How to compare two columns of the same dataframe?

Question

I have a dataframe like this:

 match_id inn1 bat bowl runs1 inn2 runs2 is_score_chased 1 1 KKR RCB 222 2 82 1 2 1 CSK KXIP 240 2 207 1 8 1 CSK MI 208 2 202 1 9 1 DC RR 214 2 217 1 33 1 KKR DC 204 2 181 1

Now i want to change the values in is_score_chased column by comparing the values in runs1 and runs2 . If runs1>runs2, then the corresponding value in the row should be 'yes' else it should be no. I tried the following code:

for i in (high_scores1): if(high_scores1['runs1']>=high_scores1['runs2']): high_scores1['is_score_chased']='yes' else: high_scores1['is_score_chased']='no'

But it didn't work. How do i change the values in the column?

miradulo · Accepted Answer · 2017-02-23 01:58:42Z

You can more easily use np.where.

high_scores1['is_score_chased'] = np.where(high_scores1['runs1']>=high_scores1['runs2'], 'yes', 'no')

Typically, if you find yourself trying to iterate explicitly as you were to set a column, there is an abstraction like apply or where which will be both faster and more concise.

Community · Accepted Answer · 2017-05-23 10:29:49Z

This is a good case for using apply.

Here there is an example of using apply on two columns.

You can adapt it to your question with this:

def f(x): return 'yes' if x['run1'] > x['run2'] else 'no' df['is_score_chased'] = df.apply(f, axis=1)

However, I would suggest filling your column with booleans so you can make it more simple

def f(x): return x['run1'] > x['run2']

And also using lambdas so you make it in one line

df['is_score_chased'] = df.apply(lambda x: x['run1'] > x['run2'], axis=1)

Robert Honeybul · Accepted Answer · 2017-02-23 01:46:59Z

You need to reference the fact that you are iterating through the dataframe, so;

for i in (high_scores1): if(high_scores1['runs1'][i]>=high_scores1['runs2'][i]): high_scores1['is_score_chased'][i]='yes' else: high_scores1['is_score_chased'][i]='no'

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