Overview

Dataset statistics

Number of variables11
Number of observations182
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory53.7 B

Variable types

Numeric3
Categorical8

Alerts

fare is highly correlated with sibsp and 1 other fieldsHigh correlation
survived is highly correlated with maleHigh correlation
sibsp is highly correlated with fareHigh correlation
male is highly correlated with survivedHigh correlation
parch is highly correlated with fareHigh correlation
df_index has unique values Unique
fare has 2 (1.1%) zeros Zeros

Reproduction

Analysis started2023-01-09 05:38:22.937071
Analysis finished2023-01-09 05:38:30.570414
Duration7.63 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455
Minimum1
Maximum889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-01-09T11:08:30.664069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54.4
Q1262.25
median458
Q3677
95-th percentile834.4
Maximum889
Range888
Interquartile range (IQR)414.75

Descriptive statistics

Standard deviation247.5847307
Coefficient of variation (CV)0.5441422654
Kurtosis-1.098496479
Mean455
Median Absolute Deviation (MAD)205
Skewness-0.06092618352
Sum82810
Variance61298.1989
MonotonicityStrictly increasing
2023-01-09T11:08:30.750991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.5%
5711
 
0.5%
5771
 
0.5%
5811
 
0.5%
5831
 
0.5%
5851
 
0.5%
5871
 
0.5%
5911
 
0.5%
5991
 
0.5%
6091
 
0.5%
Other values (172)172
94.5%
ValueCountFrequency (%)
11
0.5%
31
0.5%
61
0.5%
101
0.5%
111
0.5%
211
0.5%
231
0.5%
271
0.5%
521
0.5%
541
0.5%
ValueCountFrequency (%)
8891
0.5%
8871
0.5%
8791
0.5%
8721
0.5%
8711
0.5%
8671
0.5%
8621
0.5%
8571
0.5%
8531
0.5%
8351
0.5%

survived
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
1
123 
0
59 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1123
67.6%
059
32.4%

Length

2023-01-09T11:08:30.923708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:31.017935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1123
67.6%
059
32.4%

Most occurring characters

ValueCountFrequency (%)
1123
67.6%
059
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1123
67.6%
059
32.4%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1123
67.6%
059
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1123
67.6%
059
32.4%

age
Real number (ℝ≥0)

Distinct63
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.62318681
Minimum0.92
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-01-09T11:08:31.096586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile6.25
Q124
median36
Q347.75
95-th percentile60.95
Maximum80
Range79.08
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation15.67161536
Coefficient of variation (CV)0.4399273832
Kurtosis-0.2309427736
Mean35.62318681
Median Absolute Deviation (MAD)12
Skewness0.01841894051
Sum6483.42
Variance245.5995279
MonotonicityNot monotonic
2023-01-09T11:08:31.190271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3611
 
6.0%
249
 
4.9%
196
 
3.3%
356
 
3.3%
315
 
2.7%
295
 
2.7%
495
 
2.7%
475
 
2.7%
275
 
2.7%
585
 
2.7%
Other values (53)120
65.9%
ValueCountFrequency (%)
0.921
 
0.5%
11
 
0.5%
23
1.6%
31
 
0.5%
43
1.6%
61
 
0.5%
111
 
0.5%
141
 
0.5%
151
 
0.5%
163
1.6%
ValueCountFrequency (%)
801
 
0.5%
711
 
0.5%
701
 
0.5%
652
 
1.1%
641
 
0.5%
631
 
0.5%
621
 
0.5%
612
 
1.1%
602
 
1.1%
585
2.7%

sibsp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0
109 
1
64 
2
 
6
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
Distinct characters4
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 row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

Length

2023-01-09T11:08:31.268858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:31.347443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0109
59.9%
164
35.2%
26
 
3.3%
33
 
1.6%

parch
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0
121 
1
37 
2
23 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
Distinct characters4
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

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

Length

2023-01-09T11:08:31.426028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:31.497563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0121
66.5%
137
 
20.3%
223
 
12.6%
41
 
0.5%

fare
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct93
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.91973516
Minimum0
Maximum512.3292
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-01-09T11:08:31.582745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.5
Q129.7
median57
Q390
95-th percentile246.52101
Maximum512.3292
Range512.3292
Interquartile range (IQR)60.3

Descriptive statistics

Standard deviation76.49077401
Coefficient of variation (CV)0.9692223859
Kurtosis10.69069789
Mean78.91973516
Median Absolute Deviation (MAD)29.975
Skewness2.707368315
Sum14363.3918
Variance5850.838509
MonotonicityNot monotonic
2023-01-09T11:08:31.697778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.557
 
3.8%
53.15
 
2.7%
904
 
2.2%
2634
 
2.2%
10.54
 
2.2%
1204
 
2.2%
304
 
2.2%
134
 
2.2%
153.46253
 
1.6%
113.2753
 
1.6%
Other values (83)140
76.9%
ValueCountFrequency (%)
02
1.1%
51
 
0.5%
7.653
1.6%
8.051
 
0.5%
10.46252
1.1%
10.54
2.2%
12.4752
1.1%
12.8751
 
0.5%
134
2.2%
13.79171
 
0.5%
ValueCountFrequency (%)
512.32922
1.1%
2634
2.2%
262.3752
1.1%
247.52082
1.1%
227.5252
1.1%
211.51
 
0.5%
211.33753
1.6%
164.86671
 
0.5%
153.46253
1.6%
151.553
1.6%

male
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
1
94 
0
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
194
51.6%
088
48.4%

Length

2023-01-09T11:08:31.771337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:31.849552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
194
51.6%
088
48.4%

Most occurring characters

ValueCountFrequency (%)
194
51.6%
088
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
194
51.6%
088
48.4%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
194
51.6%
088
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
194
51.6%
088
48.4%

Q
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0
180 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 (%)
0180
98.9%
12
 
1.1%

Length

2023-01-09T11:08:31.912158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:31.990317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0180
98.9%
12
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0180
98.9%
12
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0180
98.9%
12
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0180
98.9%
12
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0180
98.9%
12
 
1.1%

S
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
1
115 
0
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 row1

Common Values

ValueCountFrequency (%)
1115
63.2%
067
36.8%

Length

2023-01-09T11:08:32.037655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:32.116227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1115
63.2%
067
36.8%

Most occurring characters

ValueCountFrequency (%)
1115
63.2%
067
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1115
63.2%
067
36.8%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1115
63.2%
067
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1115
63.2%
067
36.8%

2
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0
167 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 (%)
0167
91.8%
115
 
8.2%

Length

2023-01-09T11:08:32.178731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:32.257318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0167
91.8%
115
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0167
91.8%
115
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0167
91.8%
115
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0167
91.8%
115
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0167
91.8%
115
 
8.2%

3
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
0
172 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182
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 row1
5th row0

Common Values

ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Length

2023-01-09T11:08:32.320273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-09T11:08:32.382780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0172
94.5%
110
 
5.5%

Interactions

2023-01-09T11:08:30.065678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.530459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.816021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:30.149205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.644523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.899057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:30.223513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.730232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-09T11:08:29.982438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-09T11:08:32.445737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2023-01-09T11:08:32.571288image/svg+xmlMatplotlib v3.5.1, 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.
2023-01-09T11:08:32.700358image/svg+xmlMatplotlib v3.5.1, 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.
2023-01-09T11:08:32.838371image/svg+xmlMatplotlib v3.5.1, 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.
2023-01-09T11:08:33.101186image/svg+xmlMatplotlib v3.5.1, 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.
2023-01-09T11:08:33.201572image/svg+xmlMatplotlib v3.5.1, 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.

Missing values

2023-01-09T11:08:30.345535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-09T11:08:30.509682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexsurvivedagesibspparchfaremaleQS23
01138.01071.283300000
13135.01053.100000100
26054.00051.862510100
31014.01116.700000101
411158.00026.550000100
521134.00013.000010110
623128.00035.500010100
727019.032263.000010100
852149.01076.729200000
954065.00161.979210000

Last rows

df_indexsurvivedagesibspparchfaremaleQS23
172835139.01183.158300000
173853116.00139.400000100
174857151.00026.550010100
175862148.00025.929200100
176867031.00050.495810100
177871147.01152.554200100
178872033.0005.000010100
179879156.00183.158300000
180887119.00030.000000100
181889126.00030.000010000