更新时间:2023-12-01 14:34:10
You can use ast.literal_eval()
to convert the strings to lists.
ast.literal_eval()
-
>>> import ast
>>> l = ast.literal_eval('[10,20,30]')
>>> type(l)
<class 'list'>
对于您的情况,可以将其传递给Series.apply
,以便(安全地)评估系列中的每个元素.示例-
For your case, you can pass it to Series.apply
, so that each element in the series is evaluated (safely). Example -
df = pd.read_csv('DataFrame.txt', sep='\t')
import ast
df['neg_list'] = df['neg'].apply(ast.literal_eval)
df = df.drop('neg',axis=1)
df['pos_list'] = df['pos'].apply(ast.literal_eval)
df = df.drop('pos',axis=1)
演示-
In [15]: import pandas as pd
In [16]: dict2df = {"euNOG": ["ENOG410IF52", "KOG2956", "KOG1997"],
....: "neg": [[58], [1332, 753, 716, 782], [187]],
....: "pos": [[96], [659, 661, 705, 1228], [1414]]}
In [17]: df = pd.DataFrame(dict2df)
In [18]: df.to_csv('DataFrame.txt', sep='\t', header=True, index=False)
In [19]: newdf = pd.read_csv('DataFrame.txt', sep='\t')
In [20]: newdf['neg']
Out[20]:
0 [58]
1 [1332, 753, 716, 782]
2 [187]
Name: neg, dtype: object
In [21]: newdf['neg'][0]
Out[21]: '[58]'
In [22]: import ast
In [23]: newdf['neg_list'] = newdf['neg'].apply(ast.literal_eval)
In [24]: newdf = newdf.drop('neg',axis=1)
In [25]: newdf['pos_list'] = newdf['pos'].apply(ast.literal_eval)
In [26]: newdf = newdf.drop('pos',axis=1)
In [27]: newdf
Out[27]:
euNOG neg_list pos_list
0 ENOG410IF52 [58] [96]
1 KOG2956 [1332, 753, 716, 782] [659, 661, 705, 1228]
2 KOG1997 [187] [1414]
In [28]: newdf['neg_list'][0]
Out[28]: [58]