I have a dataframe and I want to count how many times a string (say 'Yes') has occurred in all other columns. I want to add count into new column and call it 'Yes-Count'.
I have it working using lamda and following example Creating a new column based on if-elif-else condition
I am curious if this can be done in one line.
This is sample data and code.
import pandas as pd
def finalCount(row):
count = 0
if row['Col1'] == 'Yes':
count = count + 1
if row['Col2'] == 'Yes':
count = count + 1
if row['Col3'] == 'Yes':
count = count + 1
if row['Col4'] == 'Yes':
count = count + 1
return count
data = {
'Col1': ['Yes', 1, 'No', 'Yes'],
'Col2': ['Yes', 2, 'No', 'Yes'],
'Col3': ['No', 3, 'Yes', 'Yes'],
'Col4': ['Yes', 4, 'No', 'Yes'],
}
dfData = pd.DataFrame(data, columns= ['Col1','Col2','Col3','Col4'])
dfData['Yes-Count'] = dfData.apply(finalCount, axis =1)
I get result as expected.
Is there a way to get rid of finalCount method and do this in one line?
Here's one way using a boolean mask and sum:
dfData["Yes-Count"] = dfData.eq('Yes').sum(axis=1)
print(dfData)
# Col1 Col2 Col3 Col4 Yes-Count
#0 Yes Yes No Yes 3
#1 1 2 3 4 0
#2 No No Yes No 1
#3 Yes Yes Yes Yes 4
Explanation
dfData.eq("Yes")
returns a dataframe of equal shape with boolean values representing if the value in that location is equal to "Yes"
Here is another approach using the isin()
function:
list_of_words = ['Yes']
dfData["Yes-Count"] = dfData.isin(list_of_words).sum(axis='columns')
Using this approach you can compare your DataFrame
elements with multiple values. The isin()
function returns a boolean DataFrame
which shows whether your DataFrame
elements match to any of the words in list_of_words
.
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