Overview

Dataset statistics

Number of variables20
Number of observations673
Missing cells7424
Missing cells (%)55.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory116.5 KiB
Average record size in memory177.2 B

Variable types

Text2
DateTime1
Numeric16
Categorical1

Dataset

DescriptionOver the last several years, the original standardized plans (A ‐ J) have been updated, and several new plans have been added. However, for historical rate increase purposes, this data includes all plans whether they are available for sale or not. Only plans with asterisks can be sold under the MIPPA act: A, B, C, D, F, HD‐F, G, K, L, M, and N.
Author미국
URLhttps://catalog.data.gov/dataset/iowa-standardized-medicare-supplement-rate-changes

Alerts

A is highly overall correlated with B and 13 other fieldsHigh correlation
B is highly overall correlated with A and 14 other fieldsHigh correlation
C is highly overall correlated with A and 13 other fieldsHigh correlation
D is highly overall correlated with A and 14 other fieldsHigh correlation
E is highly overall correlated with A and 9 other fieldsHigh correlation
F is highly overall correlated with A and 12 other fieldsHigh correlation
G is highly overall correlated with A and 12 other fieldsHigh correlation
H is highly overall correlated with A and 13 other fieldsHigh correlation
I is highly overall correlated with A and 10 other fieldsHigh correlation
J is highly overall correlated with A and 11 other fieldsHigh correlation
HDF is highly overall correlated with B and 9 other fieldsHigh correlation
HDG is highly overall correlated with HDFHigh correlation
K is highly overall correlated with A and 7 other fieldsHigh correlation
L is highly overall correlated with A and 13 other fieldsHigh correlation
M is highly overall correlated with A and 11 other fieldsHigh correlation
N is highly overall correlated with A and 9 other fieldsHigh correlation
HDJ is highly overall correlated with A and 11 other fieldsHigh correlation
HDJ is highly imbalanced (96.9%)Imbalance
Form Numbers has 7 (1.0%) missing valuesMissing
A has 142 (21.1%) missing valuesMissing
B has 473 (70.3%) missing valuesMissing
C has 396 (58.8%) missing valuesMissing
D has 474 (70.4%) missing valuesMissing
E has 609 (90.5%) missing valuesMissing
F has 35 (5.2%) missing valuesMissing
G has 171 (25.4%) missing valuesMissing
H has 637 (94.7%) missing valuesMissing
I has 618 (91.8%) missing valuesMissing
J has 604 (89.7%) missing valuesMissing
HDF has 495 (73.6%) missing valuesMissing
HDG has 614 (91.2%) missing valuesMissing
K has 607 (90.2%) missing valuesMissing
L has 599 (89.0%) missing valuesMissing
M has 639 (94.9%) missing valuesMissing
N has 304 (45.2%) missing valuesMissing
A has 146 (21.7%) zerosZeros
B has 46 (6.8%) zerosZeros
C has 71 (10.5%) zerosZeros
D has 55 (8.2%) zerosZeros
E has 17 (2.5%) zerosZeros
F has 116 (17.2%) zerosZeros
G has 71 (10.5%) zerosZeros
I has 11 (1.6%) zerosZeros
J has 15 (2.2%) zerosZeros
HDF has 65 (9.7%) zerosZeros
HDG has 40 (5.9%) zerosZeros
K has 29 (4.3%) zerosZeros
L has 31 (4.6%) zerosZeros
M has 16 (2.4%) zerosZeros
N has 63 (9.4%) zerosZeros

Reproduction

Analysis started2024-02-29 05:18:02.700681
Analysis finished2024-02-29 05:19:19.536377
Duration1 minute and 16.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct126
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2024-02-29T14:19:19.784930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length52
Mean length37.500743
Min length18

Characters and Unicode

Total characters25238
Distinct characters55
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.2%

Sample

1st rowMedico Life and Health Insurance Company
2nd rowPhysicians Life Insurance Company
3rd rowNew York Life Insurance Company
4th rowAvera Health Plans
5th rowMedico Insurance Company
ValueCountFrequency (%)
company 604
17.8%
insurance 573
16.9%
life 424
 
12.5%
of 105
 
3.1%
american 88
 
2.6%
health 67
 
2.0%
56
 
1.6%
united 55
 
1.6%
national 54
 
1.6%
and 51
 
1.5%
Other values (162) 1320
38.9%
2024-02-29T14:19:20.505835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2794
 
11.1%
2724
 
10.8%
a 2344
 
9.3%
e 2189
 
8.7%
r 1230
 
4.9%
i 1204
 
4.8%
o 1199
 
4.8%
s 1020
 
4.0%
c 982
 
3.9%
m 906
 
3.6%
Other values (45) 8646
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18892
74.9%
Uppercase Letter 3365
 
13.3%
Space Separator 2724
 
10.8%
Other Punctuation 102
 
0.4%
Open Punctuation 60
 
0.2%
Close Punctuation 60
 
0.2%
Dash Punctuation 31
 
0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2794
14.8%
a 2344
12.4%
e 2189
11.6%
r 1230
 
6.5%
i 1204
 
6.4%
o 1199
 
6.3%
s 1020
 
5.4%
c 982
 
5.2%
m 906
 
4.8%
u 853
 
4.5%
Other values (14) 4171
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 804
23.9%
I 645
19.2%
L 482
14.3%
A 324
9.6%
S 140
 
4.2%
M 104
 
3.1%
H 99
 
2.9%
U 96
 
2.9%
F 94
 
2.8%
G 76
 
2.3%
Other values (13) 501
14.9%
Other Punctuation
ValueCountFrequency (%)
& 36
35.3%
. 34
33.3%
, 32
31.4%
Space Separator
ValueCountFrequency (%)
2724
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22257
88.2%
Common 2981
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2794
 
12.6%
a 2344
 
10.5%
e 2189
 
9.8%
r 1230
 
5.5%
i 1204
 
5.4%
o 1199
 
5.4%
s 1020
 
4.6%
c 982
 
4.4%
m 906
 
4.1%
u 853
 
3.8%
Other values (37) 7536
33.9%
Common
ValueCountFrequency (%)
2724
91.4%
( 60
 
2.0%
) 60
 
2.0%
& 36
 
1.2%
. 34
 
1.1%
, 32
 
1.1%
- 31
 
1.0%
_ 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2794
 
11.1%
2724
 
10.8%
a 2344
 
9.3%
e 2189
 
8.7%
r 1230
 
4.9%
i 1204
 
4.8%
o 1199
 
4.8%
s 1020
 
4.0%
c 982
 
3.9%
m 906
 
3.6%
Other values (45) 8646
34.3%

Form Numbers
Text

MISSING 

Distinct187
Distinct (%)28.1%
Missing7
Missing (%)1.0%
Memory size5.4 KiB
2024-02-29T14:19:20.957772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length45
Mean length31.168168
Min length4

Characters and Unicode

Total characters20758
Distinct characters73
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.1%

Sample

1st rowStandardized MIPPA MLHMS2023G et al
2nd rowForms L265 et al
3rd rowNYM1 et al
4th rowIA-SEL-MF (06/10) et al
5th row2010 Group MIPPA Plans: MSA21A, D, F, M, and N
ValueCountFrequency (%)
al 414
 
11.0%
et 410
 
10.9%
mippa 350
 
9.3%
plans 196
 
5.2%
2010 176
 
4.7%
f 124
 
3.3%
123
 
3.3%
and 100
 
2.6%
a 98
 
2.6%
g 78
 
2.1%
Other values (326) 1706
45.2%
2024-02-29T14:19:21.811909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3195
 
15.4%
A 1197
 
5.8%
0 1172
 
5.6%
P 1015
 
4.9%
a 1010
 
4.9%
M 1006
 
4.8%
e 886
 
4.3%
l 768
 
3.7%
1 743
 
3.6%
- 733
 
3.5%
Other values (63) 9033
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6189
29.8%
Uppercase Letter 6177
29.8%
Decimal Number 3410
16.4%
Space Separator 3195
15.4%
Dash Punctuation 733
 
3.5%
Other Punctuation 614
 
3.0%
Close Punctuation 198
 
1.0%
Open Punctuation 198
 
1.0%
Math Symbol 26
 
0.1%
Connector Punctuation 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1197
19.4%
P 1015
16.4%
M 1006
16.3%
I 638
10.3%
S 493
8.0%
F 354
 
5.7%
G 344
 
5.6%
N 192
 
3.1%
L 170
 
2.8%
C 158
 
2.6%
Other values (14) 610
9.9%
Lowercase Letter
ValueCountFrequency (%)
a 1010
16.3%
e 886
14.3%
l 768
12.4%
n 613
9.9%
t 581
9.4%
d 508
8.2%
s 312
 
5.0%
r 305
 
4.9%
i 259
 
4.2%
p 246
 
4.0%
Other values (13) 701
11.3%
Decimal Number
ValueCountFrequency (%)
0 1172
34.4%
1 743
21.8%
2 528
15.5%
9 232
 
6.8%
4 182
 
5.3%
5 150
 
4.4%
6 121
 
3.5%
3 118
 
3.5%
7 91
 
2.7%
8 73
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 455
74.1%
: 68
 
11.1%
. 32
 
5.2%
* 20
 
3.3%
/ 15
 
2.4%
; 12
 
2.0%
& 8
 
1.3%
' 4
 
0.7%
Math Symbol
ValueCountFrequency (%)
| 20
76.9%
< 6
 
23.1%
Space Separator
ValueCountFrequency (%)
3195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 733
100.0%
Close Punctuation
ValueCountFrequency (%)
) 198
100.0%
Open Punctuation
ValueCountFrequency (%)
( 198
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Control
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12366
59.6%
Common 8392
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1197
 
9.7%
P 1015
 
8.2%
a 1010
 
8.2%
M 1006
 
8.1%
e 886
 
7.2%
l 768
 
6.2%
I 638
 
5.2%
n 613
 
5.0%
t 581
 
4.7%
d 508
 
4.1%
Other values (37) 4144
33.5%
Common
ValueCountFrequency (%)
3195
38.1%
0 1172
 
14.0%
1 743
 
8.9%
- 733
 
8.7%
2 528
 
6.3%
, 455
 
5.4%
9 232
 
2.8%
) 198
 
2.4%
( 198
 
2.4%
4 182
 
2.2%
Other values (16) 756
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3195
 
15.4%
A 1197
 
5.8%
0 1172
 
5.6%
P 1015
 
4.9%
a 1010
 
4.9%
M 1006
 
4.8%
e 886
 
4.3%
l 768
 
3.7%
1 743
 
3.6%
- 733
 
3.5%
Other values (63) 9033
43.5%
Distinct117
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2020-03-01 00:00:00
Maximum2024-07-01 00:00:00
2024-02-29T14:19:22.095657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:22.692967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

A
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct66
Distinct (%)12.4%
Missing142
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean0.061135593
Minimum-0.05
Maximum0.195
Zeros146
Zeros (%)21.7%
Negative5
Negative (%)0.7%
Memory size6.0 KiB
2024-02-29T14:19:23.285634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.05
5-th percentile0
Q10
median0.06
Q30.094
95-th percentile0.15
Maximum0.195
Range0.245
Interquartile range (IQR)0.094

Descriptive statistics

Standard deviation0.051826238
Coefficient of variation (CV)0.8477261
Kurtosis-0.81257975
Mean0.061135593
Median Absolute Deviation (MAD)0.04
Skewness0.30528802
Sum32.463
Variance0.0026859589
MonotonicityNot monotonic
2024-02-29T14:19:23.888269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 146
21.7%
0.06 33
 
4.9%
0.05 30
 
4.5%
0.09 27
 
4.0%
0.15 26
 
3.9%
0.07 25
 
3.7%
0.08 18
 
2.7%
0.12 16
 
2.4%
0.03 15
 
2.2%
0.065 15
 
2.2%
Other values (56) 180
26.7%
(Missing) 142
21.1%
ValueCountFrequency (%)
-0.05 3
 
0.4%
-0.045 1
 
0.1%
-0.02 1
 
0.1%
0.0 146
21.7%
0.01 1
 
0.1%
0.02 4
 
0.6%
0.025 7
 
1.0%
0.03 15
 
2.2%
0.033 1
 
0.1%
0.034 1
 
0.1%
ValueCountFrequency (%)
0.195 1
 
0.1%
0.18 1
 
0.1%
0.179 2
0.3%
0.175 1
 
0.1%
0.174 1
 
0.1%
0.17 3
0.4%
0.169 1
 
0.1%
0.164 1
 
0.1%
0.16 2
0.3%
0.155 2
0.3%

B
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct44
Distinct (%)22.0%
Missing473
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean0.05578
Minimum-0.05
Maximum0.18
Zeros46
Zeros (%)6.8%
Negative1
Negative (%)0.1%
Memory size6.0 KiB
2024-02-29T14:19:24.206284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.05
5-th percentile0
Q10.025
median0.06
Q30.08
95-th percentile0.14405
Maximum0.18
Range0.23
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.043440037
Coefficient of variation (CV)0.77877441
Kurtosis0.023507874
Mean0.05578
Median Absolute Deviation (MAD)0.03
Skewness0.41919673
Sum11.156
Variance0.0018870368
MonotonicityNot monotonic
2024-02-29T14:19:24.500133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 46
 
6.8%
0.06 19
 
2.8%
0.07 15
 
2.2%
0.05 13
 
1.9%
0.03 11
 
1.6%
0.09 9
 
1.3%
0.08 7
 
1.0%
0.079 7
 
1.0%
0.055 6
 
0.9%
0.15 5
 
0.7%
Other values (34) 62
 
9.2%
(Missing) 473
70.3%
ValueCountFrequency (%)
-0.05 1
 
0.1%
0.0 46
6.8%
0.01 1
 
0.1%
0.02 1
 
0.1%
0.025 3
 
0.4%
0.03 11
 
1.6%
0.033 1
 
0.1%
0.035 5
 
0.7%
0.04 3
 
0.4%
0.045 1
 
0.1%
ValueCountFrequency (%)
0.18 1
 
0.1%
0.179 1
 
0.1%
0.169 1
 
0.1%
0.15 5
0.7%
0.149 1
 
0.1%
0.145 1
 
0.1%
0.144 1
 
0.1%
0.14 2
 
0.3%
0.135 1
 
0.1%
0.129 1
 
0.1%

C
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct52
Distinct (%)18.8%
Missing396
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean0.055169675
Minimum-0.05
Maximum0.179
Zeros71
Zeros (%)10.5%
Negative1
Negative (%)0.1%
Memory size6.0 KiB
2024-02-29T14:19:24.776875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.05
5-th percentile0
Q10
median0.06
Q30.08
95-th percentile0.1452
Maximum0.179
Range0.229
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.044420546
Coefficient of variation (CV)0.80516236
Kurtosis-0.34175001
Mean0.055169675
Median Absolute Deviation (MAD)0.03
Skewness0.38943375
Sum15.282
Variance0.0019731849
MonotonicityNot monotonic
2024-02-29T14:19:25.084039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 71
 
10.5%
0.06 25
 
3.7%
0.05 23
 
3.4%
0.07 14
 
2.1%
0.08 13
 
1.9%
0.03 10
 
1.5%
0.09 10
 
1.5%
0.095 9
 
1.3%
0.079 8
 
1.2%
0.1 8
 
1.2%
Other values (42) 86
 
12.8%
(Missing) 396
58.8%
ValueCountFrequency (%)
-0.05 1
 
0.1%
0.0 71
10.5%
0.01 1
 
0.1%
0.02 2
 
0.3%
0.025 5
 
0.7%
0.03 10
 
1.5%
0.032 1
 
0.1%
0.033 1
 
0.1%
0.035 6
 
0.9%
0.036 1
 
0.1%
ValueCountFrequency (%)
0.179 1
 
0.1%
0.169 1
 
0.1%
0.164 1
 
0.1%
0.155 3
0.4%
0.15 6
0.9%
0.149 1
 
0.1%
0.146 1
 
0.1%
0.145 1
 
0.1%
0.144 1
 
0.1%
0.137 1
 
0.1%

D
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)19.6%
Missing474
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean0.045271357
Minimum-0.1
Maximum0.15
Zeros55
Zeros (%)8.2%
Negative3
Negative (%)0.4%
Memory size6.0 KiB
2024-02-29T14:19:25.428264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1
5-th percentile0
Q10
median0.05
Q30.0715
95-th percentile0.1
Maximum0.15
Range0.25
Interquartile range (IQR)0.0715

Descriptive statistics

Standard deviation0.039000087
Coefficient of variation (CV)0.86147379
Kurtosis0.037799142
Mean0.045271357
Median Absolute Deviation (MAD)0.03
Skewness-0.18758697
Sum9.009
Variance0.0015210068
MonotonicityNot monotonic
2024-02-29T14:19:25.775913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 55
 
8.2%
0.06 18
 
2.7%
0.05 16
 
2.4%
0.07 16
 
2.4%
0.03 11
 
1.6%
0.08 10
 
1.5%
0.04 6
 
0.9%
0.09 6
 
0.9%
0.1 6
 
0.9%
0.079 5
 
0.7%
Other values (29) 50
 
7.4%
(Missing) 474
70.4%
ValueCountFrequency (%)
-0.1 1
 
0.1%
-0.05 2
 
0.3%
0.0 55
8.2%
0.01 1
 
0.1%
0.015 1
 
0.1%
0.02 1
 
0.1%
0.025 4
 
0.6%
0.03 11
 
1.6%
0.033 1
 
0.1%
0.035 4
 
0.6%
ValueCountFrequency (%)
0.15 1
 
0.1%
0.13 2
 
0.3%
0.129 1
 
0.1%
0.124 1
 
0.1%
0.12 1
 
0.1%
0.11 1
 
0.1%
0.1 6
0.9%
0.099 2
 
0.3%
0.095 4
0.6%
0.09 6
0.9%

E
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)37.5%
Missing609
Missing (%)90.5%
Infinite0
Infinite (%)0.0%
Mean0.040078125
Minimum0
Maximum0.095
Zeros17
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:26.087699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0395
Q30.06925
95-th percentile0.08
Maximum0.095
Range0.095
Interquartile range (IQR)0.06925

Descriptive statistics

Standard deviation0.030162157
Coefficient of variation (CV)0.75258403
Kurtosis-1.2999087
Mean0.040078125
Median Absolute Deviation (MAD)0.0305
Skewness-0.034566083
Sum2.565
Variance0.0009097557
MonotonicityNot monotonic
2024-02-29T14:19:26.460078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 17
 
2.5%
0.03 8
 
1.2%
0.07 6
 
0.9%
0.025 4
 
0.6%
0.05 3
 
0.4%
0.08 3
 
0.4%
0.075 2
 
0.3%
0.095 2
 
0.3%
0.06 2
 
0.3%
0.079 2
 
0.3%
Other values (14) 15
 
2.2%
(Missing) 609
90.5%
ValueCountFrequency (%)
0.0 17
2.5%
0.025 4
 
0.6%
0.029 1
 
0.1%
0.03 8
1.2%
0.035 1
 
0.1%
0.039 1
 
0.1%
0.04 2
 
0.3%
0.047 1
 
0.1%
0.05 3
 
0.4%
0.053 1
 
0.1%
ValueCountFrequency (%)
0.095 2
 
0.3%
0.081 1
 
0.1%
0.08 3
0.4%
0.079 2
 
0.3%
0.075 2
 
0.3%
0.07 6
0.9%
0.069 1
 
0.1%
0.066 1
 
0.1%
0.065 1
 
0.1%
0.064 1
 
0.1%

F
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct75
Distinct (%)11.8%
Missing35
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean0.068222571
Minimum-0.16
Maximum0.199
Zeros116
Zeros (%)17.2%
Negative6
Negative (%)0.9%
Memory size6.0 KiB
2024-02-29T14:19:26.808854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.16
5-th percentile0
Q10.03
median0.065
Q30.1
95-th percentile0.15
Maximum0.199
Range0.359
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.051045127
Coefficient of variation (CV)0.74821466
Kurtosis-0.11368133
Mean0.068222571
Median Absolute Deviation (MAD)0.035
Skewness0.062449832
Sum43.526
Variance0.002605605
MonotonicityNot monotonic
2024-02-29T14:19:27.169316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 116
17.2%
0.05 41
 
6.1%
0.06 35
 
5.2%
0.12 31
 
4.6%
0.09 30
 
4.5%
0.07 28
 
4.2%
0.03 27
 
4.0%
0.08 25
 
3.7%
0.15 25
 
3.7%
0.065 23
 
3.4%
Other values (65) 257
38.2%
(Missing) 35
 
5.2%
ValueCountFrequency (%)
-0.16 1
 
0.1%
-0.06 1
 
0.1%
-0.05 4
 
0.6%
0.0 116
17.2%
0.01 3
 
0.4%
0.02 9
 
1.3%
0.025 8
 
1.2%
0.026 1
 
0.1%
0.03 27
 
4.0%
0.032 1
 
0.1%
ValueCountFrequency (%)
0.199 2
0.3%
0.195 1
 
0.1%
0.18 4
0.6%
0.179 3
0.4%
0.175 2
0.3%
0.174 1
 
0.1%
0.17 3
0.4%
0.169 2
0.3%
0.164 1
 
0.1%
0.161 1
 
0.1%

G
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct74
Distinct (%)14.7%
Missing171
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean0.076697211
Minimum-0.1
Maximum0.199
Zeros71
Zeros (%)10.5%
Negative7
Negative (%)1.0%
Memory size6.0 KiB
2024-02-29T14:19:27.561203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1
5-th percentile0
Q10.04
median0.075
Q30.12
95-th percentile0.155
Maximum0.199
Range0.299
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.05293294
Coefficient of variation (CV)0.69015469
Kurtosis-0.53005412
Mean0.076697211
Median Absolute Deviation (MAD)0.045
Skewness-0.06988169
Sum38.502
Variance0.0028018962
MonotonicityNot monotonic
2024-02-29T14:19:27.893301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 71
 
10.5%
0.05 31
 
4.6%
0.12 30
 
4.5%
0.15 29
 
4.3%
0.09 27
 
4.0%
0.06 23
 
3.4%
0.07 22
 
3.3%
0.03 20
 
3.0%
0.08 20
 
3.0%
0.065 19
 
2.8%
Other values (64) 210
31.2%
(Missing) 171
25.4%
ValueCountFrequency (%)
-0.1 1
 
0.1%
-0.08 1
 
0.1%
-0.05 3
 
0.4%
-0.045 1
 
0.1%
-0.02 1
 
0.1%
0.0 71
10.5%
0.01 4
 
0.6%
0.019 1
 
0.1%
0.02 5
 
0.7%
0.025 3
 
0.4%
ValueCountFrequency (%)
0.199 2
0.3%
0.195 1
 
0.1%
0.18 3
0.4%
0.179 4
0.6%
0.175 2
0.3%
0.174 1
 
0.1%
0.17 2
0.3%
0.169 3
0.4%
0.164 1
 
0.1%
0.161 1
 
0.1%

H
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)52.8%
Missing637
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean0.058472222
Minimum0
Maximum0.13
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:28.173715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.04525
median0.0585
Q30.079
95-th percentile0.09225
Maximum0.13
Range0.13
Interquartile range (IQR)0.03375

Descriptive statistics

Standard deviation0.025732121
Coefficient of variation (CV)0.44007428
Kurtosis0.68398251
Mean0.058472222
Median Absolute Deviation (MAD)0.0195
Skewness0.18911913
Sum2.105
Variance0.00066214206
MonotonicityNot monotonic
2024-02-29T14:19:28.432372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.05 6
 
0.9%
0.03 5
 
0.7%
0.065 4
 
0.6%
0.079 4
 
0.6%
0.07 2
 
0.3%
0.02 2
 
0.3%
0.075 1
 
0.1%
0.081 1
 
0.1%
0.04 1
 
0.1%
0.13 1
 
0.1%
Other values (9) 9
 
1.3%
(Missing) 637
94.7%
ValueCountFrequency (%)
0.0 1
 
0.1%
0.02 2
 
0.3%
0.03 5
0.7%
0.04 1
 
0.1%
0.047 1
 
0.1%
0.05 6
0.9%
0.055 1
 
0.1%
0.057 1
 
0.1%
0.06 1
 
0.1%
0.065 4
0.6%
ValueCountFrequency (%)
0.13 1
 
0.1%
0.099 1
 
0.1%
0.09 1
 
0.1%
0.085 1
 
0.1%
0.081 1
 
0.1%
0.08 1
 
0.1%
0.079 4
0.6%
0.075 1
 
0.1%
0.07 2
0.3%
0.065 4
0.6%

I
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)38.2%
Missing618
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean0.050309091
Minimum0
Maximum0.13
Zeros11
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:28.708728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.055
Q30.077
95-th percentile0.0927
Maximum0.13
Range0.13
Interquartile range (IQR)0.047

Descriptive statistics

Standard deviation0.032772997
Coefficient of variation (CV)0.65143291
Kurtosis-0.6700662
Mean0.050309091
Median Absolute Deviation (MAD)0.025
Skewness-0.16616665
Sum2.767
Variance0.0010740694
MonotonicityNot monotonic
2024-02-29T14:19:28.975493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 11
 
1.6%
0.05 5
 
0.7%
0.03 5
 
0.7%
0.079 4
 
0.6%
0.065 4
 
0.6%
0.08 3
 
0.4%
0.07 3
 
0.4%
0.09 3
 
0.4%
0.02 2
 
0.3%
0.06 2
 
0.3%
Other values (11) 13
 
1.9%
(Missing) 618
91.8%
ValueCountFrequency (%)
0.0 11
1.6%
0.02 2
 
0.3%
0.03 5
0.7%
0.04 1
 
0.1%
0.046 1
 
0.1%
0.047 1
 
0.1%
0.05 5
0.7%
0.055 2
 
0.3%
0.057 1
 
0.1%
0.06 2
 
0.3%
ValueCountFrequency (%)
0.13 1
 
0.1%
0.099 2
0.3%
0.09 3
0.4%
0.085 1
 
0.1%
0.08 3
0.4%
0.079 4
0.6%
0.075 1
 
0.1%
0.07 3
0.4%
0.065 4
0.6%
0.062 1
 
0.1%

J
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)36.2%
Missing604
Missing (%)89.7%
Infinite0
Infinite (%)0.0%
Mean0.048478261
Minimum0
Maximum0.12
Zeros15
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:29.227248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.05
Q30.07
95-th percentile0.0968
Maximum0.12
Range0.12
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.032049873
Coefficient of variation (CV)0.66111846
Kurtosis-0.59552134
Mean0.048478261
Median Absolute Deviation (MAD)0.02
Skewness-0.10601536
Sum3.345
Variance0.0010271944
MonotonicityNot monotonic
2024-02-29T14:19:29.472682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 15
 
2.2%
0.05 8
 
1.2%
0.065 6
 
0.9%
0.03 5
 
0.7%
0.079 4
 
0.6%
0.07 3
 
0.4%
0.055 3
 
0.4%
0.08 3
 
0.4%
0.12 2
 
0.3%
0.045 2
 
0.3%
Other values (15) 18
 
2.7%
(Missing) 604
89.7%
ValueCountFrequency (%)
0.0 15
2.2%
0.03 5
 
0.7%
0.032 1
 
0.1%
0.035 1
 
0.1%
0.04 2
 
0.3%
0.045 2
 
0.3%
0.047 1
 
0.1%
0.049 2
 
0.3%
0.05 8
1.2%
0.055 3
 
0.4%
ValueCountFrequency (%)
0.12 2
0.3%
0.1 1
 
0.1%
0.098 1
 
0.1%
0.095 1
 
0.1%
0.09 1
 
0.1%
0.085 1
 
0.1%
0.08 3
0.4%
0.079 4
0.6%
0.075 2
0.3%
0.07 3
0.4%

HDF
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct37
Distinct (%)20.8%
Missing495
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean0.04905618
Minimum-0.045
Maximum0.17
Zeros65
Zeros (%)9.7%
Negative2
Negative (%)0.3%
Memory size6.0 KiB
2024-02-29T14:19:29.785822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.045
5-th percentile0
Q10
median0.05
Q30.08
95-th percentile0.1315
Maximum0.17
Range0.215
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.046947587
Coefficient of variation (CV)0.95701678
Kurtosis-0.81076375
Mean0.04905618
Median Absolute Deviation (MAD)0.05
Skewness0.3996409
Sum8.732
Variance0.0022040759
MonotonicityNot monotonic
2024-02-29T14:19:30.086792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 65
 
9.7%
0.06 12
 
1.8%
0.09 9
 
1.3%
0.08 7
 
1.0%
0.065 7
 
1.0%
0.05 7
 
1.0%
0.07 6
 
0.9%
0.12 6
 
0.9%
0.15 5
 
0.7%
0.095 4
 
0.6%
Other values (27) 50
 
7.4%
(Missing) 495
73.6%
ValueCountFrequency (%)
-0.045 1
 
0.1%
-0.02 1
 
0.1%
0.0 65
9.7%
0.015 1
 
0.1%
0.02 1
 
0.1%
0.025 2
 
0.3%
0.03 4
 
0.6%
0.04 4
 
0.6%
0.045 1
 
0.1%
0.047 1
 
0.1%
ValueCountFrequency (%)
0.17 1
 
0.1%
0.15 5
0.7%
0.14 3
0.4%
0.13 3
0.4%
0.129 1
 
0.1%
0.12 6
0.9%
0.11 1
 
0.1%
0.1 3
0.4%
0.099 3
0.4%
0.098 1
 
0.1%

HDG
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)23.7%
Missing614
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean0.015050847
Minimum-0.224
Maximum0.095
Zeros40
Zeros (%)5.9%
Negative1
Negative (%)0.1%
Memory size6.0 KiB
2024-02-29T14:19:30.403033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.224
5-th percentile0
Q10
median0
Q30.0425
95-th percentile0.081
Maximum0.095
Range0.319
Interquartile range (IQR)0.0425

Descriptive statistics

Standard deviation0.043797112
Coefficient of variation (CV)2.9099433
Kurtosis14.7021
Mean0.015050847
Median Absolute Deviation (MAD)0
Skewness-2.3645928
Sum0.888
Variance0.001918187
MonotonicityNot monotonic
2024-02-29T14:19:30.636909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 40
 
5.9%
0.065 3
 
0.4%
0.06 3
 
0.4%
0.09 2
 
0.3%
0.055 2
 
0.3%
0.05 1
 
0.1%
0.08 1
 
0.1%
0.04 1
 
0.1%
0.037 1
 
0.1%
0.045 1
 
0.1%
Other values (4) 4
 
0.6%
(Missing) 614
91.2%
ValueCountFrequency (%)
-0.224 1
 
0.1%
0.0 40
5.9%
0.03 1
 
0.1%
0.037 1
 
0.1%
0.04 1
 
0.1%
0.045 1
 
0.1%
0.05 1
 
0.1%
0.055 2
 
0.3%
0.06 3
 
0.4%
0.065 3
 
0.4%
ValueCountFrequency (%)
0.095 1
 
0.1%
0.09 2
0.3%
0.08 1
 
0.1%
0.07 1
 
0.1%
0.065 3
0.4%
0.06 3
0.4%
0.055 2
0.3%
0.05 1
 
0.1%
0.045 1
 
0.1%
0.04 1
 
0.1%

HDJ
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
<NA>
669 
0.03
 
2
0.04
 
1
0.047
 
1

Length

Max length5
Median length4
Mean length4.0014859
Min length4

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 669
99.4%
0.03 2
 
0.3%
0.04 1
 
0.1%
0.047 1
 
0.1%

Length

2024-02-29T14:19:31.011383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-29T14:19:31.247878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 669
99.4%
0.03 2
 
0.3%
0.04 1
 
0.1%
0.047 1
 
0.1%

K
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)30.3%
Missing607
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean0.038090909
Minimum0
Maximum0.15
Zeros29
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:31.486318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0395
Q30.068
95-th percentile0.09875
Maximum0.15
Range0.15
Interquartile range (IQR)0.068

Descriptive statistics

Standard deviation0.038380174
Coefficient of variation (CV)1.0075941
Kurtosis-0.71606409
Mean0.038090909
Median Absolute Deviation (MAD)0.0395
Skewness0.46975452
Sum2.514
Variance0.0014730378
MonotonicityNot monotonic
2024-02-29T14:19:31.761532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 29
 
4.3%
0.055 6
 
0.9%
0.065 3
 
0.4%
0.1 3
 
0.4%
0.079 3
 
0.4%
0.05 3
 
0.4%
0.03 3
 
0.4%
0.07 2
 
0.3%
0.085 2
 
0.3%
0.06 2
 
0.3%
Other values (10) 10
 
1.5%
(Missing) 607
90.2%
ValueCountFrequency (%)
0.0 29
4.3%
0.029 1
 
0.1%
0.03 3
 
0.4%
0.049 1
 
0.1%
0.05 3
 
0.4%
0.055 6
 
0.9%
0.06 2
 
0.3%
0.064 1
 
0.1%
0.065 3
 
0.4%
0.069 1
 
0.1%
ValueCountFrequency (%)
0.15 1
 
0.1%
0.1 3
0.4%
0.095 1
 
0.1%
0.09 1
 
0.1%
0.085 2
0.3%
0.081 1
 
0.1%
0.08 1
 
0.1%
0.079 3
0.4%
0.075 1
 
0.1%
0.07 2
0.3%

L
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)33.8%
Missing599
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean0.034418919
Minimum0
Maximum0.15
Zeros31
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:32.085999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.055
95-th percentile0.1
Maximum0.15
Range0.15
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.036501082
Coefficient of variation (CV)1.0604947
Kurtosis0.30968856
Mean0.034418919
Median Absolute Deviation (MAD)0.03
Skewness0.87732145
Sum2.547
Variance0.001332329
MonotonicityNot monotonic
2024-02-29T14:19:32.343461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 31
 
4.6%
0.03 5
 
0.7%
0.05 5
 
0.7%
0.055 4
 
0.6%
0.08 3
 
0.4%
0.1 3
 
0.4%
0.095 2
 
0.3%
0.06 2
 
0.3%
0.029 2
 
0.3%
0.047 2
 
0.3%
Other values (15) 15
 
2.2%
(Missing) 599
89.0%
ValueCountFrequency (%)
0.0 31
4.6%
0.02 1
 
0.1%
0.025 1
 
0.1%
0.029 2
 
0.3%
0.03 5
 
0.7%
0.032 1
 
0.1%
0.039 1
 
0.1%
0.04 1
 
0.1%
0.045 1
 
0.1%
0.047 2
 
0.3%
ValueCountFrequency (%)
0.15 1
 
0.1%
0.13 1
 
0.1%
0.1 3
0.4%
0.095 2
0.3%
0.08 3
0.4%
0.075 1
 
0.1%
0.07 1
 
0.1%
0.069 1
 
0.1%
0.066 1
 
0.1%
0.061 1
 
0.1%

M
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)35.3%
Missing639
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean0.042558824
Minimum0
Maximum0.15
Zeros16
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-02-29T14:19:32.630767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0525
Q30.07375
95-th percentile0.1175
Maximum0.15
Range0.15
Interquartile range (IQR)0.07375

Descriptive statistics

Standard deviation0.045655622
Coefficient of variation (CV)1.0727651
Kurtosis-0.36391306
Mean0.042558824
Median Absolute Deviation (MAD)0.0525
Skewness0.65515226
Sum1.447
Variance0.0020844358
MonotonicityNot monotonic
2024-02-29T14:19:32.860169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 16
 
2.4%
0.06 4
 
0.6%
0.08 3
 
0.4%
0.15 2
 
0.3%
0.07 2
 
0.3%
0.095 1
 
0.1%
0.05 1
 
0.1%
0.075 1
 
0.1%
0.1 1
 
0.1%
0.065 1
 
0.1%
Other values (2) 2
 
0.3%
(Missing) 639
94.9%
ValueCountFrequency (%)
0.0 16
2.4%
0.05 1
 
0.1%
0.055 1
 
0.1%
0.06 4
 
0.6%
0.065 1
 
0.1%
0.07 2
 
0.3%
0.075 1
 
0.1%
0.08 3
 
0.4%
0.087 1
 
0.1%
0.095 1
 
0.1%
ValueCountFrequency (%)
0.15 2
0.3%
0.1 1
 
0.1%
0.095 1
 
0.1%
0.087 1
 
0.1%
0.08 3
0.4%
0.075 1
 
0.1%
0.07 2
0.3%
0.065 1
 
0.1%
0.06 4
0.6%
0.055 1
 
0.1%

N
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct72
Distinct (%)19.5%
Missing304
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean0.0720271
Minimum-0.131
Maximum0.199
Zeros63
Zeros (%)9.4%
Negative9
Negative (%)1.3%
Memory size6.0 KiB
2024-02-29T14:19:33.220713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.131
5-th percentile0
Q10.03
median0.07
Q30.119
95-th percentile0.153
Maximum0.199
Range0.33
Interquartile range (IQR)0.089

Descriptive statistics

Standard deviation0.056203428
Coefficient of variation (CV)0.78030947
Kurtosis-0.042153441
Mean0.0720271
Median Absolute Deviation (MAD)0.04
Skewness-0.20824277
Sum26.578
Variance0.0031588254
MonotonicityNot monotonic
2024-02-29T14:19:33.784410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 63
 
9.4%
0.06 28
 
4.2%
0.15 23
 
3.4%
0.12 17
 
2.5%
0.09 15
 
2.2%
0.03 14
 
2.1%
0.05 14
 
2.1%
0.08 13
 
1.9%
0.1 13
 
1.9%
0.04 11
 
1.6%
Other values (62) 158
23.5%
(Missing) 304
45.2%
ValueCountFrequency (%)
-0.131 1
 
0.1%
-0.1 3
 
0.4%
-0.05 2
 
0.3%
-0.04 1
 
0.1%
-0.03 1
 
0.1%
-0.025 1
 
0.1%
0.0 63
9.4%
0.01 3
 
0.4%
0.02 4
 
0.6%
0.025 1
 
0.1%
ValueCountFrequency (%)
0.199 2
0.3%
0.195 1
0.1%
0.18 2
0.3%
0.179 2
0.3%
0.175 1
0.1%
0.174 1
0.1%
0.17 2
0.3%
0.169 2
0.3%
0.164 1
0.1%
0.16 1
0.1%

Interactions

2024-02-29T14:19:13.472383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:22.023865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:25.714106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:29.274303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:32.863137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:36.653862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:39.958984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:43.386606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:47.059559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:50.089947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.278191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:56.363050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:59.775534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:02.805937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:06.342178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:09.847098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:13.714294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:22.332813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:25.942852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:29.501903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:33.143593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:36.935305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:40.175395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:43.649100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:47.259198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:50.272681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.472821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:56.512355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.010403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:03.022266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:06.542211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:10.042390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:13.908230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:22.529706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:26.149883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:29.765603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:33.633297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:37.194769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:40.355109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:43.836033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:47.450168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:50.470978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.662807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:56.759053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.225497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:03.256914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:06.770059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:10.233798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:14.184517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:22.732445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:26.440810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:30.009685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:33.838548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:37.398364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:40.560611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:44.104940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:47.617553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:50.690773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.817608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:56.984568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.399994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:03.475752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:06.966986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:10.522875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:14.390520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:22.990850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:26.651582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:30.343233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:34.003454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:37.575233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:40.727227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:44.292804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:47.848296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:50.906308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.990125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:57.196260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.552640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:03.689826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:07.149353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:10.717091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:14.541352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:23.177702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:26.843520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:30.576541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:34.148654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:37.737978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:40.874021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:44.448881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.094891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:51.091734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:54.154357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:57.677844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.700022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:03.994131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:07.330656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:10.894478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:14.772413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:23.500542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:27.036951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:30.851928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:34.396854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:37.929954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:41.095478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:44.659971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.254654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:51.309717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:54.339131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:57.845421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:00.875333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:04.232210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:07.512550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:11.112343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:15.086129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:23.763362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:27.230874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.026283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:34.606777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:38.131866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:41.350840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:44.880634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.439630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:51.496767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:54.596308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.028994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:01.102532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:04.486270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:07.763124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:11.312812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:15.270936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:23.953618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:27.467662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.193515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:34.798591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:38.355906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:41.553949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:45.074287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.658699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:51.675952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:54.777320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.199554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:01.275546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:04.685381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:07.938832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:11.511570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:15.537351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:24.207233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:27.658498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.409171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:35.003251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:38.523369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:41.862656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:45.260848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.829599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:51.862109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:54.997000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.371589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:01.451766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:04.876501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:08.164386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:11.716915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:15.825699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:24.410573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:27.943073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.571082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:35.234932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:38.686754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:42.073681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:45.437315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:48.995019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:52.043560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:55.192833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.574836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:01.660155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:05.066830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:08.437001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:11.947725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:16.057718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:24.590555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:28.165773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.793765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:35.463218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:38.930645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:42.260690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:45.664085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:49.164633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:52.266912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:55.383746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.739122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:01.812009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:05.251397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:08.631046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:12.174672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:16.287837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:24.795671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:28.355719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:31.975297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:35.664126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:39.148937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:42.477188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:46.210138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:49.318981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:52.470662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:55.530441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:58.941192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:02.002206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:05.424612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:08.791299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:12.392307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:16.609290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:25.036427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:28.633270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:32.184076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:35.873063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:39.378541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:42.734287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:46.453651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:49.493162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:52.658553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:55.746343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:59.155340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:02.237243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:05.688300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:09.016275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:12.666126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:16.883372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:25.266057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:28.834561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:32.469630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:36.134892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:39.605817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:42.944021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:46.661872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:49.669569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:52.873981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:55.952721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:59.387422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:02.432685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:05.885556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:09.409336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:12.991953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:17.216055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:25.484722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:29.046668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:32.674451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:36.399783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:39.800787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:43.137594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:46.847979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:49.854937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:53.088332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:56.203049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:18:59.602006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:02.627136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:06.122429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:09.617978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-29T14:19:13.229058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-29T14:19:34.057681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ABCDEFGHIJHDFHDGHDJKLMN
A1.0000.9810.9750.9760.8550.8460.9720.7120.9620.9470.9510.380NaN0.8310.8040.8540.810
B0.9811.0000.9990.9120.9650.9480.9360.9020.9980.8340.8570.0001.0000.7470.6680.8190.952
C0.9750.9991.0000.9550.8860.9200.9350.8330.8820.9240.9590.0001.0000.7230.7330.6430.867
D0.9760.9120.9551.0000.9610.8700.9780.8680.9170.9540.7900.000NaN0.9000.8620.9530.944
E0.8550.9650.8860.9611.0000.8620.7830.9790.8310.8830.669NaN1.0000.9060.4930.4160.528
F0.8460.9480.9200.8700.8621.0000.8340.9620.8670.7630.7940.723NaN0.5490.6770.6500.928
G0.9720.9360.9350.9780.7830.8341.0000.5810.5870.8270.8510.279NaN0.7330.7500.6930.847
H0.7120.9020.8330.8680.9790.9620.5811.0000.9990.9280.926NaN1.0001.0000.9970.000NaN
I0.9620.9980.8820.9170.8310.8670.5870.9991.0000.9280.926NaN1.0001.0000.8510.0001.000
J0.9470.8340.9240.9540.8830.7630.8270.9280.9281.0000.769NaN1.0000.9300.747NaN1.000
HDF0.9510.8570.9590.7900.6690.7940.8510.9260.9260.7691.0000.7131.0000.8290.8740.8760.779
HDG0.3800.0000.0000.000NaN0.7230.279NaNNaNNaN0.7131.000NaN0.0000.0000.0000.899
HDJNaN1.0001.000NaN1.000NaNNaN1.0001.0001.0001.000NaN1.000NaN1.000NaNNaN
K0.8310.7470.7230.9000.9060.5490.7331.0001.0000.9300.8290.000NaN1.0000.9840.9570.916
L0.8040.6680.7330.8620.4930.6770.7500.9970.8510.7470.8740.0001.0000.9841.0000.9940.684
M0.8540.8190.6430.9530.4160.6500.6930.0000.000NaN0.8760.000NaN0.9570.9941.0000.776
N0.8100.9520.8670.9440.5280.9280.847NaN1.0001.0000.7790.899NaN0.9160.6840.7761.000
2024-02-29T14:19:34.442841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ABCDEFGHIJHDFHDGKLMNHDJ
A1.0000.9540.9430.8040.8670.7640.7120.8980.8490.7910.386-0.1080.5070.6790.5940.6731.000
B0.9541.0000.9520.8800.9990.9320.8720.9500.9610.8500.699-0.3820.7420.6410.7990.7710.707
C0.9430.9521.0000.8370.8700.8630.7210.8870.8590.8790.494-0.3910.6550.6010.7940.6130.707
D0.8040.8800.8371.0000.9950.7440.8140.8970.9820.9640.764-0.3510.5920.6500.7970.6711.000
E0.8670.9990.8700.9951.0000.6300.5760.9500.4380.8350.775NaN0.2140.4500.229-0.0770.707
F0.7640.9320.8630.7440.6301.0000.8650.8480.7380.6730.327-0.0120.3930.5930.6580.8011.000
G0.7120.8720.7210.8140.5760.8651.0000.7330.4490.6830.342-0.0370.5400.6460.6580.8111.000
H0.8980.9500.8870.8970.9500.8480.7331.0000.9400.9980.999NaN0.4000.9771.0001.0000.707
I0.8490.9610.8590.9820.4380.7380.4490.9401.0000.9980.999NaN0.3160.7261.0000.2900.707
J0.7910.8500.8790.9640.8350.6730.6830.9980.9981.0000.103NaN0.1200.559NaN0.5000.707
HDF0.3860.6990.4940.7640.7750.3270.3420.9990.9990.1031.0000.7760.5880.5610.9060.3920.707
HDG-0.108-0.382-0.391-0.351NaN-0.012-0.037NaNNaNNaN0.7761.000-0.062-0.2890.3280.0810.000
K0.5070.7420.6550.5920.2140.3930.5400.4000.3160.1200.588-0.0621.0000.8650.9370.4330.000
L0.6790.6410.6010.6500.4500.5930.6460.9770.7260.5590.561-0.2890.8651.0000.9780.7971.000
M0.5940.7990.7940.7970.2290.6580.6581.0001.000NaN0.9060.3280.9370.9781.0000.8590.000
N0.6730.7710.6130.671-0.0770.8010.8111.0000.2900.5000.3920.0810.4330.7970.8591.0000.000
HDJ1.0000.7070.7071.0000.7071.0001.0000.7070.7070.7070.7070.0000.0001.0000.0000.0001.000

Missing values

2024-02-29T14:19:17.689580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-29T14:19:18.398025image/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.
2024-02-29T14:19:19.009610image/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

Company NameForm NumbersEffectiveDateABCDEFGHIJHDFHDGHDJKLMN
0Medico Life and Health Insurance CompanyStandardized MIPPA MLHMS2023G et al05/01/20240.05<NA><NA><NA><NA>0.050.05<NA><NA><NA>0.050.05<NA><NA><NA><NA>0.05
1Physicians Life Insurance CompanyForms L265 et al06/01/20240.120.12<NA><NA><NA>0.120.12<NA><NA><NA>0.12<NA><NA><NA><NA><NA><NA>
2New York Life Insurance CompanyNYM1 et al03/01/20240.0<NA>0.0<NA><NA>0.0<NA><NA>0.0<NA><NA><NA><NA><NA><NA><NA><NA>
3Avera Health PlansIA-SEL-MF (06/10) et al04/01/20240.1440.1440.144<NA><NA>0.144<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4Medico Insurance Company2010 Group MIPPA Plans: MSA21A, D, F, M, and N07/01/20240.0<NA><NA>0.0<NA>0.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0.0
5Shenandoah Life Insurance Company2010 MIPPA -- MS-AF 8-14 GN et al04/01/2024<NA><NA><NA><NA><NA>0.1690.169<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.169
6Medico Insurance CompanyMS992A et al07/01/20240.00.00.00.0<NA>0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7Medico Insurance CompanyGroup MSA11A, MSA11D, MSA11F07/01/20240.0<NA><NA>0.0<NA>0.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8United World Life Insurance Company2010 MIPPA plans WM24 (Plan F) et al03/01/20240.0<NA><NA><NA><NA>0.180.18<NA><NA><NA>0.060.0<NA><NA><NA><NA>0.18
9Royal Neighbors of America20069AA-IA et al04/01/20240.080.080.080.080.080.080.08<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
Company NameForm NumbersEffectiveDateABCDEFGHIJHDFHDGHDJKLMN
663Oxford Life Insurance Company (National States)ASM-1 et al05/01/20200.00.00.00.07<NA>0.0<NA><NA><NA>0.07<NA><NA><NA><NA><NA><NA><NA>
664Medico Corp Life Insurance Company (FKA World Corp Ins. Co)Standardized MIPPA MSM70A, MSM70F, and MSM70N04/01/20200.1<NA><NA><NA><NA>0.110.13<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.1
665SILAC Insurance CompanyForm 2070 (2010 MIPPA plans A, F, G, and N)04/01/20200.12<NA><NA><NA><NA>0.150.15<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.15
666American Family Life Assurance Company - AFLACA-1940F-28 et al08/01/20200.0350.0350.0350.0350.0350.0350.035<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
667Transamerica Life Insurance Company (formerly Life Investors)Group MS5000GPT-A.IA, MS9000GPT-A.IA (pre/post MIPPA)03/01/20200.0950.0950.0950.0950.0950.0950.0<NA><NA><NA><NA><NA><NA>0.00.00.00.0
668Assured Life AssociationIAMSIF06ST et al03/01/20200.070.070.070.07<NA>0.070.07<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.07
669SILAC Insurance Company920 Plans A-J05/01/20200.0470.0470.0470.0470.0470.0470.0470.0470.0470.0470.047<NA>0.047<NA>0.047<NA><NA>
670Loyal American Life Insurance CompanyL-6200 series04/21/2020<NA><NA><NA><NA><NA><NA><NA>0.090.090.09<NA><NA><NA><NA><NA><NA><NA>
671United World Life Insurance Company2010 MIPPA plans WM24 (Plan F) et al03/01/20200.1<NA><NA><NA><NA>0.080.1<NA><NA><NA>0.0<NA><NA><NA><NA><NA>0.08
672American Family Mutual Insurance CompanyH-6504/01/20200.025<NA>0.025<NA><NA>0.025<NA><NA><NA><NA><NA><NA><NA><NA>0.0<NA><NA>