Python – Compare two columns using pandas

dataframeif statementpandaspython

Using this as a starting point:

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

Out[8]: 
  one  two three
0   10  1.2   4.2
1   15  70   0.03
2    8   5     0

I want to use something like an if statement within pandas.

if df['one'] >= df['two'] and df['one'] <= df['three']:
    df['que'] = df['one']

Basically, check each row via the if statement, create new column.

The docs say to use .all but there is no example…

Best Answer

You could use np.where. If cond is a boolean array, and A and B are arrays, then

C = np.where(cond, A, B)

defines C to be equal to A where cond is True, and B where cond is False.

import 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)

yields

  one  two three  que
0  10  1.2   4.2   10
1  15   70  0.03  NaN
2   8    5     0  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)

yields

  one  two three  que
0  10  1.2   4.2   10
1  15   70  0.03   70
2   8    5     0  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']]

(The assumption may not be true if df['one'] or df['two'] contain NaNs.)


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.

In [61]: '10' <= '4.2'
Out[61]: True

In [62]: 10 <= 4.2
Out[62]: False
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