熊貓 - 檢查數據幀列是否包含字典中的鍵值對

Question

此問題與我發佈的另一個問題有關。 Pandas - check if a string column in one dataframe contains a pair of strings from another dataframe

我的目標是檢查數據框的兩個不同列是否包含一對字符串值，如果滿足條件，則提取其中一個值。

我有兩個dataframes這樣的：

df1 = pd.DataFrame({'consumption':['squirrelate apple', 'monkey likesapple', 
            'monkey banana gets', 'badger/getsbanana', 'giraffe eats grass', 'badger apple.loves', 'elephant is huge', 'elephant/eats/', 'squirrel.digsingrass'], 
        'name': ['apple', 'appleisred', 'banana is tropical', 'banana is soft', 'lemon is sour', 'washington apples', 'kiwi', 'bananas', 'apples']}) 

df2 = pd.DataFrame({'food':['apple', 'apple', 'banana', 'banana'], 'creature':['squirrel', 'badger', 'monkey', 'elephant']}) 

In [187]:df1 
Out[187]: 
      consumption    name 
0  squirrelate apple    apple 
1  monkey likesapple   appleisred 
2 monkey banana gets banana is tropical 
3  badger/getsbanana  banana is soft 
4 giraffe eats grass  lemon is sour 
5 badger apple.loves washington apples 
6  elephant is huge    kiwi 
7  elephant/eats/    bananas 
8 squirrel.digsingrass    apples 

In[188]: df2 
Out[188]: 
    creature food 
0 squirrel apple 
1 badger apple 
2 monkey banana 
3 elephant banana 

我想要做的就是測試，如果「蘋果」發生在df1['name']和「松鼠」在df1['consumption']發生，如果兩個條件都滿足，那麼提取「松鼠」從df1['consumption']轉換爲新列df['creature']。結果應該是這樣的：

Out[189]: 
      consumption creature    name 
0  squirrelate apple squirrel    apple 
1  monkey likesapple  NaN   appleisred 
2 monkey banana gets monkey banana is tropical 
3  badger/getsbanana  NaN  banana is soft 
4 giraffe eats grass  NaN  lemon is sour 
5 badger apple.loves badger washington apples 
6  elephant is huge  NaN    kiwi 
7  elephant/eats/ elephant    bananas 
8 squirrel.digsingrass  NaN    apples 

如果沒有配對值約束，我可以做喜歡的事很簡單：

np.where((df1['consumption'].str.contains(<creature_string>, case = False)) & (df1['name'].str.contains(<food_string>, case = False)), df['consumption'].str.extract(<creature_string>), np.nan) 

但我必須檢查對，所以我試圖讓一個字典食物鍵和動物，因爲值，則會使所有的生物的字符串VAR對於給定食健，尋找那些使用str.contains：

unique_food = df2.food.unique() 
food_dict = {elem : pd.DataFrame for elem in unique_food} 
for key in food_dict.keys(): 
    food_dict[key] = df2[:][df2.food == key] 

# create key:value pairs of food key and creature strings 
food_strings = {} 
for key, values in food_dict.items(): 
    food_strings.update({key: '|'.join(map(str, list(food_dict[key]['creature'].unique())))}) 

In[199]: food_strings 
Out[199]: {'apple': 'squirrel|badger', 'banana': 'monkey|elephant'} 

問題是，當我現在嘗試pply str.contains：

for key, value in food_strings.items(): 
    np.where((df1['name'].str.contains('('+food_strings[key]+')', case = False)) & 
      (df1['consumption'].str.contains('('+food_strings[value]+')', case = False)), df1['consumptions'].str.extract('('+food_strings[value]+')'), np.nan) 

我得到一個KeyError:。

--------------------------------------------------------------------------- 
KeyError         Traceback (most recent call last) 
<ipython-input-62-7ab718066040> in <module>() 
     1 for key, value in food_strings.items(): 
     2  np.where((df1['name'].str.contains('('+food_strings[key]+')', case = False)) & 
----> 3    (df1['consumption'].str.contains('('+food_strings[value]+')', case = False)), df1['consumption'].str.extract('('+food_strings[value]+')'), np.nan) 

KeyError: 'squirrel|badger' 

當我只是儘量只值，而不是關鍵，它爲第一個鍵：值對，但不是第二：

for key in food_strings.keys(): 
    df1['test'] = np.where(df1['consumption'].str.contains('('+food_strings[key]+')', case =False), 
           df1['consumption'].str.extract('('+food_strings[key]+')', expand=False), 
           np.nan) 

df1 
Out[196]: 
      consumption    name  test 
0  squirrelate apple    apple squirrel 
1  monkey likesapple   appleisred  NaN 
2 monkey banana gets banana is tropical  NaN 
3  badger/getsbanana  banana is soft badger 
4 giraffe eats grass  lemon is sour  NaN 
5 badger apple.loves washington apples badger 
6  elephant is huge    kiwi  NaN 
7  elephant/eats/    bananas  NaN 
8 squirrel.digsingrass    apples squirrel 

我得到的那些匹配蘋果，松鼠|獾，但錯過了香蕉：猴子。

有人可以幫忙嗎？

Answer 1

d1 = df1.dropna() 
d2 = df2.dropna() 

sump = d1.consumption.values.tolist() 
name = d1.name.values.tolist() 
cret = d2.creature.values.tolist() 
food = d2.food.values.tolist() 

check = np.array(
    [ 
     [c in s and f in n for c, f in zip(cret, food)] 
     for s, n in zip(sump, name) 
    ] 
) 

# create a new series with the index of `d1` where we dropped na 
# then reindex with `df1.index` prior to `assign` 
test = pd.Series(check.dot(d2[['creature']].values).ravel(), d1.index) 
test = test.reindex(df1.index, fill_value='') 
df1.assign(test=test) 

      consumption    name  test 
0  squirrelate apple    apple squirrel 
1  monkey likesapple   appleisred   
2 monkey banana gets banana is tropical monkey 
3  badger/getsbanana  banana is soft   
4 giraffe eats grass  lemon is sour   
5 badger apple.loves washington apples badger 
6  elephant is huge    kiwi   
7  elephant/eats/    bananas elephant 
8 squirrel.digsingrass    apples squirrel