Selecting data from Pandas dataframe based on criteria stored in a dict

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I have a Pandas dataframe that contains a large number of variables. This can be simplified as:

tempDF = pd.DataFrame({ 'var1': [12,12,12,12,45,45,45,51,51,51],
                        'var2': ['a','a','b','b','b','b','b','c','c','d'],
                        'var3': ['e','f','f','f','f','g','g','g','g','g'],
                        'var4': [1,2,3,3,4,5,6,6,6,7]})

If I wanted to select a subset of the dataframe (e.g. var2='b' and var4=3), I would use:

tempDF.loc[(tempDF['var2']=='b') & (tempDF['var4']==3),:]

However, is it possible to select a subset of the dataframe if the matching criteria are stored within a dict, such as:

tempDict = {'var2': 'b','var4': 3}

It's important that the variable names are not predefined and the number of variables included in the dict is changeable.

I've been puzzling over this for a while and so any suggestions would be greatly appreciated.

pandas dataframe python
4 Answers

Here's a function I have in my personal utils which accepts single values or lists to subset on:

def subsetdict(df, sdict):
    subsetter_list = [df[i].isin([j]) if not isinstance(j, list) else df[i].isin(j) for i, j in sdict.items()]
    subsetter = pd.concat(subsetter_list, axis=1).all(1)
    return df.loc[subsetter, :]
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