Python Pandas 数据框/Numpy 数组“轴”定义中的歧义
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Ambiguity in Pandas Dataframe / Numpy Array "axis" definition
提问by hlin117
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:
我一直很困惑如何定义 python 轴，以及它们是指 DataFrame 的行还是列。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:
因此，如果我们调用df.mean(axis=1)，我们将得到各行的均值：
>>> 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.drop(name, axis=1)，我们实际上删除了一个列，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?
有人能帮我理解 pandas/numpy/scipy 中的“轴”是什么意思吗？
A side note, DataFrame.meanjust might be defined wrong. It says in the documentation for DataFrame.meanthat axis=1is supposed to mean a mean over the columns, not the rows...
旁注，DataFrame.mean只是可能定义错误。它在文档中DataFrame.mean说这axis=1应该意味着列的平均值，而不是行......
采纳答案by Alex Riley
It's perhaps simplest to remember it as 0=downand 1=across.
将其记为0=down和1=across可能是最简单的。
This means:
这意味着：
- Use
axis=0to apply a method down each column, or to the row labels (the index). - Use
axis=1to apply a method across each row, or to the column labels.
- 使用
axis=0的方法应用于沿着每列，或行标签（索引）。 - 使用
axis=1的方法应用在每个行，或列标签。
Here's a picture to show the parts of a DataFrame that each axis refers to:
这是一张图片，显示了每个轴所指的 DataFrame 的各个部分：
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 Pandas 遵循 NumPy 对单词的使用也很有用axis。NumPy 的术语表中解释了用法：
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]
轴是为多维数组定义的。二维数组有两个对应的轴：第一个垂直向下跨行（轴 0），第二个水平跨列（轴 1）。[我的重点]
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), 似乎被正确定义。它采用水平跨列的条目的平均值，即沿着每个单独的行。另一方面，df.mean(axis=0)将是跨行垂直向下作用的操作。
Similarly, df.drop(name, axis=1)refers to an action on column labels, because they intuitively go across the horizontal axis. Specifying axis=0would make the method act on rows instead.
同样，df.drop(name, axis=1)指的是对列标签的操作，因为它们直观地跨过水平轴。指定axis=0将使方法改为作用于行。
回答by o0omycomputero0o
Another way to explain:
另一种解释方式：
// 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)
关于df.drop（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)
关于df.apply（轴表示方向）
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
回答by jeongmin.cha
There are already proper answers, but I give you another example with > 2 dimensions.
已经有正确的答案，但我给你另一个> 2维的例子。
The parameter axismeans axis to be changed.
For example, consider that there is a dataframe with dimension a x b x c.
该参数axis表示要更改的轴。
例如，考虑有一个维度为axbxc的数据框。
df.mean(axis=1)returns a dataframe with dimenstion a x 1 x c.df.drop("col4", axis=1)returns a dataframe with dimension a x (b-1) x c.
df.mean(axis=1)返回一个维度ax 1 xc的数据帧。df.drop("col4", axis=1)返回维度为ax (b-1) xc的数据帧。
Here, axis=1means the second axis which is b, so bvalue will be changed in these examples.
在这里，axis=1表示第二个轴是b，因此b在这些示例中值将被更改。
回答by Ted Petrou
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'.
更广为人知的是，字符串别名“index”和“columns”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。'index' 的另一个别名是'rows'。
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axis='index'被使用，那么计算下来发生在列，这是混淆。但是，我记得它得到的结果与另一行的大小相同。
Let's get some data on the screen to see what I am talking about:
让我们在屏幕上获取一些数据，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:
当我们想取所有列的平均值时，我们使用axis='index'以下内容：
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:
将得到相同的结果：
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'。我记得我认为可以向我的 DataFrame 添加一个额外的列：
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:
将得到相同的结果：
df.mean(axis=1)
Add a new row with axis=0/index/rows
添加一个 axis=0/index/rows 的新行
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/rows 时，就像获取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
添加一个axis=1/columns的新列
Similarly, when axis=1/columns it will create data that can be easily made into its own column:
类似地，当 axis=1/columns 时，它会创建可以轻松放入自己的列中的数据：
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具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'}
回答by newbie
When axis='rows' or axis=0, it means access elements in the direction of the rows, up to down. If applying sum along axis=0, it will give us totals of each column.
当axis='rows' 或axis=0 时，表示按行的方向从上到下访问元素。如果沿轴应用 sum=0，它将为我们提供每列的总数。
When axis='columns' or axis=1, it means access elements in the direction of the columns, left to right. If applying sum along axis=1, we will get totals of each row.
当axis='columns' 或axis=1 时，表示按列的方向从左到右访问元素。如果沿轴应用 sum=1，我们将得到每行的总数。
Still confusing! But the above makes it a bit easier for me.
还是一头雾水！但以上让我更容易一些。
