comparar valores de dos columnas en el ejemplo de código de pandas

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Ejemplo 1: comparar dos columnas de marco de datos

comparison_column = np.where(df["col1"]== df["col2"],True,False)

Ejemplo 2: los pandas comparan dos columnas

# Syntax:# C = np.where(condition, A, B) # equal to A when condition true and B when falseimport numpy as np
import pandas as pd

a =[['10','1.2','4.2'],['15','70','0.03'],['8','5','0']]
df = pd.DataFrame(a, columns=['one','two','three'])

df['que']= np.where((df['one']>= df['two'])&(df['one']<= df['three']), df['one'], np.nan)# If you have more than one condition, then you could use np.select # instead. For example, if you wish df['que'] to equal # df['two'] when df['one'] < df['two'], then
conditions =[(df['one']>= df['two'])&(df['one']<= df['three']), 
    df['one']< df['two']]

choices =[df['one'], df['two']]

df['que']= np.select(conditions, choices, default=np.nan)# If we can assume that df['one'] >= df['two'] when # df['one'] < df['two'] is False, then the conditions and # choices could be simplified to
conditions =[
    df['one']< df['two'],
    df['one']<= df['three']]

choices =[df['two'], df['one']]# Note that: 
a =[['10','1.2','4.2'],['15','70','0.03'],['8','5','0']]
df = pd.DataFrame(a, columns=['one','two','three'])# defines a DataFrame with string values. Since they look numeric, # you might be better off converting those strings to floats:
df2 = df.astype(float)# This changes the results, however, since strings compare # character-by-character, while floats are compared numerically.

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