在数据框列中找到字典的值并对其进行修改 [英] Find a value of a dictionary in dataframe column and modify it

问题描述

我现在正在处理DataFrames和Dictionary，但是我遇到了问题， 我有一本字典"水果"

I dealing with DataFrames and Dictionaries now, and i have a problem, I have a Dictionary "Fruits"

{BN:'Banana', LM:'Lemon', AP:'Apple' ..... etc}

还有一个DataFrame-" Stock ":

And a DataFrame- "Stock":

   Fruit             Price
0  Sweet Mango           1
1  Green Apple           2
2  Few blue Banana       0
3  Black Banana          5

我想做下一件事情: 用Fruits.values()替换Stock['Fruit']中的所有值，方法是: 如果水果"中的值出现在Stock['Fruit']行中，则它将通过以下方式替换:

I wand to do the next thing: replace all the values from Stock['Fruit'] with the Fruits.values() this way: if the value from Fruits appears in the Stock['Fruit'] row it will be replaced this way:

很少有蓝色香蕉 ---> 香蕉

Few blue Banana ---> Banana

黑色香蕉 ---> 香蕉

现在，DataFrame Stock 会看起来像这样:

now the DataFrame Stock will look this way:

   Fruit             Price
0  Sweet Mango           1
1  Green Apple           2
2  Banana                0
3  Banana                5

我找到了不同的代码来替换或检查Dicitionary中的值是否出现在DataFrame中

I found different codes to replace or to check if values from the Dicitionary appears in the DataFrame

Stock['Fruit'] = Stock.Fruit.map(Fruits)

if (Fruits.values() in Stock['Fruit'] for item in Stock)

any('Mango' in Stock['Fruit'] for index,item in Stock.iterrows())

但是我找不到任何可以更新DataFrame行的东西

But i cant find any thing to update the rows of the DataFrame

推荐答案

使用字符串方法确定条件并提取所需的值，

Use string methods for condition and extracting required values,

pat = r'({})'.format('|'.join(d.values()))
cond = df['Fruit'].str.contains('|'.join(d.values()))
df.loc[cond, 'Fruit'] = df['Fruit'].str.extract((pat), expand = False)

    Fruit       Price
0   Sweet Mango 1
1   Apple       2
2   Banana      0
3   Banana      5

按照@ user3483203的建议，一旦提取出图案，就可以用原始的填充缺失的值.

As @user3483203 suggested, you can fill the missing values with original once the pattern is extracted.

df['Fruit'] = df['Fruit'].str.extract(pat).fillna(df.Fruit)

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