Overview

Dataset statistics

Number of variables27
Number of observations136916
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.2 MiB
Average record size in memory216.0 B

Variable types

Categorical1
Text1
Numeric25

Alerts

AANTAL_BSN is highly skewed (γ1 = 359.9627084)Skewed
AANTAL_VERZEKERDEJAREN is highly skewed (γ1 = 339.5691361)Skewed
KOSTEN_MEDISCH_SPECIALISTISCHE_ZORG is highly skewed (γ1 = 52.17140102)Skewed
KOSTEN_FARMACIE is highly skewed (γ1 = 29.41423327)Skewed
KOSTEN_SPECIALISTISCHE_GGZ is highly skewed (γ1 = 27.03511978)Skewed
KOSTEN_HUISARTS_INSCHRIJFTARIEF is highly skewed (γ1 = 190.2163179)Skewed
KOSTEN_HUISARTS_CONSULT is highly skewed (γ1 = 44.70295717)Skewed
KOSTEN_HUISARTS_OVERIG is highly skewed (γ1 = 195.4210711)Skewed
KOSTEN_ZIEKENVERVOER_LIGGEND is highly skewed (γ1 = 80.99502686)Skewed
KOSTEN_GENERALISTISCHE_BASIS_GGZ is highly skewed (γ1 = 26.69994527)Skewed
KOSTEN_GRENSOVERSCHRIJDENDE_ZORG is highly skewed (γ1 = 366.9142618)Skewed
KOSTEN_SPECIALISTISCHE_GGZ has 52814 (38.6%) zerosZeros
KOSTEN_HUISARTS_MDZ has 11082 (8.1%) zerosZeros
KOSTEN_HULPMIDDELEN has 6525 (4.8%) zerosZeros
KOSTEN_MONDZORG has 27688 (20.2%) zerosZeros
KOSTEN_PARAMEDISCHE_ZORG_FYSIOTHERAPIE has 29337 (21.4%) zerosZeros
KOSTEN_PARAMEDISCHE_ZORG_OVERIG has 22856 (16.7%) zerosZeros
KOSTEN_ZIEKENVERVOER_ZITTEND has 88328 (64.5%) zerosZeros
KOSTEN_ZIEKENVERVOER_LIGGEND has 32887 (24.0%) zerosZeros
KOSTEN_KRAAMZORG has 119707 (87.4%) zerosZeros
KOSTEN_VERLOSKUNDIGE_ZORG has 118983 (86.9%) zerosZeros
KOSTEN_GENERALISTISCHE_BASIS_GGZ has 75148 (54.9%) zerosZeros
KOSTEN_LANGDURIGE_GGZ has 134601 (98.3%) zerosZeros
KOSTEN_GRENSOVERSCHRIJDENDE_ZORG has 53547 (39.1%) zerosZeros
KOSTEN_EERSTELIJNS_ONDERSTEUNING has 58122 (42.5%) zerosZeros
KOSTEN_GERIATRISCHE_REVALIDATIEZORG has 113073 (82.6%) zerosZeros
KOSTEN_VERPLEGING_EN_VERZORGING has 63022 (46.0%) zerosZeros
KOSTEN_OVERIG has 48825 (35.7%) zerosZeros

Reproduction

Analysis started2024-03-18 18:30:45.213851
Analysis finished2024-03-18 18:32:26.706851
Duration1 minute and 41.49 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

GESLACHT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
V
68671 
M
68244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters136915
Distinct characters2
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 rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
V 68671
50.2%
M 68244
49.8%
(Missing) 1
 
< 0.1%

Length

2024-03-18T18:32:26.800280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T18:32:26.941989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
v 68671
50.2%
m 68244
49.8%

Most occurring characters

ValueCountFrequency (%)
V 68671
50.2%
M 68244
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 68671
50.2%
M 68244
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 68671
50.2%
M 68244
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 68671
50.2%
M 68244
49.8%
Distinct91
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:27.210377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.8997334
Min length1

Characters and Unicode

Total characters260102
Distinct characters11
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 row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
54 1562
 
1.1%
48 1559
 
1.1%
57 1559
 
1.1%
50 1558
 
1.1%
52 1558
 
1.1%
55 1558
 
1.1%
47 1557
 
1.1%
60 1556
 
1.1%
51 1556
 
1.1%
49 1556
 
1.1%
Other values (81) 121336
88.6%
2024-03-18T18:32:27.840449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 29124
11.2%
1 29067
11.2%
6 29022
11.2%
4 29012
11.2%
3 28759
11.1%
2 28743
11.1%
7 28500
11.0%
8 26575
10.2%
0 15142
5.8%
9 14724
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 260102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 29124
11.2%
1 29067
11.2%
6 29022
11.2%
4 29012
11.2%
3 28759
11.1%
2 28743
11.1%
7 28500
11.0%
8 26575
10.2%
0 15142
5.8%
9 14724
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 260102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 29124
11.2%
1 29067
11.2%
6 29022
11.2%
4 29012
11.2%
3 28759
11.1%
2 28743
11.1%
7 28500
11.0%
8 26575
10.2%
0 15142
5.8%
9 14724
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 260102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 29124
11.2%
1 29067
11.2%
6 29022
11.2%
4 29012
11.2%
3 28759
11.1%
2 28743
11.1%
7 28500
11.0%
8 26575
10.2%
0 15142
5.8%
9 14724
5.7%

POSTCODE_3
Real number (ℝ)

Distinct794
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean541.17856
Minimum0
Maximum999
Zeros182
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:28.061780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile144
Q1318
median539
Q3763
95-th percentile955
Maximum999
Range999
Interquartile range (IQR)445

Descriptive statistics

Standard deviation258.22974
Coefficient of variation (CV)0.47716181
Kurtosis-1.1671681
Mean541.17856
Median Absolute Deviation (MAD)223
Skewness0.035766181
Sum74095462
Variance66682.597
MonotonicityNot monotonic
2024-03-18T18:32:28.272351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
487 182
 
0.1%
721 182
 
0.1%
947 182
 
0.1%
723 182
 
0.1%
462 182
 
0.1%
461 182
 
0.1%
724 182
 
0.1%
725 182
 
0.1%
456 182
 
0.1%
727 182
 
0.1%
Other values (784) 135095
98.7%
ValueCountFrequency (%)
0 182
0.1%
101 182
0.1%
102 182
0.1%
103 182
0.1%
104 16
 
< 0.1%
105 182
0.1%
106 182
0.1%
107 182
0.1%
108 182
0.1%
109 182
0.1%
ValueCountFrequency (%)
999 166
0.1%
998 182
0.1%
997 172
0.1%
996 176
0.1%
995 180
0.1%
994 171
0.1%
993 182
0.1%
992 84
0.1%
991 174
0.1%
990 182
0.1%

AANTAL_BSN
Real number (ℝ)

SKEWED 

Distinct1126
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.83875
Minimum10
Maximum356551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:28.465956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16
Q141
median82
Q3163
95-th percentile381
Maximum356551
Range356541
Interquartile range (IQR)122

Descriptive statistics

Standard deviation972.15351
Coefficient of variation (CV)7.6045294
Kurtosis131972.19
Mean127.83875
Median Absolute Deviation (MAD)50
Skewness359.96271
Sum17503170
Variance945082.45
MonotonicityNot monotonic
2024-03-18T18:32:28.678989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 1161
 
0.8%
21 1148
 
0.8%
29 1147
 
0.8%
28 1140
 
0.8%
22 1140
 
0.8%
20 1137
 
0.8%
18 1133
 
0.8%
33 1128
 
0.8%
35 1127
 
0.8%
30 1123
 
0.8%
Other values (1116) 125532
91.7%
ValueCountFrequency (%)
10 1022
0.7%
11 1034
0.8%
12 1078
0.8%
13 1066
0.8%
14 1066
0.8%
15 1081
0.8%
16 1095
0.8%
17 1102
0.8%
18 1133
0.8%
19 1161
0.8%
ValueCountFrequency (%)
356551 1
< 0.1%
2311 1
< 0.1%
2292 1
< 0.1%
2232 1
< 0.1%
2130 1
< 0.1%
2125 1
< 0.1%
2097 1
< 0.1%
2020 1
< 0.1%
2019 1
< 0.1%
1982 1
< 0.1%

AANTAL_VERZEKERDEJAREN
Real number (ℝ)

SKEWED 

Distinct34846
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.52548
Minimum3.09
Maximum196219.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:28.890192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3.09
5-th percentile15.27
Q140
median81
Q3160.34
95-th percentile376.0325
Maximum196219.74
Range196216.65
Interquartile range (IQR)120.34

Descriptive statistics

Standard deviation545.37184
Coefficient of variation (CV)4.3796005
Kurtosis122082.86
Mean124.52548
Median Absolute Deviation (MAD)50
Skewness339.56914
Sum17049530
Variance297430.44
MonotonicityNot monotonic
2024-03-18T18:32:29.110153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 749
 
0.5%
11 738
 
0.5%
19 718
 
0.5%
21 710
 
0.5%
15 707
 
0.5%
10 704
 
0.5%
16 703
 
0.5%
14 701
 
0.5%
13 689
 
0.5%
18 682
 
0.5%
Other values (34836) 129815
94.8%
ValueCountFrequency (%)
3.09 1
< 0.1%
3.95 1
< 0.1%
4.1 1
< 0.1%
4.33 1
< 0.1%
4.47 1
< 0.1%
4.55 1
< 0.1%
4.6 1
< 0.1%
4.7 1
< 0.1%
4.71 1
< 0.1%
4.75 2
< 0.1%
ValueCountFrequency (%)
196219.74 1
< 0.1%
2220.62 1
< 0.1%
2191.36 1
< 0.1%
2130.01 1
< 0.1%
2047.13 1
< 0.1%
2044.54 1
< 0.1%
2015.59 1
< 0.1%
1935.15 1
< 0.1%
1920.07 1
< 0.1%
1888.45 1
< 0.1%

KOSTEN_MEDISCH_SPECIALISTISCHE_ZORG
Real number (ℝ)

SKEWED 

Distinct136549
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165218.8
Minimum-7777
Maximum46978767
Zeros19
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:29.325435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-7777
5-th percentile6303.62
Q132441.967
median88496.32
Q3215527.94
95-th percentile576265.55
Maximum46978767
Range46986544
Interquartile range (IQR)183085.97

Descriptive statistics

Standard deviation246118.06
Coefficient of variation (CV)1.4896492
Kurtosis9567.9535
Mean165218.8
Median Absolute Deviation (MAD)68699.18
Skewness52.171401
Sum2.2621098 × 1010
Variance6.0574099 × 1010
MonotonicityNot monotonic
2024-03-18T18:32:29.535643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
< 0.1%
9678.78 2
 
< 0.1%
178377 2
 
< 0.1%
10727.73 2
 
< 0.1%
47971.66 2
 
< 0.1%
14471.36 2
 
< 0.1%
12525.43 2
 
< 0.1%
14029.62 2
 
< 0.1%
12914.14 2
 
< 0.1%
174692.61 2
 
< 0.1%
Other values (136539) 136879
> 99.9%
ValueCountFrequency (%)
-7777 1
 
< 0.1%
0 19
< 0.1%
12.52 1
 
< 0.1%
23 1
 
< 0.1%
25.71 1
 
< 0.1%
31.35 1
 
< 0.1%
32.76 1
 
< 0.1%
38.36 1
 
< 0.1%
39.38 1
 
< 0.1%
48.52 1
 
< 0.1%
ValueCountFrequency (%)
46978766.76 1
< 0.1%
4462180.08 1
< 0.1%
4142347.5 1
< 0.1%
3839430.5 1
< 0.1%
3616606.71 1
< 0.1%
3608489.09 1
< 0.1%
3254328.15 1
< 0.1%
3132180.45 1
< 0.1%
3086536 1
< 0.1%
2956602.55 1
< 0.1%

KOSTEN_FARMACIE
Real number (ℝ)

SKEWED 

Distinct134842
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33974.632
Minimum14.11
Maximum8368957.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:29.740176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum14.11
5-th percentile906.0675
Q14957.82
median15929.88
Q343154.03
95-th percentile125922.99
Maximum8368957.1
Range8368943
Interquartile range (IQR)38196.21

Descriptive statistics

Standard deviation54331.6
Coefficient of variation (CV)1.5991814
Kurtosis4067.4161
Mean33974.632
Median Absolute Deviation (MAD)13287.735
Skewness29.414233
Sum4.6516707 × 109
Variance2.9519227 × 109
MonotonicityNot monotonic
2024-03-18T18:32:29.949127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102.83 3
 
< 0.1%
913.71 3
 
< 0.1%
2911.12 3
 
< 0.1%
7758.7 3
 
< 0.1%
1991.88 3
 
< 0.1%
263.26 3
 
< 0.1%
2217.62 3
 
< 0.1%
6247.63 3
 
< 0.1%
15623.32 3
 
< 0.1%
2447.93 3
 
< 0.1%
Other values (134832) 136886
> 99.9%
ValueCountFrequency (%)
14.11 1
< 0.1%
14.14 1
< 0.1%
14.16 1
< 0.1%
14.32 1
< 0.1%
14.98 1
< 0.1%
15 1
< 0.1%
16.26 1
< 0.1%
16.32 1
< 0.1%
16.83 1
< 0.1%
19.87 1
< 0.1%
ValueCountFrequency (%)
8368957.11 1
< 0.1%
1231629.74 1
< 0.1%
1166271.42 1
< 0.1%
1147381.84 1
< 0.1%
1015539.56 1
< 0.1%
970984.66 1
< 0.1%
927593.72 1
< 0.1%
923080.7 1
< 0.1%
904922.34 1
< 0.1%
892637.9 1
< 0.1%

KOSTEN_SPECIALISTISCHE_GGZ
Real number (ℝ)

SKEWED  ZEROS 

Distinct69094
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21524.512
Minimum-66348.6
Maximum7669881.8
Zeros52814
Zeros (%)38.6%
Negative5
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:30.153515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-66348.6
5-th percentile0
Q10
median2561.32
Q319746.377
95-th percentile108695.9
Maximum7669881.8
Range7736230.4
Interquartile range (IQR)19746.377

Descriptive statistics

Standard deviation51905.62
Coefficient of variation (CV)2.4114655
Kurtosis3468.2366
Mean21524.512
Median Absolute Deviation (MAD)2561.32
Skewness27.03512
Sum2.9470501 × 109
Variance2.6941934 × 109
MonotonicityNot monotonic
2024-03-18T18:32:30.376968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52814
38.6%
616.73 181
 
0.1%
1109.94 163
 
0.1%
2502.32 145
 
0.1%
1028.98 125
 
0.1%
2500.54 106
 
0.1%
2450.72 98
 
0.1%
585.66 93
 
0.1%
635.02 88
 
0.1%
320.74 87
 
0.1%
Other values (69084) 83016
60.6%
ValueCountFrequency (%)
-66348.6 1
 
< 0.1%
-56879.22 1
 
< 0.1%
-28221.91 1
 
< 0.1%
-17118.93 1
 
< 0.1%
-15556.56 1
 
< 0.1%
0 52814
38.6%
26.95 1
 
< 0.1%
95.8 1
 
< 0.1%
99.88 1
 
< 0.1%
102.26 1
 
< 0.1%
ValueCountFrequency (%)
7669881.83 1
< 0.1%
1160281.64 1
< 0.1%
910582.08 1
< 0.1%
890716.44 1
< 0.1%
857109.74 1
< 0.1%
847733.64 1
< 0.1%
830797.11 1
< 0.1%
810819 1
< 0.1%
808372.35 1
< 0.1%
775105.51 1
< 0.1%

KOSTEN_HUISARTS_INSCHRIJFTARIEF
Real number (ℝ)

SKEWED 

Distinct35375
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8055.8898
Minimum59.96
Maximum3973069.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:30.595929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum59.96
5-th percentile1074
Q12698.2
median5456.36
Q310611.45
95-th percentile23699.993
Maximum3973069.5
Range3973009.5
Interquartile range (IQR)7913.25

Descriptive statistics

Standard deviation13390.557
Coefficient of variation (CV)1.662207
Kurtosis56149.538
Mean8055.8898
Median Absolute Deviation (MAD)3327.78
Skewness190.21632
Sum1.1029802 × 109
Variance1.7930701 × 108
MonotonicityNot monotonic
2024-03-18T18:32:30.960003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
719.52 529
 
0.4%
659.56 488
 
0.4%
1259.16 483
 
0.4%
1019.32 477
 
0.3%
1139.24 474
 
0.3%
1199.2 469
 
0.3%
959.36 465
 
0.3%
899.4 453
 
0.3%
1319.12 452
 
0.3%
599.6 446
 
0.3%
Other values (35365) 132180
96.5%
ValueCountFrequency (%)
59.96 1
 
< 0.1%
104.93 1
 
< 0.1%
149.9 2
 
< 0.1%
164.89 8
< 0.1%
179.88 6
< 0.1%
194.87 7
< 0.1%
196.54 1
 
< 0.1%
209.86 11
< 0.1%
211.53 1
 
< 0.1%
224.85 6
< 0.1%
ValueCountFrequency (%)
3973069.45 1
< 0.1%
133923.66 1
< 0.1%
130108.5 1
< 0.1%
127593.25 1
< 0.1%
121932.84 1
< 0.1%
119787.76 1
< 0.1%
118717.95 1
< 0.1%
116719.14 1
< 0.1%
109312.39 1
< 0.1%
108843.81 1
< 0.1%

KOSTEN_HUISARTS_CONSULT
Real number (ℝ)

SKEWED 

Distinct95969
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5202.9354
Minimum0
Maximum1261062.7
Zeros38
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:31.180809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile429.185
Q11440.44
median3217.68
Q36764.4125
95-th percentile16346.91
Maximum1261062.7
Range1261062.7
Interquartile range (IQR)5323.9725

Descriptive statistics

Standard deviation7016.1062
Coefficient of variation (CV)1.34849
Kurtosis7525.289
Mean5202.9354
Median Absolute Deviation (MAD)2165.86
Skewness44.702957
Sum7.123651 × 108
Variance49225747
MonotonicityNot monotonic
2024-03-18T18:32:31.402375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
< 0.1%
198.49 22
 
< 0.1%
249.27 20
 
< 0.1%
272.36 20
 
< 0.1%
337 19
 
< 0.1%
355.45 19
 
< 0.1%
332.37 19
 
< 0.1%
226.2 19
 
< 0.1%
360.07 18
 
< 0.1%
300.04 18
 
< 0.1%
Other values (95959) 136704
99.8%
ValueCountFrequency (%)
0 38
< 0.1%
1.75 1
 
< 0.1%
2 1
 
< 0.1%
2.48 3
 
< 0.1%
2.92 1
 
< 0.1%
3.12 2
 
< 0.1%
3.4 1
 
< 0.1%
3.5 1
 
< 0.1%
3.8 1
 
< 0.1%
4.62 17
< 0.1%
ValueCountFrequency (%)
1261062.66 1
< 0.1%
236449.85 1
< 0.1%
166311.62 1
< 0.1%
150052.21 1
< 0.1%
144627.1 1
< 0.1%
144054.24 1
< 0.1%
137037.69 1
< 0.1%
118429.84 1
< 0.1%
118425.91 1
< 0.1%
117121.59 1
< 0.1%

KOSTEN_HUISARTS_MDZ
Real number (ℝ)

ZEROS 

Distinct102579
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4141.7536
Minimum0
Maximum793129.65
Zeros11082
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:31.613406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1168.6
median1474.31
Q35177.05
95-th percentile17146.29
Maximum793129.65
Range793129.65
Interquartile range (IQR)5008.45

Descriptive statistics

Standard deviation7124.6244
Coefficient of variation (CV)1.7201951
Kurtosis1113.4781
Mean4141.7536
Median Absolute Deviation (MAD)1453.43
Skewness12.896496
Sum5.6707234 × 108
Variance50760272
MonotonicityNot monotonic
2024-03-18T18:32:31.834726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11082
 
8.1%
0.22 177
 
0.1%
22.82 160
 
0.1%
0.33 157
 
0.1%
41.82 137
 
0.1%
414.48 124
 
0.1%
235 122
 
0.1%
0.26 121
 
0.1%
3.2 121
 
0.1%
0.55 112
 
0.1%
Other values (102569) 124603
91.0%
ValueCountFrequency (%)
0 11082
8.1%
0.11 68
 
< 0.1%
0.12 2
 
< 0.1%
0.13 26
 
< 0.1%
0.16 6
 
< 0.1%
0.17 6
 
< 0.1%
0.18 17
 
< 0.1%
0.2 2
 
< 0.1%
0.21 4
 
< 0.1%
0.22 177
 
0.1%
ValueCountFrequency (%)
793129.65 1
< 0.1%
148980.28 1
< 0.1%
134874.46 1
< 0.1%
116405.58 1
< 0.1%
107586.71 1
< 0.1%
98644.7 1
< 0.1%
96674.67 1
< 0.1%
87317.59 1
< 0.1%
84933.97 1
< 0.1%
81301.22 1
< 0.1%

KOSTEN_HUISARTS_OVERIG
Real number (ℝ)

SKEWED 

Distinct128820
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6182.3304
Minimum-236.47
Maximum3402557.1
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:32.051969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-236.47
5-th percentile725.16
Q11980.7975
median3995.355
Q37954.2975
95-th percentile18781
Maximum3402557.1
Range3402793.5
Interquartile range (IQR)5973.5

Descriptive statistics

Standard deviation11369.879
Coefficient of variation (CV)1.8390928
Kurtosis58158.583
Mean6182.3304
Median Absolute Deviation (MAD)2480.33
Skewness195.42107
Sum8.4645995 × 108
Variance1.2927416 × 108
MonotonicityNot monotonic
2024-03-18T18:32:32.270028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3563.21 4
 
< 0.1%
3999 4
 
< 0.1%
4844.22 4
 
< 0.1%
2232.35 4
 
< 0.1%
1770.46 4
 
< 0.1%
2543.64 4
 
< 0.1%
1342.71 4
 
< 0.1%
1966.38 4
 
< 0.1%
4802.8 4
 
< 0.1%
1038.09 4
 
< 0.1%
Other values (128810) 136876
> 99.9%
ValueCountFrequency (%)
-236.47 1
< 0.1%
31.38 1
< 0.1%
70.32 1
< 0.1%
72.26 1
< 0.1%
82.15 1
< 0.1%
82.43 1
< 0.1%
84.36 1
< 0.1%
87.57 1
< 0.1%
87.71 1
< 0.1%
89.85 1
< 0.1%
ValueCountFrequency (%)
3402557.07 1
< 0.1%
192792.03 1
< 0.1%
161228.24 1
< 0.1%
143081.98 1
< 0.1%
137433.24 1
< 0.1%
136809.62 1
< 0.1%
117383.66 1
< 0.1%
111467.95 1
< 0.1%
110544.05 1
< 0.1%
107780.39 1
< 0.1%

KOSTEN_HULPMIDDELEN
Real number (ℝ)

ZEROS 

Distinct119605
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10638.331
Minimum0
Maximum1675945.1
Zeros6525
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:32.480661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.4075
Q11028.1325
median5060.01
Q313791.972
95-th percentile39410.215
Maximum1675945.1
Range1675945.1
Interquartile range (IQR)12763.84

Descriptive statistics

Standard deviation16990.324
Coefficient of variation (CV)1.5970855
Kurtosis755.64183
Mean10638.331
Median Absolute Deviation (MAD)4713.01
Skewness12.122831
Sum1.4565577 × 109
Variance2.886711 × 108
MonotonicityNot monotonic
2024-03-18T18:32:32.697903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6525
 
4.8%
29.1 838
 
0.6%
30.55 285
 
0.2%
23.5 273
 
0.2%
58.2 173
 
0.1%
30.71 141
 
0.1%
29.42 131
 
0.1%
46 126
 
0.1%
32.33 118
 
0.1%
59.65 86
 
0.1%
Other values (119595) 128220
93.6%
ValueCountFrequency (%)
0 6525
4.8%
0.18 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 3
 
< 0.1%
0.21 2
 
< 0.1%
0.24 1
 
< 0.1%
0.26 1
 
< 0.1%
0.27 2
 
< 0.1%
0.29 1
 
< 0.1%
0.31 2
 
< 0.1%
ValueCountFrequency (%)
1675945.12 1
< 0.1%
706827.19 1
< 0.1%
543395.35 1
< 0.1%
461447.34 1
< 0.1%
454747.29 1
< 0.1%
436153.56 1
< 0.1%
412941.03 1
< 0.1%
412223.07 1
< 0.1%
405370.9 1
< 0.1%
405047.17 1
< 0.1%

KOSTEN_MONDZORG
Real number (ℝ)

ZEROS 

Distinct95906
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5379.1956
Minimum-444.08
Maximum938749.29
Zeros27688
Zeros (%)20.2%
Negative20
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:32.913937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-444.08
5-th percentile0
Q1144.4775
median1653.65
Q35704.5075
95-th percentile22786.168
Maximum938749.29
Range939193.37
Interquartile range (IQR)5560.03

Descriptive statistics

Standard deviation11338.419
Coefficient of variation (CV)2.1078279
Kurtosis395.02844
Mean5379.1956
Median Absolute Deviation (MAD)1653.65
Skewness9.6610261
Sum7.3649794 × 108
Variance1.2855974 × 108
MonotonicityNot monotonic
2024-03-18T18:32:33.117369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27688
 
20.2%
84.98 267
 
0.2%
20.44 222
 
0.2%
74.19 198
 
0.1%
40.88 128
 
0.1%
61.32 90
 
0.1%
59.53 79
 
0.1%
162.55 75
 
0.1%
120.3 75
 
0.1%
156.31 70
 
0.1%
Other values (95896) 108024
78.9%
ValueCountFrequency (%)
-444.08 1
 
< 0.1%
-441.9 1
 
< 0.1%
-83.5 13
 
< 0.1%
-82.88 1
 
< 0.1%
-48.02 1
 
< 0.1%
-46.85 1
 
< 0.1%
-35.75 1
 
< 0.1%
-15.03 1
 
< 0.1%
0 27688
20.2%
0.01 2
 
< 0.1%
ValueCountFrequency (%)
938749.29 1
< 0.1%
271261.5 1
< 0.1%
270464.06 1
< 0.1%
265425.58 1
< 0.1%
261961.26 1
< 0.1%
255148.13 1
< 0.1%
254949.17 1
< 0.1%
234917.24 1
< 0.1%
232668.48 1
< 0.1%
226923.05 1
< 0.1%
Distinct76018
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3625.3103
Minimum0
Maximum340085.91
Zeros29337
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:33.312088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1171
median1768.425
Q34883.5
95-th percentile13733.587
Maximum340085.91
Range340085.91
Interquartile range (IQR)4712.5

Descriptive statistics

Standard deviation5253.3449
Coefficient of variation (CV)1.4490746
Kurtosis139.11742
Mean3625.3103
Median Absolute Deviation (MAD)1768.425
Skewness4.8664849
Sum4.9636298 × 108
Variance27597633
MonotonicityNot monotonic
2024-03-18T18:32:33.661214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29337
 
21.4%
29.5 131
 
0.1%
28.5 119
 
0.1%
59 98
 
0.1%
85.5 97
 
0.1%
57 93
 
0.1%
88.5 91
 
0.1%
206.5 89
 
0.1%
32.75 89
 
0.1%
29.25 86
 
0.1%
Other values (76008) 106686
77.9%
ValueCountFrequency (%)
0 29337
21.4%
7.12 1
 
< 0.1%
9.05 1
 
< 0.1%
10 1
 
< 0.1%
12.65 1
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
16.6 1
 
< 0.1%
17.5 1
 
< 0.1%
17.86 2
 
< 0.1%
ValueCountFrequency (%)
340085.91 1
< 0.1%
121763.26 1
< 0.1%
92821.35 1
< 0.1%
88719.4 1
< 0.1%
79761.98 1
< 0.1%
75395.72 1
< 0.1%
74152.85 1
< 0.1%
73666.86 1
< 0.1%
70915.7 1
< 0.1%
70697 1
< 0.1%

KOSTEN_PARAMEDISCHE_ZORG_OVERIG
Real number (ℝ)

ZEROS 

Distinct74302
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1736.7396
Minimum-46.02
Maximum258441.47
Zeros22856
Zeros (%)16.7%
Negative2
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:33.870751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-46.02
5-th percentile0
Q1122.045
median518.47
Q31514.2375
95-th percentile6435.145
Maximum258441.47
Range258487.49
Interquartile range (IQR)1392.1925

Descriptive statistics

Standard deviation5016.9489
Coefficient of variation (CV)2.8887169
Kurtosis247.18961
Mean1736.7396
Median Absolute Deviation (MAD)518.47
Skewness11.488014
Sum2.3778743 × 108
Variance25169776
MonotonicityNot monotonic
2024-03-18T18:32:34.083506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22856
 
16.7%
176.88 512
 
0.4%
178.8 455
 
0.3%
186 422
 
0.3%
93 376
 
0.3%
88.44 375
 
0.3%
117.92 358
 
0.3%
180 344
 
0.3%
89.4 302
 
0.2%
124 300
 
0.2%
Other values (74292) 110616
80.8%
ValueCountFrequency (%)
-46.02 1
 
< 0.1%
-28.9 1
 
< 0.1%
0 22856
16.7%
3.88 1
 
< 0.1%
8.94 1
 
< 0.1%
9 3
 
< 0.1%
9.68 1
 
< 0.1%
9.92 3
 
< 0.1%
10 1
 
< 0.1%
10.22 1
 
< 0.1%
ValueCountFrequency (%)
258441.47 1
< 0.1%
219682.47 1
< 0.1%
207652.46 1
< 0.1%
184182.51 1
< 0.1%
168919.61 1
< 0.1%
165150.9 1
< 0.1%
154776.24 1
< 0.1%
142722.86 1
< 0.1%
141009.93 1
< 0.1%
131540.9 1
< 0.1%

KOSTEN_ZIEKENVERVOER_ZITTEND
Real number (ℝ)

ZEROS 

Distinct33337
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean760.08146
Minimum-114.92
Maximum136850
Zeros88328
Zeros (%)64.5%
Negative3
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:34.278716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-114.92
5-th percentile0
Q10
median0
Q3412.985
95-th percentile4466.335
Maximum136850
Range136964.92
Interquartile range (IQR)412.985

Descriptive statistics

Standard deviation2134.0316
Coefficient of variation (CV)2.8076354
Kurtosis162.84722
Mean760.08146
Median Absolute Deviation (MAD)0
Skewness7.0258945
Sum1.0406731 × 108
Variance4554091
MonotonicityNot monotonic
2024-03-18T18:32:34.489302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88328
64.5%
68 53
 
< 0.1%
0.8 46
 
< 0.1%
101.6 45
 
< 0.1%
28.8 42
 
< 0.1%
12 40
 
< 0.1%
236 39
 
< 0.1%
202.4 38
 
< 0.1%
404 35
 
< 0.1%
14.4 34
 
< 0.1%
Other values (33327) 48216
35.2%
ValueCountFrequency (%)
-114.92 1
 
< 0.1%
-100 1
 
< 0.1%
-39.12 1
 
< 0.1%
0 88328
64.5%
0.01 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.12 1
 
< 0.1%
0.15 2
 
< 0.1%
ValueCountFrequency (%)
136850 1
< 0.1%
46078.71 1
< 0.1%
42960.97 1
< 0.1%
41745.03 1
< 0.1%
41298.8 1
< 0.1%
40493.05 1
< 0.1%
39954.89 1
< 0.1%
39794.51 1
< 0.1%
39333.8 1
< 0.1%
39110.92 1
< 0.1%

KOSTEN_ZIEKENVERVOER_LIGGEND
Real number (ℝ)

SKEWED  ZEROS 

Distinct34449
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4087.4063
Minimum0
Maximum1863993.8
Zeros32887
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:34.700258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1509.39
median1781.67
Q35094.565
95-th percentile15605.573
Maximum1863993.8
Range1863993.8
Interquartile range (IQR)4585.175

Descriptive statistics

Standard deviation8478.1134
Coefficient of variation (CV)2.0742037
Kurtosis16981.064
Mean4087.4063
Median Absolute Deviation (MAD)1781.67
Skewness80.995027
Sum5.5963132 × 108
Variance71878407
MonotonicityNot monotonic
2024-03-18T18:32:34.916178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32887
 
24.0%
697.89 903
 
0.7%
721.29 901
 
0.7%
693.99 829
 
0.6%
729.09 799
 
0.6%
701.79 774
 
0.6%
736.89 764
 
0.6%
713.49 762
 
0.6%
717.39 759
 
0.6%
705.69 743
 
0.5%
Other values (34439) 96795
70.7%
ValueCountFrequency (%)
0 32887
24.0%
6.24 1
 
< 0.1%
7.6 1
 
< 0.1%
18.44 1
 
< 0.1%
23.68 1
 
< 0.1%
32.16 1
 
< 0.1%
34.74 1
 
< 0.1%
57.36 1
 
< 0.1%
59.04 1
 
< 0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
1863993.75 1
< 0.1%
334469.07 1
< 0.1%
277647.96 1
< 0.1%
246295.68 1
< 0.1%
227647.73 1
< 0.1%
203302.08 1
< 0.1%
196771.07 1
< 0.1%
196004.54 1
< 0.1%
189897.13 1
< 0.1%
189817.37 1
< 0.1%

KOSTEN_KRAAMZORG
Real number (ℝ)

ZEROS 

Distinct15582
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2159.7611
Minimum-144.51
Maximum923607.73
Zeros119707
Zeros (%)87.4%
Negative1
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:35.127642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-144.51
5-th percentile0
Q10
median0
Q30
95-th percentile11973.157
Maximum923607.73
Range923752.24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10594.811
Coefficient of variation (CV)4.9055474
Kurtosis528.74078
Mean2159.7611
Median Absolute Deviation (MAD)0
Skewness13.042078
Sum2.9570586 × 108
Variance1.1225001 × 108
MonotonicityNot monotonic
2024-03-18T18:32:35.330925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119707
87.4%
21.64 223
 
0.2%
86.53 122
 
0.1%
43.27 100
 
0.1%
108.16 85
 
0.1%
104.89 62
 
< 0.1%
40 52
 
< 0.1%
10 38
 
< 0.1%
43.29 32
 
< 0.1%
30 25
 
< 0.1%
Other values (15572) 16470
 
12.0%
ValueCountFrequency (%)
-144.51 1
 
< 0.1%
0 119707
87.4%
2.1 1
 
< 0.1%
8.25 1
 
< 0.1%
8.32 12
 
< 0.1%
8.55 1
 
< 0.1%
10 38
 
< 0.1%
16.23 1
 
< 0.1%
18.32 1
 
< 0.1%
21.63 4
 
< 0.1%
ValueCountFrequency (%)
923607.73 1
< 0.1%
342528.49 1
< 0.1%
340512.79 1
< 0.1%
323931.42 1
< 0.1%
277844.88 1
< 0.1%
276128.64 1
< 0.1%
272066.02 1
< 0.1%
267206.27 1
< 0.1%
258922.27 1
< 0.1%
249634.91 1
< 0.1%

KOSTEN_VERLOSKUNDIGE_ZORG
Real number (ℝ)

ZEROS 

Distinct14541
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1534.488
Minimum-152.78
Maximum845092.35
Zeros118983
Zeros (%)86.9%
Negative1
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:35.530690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-152.78
5-th percentile0
Q10
median0
Q30
95-th percentile7889.535
Maximum845092.35
Range845245.13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8216.9225
Coefficient of variation (CV)5.3548299
Kurtosis1013.1146
Mean1534.488
Median Absolute Deviation (MAD)0
Skewness18.26861
Sum2.1009597 × 108
Variance67517815
MonotonicityNot monotonic
2024-03-18T18:32:35.731322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118983
86.9%
294.18 290
 
0.2%
36.48 200
 
0.1%
1563.06 89
 
0.1%
1636.02 85
 
0.1%
1599.54 81
 
0.1%
43.77 78
 
0.1%
211.54 77
 
0.1%
167.77 68
 
< 0.1%
72.96 62
 
< 0.1%
Other values (14531) 16903
 
12.3%
ValueCountFrequency (%)
-152.78 1
 
< 0.1%
0 118983
86.9%
27.63 8
 
< 0.1%
34.8 2
 
< 0.1%
36.48 200
 
0.1%
42.03 2
 
< 0.1%
43.62 13
 
< 0.1%
43.77 78
 
0.1%
46.22 1
 
< 0.1%
47.09 2
 
< 0.1%
ValueCountFrequency (%)
845092.35 1
< 0.1%
333279.3 1
< 0.1%
326058.55 1
< 0.1%
321894.7 1
< 0.1%
305078.57 1
< 0.1%
253928.23 1
< 0.1%
249941.54 1
< 0.1%
241222.06 1
< 0.1%
237421.58 1
< 0.1%
235764.89 1
< 0.1%

KOSTEN_GENERALISTISCHE_BASIS_GGZ
Real number (ℝ)

SKEWED  ZEROS 

Distinct29818
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1534.6378
Minimum-218.41
Maximum526191.06
Zeros75148
Zeros (%)54.9%
Negative2
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:35.929971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-218.41
5-th percentile0
Q10
median0
Q31686.58
95-th percentile7315.235
Maximum526191.06
Range526409.47
Interquartile range (IQR)1686.58

Descriptive statistics

Standard deviation3650.8531
Coefficient of variation (CV)2.3789673
Kurtosis3180.7882
Mean1534.6378
Median Absolute Deviation (MAD)0
Skewness26.699945
Sum2.1011646 × 108
Variance13328729
MonotonicityNot monotonic
2024-03-18T18:32:36.142799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75148
54.9%
1262.82 2028
 
1.5%
1199.68 1122
 
0.8%
1136.54 905
 
0.7%
1165.48 895
 
0.7%
805.34 707
 
0.5%
1212.31 701
 
0.5%
765.07 660
 
0.5%
724.81 607
 
0.4%
192.92 521
 
0.4%
Other values (29808) 53622
39.2%
ValueCountFrequency (%)
-218.41 1
 
< 0.1%
-100.32 1
 
< 0.1%
0 75148
54.9%
4.6 1
 
< 0.1%
12.86 1
 
< 0.1%
18.63 1
 
< 0.1%
35.92 1
 
< 0.1%
44.29 1
 
< 0.1%
58.5 1
 
< 0.1%
59.34 1
 
< 0.1%
ValueCountFrequency (%)
526191.06 1
< 0.1%
114056.4 1
< 0.1%
105632.97 1
< 0.1%
105581.3 1
< 0.1%
99291.36 1
< 0.1%
96471.45 1
< 0.1%
93100.25 1
< 0.1%
92134.18 1
< 0.1%
87265.94 1
< 0.1%
83322.56 1
< 0.1%

KOSTEN_LANGDURIGE_GGZ
Real number (ℝ)

ZEROS 

Distinct2031
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1069.9804
Minimum0
Maximum590133.67
Zeros134601
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:36.339179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum590133.67
Range590133.67
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11172.989
Coefficient of variation (CV)10.442238
Kurtosis381.10338
Mean1069.9804
Median Absolute Deviation (MAD)0
Skewness16.266098
Sum1.4649743 × 108
Variance1.2483569 × 108
MonotonicityNot monotonic
2024-03-18T18:32:36.547810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134601
98.3%
87191.2 26
 
< 0.1%
87906.6 23
 
< 0.1%
88618.35 21
 
< 0.1%
144295.45 10
 
< 0.1%
87913.9 8
 
< 0.1%
89330.1 8
 
< 0.1%
79679.5 7
 
< 0.1%
85055.95 7
 
< 0.1%
143025.25 7
 
< 0.1%
Other values (2021) 2198
 
1.6%
ValueCountFrequency (%)
0 134601
98.3%
216.47 1
 
< 0.1%
239.47 1
 
< 0.1%
477.76 1
 
< 0.1%
478.94 1
 
< 0.1%
479.72 1
 
< 0.1%
482.64 1
 
< 0.1%
483.04 1
 
< 0.1%
519.96 1
 
< 0.1%
604.77 1
 
< 0.1%
ValueCountFrequency (%)
590133.67 1
< 0.1%
475537.49 1
< 0.1%
448251.02 1
< 0.1%
427507.44 1
< 0.1%
415939.05 1
< 0.1%
413689.14 1
< 0.1%
402215.01 1
< 0.1%
378518.77 1
< 0.1%
376641.25 1
< 0.1%
376625.25 1
< 0.1%

KOSTEN_GRENSOVERSCHRIJDENDE_ZORG
Real number (ℝ)

SKEWED  ZEROS 

Distinct64331
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2240.297
Minimum-378.77
Maximum38535043
Zeros53547
Zeros (%)39.1%
Negative1
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:36.896292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-378.77
5-th percentile0
Q10
median98.35
Q3839.0575
95-th percentile9513.81
Maximum38535043
Range38535422
Interquartile range (IQR)839.0575

Descriptive statistics

Standard deviation104431.15
Coefficient of variation (CV)46.614867
Kurtosis135381.91
Mean2240.297
Median Absolute Deviation (MAD)98.35
Skewness366.91426
Sum3.067325 × 108
Variance1.0905865 × 1010
MonotonicityNot monotonic
2024-03-18T18:32:37.121413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53547
39.1%
27.63 455
 
0.3%
25 336
 
0.2%
50 292
 
0.2%
27.65 167
 
0.1%
55.27 151
 
0.1%
100 145
 
0.1%
60 140
 
0.1%
40 132
 
0.1%
80 116
 
0.1%
Other values (64321) 81435
59.5%
ValueCountFrequency (%)
-378.77 1
 
< 0.1%
0 53547
39.1%
0.1 2
 
< 0.1%
0.12 1
 
< 0.1%
0.14 1
 
< 0.1%
0.3 1
 
< 0.1%
0.59 1
 
< 0.1%
0.62 1
 
< 0.1%
0.75 1
 
< 0.1%
0.79 1
 
< 0.1%
ValueCountFrequency (%)
38535043.17 1
< 0.1%
565883.31 1
< 0.1%
390986.86 1
< 0.1%
381327.75 1
< 0.1%
341457.94 1
< 0.1%
315654.54 1
< 0.1%
306170.23 1
< 0.1%
265454.14 1
< 0.1%
262315.63 1
< 0.1%
261113.43 1
< 0.1%

KOSTEN_EERSTELIJNS_ONDERSTEUNING
Real number (ℝ)

ZEROS 

Distinct687
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.014788
Minimum0
Maximum753.88
Zeros58122
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:37.333176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.88
Q35.64
95-th percentile35.72
Maximum753.88
Range753.88
Interquartile range (IQR)5.64

Descriptive statistics

Standard deviation25.490399
Coefficient of variation (CV)3.1804209
Kurtosis64.744321
Mean8.014788
Median Absolute Deviation (MAD)1.88
Skewness6.9966261
Sum1097352.7
Variance649.76046
MonotonicityNot monotonic
2024-03-18T18:32:37.542886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58122
42.5%
1.88 26573
19.4%
3.76 13272
 
9.7%
5.64 7216
 
5.3%
7.52 4389
 
3.2%
9.4 2690
 
2.0%
11.28 1991
 
1.5%
13.16 1389
 
1.0%
15.04 1131
 
0.8%
1.41 977
 
0.7%
Other values (677) 19166
 
14.0%
ValueCountFrequency (%)
0 58122
42.5%
0.47 384
 
0.3%
0.94 412
 
0.3%
1.41 977
 
0.7%
1.88 26573
19.4%
2.35 274
 
0.2%
2.82 329
 
0.2%
3.29 817
 
0.6%
3.76 13272
 
9.7%
4.23 177
 
0.1%
ValueCountFrequency (%)
753.88 1
< 0.1%
452.61 1
< 0.1%
447.91 1
< 0.1%
429.11 1
< 0.1%
412.66 1
< 0.1%
402.32 1
< 0.1%
401.85 1
< 0.1%
399.5 1
< 0.1%
398.56 1
< 0.1%
395.27 1
< 0.1%

KOSTEN_GERIATRISCHE_REVALIDATIEZORG
Real number (ℝ)

ZEROS 

Distinct11575
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5202.7493
Minimum0
Maximum917725.19
Zeros113073
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-03-18T18:32:37.752259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33003.253
Maximum917725.19
Range917725.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18580.47
Coefficient of variation (CV)3.5712792
Kurtosis231.60991
Mean5202.7493
Median Absolute Deviation (MAD)0
Skewness10.055089
Sum7.1233963 × 108
Variance3.4523388 × 108
MonotonicityNot monotonic
2024-03-18T18:32:37.954702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113073
82.6%
10821.25 408
 
0.3%
10663.03 277
 
0.2%
12914.82 201
 
0.1%
19172.94 184
 
0.1%
5677.02 182
 
0.1%
10932.81 167
 
0.1%
5594.02 154
 
0.1%
14034.6 149
 
0.1%
18892.62 144
 
0.1%
Other values (11565) 21977
 
16.1%
ValueCountFrequency (%)
0 113073
82.6%
256.93 1
 
< 0.1%
258.05 1
 
< 0.1%
262.08 1
 
< 0.1%
263.42 1
 
< 0.1%
263.61 1
 
< 0.1%
293.39 1
 
< 0.1%
293.41 2
 
< 0.1%
296.39 1
 
< 0.1%
296.4 1
 
< 0.1%
ValueCountFrequency (%)
917725.19 1
< 0.1%
838777.4 1
< 0.1%
799103 1
< 0.1%
775131.87 1
< 0.1%
646977.02 1
< 0.1%
631471.51 1
< 0.1%
600087.92 1
< 0.1%
592651.66 1
< 0.1%
583976.09 1
< 0.1%
499252.04 1
< 0.1%

KOSTEN_VERPLEGING_EN_VERZORGING
Real number (ℝ)

ZEROS 

Distinct67257
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25079.221
Minimum-2784.46
Maximum5598974.6
Zeros63022
Zeros (%)46.0%
Negative3
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:38.147712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2784.46
5-th percentile0
Q10
median244.635
Q318909.315
95-th percentile121325.88
Maximum5598974.6
Range5601759.1
Interquartile range (IQR)18909.315

Descriptive statistics

Standard deviation84878.18
Coefficient of variation (CV)3.3844025
Kurtosis463.31233
Mean25079.221
Median Absolute Deviation (MAD)244.635
Skewness15.294696
Sum3.4337467 × 109
Variance7.2043055 × 109
MonotonicityNot monotonic
2024-03-18T18:32:38.348223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63022
46.0%
682 159
 
0.1%
1364 59
 
< 0.1%
162.96 54
 
< 0.1%
53.52 40
 
< 0.1%
722 40
 
< 0.1%
108.96 37
 
< 0.1%
54.48 35
 
< 0.1%
163.44 33
 
< 0.1%
81.72 32
 
< 0.1%
Other values (67247) 73405
53.6%
ValueCountFrequency (%)
-2784.46 1
 
< 0.1%
-1374.84 1
 
< 0.1%
-988.26 1
 
< 0.1%
0 63022
46.0%
3.77 1
 
< 0.1%
4.26 1
 
< 0.1%
4.34 1
 
< 0.1%
4.41 1
 
< 0.1%
4.44 3
 
< 0.1%
4.46 1
 
< 0.1%
ValueCountFrequency (%)
5598974.62 1
< 0.1%
4198428.66 1
< 0.1%
3974729.99 1
< 0.1%
3292775.15 1
< 0.1%
3285374.97 1
< 0.1%
3232666.39 1
< 0.1%
3098720.72 1
< 0.1%
2990976.75 1
< 0.1%
2699196.45 1
< 0.1%
2677345.29 1
< 0.1%

KOSTEN_OVERIG
Real number (ℝ)

ZEROS 

Distinct58737
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3979.0777
Minimum-1432.6
Maximum834029.68
Zeros48825
Zeros (%)35.7%
Negative7
Negative (%)< 0.1%
Memory size1.0 MiB
2024-03-18T18:32:38.553360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1432.6
5-th percentile0
Q10
median253.2
Q31969.3775
95-th percentile20354.397
Maximum834029.68
Range835462.28
Interquartile range (IQR)1969.3775

Descriptive statistics

Standard deviation13933.26
Coefficient of variation (CV)3.5016305
Kurtosis335.87418
Mean3979.0777
Median Absolute Deviation (MAD)253.2
Skewness12.631014
Sum5.4479941 × 108
Variance1.9413573 × 108
MonotonicityNot monotonic
2024-03-18T18:32:38.754661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48825
35.7%
90 886
 
0.6%
63.03 756
 
0.6%
354.76 471
 
0.3%
72.74 436
 
0.3%
98.91 377
 
0.3%
2222.36 370
 
0.3%
262.39 357
 
0.3%
50.19 356
 
0.3%
67.86 354
 
0.3%
Other values (58727) 83728
61.2%
ValueCountFrequency (%)
-1432.6 1
 
< 0.1%
-761.82 1
 
< 0.1%
-564.29 1
 
< 0.1%
-119.78 1
 
< 0.1%
-114.66 1
 
< 0.1%
-56.04 1
 
< 0.1%
-31.95 1
 
< 0.1%
0 48825
35.7%
1 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
834029.68 1
< 0.1%
762560.69 1
< 0.1%
548580.59 1
< 0.1%
495262.09 1
< 0.1%
477522.07 1
< 0.1%
449540.1 1
< 0.1%
437208.27 1
< 0.1%
415266.05 1
< 0.1%
395285.25 1
< 0.1%
384571.36 1
< 0.1%

Interactions

2024-03-18T18:32:21.493785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:49.017952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:52.463053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:56.465817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:00.584688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:04.297470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:08.104329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:12.184570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:16.377897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:20.192475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:24.273540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:28.287633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:32.318612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.698132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:39.177901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:42.702067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:46.732251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.805892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:54.473764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:58.239617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:02.099104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.889976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.966856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.937126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.764140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:21.632131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:49.162808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-18T18:30:55.128962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:59.197691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.134431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:06.959291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:10.942604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:14.976828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:18.989555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.039538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.063336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.100994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:34.719623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.107020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:41.505052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:45.505631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:49.444974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:53.326153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.100224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:00.822365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:04.705931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:08.711526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:12.732120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:16.477568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:20.317990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.231662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:51.460130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:55.270774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:59.346456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.272014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.095797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.089682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:15.127877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.132556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.186249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.207340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.245248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:34.817851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.214109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:41.645790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:45.649867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:49.591395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:53.462608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.232911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:00.957481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:04.845540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:08.858419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:12.874114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:16.753968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:20.453561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.370235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:51.604488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:55.542128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:59.499293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.413802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.240282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.241823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:15.287027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.278831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.337731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.359200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.394661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:34.923877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.326748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:41.796039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:45.803786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:49.741939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:53.603990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.371809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.099582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:04.992271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.010297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.025244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:16.896586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:20.599340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.514038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:51.753292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:55.697232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:59.659883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.562425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.384337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.400611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:15.449608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.433269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.493962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.515077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.550766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.031507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.444012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:41.949269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:45.961986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.028852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:53.749148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.518381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.361288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.142005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.168711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.178646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.044259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:20.748209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.670559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:51.897881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:55.865484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:59.832868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.722801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.544354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.571566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:15.749856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.597896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.663990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.684647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.718947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.151574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.609966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:42.117703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:46.129766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.198153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:53.911164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.677990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.524094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.309613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.339038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.346930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.203605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:20.913476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.819968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:52.034409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:56.026602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:00.126513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:03.877349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.693333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.734047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:15.916672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.757353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.828321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.846639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:31.877939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.265082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.774268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:42.274258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:46.290037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.358660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:54.063153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.829318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.677967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.465086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.506919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.502918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.354889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:21.067676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:24.952225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:52.175496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:56.170478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:00.278324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:04.015038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.829286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:11.884244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:16.067427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:19.899824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:23.973839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:27.991323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:32.023934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.494240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:38.921465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:42.417413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:46.433310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.505661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:54.199530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:57.961806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.815876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.605289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.659058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.646508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.487581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:21.209236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:25.091380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:52.321587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:30:56.321065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:00.433266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:04.159786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:07.968932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:12.036009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:16.226697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:20.050148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:24.128229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:28.143385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:32.173600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:35.598337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:39.071298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:42.562512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:46.587260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:50.658004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:54.340155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:31:58.105822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:01.959936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:05.750268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:09.815656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:13.795919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:17.631468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-18T18:32:21.353158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-18T18:32:25.341999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T18:32:26.001468image/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

GESLACHTLEEFTIJDSKLASSEPOSTCODE_3AANTAL_BSNAANTAL_VERZEKERDEJARENKOSTEN_MEDISCH_SPECIALISTISCHE_ZORGKOSTEN_FARMACIEKOSTEN_SPECIALISTISCHE_GGZKOSTEN_HUISARTS_INSCHRIJFTARIEFKOSTEN_HUISARTS_CONSULTKOSTEN_HUISARTS_MDZKOSTEN_HUISARTS_OVERIGKOSTEN_HULPMIDDELENKOSTEN_MONDZORGKOSTEN_PARAMEDISCHE_ZORG_FYSIOTHERAPIEKOSTEN_PARAMEDISCHE_ZORG_OVERIGKOSTEN_ZIEKENVERVOER_ZITTENDKOSTEN_ZIEKENVERVOER_LIGGENDKOSTEN_KRAAMZORGKOSTEN_VERLOSKUNDIGE_ZORGKOSTEN_GENERALISTISCHE_BASIS_GGZKOSTEN_LANGDURIGE_GGZKOSTEN_GRENSOVERSCHRIJDENDE_ZORGKOSTEN_EERSTELIJNS_ONDERSTEUNINGKOSTEN_GERIATRISCHE_REVALIDATIEZORGKOSTEN_VERPLEGING_EN_VERZORGINGKOSTEN_OVERIG
0NaNNaNNaN356551196219.7446978766.768368957.117669881.833973069.451261062.66793129.653402557.071675945.12938749.29340085.91258441.47136850.01863993.75923607.73845092.35526191.06120330.7738535043.17753.88281934.95696777.02495262.09
1M00.0330145.141124998.0225381.100.005278.156296.83122.1412654.511338.102062.2716851.352565.800.011936.650.000.000.000.004178.5346.060.000.000.00
2M0101.0579278.411786934.2632112.930.008995.6711378.90619.7121995.331736.69219.5118708.97122.800.013096.150.000.000.000.005221.256.580.00200.252211.57
3M0102.0312157.66950071.2512782.990.005950.247131.80135.5717602.211024.3997.5910164.69265.280.09365.320.000.000.000.001206.223.760.000.00131.87
4M0103.0341166.931344150.9124840.020.005616.567491.80195.6519003.186021.99223.1911792.31561.500.010201.430.000.000.000.00293.444.230.003333.95445.49
5M0105.0957454.982704233.2449095.530.0016008.6119426.951307.5943927.747059.73539.3130362.551097.200.031900.820.000.000.000.002435.0310.340.00176.67593.30
6M0106.01099546.944142347.5090956.510.0020634.1726067.421652.9873371.208767.5165.0947360.312185.770.037907.350.000.000.000.004916.7911.750.00843.361549.87
7M0107.0653327.822460736.5666963.340.0011042.9013761.072920.7329109.527507.39841.4637536.902384.040.023290.240.000.000.000.006923.865.170.009170.22570.07
8M0108.0285143.231171622.5233917.010.004768.536665.661576.6215280.092583.47149.5515982.32159.500.013941.080.000.000.000.0044956.420.470.00844.44153.20
9M0109.0572286.341566585.2548131.650.0010077.9812631.00760.4531746.885188.4144.5617210.121304.090.011347.650.000.000.000.001202.488.460.0013.62105.15
GESLACHTLEEFTIJDSKLASSEPOSTCODE_3AANTAL_BSNAANTAL_VERZEKERDEJARENKOSTEN_MEDISCH_SPECIALISTISCHE_ZORGKOSTEN_FARMACIEKOSTEN_SPECIALISTISCHE_GGZKOSTEN_HUISARTS_INSCHRIJFTARIEFKOSTEN_HUISARTS_CONSULTKOSTEN_HUISARTS_MDZKOSTEN_HUISARTS_OVERIGKOSTEN_HULPMIDDELENKOSTEN_MONDZORGKOSTEN_PARAMEDISCHE_ZORG_FYSIOTHERAPIEKOSTEN_PARAMEDISCHE_ZORG_OVERIGKOSTEN_ZIEKENVERVOER_ZITTENDKOSTEN_ZIEKENVERVOER_LIGGENDKOSTEN_KRAAMZORGKOSTEN_VERLOSKUNDIGE_ZORGKOSTEN_GENERALISTISCHE_BASIS_GGZKOSTEN_LANGDURIGE_GGZKOSTEN_GRENSOVERSCHRIJDENDE_ZORGKOSTEN_EERSTELIJNS_ONDERSTEUNINGKOSTEN_GERIATRISCHE_REVALIDATIEZORGKOSTEN_VERPLEGING_EN_VERZORGINGKOSTEN_OVERIG
136906V90+986.06055.3268436.7146633.441990.645348.8822070.385363.1310702.9640854.991214.960.00640.500.08331.360.00.00.000.00.001.880.00159632.5026012.70
136907V90+990.0123107.37169368.3285783.6219345.6310120.9222429.307599.0212402.0074884.341317.7617015.90582.201330.021390.440.00.01010.260.00.000.0021124.01616432.2633011.82
136908V90+991.05447.1251423.6853651.952101.255060.4627292.245858.437414.4243662.201057.091507.80700.550.07662.950.00.00.000.00.000.005677.02264190.6413587.84
136909V90+993.0183166.36296607.32143427.3835774.2415181.3826272.7510969.6230615.40109523.555642.257050.461673.300.036787.590.00.00.000.0252.513.7637599.55784496.2634423.08
136910V90+994.03330.9356171.6322300.355711.652622.005254.563785.282848.5911561.42208.020.000.000.04708.220.00.00.000.00.000.0019172.94113591.452434.56
136911V90+995.09783.20172813.8766113.712554.207161.4018886.377805.7717350.1140173.96601.987668.551741.420.032196.160.00.00.000.00.001.8889439.64233745.5543097.16
136912V90+996.04441.9154520.2628462.631041.713198.8412674.726218.025870.8216935.03344.950.000.000.08858.280.00.00.000.00.000.005677.02227809.890.00
136913V90+997.02423.1752800.3815782.661866.952438.469265.173313.013345.8017489.76760.94543.00274.170.07321.680.00.00.000.00.000.940.00193640.65610.20
136914V90+998.0122106.33295089.29108384.1827094.7911169.7235104.058219.9517497.8599369.30884.532257.503618.5728.937321.470.00.00.000.00.005.17143678.40965501.1222138.04
136915V90+999.03330.00106264.5133995.484223.833303.726679.173577.742842.4628172.65176.8245.15588.506922.45750.610.00.00.000.00.000.0032913.7581615.4025823.22