Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations375707
Missing cells58690
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.1 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 "2024-10-04" Constant
PEILDATUM has constant value "2024-10-01" 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 58690 (15.6%) missing values Missing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.38814196) Skewed

Reproduction

Analysis started2024-10-16 08:55:34.926824
Analysis finished2024-10-16 08:55:49.042206
Duration14.12 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
1.0
375707 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1127121
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 375707
100.0%

Length

2024-10-16T08:55:49.116981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T08:55:49.220456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 375707
100.0%

Most occurring characters

ValueCountFrequency (%)
1 375707
33.3%
. 375707
33.3%
0 375707
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1127121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 375707
33.3%
. 375707
33.3%
0 375707
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1127121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 375707
33.3%
. 375707
33.3%
0 375707
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1127121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 375707
33.3%
. 375707
33.3%
0 375707
33.3%

DATUM_BESTAND
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-10-04
375707 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3757070
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 row2024-10-04
2nd row2024-10-04
3rd row2024-10-04
4th row2024-10-04
5th row2024-10-04

Common Values

ValueCountFrequency (%)
2024-10-04 375707
100.0%

Length

2024-10-16T08:55:49.328382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T08:55:49.430387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-10-04 375707
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
4 751414
20.0%
- 751414
20.0%
1 375707
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
4 751414
20.0%
- 751414
20.0%
1 375707
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
4 751414
20.0%
- 751414
20.0%
1 375707
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
4 751414
20.0%
- 751414
20.0%
1 375707
 
10.0%

PEILDATUM
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-10-01
375707 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3757070
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 row2024-10-01
2nd row2024-10-01
3rd row2024-10-01
4th row2024-10-01
5th row2024-10-01

Common Values

ValueCountFrequency (%)
2024-10-01 375707
100.0%

Length

2024-10-16T08:55:49.537998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T08:55:49.639313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-10-01 375707
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
- 751414
20.0%
1 751414
20.0%
4 375707
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
- 751414
20.0%
1 751414
20.0%
4 375707
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
- 751414
20.0%
1 751414
20.0%
4 375707
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3757070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1127121
30.0%
2 751414
20.0%
- 751414
20.0%
1 751414
20.0%
4 375707
 
10.0%

JAAR
Date

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Minimum2012-01-01 00:00:00
Maximum2024-01-01 00:00:00
2024-10-16T08:55:49.731961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:49.851460image/svg+xmlMatplotlib v3.9.2, 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%
Mean463.10297
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:49.980325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1083.5567
Coefficient of variation (CV)2.3397749
Kurtosis49.829301
Mean463.10297
Median Absolute Deviation (MAD)8
Skewness7.1950194
Sum1.7399103 × 108
Variance1174095.1
MonotonicityNot monotonic
2024-10-16T08:55:50.124322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 52500
14.0%
313 48881
13.0%
303 43232
11.5%
330 29463
 
7.8%
316 25386
 
6.8%
308 20544
 
5.5%
306 15799
 
4.2%
324 15391
 
4.1%
301 14983
 
4.0%
304 12263
 
3.3%
Other values (18) 97265
25.9%
ValueCountFrequency (%)
301 14983
 
4.0%
302 8255
 
2.2%
303 43232
11.5%
304 12263
 
3.3%
305 52500
14.0%
306 15799
 
4.2%
307 6627
 
1.8%
308 20544
 
5.5%
310 4085
 
1.1%
313 48881
13.0%
ValueCountFrequency (%)
8418 5110
 
1.4%
8416 1724
 
0.5%
1900 248
 
0.1%
390 1043
 
0.3%
389 3927
 
1.0%
362 4657
 
1.2%
361 2761
 
0.7%
335 3755
 
1.0%
330 29463
7.8%
329 979
 
0.3%
Distinct1907
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:50.501507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3533472
Min length2

Characters and Unicode

Total characters1259876
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 row205
2nd row205
3rd row205
4th row205
5th row205
ValueCountFrequency (%)
101 1610
 
0.4%
402 1543
 
0.4%
301 1515
 
0.4%
403 1513
 
0.4%
201 1442
 
0.4%
203 1396
 
0.4%
404 1256
 
0.3%
401 1256
 
0.3%
409 1228
 
0.3%
802 1211
 
0.3%
Other values (1897) 361737
96.3%
2024-10-16T08:55:51.021615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 240770
19.1%
0 232023
18.4%
2 166895
13.2%
3 136110
10.8%
5 97329
7.7%
9 90576
 
7.2%
4 89092
 
7.1%
7 74261
 
5.9%
6 65759
 
5.2%
8 54329
 
4.3%
Other values (15) 12732
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1259876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 240770
19.1%
0 232023
18.4%
2 166895
13.2%
3 136110
10.8%
5 97329
7.7%
9 90576
 
7.2%
4 89092
 
7.1%
7 74261
 
5.9%
6 65759
 
5.2%
8 54329
 
4.3%
Other values (15) 12732
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1259876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 240770
19.1%
0 232023
18.4%
2 166895
13.2%
3 136110
10.8%
5 97329
7.7%
9 90576
 
7.2%
4 89092
 
7.1%
7 74261
 
5.9%
6 65759
 
5.2%
8 54329
 
4.3%
Other values (15) 12732
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1259876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 240770
19.1%
0 232023
18.4%
2 166895
13.2%
3 136110
10.8%
5 97329
7.7%
9 90576
 
7.2%
4 89092
 
7.1%
7 74261
 
5.9%
6 65759
 
5.2%
8 54329
 
4.3%
Other values (15) 12732
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6286
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4021143 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:51.176811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199799069
median1.4959903 × 108
Q39.9000302 × 108
95-th percentile9.90616 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020396 × 108

Descriptive statistics

Standard deviation4.2876883 × 108
Coefficient of variation (CV)0.97400659
Kurtosis-1.7344348
Mean4.4021143 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.47062109
Sum1.6539052 × 1014
Variance1.8384271 × 1017
MonotonicityNot monotonic
2024-10-16T08:55:51.336565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2748
 
0.7%
990004007 2701
 
0.7%
990003004 2575
 
0.7%
990004006 2193
 
0.6%
990356076 2014
 
0.5%
131999228 1912
 
0.5%
131999164 1887
 
0.5%
990356073 1869
 
0.5%
131999194 1703
 
0.5%
990003007 1642
 
0.4%
Other values (6276) 354463
94.3%
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 194
0.1%
998418080 178
< 0.1%
998418079 44
 
< 0.1%
998418077 10
 
< 0.1%
998418076 10
 
< 0.1%
998418075 8
 
< 0.1%
998418074 267
0.1%
998418073 267
0.1%
998418072 10
 
< 0.1%
998418071 10
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

High correlation 

Distinct10937
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516.40689
Minimum1
Maximum170376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:51.611674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3103
95-th percentile1750
Maximum170376
Range170375
Interquartile range (IQR)100

Descriptive statistics

Standard deviation3207.2229
Coefficient of variation (CV)6.2106508
Kurtosis422.70488
Mean516.40689
Median Absolute Deviation (MAD)13
Skewness16.94388
Sum1.9401768 × 108
Variance10286278
MonotonicityNot monotonic
2024-10-16T08:55:51.767114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62355
 
16.6%
2 30323
 
8.1%
3 19839
 
5.3%
4 14527
 
3.9%
5 11382
 
3.0%
6 9592
 
2.6%
7 8016
 
2.1%
8 6763
 
1.8%
9 6145
 
1.6%
10 5478
 
1.5%
Other values (10927) 201287
53.6%
ValueCountFrequency (%)
1 62355
16.6%
2 30323
8.1%
3 19839
 
5.3%
4 14527
 
3.9%
5 11382
 
3.0%
6 9592
 
2.6%
7 8016
 
2.1%
8 6763
 
1.8%
9 6145
 
1.6%
10 5478
 
1.5%
ValueCountFrequency (%)
170376 1
< 0.1%
165181 1
< 0.1%
164558 1
< 0.1%
164145 1
< 0.1%
155866 1
< 0.1%
154635 1
< 0.1%
154254 1
< 0.1%
144709 1
< 0.1%
118398 1
< 0.1%
115934 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

High correlation  Skewed 

Distinct11809
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean614.3715
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:51.919773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3113
95-th percentile2001
Maximum240002
Range240001
Interquartile range (IQR)110

Descriptive statistics

Standard deviation4159.6533
Coefficient of variation (CV)6.7705831
Kurtosis722.46335
Mean614.3715
Median Absolute Deviation (MAD)14
Skewness21.388142
Sum2.3082367 × 108
Variance17302716
MonotonicityNot monotonic
2024-10-16T08:55:52.073761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60038
 
16.0%
2 29802
 
7.9%
3 19626
 
5.2%
4 14327
 
3.8%
5 11289
 
3.0%
6 9555
 
2.5%
7 7947
 
2.1%
8 6675
 
1.8%
9 6082
 
1.6%
10 5499
 
1.5%
Other values (11799) 204867
54.5%
ValueCountFrequency (%)
1 60038
16.0%
2 29802
7.9%
3 19626
 
5.2%
4 14327
 
3.8%
5 11289
 
3.0%
6 9555
 
2.5%
7 7947
 
2.1%
8 6675
 
1.8%
9 6082
 
1.6%
10 5499
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231945 1
< 0.1%
230934 1
< 0.1%
227921 1
< 0.1%
227409 1
< 0.1%
226728 1
< 0.1%
223888 1
< 0.1%
218673 1
< 0.1%
216487 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

High correlation 

Distinct9902
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7742.4086
Minimum1
Maximum242406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:52.218914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1397
median1711
Q36318
95-th percentile37149
Maximum242406
Range242405
Interquartile range (IQR)5921

Descriptive statistics

Standard deviation18052.725
Coefficient of variation (CV)2.3316678
Kurtosis35.401952
Mean7742.4086
Median Absolute Deviation (MAD)1567
Skewness5.1307025
Sum2.9088771 × 109
Variance3.2590087 × 108
MonotonicityNot monotonic
2024-10-16T08:55:52.371963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 601
 
0.2%
14 565
 
0.2%
8 565
 
0.2%
23 562
 
0.1%
9 555
 
0.1%
26 552
 
0.1%
4 550
 
0.1%
19 543
 
0.1%
12 537
 
0.1%
17 536
 
0.1%
Other values (9892) 370141
98.5%
ValueCountFrequency (%)
1 441
0.1%
2 517
0.1%
3 527
0.1%
4 550
0.1%
5 511
0.1%
6 525
0.1%
7 510
0.1%
8 565
0.2%
9 555
0.1%
10 498
0.1%
ValueCountFrequency (%)
242406 23
< 0.1%
233774 23
< 0.1%
233281 23
< 0.1%
227992 23
< 0.1%
218540 24
< 0.1%
214501 17
< 0.1%
213510 25
< 0.1%
211573 17
< 0.1%
210409 19
< 0.1%
205333 17
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

High correlation 

Distinct11094
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11315.578
Minimum1
Maximum382138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:52.520714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q1527
median2396
Q39236
95-th percentile52933
Maximum382138
Range382137
Interquartile range (IQR)8709

Descriptive statistics

Standard deviation27323.145
Coefficient of variation (CV)2.4146487
Kurtosis38.533963
Mean11315.578
Median Absolute Deviation (MAD)2207
Skewness5.3536484
Sum4.2513418 × 109
Variance7.4655426 × 108
MonotonicityNot monotonic
2024-10-16T08:55:52.680758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 481
 
0.1%
20 477
 
0.1%
3 455
 
0.1%
17 452
 
0.1%
11 448
 
0.1%
6 445
 
0.1%
4 444
 
0.1%
23 437
 
0.1%
12 431
 
0.1%
31 431
 
0.1%
Other values (11084) 371206
98.8%
ValueCountFrequency (%)
1 346
0.1%
2 376
0.1%
3 455
0.1%
4 444
0.1%
5 418
0.1%
6 445
0.1%
7 424
0.1%
8 398
0.1%
9 353
0.1%
10 405
0.1%
ValueCountFrequency (%)
382138 23
< 0.1%
371149 23
< 0.1%
370126 23
< 0.1%
366193 23
< 0.1%
348469 25
< 0.1%
344894 24
< 0.1%
341641 19
< 0.1%
323740 20
< 0.1%
315767 17
< 0.1%
310748 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

High correlation 

Distinct353
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean670850.95
Minimum1610
Maximum1487618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:52.836508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile40711
Q1277881
median759520
Q31027654
95-th percentile1339993
Maximum1487618
Range1486008
Interquartile range (IQR)749773

Descriptive statistics

Standard deviation416612.38
Coefficient of variation (CV)0.62102079
Kurtosis-1.1630863
Mean670850.95
Median Absolute Deviation (MAD)319393
Skewness-0.0071497421
Sum2.520434 × 1011
Variance1.7356588 × 1011
MonotonicityNot monotonic
2024-10-16T08:55:52.995322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880928 5102
 
1.4%
874125 4354
 
1.2%
843977 4347
 
1.2%
894349 4333
 
1.2%
880491 4273
 
1.1%
897734 4212
 
1.1%
765033 4089
 
1.1%
814242 4048
 
1.1%
805793 4032
 
1.1%
1076639 3938
 
1.0%
Other values (343) 332979
88.6%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1831 138
 
< 0.1%
1922 131
 
< 0.1%
2495 173
< 0.1%
2633 193
0.1%
4309 108
 
< 0.1%
4657 181
< 0.1%
5528 67
 
< 0.1%
6810 380
0.1%
6977 323
0.1%
ValueCountFrequency (%)
1487618 2975
0.8%
1450393 3048
0.8%
1421770 3564
0.9%
1344574 3543
0.9%
1340864 3441
0.9%
1339993 3413
0.9%
1332660 3545
0.9%
1316655 3463
0.9%
1283016 3576
1.0%
1271629 3355
0.9%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

High correlation 

Distinct353
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1097877.4
Minimum1861
Maximum2682876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:53.163560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile46768
Q1454136
median1128103
Q31813050
95-th percentile2601018
Maximum2682876
Range2681015
Interquartile range (IQR)1358914

Descriptive statistics

Standard deviation760119.46
Coefficient of variation (CV)0.6923537
Kurtosis-0.80183896
Mean1097877.4
Median Absolute Deviation (MAD)684947
Skewness0.37331294
Sum4.1248021 × 1011
Variance5.7778159 × 1011
MonotonicityNot monotonic
2024-10-16T08:55:53.321725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211832 5102
 
1.4%
1281603 4354
 
1.2%
1216290 4347
 
1.2%
1315699 4333
 
1.2%
1300575 4273
 
1.1%
1342014 4212
 
1.1%
1156074 4089
 
1.1%
1230538 4048
 
1.1%
1209719 4032
 
1.1%
2680353 3938
 
1.0%
Other values (343) 332979
88.6%
ValueCountFrequency (%)
1861 130
 
< 0.1%
2102 138
 
< 0.1%
2198 131
 
< 0.1%
2817 173
< 0.1%
3453 193
0.1%
4593 108
 
< 0.1%
5552 181
< 0.1%
5767 67
 
< 0.1%
7111 323
0.1%
7389 380
0.1%
ValueCountFrequency (%)
2682876 3796
1.0%
2680353 3938
1.0%
2671992 3866
1.0%
2626220 3788
1.0%
2601018 3843
1.0%
2555580 3890
1.0%
2546348 3930
1.0%
2486742 3851
1.0%
2183750 3757
1.0%
2066313 3810
1.0%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Missing 

Distinct3822
Distinct (%)1.2%
Missing58690
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean3647.6407
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-10-16T08:55:53.470447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile145
Q1485
median1260
Q34220
95-th percentile13925
Maximum287220
Range287150
Interquartile range (IQR)3735

Descriptive statistics

Standard deviation6576.8368
Coefficient of variation (CV)1.8030385
Kurtosis126.69007
Mean3647.6407
Median Absolute Deviation (MAD)1030
Skewness6.7276893
Sum1.1563641 × 109
Variance43254782
MonotonicityNot monotonic
2024-10-16T08:55:53.778771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2090
 
0.6%
105 2064
 
0.5%
110 1821
 
0.5%
185 1707
 
0.5%
180 1702
 
0.5%
140 1563
 
0.4%
175 1561
 
0.4%
145 1504
 
0.4%
125 1498
 
0.4%
165 1477
 
0.4%
Other values (3812) 300030
79.9%
(Missing) 58690
 
15.6%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.2%
90 671
 
0.2%
95 721
 
0.2%
100 1036
0.3%
105 2064
0.5%
110 1821
0.5%
115 1120
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

2024-10-16T08:55:46.929999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:37.801422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.945361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.115825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.230207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.295296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.487696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.629344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.738652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.061528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:37.934704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.160468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.248048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.356630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.420563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.620931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.760502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.864343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.180470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.056437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.273342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.365290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.470674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.535090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.743774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.878453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.982903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.304553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.185880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.397699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.491534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.590534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.657691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.873439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.006045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.106781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.423390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.306576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.511858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.608059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.701606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.770088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.992922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.123829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.223226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.540566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.426701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.627089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.726774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.814156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.881872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.113396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.243167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.337052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.668378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.559299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.756711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.857680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.938523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.132368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.245572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.371907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.464775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.795948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.690067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.879297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:40.984984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.062687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.254480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.378972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.496076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.586522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:47.913707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:38.815354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:39.997201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:41.106785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:42.177530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:43.370466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:44.504110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:45.615175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T08:55:46.810772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T08:55:53.886070image/svg+xmlMatplotlib v3.9.2, 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_DIAG1.0000.3330.3240.9870.3160.321-0.0620.034-0.177
AANTAL_PAT_PER_SPC0.3331.0000.0780.3500.9620.082-0.535-0.007-0.365
AANTAL_PAT_PER_ZPD0.3240.0781.0000.3220.0860.9960.008-0.300-0.139
AANTAL_SUBTRAJECT_PER_DIAG0.9870.3500.3221.0000.3500.323-0.0550.042-0.208
AANTAL_SUBTRAJECT_PER_SPC0.3160.9620.0860.3501.0000.093-0.456-0.008-0.391
AANTAL_SUBTRAJECT_PER_ZPD0.3210.0820.9960.3230.0931.0000.013-0.303-0.148
BEHANDELEND_SPECIALISME_CD-0.062-0.5350.008-0.055-0.4560.0131.0000.0450.212
GEMIDDELDE_VERKOOPPRIJS0.034-0.007-0.3000.042-0.008-0.3030.0451.0000.029
ZORGPRODUCT_CD-0.177-0.365-0.139-0.208-0.391-0.1480.2120.0291.000

Missing values

2024-10-16T08:55:48.101344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T08:55:48.548976image/svg+xmlMatplotlib v3.9.2, 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
01.02024-10-042024-10-012012-01-01301205794990038878779853126523418047711100.0
11.02024-10-042024-10-012012-01-0130120579499008117877985312652341804771NaN
21.02024-10-042024-10-012012-01-01301205990003003227877985312652341804771110.0
31.02024-10-042024-10-012012-01-0130120579499012777877985312652341804771NaN
41.02024-10-042024-10-012012-01-0130120579499011227877985312652341804771NaN
51.02024-10-042024-10-012012-01-01301655797990172532598258976412652341804771450.0
61.02024-10-042024-10-012012-01-01301655797990297728018258976412652341804771275.0
71.02024-10-042024-10-012012-01-01301655797990391331468258976412652341804771410.0
81.02024-10-042024-10-012012-01-0130165599000300334348258976412652341804771110.0
91.02024-10-042024-10-012012-01-0130165579799011192082589764126523418047712070.0
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
3756971.02024-10-042024-10-012019-01-013082820297990781719649482822128091185.0
3756981.02024-10-042024-10-012019-01-013082820972802100336494828221280917145.0
3756991.02024-10-042024-10-012019-01-013082820972802091666494828221280919575.0
3757001.02024-10-042024-10-012019-01-0130828209728020933364948282212809114820.0
3757011.02024-10-042024-10-012019-01-013082820972802090116494828221280919730.0
3757021.02024-10-042024-10-012019-01-0130828209728020813364948282212809114065.0
3757031.02024-10-042024-10-012019-01-01308282029799069116494828221280912915.0
3757041.02024-10-042024-10-012019-01-01308282029799072116494828221280911340.0
3757051.02024-10-042024-10-012019-01-013082820297990771414649482822128091300.0
3757061.02024-10-042024-10-012019-01-0130828209728021112264948282212809113240.0