Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations946701
Missing cells187361
Missing cells (%)2.0%
Duplicate rows11070
Duplicate rows (%)1.2%
Total size in memory72.2 MiB
Average record size in memory80.0 B

Variable types

DateTime1
Text5
Categorical4

Alerts

Dataset has 11070 (1.2%) duplicate rowsDuplicates
FIRMA is highly overall correlated with OSOBAHigh correlation
OSOBA is highly overall correlated with FIRMAHigh correlation
OZNAM is highly imbalanced (58.2%) Imbalance
PRAVFOR is highly imbalanced (69.2%) Imbalance
CASSK has 43104 (4.6%) missing values Missing
MPZ has 23167 (2.4%) missing values Missing
TOVZN has 117380 (12.4%) missing values Missing

Reproduction

Analysis started2024-11-11 11:52:48.600615
Analysis finished2024-11-11 11:53:34.323800
Duration45.72 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

DATSK
Date

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-11-11T12:53:34.686471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:53:34.870931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CASSK
Text

Missing 

Distinct82048
Distinct (%)9.1%
Missing43104
Missing (%)4.6%
Memory size7.2 MiB
2024-11-11T12:53:35.252595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length6.1128468
Min length4

Characters and Unicode

Total characters5523550
Distinct characters13
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

Unique9839 ?
Unique (%)1.1%

Sample

1st row13:35:52
2nd row16:39:45
3rd row16:47:27
4th row15:05:02
5th row21:33:53
ValueCountFrequency (%)
09:30 1841
 
0.2%
09:15 1785
 
0.2%
09:10 1758
 
0.2%
09:20 1748
 
0.2%
09:25 1710
 
0.2%
09:35 1628
 
0.2%
09:40 1612
 
0.2%
08:20 1568
 
0.2%
08:30 1560
 
0.2%
09:05 1520
 
0.2%
Other values (82038) 886867
98.1%
2024-11-11T12:53:36.018807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 1238784
22.4%
1 891252
16.1%
0 794305
14.4%
2 569682
10.3%
3 428427
 
7.8%
4 392477
 
7.1%
5 386024
 
7.0%
9 250795
 
4.5%
8 227086
 
4.1%
7 179390
 
3.2%
Other values (3) 165328
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5523550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 1238784
22.4%
1 891252
16.1%
0 794305
14.4%
2 569682
10.3%
3 428427
 
7.8%
4 392477
 
7.1%
5 386024
 
7.0%
9 250795
 
4.5%
8 227086
 
4.1%
7 179390
 
3.2%
Other values (3) 165328
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5523550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 1238784
22.4%
1 891252
16.1%
0 794305
14.4%
2 569682
10.3%
3 428427
 
7.8%
4 392477
 
7.1%
5 386024
 
7.0%
9 250795
 
4.5%
8 227086
 
4.1%
7 179390
 
3.2%
Other values (3) 165328
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5523550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 1238784
22.4%
1 891252
16.1%
0 794305
14.4%
2 569682
10.3%
3 428427
 
7.8%
4 392477
 
7.1%
5 386024
 
7.0%
9 250795
 
4.5%
8 227086
 
4.1%
7 179390
 
3.2%
Other values (3) 165328
 
3.0%

MISTOSK
Text

Distinct100601
Distinct (%)10.6%
Missing1958
Missing (%)0.2%
Memory size7.2 MiB
2024-11-11T12:53:36.354620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length145
Median length108
Mean length29.346593
Min length1

Characters and Unicode

Total characters27724988
Distinct characters129
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

Unique50540 ?
Unique (%)5.3%

Sample

1st rowMichalská 12 Praha 1 u domu
2nd rowMichalská 16 Praha 1 u domu
3rd rowVeleslavínova 2a Praha 1 u domu
4th rowMaltézské náměstí 15 Praha 1 u domu
5th rowJungmannova 18 Praha 1 u domu
ValueCountFrequency (%)
domu 414038
 
8.8%
směr 400707
 
8.5%
u 392076
 
8.3%
132474
 
2.8%
tunel 129287
 
2.7%
jižní 113603
 
2.4%
spojka 113491
 
2.4%
centrum 90417
 
1.9%
ul 85363
 
1.8%
čís.sloupu.vo 74472
 
1.6%
Other values (27818) 2756836
58.6%
2024-11-11T12:53:37.069625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4082151
 
14.7%
o 2126263
 
7.7%
u 1565013
 
5.6%
r 1302166
 
4.7%
s 1277610
 
4.6%
m 1213056
 
4.4%
a 1130592
 
4.1%
k 989092
 
3.6%
n 879515
 
3.2%
v 858994
 
3.1%
Other values (119) 12300536
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27724988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4082151
 
14.7%
o 2126263
 
7.7%
u 1565013
 
5.6%
r 1302166
 
4.7%
s 1277610
 
4.6%
m 1213056
 
4.4%
a 1130592
 
4.1%
k 989092
 
3.6%
n 879515
 
3.2%
v 858994
 
3.1%
Other values (119) 12300536
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27724988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4082151
 
14.7%
o 2126263
 
7.7%
u 1565013
 
5.6%
r 1302166
 
4.7%
s 1277610
 
4.6%
m 1213056
 
4.4%
a 1130592
 
4.1%
k 989092
 
3.6%
n 879515
 
3.2%
v 858994
 
3.1%
Other values (119) 12300536
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27724988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4082151
 
14.7%
o 2126263
 
7.7%
u 1565013
 
5.6%
r 1302166
 
4.7%
s 1277610
 
4.6%
m 1213056
 
4.4%
a 1130592
 
4.1%
k 989092
 
3.6%
n 879515
 
3.2%
v 858994
 
3.1%
Other values (119) 12300536
44.4%

PRAHA
Text

Distinct59
Distinct (%)< 0.1%
Missing1752
Missing (%)0.2%
Memory size7.2 MiB
2024-11-11T12:53:37.228514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length21
Median length7
Mean length7.1815357
Min length7

Characters and Unicode

Total characters6786185
Distinct characters59
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPraha 1
2nd rowPraha 1
3rd rowPraha 1
4th rowPraha 1
5th rowPraha 1
ValueCountFrequency (%)
praha 942768
49.9%
4 207400
 
11.0%
6 167246
 
8.9%
5 116610
 
6.2%
1 88761
 
4.7%
9 78600
 
4.2%
10 78436
 
4.2%
12 42648
 
2.3%
2 35911
 
1.9%
7 35028
 
1.9%
Other values (52) 94772
 
5.0%
2024-11-11T12:53:37.491898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1752744
25.8%
P 945213
13.9%
943231
13.9%
r 876853
12.9%
h 876239
12.9%
1 247079
 
3.6%
4 208999
 
3.1%
6 195492
 
2.9%
A 138024
 
2.0%
5 117815
 
1.7%
Other values (49) 484496
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6786185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1752744
25.8%
P 945213
13.9%
943231
13.9%
r 876853
12.9%
h 876239
12.9%
1 247079
 
3.6%
4 208999
 
3.1%
6 195492
 
2.9%
A 138024
 
2.0%
5 117815
 
1.7%
Other values (49) 484496
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6786185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1752744
25.8%
P 945213
13.9%
943231
13.9%
r 876853
12.9%
h 876239
12.9%
1 247079
 
3.6%
4 208999
 
3.1%
6 195492
 
2.9%
A 138024
 
2.0%
5 117815
 
1.7%
Other values (49) 484496
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6786185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1752744
25.8%
P 945213
13.9%
943231
13.9%
r 876853
12.9%
h 876239
12.9%
1 247079
 
3.6%
4 208999
 
3.1%
6 195492
 
2.9%
A 138024
 
2.0%
5 117815
 
1.7%
Other values (49) 484496
 
7.1%

OZNAM
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
MPP
866709 
PČR
 
79992

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2840103
Distinct characters4
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 rowMPP
2nd rowMPP
3rd rowMPP
4th rowMPP
5th rowMPP

Common Values

ValueCountFrequency (%)
MPP 866709
91.6%
PČR 79992
 
8.4%

Length

2024-11-11T12:53:37.619860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:37.786599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
mpp 866709
91.6%
pčr 79992
 
8.4%

Most occurring characters

ValueCountFrequency (%)
P 1813410
63.9%
M 866709
30.5%
Č 79992
 
2.8%
R 79992
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2840103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1813410
63.9%
M 866709
30.5%
Č 79992
 
2.8%
R 79992
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2840103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1813410
63.9%
M 866709
30.5%
Č 79992
 
2.8%
R 79992
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2840103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1813410
63.9%
M 866709
30.5%
Č 79992
 
2.8%
R 79992
 
2.8%

MPZ
Text

Missing 

Distinct80
Distinct (%)< 0.1%
Missing23167
Missing (%)2.4%
Memory size7.2 MiB
2024-11-11T12:53:38.082611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.9690612
Min length1

Characters and Unicode

Total characters1818495
Distinct characters31
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

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowCZ
2nd rowCZ
3rd rowD
4th rowCZ
5th rowCZ
ValueCountFrequency (%)
cz 795967
86.2%
ua 44304
 
4.8%
d 22696
 
2.5%
sk 18821
 
2.0%
pl 13874
 
1.5%
ro 9026
 
1.0%
md 2996
 
0.3%
a 2091
 
0.2%
bg 1897
 
0.2%
f 1646
 
0.2%
Other values (68) 10216
 
1.1%
2024-11-11T12:53:38.328867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 796447
43.8%
Z 795994
43.8%
A 46616
 
2.6%
U 44602
 
2.5%
D 26045
 
1.4%
S 20759
 
1.1%
K 19268
 
1.1%
L 15703
 
0.9%
P 13915
 
0.8%
R 11172
 
0.6%
Other values (21) 27974
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1818495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 796447
43.8%
Z 795994
43.8%
A 46616
 
2.6%
U 44602
 
2.5%
D 26045
 
1.4%
S 20759
 
1.1%
K 19268
 
1.1%
L 15703
 
0.9%
P 13915
 
0.8%
R 11172
 
0.6%
Other values (21) 27974
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1818495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 796447
43.8%
Z 795994
43.8%
A 46616
 
2.6%
U 44602
 
2.5%
D 26045
 
1.4%
S 20759
 
1.1%
K 19268
 
1.1%
L 15703
 
0.9%
P 13915
 
0.8%
R 11172
 
0.6%
Other values (21) 27974
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1818495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 796447
43.8%
Z 795994
43.8%
A 46616
 
2.6%
U 44602
 
2.5%
D 26045
 
1.4%
S 20759
 
1.1%
K 19268
 
1.1%
L 15703
 
0.9%
P 13915
 
0.8%
R 11172
 
0.6%
Other values (21) 27974
 
1.5%

TOVZN
Text

Missing 

Distinct4026
Distinct (%)0.5%
Missing117380
Missing (%)12.4%
Memory size7.2 MiB
2024-11-11T12:53:38.667554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length46
Median length42
Mean length6.4111412
Min length2

Characters and Unicode

Total characters5316894
Distinct characters101
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

Unique2262 ?
Unique (%)0.3%

Sample

1st rowAudi
2nd rowVolkswagen
3rd rowPeugeot
4th rowŠkoda
5th rowNeuvedeno
ValueCountFrequency (%)
škoda 204375
23.3%
volkswagen 105467
12.0%
mercedes 67989
 
7.8%
bmw 56409
 
6.4%
ford 48782
 
5.6%
neuvedeno 39083
 
4.5%
audi 38497
 
4.4%
hyundai 26550
 
3.0%
toyota 26366
 
3.0%
renault 24173
 
2.8%
Other values (3931) 238082
27.2%
2024-11-11T12:53:39.442533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 552584
 
10.4%
o 552263
 
10.4%
a 464773
 
8.7%
d 440668
 
8.3%
k 308568
 
5.8%
n 231978
 
4.4%
Š 204376
 
3.8%
s 201487
 
3.8%
l 173724
 
3.3%
u 171758
 
3.2%
Other values (91) 2014715
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5316894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 552584
 
10.4%
o 552263
 
10.4%
a 464773
 
8.7%
d 440668
 
8.3%
k 308568
 
5.8%
n 231978
 
4.4%
Š 204376
 
3.8%
s 201487
 
3.8%
l 173724
 
3.3%
u 171758
 
3.2%
Other values (91) 2014715
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5316894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 552584
 
10.4%
o 552263
 
10.4%
a 464773
 
8.7%
d 440668
 
8.3%
k 308568
 
5.8%
n 231978
 
4.4%
Š 204376
 
3.8%
s 201487
 
3.8%
l 173724
 
3.3%
u 171758
 
3.2%
Other values (91) 2014715
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5316894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 552584
 
10.4%
o 552263
 
10.4%
a 464773
 
8.7%
d 440668
 
8.3%
k 308568
 
5.8%
n 231978
 
4.4%
Š 204376
 
3.8%
s 201487
 
3.8%
l 173724
 
3.3%
u 171758
 
3.2%
Other values (91) 2014715
37.9%

OSOBA
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
NE
554040 
ANO
392661 

Length

Max length3
Median length2
Mean length2.4147677
Min length2

Characters and Unicode

Total characters2286063
Distinct characters4
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 rowNE
2nd rowNE
3rd rowANO
4th rowANO
5th rowANO

Common Values

ValueCountFrequency (%)
NE 554040
58.5%
ANO 392661
41.5%

Length

2024-11-11T12:53:39.593886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:39.678939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ne 554040
58.5%
ano 392661
41.5%

Most occurring characters

ValueCountFrequency (%)
N 946701
41.4%
E 554040
24.2%
A 392661
17.2%
O 392661
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2286063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 946701
41.4%
E 554040
24.2%
A 392661
17.2%
O 392661
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2286063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 946701
41.4%
E 554040
24.2%
A 392661
17.2%
O 392661
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2286063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 946701
41.4%
E 554040
24.2%
A 392661
17.2%
O 392661
17.2%

FIRMA
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
NE
545626 
ANO
401075 

Length

Max length3
Median length2
Mean length2.4236554
Min length2

Characters and Unicode

Total characters2294477
Distinct characters4
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 rowANO
2nd rowANO
3rd rowNE
4th rowNE
5th rowNE

Common Values

ValueCountFrequency (%)
NE 545626
57.6%
ANO 401075
42.4%

Length

2024-11-11T12:53:39.769717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:39.857593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ne 545626
57.6%
ano 401075
42.4%

Most occurring characters

ValueCountFrequency (%)
N 946701
41.3%
E 545626
23.8%
A 401075
17.5%
O 401075
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2294477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 946701
41.3%
E 545626
23.8%
A 401075
17.5%
O 401075
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2294477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 946701
41.3%
E 545626
23.8%
A 401075
17.5%
O 401075
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2294477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 946701
41.3%
E 545626
23.8%
A 401075
17.5%
O 401075
17.5%

PRAVFOR
Categorical

Imbalance 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
§ 125c odst. 1 písm. k) zákona o silničním provozu
485070 
§ 125c odst. 1 písm. f) bod 4 zákona o silničním provozu
325469 
§ 125c odst. 1 písm. f) bod 3 zákona o silničním provozu
70921 
§ 125c/1k) zákona o silničním provozu
 
34251
§ 125c odst. 1 písm. f) bod 5 zákona o silničním provozu
 
13185
Other values (44)
 
17805

Length

Max length58
Median length50
Mean length52.106048
Min length20

Characters and Unicode

Total characters49328848
Distinct characters44
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row§ 125c/1k) zákona o silničním provozu
2nd row§ 125c/1k) zákona o silničním provozu
3rd row§ 125c/1k) zákona o silničním provozu
4th row§ 125c/1k) zákona o silničním provozu
5th row§ 125c/1k) zákona o silničním provozu

Common Values

ValueCountFrequency (%)
§ 125c odst. 1 písm. k) zákona o silničním provozu 485070
51.2%
§ 125c odst. 1 písm. f) bod 4 zákona o silničním provozu 325469
34.4%
§ 125c odst. 1 písm. f) bod 3 zákona o silničním provozu 70921
 
7.5%
§ 125c/1k) zákona o silničním provozu 34251
 
3.6%
§ 125c odst. 1 písm. f) bod 5 zákona o silničním provozu 13185
 
1.4%
§ 125c odst. 1 písm. f) bod 11 zákona o silničním provozu 8952
 
0.9%
§ 125c/1b) zákona o silničním provozu 1638
 
0.2%
§ 125c/1e) bod 1 zákona o silničním provozu 1285
 
0.1%
§ 125c/1d) zákona o silničním provozu 1100
 
0.1%
§ 125c/1f) bod 3 zákona o silničním provozu 788
 
0.1%
Other values (39) 4042
 
0.4%

Length

2024-11-11T12:53:40.242265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
§ 946701
9.3%
zákona 946687
9.3%
o 946687
9.3%
silničním 946687
9.3%
provozu 946687
9.3%
1 905429
8.9%
125c 903597
8.9%
odst 903597
8.9%
písm 903597
8.9%
k 485070
 
4.8%
Other values (41) 1307981
12.9%

Most occurring characters

ValueCountFrequency (%)
9196032
18.6%
o 5114409
 
10.4%
n 2840062
 
5.8%
s 2754529
 
5.6%
1 1913642
 
3.9%
i 1893647
 
3.8%
z 1893507
 
3.8%
í 1850285
 
3.8%
p 1850285
 
3.8%
m 1850284
 
3.8%
Other values (34) 18172166
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49328848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9196032
18.6%
o 5114409
 
10.4%
n 2840062
 
5.8%
s 2754529
 
5.6%
1 1913642
 
3.9%
i 1893647
 
3.8%
z 1893507
 
3.8%
í 1850285
 
3.8%
p 1850285
 
3.8%
m 1850284
 
3.8%
Other values (34) 18172166
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49328848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9196032
18.6%
o 5114409
 
10.4%
n 2840062
 
5.8%
s 2754529
 
5.6%
1 1913642
 
3.9%
i 1893647
 
3.8%
z 1893507
 
3.8%
í 1850285
 
3.8%
p 1850285
 
3.8%
m 1850284
 
3.8%
Other values (34) 18172166
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49328848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9196032
18.6%
o 5114409
 
10.4%
n 2840062
 
5.8%
s 2754529
 
5.6%
1 1913642
 
3.9%
i 1893647
 
3.8%
z 1893507
 
3.8%
í 1850285
 
3.8%
p 1850285
 
3.8%
m 1850284
 
3.8%
Other values (34) 18172166
36.8%

Correlations

2024-11-11T12:53:40.328978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
FIRMAOSOBAOZNAMPRAVFOR
FIRMA1.0000.7220.1140.099
OSOBA0.7221.0000.0970.277
OZNAM0.1140.0971.0000.422
PRAVFOR0.0990.2770.4221.000

Missing values

2024-11-11T12:53:29.740653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-11T12:53:31.019757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATSKCASSKMISTOSKPRAHAOZNAMMPZTOVZNOSOBAFIRMAPRAVFOR
02023-02-18NaNMichalská 12 Praha 1 u domuPraha 1MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu
12023-02-18NaNMichalská 16 Praha 1 u domuPraha 1MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu
22023-01-21NaNVeleslavínova 2a Praha 1 u domuPraha 1MPPDNaNANONE§ 125c/1k) zákona o silničním provozu
32023-01-24NaNMaltézské náměstí 15 Praha 1 u domuPraha 1MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu
42023-02-21NaNJungmannova 18 Praha 1 u domuPraha 1MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu
52023-02-15NaNU lužického semináře 104/28 Praha 1 u domuPraha 1MPPCZNaNANONE§ 125c/1f) bod 11 zákona o silničním provozu
62023-02-18NaNMichalská 12 Praha 1 u domuPraha 1MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu
72023-02-15NaNMalostranské náměstí 13 Praha 1 u domuPraha 1MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu
82023-01-31NaNJungmannovo náměstí Praha 1 u domu kruhový objezdPraha 1MPPCZNaNANONE§ 125c/1f) bod 8 zákona o silničním provozu
92023-02-12NaNVeleslavínova 2a Praha 1 u domuPraha 1MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu
DATSKCASSKMISTOSKPRAHAOZNAMMPZTOVZNOSOBAFIRMAPRAVFOR
9466912023-04-16NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466922023-06-11NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNANONE§ 125f odst.1 zákona o silničním provozu
9466932023-06-12NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466942023-02-26NaNul. Lipská (v blízkosti sloupu VO č. 606803 - již mimo obec Praha), ve směru ke Slanýmimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466952023-03-11NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNANONE§ 125f odst.1 zákona o silničním provozu
9466962023-07-01NaNul. Lipské (v blízkosti sloupu VO č. 606803) - již mimo obec Praha, ve směru Slanýmimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466972023-03-09NaNul. Lipské (v blízkosti sloupu VO č. 606800 - již mimo obec Praha, ve směru k Pražskému okruhu)mimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466982023-01-23NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNNEANO§ 125f odst.1 zákona o silničním provozu
9466992023-02-09NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNANONE§ 125f odst.1 zákona o silničním provozu
9467002023-02-15NaNul. Lipské (v blízkosti sloupu VO č. 606803 - již mimo obec Praha, ve směru ke Slanému)mimo PrahuMPPCZNaNANONE§ 125f odst.1 zákona o silničním provozu

Duplicate rows

Most frequently occurring

DATSKCASSKMISTOSKPRAHAOZNAMMPZTOVZNOSOBAFIRMAPRAVFOR# duplicates
37992023-04-23NaNŠvehlova 30 Praha 10 u domuPraha 15MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu38
44502023-05-10NaNŠvehlova 30 Praha 10 u domuPraha 15MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu27
93682023-10-29NaNnáměstí Míru 7 Praha 2 naproti domu u kostelaPraha 2MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu27
47402023-05-17NaNŠrobárova 48 Praha 10 u domuPraha 10MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu23
50922023-05-27NaNHolešovické nábřeží Praha 7 čís.sloupu.vo 703700Praha 7MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu23
73592023-08-31NaNKostelní 44 Praha 7 u domuPraha 7MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu21
77562023-09-14NaNŠrobárova 50 Praha 10 u domuPraha 10MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu20
90992023-10-21NaNnáměstí Míru 7 Praha 2 naproti domu u kostelaPraha 2MPPCZNaNNEANO§ 125c/1k) zákona o silničním provozu19
42692023-05-06NaNK letišti 1068/30 Praha 6 u domu letiště Václava Havla Praha, odstavná plocha SmartwingsPraha 6MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu18
47882023-05-18NaNŠvehlova 30 Praha 10 u domuPraha 15MPPCZNaNANONE§ 125c/1k) zákona o silničním provozu18