Pandas cheat sheet

Data Frame - cleaning column names

df.columns = df.columns.str.replace(r'\s+', '_') #This replaces ' ' with '_' in columns  

df.columns is of type index dence can be converted to string and cleaned up in a chained fashion

df.columns = df.columns.str.replace('.', '_').str.replace('(','').replace(')','')

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Data Frame - Efficiently iterating over all rows

for row in df.itertuples():
    print(f"{row.Col_1} : {row.Col_2}")

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Data Frame - Filtration

This Stack Overflow Link has in-depth analysis with performance plotted in a great amount of detail. Here are a few convenient ways:-

df[df['col'] == val] # Slows down with df length
df[df['col'].values == val] # np version scales up very well
df.query('col == val') # Scales up well

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Data Frame - Update all the values of a particular column

data frame before

df['Name'] = df['Name'].str[:5]   #Strip name to 5 char s only
df = df.assign(PassengerId = 420) #Update all Passenger's ID to 420 
df['Survived'] = df['Survived'].apply(lambda x: 'Fortunate' if x==1 else 'Unfortunate')  

data frame after

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Data Frame - Transform categorical features to numeric

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