compare multiple columns in pandas

1comparison_column = np.where(df["col1"] == df["col2"], True, False)

1# Syntax:
2# C = np.where(condition, A, B) 
3# equal to A when condition true and B when false
4import numpy as np
5import pandas as pd
6
7a = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]
8df = pd.DataFrame(a, columns=['one', 'two', 'three'])
9
10df['que'] = np.where((df['one'] >= df['two']) & (df['one'] <= df['three'])
11                     , df['one'], np.nan)
12
13# If you have more than one condition, then you could use np.select 
14# instead. For example, if you wish df['que'] to equal 
15# df['two'] when df['one'] < df['two'], then
16conditions = [
17    (df['one'] >= df['two']) & (df['one'] <= df['three']), 
18    df['one'] < df['two']]
19
20choices = [df['one'], df['two']]
21
22df['que'] = np.select(conditions, choices, default=np.nan)
23
24# If we can assume that df['one'] >= df['two'] when 
25# df['one'] < df['two'] is False, then the conditions and 
26# choices could be simplified to
27conditions = [
28    df['one'] < df['two'],
29    df['one'] <= df['three']]
30
31choices = [df['two'], df['one']]
32
33# Note that: 
34a = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]
35df = pd.DataFrame(a, columns=['one', 'two', 'three'])
36
37# defines a DataFrame with string values. Since they look numeric, 
38# you might be better off converting those strings to floats:
39df2 = df.astype(float)
40
41# This changes the results, however, since strings compare 
42# character-by-character, while floats are compared numerically.
43
44
45
46

1df['Result'] = (df.iloc[:, 1:4].values < df[['E']].values).all(axis=1).astype(int)
2print (df)
3   A   B   C   D   E  Result
40  V  10   5  18  20       1
51  W   9  18  11  13       0
62  X   8   7  12   5       0
73  Y   7   9   7   8       0
84  Z   6   5   3  90       1
9