正则表达式用 pandas dataframe 中的总和替换用逗号分隔的字符串

Question

I have a tab-separated data frame which looks like (for example):

   A                                 B                      C
gene1  AHX21832.1                        EEL39984.1,ARO60330.1  EEL39984.1
gene2  EEL39984.1,ARO60330.1             ARO60330.1             ARO60330.1
gene3  AYF09030.1,EEL37774.1,AQY42173.1  AQY42173.1             AQY42173.1

The following script work well on list:

values = ["AHX21832.1", "EEL39984.1,ARO60330.1", "AYF09030.1,EEL37774.1,AQY42173.1"]

script

How I can implement this script on my panda's data frame? Since there is no re.findall in pandas.

Answer 1

Take a look at https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.findall.html . It looks like it is possible to do do the equivalent of re.findall on a dataframe.

for column, data in df.iteritems():
    res = data.str.findall("[A-Z0-9]\.(\d+)")

So for the code you posted in your repl.it link, you could get the same results by doing:

import pandas as pd

values = pd.Series(["AHX21832.1",
"EEL39984.1,ARO60330.1",
"AYF09030.1,EEL37774.1,AQY42173.1"])

res = values.str.findall("[A-Z0-9]\.(\d+)")

for x in res:
    print("Found", x)
print("total", res.shape[0])