Dynamically filtering a pandas dataframe

An alternative to @coldspeed 's version:

conditions = None
for key, val in limit_dic.items():
    cond = df[key] > val
    if conditions is None:
        conditions = cond
    else:
        conditions = conditions & cond
print(df[conditions])

An alternative to both posted, that may or may not be more pythonic:

import pandas as pd
import operator
from functools import reduce

df = pd.DataFrame({"A": [6, 2, 10, -5, 3],
                   "B": [2, 5, 3, 2, 6],
                   "C": [-5, 2, 1, 8, 2]})

limits_dic = {"A": 0, "B": 2, "C": -1}

# equiv to [df['A'] > 0, df['B'] > 2 ...]
loc_elements = [df[key] > val for key, val in limits_dic.items()]

df = df.loc[reduce(operator.and_, loc_elements)]

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.

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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

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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

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For information on the pd.eval() family of functions, their features and use cases, please visit Dynamic Expression Evaluation in pandas using pd.eval().