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如何在python中组合多个字符串列列表?

[英]How to combine multiple lists of string columns in python?

我有一个Python Pandas数据框。

我尝试创建一个新列total_str ,它是colAcolB中的值的列表。

这是预期的输出:

       colA           colB              total_str
0  ['a','b','c'] ['a','b','c']   ['a','b','c','a','b','c']
1  ['a','b','c']      nan        ['a','b','c']
2  ['a','b','c']   ['d','e']     ['a','b','c','d','e']
#replace nan with empty list and then concatenate colA and colB using sum.
df['total_str'] = df.applymap(lambda x: [] if x is np.nan else x).apply(lambda x: sum(x,[]), axis=1)

df
Out[705]: 
        colA       colB           total_str
0  [a, b, c]  [a, b, c]  [a, b, c, a, b, c]
1  [a, b, c]        NaN           [a, b, c]
2  [a, b, c]     [d, e]     [a, b, c, d, e]

如果DF中还有其他列,则可以使用:

df['total_str'] = df.applymap(lambda x: [] if x is np.nan else x).apply(lambda x: x.colA+x.colB, axis=1)

chain为您做这个技巧。

itertools.chain(*filter(bool, [colA, colB]))

这将返回一个迭代器,如果需要,您可以使用list结果来获取列表,例如

import itertools

def test(colA, colB):
    total_str = itertools.chain(*filter(bool, [colA, colB]))
    print list(total_str)


test(['a', 'b'], ['c'])  # output: ['a', 'b', 'c']
test(['a', 'b', 'd'], None)  # output: ['a', 'b', 'c']
test(['a', 'b', 'd'], ['x', 'y', 'z'])  # ['a', 'b', 'd', 'x', 'y', 'z']
test(None, None)  # output []

我假设您要在数据numpy.nan处理numpy.nanNone 您可以简单地编写一个辅助函数,以在创建新列时将它们替换为空列表。 这不是干净的,但可以。

def helper(x):
    return x if x is not np.nan and x is not None else []

dataframe['total_str'] = dataframe['colA'].map(helper) + dataframe['colB'].map(helper)

使用combine_firstNaN替换为空list以实现更快的解决方案:

df['total_str'] = df['colA'] + df['colB'].combine_first(pd.Series([[]], index=df.index))
print (df)
        colA       colB           total_str
0  [a, b, c]  [a, b, c]  [a, b, c, a, b, c]
1  [a, b, c]        NaN           [a, b, c]
2  [a, b, c]     [d, e]     [a, b, c, d, e]

df['total_str'] = df['colA'].add(df['colB'].combine_first(pd.Series([[]], index=df.index)))
print (df)
        colA       colB           total_str
0  [a, b, c]  [a, b, c]  [a, b, c, a, b, c]
1  [a, b, c]        NaN           [a, b, c]
2  [a, b, c]     [d, e]     [a, b, c, d, e]

时间

df = pd.DataFrame({'colA': [['a','b','c']] * 3,  'colB':[['a','b','c'], np.nan, ['d','e']]})
#[30000 rows x 2 columns]
df = pd.concat([df]*10000).reset_index(drop=True)
#print (df)

In [62]: %timeit df['total_str'] = df['colA'].combine_first(pd.Series([[]], index=df.index)) + df['colB'].combine_first(pd.Series([[]], index=df.index))
100 loops, best of 3: 8.1 ms per loop

In [63]: %timeit df['total_str1'] = df['colA'].fillna(pd.Series([[]], index=df.index)) + df['colB'].fillna(pd.Series([[]], index=df.index))
100 loops, best of 3: 9.1 ms per loop

In [64]: %timeit df['total_str2'] = df.applymap(lambda x: [] if x is np.nan else x).apply(lambda x: x.colA+x.colB, axis=1)
1 loop, best of 3: 960 ms per loop

您可以像这样在熊猫中添加列:

dataframe['total_str'] = dataframe['colA'] + dataframe['colB']

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