如何使用 Pandas 使用来自第二个数据帧但依赖于当前数据帧中不同现有列的值填充新列

Question

I created an informational table, which is very long (think nearly 100 observations or just under). I have a main table (around 70K onservations) where I need to create a new column and fill it based on matching values between my two dataframes but I need to fill the new column the cells with data from my information table.

I created a small dataset but my real dataset (which I can't share because my prof signed a non-diclosure) has like 70K observations.

data_1 (info table)

 Animal          Food
  Dog            Stake
  Cat            Fish
  Rabbit         Carrot

data 2 (original table)

 Name     Animal     Age
 Binxy    Dog         1
 Al       Rabbit      4
 Sam      Dog         11
 Dexter   Cat         9
 Dory     Hamster     6
 Chloe    Cat         5

desired data_frame

 Name     Animal     Age    Fed [new column]
 Binxy    Dog         1     Stake
 Al       Rabbit      4     Carrot
 Sam      Dog         11    Stake
 Dexter   Cat         9     Fish
 Dory     Hamster     6     NaN
 Chloe    Cat         5     Fish

my gut tells me it probably has to do with iloc or loc and using some bloonean values. Verbally I would say:

1. If data_2["Animal"] == data_2["Animal"]
2. Then fill new column data_2["Fed"] with corresponding food found in data_1["Food"]

I think merge might work but I'm not certain if it would fill it for every matching value. I'm not very good at merges because I struggle to understand the join feature but I don't think values would be inserted where I need them because my dataframes are not the same length.

edit: I've don't manually before but I had like only two or three values in fill in and I don't want to manually do this for 100. But this is as far as my basic understanding goes.

 New_Categorized_Full.loc[
      (New_Categorized["Produce"] == "Apple"), "Fruit"] = "Fuji"

Answer 1

Use series.map

df2['Fed'] = df2.Animal.map(dict(df1[['Animal','Food']].to_numpy()))


Out[10]:
     Name   Animal  Age     Fed
0   Binxy      Dog    1   Stake
1      Al   Rabbit    4  Carrot
2     Sam      Dog   11   Stake
3  Dexter      Cat    9    Fish
4    Dory  Hamster    6     NaN
5   Chloe      Cat    5    Fish