在 Pandas DataFrame 中查找值的 VLOOKUP 等效函数
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VLOOKUP equivalent function to look up value in pandas DataFrame
提问by Alexis Eggermont
I have a pandas dataframe with the following structure:
我有一个具有以下结构的Pandas数据框：
DF_Cell, DF_Site
C1,A
C2,A
C3,B
C4,B
C5,B
And I have a very long loop (100 million iterations) in which I treat one by one strings that correspond to the "DF_Cell" column in the DataFrame (first loop iteration creates C1, second iteration creates C2, etc...).
而且我有一个很长的循环（1 亿次迭代），我在其中逐个处理对应于 DataFrame 中“DF_Cell”列的字符串（第一次循环迭代创建 C1，第二次迭代创建 C2，等等）。
I would like to lookup in the dataframe the DF_Site corresponding to the cell (DF_Cell) treated in the loop.
我想在数据框中查找与循环中处理的单元格 (DF_Cell) 相对应的 DF_Site。
One way I could think of was to put the treated cell in a one-cell DataFrame and then doing a left merge on it, but this is much too inefficient for such big data.
我能想到的一种方法是将处理过的单元格放在一个单元格的 DataFrame 中，然后对其进行左合并，但这对于如此大的数据来说效率太低了。
Is there a better way?
有没有更好的办法？
回答by Andy Hayden
Perhaps you want to set DF_Cell as the index*:
也许您想将 DF_Cell 设置为索引*：
In [11]: df = pd.read_csv('foo.csv', index_col='DF_Cell')
# or df.set_index('DF_Cell', inplace=True)
In [12]: df
Out[12]:
DF_Site
DF_Cell
C1 A
C2 A
C3 B
C4 B
C5 B
You can then refer to the row, or specific entry, using loc:
然后，您可以使用 loc 引用该行或特定条目：
In [13]: df.loc['C1']
Out[13]:
DF_Site A
Name: C1, dtype: object
In [14]: df.loc['C1', 'DF_Site']
Out[14]: 'A'
*Assuming this has two columns, you could use squeeze=True.
*假设这有两列，您可以使用squeeze=True.
回答by Sean Geoffrey Pietz
I don't really understand what you mean in your first paragraph, but to be able to look up a field value by reference to the corresponding type in a different column, I agree with Alexis' example as the most idiomatic and efficient way to do it in pandas. However if this is really representative of your data structure you can just use a dict.
我不太明白你在第一段中的意思，但是为了能够通过引用不同列中的相应类型来查找字段值，我同意 Alexis 的例子，这是最惯用和最有效的方法它在Pandas中。但是，如果这真的代表了您的数据结构，您可以只使用 dict。
data = {'a': 1, 'b': 2, 'c': 3}
data['a']
# 2
map(lambda y: x[y]+1, ['c', 'b', 'a'])
# [4, 3, 2]
