Pandas DataFrame : How to select rows on multiple conditions?

Question

I'm trying to select rows of a DataFrame based on a list of conditions that needs to be all satisfied. Those conditions are stored in a dictionary and are of the form {column: max-value}.

This is an example: dict = {'name': 4.0, 'sex': 0.0, 'city': 2, 'age': 3.0}

I need to select all DataFrame rows where the corresponding attribute is less than or equal to the corresponding value in the dictionary.

I know that for selecting rows based on two or more conditions I can write:

rows = df[(df[column1] <= dict[column1]) & (df[column2] <= dict[column2])]

My question is, how can I select rows that matches the conditions present in a dictionary in a Pythonic way? I tried this way,

keys = dict.keys() 
rows = df[(df[kk] <= dict[kk]) for kk in keys]

but it gives me an error = "[ expected" that doesn't disappear even putting the [ symbol.

Answer 1

we can use DataFrame.query() method like this:

In [109]: dct = {'name': 4.0, 'sex': 0.0, 'city': 2, 'age': 3.0}

In [110]: qry = ' and '.join(['{} <= {}'.format(k,v) for k,v in dct.items()])

In [111]: qry
Out[111]: 'name <= 4.0 and sex <= 0.0 and city <= 2 and age <= 3.0'

In [112]: df.query(qry)
...

Answer 2

You could take advantage of Pandas' automatic axis alignment. Given a DataFrame with columns ['age', 'city', 'name', 'sex'] and a Series with the same index, you can compare every entry in the DataFrame against the corresponding value in the Series using

In [29]: df < pd.Series(dct)
Out[29]: 
      age   city   name    sex
0   False  False  False  False
1   False  False  False  False
2    True  False  False  False
3   False   True  False  False
4    True   True   True  False
...

Then you can find the rows which are all True using

mask = (df <= pd.Series(dct)).all(axis=1)

and select those rows with df.loc[mask, :]. For example,

import numpy as np
import pandas as pd
np.random.seed(2017)
N = 300
df = pd.DataFrame({'name':np.random.randint(10, size=N),
                   'sex':np.random.randint(2, size=N),
                   'city':np.random.randint(10, size=N),
                   'age':np.random.randint(10, size=N)})
dct = {'name': 4.0, 'sex': 0.0, 'city': 2, 'age': 3.0}

mask = (df <= pd.Series(dct)).all(axis=1)
print(df.loc[mask, :])

yields

     age  city  name  sex
7      3     2     0    0
10     1     2     4    0
150    1     2     4    0
188    2     2     2    0
198    3     2     3    0
229    1     2     0    0
254    1     2     2    0
275    3     2     1    0
276    0     1     4    0
299    3     1     2    0