How to filter the rows of a dataframe based on the presence of the column values in a separate dataframe and append columns from the second dataframe

You can do this in two steps:

1. Mask your dataframe1 such that it only contains rows where both fruit and vegetable exits in dataframe2.Item

2. Use Series.map to obtain the values associated with the remaining rows, and add them together to get the combination price.

# Make our df2 information easier to work with. 
#  It is now a Series whose index is the Item and values are the prices. 
#  This allows us to work with it like a dictionary
>>> item_pricing = df2.set_index("Item")["Price/lb"]
>>> items = item_pricing.index

# get rows where BOTH fruit is in items & Vegetable is in items
>>> mask = df1["Fruit"].isin(items) & df1["Vegetable"].isin(items)
>>> subset = df1.loc[mask].copy()  # .copy() tells pandas we want this subset to be independent of the larger dataframe
>>> print(subset)
   Fruit Vegetable
0  Mango   Spinach
3  Peach  Zucchini

# On each column (fruit and vegetable) use .map to obtain the price of those items
#  then sum those columns together into a single price
>>> subset["combo_price"] = subset.apply(lambda s: s.map(item_pricing)).sum(axis=1)
>>> print(subset)
   Fruit Vegetable  combo_price
0  Mango   Spinach            3
3  Peach  Zucchini            3

All together with no comments:

item_pricing = df2.set_index("Item")["Price/lb"]
items = item_pricing.index

mask = df1["Fruit"].isin(items) & df1["Vegetable"].isin(items)
subset = df1.loc[mask].copy()
subset["combo_price"] = subset.apply(lambda s: s.map(item_pricing)).sum(axis=1)

A combination of melt,merge and unstack:

(df1[(df1['Fruit'].isin(df2['Item'])) & (df1['Vegetable'].isin(df2['Item']))]
    .reset_index()
    .melt(id_vars = 'index',value_vars = ['Fruit','Vegetable'])
    .merge(df2,left_on='value',right_on = 'Item')
    .drop(columns = 'Item')
    .set_index(['index','variable']).unstack(level = 1)
    .transform(lambda g: g.assign(Combination_Price=g.xs('Price/lb',axis=1,level=0).sum(axis=1)))
)

produces combination price and breakdown by ingredient, just in case

            value               Price/lb           Combination_Price
variable    Fruit   Vegetable   Fruit   Vegetable   
index                   
0           Mango   Spinach     2       1         3
3           Peach   Zucchini    2       1         3

You can do this with two inner joins, as shown below. The final result is contained in df3.

df_temp = pd.merge(df1, df2, left_on='Fruit', right_on='Item', how='inner')
df3 = pd.merge(df_temp, df2, left_on='Vegetable', right_on='Item', how='inner')
df3['Combined price'] = df3['Price/lb_x'] + df3['Price/lb_y']
df3.drop(columns = ['Item_x','Price/lb_x','Item_y','Price/lb_y'], inplace = True)