Overview

Dataset statistics

Number of variables14
Number of observations22
Missing cells213
Missing cells (%)69.2%
Duplicate rows1
Duplicate rows (%)4.5%
Total size in memory2.6 KiB
Average record size in memory123.0 B

Variable types

Unsupported3
Text7
Numeric3
DateTime1

Dataset

Description2016년도전북소방본부구조통계
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202846

Alerts

Unnamed: 5 has constant value ""Constant
Unnamed: 8 has constant value ""Constant
Unnamed: 10 has constant value ""Constant
Unnamed: 11 has constant value ""Constant
Dataset has 1 (4.5%) duplicate rowsDuplicates
Unnamed: 3 is highly overall correlated with Unnamed: 7 and 1 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 3 and 1 other fieldsHigh correlation
Unnamed: 12 is highly overall correlated with Unnamed: 3 and 1 other fieldsHigh correlation
서소별 구조건수 및 구조인원 현황 has 22 (100.0%) missing valuesMissing
Unnamed: 1 has 5 (22.7%) missing valuesMissing
Unnamed: 2 has 9 (40.9%) missing valuesMissing
Unnamed: 3 has 11 (50.0%) missing valuesMissing
Unnamed: 4 has 9 (40.9%) missing valuesMissing
Unnamed: 5 has 21 (95.5%) missing valuesMissing
Unnamed: 6 has 22 (100.0%) missing valuesMissing
Unnamed: 7 has 11 (50.0%) missing valuesMissing
Unnamed: 8 has 21 (95.5%) missing valuesMissing
Unnamed: 9 has 9 (40.9%) missing valuesMissing
Unnamed: 10 has 21 (95.5%) missing valuesMissing
Unnamed: 11 has 21 (95.5%) missing valuesMissing
Unnamed: 12 has 10 (45.5%) missing valuesMissing
Unnamed: 13 has 21 (95.5%) missing valuesMissing
서소별 구조건수 및 구조인원 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 15:50:07.245533
Analysis finished2023-12-10 15:50:09.983864
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서소별 구조건수 및 구조인원 현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22
Missing (%)100.0%
Memory size330.0 B

Unnamed: 1
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Memory size308.0 B
2023-12-11T00:50:10.125147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length8
Mean length6.5882353
Min length2

Characters and Unicode

Total characters112
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row서소 : 전라북도 - 전북소방안전본부 - 모두
2nd row구 분
3rd row합계
4th row전주덕진소방서
5th row전주완산소방서
ValueCountFrequency (%)
3
 
12.5%
서소 1
 
4.2%
구급상황관리센터 1
 
4.2%
소방_항공대 1
 
4.2%
소방항공대 1
 
4.2%
무진장소방서 1
 
4.2%
부안소방서 1
 
4.2%
고창소방서 1
 
4.2%
김제소방서 1
 
4.2%
남원소방서 1
 
4.2%
Other values (12) 12
50.0%
2023-12-11T00:50:10.646934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 14
 
12.5%
방 13
 
11.6%
서 11
 
9.8%
7
 
6.2%
전 5
 
4.5%
산 4
 
3.6%
구 3
 
2.7%
터 2
 
1.8%
센 2
 
1.8%
대 2
 
1.8%
Other values (40) 49
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
90.2%
Space Separator 7
 
6.2%
Dash Punctuation 2
 
1.8%
Connector Punctuation 1
 
0.9%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 14
 
13.9%
방 13
 
12.9%
서 11
 
10.9%
전 5
 
5.0%
산 4
 
4.0%
구 3
 
3.0%
터 2
 
2.0%
센 2
 
2.0%
대 2
 
2.0%
공 2
 
2.0%
Other values (36) 43
42.6%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101
90.2%
Common 11
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 14
 
13.9%
방 13
 
12.9%
서 11
 
10.9%
전 5
 
5.0%
산 4
 
4.0%
구 3
 
3.0%
터 2
 
2.0%
센 2
 
2.0%
대 2
 
2.0%
공 2
 
2.0%
Other values (36) 43
42.6%
Common
ValueCountFrequency (%)
7
63.6%
- 2
 
18.2%
_ 1
 
9.1%
: 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
90.2%
ASCII 11
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 14
 
13.9%
방 13
 
12.9%
서 11
 
10.9%
전 5
 
5.0%
산 4
 
4.0%
구 3
 
3.0%
터 2
 
2.0%
센 2
 
2.0%
대 2
 
2.0%
공 2
 
2.0%
Other values (36) 43
42.6%
ASCII
ValueCountFrequency (%)
7
63.6%
- 2
 
18.2%
_ 1
 
9.1%
: 1
 
9.1%

Unnamed: 2
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2023-12-11T00:50:10.980851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9230769
Min length2

Characters and Unicode

Total characters51
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row출동건수
2nd row40219
3rd row6112
4th row6648
5th row5666
ValueCountFrequency (%)
출동건수 1
 
7.7%
40219 1
 
7.7%
6112 1
 
7.7%
6648 1
 
7.7%
5666 1
 
7.7%
5234 1
 
7.7%
3261 1
 
7.7%
3615 1
 
7.7%
2395 1
 
7.7%
2278 1
 
7.7%
Other values (3) 3
23.1%
2023-12-11T00:50:11.666765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8
15.7%
6 8
15.7%
2 7
13.7%
8 6
11.8%
3 5
9.8%
4 4
7.8%
5 4
7.8%
0 2
 
3.9%
9 2
 
3.9%
출 1
 
2.0%
Other values (4) 4
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47
92.2%
Other Letter 4
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
17.0%
6 8
17.0%
2 7
14.9%
8 6
12.8%
3 5
10.6%
4 4
8.5%
5 4
8.5%
0 2
 
4.3%
9 2
 
4.3%
7 1
 
2.1%
Other Letter
ValueCountFrequency (%)
출 1
25.0%
동 1
25.0%
건 1
25.0%
수 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
92.2%
Hangul 4
 
7.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
17.0%
6 8
17.0%
2 7
14.9%
8 6
12.8%
3 5
10.6%
4 4
8.5%
5 4
8.5%
0 2
 
4.3%
9 2
 
4.3%
7 1
 
2.1%
Hangul
ValueCountFrequency (%)
출 1
25.0%
동 1
25.0%
건 1
25.0%
수 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
92.2%
Hangul 4
 
7.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8
17.0%
6 8
17.0%
2 7
14.9%
8 6
12.8%
3 5
10.6%
4 4
8.5%
5 4
8.5%
0 2
 
4.3%
9 2
 
4.3%
7 1
 
2.1%
Hangul
ValueCountFrequency (%)
출 1
25.0%
동 1
25.0%
건 1
25.0%
수 1
25.0%

Unnamed: 3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.18181818
Minimum0.04
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T00:50:11.896294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.05
Q10.07
median0.09
Q30.145
95-th percentile0.585
Maximum1
Range0.96
Interquartile range (IQR)0.075

Descriptive statistics

Standard deviation0.27458398
Coefficient of variation (CV)1.5102119
Kurtosis10.322571
Mean0.18181818
Median Absolute Deviation (MAD)0.04
Skewness3.1768386
Sum2
Variance0.075396364
MonotonicityNot monotonic
2023-12-11T00:50:12.131513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.08 2
 
9.1%
0.06 2
 
9.1%
1.0 1
 
4.5%
0.15 1
 
4.5%
0.17 1
 
4.5%
0.14 1
 
4.5%
0.13 1
 
4.5%
0.09 1
 
4.5%
0.04 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
0.04 1
4.5%
0.06 2
9.1%
0.08 2
9.1%
0.09 1
4.5%
0.13 1
4.5%
0.14 1
4.5%
0.15 1
4.5%
0.17 1
4.5%
1.0 1
4.5%
ValueCountFrequency (%)
1.0 1
4.5%
0.17 1
4.5%
0.15 1
4.5%
0.14 1
4.5%
0.13 1
4.5%
0.09 1
4.5%
0.08 2
9.1%
0.06 2
9.1%
0.04 1
4.5%

Unnamed: 4
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2023-12-11T00:50:12.515873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9230769
Min length2

Characters and Unicode

Total characters51
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row구조건수
2nd row34192
3rd row4864
4th row5749
5th row4570
ValueCountFrequency (%)
구조건수 1
 
7.7%
34192 1
 
7.7%
4864 1
 
7.7%
5749 1
 
7.7%
4570 1
 
7.7%
4438 1
 
7.7%
3004 1
 
7.7%
3203 1
 
7.7%
1944 1
 
7.7%
2058 1
 
7.7%
Other values (3) 3
23.1%
2023-12-11T00:50:13.325539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 11
21.6%
3 6
11.8%
8 5
9.8%
5 5
9.8%
0 5
9.8%
9 4
 
7.8%
2 4
 
7.8%
1 3
 
5.9%
6 2
 
3.9%
7 2
 
3.9%
Other values (4) 4
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47
92.2%
Other Letter 4
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11
23.4%
3 6
12.8%
8 5
10.6%
5 5
10.6%
0 5
10.6%
9 4
 
8.5%
2 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%
7 2
 
4.3%
Other Letter
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
건 1
25.0%
수 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
92.2%
Hangul 4
 
7.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 11
23.4%
3 6
12.8%
8 5
10.6%
5 5
10.6%
0 5
10.6%
9 4
 
8.5%
2 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%
7 2
 
4.3%
Hangul
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
건 1
25.0%
수 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
92.2%
Hangul 4
 
7.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 11
23.4%
3 6
12.8%
8 5
10.6%
5 5
10.6%
0 5
10.6%
9 4
 
8.5%
2 4
 
8.5%
1 3
 
6.4%
6 2
 
4.3%
7 2
 
4.3%
Hangul
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
건 1
25.0%
수 1
25.0%

Unnamed: 5
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing21
Missing (%)95.5%
Memory size308.0 B
2023-12-11T00:50:13.540319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1/1
ValueCountFrequency (%)
1/1 1
100.0%
2023-12-11T00:50:13.991171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2
66.7%
/ 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
66.7%
/ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
66.7%
/ 1
33.3%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22
Missing (%)100.0%
Memory size330.0 B

Unnamed: 7
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)72.7%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.18181818
Minimum0.05
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T00:50:14.460165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.055
Q10.07
median0.09
Q30.135
95-th percentile0.585
Maximum1
Range0.95
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.27403716
Coefficient of variation (CV)1.5072044
Kurtosis10.434251
Mean0.18181818
Median Absolute Deviation (MAD)0.04
Skewness3.2002676
Sum2
Variance0.075096364
MonotonicityNot monotonic
2023-12-11T00:50:14.833201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.13 2
 
9.1%
0.09 2
 
9.1%
0.06 2
 
9.1%
1.0 1
 
4.5%
0.14 1
 
4.5%
0.17 1
 
4.5%
0.05 1
 
4.5%
0.08 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
0.05 1
4.5%
0.06 2
9.1%
0.08 1
4.5%
0.09 2
9.1%
0.13 2
9.1%
0.14 1
4.5%
0.17 1
4.5%
1.0 1
4.5%
ValueCountFrequency (%)
1.0 1
4.5%
0.17 1
4.5%
0.14 1
4.5%
0.13 2
9.1%
0.09 2
9.1%
0.08 1
4.5%
0.06 2
9.1%
0.05 1
4.5%

Unnamed: 8
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing21
Missing (%)95.5%
Memory size308.0 B
2023-12-11T00:50:15.125993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row집계년도: 2016 년
ValueCountFrequency (%)
집계년도 1
33.3%
2016 1
33.3%
년 1
33.3%
2023-12-11T00:50:15.952138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
년 2
16.7%
2
16.7%
집 1
8.3%
계 1
8.3%
도 1
8.3%
: 1
8.3%
2 1
8.3%
0 1
8.3%
1 1
8.3%
6 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
41.7%
Decimal Number 4
33.3%
Space Separator 2
 
16.7%
Other Punctuation 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
년 2
40.0%
집 1
20.0%
계 1
20.0%
도 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
0 1
25.0%
1 1
25.0%
6 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7
58.3%
Hangul 5
41.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2
28.6%
: 1
14.3%
2 1
14.3%
0 1
14.3%
1 1
14.3%
6 1
14.3%
Hangul
ValueCountFrequency (%)
년 2
40.0%
집 1
20.0%
계 1
20.0%
도 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
58.3%
Hangul 5
41.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
년 2
40.0%
집 1
20.0%
계 1
20.0%
도 1
20.0%
ASCII
ValueCountFrequency (%)
2
28.6%
: 1
14.3%
2 1
14.3%
0 1
14.3%
1 1
14.3%
6 1
14.3%

Unnamed: 9
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9
Missing (%)40.9%
Memory size308.0 B
2023-12-11T00:50:16.231462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0769231
Min length2

Characters and Unicode

Total characters40
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row구조인원
2nd row4937
3rd row470
4th row841
5th row420
ValueCountFrequency (%)
구조인원 1
 
7.7%
4937 1
 
7.7%
470 1
 
7.7%
841 1
 
7.7%
420 1
 
7.7%
820 1
 
7.7%
436 1
 
7.7%
601 1
 
7.7%
328 1
 
7.7%
225 1
 
7.7%
Other values (3) 3
23.1%
2023-12-11T00:50:16.778845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 6
15.0%
2 6
15.0%
3 4
10.0%
0 4
10.0%
1 4
10.0%
8 3
7.5%
6 3
7.5%
9 2
 
5.0%
7 2
 
5.0%
5 2
 
5.0%
Other values (4) 4
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36
90.0%
Other Letter 4
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6
16.7%
2 6
16.7%
3 4
11.1%
0 4
11.1%
1 4
11.1%
8 3
8.3%
6 3
8.3%
9 2
 
5.6%
7 2
 
5.6%
5 2
 
5.6%
Other Letter
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
인 1
25.0%
원 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
90.0%
Hangul 4
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6
16.7%
2 6
16.7%
3 4
11.1%
0 4
11.1%
1 4
11.1%
8 3
8.3%
6 3
8.3%
9 2
 
5.6%
7 2
 
5.6%
5 2
 
5.6%
Hangul
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
인 1
25.0%
원 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
90.0%
Hangul 4
 
10.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6
16.7%
2 6
16.7%
3 4
11.1%
0 4
11.1%
1 4
11.1%
8 3
8.3%
6 3
8.3%
9 2
 
5.6%
7 2
 
5.6%
5 2
 
5.6%
Hangul
ValueCountFrequency (%)
구 1
25.0%
조 1
25.0%
인 1
25.0%
원 1
25.0%

Unnamed: 10
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing21
Missing (%)95.5%
Memory size308.0 B
2023-12-11T00:50:16.992974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters6
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

Unique1 ?
Unique (%)100.0%

Sample

1st row출력일시 :
ValueCountFrequency (%)
출력일시 1
50.0%
1
50.0%
2023-12-11T00:50:17.370088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
28.6%
출 1
14.3%
력 1
14.3%
일 1
14.3%
시 1
14.3%
: 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
57.1%
Space Separator 2
28.6%
Other Punctuation 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
출 1
25.0%
력 1
25.0%
일 1
25.0%
시 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
57.1%
Common 3
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
출 1
25.0%
력 1
25.0%
일 1
25.0%
시 1
25.0%
Common
ValueCountFrequency (%)
2
66.7%
: 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4
57.1%
ASCII 3
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
66.7%
: 1
33.3%
Hangul
ValueCountFrequency (%)
출 1
25.0%
력 1
25.0%
일 1
25.0%
시 1
25.0%

Unnamed: 11
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing21
Missing (%)95.5%
Memory size308.0 B
Minimum2017-03-29 00:00:00
Maximum2017-03-29 00:00:00
2023-12-11T00:50:17.546104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:17.677558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 12
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)75.0%
Missing10
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean0.16833333
Minimum0.01
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-11T00:50:17.863797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.032
Q10.0675
median0.09
Q30.1325
95-th percentile0.5435
Maximum1
Range0.99
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.26587192
Coefficient of variation (CV)1.5794372
Kurtosis11.091168
Mean0.16833333
Median Absolute Deviation (MAD)0.03
Skewness3.2825848
Sum2.02
Variance0.070687879
MonotonicityNot monotonic
2023-12-11T00:50:18.048321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.09 3
 
13.6%
0.17 2
 
9.1%
1.0 1
 
4.5%
0.1 1
 
4.5%
0.12 1
 
4.5%
0.07 1
 
4.5%
0.05 1
 
4.5%
0.06 1
 
4.5%
0.01 1
 
4.5%
(Missing) 10
45.5%
ValueCountFrequency (%)
0.01 1
 
4.5%
0.05 1
 
4.5%
0.06 1
 
4.5%
0.07 1
 
4.5%
0.09 3
13.6%
0.1 1
 
4.5%
0.12 1
 
4.5%
0.17 2
9.1%
1.0 1
 
4.5%
ValueCountFrequency (%)
1.0 1
 
4.5%
0.17 2
9.1%
0.12 1
 
4.5%
0.1 1
 
4.5%
0.09 3
13.6%
0.07 1
 
4.5%
0.06 1
 
4.5%
0.05 1
 
4.5%
0.01 1
 
4.5%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)95.5%
Memory size308.0 B

Interactions

2023-12-11T00:50:08.730991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:07.773112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:08.223262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:08.884182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:07.929351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:08.361192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:09.007618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:08.082062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:50:08.543327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:50:18.200015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 7Unnamed: 9Unnamed: 12
Unnamed: 11.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0000.9551.0000.905
Unnamed: 41.0001.0001.0001.0001.0001.0001.000
Unnamed: 71.0001.0000.9551.0001.0001.0000.955
Unnamed: 91.0001.0001.0001.0001.0001.0001.000
Unnamed: 121.0001.0000.9051.0000.9551.0001.000
2023-12-11T00:50:18.390729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 7Unnamed: 12
Unnamed: 31.0000.9910.873
Unnamed: 70.9911.0000.882
Unnamed: 120.8730.8821.000

Missing values

2023-12-11T00:50:09.199995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:50:09.510887image/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.
2023-12-11T00:50:09.776624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

서소별 구조건수 및 구조인원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
1<NA>서소 : 전라북도 - 전북소방안전본부 - 모두<NA><NA><NA><NA><NA><NA>집계년도: 2016 년<NA><NA>NaT<NA>NaN
2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
3<NA>구 분출동건수<NA>구조건수<NA><NA><NA><NA>구조인원<NA>NaT<NA>NaN
4<NA>합계402191.034192<NA><NA>1.0<NA>4937<NA>NaT1.0NaN
5<NA>전주덕진소방서61120.154864<NA><NA>0.14<NA>470<NA>NaT0.1NaN
6<NA>전주완산소방서66480.175749<NA><NA>0.17<NA>841<NA>NaT0.17NaN
7<NA>군산소방서56660.144570<NA><NA>0.13<NA>420<NA>NaT0.09NaN
8<NA>익산소방서52340.134438<NA><NA>0.13<NA>820<NA>NaT0.17NaN
9<NA>정읍소방서32610.083004<NA><NA>0.09<NA>436<NA>NaT0.09NaN
서소별 구조건수 및 구조인원 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
12<NA>고창소방서22780.062058<NA><NA>0.06<NA>225<NA>NaT0.05NaN
13<NA>부안소방서18040.041584<NA><NA>0.05<NA>319<NA>NaT0.06NaN
14<NA>무진장소방서31180.082693<NA><NA>0.08<NA>426<NA>NaT0.09NaN
15<NA>소방항공대<NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
16<NA>소방_항공대88<NA>85<NA><NA><NA><NA>51<NA>NaT0.01NaN
17<NA>구급상황관리센터<NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
18<NA>익산화학구조센터<NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>NaN
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>출력일시 :2017-03-29<NA>16:36:19
21<NA><NA><NA><NA><NA>1/1<NA><NA><NA><NA><NA>NaT<NA>NaN

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>NaT<NA>3