标签：lower csv df Kaggle summary v2 values total outliers

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.stats import kurtosis,skew
from scipy import stats

函数定义

def resumetable(df):
    print(f"Dataset Shape: {df.shape}")
    summary = pd.DataFrame(df.dtypes,columns=['dtypes'])
    summary = summary.reset_index()
    summary['Name'] = summary['index']
    summary = summary[['Name','dtypes']]
    summary['Missing'] = df.isnull().sum().values    
    summary['Uniques'] = df.nunique().values
    summary['First Value'] = df.loc[0].values
    summary['Second Value'] = df.loc[1].values
    summary['Third Value'] = df.loc[2].values

    for name in summary['Name'].value_counts().index:
        summary.loc[summary['Name'] == name, 'Entropy'] = round(stats.entropy(df[name].value_counts(normalize=True), base=2),2) 

    return summary

def CalcOutliers(df_num): 
    '''
    
    Leonardo Ferreira 20/10/2018
    Set a numerical value and it will calculate the upper, lower and total number of outliers
    It will print a lot of statistics of the numerical feature that you set on input
    
    '''
    # calculating mean and std of the array
    data_mean, data_std = np.mean(df_num), np.std(df_num)

    # seting the cut line to both higher and lower values
    # You can change this value
    cut = data_std * 3

    #Calculating the higher and lower cut values
    lower, upper = data_mean - cut, data_mean + cut

    # creating an array of lower, higher and total outlier values 
    outliers_lower = [x for x in df_num if x < lower]
    outliers_higher = [x for x in df_num if x > upper]
    outliers_total = [x for x in df_num if x < lower or x > upper]

    # array without outlier values
    outliers_removed = [x for x in df_num if x > lower and x < upper]
    
    print('Identified lowest outliers: %d' % len(outliers_lower)) # printing total number of values in lower cut of outliers
    print('Identified upper outliers: %d' % len(outliers_higher)) # printing total number of values in higher cut of outliers
    print('Identified outliers: %d' % len(outliers_total)) # printing total number of values outliers of both sides
    print('Non-outlier observations: %d' % len(outliers_removed)) # printing total number of non outlier values
    print("Total percentual of Outliers: ", round((len(outliers_total) / len(outliers_removed) )*100, 4)) # Percentual of outliers in points
    
    return

标签：lower,csv,df,Kaggle,summary,v2,values,total,outliers
来源： https://blog.csdn.net/sinat_37574187/article/details/120219162

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