Overview

Dataset statistics

Number of variables14
Number of observations318926
Missing cells48866
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.1 MiB
Average record size in memory112.0 B

Variable types

Categorical4
DateTime1
Numeric9

Alerts

VERSIE has constant value "1.0"Constant
DATUM_BESTAND has constant value "2023-01-06"Constant
PEILDATUM has constant value "2023-01-01"Constant
TYPERENDE_DIAGNOSE_CD has a high cardinality: 1897 distinct valuesHigh cardinality
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 48866 (15.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.29093219)Skewed

Reproduction

Analysis started2023-01-25 14:27:00.877607
Analysis finished2023-01-25 14:27:18.804928
Duration17.93 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1.0
318926 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters956778
Distinct characters3
Distinct categories2 ?
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 318926
100.0%

Length

2023-01-25T14:27:18.857869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T14:27:18.972315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 318926
100.0%

Most occurring characters

ValueCountFrequency (%)
1 318926
33.3%
. 318926
33.3%
0 318926
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 637852
66.7%
Other Punctuation 318926
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 318926
50.0%
0 318926
50.0%
Other Punctuation
ValueCountFrequency (%)
. 318926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 956778
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 318926
33.3%
. 318926
33.3%
0 318926
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 956778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 318926
33.3%
. 318926
33.3%
0 318926
33.3%

DATUM_BESTAND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2023-01-06
318926 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3189260
Distinct characters6
Distinct categories2 ?
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 row2023-01-06
2nd row2023-01-06
3rd row2023-01-06
4th row2023-01-06
5th row2023-01-06

Common Values

ValueCountFrequency (%)
2023-01-06 318926
100.0%

Length

2023-01-25T14:27:19.061330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T14:27:19.172145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-06 318926
100.0%

Most occurring characters

ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
3 318926
 
10.0%
1 318926
 
10.0%
6 318926
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2551408
80.0%
Dash Punctuation 637852
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 956778
37.5%
2 637852
25.0%
3 318926
 
12.5%
1 318926
 
12.5%
6 318926
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 637852
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3189260
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
3 318926
 
10.0%
1 318926
 
10.0%
6 318926
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3189260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
3 318926
 
10.0%
1 318926
 
10.0%
6 318926
 
10.0%

PEILDATUM
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2023-01-01
318926 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3189260
Distinct characters5
Distinct categories2 ?
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 row2023-01-01
2nd row2023-01-01
3rd row2023-01-01
4th row2023-01-01
5th row2023-01-01

Common Values

ValueCountFrequency (%)
2023-01-01 318926
100.0%

Length

2023-01-25T14:27:19.263300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T14:27:19.373948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01 318926
100.0%

Most occurring characters

ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
1 637852
20.0%
3 318926
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2551408
80.0%
Dash Punctuation 637852
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 956778
37.5%
2 637852
25.0%
1 637852
25.0%
3 318926
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 637852
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3189260
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
1 637852
20.0%
3 318926
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3189260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 956778
30.0%
2 637852
20.0%
- 637852
20.0%
1 637852
20.0%
3 318926
 
10.0%

JAAR
Date

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Minimum2012-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-01-25T14:27:19.454613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:27:19.550082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.6308
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:19.671362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile335
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation986.98774
Coefficient of variation (CV)2.2552977
Kurtosis61.271343
Mean437.6308
Median Absolute Deviation (MAD)8
Skewness7.9487585
Sum1.3957184 × 108
Variance974144.79
MonotonicityNot monotonic
2023-01-25T14:27:19.794359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 45051
14.1%
313 41353
13.0%
303 36720
11.5%
330 25240
 
7.9%
316 21706
 
6.8%
308 17147
 
5.4%
306 13380
 
4.2%
324 13159
 
4.1%
301 12804
 
4.0%
304 10371
 
3.3%
Other values (18) 81995
25.7%
ValueCountFrequency (%)
301 12804
 
4.0%
302 6991
 
2.2%
303 36720
11.5%
304 10371
 
3.3%
305 45051
14.1%
306 13380
 
4.2%
307 5573
 
1.7%
308 17147
 
5.4%
310 3496
 
1.1%
313 41353
13.0%
ValueCountFrequency (%)
8418 4253
 
1.3%
8416 543
 
0.2%
1900 210
 
0.1%
390 862
 
0.3%
389 3363
 
1.1%
362 4301
 
1.3%
361 2289
 
0.7%
335 3227
 
1.0%
330 25240
7.9%
329 834
 
0.3%

TYPERENDE_DIAGNOSE_CD
Categorical

Distinct1897
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
101
 
1347
402
 
1306
403
 
1278
301
 
1275
201
 
1202
Other values (1892)
312518 

Length

Max length4
Median length3
Mean length3.3522479
Min length2

Characters and Unicode

Total characters1069119
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row07
2nd row14
3rd row03
4th row17
5th row01

Common Values

ValueCountFrequency (%)
101 1347
 
0.4%
402 1306
 
0.4%
403 1278
 
0.4%
301 1275
 
0.4%
201 1202
 
0.4%
203 1193
 
0.4%
401 1067
 
0.3%
404 1057
 
0.3%
802 1039
 
0.3%
409 1030
 
0.3%
Other values (1887) 307132
96.3%

Length

2023-01-25T14:27:19.936525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
101 1347
 
0.4%
402 1306
 
0.4%
403 1278
 
0.4%
301 1275
 
0.4%
201 1202
 
0.4%
203 1193
 
0.4%
401 1067
 
0.3%
404 1057
 
0.3%
802 1039
 
0.3%
409 1030
 
0.3%
Other values (1887) 307132
96.3%

Most occurring characters

ValueCountFrequency (%)
1 204571
19.1%
0 195938
18.3%
2 141707
13.3%
3 115848
10.8%
5 82478
7.7%
9 77055
 
7.2%
4 75865
 
7.1%
7 62932
 
5.9%
6 55871
 
5.2%
8 46039
 
4.3%
Other values (15) 10815
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1058304
99.0%
Uppercase Letter 10815
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2017
18.7%
M 1830
16.9%
B 1300
12.0%
E 910
8.4%
Z 902
8.3%
D 725
 
6.7%
A 703
 
6.5%
F 674
 
6.2%
C 357
 
3.3%
K 350
 
3.2%
Other values (5) 1047
9.7%
Decimal Number
ValueCountFrequency (%)
1 204571
19.3%
0 195938
18.5%
2 141707
13.4%
3 115848
10.9%
5 82478
7.8%
9 77055
 
7.3%
4 75865
 
7.2%
7 62932
 
5.9%
6 55871
 
5.3%
8 46039
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1058304
99.0%
Latin 10815
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2017
18.7%
M 1830
16.9%
B 1300
12.0%
E 910
8.4%
Z 902
8.3%
D 725
 
6.7%
A 703
 
6.5%
F 674
 
6.2%
C 357
 
3.3%
K 350
 
3.2%
Other values (5) 1047
9.7%
Common
ValueCountFrequency (%)
1 204571
19.3%
0 195938
18.5%
2 141707
13.4%
3 115848
10.9%
5 82478
7.8%
9 77055
 
7.3%
4 75865
 
7.2%
7 62932
 
5.9%
6 55871
 
5.3%
8 46039
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1069119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 204571
19.1%
0 195938
18.3%
2 141707
13.3%
3 115848
10.8%
5 82478
7.7%
9 77055
 
7.2%
4 75865
 
7.1%
7 62932
 
5.9%
6 55871
 
5.2%
8 46039
 
4.3%
Other values (15) 10815
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6011
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4167032 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:20.202684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999038
Q199799068
median1.4959903 × 108
Q39.90004 × 108
95-th percentile9.9051604 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020494 × 108

Descriptive statistics

Standard deviation4.2921687 × 108
Coefficient of variation (CV)0.97180375
Kurtosis-1.7415168
Mean4.4167032 × 108
Median Absolute Deviation (MAD)1.1970002 × 108
Skewness0.46328001
Sum1.4086015 × 1014
Variance1.8422712 × 1017
MonotonicityNot monotonic
2023-01-25T14:27:20.350858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2321
 
0.7%
990004007 2284
 
0.7%
990003004 2233
 
0.7%
990004006 1857
 
0.6%
990356076 1689
 
0.5%
990356073 1559
 
0.5%
131999228 1485
 
0.5%
131999164 1473
 
0.5%
990003007 1458
 
0.5%
131999194 1349
 
0.4%
Other values (6001) 301218
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 11
< 0.1%
10501005 11
< 0.1%
10501007 3
 
< 0.1%
10501008 11
< 0.1%
10501010 11
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 11
< 0.1%
ValueCountFrequency (%)
998418081 160
0.1%
998418080 145
< 0.1%
998418079 38
 
< 0.1%
998418077 8
 
< 0.1%
998418076 8
 
< 0.1%
998418075 6
 
< 0.1%
998418074 214
0.1%
998418073 214
0.1%
998418072 8
 
< 0.1%
998418071 8
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10070
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.47549
Minimum1
Maximum165142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:20.497556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3104
95-th percentile1753
Maximum165142
Range165141
Interquartile range (IQR)101

Descriptive statistics

Standard deviation3174.6685
Coefficient of variation (CV)6.1587187
Kurtosis404.01663
Mean515.47549
Median Absolute Deviation (MAD)13
Skewness16.643127
Sum1.6439854 × 108
Variance10078520
MonotonicityNot monotonic
2023-01-25T14:27:20.637161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52528
 
16.5%
2 25677
 
8.1%
3 16790
 
5.3%
4 12347
 
3.9%
5 9613
 
3.0%
6 8124
 
2.5%
7 6771
 
2.1%
8 5742
 
1.8%
9 5205
 
1.6%
10 4720
 
1.5%
Other values (10060) 171409
53.7%
ValueCountFrequency (%)
1 52528
16.5%
2 25677
8.1%
3 16790
 
5.3%
4 12347
 
3.9%
5 9613
 
3.0%
6 8124
 
2.5%
7 6771
 
2.1%
8 5742
 
1.8%
9 5205
 
1.6%
10 4720
 
1.5%
ValueCountFrequency (%)
165142 1
< 0.1%
156984 1
< 0.1%
155884 1
< 0.1%
154269 1
< 0.1%
154185 1
< 0.1%
144724 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
110606 1
< 0.1%
110520 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct10744
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.76077
Minimum1
Maximum239709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:20.785586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3114
95-th percentile1997
Maximum239709
Range239708
Interquartile range (IQR)111

Descriptive statistics

Standard deviation4082.7328
Coefficient of variation (CV)6.7066294
Kurtosis722.57756
Mean608.76077
Median Absolute Deviation (MAD)14
Skewness21.290932
Sum1.9414964 × 108
Variance16668707
MonotonicityNot monotonic
2023-01-25T14:27:20.939393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50593
 
15.9%
2 25228
 
7.9%
3 16626
 
5.2%
4 12140
 
3.8%
5 9511
 
3.0%
6 8123
 
2.5%
7 6724
 
2.1%
8 5695
 
1.8%
9 5135
 
1.6%
10 4715
 
1.5%
Other values (10734) 174436
54.7%
ValueCountFrequency (%)
1 50593
15.9%
2 25228
7.9%
3 16626
 
5.2%
4 12140
 
3.8%
5 9511
 
3.0%
6 8123
 
2.5%
7 6724
 
2.1%
8 5695
 
1.8%
9 5135
 
1.6%
10 4715
 
1.5%
ValueCountFrequency (%)
239709 1
< 0.1%
232466 1
< 0.1%
231983 1
< 0.1%
230952 1
< 0.1%
227936 1
< 0.1%
227457 1
< 0.1%
225982 1
< 0.1%
223938 1
< 0.1%
218449 1
< 0.1%
215070 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct8960
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7725.6066
Minimum1
Maximum227968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:21.089252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1411
median1736
Q36417
95-th percentile36952
Maximum227968
Range227967
Interquartile range (IQR)6006

Descriptive statistics

Standard deviation17860.004
Coefficient of variation (CV)2.3117931
Kurtosis34.156965
Mean7725.6066
Median Absolute Deviation (MAD)1577
Skewness5.0595106
Sum2.4638968 × 109
Variance3.1897975 × 108
MonotonicityNot monotonic
2023-01-25T14:27:21.225166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 529
 
0.2%
12 482
 
0.2%
25 475
 
0.1%
8 465
 
0.1%
17 451
 
0.1%
9 450
 
0.1%
37 448
 
0.1%
28 430
 
0.1%
22 429
 
0.1%
14 425
 
0.1%
Other values (8950) 314342
98.6%
ValueCountFrequency (%)
1 353
0.1%
2 408
0.1%
3 396
0.1%
4 409
0.1%
5 367
0.1%
6 411
0.1%
7 375
0.1%
8 465
0.1%
9 450
0.1%
10 373
0.1%
ValueCountFrequency (%)
227968 23
< 0.1%
224643 23
< 0.1%
217853 24
< 0.1%
214514 17
< 0.1%
213536 25
< 0.1%
211593 17
< 0.1%
210434 19
< 0.1%
205348 17
< 0.1%
200603 16
< 0.1%
198527 20
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

Distinct10035
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11122.475
Minimum1
Maximum369838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:21.363859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1547
median2401
Q39151
95-th percentile52146
Maximum369838
Range369837
Interquartile range (IQR)8604

Descriptive statistics

Standard deviation26617.827
Coefficient of variation (CV)2.3931568
Kurtosis37.888792
Mean11122.475
Median Absolute Deviation (MAD)2200
Skewness5.3149377
Sum3.5472465 × 109
Variance7.0850869 × 108
MonotonicityNot monotonic
2023-01-25T14:27:21.502024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 367
 
0.1%
23 351
 
0.1%
17 348
 
0.1%
13 346
 
0.1%
11 342
 
0.1%
40 341
 
0.1%
25 341
 
0.1%
24 340
 
0.1%
52 339
 
0.1%
5 338
 
0.1%
Other values (10025) 315473
98.9%
ValueCountFrequency (%)
1 280
0.1%
2 312
0.1%
3 325
0.1%
4 308
0.1%
5 338
0.1%
6 334
0.1%
7 367
0.1%
8 266
0.1%
9 255
0.1%
10 309
0.1%
ValueCountFrequency (%)
369838 23
< 0.1%
352154 23
< 0.1%
348523 25
< 0.1%
343082 24
< 0.1%
341692 19
< 0.1%
323791 20
< 0.1%
315783 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%
289045 16
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct296
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean673592.38
Minimum1610
Maximum1487640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:21.653024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1610
5-th percentile42576
Q1288775
median746967
Q31026558
95-th percentile1340822
Maximum1487640
Range1486030
Interquartile range (IQR)737783

Descriptive statistics

Standard deviation411429.82
Coefficient of variation (CV)0.6107994
Kurtosis-1.0984928
Mean673592.38
Median Absolute Deviation (MAD)314315
Skewness0.0040716922
Sum2.1482612 × 1011
Variance1.6927449 × 1011
MonotonicityNot monotonic
2023-01-25T14:27:21.794802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880941 5102
 
1.6%
874118 4354
 
1.4%
843980 4347
 
1.4%
894334 4333
 
1.4%
880496 4273
 
1.3%
897718 4212
 
1.3%
764865 4088
 
1.3%
784849 4002
 
1.3%
1081338 3890
 
1.2%
1100486 3866
 
1.2%
Other values (286) 276459
86.7%
ValueCountFrequency (%)
1610 130
 
< 0.1%
1702 138
 
< 0.1%
1716 131
 
< 0.1%
1920 131
 
< 0.1%
2260 183
 
0.1%
2495 173
 
0.1%
6806 380
0.1%
9635 74
 
< 0.1%
14465 384
0.1%
14969 481
0.2%
ValueCountFrequency (%)
1487640 2975
0.9%
1450407 3048
1.0%
1421741 3564
1.1%
1344533 3543
1.1%
1340822 3441
1.1%
1332449 3545
1.1%
1316660 3463
1.1%
1282958 3576
1.1%
1265248 1177
 
0.4%
1262542 1201
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct297
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1085881.9
Minimum1861
Maximum2666060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:21.945290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1861
5-th percentile47348
Q1491301
median1106901
Q31756673
95-th percentile2549839
Maximum2666060
Range2664199
Interquartile range (IQR)1265372

Descriptive statistics

Standard deviation737059.47
Coefficient of variation (CV)0.67876576
Kurtosis-0.77451377
Mean1085881.9
Median Absolute Deviation (MAD)621238
Skewness0.36928889
Sum3.4631598 × 1011
Variance5.4325666 × 1011
MonotonicityNot monotonic
2023-01-25T14:27:22.094190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211791 5102
 
1.6%
1281518 4354
 
1.4%
1216262 4347
 
1.4%
1315601 4333
 
1.4%
1300468 4273
 
1.3%
1341881 4212
 
1.3%
1155430 4088
 
1.3%
1171476 4002
 
1.3%
2549839 3890
 
1.2%
2666060 3866
 
1.2%
Other values (287) 276459
86.7%
ValueCountFrequency (%)
1861 130
 
< 0.1%
1961 138
 
< 0.1%
1981 131
 
< 0.1%
2195 131
 
< 0.1%
2816 173
 
0.1%
3012 183
 
0.1%
7385 380
0.1%
10290 74
 
< 0.1%
16683 384
0.1%
17333 481
0.2%
ValueCountFrequency (%)
2666060 3866
1.2%
2620465 3787
1.2%
2599704 3788
1.2%
2595180 3844
1.2%
2549839 3890
1.2%
2481560 3851
1.2%
2179643 3757
1.2%
2062159 3810
1.2%
2052304 1168
 
0.4%
1990242 1167
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Distinct3519
Distinct (%)1.3%
Missing48866
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean3563.1378
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-01-25T14:27:22.239800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1475
median1250
Q34145
95-th percentile13455
Maximum287220
Range287150
Interquartile range (IQR)3670

Descriptive statistics

Standard deviation6512.3789
Coefficient of variation (CV)1.827709
Kurtosis148.50978
Mean3563.1378
Median Absolute Deviation (MAD)1020
Skewness7.2550817
Sum9.62261 × 108
Variance42411079
MonotonicityNot monotonic
2023-01-25T14:27:22.376340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2003
 
0.6%
110 1874
 
0.6%
105 1839
 
0.6%
180 1578
 
0.5%
185 1481
 
0.5%
145 1380
 
0.4%
300 1373
 
0.4%
175 1373
 
0.4%
120 1335
 
0.4%
125 1247
 
0.4%
Other values (3509) 254577
79.8%
(Missing) 48866
 
15.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 689
 
0.2%
95 695
 
0.2%
100 923
0.3%
105 1839
0.6%
110 1874
0.6%
115 1018
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
96880 5
< 0.1%

Interactions

2023-01-25T14:27:16.202418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:27:05.430384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:27:06.799684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:27:08.209285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-25T14:27:09.496282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/