Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.3 KiB
Average record size in memory68.3 B

Variable types

Numeric4
Categorical2
Text2

Alerts

COUNTRY_NM has constant value ""Constant
CTY_NM has constant value ""Constant
RSTRNT_ID is highly overall correlated with RSTRNT_TEL_NOHigh correlation
RSTRNT_TEL_NO is highly overall correlated with RSTRNT_IDHigh correlation
RSTRNT_ID has unique valuesUnique
RSTRNT_NM has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:02:46.311917
Analysis finished2023-12-10 10:02:51.113728
Duration4.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.764
Minimum3
Maximum549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:02:51.232735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.95
Q1140.75
median272.5
Q3407.25
95-th percentile521.05
Maximum549
Range546
Interquartile range (IQR)266.5

Descriptive statistics

Standard deviation157.15317
Coefficient of variation (CV)0.57404615
Kurtosis-1.17545
Mean273.764
Median Absolute Deviation (MAD)134
Skewness0.016174137
Sum136882
Variance24697.119
MonotonicityStrictly increasing
2023-12-10T19:02:51.457708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.2%
358 1
 
0.2%
371 1
 
0.2%
370 1
 
0.2%
369 1
 
0.2%
368 1
 
0.2%
367 1
 
0.2%
366 1
 
0.2%
365 1
 
0.2%
364 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
3 1
0.2%
4 1
0.2%
5 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
11 1
0.2%
12 1
0.2%
13 1
0.2%
14 1
0.2%
ValueCountFrequency (%)
549 1
0.2%
547 1
0.2%
546 1
0.2%
545 1
0.2%
543 1
0.2%
542 1
0.2%
541 1
0.2%
540 1
0.2%
539 1
0.2%
538 1
0.2%

COUNTRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
JAPAN
500 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
JAPAN 500
100.0%

Length

2023-12-10T19:02:51.673643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:51.825479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
japan 500
100.0%

CTY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Tokyo
500 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Tokyo 500
100.0%

Length

2023-12-10T19:02:52.012547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:52.197662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tokyo 500
100.0%

RSTRNT_NM
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:02:52.594650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21
Mean length8.654
Min length2

Characters and Unicode

Total characters4327
Distinct characters611
Distinct categories10 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowリトル小岩井
2nd rowコカレストラン＆マンゴツリーカフェ 有楽町
3rd rowラ・メゾン・デュ・ショコラ(丸の内店)
4th rowどうとんぼり神座(新宿歌舞伎町店)
5th rowすずや(新宿本店)
ValueCountFrequency (%)
銀座 5
 
0.8%
中華そば 4
 
0.6%
ジョエル・ロブション 3
 
0.5%
cafe 3
 
0.5%
レストラン 3
 
0.5%
カフェ 3
 
0.5%
つけ麺屋 3
 
0.5%
本店 2
 
0.3%
新宿 2
 
0.3%
東京 2
 
0.3%
Other values (616) 630
95.5%
2023-12-10T19:02:53.216717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
店 186
 
4.3%
) 171
 
4.0%
( 171
 
4.0%
155
 
3.6%
ー 139
 
3.2%
ン 116
 
2.7%
・ 97
 
2.2%
ル 85
 
2.0%
本 84
 
1.9%
ラ 73
 
1.7%
Other values (601) 3050
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3415
78.9%
Close Punctuation 171
 
4.0%
Open Punctuation 171
 
4.0%
Space Separator 161
 
3.7%
Modifier Letter 140
 
3.2%
Other Punctuation 105
 
2.4%
Uppercase Letter 101
 
2.3%
Lowercase Letter 53
 
1.2%
Decimal Number 8
 
0.2%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
店 186
 
5.4%
ン 116
 
3.4%
ル 85
 
2.5%
本 84
 
2.5%
ラ 73
 
2.1%
ス 53
 
1.6%
リ 53
 
1.6%
カ 51
 
1.5%
ト 48
 
1.4%
ん 43
 
1.3%
Other values (531) 2623
76.8%
Uppercase Letter
ValueCountFrequency (%)
A 14
13.9%
E 8
 
7.9%
U 7
 
6.9%
O 7
 
6.9%
I 6
 
5.9%
R 6
 
5.9%
C 6
 
5.9%
S 5
 
5.0%
N 5
 
5.0%
B 4
 
4.0%
Other values (19) 33
32.7%
Lowercase Letter
ValueCountFrequency (%)
a 8
15.1%
e 6
 
11.3%
o 6
 
11.3%
i 3
 
5.7%
t 3
 
5.7%
c 3
 
5.7%
ｓ 2
 
3.8%
l 2
 
3.8%
p 2
 
3.8%
n 2
 
3.8%
Other values (15) 16
30.2%
Other Punctuation
ValueCountFrequency (%)
・ 97
92.4%
＆ 4
 
3.8%
& 1
 
1.0%
% 1
 
1.0%
･ 1
 
1.0%
. 1
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
0 3
37.5%
2 2
25.0%
Space Separator
ValueCountFrequency (%)
155
96.3%
　 6
 
3.7%
Modifier Letter
ValueCountFrequency (%)
ー 139
99.3%
々 1
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 1506
34.8%
Katakana 1421
32.8%
Common 757
17.5%
Hiragana 489
 
11.3%
Latin 154
 
3.6%

Most frequent character per script

Han
ValueCountFrequency (%)
店 186
 
12.4%
本 84
 
5.6%
屋 32
 
2.1%
座 27
 
1.8%
銀 27
 
1.8%
新 21
 
1.4%
宿 20
 
1.3%
谷 20
 
1.3%
神 17
 
1.1%
田 16
 
1.1%
Other values (390) 1056
70.1%
Katakana
ValueCountFrequency (%)
ン 116
 
8.2%
ル 85
 
6.0%
ラ 73
 
5.1%
ス 53
 
3.7%
リ 53
 
3.7%
カ 51
 
3.6%
ト 48
 
3.4%
ア 39
 
2.7%
ッ 37
 
2.6%
イ 35
 
2.5%
Other values (70) 831
58.5%
Hiragana
ValueCountFrequency (%)
ん 43
 
8.8%
や 25
 
5.1%
う 22
 
4.5%
つ 22
 
4.5%
い 21
 
4.3%
の 19
 
3.9%
か 19
 
3.9%
ら 17
 
3.5%
と 16
 
3.3%
ば 15
 
3.1%
Other values (52) 270
55.2%
Latin
ValueCountFrequency (%)
A 14
 
9.1%
E 8
 
5.2%
a 8
 
5.2%
U 7
 
4.5%
O 7
 
4.5%
e 6
 
3.9%
I 6
 
3.9%
o 6
 
3.9%
R 6
 
3.9%
C 6
 
3.9%
Other values (44) 80
51.9%
Common
ValueCountFrequency (%)
) 171
22.6%
( 171
22.6%
155
20.5%
ー 139
18.4%
・ 97
12.8%
　 6
 
0.8%
＆ 4
 
0.5%
1 3
 
0.4%
0 3
 
0.4%
- 2
 
0.3%
Other values (5) 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Katakana 1657
38.3%
CJK 1505
34.8%
ASCII 649
 
15.0%
Hiragana 489
 
11.3%
None 27
 
0.6%

Most frequent character per block

CJK
ValueCountFrequency (%)
店 186
 
12.4%
本 84
 
5.6%
屋 32
 
2.1%
座 27
 
1.8%
銀 27
 
1.8%
新 21
 
1.4%
宿 20
 
1.3%
谷 20
 
1.3%
神 17
 
1.1%
田 16
 
1.1%
Other values (389) 1055
70.1%
ASCII
ValueCountFrequency (%)
) 171
26.3%
( 171
26.3%
155
23.9%
A 14
 
2.2%
E 8
 
1.2%
a 8
 
1.2%
U 7
 
1.1%
O 7
 
1.1%
e 6
 
0.9%
I 6
 
0.9%
Other values (40) 96
14.8%
Katakana
ValueCountFrequency (%)
ー 139
 
8.4%
ン 116
 
7.0%
・ 97
 
5.9%
ル 85
 
5.1%
ラ 73
 
4.4%
ス 53
 
3.2%
リ 53
 
3.2%
カ 51
 
3.1%
ト 48
 
2.9%
ア 39
 
2.4%
Other values (72) 903
54.5%
Hiragana
ValueCountFrequency (%)
ん 43
 
8.8%
や 25
 
5.1%
う 22
 
4.5%
つ 22
 
4.5%
い 21
 
4.3%
の 19
 
3.9%
か 19
 
3.9%
ら 17
 
3.5%
と 16
 
3.3%
ば 15
 
3.1%
Other values (52) 270
55.2%
None
ValueCountFrequency (%)
　 6
22.2%
＆ 4
14.8%
ｓ 2
 
7.4%
Ｎ 1
 
3.7%
･ 1
 
3.7%
Ｄ 1
 
3.7%
Ｅ 1
 
3.7%
ｅ 1
 
3.7%
Ｔ 1
 
3.7%
ｕ 1
 
3.7%
Other values (8) 8
29.6%

RSTRNT_ADDR
Text

Distinct489
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:02:53.611157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length32
Mean length21.1
Min length11

Characters and Unicode

Total characters10550
Distinct characters484
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)96.0%

Sample

1st row東京都千代田区大手町1-6-1 大手町ビル B2F
2nd row東京都千代田区有楽町1-11-1 ビックカメラ有楽町店 ６Ｆ
3rd row東京都千代田区丸の内3-4-1 新国際ビル　１Ｆ
4th row東京都新宿区歌舞伎町1-14-1
5th row東京都新宿区歌舞伎町1-23-15 杉山ビル　２Ｆ
ValueCountFrequency (%)
１ｆ 80
 
7.4%
1f 30
 
2.8%
ｂ１ｆ 24
 
2.2%
２ｆ 20
 
1.9%
b1f 15
 
1.4%
2f 11
 
1.0%
丸の内ビルディング 6
 
0.6%
東京都千代田区丸の内2-4-1 6
 
0.6%
1ｆ 5
 
0.5%
１ｆ・２ｆ 4
 
0.4%
Other values (795) 877
81.4%
2023-12-10T19:02:54.383342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 935
 
8.9%
1 671
 
6.4%
東 581
 
5.5%
京 544
 
5.2%
区 501
 
4.7%
都 500
 
4.7%
411
 
3.9%
2 338
 
3.2%
3 245
 
2.3%
ル 201
 
1.9%
Other values (474) 5623
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6277
59.5%
Decimal Number 2212
 
21.0%
Dash Punctuation 935
 
8.9%
Space Separator 582
 
5.5%
Uppercase Letter 404
 
3.8%
Modifier Letter 87
 
0.8%
Other Punctuation 35
 
0.3%
Lowercase Letter 16
 
0.2%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
東 581
 
9.3%
京 544
 
8.7%
区 501
 
8.0%
都 500
 
8.0%
ル 201
 
3.2%
ビ 162
 
2.6%
田 159
 
2.5%
新 131
 
2.1%
谷 120
 
1.9%
宿 111
 
1.8%
Other values (399) 3267
52.0%
Uppercase Letter
ValueCountFrequency (%)
Ｆ 193
47.8%
F 87
21.5%
Ｂ 35
 
8.7%
B 20
 
5.0%
Ｔ 9
 
2.2%
Ｋ 5
 
1.2%
Ｅ 4
 
1.0%
Ｎ 4
 
1.0%
Ｏ 4
 
1.0%
Ａ 3
 
0.7%
Other values (25) 40
 
9.9%
Decimal Number
ValueCountFrequency (%)
1 671
30.3%
2 338
15.3%
3 245
 
11.1%
4 154
 
7.0%
１ 141
 
6.4%
5 138
 
6.2%
6 123
 
5.6%
7 94
 
4.2%
8 81
 
3.7%
0 72
 
3.3%
Other values (10) 155
 
7.0%
Lowercase Letter
ValueCountFrequency (%)
ａ 4
25.0%
ｙ 2
12.5%
ｔ 2
12.5%
ｅ 1
 
6.2%
ｒ 1
 
6.2%
ｏ 1
 
6.2%
ｍ 1
 
6.2%
ｉ 1
 
6.2%
ｕ 1
 
6.2%
ｂ 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
・ 32
91.4%
＆ 2
 
5.7%
． 1
 
2.9%
Space Separator
ValueCountFrequency (%)
411
70.6%
　 171
29.4%
Modifier Letter
ValueCountFrequency (%)
ー 80
92.0%
々 7
 
8.0%
Dash Punctuation
ValueCountFrequency (%)
- 935
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 5117
48.5%
Common 3844
36.4%
Katakana 1119
 
10.6%
Latin 422
 
4.0%
Hiragana 48
 
0.5%

Most frequent character per script

Han
ValueCountFrequency (%)
東 581
 
11.4%
京 544
 
10.6%
区 501
 
9.8%
都 500
 
9.8%
田 159
 
3.1%
新 131
 
2.6%
谷 120
 
2.3%
宿 111
 
2.2%
中 99
 
1.9%
渋 94
 
1.8%
Other values (307) 2277
44.5%
Katakana
ValueCountFrequency (%)
ル 201
18.0%
ビ 162
 
14.5%
ン 81
 
7.2%
ト 37
 
3.3%
ス 37
 
3.3%
イ 36
 
3.2%
ラ 34
 
3.0%
シ 26
 
2.3%
タ 24
 
2.1%
ア 22
 
2.0%
Other values (62) 459
41.0%
Latin
ValueCountFrequency (%)
Ｆ 193
45.7%
F 87
20.6%
Ｂ 35
 
8.3%
B 20
 
4.7%
Ｔ 9
 
2.1%
Ｋ 5
 
1.2%
Ｅ 4
 
0.9%
Ｎ 4
 
0.9%
ａ 4
 
0.9%
Ｏ 4
 
0.9%
Other values (37) 57
 
13.5%
Common
ValueCountFrequency (%)
- 935
24.3%
1 671
17.5%
411
10.7%
2 338
 
8.8%
3 245
 
6.4%
　 171
 
4.4%
4 154
 
4.0%
１ 141
 
3.7%
5 138
 
3.6%
6 123
 
3.2%
Other values (17) 517
13.4%
Hiragana
ValueCountFrequency (%)
の 19
39.6%
が 5
 
10.4%
し 2
 
4.2%
ど 2
 
4.2%
き 2
 
4.2%
ら 2
 
4.2%
ん 2
 
4.2%
み 1
 
2.1%
た 1
 
2.1%
ば 1
 
2.1%
Other values (11) 11
22.9%

Most occurring blocks

ValueCountFrequency (%)
CJK 5110
48.4%
ASCII 3442
32.6%
Katakana 1231
 
11.7%
None 717
 
6.8%
Hiragana 48
 
0.5%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 935
27.2%
1 671
19.5%
411
11.9%
2 338
 
9.8%
3 245
 
7.1%
4 154
 
4.5%
5 138
 
4.0%
6 123
 
3.6%
7 94
 
2.7%
F 87
 
2.5%
Other values (13) 246
 
7.1%
CJK
ValueCountFrequency (%)
東 581
 
11.4%
京 544
 
10.6%
区 501
 
9.8%
都 500
 
9.8%
田 159
 
3.1%
新 131
 
2.6%
谷 120
 
2.3%
宿 111
 
2.2%
中 99
 
1.9%
渋 94
 
1.8%
Other values (306) 2270
44.4%
Katakana
ValueCountFrequency (%)
ル 201
 
16.3%
ビ 162
 
13.2%
ン 81
 
6.6%
ー 80
 
6.5%
ト 37
 
3.0%
ス 37
 
3.0%
イ 36
 
2.9%
ラ 34
 
2.8%
・ 32
 
2.6%
シ 26
 
2.1%
Other values (64) 505
41.0%
None
ValueCountFrequency (%)
Ｆ 193
26.9%
　 171
23.8%
１ 141
19.7%
２ 40
 
5.6%
Ｂ 35
 
4.9%
３ 11
 
1.5%
Ｔ 9
 
1.3%
４ 9
 
1.3%
０ 9
 
1.3%
５ 8
 
1.1%
Other values (39) 91
12.7%
Hiragana
ValueCountFrequency (%)
の 19
39.6%
が 5
 
10.4%
し 2
 
4.2%
ど 2
 
4.2%
き 2
 
4.2%
ら 2
 
4.2%
ん 2
 
4.2%
み 1
 
2.1%
た 1
 
2.1%
ば 1
 
2.1%
Other values (11) 11
22.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

RSTRNT_TEL_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7204902 × 1010
Minimum8.1120428 × 1010
Maximum8.1705202 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:02:54.830599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.1120428 × 1010
5-th percentile8.1332511 × 1010
Q18.1334293 × 1010
median8.1335838 × 1010
Q38.1353421 × 1010
95-th percentile8.135931 × 1010
Maximum8.1705202 × 1011
Range7.3593159 × 1011
Interquartile range (IQR)19128136

Descriptive statistics

Standard deviation6.5364353 × 1010
Coefficient of variation (CV)0.74954906
Kurtosis121.23125
Mean8.7204902 × 1010
Median Absolute Deviation (MAD)2573810
Skewness11.079068
Sum4.3602451 × 1013
Variance4.2724986 × 1021
MonotonicityNot monotonic
2023-12-10T19:02:55.223827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81353685147 2
 
0.4%
81333430889 2
 
0.4%
81332012024 1
 
0.2%
81352960080 1
 
0.2%
81353891077 1
 
0.2%
81332261288 1
 
0.2%
81353688823 1
 
0.2%
81353678355 1
 
0.2%
81353674123 1
 
0.2%
81353611381 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
81120428485 1
0.2%
81332008836 1
0.2%
81332012024 1
0.2%
81332015489 1
0.2%
81332016006 1
0.2%
81332073369 1
0.2%
81332093790 1
0.2%
81332094480 1
0.2%
81332095615 1
0.2%
81332141361 1
0.2%
ValueCountFrequency (%)
817052015690 1
0.2%
813578133501 1
0.2%
813378138428 1
0.2%
813358131451 1
0.2%
81369142634 1
0.2%
81368086161 1
0.2%
81367182822 1
0.2%
81367170935 1
0.2%
81367170932 1
0.2%
81364200668 1
0.2%

RSTRNT_LA
Real number (ℝ)

Distinct370
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.674934
Minimum35.559799
Maximum35.732201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:02:55.690791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.559799
5-th percentile35.627691
Q135.659451
median35.67285
Q335.694901
95-th percentile35.720305
Maximum35.732201
Range0.1724014
Interquartile range (IQR)0.035450925

Descriptive statistics

Standard deviation0.027824418
Coefficient of variation (CV)0.00077994307
Kurtosis0.90455509
Mean35.674934
Median Absolute Deviation (MAD)0.01745035
Skewness-0.42779935
Sum17837.467
Variance0.00077419821
MonotonicityNot monotonic
2023-12-10T19:02:56.019274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.6703987 5
 
1.0%
35.6456986 4
 
0.8%
35.6609001 4
 
0.8%
35.6581001 4
 
0.8%
35.6918984 4
 
0.8%
35.6682015 3
 
0.6%
35.6702995 3
 
0.6%
35.6915016 3
 
0.6%
35.668499 3
 
0.6%
35.6852989 3
 
0.6%
Other values (360) 464
92.8%
ValueCountFrequency (%)
35.5597992 1
0.2%
35.5642014 1
0.2%
35.5646019 1
0.2%
35.6041985 1
0.2%
35.6064987 1
0.2%
35.6069984 1
0.2%
35.607399 1
0.2%
35.6076012 1
0.2%
35.6077995 1
0.2%
35.6096993 1
0.2%
ValueCountFrequency (%)
35.7322006 1
0.2%
35.7318001 1
0.2%
35.7314987 1
0.2%
35.7308998 1
0.2%
35.7307015 1
0.2%
35.7304993 1
0.2%
35.7304001 1
0.2%
35.7300987 1
0.2%
35.7299995 1
0.2%
35.7298012 1
0.2%

RSTRNT_LO
Real number (ℝ)

Distinct120
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.73696
Minimum139.625
Maximum139.883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:02:56.320632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139.625
5-th percentile139.69099
Q1139.707
median139.7375
Q3139.765
95-th percentile139.79406
Maximum139.883
Range0.2579956
Interquartile range (IQR)0.0579987

Descriptive statistics

Standard deviation0.036351033
Coefficient of variation (CV)0.000260139
Kurtosis0.23727013
Mean139.73696
Median Absolute Deviation (MAD)0.02949525
Skewness0.044075268
Sum69868.48
Variance0.0013213976
MonotonicityNot monotonic
2023-12-10T19:02:56.605775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.7689972 14
 
2.8%
139.6990051 12
 
2.4%
139.6979981 12
 
2.4%
139.7700043 11
 
2.2%
139.6970062 11
 
2.2%
139.7640076 11
 
2.2%
139.7630005 10
 
2.0%
139.7380066 10
 
2.0%
139.7649994 10
 
2.0%
139.7149963 10
 
2.0%
Other values (110) 389
77.8%
ValueCountFrequency (%)
139.625 1
 
0.2%
139.6260071 1
 
0.2%
139.6369934 1
 
0.2%
139.6410065 1
 
0.2%
139.6439972 2
 
0.4%
139.6519928 1
 
0.2%
139.6670075 5
1.0%
139.6679993 3
0.6%
139.6690064 1
 
0.2%
139.6699982 3
0.6%
ValueCountFrequency (%)
139.8829956 1
 
0.2%
139.875 1
 
0.2%
139.8269959 1
 
0.2%
139.826004 1
 
0.2%
139.8200073 1
 
0.2%
139.8139954 1
 
0.2%
139.8070068 1
 
0.2%
139.8009949 1
 
0.2%
139.798996 4
0.8%
139.7980042 2
0.4%

Interactions

2023-12-10T19:02:49.781792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:47.273165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:48.089754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:48.966547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:49.934356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:47.447325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:48.398848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:49.159538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:50.471460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:47.632238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:48.582735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:49.368067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:50.633350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:47.820418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:48.778414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:02:49.614326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:02:56.814860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.0880.6370.490
RSTRNT_TEL_NO0.0881.0000.3120.054
RSTRNT_LA0.6370.3121.0000.754
RSTRNT_LO0.4900.0540.7541.000
2023-12-10T19:02:57.097123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.687-0.146-0.039
RSTRNT_TEL_NO0.6871.000-0.1480.015
RSTRNT_LA-0.146-0.1481.0000.371
RSTRNT_LO-0.0390.0150.3711.000

Missing values

2023-12-10T19:02:50.830134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:02:51.037701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RSTRNT_IDCOUNTRY_NMCTY_NMRSTRNT_NMRSTRNT_ADDRRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
03JAPANTokyoリトル小岩井東京都千代田区大手町1-6-1 大手町ビル B2F8133201202435.686401139.764999
14JAPANTokyoコカレストラン＆マンゴツリーカフェ 有楽町東京都千代田区有楽町1-11-1 ビックカメラ有楽町店 ６Ｆ8133201548935.6754139.763001
25JAPANTokyoラ・メゾン・デュ・ショコラ(丸の内店)東京都千代田区丸の内3-4-1 新国際ビル　１Ｆ8133201600635.6763139.761993
38JAPANTokyoどうとんぼり神座(新宿歌舞伎町店)東京都新宿区歌舞伎町1-14-18133209379035.694401139.701996
49JAPANTokyoすずや(新宿本店)東京都新宿区歌舞伎町1-23-15 杉山ビル　２Ｆ8133209448035.694139.701004
510JAPANTokyo渡なべ東京都新宿区高田馬場2-1-48133209561535.711899139.710007
611JAPANTokyoアピシウス東京都千代田区有楽町1-9-4 蚕糸会館　Ｂ１Ｆ8133214136135.674999139.761002
712JAPANTokyo有楽町コパン・コパン東京都千代田区丸の内3-6-18133217377735.676498139.764999
813JAPANTokyoパティスリー SATSUKI東京都千代田区紀尾井町4-1 ホテルニューオータニ東京 ロビィ階8133221725235.681099139.733993
914JAPANTokyoさぶちゃん東京都千代田区神田神保町2-24 木下ビル １Ｆ8133230125235.697399139.757004
RSTRNT_IDCOUNTRY_NMCTY_NMRSTRNT_NMRSTRNT_ADDRRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
490538JAPANTokyoロックフィッシュ東京都中央区銀座7-2-14 第26ポールスタービル 2F8135537690035.670399139.759995
491539JAPANTokyoさいとう東京都港区赤坂1-9-15 自転車会館　１Ｆ8133589441235.665402139.738998
492540JAPANTokyo立ち飲み 竜馬東京都港区新橋2-13-3 ＡＬＣ．ＢＩＤ　１Ｆ8133591175735.666901139.755005
493541JAPANTokyo信濃神麺 烈士洵名東京都文京区西片1-15-68135684226335.711399139.753998
494542JAPANTokyoランテルナ・マジカ東京都品川区上大崎2-9-26 Ｔ＆Ｈ　Ｍｅｍｏｒｙ　 1Ｆ8136408148835.636601139.716003
495543JAPANTokyo鮨 水谷東京都中央区銀座8-7-7 JUNOビル　９F8133573525835.6684139.761002
496545JAPANTokyo正泰苑(銀座店)東京都中央区銀座5-9-5 チアーズ銀座　９Ｆ8136274500335.6702139.766007
497546JAPANTokyoカシータ東京都渋谷区神宮前5-51-8 ラ・ポルト青山 3Ｆ8135485735335.6628139.710007
498547JAPANTokyoダルマット(西麻布店)東京都港区西麻布1-10-8 第２大晃ビル　Ｂ１Ｆ8133470989935.6609139.723999
499549JAPANTokyo食彩 かどた東京都渋谷区恵比寿西1-1-2 しんみつビル B1F8133780108035.647999139.709