How to convert values like '2+3' in a Python Pandas column to its aggregated value

Question

I have a column in a DataFrame named fatalities in which few of the values are like below: data[''fatalities']= [1, 4, , 10, 1+8, 5, 2+9, , 16, 4+5]

I want the values of like '1+8', '2+9', etc to be converted to its aggregated value i.e, data[''fatalities']= [1, 4, , 10, 9, 5, 11, , 16, 9]

I not sure how to write a code to perform above aggregation for one of the column in pandas DataFrame in Python. But when I tried with the below code its throwing an error.

def addition(col):
  col= col.split('+')
  col= int(col[0]) + int(col[1])
  return col

data['fatalities']= [addition(row) for row in data['fatalities']]

Error:

IndexError: list index out of range

jezrael · Accepted Answer · 2020-04-28T05:43:37.927

2

Use pandas.eval what is different like pure python eval:

data['fatalities'] = pd.eval(data['fatalities'])
print (data)
  fatalities
0          1
1          4
2         10
3          9
4          5
5         11
6         16
7          9

But because this working only to 100 rows because bug:

AttributeError: 'PandasExprVisitor' object has no attribute 'visit_Ellipsis'

Then solution is:

data['fatalities'] = data['fatalities'].apply(pd.eval)

edited Apr 28 '20 at 05:43

answered Apr 28 '20 at 05:38

jezrael

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didn;t know about the bug is there GH tracker for it? – Umar.H Apr 28 '20 at 05:41
1

@Datanovice - I once debug this bug so remember it. – jezrael Apr 28 '20 at 05:44
you're right, I get `AttributeError: 'PandasExprVisitor' object has no attribute 'visit_Ellipsis'` for 101 rows – Umar.H Apr 28 '20 at 05:46
I even have nan values in the data column and when I am trying the above code its throwing me an error as below: ` UndefinedVariableError: name 'nan' is not defined ` – Naveen kumar Apr 28 '20 at 06:08
@Naveenkumar - Can you try `data['fatalities'] = data['fatalities'].fillna(0).apply(pd.eval)` ? – jezrael Apr 28 '20 at 06:09
1

@jezrael, this works for me now after filling the nan value with 0. Thank you! – Naveen kumar Apr 28 '20 at 06:12

score 1 · Answer 2 · answered Apr 28 '20 at 05:39

using .map and .astype(str) to force conversion if you have mixed data types.

df['fatalities'].astype(str).map(eval)
print(df)
   fatalities
0           1
1           4
2          10
3           9
4           5
5          11
6          16
7           9

How to convert values like '2+3' in a Python Pandas column to its aggregated value

2 Answers2