Overview

Dataset statistics

Number of variables12
Number of observations891
Missing cells866
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory315.0 KiB
Average record size in memory362.1 B

Variable types

Numeric5
Categorical7

Alerts

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
Pclass is highly correlated with FareHigh correlation
Fare is highly correlated with PclassHigh correlation
Sex is highly correlated with SurvivedHigh correlation
Survived is highly correlated with SexHigh correlation
Survived is highly correlated with SexHigh correlation
Pclass is highly correlated with Fare and 1 other fieldsHigh correlation
Sex is highly correlated with SurvivedHigh correlation
SibSp is highly correlated with ParchHigh correlation
Parch is highly correlated with SibSpHigh correlation
Fare is highly correlated with PclassHigh correlation
Embarked is highly correlated with PclassHigh 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
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started2022-09-06 19:01:31.272531
Analysis finished2022-09-06 19:01:36.242695
Duration4.97 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ≥0)

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
2022-09-06T19:01:36.307203image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:01:36.428493image/svg+xmlMatplotlib v3.5.3, 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
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
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 row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Length

2022-09-06T19:01:36.538851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:01:36.636911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring characters

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring scripts

ValueCountFrequency (%)
Common891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Pclass
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
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 row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Length

2022-09-06T19:01:36.720313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:01:36.943940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number891
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size73.2 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 length52
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

2022-09-06T19:01:37.064512image/svg+xmlMatplotlib v3.5.3, 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%
george24
 
0.7%
james24
 
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%
Close Punctuation144
 
0.6%
Open 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%
Close Punctuation
ValueCountFrequency (%)
)144
100.0%
Open 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
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.8 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

2022-09-06T19:01:37.190434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:01:37.299123image/svg+xmlMatplotlib v3.5.3, 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)

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
2022-09-06T19:01:37.400720image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:01:37.525615image/svg+xmlMatplotlib v3.5.3, 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
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
2022-09-06T19:01:37.635640image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:01:37.722287image/svg+xmlMatplotlib v3.5.3, 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
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
2022-09-06T19:01:37.814114image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:01:37.893922image/svg+xmlMatplotlib v3.5.3, 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 size55.6 KiB
347082
 
7
CA. 2343
 
7
1601
 
7
3101295
 
6
CA 2144
 
6
Other values (676)
858 

Length

Max length18
Median length17
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

2022-09-06T19:01:38.003453image/svg+xmlMatplotlib v3.5.3, 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
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
2022-09-06T19:01:38.121602image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:01:38.249521image/svg+xmlMatplotlib v3.5.3, 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 size33.7 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

2022-09-06T19:01:38.368602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c234
 
1.7%
c274
 
1.7%
g64
 
1.7%
b964
 
1.7%
b984
 
1.7%
f4
 
1.7%
c254
 
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%
437
 
5.1%
837
 
5.1%
34
 
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%
437
8.0%
837
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%
437
7.5%
837
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%
437
 
5.1%
837
 
5.1%
34
 
4.6%
Other values (9)201
27.5%

Embarked
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size50.5 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

2022-09-06T19:01:38.475379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:01:38.585350image/svg+xmlMatplotlib v3.5.3, 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

2022-09-06T19:01:35.160292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:01:32.758510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:01:33.349088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:01:33.936759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/