Overview

Dataset statistics

Number of variables23
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory194.6 B

Variable types

Text1
Categorical14
Numeric2
Boolean6

Alerts

FNCTECH_MTHD_FUND_FTRS_AT has constant value ""Constant
FNCTECH_MTHD_PCUMTL_AT has constant value ""Constant
FNCTECH_MTHD_GLD_AT has constant value ""Constant
INVT_FNCTECH_SE is highly imbalanced (75.8%)Imbalance
INVT_FNCTECH_NM is highly imbalanced (75.8%)Imbalance
FNCTECH_MTHD_INDVDL_INTNET_LON_AT is highly imbalanced (85.9%)Imbalance
FNCTECH_MTHD_GNRLZ_AT is highly imbalanced (85.9%)Imbalance
USID has unique valuesUnique
RESIDE_PROV_CL has 9 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:05:41.719845
Analysis finished2023-12-10 10:05:42.165565
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

USID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:05:42.452044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1800
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowe44d0b36-3949-4a79-8200-0319f12fd071
2nd rowb518f2bc-5307-4ae6-8120-c4127c0704c5
3rd row40b37bd1-866b-4f9d-859e-59dcfda25afb
4th rowded94db7-61db-42a2-82da-100feb498161
5th row01b35055-8a62-44e3-837e-4be25df68caf
ValueCountFrequency (%)
e44d0b36-3949-4a79-8200-0319f12fd071 1
 
2.0%
2b64a8e4-5031-4191-b917-8dafb01f67c4 1
 
2.0%
29cca72e-c021-4491-b6ae-0bd805672b3f 1
 
2.0%
e8d4a889-297a-4962-81e6-0c6ac0728012 1
 
2.0%
6eb69106-2f51-48d7-8f02-0d6ab2d692a6 1
 
2.0%
31294d62-7811-4315-9b2f-5efd422c3441 1
 
2.0%
2b99a960-a8e8-4bc9-b555-f4f1fa4d080d 1
 
2.0%
9f8eb632-bdbc-41e0-806a-0ffc1493715a 1
 
2.0%
39ef1f19-b90b-49d8-ae56-49bd3e736c55 1
 
2.0%
efcea5ad-dbcb-4d82-b6a4-4b54eadf3c5c 1
 
2.0%
Other values (40) 40
80.0%
2023-12-10T19:05:43.067418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200
 
11.1%
4 166
 
9.2%
b 123
 
6.8%
9 118
 
6.6%
a 111
 
6.2%
0 107
 
5.9%
8 99
 
5.5%
6 99
 
5.5%
e 93
 
5.2%
f 92
 
5.1%
Other values (7) 592
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1015
56.4%
Lowercase Letter 585
32.5%
Dash Punctuation 200
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 166
16.4%
9 118
11.6%
0 107
10.5%
8 99
9.8%
6 99
9.8%
2 90
8.9%
1 87
8.6%
5 87
8.6%
7 84
8.3%
3 78
7.7%
Lowercase Letter
ValueCountFrequency (%)
b 123
21.0%
a 111
19.0%
e 93
15.9%
f 92
15.7%
d 91
15.6%
c 75
12.8%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1215
67.5%
Latin 585
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 200
16.5%
4 166
13.7%
9 118
9.7%
0 107
8.8%
8 99
8.1%
6 99
8.1%
2 90
7.4%
1 87
7.2%
5 87
7.2%
7 84
6.9%
Latin
ValueCountFrequency (%)
b 123
21.0%
a 111
19.0%
e 93
15.9%
f 92
15.7%
d 91
15.6%
c 75
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 200
 
11.1%
4 166
 
9.2%
b 123
 
6.8%
9 118
 
6.6%
a 111
 
6.2%
0 107
 
5.9%
8 99
 
5.5%
6 99
 
5.5%
e 93
 
5.2%
f 92
 
5.1%
Other values (7) 592
32.9%

CITY_GRAD_CL
Categorical

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
1
21 
2
13 
3
11 
4
3 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 21
42.0%
2 13
26.0%
3 11
22.0%
4 3
 
6.0%
5 2
 
4.0%

Length

2023-12-10T19:05:43.324062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:43.528013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 21
42.0%
2 13
26.0%
3 11
22.0%
4 3
 
6.0%
5 2
 
4.0%

CITY_GRAD_NM
Categorical

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
1선도시
21 
신1선도시
13 
2선도시
11 
3선도시
3 
4선도시
 
2

Length

Max length5
Median length4
Mean length4.26
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신1선도시
2nd row2선도시
3rd row1선도시
4th row신1선도시
5th row2선도시

Common Values

ValueCountFrequency (%)
1선도시 21
42.0%
신1선도시 13
26.0%
2선도시 11
22.0%
3선도시 3
 
6.0%
4선도시 2
 
4.0%

Length

2023-12-10T19:05:43.754440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:43.948306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1선도시 21
42.0%
신1선도시 13
26.0%
2선도시 11
22.0%
3선도시 3
 
6.0%
4선도시 2
 
4.0%

RESIDE_PROV_CL
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2
Minimum0
Maximum24
Zeros9
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:05:44.165112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37.75
95-th percentile17.75
Maximum24
Range24
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation5.9074495
Coefficient of variation (CV)1.136048
Kurtosis1.8034962
Mean5.2
Median Absolute Deviation (MAD)3
Skewness1.5075375
Sum260
Variance34.897959
MonotonicityNot monotonic
2023-12-10T19:05:44.459247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 9
18.0%
0 9
18.0%
2 6
12.0%
6 5
10.0%
4 4
8.0%
8 3
 
6.0%
15 3
 
6.0%
10 2
 
4.0%
20 2
 
4.0%
3 2
 
4.0%
Other values (5) 5
10.0%
ValueCountFrequency (%)
0 9
18.0%
1 9
18.0%
2 6
12.0%
3 2
 
4.0%
4 4
8.0%
5 1
 
2.0%
6 5
10.0%
7 1
 
2.0%
8 3
 
6.0%
9 1
 
2.0%
ValueCountFrequency (%)
24 1
 
2.0%
20 2
 
4.0%
15 3
6.0%
13 1
 
2.0%
10 2
 
4.0%
9 1
 
2.0%
8 3
6.0%
7 1
 
2.0%
6 5
10.0%
5 1
 
2.0%

RESIDE_PROV_NM
Categorical

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
광동
9 
북경
9 
상해
6 
강소
5 
호북
4 
Other values (10)
17 

Length

Max length3
Median length2
Mean length2.04
Min length2

Unique

Unique5 ?
Unique (%)10.0%

Sample

1st row호북
2nd row신강
3rd row광동
4th row강소
5th row광동

Common Values

ValueCountFrequency (%)
광동 9
18.0%
북경 9
18.0%
상해 6
12.0%
강소 5
10.0%
호북 4
8.0%
산동 3
 
6.0%
복건 3
 
6.0%
사천 2
 
4.0%
흑룡강 2
 
4.0%
하남 2
 
4.0%
Other values (5) 5
10.0%

Length

2023-12-10T19:05:44.704596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광동 9
18.0%
북경 9
18.0%
상해 6
12.0%
강소 5
10.0%
호북 4
8.0%
산동 3
 
6.0%
복건 3
 
6.0%
사천 2
 
4.0%
흑룡강 2
 
4.0%
하남 2
 
4.0%
Other values (5) 5
10.0%

ONLINE_CNSMP_ABLTY_SE
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
27 
1
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 27
54.0%
1 23
46.0%

Length

2023-12-10T19:05:44.903542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:45.082436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
54.0%
1 23
46.0%

ONLINE_CNSMP_ABLTY_NM
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
하
27 
상
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하
2nd row하
3rd row상
4th row하
5th row상

Common Values

ValueCountFrequency (%)
하 27
54.0%
상 23
46.0%

Length

2023-12-10T19:05:45.256484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:45.420370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하 27
54.0%
상 23
46.0%

ONLINE_CNSMP_INTEN_SE
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
27 
1
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 27
54.0%
1 23
46.0%

Length

2023-12-10T19:05:45.710067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:45.892708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
54.0%
1 23
46.0%

ONLINE_CNSMP_INTEN_NM
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
하
27 
상
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하
2nd row하
3rd row상
4th row하
5th row상

Common Values

ValueCountFrequency (%)
하 27
54.0%
상 23
46.0%

Length

2023-12-10T19:05:46.079115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:46.255716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하 27
54.0%
상 23
46.0%

ASSETS_IDEX_SE
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
28 
1
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 28
56.0%
1 22
44.0%

Length

2023-12-10T19:05:46.449786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:46.645737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
56.0%
1 22
44.0%

ASSETS_IDEX_NM
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
하
28 
상
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하
2nd row하
3rd row상
4th row하
5th row상

Common Values

ValueCountFrequency (%)
하 28
56.0%
상 22
44.0%

Length

2023-12-10T19:05:46.833404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:47.053723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하 28
56.0%
상 22
44.0%

INVT_FNCTECH_SE
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
48 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Length

2023-12-10T19:05:47.263038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:47.477849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

INVT_FNCTECH_NM
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
하
48 
상
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
하 48
96.0%
상 2
 
4.0%

Length

2023-12-10T19:05:47.661175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:47.812498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하 48
96.0%
상 2
 
4.0%

TRMNL_BRAND_CL
Real number (ℝ)

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.08
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:05:47.961334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32.75
95-th percentile56.7
Maximum99
Range98
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation23.21843
Coefficient of variation (CV)2.8735681
Kurtosis13.083572
Mean8.08
Median Absolute Deviation (MAD)0
Skewness3.812901
Sum404
Variance539.09551
MonotonicityNot monotonic
2023-12-10T19:05:48.180663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 33
66.0%
4 5
 
10.0%
3 4
 
8.0%
1 4
 
8.0%
99 3
 
6.0%
5 1
 
2.0%
ValueCountFrequency (%)
1 4
 
8.0%
2 33
66.0%
3 4
 
8.0%
4 5
 
10.0%
5 1
 
2.0%
99 3
 
6.0%
ValueCountFrequency (%)
99 3
 
6.0%
5 1
 
2.0%
4 5
 
10.0%
3 4
 
8.0%
2 33
66.0%
1 4
 
8.0%

TRMNL_BRAND_NM
Categorical

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
화웨이
33 
샤오미
5 
OPPO
4 
VIVO
4 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.2
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row샤오미
2nd row화웨이
3rd rowOPPO
4th row화웨이
5th row화웨이

Common Values

ValueCountFrequency (%)
화웨이 33
66.0%
샤오미 5
 
10.0%
OPPO 4
 
8.0%
VIVO 4
 
8.0%
<NA> 3
 
6.0%
삼성 1
 
2.0%

Length

2023-12-10T19:05:48.450088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:48.665604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화웨이 33
66.0%
샤오미 5
 
10.0%
oppo 4
 
8.0%
vivo 4
 
8.0%
na 3
 
6.0%
삼성 1
 
2.0%

TRMNL_PC_CL
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3
26 
1
13 
2
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 26
52.0%
1 13
26.0%
2 11
22.0%

Length

2023-12-10T19:05:48.883952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:49.053910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 26
52.0%
1 13
26.0%
2 11
22.0%

TRMNL_PC_NM
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3000CNY이상
26 
1000-2000CNY
13 
2000-3000CNY
11 

Length

Max length12
Median length9
Mean length10.44
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000-2000CNY
2nd row1000-2000CNY
3rd row3000CNY이상
4th row3000CNY이상
5th row3000CNY이상

Common Values

ValueCountFrequency (%)
3000CNY이상 26
52.0%
1000-2000CNY 13
26.0%
2000-3000CNY 11
22.0%

Length

2023-12-10T19:05:49.229006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:49.863117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000cny이상 26
52.0%
1000-2000cny 13
26.0%
2000-3000cny 11
22.0%

FNCTECH_MTHD_SCRITS_AT
Boolean

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
44 
True
6 
ValueCountFrequency (%)
False 44
88.0%
True 6
 
12.0%
2023-12-10T19:05:49.994081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FNCTECH_MTHD_INDVDL_INTNET_LON_AT
Boolean

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
49 
True
 
1
ValueCountFrequency (%)
False 49
98.0%
True 1
 
2.0%
2023-12-10T19:05:50.118499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FNCTECH_MTHD_FUND_FTRS_AT
Boolean

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
50 
ValueCountFrequency (%)
False 50
100.0%
2023-12-10T19:05:50.242496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FNCTECH_MTHD_PCUMTL_AT
Boolean

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
50 
ValueCountFrequency (%)
False 50
100.0%
2023-12-10T19:05:50.367444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FNCTECH_MTHD_GNRLZ_AT
Boolean

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
49 
True
 
1
ValueCountFrequency (%)
False 49
98.0%
True 1
 
2.0%
2023-12-10T19:05:50.555334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FNCTECH_MTHD_GLD_AT
Boolean

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
50 
ValueCountFrequency (%)
False 50
100.0%
2023-12-10T19:05:50.696201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

USIDCITY_GRAD_CLCITY_GRAD_NMRESIDE_PROV_CLRESIDE_PROV_NMONLINE_CNSMP_ABLTY_SEONLINE_CNSMP_ABLTY_NMONLINE_CNSMP_INTEN_SEONLINE_CNSMP_INTEN_NMASSETS_IDEX_SEASSETS_IDEX_NMINVT_FNCTECH_SEINVT_FNCTECH_NMTRMNL_BRAND_CLTRMNL_BRAND_NMTRMNL_PC_CLTRMNL_PC_NMFNCTECH_MTHD_SCRITS_ATFNCTECH_MTHD_INDVDL_INTNET_LON_ATFNCTECH_MTHD_FUND_FTRS_ATFNCTECH_MTHD_PCUMTL_ATFNCTECH_MTHD_GNRLZ_ATFNCTECH_MTHD_GLD_AT
0e44d0b36-3949-4a79-8200-0319f12fd0712신1선도시4호북0하0하0하0하4샤오미11000-2000CNYNNNNNN
1b518f2bc-5307-4ae6-8120-c4127c0704c532선도시24신강0하0하0하0하2화웨이11000-2000CNYNNNNNN
240b37bd1-866b-4f9d-859e-59dcfda25afb11선도시1광동1상1상1상0하3OPPO33000CNY이상NNNNNN
3ded94db7-61db-42a2-82da-100feb4981612신1선도시6강소0하0하0하0하2화웨이33000CNY이상NNNNNN
401b35055-8a62-44e3-837e-4be25df68caf32선도시1광동1상1상1상0하2화웨이33000CNY이상NNNNNN
5afcee613-276e-41f7-a0d6-b1b969ae3f4743선도시6강소0하0하0하0하2화웨이11000-2000CNYNNNNNN
68d80840a-7202-41f9-8d92-4bb20743904a11선도시2상해1상1상1상0하4샤오미11000-2000CNYNNNNNN
759984f7b-f8b9-48af-979f-35404eba686f11선도시0북경0하0하0하0하2화웨이22000-3000CNYNNNNNN
8df4b829a-48fc-490b-a56a-6d605aa4dea943선도시8산동0하0하0하0하2화웨이33000CNY이상NNNNNN
947b3e9bb-3a95-4c88-95d1-f9d3691e735911선도시2상해0하0하0하0하2화웨이22000-3000CNYYNNNNN
USIDCITY_GRAD_CLCITY_GRAD_NMRESIDE_PROV_CLRESIDE_PROV_NMONLINE_CNSMP_ABLTY_SEONLINE_CNSMP_ABLTY_NMONLINE_CNSMP_INTEN_SEONLINE_CNSMP_INTEN_NMASSETS_IDEX_SEASSETS_IDEX_NMINVT_FNCTECH_SEINVT_FNCTECH_NMTRMNL_BRAND_CLTRMNL_BRAND_NMTRMNL_PC_CLTRMNL_PC_NMFNCTECH_MTHD_SCRITS_ATFNCTECH_MTHD_INDVDL_INTNET_LON_ATFNCTECH_MTHD_FUND_FTRS_ATFNCTECH_MTHD_PCUMTL_ATFNCTECH_MTHD_GNRLZ_ATFNCTECH_MTHD_GLD_AT
4087cb1031-cd23-4c79-bd0d-a9bb2d9ff6b211선도시2상해1상1상1상0하2화웨이33000CNY이상NNNNNN
419494766c-adaf-4d92-acb2-0b418057eda932선도시6강소1상1상0하0하2화웨이11000-2000CNYNNNNNN
42abbcaf53-98f7-48c3-a653-476d9ab2254b2신1선도시1광동0하0하0하0하1VIVO33000CNY이상YNNNNN
4306fe6faa-23f0-4821-a00e-1b7492877f4611선도시1광동1상1상1상0하2화웨이33000CNY이상NNNNNN
4410eebe7a-6c0e-470d-84aa-166ffce9367911선도시2상해1상1상1상0하2화웨이33000CNY이상NNNNNN
45767821d8-6155-4624-b9d2-287bb57955d811선도시0북경0하0하0하0하99<NA>11000-2000CNYNNNNYN
4691297123-7530-48d8-a9d4-f3497023c9c432선도시15복건1상1상1상0하3OPPO22000-3000CNYNNNNNN
47b5e7be7d-9f07-4825-8278-4afbd146ddef2신1선도시5호남0하0하1상1상3OPPO22000-3000CNYNNNNNN
4829cca72e-c021-4491-b6ae-0bd805672b3f2신1선도시6강소0하0하0하0하2화웨이11000-2000CNYNNNNNN
49aa0e0494-43de-4b1c-b370-e1a46ece3e6a2신1선도시9섬서1상1상1상0하2화웨이33000CNY이상NNNNNN