Right now, I select multiple rows where certain columns have a particular value this way:
df.loc[(df['col1'] == val1) & (df['col2'] == val2)]
Is there a way I can do this programmatically, I provide the column key/value as a dict? Something like this:
def get_df(cols)
df.loc[ (df[k] == v) for k,v in cols.items() ]
But I'm not sure how to 'AND' the expressions. Any ideas?
You can create a Series from the dictionary and make the comparison:
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame(np.random.randint(0, 5, (100, 3)), columns = list("ABC"))
cols = {"A": 0, "B": 3, "C": 3}
df[(df == pd.Series(cols)).all(axis=1)]
Out:
A B C
94 0 3 3
Or, use np.logical_and with reduce:
df[np.logical_and.reduce([(df[k] == v) for k,v in cols.items()])]
A B C
94 0 3 3