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c3 = pd.Series(['China', 'US'])
df[df['countries'].isin(c1)]
# applying filter function 
df.filter(["Name", "College", "Salary"]) 

# importing pandas as pd 
import pandas as pd 
  
# Creating the dataframe  
df = pd.read_csv("nba.csv") 
  
# Using regular expression to extract all 
# columns which has letter 'a' or 'A' in its name. 
df.filter(regex ='[aA]') 
star

Sat Oct 31 2020 00:55:40 GMT+0000 (UTC) https://stackoverflow.com/questions/19960077/how-to-filter-pandas-dataframe-using-in-and-not-in-like-in-sql

#pandas #isin #filter
star

Sat Oct 31 2020 00:40:59 GMT+0000 (UTC) https://www.geeksforgeeks.org/python-pandas-dataframe-filter/

#pandas #filter #column
star

Sat Oct 31 2020 00:38:58 GMT+0000 (UTC) https://www.geeksforgeeks.org/python-pandas-dataframe-filter/

#pandas #filter

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