Overview

Dataset statistics

Number of variables11
Number of observations418
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory61.0 B

Variable types

Numeric5
Categorical6

Alerts

Embarked_C is highly overall correlated with Embarked_SHigh correlation
Embarked_S is highly overall correlated with Embarked_CHigh correlation
Sex_female is highly overall correlated with Sex_maleHigh correlation
Sex_male is highly overall correlated with Sex_femaleHigh correlation
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
SibSp has 283 (67.7%) zerosZeros
Parch has 324 (77.5%) zerosZeros

Reproduction

Analysis started2023-06-20 12:18:42.520446
Analysis finished2023-06-20 12:18:45.662900
Duration3.14 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-06-20T14:18:45.747415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityNot monotonic
2023-06-20T14:18:45.879996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 1
 
0.2%
910 1
 
0.2%
968 1
 
0.2%
971 1
 
0.2%
972 1
 
0.2%
975 1
 
0.2%
977 1
 
0.2%
979 1
 
0.2%
990 1
 
0.2%
980 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
892 1
0.2%
893 1
0.2%
894 1
0.2%
895 1
0.2%
896 1
0.2%
897 1
0.2%
898 1
0.2%
899 1
0.2%
900 1
0.2%
901 1
0.2%
ValueCountFrequency (%)
1309 1
0.2%
1308 1
0.2%
1307 1
0.2%
1306 1
0.2%
1305 1
0.2%
1304 1
0.2%
1303 1
0.2%
1302 1
0.2%
1301 1
0.2%
1300 1
0.2%

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Length

2023-06-20T14:18:45.994050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:46.106560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring characters

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Age
Real number (ℝ)

Distinct85
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.423005
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-06-20T14:18:46.226077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile10
Q123
median25
Q336.375
95-th percentile55
Maximum76
Range75.83
Interquartile range (IQR)13.375

Descriptive statistics

Standard deviation12.963036
Coefficient of variation (CV)0.44057485
Kurtosis0.67088016
Mean29.423005
Median Absolute Deviation (MAD)5
Skewness0.664077
Sum12298.816
Variance168.0403
MonotonicityNot monotonic
2023-06-20T14:18:46.362653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.52510417 50
 
12.0%
23.0734 22
 
5.3%
21 17
 
4.1%
24 17
 
4.1%
22 16
 
3.8%
30 15
 
3.6%
18 13
 
3.1%
27 12
 
2.9%
26 12
 
2.9%
25 11
 
2.6%
Other values (75) 233
55.7%
ValueCountFrequency (%)
0.17 1
 
0.2%
0.33 1
 
0.2%
0.75 1
 
0.2%
0.83 1
 
0.2%
0.92 1
 
0.2%
1 3
0.7%
2 2
0.5%
3 1
 
0.2%
5 1
 
0.2%
6 3
0.7%
ValueCountFrequency (%)
76 1
 
0.2%
67 1
 
0.2%
64 3
0.7%
63 2
0.5%
62 1
 
0.2%
61 2
0.5%
60.5 1
 
0.2%
60 3
0.7%
59 1
 
0.2%
58 1
 
0.2%

SibSp
Real number (ℝ)

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-06-20T14:18:46.481170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2023-06-20T14:18:46.577686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
8 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
5 1
 
0.2%
8 2
 
0.5%
ValueCountFrequency (%)
8 2
 
0.5%
5 1
 
0.2%
4 4
 
1.0%
3 4
 
1.0%
2 14
 
3.3%
1 110
 
26.3%
0 283
67.7%

Parch
Real number (ℝ)

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-06-20T14:18:46.682764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2023-06-20T14:18:46.772806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.5%
3 3
 
0.7%
2 33
 
7.9%
1 52
 
12.4%
0 324
77.5%

Fare
Real number (ℝ)

Distinct170
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.627188
Minimum0
Maximum512.3292
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-06-20T14:18:46.889461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2292
Q17.8958
median14.4542
Q331.5
95-th percentile151.55
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.8405
Coefficient of variation (CV)1.5673564
Kurtosis17.971266
Mean35.627188
Median Absolute Deviation (MAD)6.85
Skewness3.6915998
Sum14892.165
Variance3118.1615
MonotonicityNot monotonic
2023-06-20T14:18:47.028036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 21
 
5.0%
26 19
 
4.5%
13 17
 
4.1%
8.05 17
 
4.1%
7.8958 11
 
2.6%
10.5 11
 
2.6%
7.775 10
 
2.4%
7.2292 9
 
2.2%
7.225 9
 
2.2%
7.8542 8
 
1.9%
Other values (160) 286
68.4%
ValueCountFrequency (%)
0 2
 
0.5%
3.1708 1
 
0.2%
6.4375 2
 
0.5%
6.4958 1
 
0.2%
6.95 1
 
0.2%
7 2
 
0.5%
7.05 2
 
0.5%
7.225 9
2.2%
7.2292 9
2.2%
7.25 5
1.2%
ValueCountFrequency (%)
512.3292 1
 
0.2%
263 2
 
0.5%
262.375 5
1.2%
247.5208 1
 
0.2%
227.525 1
 
0.2%
221.7792 3
0.7%
211.5 4
1.0%
211.3375 1
 
0.2%
164.8667 2
 
0.5%
151.55 2
 
0.5%

Embarked_C
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
0
316 
1
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Length

2023-06-20T14:18:47.147548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:47.258063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 316
75.6%
1 102
 
24.4%

Embarked_Q
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
0
372 
1
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Length

2023-06-20T14:18:47.349574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:47.652115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 372
89.0%
1 46
 
11.0%

Embarked_S
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
1
270 
0
148 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Length

2023-06-20T14:18:47.746628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:47.858229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Most occurring characters

ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 270
64.6%
0 148
35.4%

Sex_female
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
0
266 
1
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Length

2023-06-20T14:18:47.951740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:48.061252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Most occurring characters

ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
63.6%
1 152
36.4%

Sex_male
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
1
266 
0
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
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 row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Length

2023-06-20T14:18:48.153764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T14:18:48.266279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Most occurring characters

ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 266
63.6%
0 152
36.4%

Interactions

2023-06-20T14:18:44.869653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:42.869037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.357634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.873385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.394459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.965163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:42.959578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.464137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.971898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.483070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:45.070734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.063090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.566828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.079411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.586558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:45.176340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.170578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.675362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.193439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.689135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:45.266849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.263092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:43.772809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.290948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T14:18:44.777139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-20T14:18:48.351824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PassengerIdAgeSibSpParchFarePclassEmbarked_CEmbarked_QEmbarked_SSex_femaleSex_male
PassengerId1.000-0.032-0.0100.0510.0190.0540.0000.1340.0000.0000.000
Age-0.0321.000-0.025-0.1220.3090.3920.1330.1850.0330.0450.045
SibSp-0.010-0.0251.0000.4120.4390.1130.0930.1300.0000.1360.136
Parch0.051-0.1220.4121.0000.3770.0000.1230.1130.0860.2130.213
Fare0.0190.3090.4390.3771.0000.4750.3480.0920.2140.1550.155
Pclass0.0540.3920.1130.0000.4751.0000.3780.2540.2590.1060.106
Embarked_C0.0000.1330.0930.1230.3480.3781.0000.1850.7610.0000.000
Embarked_Q0.1340.1850.1300.1130.0920.2540.1851.0000.4650.0960.096
Embarked_S0.0000.0330.0000.0860.2140.2590.7610.4651.0000.0880.088
Sex_female0.0000.0450.1360.2130.1550.1060.0000.0960.0881.0000.995
Sex_male0.0000.0450.1360.2130.1550.1060.0000.0960.0880.9951.000

Missing values

2023-06-20T14:18:45.410875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-20T14:18:45.591902image/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.

Sample

PassengerIdPclassAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_SSex_femaleSex_male
2081100133.000027.720810010
3501242145.000163.358310010
1221014135.001057.750010010
3431235158.0001512.329210010
1311023153.000028.500010001
3351227130.000026.000000101
1411033133.0000151.550000110
1181010136.000075.241710001
1421034161.0013262.375010001
1461038140.520051.862500101
PassengerIdPclassAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_SSex_femaleSex_male
1721064323.0000001013.900000101
1711063327.000000007.225010001
1701062324.525104007.550000101
1691061322.000000008.962500110
1671059318.0000002234.375000101
1651057326.0000001122.025000110
1631055324.525104007.000000101
161105337.0000001115.245810001
1991091323.073400008.112500110
4171309324.5251041122.358310001