Analyze is a python library that provides comprehensive statistical analysis of a dataframe in 5 lines of code. It creates significant insight for data scientists, analysts and machine learning engineers, enabling quick understanding of a dataset.
Project description
Analyze
Analyze is a python module that provides comprehensive statistical analysis of a dataframe in 5 lines of code. It creates significant insight for data scientists, analysts and machine learning engineers, enabling quick understanding of a dataset..
Installation
Install the package
pip install Analyze
Import the package into your code
from Analyze import analyze, get_dist
Read your dataset into a variable and make it a dataframe
data = pd.read_csv('file.csv') # Use the appropriate file extension
Create an instance of the analyze class
Datavalue = analyze(data)
Explore the comprehensive statistical value of your dataset using the analyse method.
Datavalue.analyze()
Sample Output
................................................
These are the categorical features in your dataset:
Name Sex \
0 Braund, Mr. Owen Harris male
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female
2 Heikkinen, Miss. Laina female
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female
4 Allen, Mr. William Henry male
5 Moran, Mr. James male
6 McCarthy, Mr. Timothy J male
7 Palsson, Master. Gosta Leonard male
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female
9 Nasser, Mrs. Nicholas (Adele Achem) female
10 Sandstrom, Miss. Marguerite Rut female
11 Bonnell, Miss. Elizabeth female
12 Saundercock, Mr. William Henry male
13 Andersson, Mr. Anders Johan male
14 Vestrom, Miss. Hulda Amanda Adolfina female
15 Hewlett, Mrs. (Mary D Kingcome) female
16 Rice, Master. Eugene male
17 Williams, Mr. Charles Eugene male
18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female
19 Masselmani, Mrs. Fatima female
Ticket Cabin Embarked
0 A/5 21171 NaN S
1 PC 17599 C85 C
2 STON/O2. 3101282 NaN S
3 113803 C123 S
4 373450 NaN S
5 330877 NaN Q
6 17463 E46 S
7 349909 NaN S
8 347742 NaN S
9 237736 NaN C
10 PP 9549 G6 S
11 113783 C103 S
12 A/5. 2151 NaN S
13 347082 NaN S
14 350406 NaN S
15 248706 NaN S
16 382652 NaN Q
17 244373 NaN S
18 345763 NaN S
19 2649 NaN C
................................................
The 10th percentile values for each column in the dataset are:
PassengerId 90.00
Survived 0.00
Pclass 1.00
Age 14.00
SibSp 0.00
Parch 0.00
Fare 7.55
Name: 0.1, dtype: float64
................................................
The median values for each column of your dataset without missing values are:
PassengerId 457.0
Survived 1.0
Pclass 1.0
Age 36.0
SibSp 0.0
Parch 0.0
Fare 57.0
dtype: float64
................................................
The interquartile ranges of your dataset columns is:
PassengerId 445.0000
Survived 1.0000
Pclass 1.0000
Age 17.8750
SibSp 1.0000
Parch 0.0000
Fare 23.0896
dtype: float64
................................................
The coefficients of variation of your dataset columns without missing values are:
PassengerId 56.618895
Survived 109.330348
Pclass 36.083963
Age 63.499659
SibSp 137.349175
Parch 217.198066
Fare 76.676637
dtype: float64
------------------------------------
................................................
These are the numerical features in your dataset:
PassengerId Survived Pclass Age SibSp Parch Fare
0 1 0 3 22.0 1 0 7.2500
1 2 1 1 38.0 1 0 71.2833
2 3 1 3 26.0 0 0 7.9250
3 4 1 1 35.0 1 0 53.1000
4 5 0 3 35.0 0 0 8.0500
5 6 0 3 NaN 0 0 8.4583
6 7 0 1 54.0 0 0 51.8625
7 8 0 3 2.0 3 1 21.0750
8 9 1 3 27.0 0 2 11.1333
9 10 1 2 14.0 1 0 30.0708
10 11 1 3 4.0 1 1 16.7000
11 12 1 1 58.0 0 0 26.5500
12 13 0 3 20.0 0 0 8.0500
13 14 0 3 39.0 1 5 31.2750
14 15 0 3 14.0 0 0 7.8542
15 16 1 2 55.0 0 0 16.0000
16 17 0 3 2.0 4 1 29.1250
17 18 1 2 NaN 0 0 13.0000
18 19 0 3 31.0 1 0 18.0000
19 20 1 3 NaN 0 0 7.2250
------------------------------------
................................................
The skew of your dataset is:
credit.policy -1.539621
int.rate 0.164420
installment 0.912522
log.annual.inc 0.028668
dti 0.023941
fico 0.471260
days.with.cr.line 1.155748
revol.bal 11.161058
revol.util 0.059985
inq.last.6mths 3.584151
delinq.2yrs 6.061793
pub.rec 5.126434
not.fully.paid 1.854592
dtype: float64
------------------------------------
................................................
The mean values for each column of your dataset are:
PassengerId 446.000000
Survived 0.383838
Pclass 2.308642
Age 29.699118
SibSp 0.523008
Parch 0.381594
Fare 32.204208
dtype: float64
------------------------------------
................................................
The kurtosis of your dataset is:
PassengerId -1.200000
Survived -1.775005
Pclass -1.280015
Age 0.178274
SibSp 17.880420
Parch 9.778125
Fare 33.398141
dtype: float64
------------------------------------
Distributions listed by Betterment of fit for int.rate:
............................................
Distribution chi_square
4 invgauss 1.029833
6 gamma 1.343415
8 lognorm 1.445072
1 norm 1.458848
3 beta 1.755474
5 uniform 2.043826
10 triang 5.287520
7 expon 14.766634
0 weibull_min 37.608865
2 weibull_max 74.688672
9 pearson3 398.094255
------------------------------------
Generating report...
<scipy.stats._continuous_distns.weibull_min_gen object at 0x11e0699a0>
(0.44201093944372993, 1.9999999999999996, 6.268594297720819)
<scipy.stats._continuous_distns.norm_gen object at 0x11dd94a30>
(28.0, 17.2490408940528)
<scipy.stats._continuous_distns.weibull_max_gen object at 0x11e069d00>
(0.2488410231665656, 58.000000000000014, 1.5915689505857427)
<scipy.stats._continuous_distns.beta_gen object at 0x11e046400>
(1.0527932282858297, 0.8439361636930744, -7.839143943009921, 65.83914394300993)
<scipy.stats._continuous_distns.invgauss_gen object at 0x11e0951c0>
(0.03838006670625667, -62.95941618248101, 2367.692288500255)
<scipy.stats._continuous_distns.uniform_gen object at 0x11e0f53d0>
(2.0, 56.0)
<scipy.stats._continuous_distns.gamma_gen object at 0x11e07cd90>
(54.156927130881755, -98.49677304019752, 2.334748847288054)
<scipy.stats._continuous_distns.expon_gen object at 0x11e05c460>
(2.0, 26.0)
<scipy.stats._continuous_distns.lognorm_gen object at 0x11e0b13d0>
(4.3245955568716274, 1.9999999876441796, 1.595318052251119)
<scipy.stats._continuous_distns.pearson3_gen object at 0x11e0cd8b0>
(0.4767375399624638, 28.000022387906775, 17.488900591625978)
<scipy.stats._continuous_distns.triang_gen object at 0x11e0e1a00>
(0.9999999999942933, -16.926163961990795, 74.92616396492964)
------------------------------------
................................................
The variance values of your dataset are:
PassengerId 66231.000000
Survived 0.236772
Pclass 0.699015
Age 211.019125
SibSp 1.216043
Parch 0.649728
Fare 2469.436846
dtype: float64
------------------------------------
Maintainers:
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