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.
You could create mask for each condition using list comprehension and then join them by converting to dataframe and using all:
In [23]: pd.DataFrame([tempDF[key] == val for key, val in tempDict.items()]).T.all(axis=1)
Out[23]:
0 False
1 False
2 True
3 True
4 False
5 False
6 False
7 False
8 False
9 False
dtype: bool
Then you could slice your dataframe with that mask:
mask = pd.DataFrame([tempDF[key] == val for key, val in tempDict.items()]).T.all(axis=1)
In [25]: tempDF[mask]
Out[25]:
var1 var2 var3 var4
2 12 b f 3
3 12 b f 3