python：作为DataFrame单个值的整数列表

问题： 如何“pd.read_csv”，使给定列中的值为类型的列表（列中每行的列表）

创建数据帧时（从dict，见下文），单个值的类型为list。问题是：在将数据帧写入文件并从文件读回数据帧后，我得到的是字符串而不是列表

import pandas as pd
dict2df = {"euNOG": ["ENOG410IF52", "KOG2956", "KOG1997"], 
           "neg": [[58], [1332, 753, 716, 782], [187]], 
           "pos": [[96], [659, 661, 705, 1228], [1414]]}
df = pd.DataFrame(dict2df)

创建数据帧

import pandas as pd
dict2df = {"euNOG": ["ENOG410IF52", "KOG2956", "KOG1997"], 
           "neg": [[58], [1332, 753, 716, 782], [187]], 
           "pos": [[96], [659, 661, 705, 1228], [1414]]}
df = pd.DataFrame(dict2df)

值是一个列表 写入文件

df.to_csv('DataFrame.txt', sep='\t', header=True, index=False)

从文件中读取

df = pd.read_csv('DataFrame.txt', sep='\t')

值是字符串而不是列表 当然，可以在这两种数据类型之间进行转换，但计算成本很高，需要额外的工作（见下文）

什么是更好的（更具pythonic）解决方案？迭代列表中的整数将非常方便，无需来回转换它们。 谢谢你的支持

您可以使用将字符串转换为列表

ast.literal\u eval（）的一个简单示例-

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]

非常感谢。从使用库而不是我自己的代码的意义上说，这更像是python。我希望找到一个解决方案，在读取数据帧时包含这种转换。
def convert_StringList2ListOfInt(string2convert):
    return [int(ele) for ele in string2convert[1:-1].split(',')]

def DataFrame_StringOfInts2ListOfInts(df, cols2convert_list):
    for column in cols2convert_list:
        column_temp = column + "_temp"
        df[column_temp] = df[column].apply(convert_StringList2ListOfInt, 1)
        df[column] = df[column_temp]
        df = df.drop(column_temp, axis=1)
    return df
df = DataFrame_StringOfInts2ListOfInts(df, ['neg', 'pos'])

>>> import ast
>>> l = ast.literal_eval('[10,20,30]')
>>> type(l)
<class 'list'>

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)

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]