I referenced the pandas explode doc :#https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.explode.html
This code works with strings.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [["1058","1057","1056","1055","1054"], np.nan, np.nan, ["10","57","56","55","54"]],
'B': 1,
'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
df.explode('A')
Gives
A B C
0 1058 1 [a, b, c]
0 1057 1 [a, b, c]
0 1056 1 [a, b, c]
0 1055 1 [a, b, c]
0 1054 1 [a, b, c]
1 NaN 1 NaN
2 NaN 1 []
3 10 1 [d, e]
3 57 1 [d, e]
3 56 1 [d, e]
3 55 1 [d, e]
3 54 1 [d, e]
How can I get the same Column A exploded results above with this dataframe that includes quotes?:
df = pd.DataFrame({'A': [['\"1058\",\"1057\",\"1056\",\"1055\",\"1054\"'], np.nan, np.nan, ['\"10\",\"57\",\"56\",\"55\",\"54\"']],
'B': 1,
'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
CodePudding user response:
Use pd.eval
before explode
:
>>> df.assign(A=df['A'].apply(lambda x: pd.eval(x) if pd.notna(x) and x else x)) \
.explode('A')
A B C
0 1058 1 [a, b, c]
0 1057 1 [a, b, c]
0 1056 1 [a, b, c]
0 1055 1 [a, b, c]
0 1054 1 [a, b, c]
1 NaN 1 NaN
2 NaN 1 []
3 10 1 [d, e]
3 57 1 [d, e]
3 56 1 [d, e]
3 55 1 [d, e]
3 54 1 [d, e]
CodePudding user response:
Use ast
which is more prefered like eval
:
import ast
df['A'] = df.A.apply(lambda x: ast.literal_eval(x[0]) if isinstance(x, list) else x)
df = df.explode('A')
print (df)
A B C
0 1058 1 [a, b, c]
0 1057 1 [a, b, c]
0 1056 1 [a, b, c]
0 1055 1 [a, b, c]
0 1054 1 [a, b, c]
1 NaN 1 NaN
2 NaN 1 []
3 10 1 [d, e]
3 57 1 [d, e]
3 56 1 [d, e]
3 55 1 [d, e]
3 54 1 [d, e]