Overview

Dataset statistics

Number of variables6
Number of observations277
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory53.5 B

Variable types

Text1
Numeric5

Dataset

Description경찰청 치안고객만족도 종합(관서별) 분야(응답자 특성, 종합만족도, 민원분야, 112신고처리분야, 교통사고조사분야, 수사분야)
Author경찰청
URLhttps://www.data.go.kr/data/15064412/fileData.do

Alerts

종합만족도 is highly overall correlated with 교통사고조사분야 and 1 other fieldsHigh correlation
교통사고조사분야 is highly overall correlated with 종합만족도High correlation
수사분야 is highly overall correlated with 종합만족도High correlation
응답자특성 has unique valuesUnique

Reproduction

Analysis started2024-03-23 06:55:45.866026
Analysis finished2024-03-23 06:55:57.281877
Duration11.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

응답자특성
Text

UNIQUE 

Distinct277
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-23T06:55:57.754040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.1046931
Min length5

Characters and Unicode

Total characters1691
Distinct characters148
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)100.0%

Sample

1st row서울특별시경찰청
2nd row부산광역시경찰청
3rd row대구광역시경찰청
4th row인천광역시경찰청
5th row광주광역시경찰청
ValueCountFrequency (%)
서울특별시경찰청 1
 
0.4%
천안서북경찰서 1
 
0.4%
태안경찰서 1
 
0.4%
청양경찰서 1
 
0.4%
금산경찰서 1
 
0.4%
서천경찰서 1
 
0.4%
부여경찰서 1
 
0.4%
예산경찰서 1
 
0.4%
당진경찰서 1
 
0.4%
양주경찰서 1
 
0.4%
Other values (267) 267
96.4%
2024-03-23T06:55:59.023615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 315
18.6%
경 285
16.9%
찰 277
16.4%
부 72
 
4.3%
산 45
 
2.7%
울 41
 
2.4%
천 36
 
2.1%
주 32
 
1.9%
청 28
 
1.7%
남 25
 
1.5%
Other values (138) 535
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1691
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 315
18.6%
경 285
16.9%
찰 277
16.4%
부 72
 
4.3%
산 45
 
2.7%
울 41
 
2.4%
천 36
 
2.1%
주 32
 
1.9%
청 28
 
1.7%
남 25
 
1.5%
Other values (138) 535
31.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1691
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 315
18.6%
경 285
16.9%
찰 277
16.4%
부 72
 
4.3%
산 45
 
2.7%
울 41
 
2.4%
천 36
 
2.1%
주 32
 
1.9%
청 28
 
1.7%
남 25
 
1.5%
Other values (138) 535
31.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1691
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 315
18.6%
경 285
16.9%
찰 277
16.4%
부 72
 
4.3%
산 45
 
2.7%
울 41
 
2.4%
천 36
 
2.1%
주 32
 
1.9%
청 28
 
1.7%
남 25
 
1.5%
Other values (138) 535
31.6%

종합만족도
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.033935
Minimum78.9
Maximum90.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T06:55:59.750665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78.9
5-th percentile80.9
Q183.6
median85
Q386.5
95-th percentile88.9
Maximum90.6
Range11.7
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.3651309
Coefficient of variation (CV)0.027813965
Kurtosis-0.19509726
Mean85.033935
Median Absolute Deviation (MAD)1.4
Skewness-0.069064408
Sum23554.4
Variance5.5938442
MonotonicityNot monotonic
2024-03-23T06:56:00.468697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6 9
 
3.2%
85.7 8
 
2.9%
84.9 8
 
2.9%
84.4 8
 
2.9%
84.1 8
 
2.9%
85.5 7
 
2.5%
88.5 7
 
2.5%
84.3 6
 
2.2%
85.8 6
 
2.2%
85.2 6
 
2.2%
Other values (88) 204
73.6%
ValueCountFrequency (%)
78.9 1
0.4%
79.2 1
0.4%
79.6 2
0.7%
79.7 2
0.7%
79.8 1
0.4%
80.1 1
0.4%
80.4 1
0.4%
80.5 1
0.4%
80.6 1
0.4%
80.7 1
0.4%
ValueCountFrequency (%)
90.6 1
0.4%
90.5 1
0.4%
90.4 1
0.4%
90.2 1
0.4%
90.1 1
0.4%
89.7 1
0.4%
89.4 1
0.4%
89.3 2
0.7%
89.2 1
0.4%
89.1 2
0.7%

민원분야
Real number (ℝ)

Distinct71
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.667148
Minimum89.6
Maximum99.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T06:56:01.068296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.6
5-th percentile92.98
Q195
median95.9
Q396.6
95-th percentile97.9
Maximum99.2
Range9.6
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.563083
Coefficient of variation (CV)0.016338764
Kurtosis2.272418
Mean95.667148
Median Absolute Deviation (MAD)0.8
Skewness-1.0902433
Sum26499.8
Variance2.4432284
MonotonicityNot monotonic
2024-03-23T06:56:01.840112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.8 18
 
6.5%
96.6 12
 
4.3%
96.1 12
 
4.3%
96.2 11
 
4.0%
96.4 11
 
4.0%
95.4 10
 
3.6%
95.5 9
 
3.2%
96.3 9
 
3.2%
95.2 9
 
3.2%
96.0 8
 
2.9%
Other values (61) 168
60.6%
ValueCountFrequency (%)
89.6 1
0.4%
90.1 2
0.7%
90.4 2
0.7%
91.1 1
0.4%
91.3 1
0.4%
91.6 2
0.7%
91.8 1
0.4%
91.9 1
0.4%
92.2 1
0.4%
92.8 1
0.4%
ValueCountFrequency (%)
99.2 1
0.4%
98.9 1
0.4%
98.7 1
0.4%
98.6 1
0.4%
98.5 1
0.4%
98.4 1
0.4%
98.3 1
0.4%
98.2 2
0.7%
98.1 2
0.7%
98.0 1
0.4%

112신고처리분야
Real number (ℝ)

Distinct78
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.906498
Minimum83.7
Maximum96.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T06:56:02.449587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83.7
5-th percentile87.2
Q188.7
median89.8
Q390.9
95-th percentile93.7
Maximum96.2
Range12.5
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.934459
Coefficient of variation (CV)0.021516342
Kurtosis1.0226035
Mean89.906498
Median Absolute Deviation (MAD)1.1
Skewness0.43034681
Sum24904.1
Variance3.7421315
MonotonicityNot monotonic
2024-03-23T06:56:02.913216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.5 10
 
3.6%
89.5 9
 
3.2%
90.4 9
 
3.2%
88.9 8
 
2.9%
91.1 8
 
2.9%
90.0 8
 
2.9%
89.1 8
 
2.9%
89.7 7
 
2.5%
89.8 7
 
2.5%
89.0 7
 
2.5%
Other values (68) 196
70.8%
ValueCountFrequency (%)
83.7 1
 
0.4%
84.6 1
 
0.4%
85.3 1
 
0.4%
85.7 1
 
0.4%
85.8 1
 
0.4%
86.3 3
1.1%
86.4 1
 
0.4%
86.9 1
 
0.4%
87.0 1
 
0.4%
87.1 2
0.7%
ValueCountFrequency (%)
96.2 1
 
0.4%
95.9 1
 
0.4%
95.3 1
 
0.4%
95.0 1
 
0.4%
94.9 1
 
0.4%
94.8 1
 
0.4%
94.6 3
1.1%
94.2 2
0.7%
94.0 1
 
0.4%
93.8 1
 
0.4%

교통사고조사분야
Real number (ℝ)

HIGH CORRELATION 

Distinct151
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.745848
Minimum67
Maximum99.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T06:56:03.696249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile77.2
Q183.6
median86.8
Q390.4
95-th percentile95.96
Maximum99.8
Range32.8
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation5.5119132
Coefficient of variation (CV)0.063540946
Kurtosis0.089361752
Mean86.745848
Median Absolute Deviation (MAD)3.4
Skewness-0.23041136
Sum24028.6
Variance30.381187
MonotonicityNot monotonic
2024-03-23T06:56:04.190269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.8 6
 
2.2%
91.0 5
 
1.8%
90.4 5
 
1.8%
86.0 4
 
1.4%
88.5 4
 
1.4%
86.8 4
 
1.4%
85.4 4
 
1.4%
87.7 4
 
1.4%
85.5 4
 
1.4%
84.4 4
 
1.4%
Other values (141) 233
84.1%
ValueCountFrequency (%)
67.0 1
 
0.4%
72.3 1
 
0.4%
73.5 1
 
0.4%
74.2 1
 
0.4%
75.0 1
 
0.4%
75.2 1
 
0.4%
75.4 1
 
0.4%
76.0 1
 
0.4%
76.9 1
 
0.4%
77.1 3
1.1%
ValueCountFrequency (%)
99.8 1
0.4%
99.1 1
0.4%
97.8 2
0.7%
97.6 1
0.4%
97.4 1
0.4%
97.2 1
0.4%
97.0 1
0.4%
96.8 2
0.7%
96.5 2
0.7%
96.3 1
0.4%

수사분야
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.820578
Minimum54.1
Maximum82.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-23T06:56:04.904043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.1
5-th percentile59.54
Q164.7
median67.9
Q370.6
95-th percentile76.4
Maximum82.8
Range28.7
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation5.0750857
Coefficient of variation (CV)0.07483106
Kurtosis0.5006592
Mean67.820578
Median Absolute Deviation (MAD)3
Skewness-0.046173555
Sum18786.3
Variance25.756495
MonotonicityNot monotonic
2024-03-23T06:56:05.322736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 6
 
2.2%
65.4 5
 
1.8%
67.1 5
 
1.8%
62.9 5
 
1.8%
65.6 5
 
1.8%
69.7 5
 
1.8%
66.9 4
 
1.4%
64.3 4
 
1.4%
71.6 4
 
1.4%
69.9 4
 
1.4%
Other values (128) 230
83.0%
ValueCountFrequency (%)
54.1 1
0.4%
54.3 1
0.4%
54.6 1
0.4%
54.8 1
0.4%
55.1 1
0.4%
56.6 2
0.7%
56.8 2
0.7%
57.6 1
0.4%
58.1 1
0.4%
58.2 1
0.4%
ValueCountFrequency (%)
82.8 1
0.4%
82.4 1
0.4%
82.1 1
0.4%
79.2 1
0.4%
78.8 1
0.4%
78.5 1
0.4%
78.3 1
0.4%
78.0 1
0.4%
77.8 2
0.7%
77.4 1
0.4%

Interactions

2024-03-23T06:55:54.222327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:46.557875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:48.413832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:50.203992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:51.960924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:54.658394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:46.927645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:48.788065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:50.517562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:52.323816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:55.038506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:47.221664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:49.135600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:51.049724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:52.717577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:55.384971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:47.576442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:49.401502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:51.411937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:53.336318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:55.655005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:48.024312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:49.882856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:51.719612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:55:53.887643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:56:05.651043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종합만족도민원분야112신고처리분야교통사고조사분야수사분야
종합만족도1.0000.3920.6750.8000.729
민원분야0.3921.0000.2530.4000.000
112신고처리분야0.6750.2531.0000.3230.145
교통사고조사분야0.8000.4000.3231.0000.290
수사분야0.7290.0000.1450.2901.000
2024-03-23T06:56:06.304794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종합만족도민원분야112신고처리분야교통사고조사분야수사분야
종합만족도1.0000.3820.4960.7790.659
민원분야0.3821.0000.1740.2370.068
112신고처리분야0.4960.1741.0000.3240.236
교통사고조사분야0.7790.2370.3241.0000.175
수사분야0.6590.0680.2360.1751.000

Missing values

2024-03-23T06:55:56.153412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:55:57.000706image/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

응답자특성종합만족도민원분야112신고처리분야교통사고조사분야수사분야
0서울특별시경찰청82.995.390.282.264.1
1부산광역시경찰청86.496.490.988.369.9
2대구광역시경찰청83.995.889.384.965.7
3인천광역시경찰청84.796.589.386.167.0
4광주광역시경찰청86.095.489.889.669.3
5대전광역시경찰청85.296.789.887.367.1
6울산광역시경찰청85.895.589.786.371.7
7세종특별자치시경찰청82.995.490.482.563.2
8경기도남부경찰청82.694.090.180.665.6
9경기도북부경찰청84.995.889.986.068.1
응답자특성종합만족도민원분야112신고처리분야교통사고조사분야수사분야
267경남고성경찰서88.594.191.796.571.8
268하동경찰서88.495.892.194.271.6
269남해경찰서90.196.193.692.878.0
270함양경찰서86.598.687.797.462.5
271산청경찰서88.096.193.889.572.5
272함안경찰서86.296.690.589.168.4
273의령경찰서86.797.585.394.469.7
274제주동부경찰서85.393.490.087.270.6
275제주서부경찰서84.495.787.387.567.2
276서귀포경찰서84.694.888.289.965.6