Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations1074
Missing cells1292
Missing cells (%)8.0%
Duplicate rows3
Duplicate rows (%)0.3%
Total size in memory126.0 KiB
Average record size in memory120.1 B

Variable types

Text5
Numeric2
Categorical8

Alerts

Dataset has 3 (0.3%) duplicate rowsDuplicates
cislo_domovní is highly overall correlated with katastralni_uzemi and 4 other fieldsHigh correlation
cislo_orientacni is highly overall correlated with psc and 2 other fieldsHigh correlation
katastralni_uzemi is highly overall correlated with cislo_domovní and 7 other fieldsHigh correlation
nazev_obce is highly overall correlated with katastralni_uzemi and 3 other fieldsHigh correlation
nazev_obvod is highly overall correlated with cislo_domovní and 7 other fieldsHigh correlation
psc is highly overall correlated with cislo_domovní and 8 other fieldsHigh correlation
spravce_kontaktni_URL is highly overall correlated with katastralni_uzemi and 5 other fieldsHigh correlation
spravce_nazev is highly overall correlated with katastralni_uzemi and 5 other fieldsHigh correlation
ucel_popis is highly overall correlated with cislo_domovní and 6 other fieldsHigh correlation
znak_cisla_orientacniho is highly overall correlated with cislo_domovní and 7 other fieldsHigh correlation
nazev_obce is highly imbalanced (82.6%) Imbalance
cislo_domovní has 20 (1.9%) missing values Missing
cislo_orientacni has 194 (18.1%) missing values Missing
znak_cisla_orientacniho has 1041 (96.9%) missing values Missing
vymera_m2 has 14 (1.3%) missing values Missing

Reproduction

Analysis started2024-11-11 11:53:41.418089
Analysis finished2024-11-11 11:53:42.993709
Duration1.58 second
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

nazev_ulice
Text

Distinct114
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-11-11T12:53:43.496864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length10.391061
Min length6

Characters and Unicode

Total characters11160
Distinct characters63
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

Unique45 ?
Unique (%)4.2%

Sample

1st rowArn. Valenty
2nd rowArn. Valenty
3rd rowArn. Valenty
4th rowArn.Valenty
5th rowArn.Valenty
ValueCountFrequency (%)
k 130
 
9.3%
bělohorská 105
 
7.5%
říčanská 90
 
6.4%
vaníčkova 69
 
4.9%
rytířská 53
 
3.8%
zátiší 49
 
3.5%
osinalická 37
 
2.6%
kinského 35
 
2.5%
zahrada 35
 
2.5%
verneráku 30
 
2.1%
Other values (117) 768
54.8%
2024-11-11T12:53:43.902355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 923
 
8.3%
o 910
 
8.2%
k 738
 
6.6%
n 687
 
6.2%
á 648
 
5.8%
618
 
5.5%
s 523
 
4.7%
r 517
 
4.6%
v 469
 
4.2%
í 358
 
3.2%
Other values (53) 4769
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 923
 
8.3%
o 910
 
8.2%
k 738
 
6.6%
n 687
 
6.2%
á 648
 
5.8%
618
 
5.5%
s 523
 
4.7%
r 517
 
4.6%
v 469
 
4.2%
í 358
 
3.2%
Other values (53) 4769
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 923
 
8.3%
o 910
 
8.2%
k 738
 
6.6%
n 687
 
6.2%
á 648
 
5.8%
618
 
5.5%
s 523
 
4.7%
r 517
 
4.6%
v 469
 
4.2%
í 358
 
3.2%
Other values (53) 4769
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 923
 
8.3%
o 910
 
8.2%
k 738
 
6.6%
n 687
 
6.2%
á 648
 
5.8%
618
 
5.5%
s 523
 
4.7%
r 517
 
4.6%
v 469
 
4.2%
í 358
 
3.2%
Other values (53) 4769
42.7%

cislo_domovní
Real number (ℝ)

High correlation  Missing 

Distinct141
Distinct (%)13.4%
Missing20
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean912.73529
Minimum1
Maximum3406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-11T12:53:44.068591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile97
Q1182
median720
Q31255
95-th percentile2526
Maximum3406
Range3405
Interquartile range (IQR)1073

Descriptive statistics

Standard deviation823.7999
Coefficient of variation (CV)0.90256168
Kurtosis0.64478903
Mean912.73529
Median Absolute Deviation (MAD)535
Skewness1.1846904
Sum962023
Variance678646.28
MonotonicityNot monotonic
2024-11-11T12:53:44.467067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 105
 
9.8%
1752 90
 
8.4%
100 89
 
8.3%
3131 49
 
4.6%
398 41
 
3.8%
901 37
 
3.4%
97 35
 
3.3%
2342 30
 
2.8%
669 29
 
2.7%
912 27
 
2.5%
Other values (131) 522
48.6%
ValueCountFrequency (%)
1 20
 
1.9%
27 1
 
0.1%
34 1
 
0.1%
36 10
 
0.9%
44 2
 
0.2%
69 1
 
0.1%
83 2
 
0.2%
97 35
 
3.3%
100 89
8.3%
132 7
 
0.7%
ValueCountFrequency (%)
3406 1
 
0.1%
3131 49
4.6%
2526 24
2.2%
2343 1
 
0.1%
2342 30
2.8%
2341 4
 
0.4%
2311 1
 
0.1%
2310 4
 
0.4%
2306 1
 
0.1%
2286 2
 
0.2%

cislo_orientacni
Real number (ℝ)

High correlation  Missing 

Distinct43
Distinct (%)4.9%
Missing194
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean32.228409
Minimum0
Maximum171
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-11-11T12:53:44.606497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q330
95-th percentile171
Maximum171
Range171
Interquartile range (IQR)27

Descriptive statistics

Standard deviation53.25162
Coefficient of variation (CV)1.6523192
Kurtosis2.6192647
Mean32.228409
Median Absolute Deviation (MAD)7
Skewness2.0561904
Sum28361
Variance2835.735
MonotonicityNot monotonic
2024-11-11T12:53:44.735019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 117
10.9%
6 110
10.2%
171 105
9.8%
2 61
 
5.7%
3 51
 
4.7%
26 45
 
4.2%
4 40
 
3.7%
8 39
 
3.6%
30 37
 
3.4%
7 33
 
3.1%
Other values (33) 242
22.5%
(Missing) 194
18.1%
ValueCountFrequency (%)
0 3
 
0.3%
1 117
10.9%
2 61
5.7%
3 51
4.7%
4 40
 
3.7%
5 11
 
1.0%
6 110
10.2%
7 33
 
3.1%
8 39
 
3.6%
9 32
 
3.0%
ValueCountFrequency (%)
171 105
9.8%
117 1
 
0.1%
111 4
 
0.4%
110 1
 
0.1%
71 1
 
0.1%
65 4
 
0.4%
58 1
 
0.1%
57 1
 
0.1%
54 1
 
0.1%
48 12
 
1.1%

znak_cisla_orientacniho
Categorical

High correlation  Missing 

Distinct3
Distinct (%)9.1%
Missing1041
Missing (%)96.9%
Memory size8.5 KiB
a
15 
11 
b
7 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa
2nd rowa
3rd rowa
4th rowb
5th rowb

Common Values

ValueCountFrequency (%)
a 15
 
1.4%
11
 
1.0%
b 7
 
0.7%
(Missing) 1041
96.9%

Length

2024-11-11T12:53:44.848820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:44.934086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
a 15
68.2%
b 7
31.8%

Most occurring characters

ValueCountFrequency (%)
a 15
45.5%
11
33.3%
b 7
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15
45.5%
11
33.3%
b 7
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15
45.5%
11
33.3%
b 7
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15
45.5%
11
33.3%
b 7
21.2%

psc
Categorical

High correlation 

Distinct42
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
16900
200 
19800
168 
11000
133 
18200
94 
10100
90 
Other values (37)
389 

Length

Max length8
Median length5
Mean length5.0223464
Min length4

Characters and Unicode

Total characters5394
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

Unique11 ?
Unique (%)1.0%

Sample

1st row19800
2nd row19800
3rd row19800
4th row19800
5th row19800

Common Values

ValueCountFrequency (%)
16900 200
18.6%
19800 168
15.6%
11000 133
12.4%
18200 94
8.8%
10100 90
8.4%
46401 49
 
4.6%
14800 49
 
4.6%
15000 37
 
3.4%
19600 36
 
3.4%
15800 26
 
2.4%
Other values (32) 192
17.9%

Length

2024-11-11T12:53:45.054040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16900 200
18.3%
19800 168
15.3%
11000 133
12.1%
18200 94
8.6%
10100 90
8.2%
46401 49
 
4.5%
14800 49
 
4.5%
15000 37
 
3.4%
19600 36
 
3.3%
15800 26
 
2.4%
Other values (32) 213
19.5%

Most occurring characters

ValueCountFrequency (%)
0 2449
45.4%
1 1299
24.1%
9 427
 
7.9%
8 394
 
7.3%
6 321
 
6.0%
4 185
 
3.4%
2 147
 
2.7%
5 100
 
1.9%
7 27
 
0.5%
25
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2449
45.4%
1 1299
24.1%
9 427
 
7.9%
8 394
 
7.3%
6 321
 
6.0%
4 185
 
3.4%
2 147
 
2.7%
5 100
 
1.9%
7 27
 
0.5%
25
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2449
45.4%
1 1299
24.1%
9 427
 
7.9%
8 394
 
7.3%
6 321
 
6.0%
4 185
 
3.4%
2 147
 
2.7%
5 100
 
1.9%
7 27
 
0.5%
25
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2449
45.4%
1 1299
24.1%
9 427
 
7.9%
8 394
 
7.3%
6 321
 
6.0%
4 185
 
3.4%
2 147
 
2.7%
5 100
 
1.9%
7 27
 
0.5%
25
 
0.5%

nazev_obce
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Praha
1008 
Frýdlant
 
49
Praha
 
10
Káraný
 
5
Bořkovice
 
2

Length

Max length9
Median length5
Mean length5.1582868
Min length5

Characters and Unicode

Total characters5540
Distinct characters21
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 rowPraha
2nd rowPraha
3rd rowPraha
4th rowPraha
5th rowPraha

Common Values

ValueCountFrequency (%)
Praha 1008
93.9%
Frýdlant 49
 
4.6%
Praha 10
 
0.9%
Káraný 5
 
0.5%
Bořkovice 2
 
0.2%

Length

2024-11-11T12:53:45.177546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:45.448423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
praha 1018
94.8%
frýdlant 49
 
4.6%
káraný 5
 
0.5%
bořkovice 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 2090
37.7%
r 1072
19.4%
P 1018
18.4%
h 1018
18.4%
ý 54
 
1.0%
n 54
 
1.0%
F 49
 
0.9%
d 49
 
0.9%
l 49
 
0.9%
t 49
 
0.9%
Other values (11) 38
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2090
37.7%
r 1072
19.4%
P 1018
18.4%
h 1018
18.4%
ý 54
 
1.0%
n 54
 
1.0%
F 49
 
0.9%
d 49
 
0.9%
l 49
 
0.9%
t 49
 
0.9%
Other values (11) 38
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2090
37.7%
r 1072
19.4%
P 1018
18.4%
h 1018
18.4%
ý 54
 
1.0%
n 54
 
1.0%
F 49
 
0.9%
d 49
 
0.9%
l 49
 
0.9%
t 49
 
0.9%
Other values (11) 38
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2090
37.7%
r 1072
19.4%
P 1018
18.4%
h 1018
18.4%
ý 54
 
1.0%
n 54
 
1.0%
F 49
 
0.9%
d 49
 
0.9%
l 49
 
0.9%
t 49
 
0.9%
Other values (11) 38
 
0.7%

nazev_obvod
Categorical

High correlation 

Distinct19
Distinct (%)1.8%
Missing7
Missing (%)0.7%
Memory size8.5 KiB
Praha 9
211 
Praha6
200 
Praha 1
134 
Praha 8
118 
Praha10
90 
Other values (14)
314 

Length

Max length8
Median length7
Mean length6.8650422
Min length6

Characters and Unicode

Total characters7325
Distinct characters20
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

Unique4 ?
Unique (%)0.4%

Sample

1st rowPraha 9
2nd rowPraha 9
3rd rowPraha 9
4th rowPraha 9
5th rowPraha 9

Common Values

ValueCountFrequency (%)
Praha 9 211
19.6%
Praha6 200
18.6%
Praha 1 134
12.5%
Praha 8 118
11.0%
Praha10 90
8.4%
Praha 4 83
 
7.7%
Praha 5 53
 
4.9%
Frýdlant 49
 
4.6%
Praha5 36
 
3.4%
Praha 10 27
 
2.5%
Other values (9) 66
 
6.1%

Length

2024-11-11T12:53:45.671937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
praha 692
39.3%
9 212
 
12.1%
praha6 200
 
11.4%
1 135
 
7.7%
8 118
 
6.7%
praha10 90
 
5.1%
4 85
 
4.8%
5 53
 
3.0%
frýdlant 49
 
2.8%
praha5 36
 
2.0%
Other values (7) 89
 
5.1%

Most occurring characters

ValueCountFrequency (%)
a 2085
28.5%
r 1067
14.6%
P 1018
13.9%
h 1018
13.9%
696
 
9.5%
1 265
 
3.6%
6 217
 
3.0%
9 212
 
2.9%
8 118
 
1.6%
0 117
 
1.6%
Other values (10) 512
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2085
28.5%
r 1067
14.6%
P 1018
13.9%
h 1018
13.9%
696
 
9.5%
1 265
 
3.6%
6 217
 
3.0%
9 212
 
2.9%
8 118
 
1.6%
0 117
 
1.6%
Other values (10) 512
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2085
28.5%
r 1067
14.6%
P 1018
13.9%
h 1018
13.9%
696
 
9.5%
1 265
 
3.6%
6 217
 
3.0%
9 212
 
2.9%
8 118
 
1.6%
0 117
 
1.6%
Other values (10) 512
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2085
28.5%
r 1067
14.6%
P 1018
13.9%
h 1018
13.9%
696
 
9.5%
1 265
 
3.6%
6 217
 
3.0%
9 212
 
2.9%
8 118
 
1.6%
0 117
 
1.6%
Other values (10) 512
 
7.0%

nazev_mestska_cast
Text

Distinct51
Distinct (%)4.8%
Missing7
Missing (%)0.7%
Memory size8.5 KiB
2024-11-11T12:53:45.863434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length23
Median length21
Mean length17.585754
Min length13

Characters and Unicode

Total characters18764
Distinct characters62
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

Unique13 ?
Unique (%)1.2%

Sample

1st rowPraha 14 - Černý Most
2nd rowPraha 14 - Černý Most
3rd rowPraha 14 - Černý Most
4th rowPraha 14 - Černý Most
5th rowPraha 14 - Černý Most
ValueCountFrequency (%)
736
21.0%
praha 692
19.7%
praha6-břevnov 200
 
5.7%
14 166
 
4.7%
most 161
 
4.6%
černý 161
 
4.6%
1 133
 
3.8%
město 120
 
3.4%
8 119
 
3.4%
ďáblice 94
 
2.7%
Other values (54) 926
26.4%
2024-11-11T12:53:46.302694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2477
 
13.2%
a 2422
 
12.9%
r 1557
 
8.3%
h 1164
 
6.2%
- 1067
 
5.7%
P 1018
 
5.4%
o 887
 
4.7%
e 708
 
3.8%
v 609
 
3.2%
n 599
 
3.2%
Other values (52) 6256
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2477
 
13.2%
a 2422
 
12.9%
r 1557
 
8.3%
h 1164
 
6.2%
- 1067
 
5.7%
P 1018
 
5.4%
o 887
 
4.7%
e 708
 
3.8%
v 609
 
3.2%
n 599
 
3.2%
Other values (52) 6256
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2477
 
13.2%
a 2422
 
12.9%
r 1557
 
8.3%
h 1164
 
6.2%
- 1067
 
5.7%
P 1018
 
5.4%
o 887
 
4.7%
e 708
 
3.8%
v 609
 
3.2%
n 599
 
3.2%
Other values (52) 6256
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2477
 
13.2%
a 2422
 
12.9%
r 1557
 
8.3%
h 1164
 
6.2%
- 1067
 
5.7%
P 1018
 
5.4%
o 887
 
4.7%
e 708
 
3.8%
v 609
 
3.2%
n 599
 
3.2%
Other values (52) 6256
33.3%

katastralni_uzemi
Categorical

High correlation 

Distinct42
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
BŘEVNOV
200 
ČERNÝ MOST
159 
VINOHRADY
96 
ĎÁBLICE
96 
STARÉ MĚSTO
71 
Other values (37)
452 

Length

Max length13
Median length11
Mean length8.2188082
Min length3

Characters and Unicode

Total characters8827
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.7%

Sample

1st rowČERNÝ MOST
2nd rowČERNÝ MOST
3rd rowČERNÝ MOST
4th rowČERNÝ MOST
5th rowČERNÝ MOST

Common Values

ValueCountFrequency (%)
BŘEVNOV 200
18.6%
ČERNÝ MOST 159
14.8%
VINOHRADY 96
8.9%
ĎÁBLICE 96
8.9%
STARÉ MĚSTO 71
 
6.6%
NOVÉ MĚSTO 50
 
4.7%
FRÝDLANT 49
 
4.6%
KUNRATICE 48
 
4.5%
SMÍCHOV 37
 
3.4%
ČAKOVICE 29
 
2.7%
Other values (32) 239
22.3%

Length

2024-11-11T12:53:46.723338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
břevnov 200
14.7%
most 159
11.7%
černý 159
11.7%
město 121
 
8.9%
vinohrady 96
 
7.1%
ďáblice 96
 
7.1%
staré 71
 
5.2%
nové 50
 
3.7%
frýdlant 49
 
3.6%
kunratice 48
 
3.5%
Other values (36) 307
22.6%

Most occurring characters

ValueCountFrequency (%)
O 840
 
9.5%
E 714
 
8.1%
V 671
 
7.6%
N 650
 
7.4%
T 527
 
6.0%
R 497
 
5.6%
S 478
 
5.4%
A 382
 
4.3%
B 373
 
4.2%
M 373
 
4.2%
Other values (24) 3322
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 840
 
9.5%
E 714
 
8.1%
V 671
 
7.6%
N 650
 
7.4%
T 527
 
6.0%
R 497
 
5.6%
S 478
 
5.4%
A 382
 
4.3%
B 373
 
4.2%
M 373
 
4.2%
Other values (24) 3322
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 840
 
9.5%
E 714
 
8.1%
V 671
 
7.6%
N 650
 
7.4%
T 527
 
6.0%
R 497
 
5.6%
S 478
 
5.4%
A 382
 
4.3%
B 373
 
4.2%
M 373
 
4.2%
Other values (24) 3322
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 840
 
9.5%
E 714
 
8.1%
V 671
 
7.6%
N 650
 
7.4%
T 527
 
6.0%
R 497
 
5.6%
S 478
 
5.4%
A 382
 
4.3%
B 373
 
4.2%
M 373
 
4.2%
Other values (24) 3322
37.6%

id_nebytoveho_prostoru
Text

Distinct277
Distinct (%)26.0%
Missing9
Missing (%)0.8%
Memory size8.5 KiB
2024-11-11T12:53:47.063275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0685446
Min length1

Characters and Unicode

Total characters3268
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

Unique173 ?
Unique (%)16.2%

Sample

1st row917.1
2nd row923.1
3rd row943,1
4th row926
5th row901.1
ValueCountFrequency (%)
101 154
 
14.4%
100 104
 
9.8%
104 69
 
6.5%
103 61
 
5.7%
102 60
 
5.6%
105 35
 
3.3%
107 31
 
2.9%
106 23
 
2.2%
401 15
 
1.4%
501 10
 
0.9%
Other values (266) 504
47.3%
2024-11-11T12:53:47.815497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1072
32.8%
0 881
27.0%
2 304
 
9.3%
3 240
 
7.3%
5 210
 
6.4%
4 177
 
5.4%
9 107
 
3.3%
7 95
 
2.9%
6 93
 
2.8%
8 52
 
1.6%
Other values (3) 37
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1072
32.8%
0 881
27.0%
2 304
 
9.3%
3 240
 
7.3%
5 210
 
6.4%
4 177
 
5.4%
9 107
 
3.3%
7 95
 
2.9%
6 93
 
2.8%
8 52
 
1.6%
Other values (3) 37
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1072
32.8%
0 881
27.0%
2 304
 
9.3%
3 240
 
7.3%
5 210
 
6.4%
4 177
 
5.4%
9 107
 
3.3%
7 95
 
2.9%
6 93
 
2.8%
8 52
 
1.6%
Other values (3) 37
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1072
32.8%
0 881
27.0%
2 304
 
9.3%
3 240
 
7.3%
5 210
 
6.4%
4 177
 
5.4%
9 107
 
3.3%
7 95
 
2.9%
6 93
 
2.8%
8 52
 
1.6%
Other values (3) 37
 
1.1%

vymera_m2
Text

Missing 

Distinct741
Distinct (%)69.9%
Missing14
Missing (%)1.3%
Memory size8.5 KiB
2024-11-11T12:53:48.258282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.7688679
Min length1

Characters and Unicode

Total characters5055
Distinct characters12
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

Unique619 ?
Unique (%)58.4%

Sample

1st row11,20
2nd row11,20
3rd row11,20
4th row13,15
5th row13,57
ValueCountFrequency (%)
14,21 26
 
2.5%
11,20 24
 
2.3%
11,00 23
 
2.2%
12,50 16
 
1.5%
16,50 12
 
1.1%
12,60 10
 
0.9%
18,00 10
 
0.9%
20,00 8
 
0.8%
13,00 7
 
0.7%
10,24 7
 
0.7%
Other values (731) 917
86.5%
2024-11-11T12:53:49.023042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 1054
20.9%
1 858
17.0%
0 691
13.7%
2 538
10.6%
5 331
 
6.5%
3 330
 
6.5%
4 318
 
6.3%
6 242
 
4.8%
8 234
 
4.6%
7 233
 
4.6%
Other values (2) 226
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 1054
20.9%
1 858
17.0%
0 691
13.7%
2 538
10.6%
5 331
 
6.5%
3 330
 
6.5%
4 318
 
6.3%
6 242
 
4.8%
8 234
 
4.6%
7 233
 
4.6%
Other values (2) 226
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 1054
20.9%
1 858
17.0%
0 691
13.7%
2 538
10.6%
5 331
 
6.5%
3 330
 
6.5%
4 318
 
6.3%
6 242
 
4.8%
8 234
 
4.6%
7 233
 
4.6%
Other values (2) 226
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 1054
20.9%
1 858
17.0%
0 691
13.7%
2 538
10.6%
5 331
 
6.5%
3 330
 
6.5%
4 318
 
6.3%
6 242
 
4.8%
8 234
 
4.6%
7 233
 
4.6%
Other values (2) 226
 
4.5%

ucel_popis
Categorical

High correlation 

Distinct38
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
garáž
247 
ostatní
143 
kancel.prostory
135 
škola
105 
kancelář
95 
Other values (33)
349 

Length

Max length27
Median length23
Mean length8.8873371
Min length5

Characters and Unicode

Total characters9545
Distinct characters49
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 (%)1.7%

Sample

1st rowgarážové stání
2nd rowgarážové stání
3rd rowgarážové stání
4th rowgaráž
5th rowgarážové stání

Common Values

ValueCountFrequency (%)
garáž 247
23.0%
ostatní 143
13.3%
kancel.prostory 135
12.6%
škola 105
9.8%
kancelář 95
 
8.8%
sklad 83
 
7.7%
sklady/archiv 77
 
7.2%
garážové stání 68
 
6.3%
NJ_kancelářské prostory 25
 
2.3%
ateliér 16
 
1.5%
Other values (28) 80
 
7.4%

Length

2024-11-11T12:53:49.181230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
garáž 247
20.4%
ostatní 151
12.5%
kancel.prostory 135
11.1%
škola 105
8.7%
kancelář 96
 
7.9%
sklad 87
 
7.2%
sklady/archiv 77
 
6.4%
stání 69
 
5.7%
garážové 68
 
5.6%
ateliér 26
 
2.1%
Other values (32) 150
12.4%

Most occurring characters

ValueCountFrequency (%)
a 1147
 
12.0%
r 780
 
8.2%
o 724
 
7.6%
s 615
 
6.4%
t 600
 
6.3%
l 582
 
6.1%
k 574
 
6.0%
n 547
 
5.7%
á 520
 
5.4%
c 365
 
3.8%
Other values (39) 3091
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1147
 
12.0%
r 780
 
8.2%
o 724
 
7.6%
s 615
 
6.4%
t 600
 
6.3%
l 582
 
6.1%
k 574
 
6.0%
n 547
 
5.7%
á 520
 
5.4%
c 365
 
3.8%
Other values (39) 3091
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1147
 
12.0%
r 780
 
8.2%
o 724
 
7.6%
s 615
 
6.4%
t 600
 
6.3%
l 582
 
6.1%
k 574
 
6.0%
n 547
 
5.7%
á 520
 
5.4%
c 365
 
3.8%
Other values (39) 3091
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1147
 
12.0%
r 780
 
8.2%
o 724
 
7.6%
s 615
 
6.4%
t 600
 
6.3%
l 582
 
6.1%
k 574
 
6.0%
n 547
 
5.7%
á 520
 
5.4%
c 365
 
3.8%
Other values (39) 3091
32.4%

ucel_detail
Text

Distinct515
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-11-11T12:53:49.444325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length86
Median length44
Mean length15.170391
Min length2

Characters and Unicode

Total characters16293
Distinct characters76
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

Unique458 ?
Unique (%)42.6%

Sample

1st rowgarážové stání
2nd rowgarážové stání
3rd rowgarážové stání
4th rowgaráž
5th rowgarážové stání
ValueCountFrequency (%)
č 457
 
17.2%
stání 246
 
9.3%
garáž 225
 
8.5%
kancelář 147
 
5.5%
sklad 96
 
3.6%
garážové 82
 
3.1%
vnitřní 66
 
2.5%
wc 64
 
2.4%
sklady/archiv/depozitář 56
 
2.1%
1 50
 
1.9%
Other values (358) 1167
43.9%
2024-11-11T12:53:50.169190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1477
 
9.1%
a 1099
 
6.7%
á 919
 
5.6%
n 846
 
5.2%
s 678
 
4.2%
t 673
 
4.1%
. 639
 
3.9%
o 634
 
3.9%
r 583
 
3.6%
í 525
 
3.2%
Other values (66) 8220
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1477
 
9.1%
a 1099
 
6.7%
á 919
 
5.6%
n 846
 
5.2%
s 678
 
4.2%
t 673
 
4.1%
. 639
 
3.9%
o 634
 
3.9%
r 583
 
3.6%
í 525
 
3.2%
Other values (66) 8220
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1477
 
9.1%
a 1099
 
6.7%
á 919
 
5.6%
n 846
 
5.2%
s 678
 
4.2%
t 673
 
4.1%
. 639
 
3.9%
o 634
 
3.9%
r 583
 
3.6%
í 525
 
3.2%
Other values (66) 8220
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1477
 
9.1%
a 1099
 
6.7%
á 919
 
5.6%
n 846
 
5.2%
s 678
 
4.2%
t 673
 
4.1%
. 639
 
3.9%
o 634
 
3.9%
r 583
 
3.6%
í 525
 
3.2%
Other values (66) 8220
50.5%

spravce_nazev
Categorical

High correlation 

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Liga-servis
485 
CENTRA-Maňákova
162 
ACTON
154 
TCP
87 
CENTRA-Ženské domovy
79 
Other values (5)
107 

Length

Max length26
Median length22
Mean length11.175978
Min length3

Characters and Unicode

Total characters12003
Distinct characters43
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 (%)0.2%

Sample

1st rowCENTRA-Ženské domovy
2nd rowCENTRA-Ženské domovy
3rd rowCENTRA-Ženské domovy
4th rowCENTRA-Ženské domovy
5th rowCENTRA-Ženské domovy

Common Values

ValueCountFrequency (%)
Liga-servis 485
45.2%
CENTRA-Maňákova 162
 
15.1%
ACTON 154
 
14.3%
TCP 87
 
8.1%
CENTRA-Ženské domovy 79
 
7.4%
Q-Facility - Hlavatého 49
 
4.6%
SOLID 36
 
3.4%
Q-Facility - Řepy 20
 
1.9%
VAS 1
 
0.1%
Q-Facility - Veronské nám. 1
 
0.1%

Length

2024-11-11T12:53:50.317867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:50.428241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
liga-servis 485
37.5%
centra-maňákova 162
 
12.5%
acton 154
 
11.9%
tcp 87
 
6.7%
centra-ženské 79
 
6.1%
domovy 79
 
6.1%
q-facility 70
 
5.4%
70
 
5.4%
hlavatého 49
 
3.8%
solid 36
 
2.8%
Other values (4) 23
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i 1110
 
9.2%
s 1050
 
8.7%
a 977
 
8.1%
- 866
 
7.2%
v 775
 
6.5%
e 585
 
4.9%
L 521
 
4.3%
r 486
 
4.0%
g 485
 
4.0%
C 482
 
4.0%
Other values (33) 4666
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1110
 
9.2%
s 1050
 
8.7%
a 977
 
8.1%
- 866
 
7.2%
v 775
 
6.5%
e 585
 
4.9%
L 521
 
4.3%
r 486
 
4.0%
g 485
 
4.0%
C 482
 
4.0%
Other values (33) 4666
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1110
 
9.2%
s 1050
 
8.7%
a 977
 
8.1%
- 866
 
7.2%
v 775
 
6.5%
e 585
 
4.9%
L 521
 
4.3%
r 486
 
4.0%
g 485
 
4.0%
C 482
 
4.0%
Other values (33) 4666
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1110
 
9.2%
s 1050
 
8.7%
a 977
 
8.1%
- 866
 
7.2%
v 775
 
6.5%
e 585
 
4.9%
L 521
 
4.3%
r 486
 
4.0%
g 485
 
4.0%
C 482
 
4.0%
Other values (33) 4666
38.9%

spravce_kontaktni_URL
Categorical

High correlation 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
https://www.liga-servis.cz/
485 
http://www.centra.eu/cs/domu
241 
http://www.acton.cz/
154 
https://www.tcp-as.cz/
87 
http://q-f.cz/
70 
Other values (2)
 
37

Length

Max length28
Median length27
Mean length24.729981
Min length14

Characters and Unicode

Total characters26560
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowhttp://www.centra.eu/cs/domu
2nd rowhttp://www.centra.eu/cs/domu
3rd rowhttp://www.centra.eu/cs/domu
4th rowhttp://www.centra.eu/cs/domu
5th rowhttp://www.centra.eu/cs/domu

Common Values

ValueCountFrequency (%)
https://www.liga-servis.cz/ 485
45.2%
http://www.centra.eu/cs/domu 241
22.4%
http://www.acton.cz/ 154
 
14.3%
https://www.tcp-as.cz/ 87
 
8.1%
http://q-f.cz/ 70
 
6.5%
http://www.solid.cz/ 36
 
3.4%
https://www.vas-vos.cz/ 1
 
0.1%

Length

2024-11-11T12:53:50.581918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T12:53:50.931658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
https://www.liga-servis.cz 485
45.2%
http://www.centra.eu/cs/domu 241
22.4%
http://www.acton.cz 154
 
14.3%
https://www.tcp-as.cz 87
 
8.1%
http://q-f.cz 70
 
6.5%
http://www.solid.cz 36
 
3.4%
https://www.vas-vos.cz 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
/ 3463
13.0%
w 3012
 
11.3%
t 2630
 
9.9%
. 2078
 
7.8%
s 1909
 
7.2%
c 1556
 
5.9%
p 1161
 
4.4%
h 1074
 
4.0%
: 1074
 
4.0%
i 1006
 
3.8%
Other values (15) 7597
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 3463
13.0%
w 3012
 
11.3%
t 2630
 
9.9%
. 2078
 
7.8%
s 1909
 
7.2%
c 1556
 
5.9%
p 1161
 
4.4%
h 1074
 
4.0%
: 1074
 
4.0%
i 1006
 
3.8%
Other values (15) 7597
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 3463
13.0%
w 3012
 
11.3%
t 2630
 
9.9%
. 2078
 
7.8%
s 1909
 
7.2%
c 1556
 
5.9%
p 1161
 
4.4%
h 1074
 
4.0%
: 1074
 
4.0%
i 1006
 
3.8%
Other values (15) 7597
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 3463
13.0%
w 3012
 
11.3%
t 2630
 
9.9%
. 2078
 
7.8%
s 1909
 
7.2%
c 1556
 
5.9%
p 1161
 
4.4%
h 1074
 
4.0%
: 1074
 
4.0%
i 1006
 
3.8%
Other values (15) 7597
28.6%

Interactions

2024-11-11T12:53:42.370740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:53:41.869865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:53:42.471887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-11T12:53:41.968313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-11T12:53:51.100092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
cislo_domovnícislo_orientacnikatastralni_uzeminazev_obcenazev_obvodpscspravce_kontaktni_URLspravce_nazevucel_popisznak_cisla_orientacniho
cislo_domovní1.000-0.4260.8510.4990.8040.8520.4820.4830.5190.683
cislo_orientacni-0.4261.0000.4980.1320.4140.5730.3240.3310.6230.820
katastralni_uzemi0.8510.4981.0000.8500.8730.8630.8820.8810.5090.683
nazev_obce0.4990.1320.8501.0000.7040.8500.1530.1850.2561.000
nazev_obvod0.8040.4140.8730.7041.0000.9160.7210.6680.6070.683
psc0.8520.5730.8630.8500.9161.0000.8800.8250.5340.683
spravce_kontaktni_URL0.4820.3240.8820.1530.7210.8801.0000.9990.5510.984
spravce_nazev0.4830.3310.8810.1850.6680.8250.9991.0000.5880.984
ucel_popis0.5190.6230.5090.2560.6070.5340.5510.5881.0000.000
znak_cisla_orientacniho0.6830.8200.6831.0000.6830.6830.9840.9840.0001.000

Missing values

2024-11-11T12:53:42.619113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-11T12:53:42.823829image/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

nazev_ulicecislo_domovnícislo_orientacniznak_cisla_orientacnihopscnazev_obcenazev_obvodnazev_mestska_castkatastralni_uzemiid_nebytoveho_prostoruvymera_m2ucel_popisucel_detailspravce_nazevspravce_kontaktni_URL
0Arn. Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST917.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
1Arn. Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST923.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
2Arn. Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST943,111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
3Arn.Valenty668.035.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST92613,15garážgarážCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
4Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST901.113,57garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
5Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST903.113,57garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
6Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST907.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
7Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST909.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
8Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST911.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
9Arn.Valenty669.033.0NaN19800PrahaPraha 9Praha 14 - Černý MostČERNÝ MOST912.111,20garážové stánígarážové stáníCENTRA-Ženské domovyhttp://www.centra.eu/cs/domu
nazev_ulicecislo_domovnícislo_orientacniznak_cisla_orientacnihopscnazev_obcenazev_obvodnazev_mestska_castkatastralni_uzemiid_nebytoveho_prostoruvymera_m2ucel_popisucel_detailspravce_nazevspravce_kontaktni_URL
1064Zátopkova100.02.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV13224,88sklady/archivparkovací stání č. 245 částLiga-servishttps://www.liga-servis.cz/
1065Zátopkova100.02.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV14019,20kancel.prostorykancelář č. 233 PLiga-servishttps://www.liga-servis.cz/
1066Zátopkova100.02.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV1182,15kancel.prostorykancelář č. 326Liga-servishttps://www.liga-servis.cz/
1067Zátopkova100.02.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV1479,26kancel.prostorykancelář. 3133Liga-servishttps://www.liga-servis.cz/
1068Zátopkova100.02.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV13617,65kancel.prostorykancelář č. 3121Liga-servishttps://www.liga-servis.cz/
1069Zátopkova100.06.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV10413,86kancel.prostorydivadelní sál/koncertní sál/společenský sál č. 3148Liga-servishttps://www.liga-servis.cz/
1070Zátopkova100.06.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV14010,79NJ_kancelářské prostorykancelář č.349Liga-servishttps://www.liga-servis.cz/
1071Zátopkova100.06.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV14030,82NJ_kancelářské prostorykancelář č.350Liga-servishttps://www.liga-servis.cz/
1072Zátopkova100.06.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV102362,25sklady/archivsklady/archiv/depozitář č. 1Liga-servishttps://www.liga-servis.cz/
1073Zátopkova100.06.0NaN16900PrahaPraha6Praha6-BřevnovBŘEVNOV11613,28kancel.prostorykancelář. 3127Liga-servishttps://www.liga-servis.cz/

Duplicate rows

Most frequently occurring

nazev_ulicecislo_domovnícislo_orientacniznak_cisla_orientacnihopscnazev_obcenazev_obvodnazev_mestska_castkatastralni_uzemiid_nebytoveho_prostoruvymera_m2ucel_popisucel_detailspravce_nazevspravce_kontaktni_URL# duplicates
0LahovskáNaNNaNNaN15900PrahaPraha 5Praha 5 - ZbraslavZBRASLAVNaNNaNostatnívodojemLiga-servishttps://www.liga-servis.cz/2
1LehovecNaNNaNNaN19800PrahaPraha 9Praha 14 - Černý MostLEHOVECNaNNaNostatnívodojem, čerpací staniceLiga-servishttps://www.liga-servis.cz/2
2Rytířská398.026.0NaN11000PrahaPraha 1Praha 1 - Staré MěstoSTARÉ MĚSTO1042,81ostatníWCTCPhttps://www.tcp-as.cz/2