Overview

Dataset statistics

Number of variables9
Number of observations344
Missing cells19
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.3 KiB
Average record size in memory72.4 B

Variable types

Numeric5
Categorical4

Alerts

rowid is highly overall correlated with bill_length_mm and 3 other fieldsHigh correlation
bill_length_mm is highly overall correlated with rowid and 4 other fieldsHigh correlation
bill_depth_mm is highly overall correlated with flipper_length_mm and 2 other fieldsHigh correlation
flipper_length_mm is highly overall correlated with bill_length_mm and 4 other fieldsHigh correlation
body_mass_g is highly overall correlated with bill_length_mm and 3 other fieldsHigh correlation
species is highly overall correlated with rowid and 5 other fieldsHigh correlation
island is highly overall correlated with rowid and 2 other fieldsHigh correlation
sex is highly overall correlated with bill_length_mm and 2 other fieldsHigh correlation
year is highly overall correlated with rowidHigh correlation
sex has 11 (3.2%) missing valuesMissing
rowid is uniformly distributedUniform
rowid has unique valuesUnique

Reproduction

Analysis started2023-11-17 19:32:31.787172
Analysis finished2023-11-17 19:32:40.846371
Duration9.06 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

rowid
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct344
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.5
Minimum1
Maximum344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-11-17T19:32:41.029096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.15
Q186.75
median172.5
Q3258.25
95-th percentile326.85
Maximum344
Range343
Interquartile range (IQR)171.5

Descriptive statistics

Standard deviation99.448479
Coefficient of variation (CV)0.57651292
Kurtosis-1.2
Mean172.5
Median Absolute Deviation (MAD)86
Skewness0
Sum59340
Variance9890
MonotonicityStrictly increasing
2023-11-17T19:32:41.317702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
227 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
228 1
 
0.3%
Other values (334) 334
97.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
344 1
0.3%
343 1
0.3%
342 1
0.3%
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
336 1
0.3%
335 1
0.3%

species
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Adelie
152 
Gentoo
124 
Chinstrap
68 

Length

Max length9
Median length6
Mean length6.5930233
Min length6

Characters and Unicode

Total characters2268
Distinct characters15
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Adelie 152
44.2%
Gentoo 124
36.0%
Chinstrap 68
19.8%

Length

2023-11-17T19:32:41.786468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T19:32:42.202489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
adelie 152
44.2%
gentoo 124
36.0%
chinstrap 68
19.8%

Most occurring characters

ValueCountFrequency (%)
e 428
18.9%
o 248
10.9%
i 220
9.7%
n 192
8.5%
t 192
8.5%
A 152
 
6.7%
d 152
 
6.7%
l 152
 
6.7%
G 124
 
5.5%
C 68
 
3.0%
Other values (5) 340
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1924
84.8%
Uppercase Letter 344
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 428
22.2%
o 248
12.9%
i 220
11.4%
n 192
10.0%
t 192
10.0%
d 152
 
7.9%
l 152
 
7.9%
h 68
 
3.5%
s 68
 
3.5%
r 68
 
3.5%
Other values (2) 136
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
A 152
44.2%
G 124
36.0%
C 68
19.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2268
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 428
18.9%
o 248
10.9%
i 220
9.7%
n 192
8.5%
t 192
8.5%
A 152
 
6.7%
d 152
 
6.7%
l 152
 
6.7%
G 124
 
5.5%
C 68
 
3.0%
Other values (5) 340
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 428
18.9%
o 248
10.9%
i 220
9.7%
n 192
8.5%
t 192
8.5%
A 152
 
6.7%
d 152
 
6.7%
l 152
 
6.7%
G 124
 
5.5%
C 68
 
3.0%
Other values (5) 340
15.0%

island
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Biscoe
168 
Dream
124 
Torgersen
52 

Length

Max length9
Median length6
Mean length6.0930233
Min length5

Characters and Unicode

Total characters2096
Distinct characters13
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Biscoe 168
48.8%
Dream 124
36.0%
Torgersen 52
 
15.1%

Length

2023-11-17T19:32:42.634933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T19:32:43.100132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
biscoe 168
48.8%
dream 124
36.0%
torgersen 52
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1752
83.6%
Uppercase Letter 344
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 396
22.6%
r 228
13.0%
s 220
12.6%
o 220
12.6%
i 168
9.6%
c 168
9.6%
a 124
 
7.1%
m 124
 
7.1%
g 52
 
3.0%
n 52
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 168
48.8%
D 124
36.0%
T 52
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

bill_length_mm
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)48.0%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean43.92193
Minimum32.1
Maximum59.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-11-17T19:32:43.548966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32.1
5-th percentile35.7
Q139.225
median44.45
Q348.5
95-th percentile51.995
Maximum59.6
Range27.5
Interquartile range (IQR)9.275

Descriptive statistics

Standard deviation5.4595837
Coefficient of variation (CV)0.124302
Kurtosis-0.87602697
Mean43.92193
Median Absolute Deviation (MAD)4.75
Skewness0.053118067
Sum15021.3
Variance29.807054
MonotonicityNot monotonic
2023-11-17T19:32:44.033873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.1 7
 
2.0%
45.2 6
 
1.7%
46.5 5
 
1.5%
46.2 5
 
1.5%
50.5 5
 
1.5%
39.6 5
 
1.5%
50 5
 
1.5%
45.5 5
 
1.5%
37.8 5
 
1.5%
47.5 4
 
1.2%
Other values (154) 290
84.3%
ValueCountFrequency (%)
32.1 1
0.3%
33.1 1
0.3%
33.5 1
0.3%
34 1
0.3%
34.1 1
0.3%
34.4 1
0.3%
34.5 1
0.3%
34.6 2
0.6%
35 2
0.6%
35.1 1
0.3%
ValueCountFrequency (%)
59.6 1
0.3%
58 1
0.3%
55.9 1
0.3%
55.8 1
0.3%
55.1 1
0.3%
54.3 1
0.3%
54.2 1
0.3%
53.5 1
0.3%
53.4 1
0.3%
52.8 1
0.3%

bill_depth_mm
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)23.4%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean17.15117
Minimum13.1
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-11-17T19:32:44.525761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile13.9
Q115.6
median17.3
Q318.7
95-th percentile20
Maximum21.5
Range8.4
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9747932
Coefficient of variation (CV)0.11514044
Kurtosis-0.90686609
Mean17.15117
Median Absolute Deviation (MAD)1.5
Skewness-0.14346463
Sum5865.7
Variance3.899808
MonotonicityNot monotonic
2023-11-17T19:32:45.008602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 12
 
3.5%
15 10
 
2.9%
18.6 10
 
2.9%
17.9 10
 
2.9%
18.5 10
 
2.9%
17.3 9
 
2.6%
18.9 9
 
2.6%
19 9
 
2.6%
17.8 9
 
2.6%
18.1 9
 
2.6%
Other values (70) 245
71.2%
ValueCountFrequency (%)
13.1 1
 
0.3%
13.2 1
 
0.3%
13.3 1
 
0.3%
13.4 1
 
0.3%
13.5 2
 
0.6%
13.6 1
 
0.3%
13.7 6
1.7%
13.8 4
1.2%
13.9 4
1.2%
14 2
 
0.6%
ValueCountFrequency (%)
21.5 1
 
0.3%
21.2 2
0.6%
21.1 3
0.9%
20.8 1
 
0.3%
20.7 3
0.9%
20.6 1
 
0.3%
20.5 1
 
0.3%
20.3 3
0.9%
20.2 1
 
0.3%
20.1 1
 
0.3%

flipper_length_mm
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)16.1%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean200.9152
Minimum172
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-11-17T19:32:45.456107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile181
Q1190
median197
Q3213
95-th percentile225
Maximum231
Range59
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.061714
Coefficient of variation (CV)0.0699883
Kurtosis-0.98427289
Mean200.9152
Median Absolute Deviation (MAD)11
Skewness0.34568183
Sum68713
Variance197.73179
MonotonicityNot monotonic
2023-11-17T19:32:45.868064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190 22
 
6.4%
195 17
 
4.9%
187 16
 
4.7%
193 15
 
4.4%
210 14
 
4.1%
191 13
 
3.8%
215 12
 
3.5%
197 10
 
2.9%
196 10
 
2.9%
185 9
 
2.6%
Other values (45) 204
59.3%
ValueCountFrequency (%)
172 1
 
0.3%
174 1
 
0.3%
176 1
 
0.3%
178 4
1.2%
179 1
 
0.3%
180 5
1.5%
181 7
2.0%
182 3
0.9%
183 2
 
0.6%
184 7
2.0%
ValueCountFrequency (%)
231 1
 
0.3%
230 7
2.0%
229 2
 
0.6%
228 4
1.2%
226 1
 
0.3%
225 4
1.2%
224 3
0.9%
223 2
 
0.6%
222 6
1.7%
221 5
1.5%

body_mass_g
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)27.5%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4201.7544
Minimum2700
Maximum6300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-11-17T19:32:46.316615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile3150
Q13550
median4050
Q34750
95-th percentile5650
Maximum6300
Range3600
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation801.95454
Coefficient of variation (CV)0.19086183
Kurtosis-0.71922187
Mean4201.7544
Median Absolute Deviation (MAD)600
Skewness0.47032933
Sum1437000
Variance643131.08
MonotonicityNot monotonic
2023-11-17T19:32:46.851342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3800 12
 
3.5%
3700 11
 
3.2%
3900 10
 
2.9%
3950 10
 
2.9%
3550 9
 
2.6%
4300 8
 
2.3%
3400 8
 
2.3%
4400 8
 
2.3%
3450 8
 
2.3%
3500 7
 
2.0%
Other values (84) 251
73.0%
ValueCountFrequency (%)
2700 1
 
0.3%
2850 2
0.6%
2900 4
1.2%
2925 1
 
0.3%
2975 1
 
0.3%
3000 2
0.6%
3050 4
1.2%
3075 1
 
0.3%
3100 1
 
0.3%
3150 4
1.2%
ValueCountFrequency (%)
6300 1
 
0.3%
6050 1
 
0.3%
6000 2
 
0.6%
5950 2
 
0.6%
5850 3
0.9%
5800 2
 
0.6%
5750 1
 
0.3%
5700 5
1.5%
5650 3
0.9%
5600 2
 
0.6%

sex
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.6%
Missing11
Missing (%)3.2%
Memory size2.8 KiB
male
168 
female
165 

Length

Max length6
Median length4
Mean length4.990991
Min length4

Characters and Unicode

Total characters1662
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 168
48.8%
female 165
48.0%
(Missing) 11
 
3.2%

Length

2023-11-17T19:32:47.366678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T19:32:47.863963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 168
50.5%
female 165
49.5%

Most occurring characters

ValueCountFrequency (%)
e 498
30.0%
m 333
20.0%
a 333
20.0%
l 333
20.0%
f 165
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1662
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 498
30.0%
m 333
20.0%
a 333
20.0%
l 333
20.0%
f 165
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1662
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 498
30.0%
m 333
20.0%
a 333
20.0%
l 333
20.0%
f 165
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 498
30.0%
m 333
20.0%
a 333
20.0%
l 333
20.0%
f 165
 
9.9%

year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2009
120 
2008
114 
2007
110 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1376
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007
2nd row2007
3rd row2007
4th row2007
5th row2007

Common Values

ValueCountFrequency (%)
2009 120
34.9%
2008 114
33.1%
2007 110
32.0%

Length

2023-11-17T19:32:48.305998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T19:32:48.813299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2009 120
34.9%
2008 114
33.1%
2007 110
32.0%

Most occurring characters

ValueCountFrequency (%)
0 688
50.0%
2 344
25.0%
9 120
 
8.7%
8 114
 
8.3%
7 110
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1376
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 688
50.0%
2 344
25.0%
9 120
 
8.7%
8 114
 
8.3%
7 110
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 688
50.0%
2 344
25.0%
9 120
 
8.7%
8 114
 
8.3%
7 110
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 688
50.0%
2 344
25.0%
9 120
 
8.7%
8 114
 
8.3%
7 110
 
8.0%

Interactions

2023-11-17T19:32:38.453474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:33.625675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:34.945379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:36.083604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:37.268554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:38.696754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:34.034569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:35.175228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:36.330182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:37.534971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:38.937673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:34.247437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:35.415181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:36.580112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:37.781622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:39.142922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:34.458543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:35.623026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:36.792990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:37.984385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:39.376428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:34.711979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:35.848871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:37.033324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-17T19:32:38.212014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-17T19:32:49.093905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
rowidbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gspeciesislandsexyear
rowid1.0000.775-0.2530.4830.2750.9530.6870.0000.745
bill_length_mm0.7751.000-0.2220.6730.5840.6500.3240.5200.117
bill_depth_mm-0.253-0.2221.000-0.523-0.4320.6350.4840.5860.107
flipper_length_mm0.4830.673-0.5231.0000.8400.7010.5010.4480.219
body_mass_g0.2750.584-0.4320.8401.0000.6050.4560.5890.000
species0.9530.6500.6350.7010.6051.0000.6570.0000.000
island0.6870.3240.4840.5010.4560.6571.0000.0000.058
sex0.0000.5200.5860.4480.5890.0000.0001.0000.000
year0.7450.1170.1070.2190.0000.0000.0580.0001.000

Missing values

2023-11-17T19:32:39.709981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T19:32:40.083366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-17T19:32:40.674773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

rowidspeciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsexyear
01AdelieTorgersen39.118.7181.03750.0male2007
12AdelieTorgersen39.517.4186.03800.0female2007
23AdelieTorgersen40.318.0195.03250.0female2007
34AdelieTorgersenNaNNaNNaNNaNNaN2007
45AdelieTorgersen36.719.3193.03450.0female2007
56AdelieTorgersen39.320.6190.03650.0male2007
67AdelieTorgersen38.917.8181.03625.0female2007
78AdelieTorgersen39.219.6195.04675.0male2007
89AdelieTorgersen34.118.1193.03475.0NaN2007
910AdelieTorgersen42.020.2190.04250.0NaN2007
rowidspeciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsexyear
334335ChinstrapDream50.218.8202.03800.0male2009
335336ChinstrapDream45.619.4194.03525.0female2009
336337ChinstrapDream51.919.5206.03950.0male2009
337338ChinstrapDream46.816.5189.03650.0female2009
338339ChinstrapDream45.717.0195.03650.0female2009
339340ChinstrapDream55.819.8207.04000.0male2009
340341ChinstrapDream43.518.1202.03400.0female2009
341342ChinstrapDream49.618.2193.03775.0male2009
342343ChinstrapDream50.819.0210.04100.0male2009
343344ChinstrapDream50.218.7198.03775.0female2009