Here I have this dataframe and I am trying to remove the duplicate elements from each array in column 2 as follows and resultant array in Column 3.
Column1 Column 2 Column3
0 [ABC|QWER|12345, ABC|QWER|12345] [ABC|QWER|12345]
1 [TBC|WERT|567890,TBC|WERT|567890] [TBC|WERT|567890]
2 [ERT|TYIO|9845366, ERT|TYIO|9845366,ERT|TYIO|5] [ERT|TYIO|9845366, ERT|TYIO|5]
3 NaN NaN
4 [SAR|QWPO|34564557,SAR|QWPO|3456455] [SAR|QWPO|34564557,SAR|QWPO|3456455]
5 NaN NaN
6 [SE|WERT|12233412] [SE|WERT|12233412]
7 NaN NaN
I m using following codes but its showing the error of malformed node or string.Please help to solve this.
import ast
def ddpe(a):
return list(dict.fromkeys(ast.literal_eval(a)))
df['column3'] = df['column2'].apply(ddpe)
CodePudding user response:
I'm assuming the values of 'column2' are strings since you are trying to use ast.literal_eval
. In that case, try this instead
import pandas as pd
import numpy as np
def ddpe(str_val):
if pd.isna(str_val): # return NaN if value is NaN
return np.nan
# Remove the square brackets, split on ',' and strip possible
# whitespaces between elements
vals = [v.strip() for v in str_val.strip('[]').split(',')]
# remove duplicates keeping the original order
return list(dict.fromkeys(vals))
df['column3'] = df['column2'].apply(ddpe)
If the column values are lists already, you just need
def ddpe(lst_val):
# return NaN is value is not a list.
# Assuming those are only the two options.
if not isinstance(lst_val, list):
return np.nan
return list(dict.fromkeys(lst_val))
df['column3'] = df['column2'].apply(ddpe)