Overview

Dataset statistics

Number of variables12
Number of observations293
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.6 KiB
Average record size in memory96.4 B

Variable types

NUM6
BOOL4
CAT2

Warnings

df_index has unique values Unique
oldpeak has 188 (64.2%) zeros Zeros

Reproduction

Analysis started2020-12-10 21:13:36.661704
Analysis finished2020-12-10 21:13:48.656092
Duration11.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.6518771
Minimum0
Maximum293
Zeros1
Zeros (%)0.3%
Memory size2.4 KiB
2020-12-11T00:13:48.868873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.6
Q173
median147
Q3220
95-th percentile278.4
Maximum293
Range293
Interquartile range (IQR)147

Descriptive statistics

Standard deviation85.12019084
Coefficient of variation (CV)0.5804234661
Kurtosis-1.203043787
Mean146.6518771
Median Absolute Deviation (MAD)74
Skewness-0.004896904897
Sum42969
Variance7245.446889
MonotocityStrictly increasing
2020-12-11T00:13:49.225015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
29310.3%
 
10910.3%
 
9210.3%
 
9310.3%
 
9410.3%
 
9510.3%
 
9610.3%
 
9710.3%
 
9810.3%
 
9910.3%
 
10010.3%
 
10110.3%
 
10310.3%
 
10410.3%
 
10510.3%
 
10610.3%
 
10710.3%
 
9110.3%
 
9010.3%
 
8910.3%
 
8010.3%
 
7410.3%
 
7510.3%
 
7610.3%
 
7710.3%
 
Other values (268)26891.5%
 
ValueCountFrequency (%) 
010.3%
 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
610.3%
 
710.3%
 
810.3%
 
910.3%
 
ValueCountFrequency (%) 
29310.3%
 
29210.3%
 
29110.3%
 
29010.3%
 
28910.3%
 
28810.3%
 
28710.3%
 
28610.3%
 
28510.3%
 
28410.3%
 

age
Real number (ℝ≥0)

Distinct38
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.8225256
Minimum28
Maximum66
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-12-11T00:13:49.519905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile34
Q142
median49
Q354
95-th percentile59
Maximum66
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.824875011
Coefficient of variation (CV)0.1636232071
Kurtosis-0.5112967113
Mean47.8225256
Median Absolute Deviation (MAD)6
Skewness-0.2822808516
Sum14012
Variance61.22866894
MonotocityNot monotonic
2020-12-11T00:13:49.843390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
54258.5%
 
48196.5%
 
52175.8%
 
55155.1%
 
49144.8%
 
46134.4%
 
53124.1%
 
43124.1%
 
50124.1%
 
39113.8%
 
41113.8%
 
47103.4%
 
56103.4%
 
5193.1%
 
5893.1%
 
5982.7%
 
3782.7%
 
4582.7%
 
4472.4%
 
4272.4%
 
4072.4%
 
3872.4%
 
3551.7%
 
5751.7%
 
3651.7%
 
Other values (13)279.2%
 
ValueCountFrequency (%) 
2810.3%
 
2920.7%
 
3010.3%
 
3120.7%
 
3241.4%
 
3320.7%
 
3441.4%
 
3551.7%
 
3651.7%
 
3782.7%
 
ValueCountFrequency (%) 
6610.3%
 
6531.0%
 
6310.3%
 
6220.7%
 
6120.7%
 
6020.7%
 
5982.7%
 
5893.1%
 
5751.7%
 
56103.4%
 

sex
Boolean

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1
213 
0
80 
ValueCountFrequency (%) 
121372.7%
 
08027.3%
 
2020-12-11T00:13:50.026898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cp
Categorical

Distinct4
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
4
123 
2
105 
3
54 
1
 
11
ValueCountFrequency (%) 
412342.0%
 
210535.8%
 
35418.4%
 
1113.8%
 
2020-12-11T00:13:50.171941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-11T00:13:50.310005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:50.468085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
412342.0%
 
210535.8%
 
35418.4%
 
1113.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number293100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
412342.0%
 
210535.8%
 
35418.4%
 
1113.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common293100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
412342.0%
 
210535.8%
 
35418.4%
 
1113.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII293100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
412342.0%
 
210535.8%
 
35418.4%
 
1113.8%
 

trestbps
Real number (ℝ≥0)

Distinct32
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.6606949
Minimum92
Maximum200
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-12-11T00:13:50.757027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile110
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range108
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.57678272
Coefficient of variation (CV)0.1324942759
Kurtosis0.8312457912
Mean132.6606949
Median Absolute Deviation (MAD)10
Skewness0.7386292738
Sum38869.58362
Variance308.9432907
MonotocityNot monotonic
2020-12-11T00:13:51.161512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
1206522.2%
 
1305418.4%
 
1405017.1%
 
150237.8%
 
110206.8%
 
160206.8%
 
12582.7%
 
10062.0%
 
18062.0%
 
14551.7%
 
17051.7%
 
13551.7%
 
11231.0%
 
11820.7%
 
12420.7%
 
11520.7%
 
12220.7%
 
10510.3%
 
9810.3%
 
132.583617710.3%
 
19010.3%
 
15510.3%
 
13210.3%
 
10810.3%
 
11310.3%
 
Other values (7)72.4%
 
ValueCountFrequency (%) 
9210.3%
 
9810.3%
 
10062.0%
 
10510.3%
 
10610.3%
 
10810.3%
 
110206.8%
 
11231.0%
 
11310.3%
 
11520.7%
 
ValueCountFrequency (%) 
20010.3%
 
19010.3%
 
18062.0%
 
17051.7%
 
160206.8%
 
15510.3%
 
150237.8%
 
14551.7%
 
14210.3%
 
1405017.1%
 

chol
Real number (ℝ≥0)

Distinct154
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.8487085
Minimum85
Maximum603
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-12-11T00:13:51.524442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile165.2
Q1211
median248
Q3277
95-th percentile350.2
Maximum603
Range518
Interquartile range (IQR)66

Descriptive statistics

Standard deviation65.05905634
Coefficient of variation (CV)0.2593557556
Kurtosis5.187495865
Mean250.8487085
Median Absolute Deviation (MAD)34
Skewness1.489808115
Sum73498.67159
Variance4232.680812
MonotocityNot monotonic
2020-12-11T00:13:51.875358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
250.8487085227.5%
 
27551.7%
 
23051.7%
 
24651.7%
 
21641.4%
 
21141.4%
 
26341.4%
 
26041.4%
 
22441.4%
 
23841.4%
 
21541.4%
 
19641.4%
 
23741.4%
 
20731.0%
 
28831.0%
 
24831.0%
 
18231.0%
 
29231.0%
 
22331.0%
 
19331.0%
 
29731.0%
 
21331.0%
 
26831.0%
 
18431.0%
 
29131.0%
 
Other values (129)18462.8%
 
ValueCountFrequency (%) 
8510.3%
 
10010.3%
 
11710.3%
 
12910.3%
 
13210.3%
 
14720.7%
 
15610.3%
 
16031.0%
 
16110.3%
 
16320.7%
 
ValueCountFrequency (%) 
60310.3%
 
52910.3%
 
51810.3%
 
49110.3%
 
46810.3%
 
46610.3%
 
41210.3%
 
40410.3%
 
39410.3%
 
39310.3%
 

fbs
Boolean

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
273 
1
 
20
ValueCountFrequency (%) 
027393.2%
 
1206.8%
 
2020-12-11T00:13:52.097796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

restecg
Categorical

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
235 
1
52 
2
 
6
ValueCountFrequency (%) 
023580.2%
 
15217.7%
 
262.0%
 
2020-12-11T00:13:52.244035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-11T00:13:52.482700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:52.650638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
052860.1%
 
.29333.3%
 
1525.9%
 
260.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number58666.7%
 
Other Punctuation29333.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
052890.1%
 
1528.9%
 
261.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.293100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common879100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
052860.1%
 
.29333.3%
 
1525.9%
 
260.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII879100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
052860.1%
 
.29333.3%
 
1525.9%
 
260.7%
 

thalach
Real number (ℝ≥0)

Distinct72
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.0584631
Minimum82
Maximum190
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2020-12-11T00:13:52.918862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile98
Q1122
median140
Q3155
95-th percentile176.8
Maximum190
Range108
Interquartile range (IQR)33

Descriptive statistics

Standard deviation23.55800299
Coefficient of variation (CV)0.169410782
Kurtosis-0.579829956
Mean139.0584631
Median Absolute Deviation (MAD)16
Skewness-0.08053560888
Sum40744.12969
Variance554.9795047
MonotocityNot monotonic
2020-12-11T00:13:53.291551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
150299.9%
 
140217.2%
 
130175.8%
 
170144.8%
 
160124.1%
 
120113.8%
 
11093.1%
 
14282.7%
 
12582.7%
 
13572.4%
 
10072.4%
 
15572.4%
 
13862.0%
 
11562.0%
 
17562.0%
 
18062.0%
 
14562.0%
 
11851.7%
 
12251.7%
 
12441.4%
 
11641.4%
 
13741.4%
 
13441.4%
 
9841.4%
 
16541.4%
 
Other values (47)7927.0%
 
ValueCountFrequency (%) 
8210.3%
 
8710.3%
 
9010.3%
 
9110.3%
 
9231.0%
 
9420.7%
 
9631.0%
 
9841.4%
 
9920.7%
 
10072.4%
 
ValueCountFrequency (%) 
19010.3%
 
18810.3%
 
18531.0%
 
18431.0%
 
18062.0%
 
17810.3%
 
17610.3%
 
17562.0%
 
17420.7%
 
17231.0%
 

exang
Boolean

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
204 
1
89 
ValueCountFrequency (%) 
020469.6%
 
18930.4%
 
2020-12-11T00:13:53.504450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

oldpeak
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5880546075
Minimum0
Maximum5
Zeros188
Zeros (%)64.2%
Memory size2.4 KiB
2020-12-11T00:13:53.644106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9095539158
Coefficient of variation (CV)1.546716758
Kurtosis2.163645114
Mean0.5880546075
Median Absolute Deviation (MAD)0
Skewness1.543805087
Sum172.3
Variance0.8272883258
MonotocityNot monotonic
2020-12-11T00:13:53.918589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018864.2%
 
14114.0%
 
23110.6%
 
1.5165.5%
 
393.1%
 
2.531.0%
 
0.520.7%
 
0.810.3%
 
510.3%
 
410.3%
 
ValueCountFrequency (%) 
018864.2%
 
0.520.7%
 
0.810.3%
 
14114.0%
 
1.5165.5%
 
23110.6%
 
2.531.0%
 
393.1%
 
410.3%
 
510.3%
 
ValueCountFrequency (%) 
510.3%
 
410.3%
 
393.1%
 
2.531.0%
 
23110.6%
 
1.5165.5%
 
14114.0%
 
0.810.3%
 
0.520.7%
 
018864.2%
 

num
Boolean

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
187 
1
106 
ValueCountFrequency (%) 
018763.8%
 
110636.2%
 
2020-12-11T00:13:54.354524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-11T00:13:38.317569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:39.071873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:39.351720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:39.562496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:39.759282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:39.978305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:40.182026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:40.376509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:40.705393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:41.036293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:41.255208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:41.585558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:41.866161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:42.233131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:42.577063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:42.867556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:43.083531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:43.294803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:43.493695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:43.698359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:43.941050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:44.152881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:44.371341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:44.594798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:44.813348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:45.113197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:45.346086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:45.647041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:45.877865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:46.072080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:46.287991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:46.479199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:46.668457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:47.001999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:47.219849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:47.409706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-11T00:13:54.481859image/svg+xmlMatplotlib v3.3.3, 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.
2020-12-11T00:13:54.929055image/svg+xmlMatplotlib v3.3.3, 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.
2020-12-11T00:13:55.235462image/svg+xmlMatplotlib v3.3.3, 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.
2020-12-11T00:13:55.562939image/svg+xmlMatplotlib v3.3.3, 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.
2020-12-11T00:13:55.825721image/svg+xmlMatplotlib v3.3.3, 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

2020-12-11T00:13:47.816029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-11T00:13:48.400710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexagesexcptrestbpscholfbsrestecgthalachexangoldpeaknum
002812130.0132.0000000.02.0185.00.00.00
112912120.0243.0000000.00.0160.00.00.00
222912140.0250.8487080.00.0170.00.00.00
333001170.0237.0000000.01.0170.00.00.00
443102100.0219.0000000.01.0150.00.00.00
553202105.0198.0000000.00.0165.00.00.00
663212110.0225.0000000.00.0184.00.00.00
773212125.0254.0000000.00.0155.00.00.00
883313120.0298.0000000.00.0185.00.00.00
993402130.0161.0000000.00.0190.00.00.00

Last rows

df_indexagesexcptrestbpscholfbsrestecgthalachexangoldpeaknum
2832844914128.0212.00.00.096.01.00.01
2842854914150.0222.00.00.0122.00.02.01
2852865014140.0231.00.01.0140.01.05.01
2862875014140.0341.00.01.0125.01.02.51
2872885214140.0266.00.00.0134.01.02.01
2882895214160.0331.00.00.094.01.02.51
2892905403130.0294.00.01.0100.01.00.01
2902915614155.0342.01.00.0150.01.03.01
2912925802180.0393.00.00.0110.01.01.01
2922936514130.0275.00.01.0115.01.01.01