在 pandas 数据框中选择多个列 [英] Selecting multiple columns in a pandas dataframe

问题描述

我在不同的列中都有数据，但是我不知道如何提取数据以将其保存在另一个变量中.

I have data in different columns but I don't know how to extract it to save it in another variable.

index  a   b   c
1      2   3   4
2      3   4   5

如何选择'a'，'b'并将其保存到df1?

How do I select 'a', 'b' and save it in to df1?

我尝试了

df1 = df['a':'b']
df1 = df.ix[:, 'a':'b']

似乎没有一个工作.

推荐答案

无法按照您尝试的方式对列名(字符串)进行切片.

The column names (which are strings) cannot be sliced in the manner you tried.

在这里，您有两种选择.如果从上下文中知道要切出哪些变量，则可以通过将列表传递给__getitem__语法([])来只返回那些列的视图.

Here you have a couple of options. If you know from context which variables you want to slice out, you can just return a view of only those columns by passing a list into the __getitem__ syntax (the []'s).

df1 = df[['a','b']]

或者，如果要对它们进行数字索引而不是按其名称进行索引(例如，您的代码应在不知道前两列的名称的情况下自动执行此操作)，则可以执行以下操作:

Alternatively, if it matters to index them numerically and not by their name (say your code should automatically do this without knowing the names of the first two columns) then you can do this instead:

df1 = df.iloc[:,0:2] # Remember that Python does not slice inclusive of the ending index.

此外，您应该熟悉Pandas对象与该对象副本的视图概念.上述方法中的第一种将在内存中返回所需子对象(所需切片)的新副本.

Additionally, you should familiarize yourself with the idea of a view into a Pandas object vs. a copy of that object. The first of the above methods will return a new copy in memory of the desired sub-object (the desired slices).

但是，有时候，Pandas中的索引约定不这样做，而是给您一个新变量，该变量只引用与原始对象中的子对象或切片相同的内存块.这将通过第二种索引方式发生，因此您可以使用copy()函数对其进行修改以获取常规副本.发生这种情况时，更改您认为是切片对象的内容有时会更改原始对象.随时注意这一点总是很高兴.

Sometimes, however, there are indexing conventions in Pandas that don't do this and instead give you a new variable that just refers to the same chunk of memory as the sub-object or slice in the original object. This will happen with the second way of indexing, so you can modify it with the copy() function to get a regular copy. When this happens, changing what you think is the sliced object can sometimes alter the original object. Always good to be on the look out for this.

df1 = df.iloc[0,0:2].copy() # To avoid the case where changing df1 also changes df

要使用iloc，您需要知道列位置(或索引).由于列位置可能会发生变化，因此可以使用iloc以及数据框对象的columns方法的get_loc函数来代替列索引，以获取列索引.

To use iloc, you need to know the column positions (or indices). As the column positions may change, instead of hard-coding indices, you can use iloc along with get_loc function of columns method of dataframe object to obtain column indices.

{df.columns.get_loc(c):c for idx, c in enumerate(df.columns)}

现在，您可以使用此词典通过名称和使用iloc来访问列.

Now you can use this dictionary to access columns through names and using iloc.

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