Python Pandas与Numpy中axis参数的意思_python axis=rows-优快云博客

本文详细解释了Python中Pandas库的数据结构DataFrame中axis参数的意义。通过实例展示，清晰地说明了axis=0和axis=1分别对应的操作方向，帮助读者理解如何在数据分析任务中正确使用这些参数。

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转自 stackoverflow.com
https://stackoverflow.com/questions/25773245/ambiguity-in-pandas-dataframe-numpy-array-axis-definition/25774395#25774395?newreg=547fe2de322e46a3a20d6d1aeeba4df9
参考 https://www.jianshu.com/p/9aa448ea397c

原文：
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:

>>> 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:

>>> 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:

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

其中有一位大神的解答非常不错：

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

This means:

Use axis=0 to apply a method down each column, or to the row labels (the index).
Use axis=1 to apply a method across each row, or to the column labels.

Here’s a picture to show the parts of a DataFrame that each axis refers to:
这里写图片描述

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:

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]

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.

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.

参考中文翻译，总结来说：
axis=0是每一列做自上而下的执行，axis=1是每一行做自左向右的执行，强调的是一种遍历的概念