python - Compare two columns using pandas

130
votes

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...

8
pythonpandasif-statementdataframe
What should the value be if the if statement is False? - Alex Riley
@Merlin: If you have numeric data in a column, it is best not to mix it with strings. Doing so changes the column's dtype to object. This allows arbitrary Python objects to be stored in the column, but it comes at the cost of slower numeric computation. Thus if the column is storing numeric data, using NaNs for not-a-numbers is preferable. - unutbu
Having integers as strings and trying to do comparison on them looks odd: a = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]. This creates confusing results with "correct" code: df['que'] = df['one'][(df['one'] >= df['two']) & (df['one'] <= df['three'])] yields 10 for the first line, while it should yield NaN if the input would have been integers. - Primer

8 Answers

178
votes

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
87
votes

You can use .equals for columns or entire dataframes.

df['col1'].equals(df['col2'])

If they're equal, that statement will return True, else False.

30
votes

You could use apply() and do something like this

df['que'] = df.apply(lambda x : x['one'] if x['one'] >= x['two'] and x['one'] <= x['three'] else "", axis=1)

or if you prefer not to use a lambda

def que(x):
    if x['one'] >= x['two'] and x['one'] <= x['three']:
        return x['one']
    return ''
df['que'] = df.apply(que, axis=1)
11
votes

One way is to use a Boolean series to index the column df['one']. This gives you a new column where the True entries have the same value as the same row as df['one'] and the False values are NaN.

The Boolean series is just given by your if statement (although it is necessary to use & instead of and):

>>> df['que'] = df['one'][(df['one'] >= df['two']) & (df['one'] <= df['three'])]
>>> df
    one two three   que
0   10  1.2 4.2      10
1   15  70  0.03    NaN
2   8   5   0       NaN

If you want the NaN values to be replaced by other values, you can use the fillna method on the new column que. I've used 0 instead of the empty string here:

>>> df['que'] = df['que'].fillna(0)
>>> df
    one two three   que
0   10  1.2   4.2    10
1   15   70  0.03     0
2    8    5     0     0
8
votes

Wrap each individual condition in parentheses, and then use the & operator to combine the conditions:

df.loc[(df['one'] >= df['two']) & (df['one'] <= df['three']), 'que'] = df['one']

You can fill the non-matching rows by just using ~ (the "not" operator) to invert the match:

df.loc[~ ((df['one'] >= df['two']) & (df['one'] <= df['three'])), 'que'] = ''

You need to use & and ~ rather than and and not because the & and ~ operators work element-by-element.

The final result:

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

Use np.select if you have multiple conditions to be checked from the dataframe and output a specific choice in a different column

conditions=[(condition1),(condition2)]
choices=["choice1","chocie2"]

df["new column"]=np.select=(condtion,choice,default=)

Note: No of conditions and no of choices should match, repeat text in choice if for two different conditions you have same choices

0
votes

I think the closest to the OP's intuition is an inline if statement:

df['que'] = (df['one'] if ((df['one'] >= df['two']) and (df['one'] <= df['three']))
0
votes

Use lambda expression:

df[df.apply(lambda x: x['col1'] != x['col2'], axis = 1)]