Query/Filter a pandas df using a dict of lists

My problem

I have a dict d that can be of varying length consisting of the following format:

d = {
  "foo": [
    50,
    100
  ],
  "bar": [
    5,
    10
  ]
}

Where the key is a column name and the value is a two length list for the min and max value of said column to filter a datframe df on. Thus, given the input above I’d like to filter df.foo between 50-100 and df.bar between 5-10.

What I have tried

Of course, I could just hard code it like so:

df.loc[(df.list(d.items())[0][0] > list(d.items())[0][1][0]) & (df.list(d.items())[0][0] < list(d.items())[0][1][1]) ...]

etc, but the number of keys (columns to filter on) may vary and also this just incredibly ugly code. Is there a cleaner/vectorized way to do this?

Context

I am building a streamlit app where a user can create n min max filters on a dataframe, and the format listed above is the format streamlit’s slider returns

>Solution :

IIUC, one way using pandas.Series.between:

# sample
import numpy as np
np.random.seed(1234)

df = pd.DataFrame({"foo": np.random.random(10) * 100,
                   "bar": np.random.random(10) * 10})

         foo       bar
0  19.151945  3.578173
1  62.210877  5.009951
2  43.772774  6.834629
3  78.535858  7.127020
4  77.997581  3.702508
5  27.259261  5.611962
6  27.646426  5.030832
7  80.187218  0.137684
8  95.813935  7.728266
9  87.593263  8.826412

Code:

new_df = df[np.logical_and(*[df[k].between(*v) for k, v in d.items()])]
print(new_df)

Output:

         foo       bar
1  62.210877  5.009951
3  78.535858  7.127020
8  95.813935  7.728266
9  87.593263  8.826412