I have a pandas dataframe where I would like to verify that column A is greater than column B (row wise). I am doing something like
tmp=df['B']-df['A']
if(any( [ v for v in tmp if v > 0])):
....
I was wondering if there was better(concise) way of doing it or if pandas dataframe had any such built in routines to accomplish this
df = pd.DataFrame({'A': [1, 2, 3], 'B': [3, 1, 1]})
temp = df['B'] - df['A']
print(temp)
0 2
1 -1
2 -2
Now you can create a Boolean series using temp > 0:
print(temp > 0)
0 True
1 False
2 False
dtype: bool
This boolean series can be fed to any and therefore you can use:
if any(temp > 0):
print('juhu!')
Or simply (which avoids temp):
if any(df['B'] > df['A']):
print('juhu')
using the same logic of creating a Boolean series first:
print(df['B'] > df['A'])
0 True
1 False
2 False
dtype: bool
df['B']>df['A'] will be pandas series in boolean datatype.
>>> (df['B']>df['A']).dtype
dtype('bool')
For example
>>> df['B']>df['A']
0 True
1 False
2 False
3 True
4 True
dtype: bool
any() function returns True if any of the item in an iterable is true
>>> if any(df['B']>df['A']):
... print(True)
...
True
I guess you wanted to check if any df[‘B'] > df[‘A’] then do something.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 0, 6, 3]})
if np.where(df['B'] > df['A'], 1, 0).sum():
print('do something')
