Overview

Dataset statistics

Number of variables6
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.0 KiB
Average record size in memory51.3 B

Variable types

Numeric3
Text1
Categorical2

Reproduction

Analysis started2023-12-10 15:02:25.897263
Analysis finished2023-12-10 15:02:28.614886
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(STD_YM)
Real number (ℝ)

Distinct34
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201705.75
Minimum201601
Maximum201810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:28.746186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201602
Q1201610
median201707
Q3201802
95-th percentile201808.05
Maximum201810
Range209
Interquartile range (IQR)192

Descriptive statistics

Standard deviation78.249309
Coefficient of variation (CV)0.00038793792
Kurtosis-1.3798766
Mean201705.75
Median Absolute Deviation (MAD)96
Skewness-0.0011407472
Sum1.0085287 × 108
Variance6122.9544
MonotonicityNot monotonic
2023-12-11T00:02:28.986027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
201807 23
 
4.6%
201805 19
 
3.8%
201706 19
 
3.8%
201712 18
 
3.6%
201710 18
 
3.6%
201711 18
 
3.6%
201803 18
 
3.6%
201708 18
 
3.6%
201602 17
 
3.4%
201709 17
 
3.4%
Other values (24) 315
63.0%
ValueCountFrequency (%)
201601 11
2.2%
201602 17
3.4%
201603 10
2.0%
201604 11
2.2%
201605 8
1.6%
201606 15
3.0%
201607 11
2.2%
201608 17
3.4%
201609 13
2.6%
201610 16
3.2%
ValueCountFrequency (%)
201810 11
2.2%
201809 14
2.8%
201808 14
2.8%
201807 23
4.6%
201806 14
2.8%
201805 19
3.8%
201804 11
2.2%
201803 18
3.6%
201802 13
2.6%
201801 16
3.2%

행정동_코드(ADMI_CD)
Real number (ℝ)

Distinct295
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11440928
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:29.237348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140578
Q111290590
median11440700
Q311620575
95-th percentile11740520
Maximum11740700
Range630185
Interquartile range (IQR)329985

Descriptive statistics

Standard deviation192411.45
Coefficient of variation (CV)0.016817819
Kurtosis-1.2544035
Mean11440928
Median Absolute Deviation (MAD)150125
Skewness-0.036346148
Sum5.7204638 × 109
Variance3.7022167 × 1010
MonotonicityNot monotonic
2023-12-11T00:02:29.540858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11320522 5
 
1.0%
11620715 5
 
1.0%
11620735 5
 
1.0%
11500603 4
 
0.8%
11290575 4
 
0.8%
11305535 4
 
0.8%
11290630 4
 
0.8%
11530510 4
 
0.8%
11590530 4
 
0.8%
11710720 4
 
0.8%
Other values (285) 457
91.4%
ValueCountFrequency (%)
11110515 2
0.4%
11110530 1
 
0.2%
11110550 2
0.4%
11110570 1
 
0.2%
11110580 1
 
0.2%
11110600 3
0.6%
11110615 1
 
0.2%
11110650 1
 
0.2%
11110680 3
0.6%
11110690 1
 
0.2%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740690 3
0.6%
11740685 1
 
0.2%
11740660 1
 
0.2%
11740650 1
 
0.2%
11740640 2
0.4%
11740620 2
0.4%
11740610 2
0.4%
11740600 2
0.4%
11740590 1
 
0.2%

행정동_이름(ADMI_NM)
Text

Distinct298
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:02:30.051453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.866
Min length2

Characters and Unicode

Total characters1933
Distinct characters170
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)31.6%

Sample

1st row대림2동
2nd row서초4동
3rd row쌍문4동
4th row논현1동
5th row행당2동
ValueCountFrequency (%)
방배4동 6
 
1.2%
신사동 5
 
1.0%
창신1동 4
 
0.8%
삼각산동 4
 
0.8%
용산2가동 4
 
0.8%
방학1동 4
 
0.8%
방이2동 4
 
0.8%
목3동 4
 
0.8%
마장동 4
 
0.8%
창신3동 4
 
0.8%
Other values (288) 457
91.4%
2023-12-11T00:02:30.774738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 501
25.9%
1 121
 
6.3%
2 107
 
5.5%
3 52
 
2.7%
신 48
 
2.5%
4 41
 
2.1%
가 33
 
1.7%
방 26
 
1.3%
계 25
 
1.3%
창 22
 
1.1%
Other values (160) 957
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1553
80.3%
Decimal Number 359
 
18.6%
Other Punctuation 21
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 501
32.3%
신 48
 
3.1%
가 33
 
2.1%
방 26
 
1.7%
계 25
 
1.6%
창 22
 
1.4%
곡 20
 
1.3%
산 19
 
1.2%
원 19
 
1.2%
정 18
 
1.2%
Other values (149) 822
52.9%
Decimal Number
ValueCountFrequency (%)
1 121
33.7%
2 107
29.8%
3 52
14.5%
4 41
 
11.4%
6 11
 
3.1%
5 10
 
2.8%
8 7
 
1.9%
7 7
 
1.9%
0 2
 
0.6%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1553
80.3%
Common 380
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 501
32.3%
신 48
 
3.1%
가 33
 
2.1%
방 26
 
1.7%
계 25
 
1.6%
창 22
 
1.4%
곡 20
 
1.3%
산 19
 
1.2%
원 19
 
1.2%
정 18
 
1.2%
Other values (149) 822
52.9%
Common
ValueCountFrequency (%)
1 121
31.8%
2 107
28.2%
3 52
13.7%
4 41
 
10.8%
. 21
 
5.5%
6 11
 
2.9%
5 10
 
2.6%
8 7
 
1.8%
7 7
 
1.8%
0 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1553
80.3%
ASCII 380
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 501
32.3%
신 48
 
3.1%
가 33
 
2.1%
방 26
 
1.7%
계 25
 
1.6%
창 22
 
1.4%
곡 20
 
1.3%
산 19
 
1.2%
원 19
 
1.2%
정 18
 
1.2%
Other values (149) 822
52.9%
ASCII
ValueCountFrequency (%)
1 121
31.8%
2 107
28.2%
3 52
13.7%
4 41
 
10.8%
. 21
 
5.5%
6 11
 
2.9%
5 10
 
2.6%
8 7
 
1.8%
7 7
 
1.8%
0 2
 
0.5%

성별(GENDER)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
F
254 
M
246 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 254
50.8%
M 246
49.2%

Length

2023-12-11T00:02:31.053743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:02:31.352399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 254
50.8%
m 246
49.2%

연령대(AGE)
Categorical

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
70_ABOVE
43 
3034
39 
6569
37 
5054
36 
4549
35 
Other values (10)
310 

Length

Max length8
Median length4
Mean length4.344
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6569
2nd row4549
3rd row70_ABOVE
4th row1519
5th row3539

Common Values

ValueCountFrequency (%)
70_ABOVE 43
 
8.6%
3034 39
 
7.8%
6569 37
 
7.4%
5054 36
 
7.2%
4549 35
 
7.0%
3539 34
 
6.8%
5559 34
 
6.8%
1519 33
 
6.6%
2024 33
 
6.6%
4044 32
 
6.4%
Other values (5) 144
28.8%

Length

2023-12-11T00:02:31.595634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
70_above 43
 
8.6%
3034 39
 
7.8%
6569 37
 
7.4%
5054 36
 
7.2%
4549 35
 
7.0%
3539 34
 
6.8%
5559 34
 
6.8%
1519 33
 
6.6%
2024 33
 
6.6%
4044 32
 
6.4%
Other values (5) 144
28.8%

유동인구_합계(POP_CNT)
Real number (ℝ)

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101146.48
Minimum152
Maximum1567717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:02:31.848450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum152
5-th percentile652.5
Q130647
median67736.5
Q3123126
95-th percentile348897.35
Maximum1567717
Range1567565
Interquartile range (IQR)92479

Descriptive statistics

Standard deviation133721.25
Coefficient of variation (CV)1.3220554
Kurtosis36.551542
Mean101146.48
Median Absolute Deviation (MAD)44308.5
Skewness4.6911567
Sum50573242
Variance1.7881374 × 1010
MonotonicityNot monotonic
2023-12-11T00:02:32.208346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
358 2
 
0.4%
419 2
 
0.4%
132396 1
 
0.2%
24772 1
 
0.2%
4145 1
 
0.2%
8256 1
 
0.2%
114887 1
 
0.2%
155233 1
 
0.2%
13858 1
 
0.2%
40993 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
152 1
0.2%
168 1
0.2%
262 1
0.2%
267 1
0.2%
274 1
0.2%
311 1
0.2%
336 1
0.2%
341 1
0.2%
348 1
0.2%
357 1
0.2%
ValueCountFrequency (%)
1567717 1
0.2%
1078273 1
0.2%
697996 1
0.2%
693524 1
0.2%
577623 1
0.2%
551626 1
0.2%
549657 1
0.2%
539870 1
0.2%
538774 1
0.2%
513366 1
0.2%

Interactions

2023-12-11T00:02:27.641252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:26.422439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:26.980621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:27.826540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:26.594114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:27.185328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:28.039642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:26.774933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:02:27.414001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:02:32.424866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동_코드(ADMI_CD)성별(GENDER)연령대(AGE)유동인구_합계(POP_CNT)
기준년월(STD_YM)1.0000.0000.0490.0000.084
행정동_코드(ADMI_CD)0.0001.0000.0450.2080.000
성별(GENDER)0.0490.0451.0000.0000.000
연령대(AGE)0.0000.2080.0001.0000.080
유동인구_합계(POP_CNT)0.0840.0000.0000.0801.000
2023-12-11T00:02:32.639969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대(AGE)성별(GENDER)
연령대(AGE)1.0000.000
성별(GENDER)0.0001.000
2023-12-11T00:02:32.786482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동_코드(ADMI_CD)유동인구_합계(POP_CNT)성별(GENDER)연령대(AGE)
기준년월(STD_YM)1.000-0.0080.0490.0470.000
행정동_코드(ADMI_CD)-0.0081.000-0.0300.0340.075
유동인구_합계(POP_CNT)0.049-0.0301.0000.0000.031
성별(GENDER)0.0470.0340.0001.0000.000
연령대(AGE)0.0000.0750.0310.0001.000

Missing values

2023-12-11T00:02:28.317273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:02:28.525606image/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

기준년월(STD_YM)행정동_코드(ADMI_CD)행정동_이름(ADMI_NM)성별(GENDER)연령대(AGE)유동인구_합계(POP_CNT)
020170411500535대림2동F6569132396
120180311590660서초4동M4549333412
220180311410710쌍문4동M70_ABOVE84393
320160311410520논현1동F1519674
420161111740600행당2동F353944638
520170211620765구로3동F30343711
620161111200535목3동M70_ABOVE94212
720160811590670양재2동M606417771
820170911410640홍제2동M2529107057
920180611740660논현1동F1014116294
기준년월(STD_YM)행정동_코드(ADMI_CD)행정동_이름(ADMI_NM)성별(GENDER)연령대(AGE)유동인구_합계(POP_CNT)
49020161111230730약수동M2024182182
49120180211320660신대방2동M6064115907
49220170911620685망원1동F1014129568
49320171111620735상계9동M0509262
49420161211260610일원2동M3034233274
49520170611215760일원1동F3034182240
49620161111110650반포3동M101476940
49720170611470590삼각산동M2024121412
49820170611410700방배4동F454989843
49920160811305645대치4동M5054126741