Automated view of dataset
Project description
Description
Requirements
- python >= 3.6
- numpy >= 1.16
- pandas >= 0.23
- matplotlib >= 3.02
- IPython >= 7.2.0
Module, provides the function view, which displays general information on the data:
- Result of method info()
- Result of method describe()
- for numeric / categorical variables - The number of nulls in the data (amount and percentage for each column)
- grid of histograms for numeric variables
- Top-5 of the most frequent categorical variables (for each)
- Split count statistic by category with less 5 unique values
- matrix correlation
- dataframe of higher correlation pair (over 0.7)
Parameters (function view):
- d - table with data
- only_numeric - True / False, default: True. True - information output only by numerical variables, False - information output by numerical and categorical variables.
- full_stats - True / False, default: False. False - output information on numerical variables without interquartile range, data boundaries without outliers, True - complete output with IQR, min and max by IQR and amount of outliers.
- histograms - True / False, default: True. True - output with building histograms for numerical variables, False - without building histograms
Top-5 elements of categorical variables
The postfix (_name / _count) is assigned to the name of the data column to create top-5 elements table:
- _name - category name
- _count - number of unique elements in this category. If there are less than 5 unique elements, then the values in the _count field are filled -1
Count statistic of numbers variables split by some categorical variables
- if number of unique elements in category variable less 5, then show count statistic
Correlation matrix
- Correlation matrix with heatmap (pearson corr index)
Install
$ pip install data_view
Usage
$ python3
from data_view import *
d = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])
view(d, only_numeric=True, histograms=False)
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