Overview

Dataset statistics

Number of variables8
Number of observations810
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.3 KiB
Average record size in memory66.2 B

Variable types

Categorical6
Numeric2

Alerts

IMAGE_ID is highly overall correlated with IMAGE_NMHigh correlation
AREA_NM is highly overall correlated with RESPOND_CO and 1 other fieldsHigh correlation
IMAGE_NM is highly overall correlated with IMAGE_IDHigh correlation
QUSTNR_ID is highly overall correlated with QUSTNR_NMHigh correlation
AREA_ID is highly overall correlated with RESPOND_CO and 1 other fieldsHigh correlation
QUSTNR_NM is highly overall correlated with QUSTNR_IDHigh correlation
RESPOND_CO is highly overall correlated with AREA_ID and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 09:41:45.646652
Analysis finished2023-12-10 09:41:47.659217
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

AREA_ID
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
ALL
 
45
GW
 
45
GG
 
45
GN
 
45
GB
 
45
Other values (13)
585 

Length

Max length4
Median length2
Mean length2.2222222
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ALL 45
 
5.6%
GW 45
 
5.6%
GG 45
 
5.6%
GN 45
 
5.6%
GB 45
 
5.6%
GJ 45
 
5.6%
DG 45
 
5.6%
DJ 45
 
5.6%
BS 45
 
5.6%
SU 45
 
5.6%
Other values (8) 360
44.4%

Length

2023-12-10T18:41:47.839347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
all 45
 
5.6%
gw 45
 
5.6%
jj 45
 
5.6%
cb 45
 
5.6%
chn 45
 
5.6%
jnbk 45
 
5.6%
jn 45
 
5.6%
ic 45
 
5.6%
us 45
 
5.6%
su 45
 
5.6%
Other values (8) 360
44.4%

AREA_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
전국
 
45
강원
 
45
경기
 
45
경남
 
45
경북
 
45
Other values (13)
585 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전국 45
 
5.6%
강원 45
 
5.6%
경기 45
 
5.6%
경남 45
 
5.6%
경북 45
 
5.6%
광주 45
 
5.6%
대구 45
 
5.6%
대전 45
 
5.6%
부산 45
 
5.6%
서울 45
 
5.6%
Other values (8) 360
44.4%

Length

2023-12-10T18:41:48.065648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전국 45
 
5.6%
강원 45
 
5.6%
제주 45
 
5.6%
충북 45
 
5.6%
충남 45
 
5.6%
전북 45
 
5.6%
전남 45
 
5.6%
인천 45
 
5.6%
울산 45
 
5.6%
서울 45
 
5.6%
Other values (8) 360
44.4%

RESPOND_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5156.463
Minimum205
Maximum69612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-10T18:41:48.307551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile426
Q11407
median2419
Q34102
95-th percentile33485
Maximum69612
Range69407
Interquartile range (IQR)2695

Descriptive statistics

Standard deviation10901.009
Coefficient of variation (CV)2.1140478
Kurtosis21.899229
Mean5156.463
Median Absolute Deviation (MAD)1193.5
Skewness4.570526
Sum4176735
Variance1.18832 × 108
MonotonicityNot monotonic
2023-12-10T18:41:48.657636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69612 15
 
1.9%
1703 15
 
1.9%
1571 15
 
1.9%
633 15
 
1.9%
939 15
 
1.9%
2958 15
 
1.9%
1238 15
 
1.9%
1719 15
 
1.9%
5531 15
 
1.9%
3159 15
 
1.9%
Other values (44) 660
81.5%
ValueCountFrequency (%)
205 15
1.9%
222 15
1.9%
426 15
1.9%
508 15
1.9%
602 15
1.9%
633 15
1.9%
842 15
1.9%
867 15
1.9%
939 15
1.9%
1000 15
1.9%
ValueCountFrequency (%)
69612 15
1.9%
36127 15
1.9%
33485 15
1.9%
9230 15
1.9%
7583 15
1.9%
6453 15
1.9%
5691 15
1.9%
5531 15
1.9%
5421 15
1.9%
5368 15
1.9%

IMAGE_ID
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
ALL_IMAGE
270 
PASSNGR_IMAGE
270 
LCLS_IMAGE
270 

Length

Max length13
Median length10
Mean length10.666667
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ALL_IMAGE 270
33.3%
PASSNGR_IMAGE 270
33.3%
LCLS_IMAGE 270
33.3%

Length

2023-12-10T18:41:49.028109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:49.231763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
all_image 270
33.3%
passngr_image 270
33.3%
lcls_image 270
33.3%

IMAGE_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
전체지역이미지
270 
여행자지역이미지
270 
현지인지역이미지
270 

Length

Max length8
Median length8
Mean length7.6666667
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체지역이미지 270
33.3%
여행자지역이미지 270
33.3%
현지인지역이미지 270
33.3%

Length

2023-12-10T18:41:49.889663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:50.093545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체지역이미지 270
33.3%
여행자지역이미지 270
33.3%
현지인지역이미지 270
33.3%

QUSTNR_ID
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
IMAGE_1
 
54
IMAGE_2
 
54
IMAGE_3
 
54
IMAGE_4
 
54
IMAGE_5
 
54
Other values (10)
540 

Length

Max length8
Median length7
Mean length7.4
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMAGE_1
2nd rowIMAGE_2
3rd rowIMAGE_3
4th rowIMAGE_4
5th rowIMAGE_5

Common Values

ValueCountFrequency (%)
IMAGE_1 54
 
6.7%
IMAGE_2 54
 
6.7%
IMAGE_3 54
 
6.7%
IMAGE_4 54
 
6.7%
IMAGE_5 54
 
6.7%
IMAGE_6 54
 
6.7%
IMAGE_7 54
 
6.7%
IMAGE_8 54
 
6.7%
IMAGE_9 54
 
6.7%
IMAGE_10 54
 
6.7%
Other values (5) 270
33.3%

Length

2023-12-10T18:41:50.307425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
image_1 54
 
6.7%
image_2 54
 
6.7%
image_3 54
 
6.7%
image_4 54
 
6.7%
image_5 54
 
6.7%
image_6 54
 
6.7%
image_7 54
 
6.7%
image_8 54
 
6.7%
image_9 54
 
6.7%
image_10 54
 
6.7%
Other values (5) 270
33.3%

QUSTNR_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
지저분한깨끗한
 
54
개발된보존된
 
54
근거리원거리
 
54
복잡한한적한
 
54
세련된촌스러운
 
54
Other values (10)
540 

Length

Max length10
Median length6
Mean length6.9333333
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지저분한깨끗한
2nd row개발된보존된
3rd row근거리원거리
4th row복잡한한적한
5th row세련된촌스러운

Common Values

ValueCountFrequency (%)
지저분한깨끗한 54
 
6.7%
개발된보존된 54
 
6.7%
근거리원거리 54
 
6.7%
복잡한한적한 54
 
6.7%
세련된촌스러운 54
 
6.7%
독특한평범한 54
 
6.7%
화려한소박한 54
 
6.7%
이국적한국적 54
 
6.7%
젊은층의중장년층 54
 
6.7%
상업적인인심좋은 54
 
6.7%
Other values (5) 270
33.3%

Length

2023-12-10T18:41:50.515298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지저분한깨끗한 54
 
6.7%
개발된보존된 54
 
6.7%
근거리원거리 54
 
6.7%
복잡한한적한 54
 
6.7%
세련된촌스러운 54
 
6.7%
독특한평범한 54
 
6.7%
화려한소박한 54
 
6.7%
이국적한국적 54
 
6.7%
젊은층의중장년층 54
 
6.7%
상업적인인심좋은 54
 
6.7%
Other values (5) 270
33.3%

QUSTNR_VALUE
Real number (ℝ)

Distinct347
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.295185
Minimum17.7
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-10T18:41:50.854378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.7
5-th percentile29.845
Q139.925
median48.1
Q359
95-th percentile64.1
Maximum74
Range56.3
Interquartile range (IQR)19.075

Descriptive statistics

Standard deviation11.258879
Coefficient of variation (CV)0.23312633
Kurtosis-0.84446143
Mean48.295185
Median Absolute Deviation (MAD)9.7
Skewness-0.16900089
Sum39119.1
Variance126.76236
MonotonicityNot monotonic
2023-12-10T18:41:51.133358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 45
 
5.6%
61.6 9
 
1.1%
43.4 7
 
0.9%
64.1 7
 
0.9%
38.6 7
 
0.9%
42.5 6
 
0.7%
59.5 6
 
0.7%
38.0 6
 
0.7%
62.3 6
 
0.7%
58.9 6
 
0.7%
Other values (337) 705
87.0%
ValueCountFrequency (%)
17.7 1
0.1%
17.8 1
0.1%
17.9 1
0.1%
20.1 1
0.1%
21.2 1
0.1%
21.5 1
0.1%
22.3 1
0.1%
22.6 2
0.2%
22.8 1
0.1%
23.0 1
0.1%
ValueCountFrequency (%)
74.0 1
0.1%
71.8 1
0.1%
71.5 1
0.1%
70.6 1
0.1%
70.0 1
0.1%
68.3 1
0.1%
68.1 2
0.2%
67.7 1
0.1%
67.5 1
0.1%
67.2 1
0.1%

Interactions

2023-12-10T18:41:46.889020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:46.455241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:47.078085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:46.680585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:41:51.348665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AREA_IDAREA_NMRESPOND_COIMAGE_IDIMAGE_NMQUSTNR_IDQUSTNR_NMQUSTNR_VALUE
AREA_ID1.0001.0000.7920.0000.0000.0000.0000.785
AREA_NM1.0001.0000.7920.0000.0000.0000.0000.785
RESPOND_CO0.7920.7921.0000.3690.3690.0000.0000.594
IMAGE_ID0.0000.0000.3691.0001.0000.0000.0000.000
IMAGE_NM0.0000.0000.3691.0001.0000.0000.0000.000
QUSTNR_ID0.0000.0000.0000.0000.0001.0001.0000.357
QUSTNR_NM0.0000.0000.0000.0000.0001.0001.0000.357
QUSTNR_VALUE0.7850.7850.5940.0000.0000.3570.3571.000
2023-12-10T18:41:51.564419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IMAGE_IDAREA_NMIMAGE_NMQUSTNR_IDAREA_IDQUSTNR_NM
IMAGE_ID1.0000.0001.0000.0000.0000.000
AREA_NM0.0001.0000.0000.0001.0000.000
IMAGE_NM1.0000.0001.0000.0000.0000.000
QUSTNR_ID0.0000.0000.0001.0000.0001.000
AREA_ID0.0001.0000.0000.0001.0000.000
QUSTNR_NM0.0000.0000.0001.0000.0001.000
2023-12-10T18:41:51.799972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RESPOND_COQUSTNR_VALUEAREA_IDAREA_NMIMAGE_IDIMAGE_NMQUSTNR_IDQUSTNR_NM
RESPOND_CO1.0000.1920.5540.5540.2990.2990.0000.000
QUSTNR_VALUE0.1921.0000.4430.4430.0000.0000.1400.140
AREA_ID0.5540.4431.0001.0000.0000.0000.0000.000
AREA_NM0.5540.4431.0001.0000.0000.0000.0000.000
IMAGE_ID0.2990.0000.0000.0001.0001.0000.0000.000
IMAGE_NM0.2990.0000.0000.0001.0001.0000.0000.000
QUSTNR_ID0.0000.1400.0000.0000.0000.0001.0001.000
QUSTNR_NM0.0000.1400.0000.0000.0000.0001.0001.000

Missing values

2023-12-10T18:41:47.312217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:41:47.573750image/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

AREA_IDAREA_NMRESPOND_COIMAGE_IDIMAGE_NMQUSTNR_IDQUSTNR_NMQUSTNR_VALUE
0ALL전국69612ALL_IMAGE전체지역이미지IMAGE_1지저분한깨끗한50.0
1ALL전국69612ALL_IMAGE전체지역이미지IMAGE_2개발된보존된50.0
2ALL전국69612ALL_IMAGE전체지역이미지IMAGE_3근거리원거리50.0
3ALL전국69612ALL_IMAGE전체지역이미지IMAGE_4복잡한한적한50.0
4ALL전국69612ALL_IMAGE전체지역이미지IMAGE_5세련된촌스러운50.0
5ALL전국69612ALL_IMAGE전체지역이미지IMAGE_6독특한평범한50.0
6ALL전국69612ALL_IMAGE전체지역이미지IMAGE_7화려한소박한50.0
7ALL전국69612ALL_IMAGE전체지역이미지IMAGE_8이국적한국적50.0
8ALL전국69612ALL_IMAGE전체지역이미지IMAGE_9젊은층의중장년층50.0
9ALL전국69612ALL_IMAGE전체지역이미지IMAGE_10상업적인인심좋은50.0
AREA_IDAREA_NMRESPOND_COIMAGE_IDIMAGE_NMQUSTNR_IDQUSTNR_NMQUSTNR_VALUE
800SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_6독특한평범한36.6
801SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_7화려한소박한30.9
802SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_8이국적한국적21.5
803SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_9젊은층의중장년층27.6
804SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_10상업적인인심좋은28.9
805SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_11무례한친절한32.7
806SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_12여성적인남성적인47.7
807SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_13친구와함께가족과함께47.1
808SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_14활동적인힐링되는44.4
809SJ세종205LCLS_IMAGE현지인지역이미지IMAGE_15고비용저비용32.4