2668851 3796
1.1%
2663706 3866
1.1%
2640049 3925
1.1%
2618208 3788
1.1%
2592938 3843
1.1%
2547668 3890
1.1%
2479530 3851
1.1%
2178204 3757
1.1%
2061927 3811
1.1%
2051811 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3707
Distinct (%)1.2%
Missing53545
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean3643.9968
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-03-18T18:23:09.150839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1485
median1270
Q34230
95-th percentile13905
Maximum287220
Range287150
Interquartile range (IQR)3745

Descriptive statistics

Standard deviation6589.8557
Coefficient of variation (CV)1.8084142
Kurtosis131.64692
Mean3643.9968
Median Absolute Deviation (MAD)1040
Skewness6.8483004
Sum1.0851312 × 109
Variance43426198
MonotonicityNot monotonic
2024-03-18T18:23:09.319817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2044
 
0.6%
105 1984
 
0.6%
110 1791
 
0.5%
180 1645
 
0.5%
185 1567
 
0.4%
140 1547
 
0.4%
125 1520
 
0.4%
175 1482
 
0.4%
165 1441
 
0.4%
300 1428
 
0.4%
Other values (3697) 281337
80.1%
(Missing) 53545
 
15.2%
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 1025
0.3%
105 1984
0.6%
110 1791
0.5%
115 1176
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%
101270 8
< 0.1%
99540 5
< 0.1%

Interactions

2024-03-18T18:23:00.651328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:48.845332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.990289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.500748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.994887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.425392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.993739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.520327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.059754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:00.828014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:48.976450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:50.115848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.678691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.162619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.598766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:56.172265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.703890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.234402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:00.988998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.095999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:50.360885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.838953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.317103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.755011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:56.335178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.867318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.391027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.160267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.222199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:50.529700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.007498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.478256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.922268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:56.508642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.045418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.559648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.318926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.339301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:50.686759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.165608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.629725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.075879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:56.673176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.210203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.716251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.478392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.458024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:50.844130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.325634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.779410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.230112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:56.834989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.372662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:59.875390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.649854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.585561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.011235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.495579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:53.942839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.394667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.008119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.548317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:00.042860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.825351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.745850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.181009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.665631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.106893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.673717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.183504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.721298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:00.211619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:01.986028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:49.867018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:51.340486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:52.830764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:54.266302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:55.834007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:57.352638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:22:58.889839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:23:00.368636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-18T18:23:09.424392image/svg+xmlMatplotlib v3.8.3, 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_DIAG1.0000.3120.3210.9870.2950.317-0.0610.028-0.179
AANTAL_PAT_PER_SPC0.3121.0000.0680.3250.9590.070-0.559-0.011-0.381
AANTAL_PAT_PER_ZPD0.3210.0681.0000.3190.0770.9960.008-0.303-0.141
AANTAL_SUBTRAJECT_PER_DIAG0.9870.3250.3191.0000.3250.320-0.0550.037-0.212
AANTAL_SUBTRAJECT_PER_SPC0.2950.9590.0770.3251.0000.084-0.478-0.014-0.411
AANTAL_SUBTRAJECT_PER_ZPD0.3170.0700.9960.3200.0841.0000.014-0.306-0.149
BEHANDELEND_SPECIALISME_CD-0.061-0.5590.008-0.055-0.4780.0141.0000.0460.214
GEMIDDELDE_VERKOOPPRIJS0.028-0.011-0.3030.037-0.014-0.3060.0461.0000.029
ZORGPRODUCT_CD-0.179-0.381-0.141-0.212-0.411-0.1490.2140.0291.000

Missing values

2024-03-18T18:23:02.243555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T18:23:02.764971image/svg+xmlMatplotlib v3.8.3, 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
01.02024-03-082024-03-012024-01-01841820399841807011113434NaN
11.02024-03-082024-03-012024-01-01841850199841806922333434NaN
21.02024-03-082024-03-012024-01-01841830999841803211113434NaN
31.02024-03-082024-03-012024-01-01841810199841805211883434NaN
41.02024-03-082024-03-012024-01-01841830399841807011443434NaN
51.02024-03-082024-03-012024-01-01841830299841806711223434NaN
61.02024-03-082024-03-012024-01-01841850399841806922773434NaN
71.02024-03-082024-03-012024-01-01841830399841806911443434NaN
81.02024-03-082024-03-012024-01-01841830399841803611443434NaN
91.02024-03-082024-03-012024-01-01841850499841806911443434NaN
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
3513211.02024-03-082024-03-012014-01-013137629790030031129923971037165206192712575.0
3513221.02024-03-082024-03-012012-01-013033271194990621181791066914876251939514NaN
3513231.02024-03-082024-03-012012-01-01303339119499054113069425414876251939514NaN
3513241.02024-03-082024-03-012015-01-0130175579799024113689928110844131653257NaN
3513251.02024-03-082024-03-012016-01-0130324919929906211628794133225818313325450.0
3513261.02024-03-082024-03-012015-01-01322130329099025112573772856442135759728NaN
3513271.02024-03-082024-03-012014-01-0131383320110030231686767510371652061927NaN
3513281.02024-03-082024-03-012014-01-0131376128999065112557545910371652061927NaN
3513291.02024-03-082024-03-012014-01-0131390429199114113280132721037165206192716420.0
3513301.02024-03-082024-03-012015-01-01322140299002203511161923564421357597282915.0