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I get the same indexing results using these two methods: 

data.loc[data['loan_status']== 'Default'].head()

data[data['loan_status'].isin(['Default'])].head()

Is there an advantage of one over the other?

Also is there a reason why isin needs a ([]) parameter to work whereas most methods simply need a ()?

Andras Deak -- Слава Україні
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NimbleTortoise
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1 Answers1

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.isin allows you to supply a list of values to check. For instance, if you were looking for 'Default' or Defaulted, or something like that, you could say:

data[data['loan_status'].isin(['Default', 'Defaulted'])].head()

Whereas otherwise, you would have to give it multiple conditions, like:

data.loc[(data['loan_status'] == 'Default') | (data['loan_status'] == 'Defaulted')]
sacuL
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    To summarise, for a single value use `==` or `pd.Series.eq`, multiple values use `pd.Series.isin`. – jpp Aug 09 '18 at 00:16