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 has unique valuesUnique
RSTRNT_NM has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:14:09.406543
Analysis finished2023-12-10 10:14:14.103493
Duration4.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.116
Minimum1
Maximum565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:14:14.274817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.95
Q1136.75
median280.5
Q3423.25
95-th percentile537.05
Maximum565
Range564
Interquartile range (IQR)286.5

Descriptive statistics

Standard deviation164.5329
Coefficient of variation (CV)0.58528474
Kurtosis-1.2174024
Mean281.116
Median Absolute Deviation (MAD)143.5
Skewness0.011896873
Sum140558
Variance27071.077
MonotonicityStrictly increasing
2023-12-10T19:14:14.600896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
374 1
 
0.2%
387 1
 
0.2%
386 1
 
0.2%
385 1
 
0.2%
384 1
 
0.2%
383 1
 
0.2%
382 1
 
0.2%
381 1
 
0.2%
380 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
565 1
0.2%
564 1
0.2%
563 1
0.2%
561 1
0.2%
560 1
0.2%
559 1
0.2%
558 1
0.2%
557 1
0.2%
556 1
0.2%
555 1
0.2%

COUNTRY_NM
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CHINA 500
100.0%

Length

2023-12-10T19:14:14.857695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:15.023681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
china 500
100.0%

CTY_NM
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Beijing 500
100.0%

Length

2023-12-10T19:14:15.192873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:15.361423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
beijing 500
100.0%

RSTRNT_NM
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:14:15.677999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length9.598
Min length2

Characters and Unicode

Total characters4799
Distinct characters754
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
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安妮意大利餐厅(朝阳公园店)
2nd row正院大宅门(惠新北里总店)
3rd row满福楼大酒楼
4th row巴国布衣风味酒楼(地安门店)
5th row莫斯科餐厅
ValueCountFrequency (%)
la 2
 
0.4%
安妮意大利餐厅(朝阳公园店 1
 
0.2%
珍巷福地四合院菜馆 1
 
0.2%
海底捞火锅(婚庆大楼店 1
 
0.2%
巴黎贝甜(中关村店 1
 
0.2%
田源鸡火锅(劲松桥店 1
 
0.2%
一茶一坐(西直门店 1
 
0.2%
麻辣诱惑(西直门店 1
 
0.2%
海底捞火锅(马家堡店 1
 
0.2%
全聚德烤鸭店(什刹海店 1
 
0.2%
Other values (498) 498
97.8%
2023-12-10T19:14:16.258973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
店 409
 
8.5%
) 372
 
7.8%
( 372
 
7.8%
西 62
 
1.3%
餐 59
 
1.2%
大 56
 
1.2%
厅 53
 
1.1%
门 45
 
0.9%
锅 43
 
0.9%
村 39
 
0.8%
Other values (744) 3289
68.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3917
81.6%
Close Punctuation 372
 
7.8%
Open Punctuation 372
 
7.8%
Lowercase Letter 61
 
1.3%
Uppercase Letter 59
 
1.2%
Space Separator 9
 
0.2%
Decimal Number 7
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
店 409
 
10.4%
西 62
 
1.6%
餐 59
 
1.5%
大 56
 
1.4%
厅 53
 
1.4%
门 45
 
1.1%
锅 43
 
1.1%
村 39
 
1.0%
酒 37
 
0.9%
楼 36
 
0.9%
Other values (696) 3078
78.6%
Uppercase Letter
ValueCountFrequency (%)
L 10
16.9%
G 6
 
10.2%
M 5
 
8.5%
B 4
 
6.8%
I 4
 
6.8%
A 4
 
6.8%
C 3
 
5.1%
V 3
 
5.1%
D 3
 
5.1%
N 2
 
3.4%
Other values (10) 15
25.4%
Lowercase Letter
ValueCountFrequency (%)
a 8
13.1%
i 8
13.1%
e 7
11.5%
s 6
9.8%
o 4
 
6.6%
l 4
 
6.6%
r 4
 
6.6%
u 3
 
4.9%
p 3
 
4.9%
t 3
 
4.9%
Other values (8) 11
18.0%
Decimal Number
ValueCountFrequency (%)
8 3
42.9%
9 1
 
14.3%
3 1
 
14.3%
2 1
 
14.3%
1 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
• 1
50.0%
' 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 372
100.0%
Open Punctuation
ValueCountFrequency (%)
( 372
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 3916
81.6%
Common 762
 
15.9%
Latin 120
 
2.5%
Hiragana 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
店 409
 
10.4%
西 62
 
1.6%
餐 59
 
1.5%
大 56
 
1.4%
厅 53
 
1.4%
门 45
 
1.1%
锅 43
 
1.1%
村 39
 
1.0%
酒 37
 
0.9%
楼 36
 
0.9%
Other values (695) 3077
78.6%
Latin
ValueCountFrequency (%)
L 10
 
8.3%
a 8
 
6.7%
i 8
 
6.7%
e 7
 
5.8%
G 6
 
5.0%
s 6
 
5.0%
M 5
 
4.2%
B 4
 
3.3%
o 4
 
3.3%
I 4
 
3.3%
Other values (28) 58
48.3%
Common
ValueCountFrequency (%)
) 372
48.8%
( 372
48.8%
9
 
1.2%
8 3
 
0.4%
• 1
 
0.1%
' 1
 
0.1%
9 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%
1 1
 
0.1%
Hiragana
ValueCountFrequency (%)
の 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 3916
81.6%
ASCII 881
 
18.4%
Punctuation 1
 
< 0.1%
Hiragana 1
 
< 0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
店 409
 
10.4%
西 62
 
1.6%
餐 59
 
1.5%
大 56
 
1.4%
厅 53
 
1.4%
门 45
 
1.1%
锅 43
 
1.1%
村 39
 
1.0%
酒 37
 
0.9%
楼 36
 
0.9%
Other values (695) 3077
78.6%
ASCII
ValueCountFrequency (%)
) 372
42.2%
( 372
42.2%
L 10
 
1.1%
9
 
1.0%
a 8
 
0.9%
i 8
 
0.9%
e 7
 
0.8%
G 6
 
0.7%
s 6
 
0.7%
M 5
 
0.6%
Other values (37) 78
 
8.9%
Punctuation
ValueCountFrequency (%)
• 1
100.0%
Hiragana
ValueCountFrequency (%)
の 1
100.0%

RSTRNT_ADDR
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:14:16.644799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length31
Mean length22.248
Min length7

Characters and Unicode

Total characters11124
Distinct characters670
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique492 ?
Unique (%)98.4%

Sample

1st row朝阳区朝阳公园老西门南侧
2nd row朝阳区惠新北里3号楼(罗马花园东)
3rd row西城区地安门内大街38号(近景山公园)
4th row东城区地安门东大街89-3号(平安大街路北)
5th row西城区西直门外大街135号北京展览馆院内(近北京动物园)
ValueCountFrequency (%)
东城区 3
 
0.6%
东城区东长安街1号东方广场东方新天地b1楼 2
 
0.4%
西城区 2
 
0.4%
朝阳区望京街9号望京国际商业中心4楼(近方恒国际中心 2
 
0.4%
西城区西直门外大街乙143号(近家乐福 2
 
0.4%
东城区崇文门外大街18号国瑞购物中心b1楼(近西花市大街 2
 
0.4%
西城区金融大街17号人寿中心大厦3楼301室 1
 
0.2%
朝阳区朝阳公园老西门南侧 1
 
0.2%
东城区前门东大街甲2号(祈年大街北口 1
 
0.2%
海淀区王庄路甲1号(西郊宾馆西门北侧 1
 
0.2%
Other values (495) 495
96.7%
2023-12-10T19:14:17.313096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
号 547
 
4.9%
区 534
 
4.8%
1 379
 
3.4%
) 351
 
3.2%
( 351
 
3.2%
大 317
 
2.8%
东 298
 
2.7%
西 292
 
2.6%
街 279
 
2.5%
楼 271
 
2.4%
Other values (660) 7505
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8814
79.2%
Decimal Number 1309
 
11.8%
Close Punctuation 351
 
3.2%
Open Punctuation 351
 
3.2%
Uppercase Letter 172
 
1.5%
Dash Punctuation 56
 
0.5%
Lowercase Letter 48
 
0.4%
Space Separator 19
 
0.2%
Other Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
号 547
 
6.2%
区 534
 
6.1%
大 317
 
3.6%
东 298
 
3.4%
西 292
 
3.3%
街 279
 
3.2%
楼 271
 
3.1%
路 271
 
3.1%
城 237
 
2.7%
北 192
 
2.2%
Other values (617) 5576
63.3%
Uppercase Letter
ValueCountFrequency (%)
B 45
26.2%
L 27
15.7%
A 20
11.6%
C 13
 
7.6%
M 9
 
5.2%
S 8
 
4.7%
O 8
 
4.7%
D 8
 
4.7%
V 8
 
4.7%
F 8
 
4.7%
Other values (6) 18
 
10.5%
Decimal Number
ValueCountFrequency (%)
1 379
29.0%
2 191
14.6%
3 158
12.1%
8 97
 
7.4%
0 92
 
7.0%
6 91
 
7.0%
5 91
 
7.0%
9 78
 
6.0%
4 74
 
5.7%
7 58
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
l 12
25.0%
g 7
14.6%
i 7
14.6%
a 6
12.5%
e 6
12.5%
s 3
 
6.2%
o 2
 
4.2%
c 2
 
4.2%
v 2
 
4.2%
r 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
， 2
66.7%
、 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 351
100.0%
Open Punctuation
ValueCountFrequency (%)
( 351
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 8814
79.2%
Common 2090
 
18.8%
Latin 220
 
2.0%

Most frequent character per script

Han
ValueCountFrequency (%)
号 547
 
6.2%
区 534
 
6.1%
大 317
 
3.6%
东 298
 
3.4%
西 292
 
3.3%
街 279
 
3.2%
楼 271
 
3.1%
路 271
 
3.1%
城 237
 
2.7%
北 192
 
2.2%
Other values (617) 5576
63.3%
Latin
ValueCountFrequency (%)
B 45
20.5%
L 27
12.3%
A 20
 
9.1%
C 13
 
5.9%
l 12
 
5.5%
M 9
 
4.1%
S 8
 
3.6%
O 8
 
3.6%
D 8
 
3.6%
V 8
 
3.6%
Other values (16) 62
28.2%
Common
ValueCountFrequency (%)
1 379
18.1%
) 351
16.8%
( 351
16.8%
2 191
9.1%
3 158
7.6%
8 97
 
4.6%
0 92
 
4.4%
6 91
 
4.4%
5 91
 
4.4%
9 78
 
3.7%
Other values (7) 211
10.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 8814
79.2%
ASCII 2307
 
20.7%
None 3
 
< 0.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
号 547
 
6.2%
区 534
 
6.1%
大 317
 
3.6%
东 298
 
3.4%
西 292
 
3.3%
街 279
 
3.2%
楼 271
 
3.1%
路 271
 
3.1%
城 237
 
2.7%
北 192
 
2.2%
Other values (617) 5576
63.3%
ASCII
ValueCountFrequency (%)
1 379
16.4%
) 351
15.2%
( 351
15.2%
2 191
8.3%
3 158
 
6.8%
8 97
 
4.2%
0 92
 
4.0%
6 91
 
3.9%
5 91
 
3.9%
9 78
 
3.4%
Other values (31) 428
18.6%
None
ValueCountFrequency (%)
， 2
66.7%
、 1
33.3%

RSTRNT_TEL_NO
Real number (ℝ)

Distinct493
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2024039 × 1012
Minimum8.610511 × 1011
Maximum8.6851812 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:14:17.667582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.610511 × 1011
5-th percentile8.6105802 × 1011
Q18.6106347 × 1011
median8.6106552 × 1011
Q38.6108257 × 1011
95-th percentile8.6400112 × 1011
Maximum8.6851812 × 1013
Range8.5990761 × 1013
Interquartile range (IQR)19099070

Descriptive statistics

Standard deviation1.013064 × 1013
Coefficient of variation (CV)4.5998101
Kurtosis65.678744
Mean2.2024039 × 1012
Median Absolute Deviation (MAD)3067579
Skewness8.173061
Sum1.1012019 × 1015
Variance1.0262986 × 1026
MonotonicityNot monotonic
2023-12-10T19:14:17.894163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864006766111 4
 
0.8%
864008209777 3
 
0.6%
864001081717 2
 
0.4%
861082151999 2
 
0.4%
861067577027 1
 
0.2%
861062310832 1
 
0.2%
861062652729 1
 
0.2%
861067022078 1
 
0.2%
861067020867 1
 
0.2%
861066171570 1
 
0.2%
Other values (483) 483
96.6%
ValueCountFrequency (%)
861051096013 1
0.2%
861051160000 1
0.2%
861051199575 1
0.2%
861051203232 1
0.2%
861051281258 1
0.2%
861051281888 1
0.2%
861051283315 1
0.2%
861051283316 1
0.2%
861051283329 1
0.2%
861051391798 1
0.2%
ValueCountFrequency (%)
86851812343628 1
0.2%
86685822331666 1
0.2%
86684122116715 1
0.2%
86683322666613 1
0.2%
86651333661679 1
0.2%
86651222776101 1
0.2%
86646533884227 1
0.2%
8664281188106 1
0.2%
8615801207597 1
0.2%
8613426209148 1
0.2%

RSTRNT_LA
Real number (ℝ)

Distinct481
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.940767
Minimum39.809181
Maximum40.426563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:14:18.137142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.809181
5-th percentile39.884227
Q139.911066
median39.936972
Q339.970212
95-th percentile39.994504
Maximum40.426563
Range0.6173821
Interquartile range (IQR)0.059145925

Descriptive statistics

Standard deviation0.043264346
Coefficient of variation (CV)0.0010832127
Kurtosis31.537639
Mean39.940767
Median Absolute Deviation (MAD)0.02714545
Skewness3.0103755
Sum19970.383
Variance0.0018718036
MonotonicityNot monotonic
2023-12-10T19:14:18.602369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.9111824 3
 
0.6%
39.9925423 3
 
0.6%
39.9926758 3
 
0.6%
39.9098091 2
 
0.4%
39.9933891 2
 
0.4%
39.9379082 2
 
0.4%
39.9076233 2
 
0.4%
39.9488297 2
 
0.4%
39.9374085 2
 
0.4%
39.9770012 2
 
0.4%
Other values (471) 477
95.4%
ValueCountFrequency (%)
39.8091812 1
0.2%
39.8461533 1
0.2%
39.846199 1
0.2%
39.8484421 1
0.2%
39.8500404 1
0.2%
39.8642502 1
0.2%
39.8656235 1
0.2%
39.865799 1
0.2%
39.8661118 1
0.2%
39.8662338 1
0.2%
ValueCountFrequency (%)
40.4265633 1
0.2%
40.1297417 1
0.2%
40.0750656 1
0.2%
40.0604706 1
0.2%
40.0540237 1
0.2%
40.0054321 1
0.2%
40.0054016 1
0.2%
40.0045128 1
0.2%
40.0040016 1
0.2%
40.0030098 1
0.2%

RSTRNT_LO
Real number (ℝ)

Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.39398
Minimum116.201
Maximum116.64803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:14:19.216403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116.201
5-th percentile116.3035
Q1116.34605
median116.40345
Q3116.44131
95-th percentile116.4738
Maximum116.64803
Range0.4470367
Interquartile range (IQR)0.095268225

Descriptive statistics

Standard deviation0.059660925
Coefficient of variation (CV)0.00051257743
Kurtosis0.4662564
Mean116.39398
Median Absolute Deviation (MAD)0.0444336
Skewness-0.27824193
Sum58196.989
Variance0.003559426
MonotonicityNot monotonic
2023-12-10T19:14:19.593734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.3392334 3
 
0.6%
116.3392258 3
 
0.6%
116.3726807 3
 
0.6%
116.4544601 2
 
0.4%
116.3730393 2
 
0.4%
116.375351 2
 
0.4%
116.411911 2
 
0.4%
116.4126129 2
 
0.4%
116.4560013 2
 
0.4%
116.4195099 2
 
0.4%
Other values (475) 477
95.4%
ValueCountFrequency (%)
116.2009964 1
0.2%
116.2031784 1
0.2%
116.2097015 1
0.2%
116.2190018 1
0.2%
116.2244263 1
0.2%
116.2259903 1
0.2%
116.2286224 1
0.2%
116.2509537 1
0.2%
116.2584381 1
0.2%
116.2602692 1
0.2%
ValueCountFrequency (%)
116.6480331 1
0.2%
116.5915527 1
0.2%
116.5179977 1
0.2%
116.5015793 1
0.2%
116.4999771 1
0.2%
116.4888001 1
0.2%
116.4850235 1
0.2%
116.4847794 1
0.2%
116.4845429 1
0.2%
116.4826126 1
0.2%

Interactions

2023-12-10T19:14:12.935173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:10.950249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.506616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:12.223672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:13.092881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.091721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.695531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:12.410947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:13.242936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.226471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.844152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:12.580252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:13.407452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:11.372969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:12.046179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:14:12.774830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:14:19.839585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.2440.0360.330
RSTRNT_TEL_NO0.2441.0000.0000.000
RSTRNT_LA0.0360.0001.0000.808
RSTRNT_LO0.3300.0000.8081.000
2023-12-10T19:14:20.054680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
RSTRNT_ID1.000-0.0810.042-0.004
RSTRNT_TEL_NO-0.0811.000-0.098-0.027
RSTRNT_LA0.042-0.0981.000-0.229
RSTRNT_LO-0.004-0.027-0.2291.000

Missing values

2023-12-10T19:14:13.674442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:14:13.976289image/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
01CHINABeijing安妮意大利餐厅(朝阳公园店)朝阳区朝阳公园老西门南侧86106591193139.937116.474968
12CHINABeijing正院大宅门(惠新北里总店)朝阳区惠新北里3号楼(罗马花园东)86106495216639.983711116.420281
23CHINABeijing满福楼大酒楼西城区地安门内大街38号(近景山公园)86106405308839.929352116.396751
34CHINABeijing巴国布衣风味酒楼(地安门店)东城区地安门东大街89-3号(平安大街路北)86106400888839.93354116.402313
45CHINABeijing莫斯科餐厅西城区西直门外大街135号北京展览馆院内(近北京动物园)86106831675839.940319116.342918
56CHINABeijing咖啡茶自助餐厅海淀区紫竹院路29号北京香格里拉饭店景阁1楼8668412211671539.944351116.308556
67CHINABeijing金湖茶餐厅(国贸总店)朝阳区建国门外大街1号国贸大厦B1楼SB127B号(近国贸二座)86106505686839.9095116.457962
78CHINABeijing星期五餐厅(中关村店)海淀区中关村南大街1号友谊宾馆贵宾楼1楼(近华宇购物中心)86106849873839.963638116.319359
89CHINABeijing福楼法餐厅朝阳区霄云路18号86106595513539.961338116.467293
910CHINABeijing马克西姆(崇文门店)东城区崇文门西大街2号崇文门饭店1楼86106512199239.900532116.417656
RSTRNT_IDCOUNTRY_NMCTY_NMRSTRNT_NMRSTRNT_ADDRRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
490555CHINABeijing川成元麻辣香锅(国瑞店)东城区崇文门外大街18号国瑞购物中心B1楼(近西花市大街)86106714785939.897839116.419792
491556CHINABeijing陈阿婆鱼火锅(长椿街店)西城区长椿街甲16号86106301638239.894501116.364166
492557CHINABeijing双流老妈兔头(双井店)朝阳区东三环南路48号(地铁双井站C口南)86106540585839.890694116.462776
493558CHINABeijing一期一会日本料理东城区鼓楼东大街南锣鼓巷黑芝麻胡同8号(近茅盾故居)861342620914839.938393116.40226
494559CHINABeijing满记甜品(来福士店)东城区东直门南大街1号来福士购物中心B1楼B1-13B商铺(东直门桥西南角)86108409422839.940064116.433243
495560CHINABeijing张生记(西湖汇店)东城区崇文门外大街18号国瑞购物中心3楼南(近西花市大街)86106713311639.898491116.419701
496561CHINABeijing巴扎童嘎藏餐吧朝阳区新东路甲5-2路(东直门外大街口西南角)86106415710739.940735116.450928
497563CHINABeijing金波亭章鱼烧东城区南锣鼓巷90号(近北兵马司胡同)861580120759739.936382116.403229
498564CHINABeijing鹿港小镇(东直门来福士店)东城区东直门南大街1号来福士购物中心内(近东直门地铁站)86108409807539.94059116.432991
499565CHINABeijing汉拿山(来福士店)东城区东直门南大街1号来福士购物中心5楼(近东直门地铁站)86108409816639.939888116.432282