Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory105.3 B

Variable types

Numeric8
Categorical4

Alerts

ctprvn_nm has constant value ""Constant
adstrd_nm is highly overall correlated with seq_no and 8 other fieldsHigh correlation
signgu_nm is highly overall correlated with seq_no and 8 other fieldsHigh correlation
str_id is highly overall correlated with seq_no and 8 other fieldsHigh correlation
seq_no is highly overall correlated with base_ym and 3 other fieldsHigh correlation
base_ym is highly overall correlated with seq_no and 3 other fieldsHigh correlation
adstrd_cd is highly overall correlated with fclty_crdnt_lo and 3 other fieldsHigh correlation
fclty_crdnt_la is highly overall correlated with str_id and 2 other fieldsHigh correlation
fclty_crdnt_lo is highly overall correlated with adstrd_cd and 3 other fieldsHigh correlation
tot_infn_co is highly overall correlated with fclty_co and 3 other fieldsHigh correlation
fclty_co is highly overall correlated with tot_infn_co and 3 other fieldsHigh correlation
seq_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:43:45.315011
Analysis finished2023-12-10 09:43:58.701193
Duration13.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.88
Minimum1
Maximum761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:58.860217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum761
Range760
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation124.67779
Coefficient of variation (CV)1.7107271
Kurtosis26.624764
Mean72.88
Median Absolute Deviation (MAD)25.5
Skewness5.1439721
Sum7288
Variance15544.551
MonotonicityNot monotonic
2023-12-10T18:43:59.332009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
761 1
1.0%
760 1
1.0%
759 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

base_ym
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202091.08
Minimum201601
Maximum202110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:59.610981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile202103
Q1202104
median202106
Q3202108
95-th percentile202110
Maximum202110
Range509
Interquartile range (IQR)4

Descriptive statistics

Standard deviation86.295087
Coefficient of variation (CV)0.00042701087
Kurtosis29.85989
Mean202091.08
Median Absolute Deviation (MAD)2
Skewness-5.5893186
Sum20209108
Variance7446.842
MonotonicityNot monotonic
2023-12-10T18:43:59.880839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
202104 15
15.0%
202106 14
14.0%
202105 14
14.0%
202107 13
13.0%
202103 12
12.0%
202108 11
11.0%
202109 10
10.0%
202110 8
8.0%
201606 1
 
1.0%
201602 1
 
1.0%
ValueCountFrequency (%)
201601 1
 
1.0%
201602 1
 
1.0%
201606 1
 
1.0%
202103 12
12.0%
202104 15
15.0%
202105 14
14.0%
202106 14
14.0%
202107 13
13.0%
202108 11
11.0%
202109 10
10.0%
ValueCountFrequency (%)
202110 8
8.0%
202109 10
10.0%
202108 11
11.0%
202107 13
13.0%
202106 14
14.0%
202105 14
14.0%
202104 15
15.0%
202103 12
12.0%
201606 1
 
1.0%
201602 1
 
1.0%

str_id
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
bonbon10
8 
nrpk05
8 
hello08
8 
starb01
8 
kingkong37
8 
Other values (11)
60 

Length

Max length10
Median length8
Mean length7.47
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbonbon10
2nd rowjump01
3rd rownrpk05
4th rowhello08
5th rowstarb01

Common Values

ValueCountFrequency (%)
bonbon10 8
 
8.0%
nrpk05 8
 
8.0%
hello08 8
 
8.0%
starb01 8
 
8.0%
kingkong37 8
 
8.0%
skykids01 8
 
8.0%
petit04 7
 
7.0%
ssbb01 7
 
7.0%
seek58 7
 
7.0%
yj38317hj 6
 
6.0%
Other values (6) 25
25.0%

Length

2023-12-10T18:44:00.212084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bonbon10 8
 
8.0%
nrpk05 8
 
8.0%
hello08 8
 
8.0%
starb01 8
 
8.0%
kingkong37 8
 
8.0%
skykids01 8
 
8.0%
petit04 7
 
7.0%
ssbb01 7
 
7.0%
seek58 7
 
7.0%
yj38317hj 6
 
6.0%
Other values (6) 25
25.0%

ctprvn_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충청남도
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 100
100.0%

Length

2023-12-10T18:44:00.470998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:00.758783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 100
100.0%

signgu_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
천안시 서북구
44 
아산시
21 
서산시
16 
예산군
8 
천안시 동남구
8 

Length

Max length7
Median length7
Mean length5.08
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row천안시 서북구
2nd row당진시
3rd row서산시
4th row천안시 서북구
5th row천안시 서북구

Common Values

ValueCountFrequency (%)
천안시 서북구 44
44.0%
아산시 21
21.0%
서산시 16
 
16.0%
예산군 8
 
8.0%
천안시 동남구 8
 
8.0%
당진시 3
 
3.0%

Length

2023-12-10T18:44:01.123679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:01.405010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
천안시 52
34.2%
서북구 44
28.9%
아산시 21
13.8%
서산시 16
 
10.5%
예산군 8
 
5.3%
동남구 8
 
5.3%
당진시 3
 
2.0%

adstrd_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
불당동
24 
부성2동
16 
배방읍
14 
성연면
8 
삽교읍
8 
Other values (5)
30 

Length

Max length4
Median length3
Mean length3.19
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row불당동
2nd row부성1동
3rd row성연면
4th row부성2동
5th row부성2동

Common Values

ValueCountFrequency (%)
불당동 24
24.0%
부성2동 16
16.0%
배방읍 14
14.0%
성연면 8
 
8.0%
삽교읍 8
 
8.0%
신관동 8
 
8.0%
청룡동 8
 
8.0%
석남동 7
 
7.0%
백석동 4
 
4.0%
부성1동 3
 
3.0%

Length

2023-12-10T18:44:01.683619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:01.921288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
불당동 24
24.0%
부성2동 16
16.0%
배방읍 14
14.0%
성연면 8
 
8.0%
삽교읍 8
 
8.0%
신관동 8
 
8.0%
청룡동 8
 
8.0%
석남동 7
 
7.0%
백석동 4
 
4.0%
부성1동 3
 
3.0%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3530012 × 109
Minimum2.1474836 × 109
Maximum4.4810253 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:02.157356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1474836 × 109
5-th percentile4.413158 × 109
Q14.4133565 × 109
median4.413359 × 109
Q34.4200253 × 109
95-th percentile4.4810253 × 109
Maximum4.4810253 × 109
Range2.3335417 × 109
Interquartile range (IQR)6668800

Descriptive statistics

Standard deviation3.9023988 × 108
Coefficient of variation (CV)0.089648464
Kurtosis29.757775
Mean4.3530012 × 109
Median Absolute Deviation (MAD)201000
Skewness-5.5753138
Sum4.3530012 × 1011
Variance1.5228716 × 1017
MonotonicityNot monotonic
2023-12-10T18:44:02.459655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4413356500 24
24.0%
4413359000 16
16.0%
4420025300 14
14.0%
4421036000 8
 
8.0%
4481025300 8
 
8.0%
4415057000 8
 
8.0%
4413158000 8
 
8.0%
4421055000 7
 
7.0%
4413356000 4
 
4.0%
2147483647 3
 
3.0%
ValueCountFrequency (%)
2147483647 3
 
3.0%
4413158000 8
 
8.0%
4413356000 4
 
4.0%
4413356500 24
24.0%
4413359000 16
16.0%
4415057000 8
 
8.0%
4420025300 14
14.0%
4421036000 8
 
8.0%
4421055000 7
 
7.0%
4481025300 8
 
8.0%
ValueCountFrequency (%)
4481025300 8
 
8.0%
4421055000 7
 
7.0%
4421036000 8
 
8.0%
4420025300 14
14.0%
4415057000 8
 
8.0%
4413359000 16
16.0%
4413356500 24
24.0%
4413356000 4
 
4.0%
4413158000 8
 
8.0%
2147483647 3
 
3.0%

fclty_crdnt_la
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.769558
Minimum36.477056
Maximum36.840105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:02.695572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.477056
5-th percentile36.477056
Q136.774834
median36.806866
Q336.823292
95-th percentile36.840105
Maximum36.840105
Range0.3630483
Interquartile range (IQR)0.0484587

Descriptive statistics

Standard deviation0.097929711
Coefficient of variation (CV)0.0026633367
Kurtosis3.937535
Mean36.769558
Median Absolute Deviation (MAD)0.0271867
Skewness-2.1937237
Sum3676.9558
Variance0.0095902283
MonotonicityNot monotonic
2023-12-10T18:44:02.950121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
36.8068658 8
 
8.0%
36.8232924 8
 
8.0%
36.8340525 8
 
8.0%
36.8401045 8
 
8.0%
36.6622102 8
 
8.0%
36.4770562 8
 
8.0%
36.7748337 7
 
7.0%
36.7748964 7
 
7.0%
36.7746673 7
 
7.0%
36.8120677 6
 
6.0%
Other values (6) 25
25.0%
ValueCountFrequency (%)
36.4770562 8
8.0%
36.6622102 8
8.0%
36.7746673 7
7.0%
36.7748337 7
7.0%
36.7748964 7
7.0%
36.7814345 6
6.0%
36.7832679 2
 
2.0%
36.8058884 4
4.0%
36.8068658 8
8.0%
36.8120677 6
6.0%
ValueCountFrequency (%)
36.8401045 8
8.0%
36.8378176 4
4.0%
36.8340525 8
8.0%
36.833202 3
 
3.0%
36.8232924 8
8.0%
36.8223949 6
6.0%
36.8120677 6
6.0%
36.8068658 8
8.0%
36.8058884 4
4.0%
36.7832679 2
 
2.0%

fclty_crdnt_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97931
Minimum126.44981
Maximum127.15278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:03.177475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.44981
5-th percentile126.44981
Q1127.0541
median127.1069
Q3127.13131
95-th percentile127.15268
Maximum127.15278
Range0.7029642
Interquartile range (IQR)0.0772051

Descriptive statistics

Standard deviation0.25376825
Coefficient of variation (CV)0.0019985007
Kurtosis0.19726029
Mean126.97931
Median Absolute Deviation (MAD)0.039076
Skewness-1.4002784
Sum12697.931
Variance0.064398327
MonotonicityNot monotonic
2023-12-10T18:44:03.388567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
127.131084 8
 
8.0%
127.1313098 7
 
7.0%
127.1064039 7
 
7.0%
127.0541047 7
 
7.0%
126.4506569 7
 
7.0%
126.6803306 7
 
7.0%
127.1459775 7
 
7.0%
126.4498116 7
 
7.0%
127.0568175 6
 
6.0%
127.1526789 6
 
6.0%
Other values (13) 31
31.0%
ValueCountFrequency (%)
126.4498116 7
7.0%
126.4506569 7
7.0%
126.450657 1
 
1.0%
126.6803306 7
7.0%
126.680331 1
 
1.0%
127.0541047 7
7.0%
127.0568175 6
6.0%
127.056818 1
 
1.0%
127.1058264 4
4.0%
127.1064039 7
7.0%
ValueCountFrequency (%)
127.1527758 2
 
2.0%
127.1526789 6
6.0%
127.145978 1
 
1.0%
127.1459775 7
7.0%
127.142527 3
 
3.0%
127.13131 1
 
1.0%
127.1313098 7
7.0%
127.131084 8
8.0%
127.1194504 4
4.0%
127.1080718 6
6.0%

dynmc_popltn_co
Real number (ℝ)

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean797.02
Minimum3
Maximum2371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:03.652780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15.45
Q1460.75
median674
Q31026.25
95-th percentile1956.9
Maximum2371
Range2368
Interquartile range (IQR)565.5

Descriptive statistics

Standard deviation546.60162
Coefficient of variation (CV)0.68580666
Kurtosis0.51059943
Mean797.02
Median Absolute Deviation (MAD)274
Skewness0.92572648
Sum79702
Variance298773.33
MonotonicityNot monotonic
2023-12-10T18:44:03.897058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3
 
3.0%
527 3
 
3.0%
476 2
 
2.0%
899 1
 
1.0%
16 1
 
1.0%
796 1
 
1.0%
479 1
 
1.0%
904 1
 
1.0%
768 1
 
1.0%
975 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
3 3
3.0%
4 1
 
1.0%
5 1
 
1.0%
16 1
 
1.0%
19 1
 
1.0%
56 1
 
1.0%
69 1
 
1.0%
131 1
 
1.0%
164 1
 
1.0%
234 1
 
1.0%
ValueCountFrequency (%)
2371 1
1.0%
2236 1
1.0%
2195 1
1.0%
2002 1
1.0%
1993 1
1.0%
1955 1
1.0%
1766 1
1.0%
1764 1
1.0%
1749 1
1.0%
1724 1
1.0%

tot_infn_co
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean882.78
Minimum76
Maximum1709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:04.115053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile76
Q1368
median1030
Q31089
95-th percentile1709
Maximum1709
Range1633
Interquartile range (IQR)721

Descriptive statistics

Standard deviation510.51801
Coefficient of variation (CV)0.57830718
Kurtosis-0.92324793
Mean882.78
Median Absolute Deviation (MAD)332
Skewness-0.08912521
Sum88278
Variance260628.64
MonotonicityNot monotonic
2023-12-10T18:44:04.857993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1030 24
24.0%
1046 16
16.0%
1709 14
14.0%
135 8
 
8.0%
76 8
 
8.0%
368 8
 
8.0%
1089 8
 
8.0%
429 7
 
7.0%
1362 4
 
4.0%
367 3
 
3.0%
ValueCountFrequency (%)
76 8
 
8.0%
135 8
 
8.0%
367 3
 
3.0%
368 8
 
8.0%
429 7
 
7.0%
1030 24
24.0%
1046 16
16.0%
1089 8
 
8.0%
1362 4
 
4.0%
1709 14
14.0%
ValueCountFrequency (%)
1709 14
14.0%
1362 4
 
4.0%
1089 8
 
8.0%
1046 16
16.0%
1030 24
24.0%
429 7
 
7.0%
368 8
 
8.0%
367 3
 
3.0%
135 8
 
8.0%
76 8
 
8.0%

fclty_co
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.18
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:05.075358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median5
Q37
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2671614
Coefficient of variation (CV)0.52866689
Kurtosis0.40996399
Mean6.18
Median Absolute Deviation (MAD)1
Skewness0.88627726
Sum618
Variance10.674343
MonotonicityNot monotonic
2023-12-10T18:44:05.383287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 27
27.0%
6 16
16.0%
13 14
14.0%
7 11
11.0%
1 8
 
8.0%
3 8
 
8.0%
4 8
 
8.0%
8 8
 
8.0%
ValueCountFrequency (%)
1 8
 
8.0%
3 8
 
8.0%
4 8
 
8.0%
5 27
27.0%
6 16
16.0%
7 11
11.0%
8 8
 
8.0%
13 14
14.0%
ValueCountFrequency (%)
13 14
14.0%
8 8
 
8.0%
7 11
11.0%
6 16
16.0%
5 27
27.0%
4 8
 
8.0%
3 8
 
8.0%
1 8
 
8.0%

Interactions

2023-12-10T18:43:56.787692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:46.281548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.790515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.208694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.433339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:51.949203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:53.839136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:55.153721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:56.962385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:46.494283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.974831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.395496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.603917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:52.110707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:53.988037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:55.329283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:57.121657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:46.668538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:48.132162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.548566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.778454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:52.303098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.161826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:55.483580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:57.288797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:46.833930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:48.285222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.677913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.961523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:52.511021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.316282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:55.650451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:57.569215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.112355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:48.455747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.837226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:51.135212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:52.711758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.491529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:55.836756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:57.723857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.269397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:48.703621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.000010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:51.310010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:52.915573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.642346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:56.057228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:57.851243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.419432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:48.851723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.136124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:51.563290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:53.073251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.771573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:56.271780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:58.033670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:47.634853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:49.037792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:50.297147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:51.791199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:53.264881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:54.991862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:56.565960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:44:05.566101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_ymstr_idsigngu_nmadstrd_nmadstrd_cdfclty_crdnt_lafclty_crdnt_lodynmc_popltn_cotot_infn_cofclty_co
seq_no1.0001.0000.8350.9390.803NaN0.0000.0000.2680.4240.117
base_ym1.0001.0001.0001.0001.000NaN0.0000.0000.0000.3380.000
str_id0.8351.0001.0001.0001.000NaN1.0001.0000.7631.0001.000
signgu_nm0.9391.0001.0001.0001.000NaN0.9740.9210.2920.9070.893
adstrd_nm0.8031.0001.0001.0001.000NaN1.0001.0000.7571.0001.000
adstrd_cdNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaN
fclty_crdnt_la0.0000.0001.0000.9741.000NaN1.0000.9460.3300.8830.964
fclty_crdnt_lo0.0000.0001.0000.9211.000NaN0.9461.0000.4190.8970.942
dynmc_popltn_co0.2680.0000.7630.2920.757NaN0.3300.4191.0000.4710.607
tot_infn_co0.4240.3381.0000.9071.000NaN0.8830.8970.4711.0000.978
fclty_co0.1170.0001.0000.8931.000NaN0.9640.9420.6070.9781.000
2023-12-10T18:44:05.837724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
adstrd_nmsigngu_nmstr_id
adstrd_nm1.0000.9780.966
signgu_nm0.9781.0000.945
str_id0.9660.9451.000
2023-12-10T18:44:06.033838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_ymadstrd_cdfclty_crdnt_lafclty_crdnt_lodynmc_popltn_cotot_infn_cofclty_costr_idsigngu_nmadstrd_nm
seq_no1.000-0.992-0.2150.0420.130-0.2800.0010.0630.6150.6820.662
base_ym-0.9921.0000.231-0.083-0.1290.303-0.013-0.0570.9260.9790.958
adstrd_cd-0.2150.2311.000-0.465-0.7240.351-0.369-0.1910.9260.9790.958
fclty_crdnt_la0.042-0.083-0.4651.0000.2530.2100.2340.0140.9350.8840.968
fclty_crdnt_lo0.130-0.129-0.7240.2531.000-0.1180.2950.1300.9350.8030.968
dynmc_popltn_co-0.2800.3030.3510.210-0.1181.000-0.226-0.2100.4120.1520.324
tot_infn_co0.001-0.013-0.3690.2340.295-0.2261.0000.8900.9500.8060.984
fclty_co0.063-0.057-0.1910.0140.130-0.2100.8901.0000.9500.8440.984
str_id0.6150.9260.9260.9350.9350.4120.9500.9501.0000.9450.966
signgu_nm0.6820.9790.9790.8840.8030.1520.8060.8440.9451.0000.978
adstrd_nm0.6620.9580.9580.9680.9680.3240.9840.9840.9660.9781.000

Missing values

2023-12-10T18:43:58.264271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:43:58.585628image/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

seq_nobase_ymstr_idctprvn_nmsigngu_nmadstrd_nmadstrd_cdfclty_crdnt_lafclty_crdnt_lodynmc_popltn_cotot_infn_cofclty_co
01202110bonbon10충청남도천안시 서북구불당동441335650036.806866127.10640441910305
1759201606jump01충청남도당진시부성1동214748364736.833202127.142527563675
23202110nrpk05충청남도서산시성연면442103600036.823292126.45065717491351
34202110hello08충청남도천안시 서북구부성2동441335900036.834052127.13108495010466
45202110starb01충청남도천안시 서북구부성2동441335900036.840105127.13131195510466
56202110kingkong37충청남도예산군삽교읍448102530036.66221126.680331957763
67202110skykids01충청남도서산시신관동441505700036.477056127.1459785273684
7760201602jump01충청남도당진시부성1동214748364736.833202127.1425272413675
89202110petit04충청남도아산시배방읍442002530036.774834127.056818746170913
910202110yj38317hj충청남도천안시 서북구불당동441335650036.822395127.10690280010305
seq_nobase_ymstr_idctprvn_nmsigngu_nmadstrd_nmadstrd_cdfclty_crdnt_lafclty_crdnt_lodynmc_popltn_cotot_infn_cofclty_co
9091202103lil29충청남도천안시 서북구불당동441335650036.812068127.10807251410305
9192202103lovely01충청남도천안시 동남구청룡동441315800036.783268127.152776410898
9293202103nrpk05충청남도서산시성연면442103600036.823292126.45065751351
9394202103monello01충청남도천안시 서북구백석동441335600036.837818127.119451913627
9495202103hello08충청남도천안시 서북구부성2동441335900036.834052127.13108488610466
9596202103starb01충청남도천안시 서북구부성2동441335900036.840105127.13131219510466
9697202103kingkong37충청남도예산군삽교읍448102530036.66221126.6803311056763
9798202103cherry01충청남도천안시 동남구청룡동441315800036.781435127.15267969510898
9899202103skykids01충청남도서산시신관동441505700036.477056127.1459785153684
99100202103seek58충청남도아산시석남동442105500036.774667126.4498123204297