Iterating across multiple columns in Pandas DF and slicing dynamically

问题

TLDR: How to iterate across all options of multiple columns in a pandas dataframe without specifying the columns or their values explicitly?

Long Version: I have a pandas dataframe that looks like this, only it has a lot more features or drug dose combinations than are listed here. Instead of just 3 types of features, it could have something like 70...:

> dosage_df

First Score Last Score  A_dose  B_dose  C_dose
22          28          1       40      130
55          11          2       40      130
15          72          3       40      130
42          67          1       90      130
90          74          2       90      130
87          89          3       90      130
14          43          1       40      700
12          61          2       40      700
41          5           3       40      700

Along with my data frame, I also have a python dictionary with the relevant ranges for each feature. The keys are the feature names, and the different values which it can take are the keys:

> dict_of_dose_ranges = {'A_dose': [1, 2, 3], 'B_dose': [40, 90], 'C_dose': [130,700]}

For my purposes, I need to generate a particular combination (say A_dose = 1, B_dose = 90, and C_dose = 700), and based on those settings take the relevant slice out of my dataframe, and do relevant calculations from that smaller subset, and save the results somewhere.

I need to do this for ALL possible combinations of ALL of my features (far more than the 3 which are here, and which will be variable in the future).

In this case, I could easily pop this into SkLearn's Parameter grid, generate the options:

> from sklearn.grid_search import ParameterGrid
> all_options = list(ParameterGrid(dict_of_dose_ranges)) 
> all_options

and get:

[{'A_dose': 1, 'B_dose': 40, 'C_dose': 130},
 {'A_dose': 1, 'B_dose': 40, 'C_dose': 700},
 {'A_dose': 1, 'B_dose': 90, 'C_dose': 130},
 {'A_dose': 1, 'B_dose': 90, 'C_dose': 700},
 {'A_dose': 2, 'B_dose': 40, 'C_dose': 130},
 {'A_dose': 2, 'B_dose': 40, 'C_dose': 700},
 {'A_dose': 2, 'B_dose': 90, 'C_dose': 130},
 {'A_dose': 2, 'B_dose': 90, 'C_dose': 700},
 {'A_dose': 3, 'B_dose': 40, 'C_dose': 130},
 {'A_dose': 3, 'B_dose': 40, 'C_dose': 700},
 {'A_dose': 3, 'B_dose': 90, 'C_dose': 130},
 {'A_dose': 3, 'B_dose': 90, 'C_dose': 700}]

This is where I run into problems:

Problem #1) I can now iterate across all_options, but I'm not sure how to now SELECT out of my dosage_df from each of the dictionary options (i.e. {'A_dose': 1, 'B_dose': 40, 'C_dose': 130}) WITHOUT doing it explicitly.

In the past, I could do something like:

dosage_df[(dosage_df.A_dose == 1) & (dosage_df.B_dose == 40) & (dosage_df.C_dose == 130)]

First Score Last Score  A_dose  B_dose  C_dose
0           22          28      140     130

But now I'm not sure what to put inside the brackets to slice it dynamically...

dosage_df[?????]

Problem #2) When I actually enter in my full dictionary of features with their respective ranges, I get an error because it deems it as having too many options...

from sklearn.grid_search import ParameterGrid
all_options = list(ParameterGrid(dictionary_of_features_and_ranges)) 
all_options

---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-138-7b73d5e248f5> in <module>()
      1 from sklearn.grid_search import ParameterGrid
----> 2 all_options = list(ParameterGrid(dictionary_of_features_and_ranges))
      3 all_options

OverflowError: long int too large to convert to int

I tried a number of alternate approaches including using double while loops, a tree / recursion method from here, another recursion method from here, but it wasn't coming together.... Any help is much appreciated.

回答1:

You can use itertools.product to generate all possible dosage combinations, and DataFrame.query to do the selection:

from itertools import product

for dosage_comb in product(*dict_of_dose_ranges.values()):
    dosage_items = zip(dict_of_dose_ranges.keys(), dosage_comb)
    query_str = ' & '.join('{} == {}'.format(*x) for x in dosage_items)
    sub_df = dosage_df.query(query_str)

    # Do Stuff...

回答2:

What about using the underlying numpy array and some boolean logic to build an array containing only the lines you want ?

dosage_df = pd.DataFrame((np.random.rand(40000,10)*100).astype(np.int))
dict_of_dose_ranges={3:[10,11,12,13,15,20],4:[20,22,23,24]}

#combined_doses will be bool array that will select all the lines that match the wanted combinations of doses

combined_doses=np.ones(dosage_df.shape[0]).astype(np.bool)
for item in dict_of_dose_ranges.items():
    #item[0] is the kind of dose
    #item[1] are the values of that kind of dose

    next_dose=np.zeros(dosage_df.shape[0]).astype(np.bool)

    #we then iterate over the wanted values
    for value in item[1]:
        # we select and "logical or" all lines matching the values
        next_dose|=(dosage_df[item[0]] == value)
    # we "logical and" all the kinds of dose
    combined_doses&=next_dose

print(dosage_df[combined_doses])

来源：https://stackoverflow.com/questions/39378510/iterating-across-multiple-columns-in-pandas-df-and-slicing-dynamically

标签

python

pandas

machine-learning

scikit-learn

grid-search