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如何在pandas中的列中间添加字符串

pandas python

Claudiu Creanga · 技术社区 · 7 年前

我在寻找一个解决办法不包含一个.apply或lambda函数,该函数在列表中循环并在所需索引处添加字符串。我有这样一个列,有很多条目:

df = pd.DataFrame(["1:77631829:-:1:77641672:-"], columns=["position"])

    position
0   1:77631829:-:1:77641672:-

    position
0   chr1:77631829:-:chr1:77641672:-

所以在第三个冒号的开头和后面插入“chr” :

我本以为这样做可以,但insert并没有在系列中实现:

"chr" + df["position"].str.split(":").insert(3, "chr").str.join(":")

"chr" + df["position"].str.split(":").str[:3].str.join(":") + "chr" + df["position"].str.split(":").str[3:].str.join(":")

1 回复 | 直到 7 年前

jezrael 7 年前

3 : ,然后提取列表的头和尾-连接头,添加 ch 中国 最后一个附加到列表 L :

df = pd.DataFrame(["1:77631829:-:1:77641672:-","1:77631829:-:1:77641672:-"], 
                  columns=["position"])
print (df)
                    position
0  1:77631829:-:1:77641672:-
1  1:77631829:-:1:77641672:-

L = []
for x in df["position"]:
    *i, j = x.split(':', 3)
    L.append(("chr" + ':'.join(i) + "chr" + j))

df['new'] = L
print (df)
                    position                             new
0  1:77631829:-:1:77641672:-  chr1:77631829:-chr1:77641672:-
1  1:77631829:-:1:77641672:-  chr1:77631829:-chr1:77641672:-

带注释的黑客解决方案:

'chr' + df['position'].str.replace('-:', '-:chr')

使用列表理解和f字串更快:

df['new'] = [f"ch{x.replace('-:', '-:chr')}" for x in df['position']]

性能

df = pd.DataFrame(["1:77631829:-:1:77641672:-","1:77631829:-:1:77641672:-"], 
                  columns=["position"])

#[20000 rows x 1 columns]
df = pd.concat([df] * 10000, ignore_index=True)

In [226]: %%timeit
     ...: L = []
     ...: for x in df["position"]:
     ...:     *i, j = x.split(':', 3)
     ...:     L.append(("chr" + ':'.join(i) + "chr" + j))
     ...: 
     ...: df['new1'] = L
     ...: 
18.9 ms Â± 1.25 ms per loop (mean Â± std. dev. of 7 runs, 100 loops each)

In [227]: %%timeit
     ...: df['new2'] = "chr" + df["position"].str.split(":").str[:3].str.join(":") + "chr" + df["position"].str.split(":").str[3:].str.join(":")
     ...: 
50.8 ms Â± 1.2 ms per loop (mean Â± std. dev. of 7 runs, 10 loops each)

In [228]: %%timeit
     ...: df['new3'] = 'chr' + df['position'].str.replace('-:', '-:chr')
     ...: 
21.5 ms Â± 140 Âµs per loop (mean Â± std. dev. of 7 runs, 10 loops each)

In [229]: %%timeit
     ...: df['new4'] = [f"ch{x.replace('-:', '-:chr')}" for x in df['position']]
     ...: 
8.59 ms Â± 130 Âµs per loop (mean Â± std. dev. of 7 runs, 100 loops each)

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