Pandas

df['column_name'] = pd.to_datetime(df['column_name'])
# new version
df.groupby(pd.Grouper(key='column_name', freq="M")).mean().plot()
df.pct_change(axis=0,fill_method='bfill')
# 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]') 
# applying filter function 
df.filter(["Name", "College", "Salary"]) 

c3 = pd.Series(['China', 'US'])
df[df['countries'].isin(c1)]

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