Python's equivalent to R's grepl and dplyr filter [duplicate]

Question

I want to find a way to recreate the same command code from R in Python using something similar as the dplyr package. In R I would do this:

 library(dplyr) df <- data.frame(Countries=c('Brazil','Venezuela','Brazil, Colombia, Paraguay','Argentina','Peru','Andorra,Argentina,Chile,Uruguay'), Code=c(1,2,3,4,5,6)) df %>% filter(grepl('(Brazil|Argentina)',Countries))

Or even:

 a=strsplit(as.character(df$Countries),',') a=lapply(a,FUN=function(t) gsub(" ","",t)) ele=unlist(lapply(a,FUN=function(t) any(t%in%c('Brazil','Argentina')))) (df[ele,])

The output that I want:

 Countries Code 1 Brazil 1 2 Brazil, Colombia, Paraguay 3 3 Argentina 4 4 Argentina,Chile,Uruguay 6

In Python I've tried this:

import pandas as pd df = pd.DataFrame(dict(Countries=['Brazil','Venezuela','Brazil, Colombia, Paraguay','Argentina','Peru','Andorra,Argentina,Chile,Uruguay'], Code=[1,2,3,4,5,6])) list_=['Brazil','Argentina'] print(df.loc[df['Countries'].isin(list_)])

But the output looks like:

 Countries Code 0 Brazil 1 3 Argentina 4

Cameron Riddell · Accepted Answer · 2020-11-09 14:09:46Z

Seems like you're looking for the .str extension of pd.Series with an object dtype (essentially you can call pd.Series.str.... for a subset of pandas functions specifically for dealing with regular expression and other string based operations- however this only works if the array is of dtype "object".

mask = df["Countries"].str.contains("Brazil|Argentina") subset = df.loc[mask] print(subset) Countries Code 0 Brazil 1 2 Brazil, Colombia, Paraguay 3 3 Argentina 4 5 Andorra,Argentina,Chile,Uruguay 6

A neat way you can use this is by using the .join function on your list_ variable to join it into a single string usable by regular expression matching patterns.

list_=['Brazil','Argentina'] pattern = "|".join(list_) # Now we have "Brazil|Argentina" as a string mask = df["Countries"].str.contains(pattern) subset = df.loc[mask] # Same subset as the previous example

See the docs and other methods besides .str.contains at the documentation https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.html

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