Overview

Dataset statistics

Number of variables12
Number of observations891
Missing cells866
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.9 KiB
Average record size in memory96.4 B

Variable types

Numeric7
Text3
Categorical2

Alerts

Fare is highly overall correlated with PclassHigh correlation
Pclass is highly overall correlated with FareHigh correlation
Sex is highly overall correlated with SurvivedHigh correlation
Survived is highly overall correlated with SexHigh correlation
Age has 177 (19.9%) missing valuesMissing
Cabin has 687 (77.1%) missing valuesMissing
PassengerId is uniformly distributedUniform
Survived is uniformly distributedUniform
Pclass is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
Survived has 549 (61.6%) zerosZeros
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2026-06-23 22:20:31.450038
Analysis finished2026-06-23 22:20:37.474846
Duration6.02 seconds
Software versiondata-profiling v0.0.dev0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:37.548617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2026-06-23T22:20:37.687617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
21
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
81
 
0.1%
91
 
0.1%
101
 
0.1%
Other values (881)881
98.9%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
8911
0.1%
8901
0.1%
8891
0.1%
8881
0.1%
8871
0.1%
8861
0.1%
8851
0.1%
8841
0.1%
8831
0.1%
8821
0.1%

Survived
Real number (ℝ)

High correlation  Uniform  Zeros 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38383838
Minimum0
Maximum1
Zeros549
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:37.802515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.48659245
Coefficient of variation (CV)1.2677014
Kurtosis-1.7750047
Mean0.38383838
Median Absolute Deviation (MAD)0
Skewness0.47852344
Sum342
Variance0.23677222
MonotonicityNot monotonic
2026-06-23T22:20:37.893345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%
ValueCountFrequency (%)
0549
61.6%
1342
38.4%
ValueCountFrequency (%)
1342
38.4%
0549
61.6%

Pclass
Real number (ℝ)

High correlation  Uniform 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.308642
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:37.977412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83607124
Coefficient of variation (CV)0.36214851
Kurtosis-1.280015
Mean2.308642
Median Absolute Deviation (MAD)0
Skewness-0.63054791
Sum2057
Variance0.69901512
MonotonicityNot monotonic
2026-06-23T22:20:38.065494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%
ValueCountFrequency (%)
1216
24.2%
2184
 
20.7%
3491
55.1%
ValueCountFrequency (%)
3491
55.1%
2184
 
20.7%
1216
24.2%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
2026-06-23T22:20:38.328723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
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

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr521
 
14.4%
miss182
 
5.0%
mrs129
 
3.6%
william64
 
1.8%
john44
 
1.2%
master40
 
1.1%
henry35
 
1.0%
james24
 
0.7%
george24
 
0.7%
charles23
 
0.6%
Other values (1515)2538
70.0%
2026-06-23T22:20:38.750272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
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 (%)
male577
64.8%
female314
35.2%

Length

2026-06-23T22:20:38.868548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-06-23T22:20:38.958284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male577
64.8%
female314
35.2%

Most occurring characters

ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Age
Real number (ℝ)

Missing 

Distinct88
Distinct (%)12.3%
Missing177
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean29.699118
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-06-23T22:20:39.052557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4
Q120.125
median28
Q338
95-th percentile56
Maximum80
Range79.58
Interquartile range (IQR)17.875

Descriptive statistics

Standard deviation14.526497
Coefficient of variation (CV)0.48912219
Kurtosis0.17827415
Mean29.699118
Median Absolute Deviation (MAD)9
Skewness0.38910778
Sum21205.17
Variance211.01912
MonotonicityNot monotonic
2026-06-23T22:20:39.195543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2430
 
3.4%
2227
 
3.0%
1826
 
2.9%
2825
 
2.8%
1925
 
2.8%
3025
 
2.8%
2124
 
2.7%
2523
 
2.6%
3622
 
2.5%
2920
 
2.2%
Other values (78)467
52.4%
(Missing)177
 
19.9%
ValueCountFrequency (%)
0.421
 
0.1%
0.671
 
0.1%
0.752
 
0.2%
0.832
 
0.2%
0.921
 
0.1%
17
0.8%
210
1.1%
36
0.7%
410
1.1%
54
 
0.4%
ValueCountFrequency (%)
801
 
0.1%
741
 
0.1%
712
0.2%
70.51
 
0.1%
702
0.2%
661
 
0.1%
653
0.3%
642
0.2%
632
0.2%
624
0.4%

SibSp
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:39.447466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2026-06-23T22:20:39.535257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
418
 
2.0%
316
 
1.8%
87
 
0.8%
55
 
0.6%
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
316
 
1.8%
418
 
2.0%
55
 
0.6%
87
 
0.8%
ValueCountFrequency (%)
87
 
0.8%
55
 
0.6%
418
 
2.0%
316
 
1.8%
228
 
3.1%
1209
 
23.5%
0608
68.2%

Parch
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:39.644624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2026-06-23T22:20:39.746492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
55
 
0.6%
35
 
0.6%
44
 
0.4%
61
 
0.1%
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
35
 
0.6%
44
 
0.4%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
44
 
0.4%
35
 
0.6%
280
 
9.0%
1118
 
13.2%
0678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2026-06-23T22:20:40.016840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
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

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc60
 
5.3%
c.a27
 
2.4%
a/517
 
1.5%
ca14
 
1.2%
ston/o12
 
1.1%
212
 
1.1%
w./c9
 
0.8%
sc/paris9
 
0.8%
soton/o.q8
 
0.7%
3470827
 
0.6%
Other values (709)955
84.5%
2026-06-23T22:20:40.402168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2026-06-23T22:20:40.522395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2026-06-23T22:20:40.662335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0543
 
4.8%
1342
 
4.7%
7.895838
 
4.3%
7.7534
 
3.8%
2631
 
3.5%
10.524
 
2.7%
7.92518
 
2.0%
7.77516
 
1.8%
26.5515
 
1.7%
7.229215
 
1.7%
Other values (238)615
69.0%
ValueCountFrequency (%)
015
1.7%
4.01251
 
0.1%
51
 
0.1%
6.23751
 
0.1%
6.43751
 
0.1%
6.451
 
0.1%
6.49582
 
0.2%
6.752
 
0.2%
6.85831
 
0.1%
6.951
 
0.1%
ValueCountFrequency (%)
512.32923
0.3%
2634
0.4%
262.3752
0.2%
247.52082
0.2%
227.5254
0.4%
221.77921
 
0.1%
211.51
 
0.1%
211.33753
0.3%
164.86672
0.2%
153.46253
0.3%

Cabin
Text

Missing 

Distinct147
Distinct (%)72.1%
Missing687
Missing (%)77.1%
Memory size732.0 B
2026-06-23T22:20:40.965802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.5882353
Min length1

Characters and Unicode

Total characters732
Distinct characters19
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

Unique101 ?
Unique (%)49.5%

Sample

1st rowC85
2nd rowC123
3rd rowE46
4th rowG6
5th rowC103
ValueCountFrequency (%)
g64
 
1.7%
c234
 
1.7%
c254
 
1.7%
c274
 
1.7%
b964
 
1.7%
b984
 
1.7%
f4
 
1.7%
f333
 
1.3%
e1013
 
1.3%
f23
 
1.3%
Other values (151)201
84.5%
2026-06-23T22:20:41.385324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
272
 
9.8%
C71
 
9.7%
B64
 
8.7%
161
 
8.3%
359
 
8.1%
651
 
7.0%
545
 
6.1%
837
 
5.1%
437
 
5.1%
34
 
4.6%
Other values (9)201
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
272
 
9.8%
C71
 
9.7%
B64
 
8.7%
161
 
8.3%
359
 
8.1%
651
 
7.0%
545
 
6.1%
837
 
5.1%
437
 
5.1%
34
 
4.6%
Other values (9)201
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
272
 
9.8%
C71
 
9.7%
B64
 
8.7%
161
 
8.3%
359
 
8.1%
651
 
7.0%
545
 
6.1%
837
 
5.1%
437
 
5.1%
34
 
4.6%
Other values (9)201
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
272
 
9.8%
C71
 
9.7%
B64
 
8.7%
161
 
8.3%
359
 
8.1%
651
 
7.0%
545
 
6.1%
837
 
5.1%
437
 
5.1%
34
 
4.6%
Other values (9)201
27.5%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size889.0 B
S
644 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters889
Distinct characters3
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 rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S644
72.3%
C168
 
18.9%
Q77
 
8.6%
(Missing)2
 
0.2%

Length

2026-06-23T22:20:41.508835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-06-23T22:20:41.594408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s644
72.4%
c168
 
18.9%
q77
 
8.7%

Most occurring characters

ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S644
72.4%
C168
 
18.9%
Q77
 
8.7%

Interactions

2026-06-23T22:20:36.328045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:31.611731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.410793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.140775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.945369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.824736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.583862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.421258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:31.707681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.508570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.247561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.057738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.930342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.681442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.518621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:31.807708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.607882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.354357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.162681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.038477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.788667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.627785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:31.921272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.722615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.484876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.281238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.157156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.901170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.733814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.026053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.835104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.601617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.388934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.261396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.012086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.971752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.132731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.941411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.719395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.609307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.371325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.123343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:37.071991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:32.319823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.042085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:33.832615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:34.711522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:35.475341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-06-23T22:20:36.226302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-06-23T22:20:41.656878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeEmbarkedFareParchPassengerIdPclassSexSibSpSurvived
Age1.0000.0000.135-0.2540.041-0.3620.079-0.182-0.053
Embarked0.0001.0000.4170.0690.0000.2600.1130.0760.166
Fare0.1350.4171.0000.410-0.014-0.6880.3120.4470.324
Parch-0.2540.0690.4101.0000.001-0.0230.2510.4500.138
PassengerId0.0410.000-0.0140.0011.000-0.0340.066-0.061-0.005
Pclass-0.3620.260-0.688-0.023-0.0341.0000.130-0.043-0.340
Sex0.0790.1130.3120.2510.0660.1301.0000.1990.540
SibSp-0.1820.0760.4470.450-0.061-0.0430.1991.0000.089
Survived-0.0530.1660.3240.138-0.005-0.3400.5400.0891.000

Missing values

2026-06-23T22:20:37.205862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-06-23T22:20:37.323173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-06-23T22:20:37.408854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.25S
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.925S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1C123S
4503Allen, Mr. William Henrymale35.0003734508.05S
5603Moran, Mr. Jamesmale003308778.4583Q
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.075S
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
088203Markun, Mr. Johannmale33.0003492577.8958S
188303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167S
288402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5S
388503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.05S
488603Rice, Mrs. William (Margaret Norton)female39.00538265229.125Q
588702Montvila, Rev. Juozasmale27.00021153613.0S
688811Graham, Miss. Margaret Edithfemale19.00011205330.0B42S
788903Johnston, Miss. Catherine Helen "Carrie"female12W./C. 660723.45S
889011Behr, Mr. Karl Howellmale26.00011136930.0C148C
989103Dooley, Mr. Patrickmale32.0003703767.75Q