熊猫Dataframe / Numpy数组“axis”定义中的歧义

I've been very confused about how python axes are defined, and whether they refer to a DataFrame's rows or columns. Consider the code below:

我对python轴是如何定义的以及它们是引用DataFrame的行还是列感到非常困惑。考虑下面的代码:

>>> df = pd.DataFrame([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], columns=["col1", "col2", "col3", "col4"])
>>> df
   col1  col2  col3  col4
0     1     1     1     1
1     2     2     2     2
2     3     3     3     3

So if we call df.mean(axis=1), we'll get a mean across the rows:

如果我们叫它df。mean(坐标轴=1)我们会得到横轴上的均值

>>> df.mean(axis=1)
0    1
1    2
2    3

However, if we call df.drop(name, axis=1), we actually drop a column, not a row:

但是，如果我们调用df。删除(name, axis=1)，我们实际上删除了一列，而不是一行:

>>> df.drop("col4", axis=1)
   col1  col2  col3
0     1     1     1
1     2     2     2
2     3     3     3

Can someone help me understand what is meant by an "axis" in pandas/numpy/scipy?

有谁能帮我理解熊猫里的“轴”是什么意思?

A side note, DataFrame.mean just might be defined wrong. It says in the documentation for DataFrame.mean that axis=1 is supposed to mean a mean over the columns, not the rows...

边注,DataFrame。平均值可能被定义为错误的。它在DataFrame的文档中说。表示坐标轴=1表示的是列的平均值，而不是行。

4 个解决方案

#1

It's perhaps simplest to remember it as 0=down and 1=across.

它可能是最简单的，记为0=down和1=across。

This means:

这意味着:

• Use axis=0 to apply a method down each column, or to the row labels (the index).
• 使用axis=0对每一列或行标签(索引)应用方法。
• Use axis=1 to apply a method across each row, or to the column labels.
• 使用axis=1跨每一行或对列标签应用方法。

Here's a picture to show the parts of a DataFrame that each axis refers to:

这是一幅图，显示了每个轴都指向的数据aframe部分:

It's also useful to remember that Pandas follows NumPy's use of the word axis. The usage is explained in NumPy's glossary of terms:

记住熊猫遵循NumPy对轴的用法也是有用的。NumPy的术语表解释了这个用法:

Axes are defined for arrays with more than one dimension. A 2-dimensional array has two corresponding axes: the first running vertically downwards across rows (axis 0), and the second running horizontally across columns (axis 1). [my emphasis]

为具有多个维度的数组定义坐标轴。二维数组有两个对应的轴:第一个轴垂直向下(轴0)，第二个轴水平地跨列(轴1)。

So, concerning the method in the question, df.mean(axis=1), seems to be correctly defined. It takes the mean of entries horizontally across columns, that is, along each individual row. On the other hand, df.mean(axis=0) would be an operation acting vertically downwards across rows.

因此，关于问题中的方法，df.mean(axis=1)似乎得到了正确的定义。它将条目的平均水平跨列，即沿着每一行。另一方面，df.mean(axis=0)将是一个跨行垂直向下的操作。

Similarly, df.drop(name, axis=1) refers to an action on column labels, because they intuitively go across the horizontal axis. Specifying axis=0 would make the method act on rows instead.

同样,df。drop(name, axis=1)指的是列标签上的操作，因为它们直观地跨越横轴。指定axis=0将使方法对行执行。

#2

Another way to explain:

另一种解释:

// Not realistic but ideal for understanding the axis parameter 
df = pd.DataFrame([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]],
                  columns=["idx1", "idx2", "idx3", "idx4"],
                  index=["idx1", "idx2", "idx3"]
                 )

---------------------------------------1
|          idx1  idx2  idx3  idx4
|    idx1     1     1     1     1
|    idx2     2     2     2     2
|    idx3     3     3     3     3
0

About df.drop (axis means the position)

df。下降(轴表示位置)

A: I wanna remove idx3.
B: **Which one**? // typing while waiting response: df.drop("idx3",
A: The one which is on axis 1
B: OK then it is >> df.drop("idx3", axis=1)

// Result
---------------------------------------1
|          idx1  idx2     idx4
|    idx1     1     1     1
|    idx2     2     2     2
|    idx3     3     3     3
0

About df.apply (axis means direction)

df。应用方向(轴)

A: I wanna apply sum.
B: Which direction? // typing while waiting response: df.apply(lambda x: x.sum(),
A: The one which is on *parallel to axis 0*
B: OK then it is >> df.apply(lambda x: x.sum(), axis=0)

// Result
idx1    6
idx2    6
idx3    6
idx4    6

#3

There are already right answers, but I give you another example with > 2 dimensions.

已经有正确的答案了，但是我给你另一个>二维的例子。

The parameter axis means axis to be changed.
For example, consider that there is a dataframe with dimension a x b x c.

参数轴表示要更改的轴。例如，考虑有一个维度为axbxc的dataframe。

• df.mean(axis=1) returns a dataframe with dimenstion a x 1 x c.
• 平均值(axis=1)返回一个具有x 1 x c维度的dataframe。
• df.drop("col4", axis=1) returns a dataframe with dimension a x (b-1) x c.
• df。drop(“col4”，axis=1)返回一个带有x (b-1) x c维度的dataframe。

#4

It should be more widely known that the string aliases 'index' and 'columns' can be used in place of the integers 0/1. The aliases are much more explicit and help me remember how the calculations take place. Another alias for 'index' is 'rows'.

应该更广泛地知道，字符串别名“索引”和“列”可以用来代替整数0/1。别名更加明确，帮助我记住计算是如何进行的。“索引”的另一个别名是“行”。

When axis='index' is used, then the calculations happen down the columns, which is confusing. But, I remember it as getting a result that is the same size as another row.

当使用axis='index'时，计算将沿着列进行，这很令人困惑。但是，我记得得到的结果和另一行一样大。

Let's get some data on the screen to see what I am talking about:

让我们在屏幕上找到一些数据，看看我在说什么:

df = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd'))
          a         b         c         d
0  0.990730  0.567822  0.318174  0.122410
1  0.144962  0.718574  0.580569  0.582278
2  0.477151  0.907692  0.186276  0.342724
3  0.561043  0.122771  0.206819  0.904330
4  0.427413  0.186807  0.870504  0.878632
5  0.795392  0.658958  0.666026  0.262191
6  0.831404  0.011082  0.299811  0.906880
7  0.749729  0.564900  0.181627  0.211961
8  0.528308  0.394107  0.734904  0.961356
9  0.120508  0.656848  0.055749  0.290897

When we want to take the mean of all the columns, we use axis='index' to get the following:

当我们要取所有列的均值时，我们用axis='index'得到如下结果:

df.mean(axis='index')
a    0.562664
b    0.478956
c    0.410046
d    0.546366
dtype: float64

The same result would be gotten by:

同样的结果可以得到:

df.mean() # default is axis=0
df.mean(axis=0)
df.mean(axis='rows')

To get use an operation left to right on the rows, use axis='columns'. I remember it by thinking that an additional column may be added to my DataFrame:

要在行上使用从左到右的操作，请使用axis='columns'。我记得我想在我的DataFrame中增加一个专栏:

df.mean(axis='columns')
0    0.499784
1    0.506596
2    0.478461
3    0.448741
4    0.590839
5    0.595642
6    0.512294
7    0.427054
8    0.654669
9    0.281000
dtype: float64

The same result would be gotten by:

同样的结果可以得到:

df.mean(axis=1)

---

Add a new row with axis=0/index/rows

Let's use these results to add additional rows or columns to complete the explanation. So, whenever using axis = 0/index/rows, its like getting a new row of the DataFrame. Let's add a row:

让我们使用这些结果添加额外的行或列来完成解释。因此，每当使用axis = 0/index/row时，就像获得DataFrame的新行一样。让我们添加一行:

df.append(df.mean(axis='rows'), ignore_index=True)

           a         b         c         d
0   0.990730  0.567822  0.318174  0.122410
1   0.144962  0.718574  0.580569  0.582278
2   0.477151  0.907692  0.186276  0.342724
3   0.561043  0.122771  0.206819  0.904330
4   0.427413  0.186807  0.870504  0.878632
5   0.795392  0.658958  0.666026  0.262191
6   0.831404  0.011082  0.299811  0.906880
7   0.749729  0.564900  0.181627  0.211961
8   0.528308  0.394107  0.734904  0.961356
9   0.120508  0.656848  0.055749  0.290897
10  0.562664  0.478956  0.410046  0.546366

---

Add a new column with axis=1/columns

Similarly, when axis=1/columns it will create data that can be easily made into its own column:

类似地，当axis=1/列时，它将创建可以很容易地创建到它自己的列中的数据:

df.assign(e=df.mean(axis='columns'))

          a         b         c         d         e
0  0.990730  0.567822  0.318174  0.122410  0.499784
1  0.144962  0.718574  0.580569  0.582278  0.506596
2  0.477151  0.907692  0.186276  0.342724  0.478461
3  0.561043  0.122771  0.206819  0.904330  0.448741
4  0.427413  0.186807  0.870504  0.878632  0.590839
5  0.795392  0.658958  0.666026  0.262191  0.595642
6  0.831404  0.011082  0.299811  0.906880  0.512294
7  0.749729  0.564900  0.181627  0.211961  0.427054
8  0.528308  0.394107  0.734904  0.961356  0.654669
9  0.120508  0.656848  0.055749  0.290897  0.281000

It appears that you can see all the aliases with the following private variables:

似乎您可以看到所有带有以下私有变量的别名:

df._AXIS_ALIASES
{'rows': 0}

df._AXIS_NUMBERS
{'columns': 1, 'index': 0}

df._AXIS_NAMES
{0: 'index', 1: 'columns'}