Overview

Dataset statistics

Number of variables12
Number of observations891
Missing cells866
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.7 KiB
Average record size in memory96.1 B

Variable types

Numeric7
Categorical5

Warnings

Name has a high cardinality: 891 distinct values High cardinality
Ticket has a high cardinality: 681 distinct values High cardinality
Cabin has a high cardinality: 147 distinct values High cardinality
Pclass is highly correlated with FareHigh correlation
Fare is highly correlated with PclassHigh correlation
Pclass is highly correlated with FareHigh correlation
Fare is highly correlated with PclassHigh correlation
PassengerId is highly correlated with Survived and 2 other fieldsHigh correlation
Survived is highly correlated with PassengerId and 2 other fieldsHigh correlation
Pclass is highly correlated with Survived and 2 other fieldsHigh correlation
Age is highly correlated with SibSp and 1 other fieldsHigh correlation
SibSp is highly correlated with PassengerId and 2 other fieldsHigh correlation
Parch is highly correlated with PassengerId and 3 other fieldsHigh correlation
Fare is highly correlated with PclassHigh correlation
Pclass is highly correlated with Fare and 1 other fieldsHigh correlation
Sex is highly correlated with SurvivedHigh correlation
Embarked is highly correlated with PclassHigh correlation
Survived is highly correlated with SexHigh correlation
SibSp is highly correlated with ParchHigh correlation
Parch is highly correlated with SibSpHigh correlation
Age has 177 (19.9%) missing values Missing
Cabin has 687 (77.1%) missing values Missing
PassengerId is uniformly distributed Uniform
Name is uniformly distributed Uniform
Ticket is uniformly distributed Uniform
Cabin is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
Survived has 549 (61.6%) zeros Zeros
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started2021-08-14 00:02:06.495597
Analysis finished2021-08-14 00:02:14.062469
Duration7.57 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ≥0)

HIGH CORRELATION
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 size7.1 KiB
2021-08-14T05:32:14.134494image/svg+xmlMatplotlib v3.4.3, 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 deviation257.353842
Coefficient of variation (CV)0.5770265516
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2021-08-14T05:32:14.264792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
5991
 
0.1%
5881
 
0.1%
5891
 
0.1%
5901
 
0.1%
5911
 
0.1%
5921
 
0.1%
5931
 
0.1%
5941
 
0.1%
5951
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3838383838
Minimum0
Maximum1
Zeros549
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:14.373933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.4865924543
Coefficient of variation (CV)1.267701394
Kurtosis-1.775004671
Mean0.3838383838
Median Absolute Deviation (MAD)0
Skewness0.4785234383
Sum342
Variance0.2367722165
MonotonicityNot monotonic
2021-08-14T05:32:14.457883image/svg+xmlMatplotlib v3.4.3, 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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.308641975
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:14.542325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.836071241
Coefficient of variation (CV)0.3621485054
Kurtosis-1.280014972
Mean2.308641975
Median Absolute Deviation (MAD)0
Skewness-0.6305479069
Sum2057
Variance0.69901512
MonotonicityNot monotonic
2021-08-14T05:32:14.629192image/svg+xmlMatplotlib v3.4.3, 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
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Braund, Mr. Owen Harris
 
1
Boulos, Mr. Hanna
 
1
Frolicher-Stehli, Mr. Maxmillian
 
1
Gilinski, Mr. Eliezer
 
1
Murdlin, Mr. Joseph
 
1
Other values (886)
886 

Length

Max length82
Median length25
Mean length26.96520763
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
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

Common Values

ValueCountFrequency (%)
Braund, Mr. Owen Harris1
 
0.1%
Boulos, Mr. Hanna1
 
0.1%
Frolicher-Stehli, Mr. Maxmillian1
 
0.1%
Gilinski, Mr. Eliezer1
 
0.1%
Murdlin, Mr. Joseph1
 
0.1%
Rintamaki, Mr. Matti1
 
0.1%
Stephenson, Mrs. Walter Bertram (Martha Eustis)1
 
0.1%
Elsbury, Mr. William James1
 
0.1%
Bourke, Miss. Mary1
 
0.1%
Chapman, Mr. John Henry1
 
0.1%
Other values (881)881
98.9%

Length

2021-08-14T05:32:14.905762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
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%

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 (%)
Lowercase Letter15446
64.3%
Uppercase Letter3645
 
15.2%
Space Separator2735
 
11.4%
Other Punctuation1899
 
7.9%
Open Punctuation144
 
0.6%
Close Punctuation144
 
0.6%
Dash Punctuation13
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1958
12.7%
e1703
11.0%
a1657
10.7%
i1325
8.6%
n1304
8.4%
s1297
8.4%
l1067
 
6.9%
o1008
 
6.5%
t667
 
4.3%
h517
 
3.3%
Other values (16)2943
19.1%
Uppercase Letter
ValueCountFrequency (%)
M1128
30.9%
A250
 
6.9%
J215
 
5.9%
H203
 
5.6%
S180
 
4.9%
C172
 
4.7%
E166
 
4.6%
W143
 
3.9%
B140
 
3.8%
L129
 
3.5%
Other values (15)919
25.2%
Other Punctuation
ValueCountFrequency (%)
.892
47.0%
,891
46.9%
"106
 
5.6%
'9
 
0.5%
/1
 
0.1%
Space Separator
ValueCountFrequency (%)
2735
100.0%
Open Punctuation
ValueCountFrequency (%)
(144
100.0%
Close Punctuation
ValueCountFrequency (%)
)144
100.0%
Dash Punctuation
ValueCountFrequency (%)
-13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19091
79.5%
Common4935
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1958
 
10.3%
e1703
 
8.9%
a1657
 
8.7%
i1325
 
6.9%
n1304
 
6.8%
s1297
 
6.8%
M1128
 
5.9%
l1067
 
5.6%
o1008
 
5.3%
t667
 
3.5%
Other values (41)5977
31.3%
Common
ValueCountFrequency (%)
2735
55.4%
.892
 
18.1%
,891
 
18.1%
(144
 
2.9%
)144
 
2.9%
"106
 
2.1%
-13
 
0.3%
'9
 
0.2%
/1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII24026
100.0%

Most frequent character per block

ASCII
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 size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826038
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

2021-08-14T05:32:15.129393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T05:32:15.203270image/svg+xmlMatplotlib v3.4.3, 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 (%)
Lowercase Letter4192
100.0%

Most frequent character per category

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

Most occurring scripts

ValueCountFrequency (%)
Latin4192
100.0%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII4192
100.0%

Most frequent character per block

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

Age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct88
Distinct (%)12.3%
Missing177
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean29.69911765
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:15.288376image/svg+xmlMatplotlib v3.4.3, 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 deviation14.52649733
Coefficient of variation (CV)0.4891221855
Kurtosis0.1782741536
Mean29.69911765
Median Absolute Deviation (MAD)9
Skewness0.3891077823
Sum21205.17
Variance211.0191247
MonotonicityNot monotonic
2021-08-14T05:32:15.492679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2430
 
3.4%
2227
 
3.0%
1826
 
2.9%
2825
 
2.8%
3025
 
2.8%
1925
 
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≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5230078563
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:15.608279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation1.102743432
Coefficient of variation (CV)2.108464374
Kurtosis17.88041973
Mean0.5230078563
Median Absolute Deviation (MAD)0
Skewness3.695351727
Sum466
Variance1.216043077
MonotonicityNot monotonic
2021-08-14T05:32:15.701986image/svg+xmlMatplotlib v3.4.3, 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≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3815937149
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:15.796347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.8060572211
Coefficient of variation (CV)2.112344071
Kurtosis9.778125179
Mean0.3815937149
Median Absolute Deviation (MAD)0
Skewness2.749117047
Sum340
Variance0.6497282437
MonotonicityNot monotonic
2021-08-14T05:32:15.882719image/svg+xmlMatplotlib v3.4.3, 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
Categorical

HIGH CARDINALITY
UNIFORM

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
347082
 
7
CA. 2343
 
7
1601
 
7
3101295
 
6
CA 2144
 
6
Other values (676)
858 

Length

Max length18
Median length6
Mean length6.750841751
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
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

Common Values

ValueCountFrequency (%)
3470827
 
0.8%
CA. 23437
 
0.8%
16017
 
0.8%
31012956
 
0.7%
CA 21446
 
0.7%
3470886
 
0.7%
S.O.C. 148795
 
0.6%
3826525
 
0.6%
LINE4
 
0.4%
PC 177574
 
0.4%
Other values (671)834
93.6%

Length

2021-08-14T05:32:16.153965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pc60
 
5.3%
c.a27
 
2.4%
a/517
 
1.5%
ca14
 
1.2%
ston/o12
 
1.1%
212
 
1.1%
sc/paris9
 
0.8%
w./c9
 
0.8%
soton/o.q8
 
0.7%
3470827
 
0.6%
Other values (709)955
84.5%

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 (%)
Decimal Number4808
79.9%
Uppercase Letter652
 
10.8%
Other Punctuation295
 
4.9%
Space Separator239
 
4.0%
Lowercase Letter21
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C151
23.2%
O100
15.3%
P98
15.0%
A82
12.6%
S74
11.3%
N40
 
6.1%
T36
 
5.5%
W16
 
2.5%
Q15
 
2.3%
I11
 
1.7%
Other values (6)29
 
4.4%
Decimal Number
ValueCountFrequency (%)
3746
15.5%
1689
14.3%
2594
12.4%
7490
10.2%
4464
9.7%
6422
8.8%
0406
8.4%
5387
8.0%
9328
6.8%
8282
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
a6
28.6%
s5
23.8%
r4
19.0%
i4
19.0%
l1
 
4.8%
e1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.197
66.8%
/98
33.2%
Space Separator
ValueCountFrequency (%)
239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5342
88.8%
Latin673
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C151
22.4%
O100
14.9%
P98
14.6%
A82
12.2%
S74
11.0%
N40
 
5.9%
T36
 
5.3%
W16
 
2.4%
Q15
 
2.2%
I11
 
1.6%
Other values (12)50
 
7.4%
Common
ValueCountFrequency (%)
3746
14.0%
1689
12.9%
2594
11.1%
7490
9.2%
4464
8.7%
6422
7.9%
0406
7.6%
5387
7.2%
9328
6.1%
8282
 
5.3%
Other values (3)534
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6015
100.0%

Most frequent character per block

ASCII
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≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.20420797
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2021-08-14T05:32:16.278857image/svg+xmlMatplotlib v3.4.3, 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 deviation49.6934286
Coefficient of variation (CV)1.543072528
Kurtosis33.39814088
Mean32.20420797
Median Absolute Deviation (MAD)6.9042
Skewness4.78731652
Sum28693.9493
Variance2469.436846
MonotonicityNot monotonic
2021-08-14T05:32:16.419678image/svg+xmlMatplotlib v3.4.3, 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%
7.229215
 
1.7%
015
 
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
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct147
Distinct (%)72.1%
Missing687
Missing (%)77.1%
Memory size7.1 KiB
C23 C25 C27
 
4
G6
 
4
B96 B98
 
4
C22 C26
 
3
D
 
3
Other values (142)
186 

Length

Max length15
Median length3
Mean length3.588235294
Min length1

Characters and Unicode

Total characters732
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
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

Common Values

ValueCountFrequency (%)
C23 C25 C274
 
0.4%
G64
 
0.4%
B96 B984
 
0.4%
C22 C263
 
0.3%
D3
 
0.3%
F333
 
0.3%
E1013
 
0.3%
F23
 
0.3%
B202
 
0.2%
E672
 
0.2%
Other values (137)173
 
19.4%
(Missing)687
77.1%

Length

2021-08-14T05:32:16.704826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b964
 
1.7%
g64
 
1.7%
c234
 
1.7%
c254
 
1.7%
c274
 
1.7%
b984
 
1.7%
f4
 
1.7%
f333
 
1.3%
e1013
 
1.3%
f23
 
1.3%
Other values (151)201
84.5%

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%
D34
 
4.6%
Other values (9)201
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number460
62.8%
Uppercase Letter238
32.5%
Space Separator34
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
272
15.7%
161
13.3%
359
12.8%
651
11.1%
545
9.8%
837
8.0%
437
8.0%
734
7.4%
933
7.2%
031
6.7%
Uppercase Letter
ValueCountFrequency (%)
C71
29.8%
B64
26.9%
D34
14.3%
E33
13.9%
A15
 
6.3%
F13
 
5.5%
G7
 
2.9%
T1
 
0.4%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common494
67.5%
Latin238
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
272
14.6%
161
12.3%
359
11.9%
651
10.3%
545
9.1%
837
7.5%
437
7.5%
34
6.9%
734
6.9%
933
6.7%
Latin
ValueCountFrequency (%)
C71
29.8%
B64
26.9%
D34
14.3%
E33
13.9%
A15
 
6.3%
F13
 
5.5%
G7
 
2.9%
T1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII732
100.0%

Most frequent character per block

ASCII
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%
D34
 
4.6%
Other values (9)201
27.5%

Embarked
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size7.1 KiB
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

2021-08-14T05:32:16.909749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T05:32:16.972829image/svg+xmlMatplotlib v3.4.3, 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 (%)
Uppercase Letter889
100.0%

Most frequent character per category

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

Most occurring scripts

ValueCountFrequency (%)
Latin889
100.0%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII889
100.0%

Most frequent character per block

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

Interactions

2021-08-14T05:32:07.713371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:07.889409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:07.990635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.100752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.203161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.310796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.413461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.515620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.619964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.725946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.838978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:08.945233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.126726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.230616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.336163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.450411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.567051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.692544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.810425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:09.931715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.050132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.167346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.276512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.388216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.506341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.622723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.732490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.844246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:10.955712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.071434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.190328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.382466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.488822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.610124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.726757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.841891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:11.949951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.060209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.179285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.289510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.405380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.516486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.627600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.735275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.844389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:12.960289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:13.069281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:13.184019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-08-14T05:32:13.293705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-08-14T05:32:17.040061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-14T05:32:17.249191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-14T05:32:17.390787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-14T05:32:17.537377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-14T05:32:17.670043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-14T05:32:13.560867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-14T05:32:13.764949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-14T05:32:13.901516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-14T05:32:13.984207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC

Last rows

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen "Carrie"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ