If you’re trying to build a dynamic query, there are easier ways. Here’s one using a list comprehension and str.join:
query = ' & '.join(['{}>{}'.format(k, v) for k, v in limits_dic.items()])
Or, using f-strings with python-3.6+,
query = ' & '.join([f'{k}>{v}' for k, v in limits_dic.items()])
print(query)
'A>0 & C>-1 & B>2'
Pass the query string to df.query, it’s meant for this very purpose:
out = df.query(query)
print(out)
A B C
1 2 5 2
2 10 3 1
4 3 6 2
What if my column names have whitespace, or other weird characters?
From pandas 0.25, you can wrap your column name in backticks so this works:
query = ' & '.join([f'`{k}`>{v}' for k, v in limits_dic.items()])
See this Stack Overflow post for more.
You could also use df.eval if you want to obtain a boolean mask for your query, and then indexing becomes straightforward after that:
mask = df.eval(query)
print(mask)
0 False
1 True
2 True
3 False
4 True
dtype: bool
out = df[mask]
print(out)
A B C
1 2 5 2
2 10 3 1
4 3 6 2
String Data
If you need to query columns that use string data, the code above will need a slight modification.
Consider (data from this answer):
df = pd.DataFrame({'gender':list('MMMFFF'),
'height':[4,5,4,5,5,4],
'age':[70,80,90,40,2,3]})
print (df)
gender height age
0 M 4 70
1 M 5 80
2 M 4 90
3 F 5 40
4 F 5 2
5 F 4 3
And a list of columns, operators, and values:
column = ['height', 'age', 'gender']
equal = ['>', '>', '==']
condition = [1.68, 20, 'F']
The appropriate modification here is:
query = ' & '.join(f'{i} {j} {repr(k)}' for i, j, k in zip(column, equal, condition))
df.query(query)
age gender height
3 40 F 5
For information on the pd.eval() family of functions, their features and use cases, please visit Dynamic Expression Evaluation in pandas using pd.eval().