Overview

由ShuYouQi为您生成

Dataset statistics

Number of variables7
Number of observations358
Missing cells50
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.7 KiB
Average record size in memory56.4 B

Variable types

DateTime1
Numeric6

Alerts

德国 is highly overall correlated with 意大利 and 4 other fieldsHigh correlation
意大利 is highly overall correlated with 德国 and 4 other fieldsHigh correlation
法国 is highly overall correlated with 德国 and 4 other fieldsHigh correlation
英国 is highly overall correlated with 德国 and 4 other fieldsHigh correlation
葡萄牙 is highly overall correlated with 德国 and 4 other fieldsHigh correlation
西班牙 is highly overall correlated with 德国 and 4 other fieldsHigh correlation
法国 has 36 (10.1%) missing values Missing
英国 has 12 (3.4%) missing values Missing
日期 has unique values Unique
意大利 has unique values Unique
德国 has unique values Unique
葡萄牙 has unique values Unique

Reproduction

Analysis started2025-03-15 07:30:26.399283
Analysis finished2025-03-15 07:30:29.285151
Duration2.89 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

日期
Date

Unique 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Minimum1990-01-01 00:00:00
Maximum2019-10-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-15T15:30:29.351276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:29.457266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

意大利
Real number (ℝ)

High correlation  Unique 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5997803.9
Minimum2543920
Maximum11933697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:29.556274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2543920
5-th percentile2956995.8
Q14306023.2
median5679990
Q37386237
95-th percentile10194287
Maximum11933697
Range9389777
Interquartile range (IQR)3080213.8

Descriptive statistics

Standard deviation2181899.8
Coefficient of variation (CV)0.36378311
Kurtosis-0.16622725
Mean5997803.9
Median Absolute Deviation (MAD)1518739.5
Skewness0.61589279
Sum2.1472138 × 109
Variance4.7606867 × 1012
MonotonicityNot monotonic
2025-03-15T15:30:29.647060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7692388 1
 
0.3%
2543920 1
 
0.3%
2871632 1
 
0.3%
3774702 1
 
0.3%
5107712 1
 
0.3%
4738376 1
 
0.3%
10728751 1
 
0.3%
9066367 1
 
0.3%
8025649 1
 
0.3%
6779476 1
 
0.3%
Other values (348) 348
97.2%
ValueCountFrequency (%)
2543920 1
0.3%
2544782 1
0.3%
2645773 1
0.3%
2684279 1
0.3%
2714727 1
0.3%
2715241 1
0.3%
2725903 1
0.3%
2726843 1
0.3%
2767519 1
0.3%
2797069 1
0.3%
ValueCountFrequency (%)
11933697 1
0.3%
11895572 1
0.3%
11817246 1
0.3%
11755553 1
0.3%
11649500 1
0.3%
11567765 1
0.3%
11506828 1
0.3%
11482751 1
0.3%
11422615 1
0.3%
11125505 1
0.3%

法国
Real number (ℝ)

High correlation  Missing 

Distinct322
Distinct (%)100.0%
Missing36
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean8521934.1
Minimum3804493
Maximum13692822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:29.739130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3804493
5-th percentile4707949.1
Q16825020.5
median8409300
Q310409036
95-th percentile11988194
Maximum13692822
Range9888329
Interquartile range (IQR)3584016

Descriptive statistics

Standard deviation2240099
Coefficient of variation (CV)0.26286274
Kurtosis-0.75667102
Mean8521934.1
Median Absolute Deviation (MAD)1745580
Skewness0.054481585
Sum2.7440628 × 109
Variance5.0180435 × 1012
MonotonicityNot monotonic
2025-03-15T15:30:29.836839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9234944 1
 
0.3%
8017670 1
 
0.3%
6640575 1
 
0.3%
6351714 1
 
0.3%
7069474 1
 
0.3%
6964427 1
 
0.3%
8894445 1
 
0.3%
10433344 1
 
0.3%
11705427 1
 
0.3%
6369735 1
 
0.3%
Other values (312) 312
87.2%
(Missing) 36
 
10.1%
ValueCountFrequency (%)
3804493 1
0.3%
4117549 1
0.3%
4132139 1
0.3%
4179622 1
0.3%
4231516 1
0.3%
4241163 1
0.3%
4260861 1
0.3%
4269970 1
0.3%
4355321 1
0.3%
4368478 1
0.3%
ValueCountFrequency (%)
13692822 1
0.3%
13411499 1
0.3%
13174390 1
0.3%
13071877 1
0.3%
13065128 1
0.3%
13060638 1
0.3%
12940902 1
0.3%
12913800 1
0.3%
12855608 1
0.3%
12670939 1
0.3%

德国
Real number (ℝ)

High correlation  Unique 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8090285
Minimum3185877
Maximum14744389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:29.935091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3185877
5-th percentile4222534
Q16112370.5
median7790691
Q39760265
95-th percentile13114148
Maximum14744389
Range11558512
Interquartile range (IQR)3647894.5

Descriptive statistics

Standard deviation2679517
Coefficient of variation (CV)0.3312018
Kurtosis-0.47924369
Mean8090285
Median Absolute Deviation (MAD)1853772
Skewness0.46531429
Sum2.896322 × 109
Variance7.1798112 × 1012
MonotonicityNot monotonic
2025-03-15T15:30:30.030391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13780441 1
 
0.3%
3185877 1
 
0.3%
3588879 1
 
0.3%
4272437 1
 
0.3%
4689424 1
 
0.3%
6045278 1
 
0.3%
13773254 1
 
0.3%
13113329 1
 
0.3%
11666825 1
 
0.3%
11004822 1
 
0.3%
Other values (348) 348
97.2%
ValueCountFrequency (%)
3185877 1
0.3%
3384978 1
0.3%
3398861 1
0.3%
3588879 1
0.3%
3599130 1
0.3%
3622211 1
0.3%
3656181 1
0.3%
3677255 1
0.3%
3683350 1
0.3%
3743291 1
0.3%
ValueCountFrequency (%)
14744389 1
0.3%
14570339 1
0.3%
14373815 1
0.3%
14302813 1
0.3%
14278769 1
0.3%
14182815 1
0.3%
14044382 1
0.3%
14009901 1
0.3%
13931767 1
0.3%
13910286 1
0.3%

葡萄牙
Real number (ℝ)

High correlation  Unique 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean965312.11
Minimum315653
Maximum2531809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:30.130412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum315653
5-th percentile395391.1
Q1629541.25
median845573
Q31162228
95-th percentile1993760.1
Maximum2531809
Range2216156
Interquartile range (IQR)532686.75

Descriptive statistics

Standard deviation467034.75
Coefficient of variation (CV)0.48381735
Kurtosis0.84183295
Mean965312.11
Median Absolute Deviation (MAD)252024
Skewness1.1262955
Sum3.4558174 × 108
Variance2.1812146 × 1011
MonotonicityNot monotonic
2025-03-15T15:30:30.231951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1995942 1
 
0.3%
325138 1
 
0.3%
381539 1
 
0.3%
493957 1
 
0.3%
635822 1
 
0.3%
609952 1
 
0.3%
1993375 1
 
0.3%
1987980 1
 
0.3%
1732371 1
 
0.3%
1480669 1
 
0.3%
Other values (348) 348
97.2%
ValueCountFrequency (%)
315653 1
0.3%
325138 1
0.3%
327298 1
0.3%
328842 1
0.3%
342045 1
0.3%
343732 1
0.3%
346012 1
0.3%
357375 1
0.3%
361735 1
0.3%
364478 1
0.3%
ValueCountFrequency (%)
2531809 1
0.3%
2433537 1
0.3%
2424592 1
0.3%
2305078 1
0.3%
2263748 1
0.3%
2208608 1
0.3%
2205705 1
0.3%
2190642 1
0.3%
2185167 1
0.3%
2143639 1
0.3%

西班牙
Real number (ℝ)

High correlation 

Distinct356
Distinct (%)100.0%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5599948.2
Minimum1532720
Maximum12893366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:30.328114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1532720
5-th percentile1955359.2
Q13544910.5
median5118159
Q37438192
95-th percentile10569705
Maximum12893366
Range11360646
Interquartile range (IQR)3893281.5

Descriptive statistics

Standard deviation2631502.8
Coefficient of variation (CV)0.46991557
Kurtosis-0.42069448
Mean5599948.2
Median Absolute Deviation (MAD)1847254.5
Skewness0.5980114
Sum1.9935816 × 109
Variance6.9248072 × 1012
MonotonicityNot monotonic
2025-03-15T15:30:30.436615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7819089 1
 
0.3%
6790660 1
 
0.3%
5714563 1
 
0.3%
4640082 1
 
0.3%
3783809 1
 
0.3%
4430436 1
 
0.3%
4725546 1
 
0.3%
7027737 1
 
0.3%
7833384 1
 
0.3%
9445871 1
 
0.3%
Other values (346) 346
96.6%
(Missing) 2
 
0.6%
ValueCountFrequency (%)
1532720 1
0.3%
1604591 1
0.3%
1645561 1
0.3%
1684492 1
0.3%
1705715 1
0.3%
1723786 1
0.3%
1732062 1
0.3%
1766397 1
0.3%
1786666 1
0.3%
1795056 1
0.3%
ValueCountFrequency (%)
12893366 1
0.3%
12500596 1
0.3%
12335399 1
0.3%
12187504 1
0.3%
12097382 1
0.3%
11908505 1
0.3%
11772679 1
0.3%
11707840 1
0.3%
11605320 1
0.3%
11344295 1
0.3%

英国
Real number (ℝ)

High correlation  Missing 

Distinct343
Distinct (%)99.1%
Missing12
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean5263240.7
Minimum1530000
Maximum8962949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-03-15T15:30:30.555271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1530000
5-th percentile2436938.5
Q14130292.5
median5273998
Q36464290.5
95-th percentile7746375.2
Maximum8962949
Range7432949
Interquartile range (IQR)2333998

Descriptive statistics

Standard deviation1650879.6
Coefficient of variation (CV)0.31366218
Kurtosis-0.66140687
Mean5263240.7
Median Absolute Deviation (MAD)1175041
Skewness-0.18456679
Sum1.8210813 × 109
Variance2.7254033 × 1012
MonotonicityNot monotonic
2025-03-15T15:30:30.671475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4120000 2
 
0.6%
2790000 2
 
0.6%
6847483 2
 
0.6%
6872769 1
 
0.3%
6209296 1
 
0.3%
6919401 1
 
0.3%
6287778 1
 
0.3%
5194091 1
 
0.3%
4368478 1
 
0.3%
3441319 1
 
0.3%
Other values (333) 333
93.0%
(Missing) 12
 
3.4%
ValueCountFrequency (%)
1530000 1
0.3%
1600000 1
0.3%
1720000 1
0.3%
1730000 1
0.3%
1767000 1
0.3%
1776000 1
0.3%
1800000 1
0.3%
1915000 1
0.3%
1920000 1
0.3%
1970000 1
0.3%
ValueCountFrequency (%)
8962949 1
0.3%
8959000 1
0.3%
8889049 1
0.3%
8803192 1
0.3%
8371376 1
0.3%
8299313 1
0.3%
8241038 1
0.3%
8218437 1
0.3%
8149346 1
0.3%
8117000 1
0.3%

Interactions

2025-03-15T15:30:28.501416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.455975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.920388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.286952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.735422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.129612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.564949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.514326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.980840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.346187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.809098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.191243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.629761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.572900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.040337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.418120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.885206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.252377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.695373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.633819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.100120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.494238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.946136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.315111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.760514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.692840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.161357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.576015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.004357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.375714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.826154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:26.853182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.222159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:27.652942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.066154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-15T15:30:28.437149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-15T15:30:30.753590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
德国意大利法国英国葡萄牙西班牙
德国1.0000.8310.8140.8180.9470.942
意大利0.8311.0000.9640.8370.9170.897
法国0.8140.9641.0000.8690.8870.904
英国0.8180.8370.8691.0000.8460.888
葡萄牙0.9470.9170.8870.8461.0000.967
西班牙0.9420.8970.9040.8880.9671.000

Missing values

2025-03-15T15:30:28.941794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-15T15:30:29.023657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-15T15:30:29.231525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

日期意大利法国德国葡萄牙西班牙英国
01990-01-012543920NaN31858773251381723786.01776000.0
11990-02-012871632NaN35888793815391885718.02250000.0
21990-03-013774702NaN42724374939572337847.02662000.0
31990-04-015107712NaN46894246358223172302.02645000.0
41990-05-014738376NaN60452786099523072480.03096000.0
51990-06-015001834NaN59044666004513086776.02986000.0
61990-07-015538456NaN61064386784463445909.03796000.0
71990-08-016215162NaN61973678351194066554.04434000.0
81990-09-015872800NaN67648337391873545411.03452000.0
91990-10-014382114NaN62727226180713032533.03360000.0
日期意大利法国德国葡萄牙西班牙英国
3482019-01-0147797807135767.0887493910125725228869.04851435.0
3492019-02-0154019077679625.0960040411482426003840.04798975.0
3502019-03-0164424519118018.01127907615242517542541.05409050.0
3512019-04-0179965643804493.01197414618884009141970.06625861.0
3522019-05-01848462310677739.013931767209832010562869.07600202.0
3532019-06-011055517712472500.013910286214363911344295.07525413.0
3542019-07-011150682813174390.014744389220570512097382.08962949.0
3552019-08-011164950013692822.014570339253180912893366.08889049.0
3562019-09-01988881711684845.0143738152263748NaN5858984.0
3572019-10-01769238810401793.0137804411995942NaN7455781.0