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Pandas data selection based on criteria stored in dict

I have a Pandas framework 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 data frame if the matching criteria is stored inside a dict, for example:

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

It is important that variable names are not predefined, and the number of variables included in the dict is changed.

I was a little puzzled by this, so any suggestions would be greatly appreciated.

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3 answers

, , dataframe 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

:

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
+1

. .

df = tempDF
d = tempDict

# `repr` returns the string representation of an object.    
>>> df[eval(" & ".join(["(df['{0}'] == {1})".format(col, repr(cond)) 
       for col, cond in d.iteritems()]))]
   var1 var2 var3  var4
2    12    b    f     3
3    12    b    f     3

, eval :

conditions = " & ".join(["(df['{0}'] == {1})".format(col, repr(cond)) 
       for col, cond in d.iteritems()])

>>> conditions
"(df['var4'] == 3) & (df['var2'] == 'b')"

>>> eval(conditions)
0    False
1    False
2     True
3     True
4    False
5    False
6    False
7    False
8    False
9    False
dtype: bool

:

>>> eval(" & ".join(["(df['{0}'] == {1})".format(col, repr(cond)) 
                      for col, cond in d.iteritems()]))
d = {'var2': ('==', "'b'"),
     'var4': ('>', 3)}

>>> df[eval(" & ".join(["(df['{0}'] {1} {2})".format(col, cond[0], cond[1]) 
       for col, cond in d.iteritems()]))]
   var1 var2 var3  var4
4    45    b    f     4
5    45    b    g     5
6    45    b    g     6

query:

qry = " & ".join('{0} {1} {2}'.format(k, cond[0], cond[1]) for k, cond in d.iteritems())

>>> qry
"var4 > 3 & var2 == 'b'"

>>> df.query(qry)
   var1 var2 var3  var4
4    45    b    f     4
5    45    b    g     5
6    45    b    g     6
+2

tempDict

In [25]: tempDF.loc[pd.np.all([tempDF[k] == tempDict[k] for k in tempDict], axis=0), :]
Out[25]:
   var1 var2 var3  var4
2    12    b    f     3
3    12    b    f     3

query .

In [33]: tempDF.query(' & '.join(['{0}=={1}'.format(k, repr(v)) for k, v in tempDict.iteritems()]))
Out[33]:
   var1 var2 var3  var4
2    12    b    f     3
3    12    b    f     3

In [34]: ' & '.join(['{0}=={1}'.format(k, repr(v)) for k, v in tempDict.iteritems()])
Out[34]: "var4==3 & var2=='b'"
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