Match Column Values To Dict

I have a dict and a dataframe like the examples v and df below. I want to search through the items in df and return the item that has the maximum number of field values in common

Solution 1:

Create one line DataFrame and merge with original:

a = pd.DataFrame(v, index=[0]).merge(df)['item']
print (a)
0    3
Name: item, dtype: int64

Another solution with query, but if strings values of dict is necessary add another ":

v1 = {k: '"{}"'.format(v) if isinstance(v, str) else v for k, v in v.items()}
print (v1)
{'size': 1, 'color': '"red"'}

df = df.query(' & '.join(['{}=={}'.format(i,j) for i, j in v1.items()]))['item']
print (df)
1    3
Name: item, dtype: int64

In output are possible 3 ways - Series with more values, one value or empty, so helper function was created:

def get_val(v):
    x = pd.DataFrame(v, index=[0]).merge(df)['item']
    if x.empty:
        return 'Not found'
    elif len(x) == 1:
        return x.values[0]
    else:
        return x.values.tolist()

print (get_val({'size':1,'color':'red'}))
3

print (get_val({'size':10,'color':'red'}))
Not found

print (get_val({'color':'red'}))
[2, 3]

Solution 2:

An alternative solution is to work with dictionaries instead of dataframes:

v = {'size': 1, 'color': 'red'}

match_count = {}

fields = df.columns[1:]

for k, value in df.to_dict(orient='index').items():
    match_count[value['item']] = sum(value[i] == v[i] for i in fields & v.keys())

Result

print(match_count)
# {2: 1, 3: 2}

res = max(match_count.items(), key=lambda x: x[1])

print(res)
# (3, 2)