blocks|key|149983|text|将其记为0=down和1=across可能是最简单的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|149984|这意味着：|149985|149986|使用axis=0将方法应用于每列或应用于行标签(索引)。|unordered-list-item|offset|length|style|CODE|149987|使用axis=1将方法应用于每行或应用于列标签。|149988|149989|下面的图片显示了每个轴引用的DataFrame的各个部分：|149990|​|149991|📷|atomic|149992|149993|记住Pandas遵循NumPy对单词axis的使用也很有用。在NumPy的glossary+of+terms中解释了用法|149994|149995|轴是为具有多个维度的数组定义的。二维数组有两个对应的轴:第一个轴垂直向下跨行运行(轴0)，第二个轴水平跨列运行(轴1)。我的重点|blockquote|BOLD|149996|149997|149998|因此，关于问题中的方法，df.mean(axis=1)似乎定义正确。它取横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)将是跨行垂直向下操作的操作。|149999|类似地，df.drop(name,+axis=1)指的是列标签上的操作，因为它们直观地横跨水平轴。指定axis=0将使该方法改为作用于行。|150000|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/20be6f300655ffa87e2b54661d75ec01.jpeg|imageAlt|1|LINK|MUTABLE|url|http://docs.scipy.org/doc/numpy/glossary.html^0|0|0|0|2|6|0|2|6|0|0|0|0|0|1|0|0|0|I|4|11|H|1|0|0|14|4|1J|4|0|0|0|C|F|1Q|F|0|4|L|1F|6|0^^$0|@$1|2|3|4|5|6|7|1N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1O|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|1P|8|@]|9|@]|A|$]]|$1|E|3|F|5|G|7|1Q|8|@$H|1R|I|1S|J|K]]|9|@]|A|$]]|$1|L|3|M|5|G|7|1T|8|@$H|1U|I|1V|J|K]]|9|@]|A|$]]|$1|N|3|-4|5|6|7|1W|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1X|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1Y|8|@]|9|@]|A|$]]|$1|S|3|T|5|U|7|1Z|8|@]|9|@$H|20|I|21|1|22]]|A|$]]|$1|V|3|R|5|6|7|23|8|@]|9|@]|A|$]]|$1|W|3|X|5|6|7|24|8|@$H|25|I|26|J|K]]|9|@$H|27|I|28|1|29]]|A|$]]|$1|Y|3|-4|5|6|7|2A|8|@]|9|@]|A|$]]|$1|Z|3|10|5|11|7|2B|8|@$H|2C|I|2D|J|12]|$H|2E|I|2F|J|12]]|9|@]|A|$]]|$1|13|3|-4|5|6|7|2G|8|@]|9|@]|A|$]]|$1|14|3|-4|5|6|7|2H|8|@]|9|@]|A|$]]|$1|15|3|16|5|6|7|2I|8|@$H|2J|I|2K|J|K]|$H|2L|I|2M|J|K]]|9|@]|A|$]]|$1|17|3|18|5|6|7|2N|8|@$H|2O|I|2P|J|K]|$H|2Q|I|2R|J|K]]|9|@]|A|$]]|$1|19|3|-4|5|6|7|2S|8|@]|9|@]|A|$]]]|1A|$1B|$5|1C|1D|1E|A|$1F|1G|1H|-4]]|1I|$5|1J|1D|1K|A|$1L|1M]]]]

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

This means:

<ul>
<li>Use <code>axis=0</code> to apply a method down each column, or to the row labels (the index).</li>
<li>Use <code>axis=1</code> to apply a method across each row, or to the column labels.</li>
</ul>

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

<img src="https://i.stack.imgur.com/DL0iQ.jpg" width="410" height="210">

It's also useful to remember that Pandas follows NumPy's use of the word <code>axis</code>. The usage is explained in NumPy's <a href="http://docs.scipy.org/doc/numpy/glossary.html">glossary of terms</a>:

<blockquote>
 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] 
</blockquote>

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

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

blocks|key|147233|text|另一种解释是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|147234|//+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
%7C++++++++++idx1++idx2++idx3++idx4
%7C++++idx1+++++1+++++1+++++1+++++1
%7C++++idx2+++++2+++++2+++++2+++++2
%7C++++idx3+++++3+++++3+++++3+++++3
0|code-block|syntax|javascript|147235|关于df.drop+(轴表示位置)|offset|length|style|CODE|147236|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
%7C++++++++++idx1++idx2+++++idx4
%7C++++idx1+++++1+++++1+++++1
%7C++++idx2+++++2+++++2+++++2
%7C++++idx3+++++3+++++3+++++3
0|147237|关于df.apply+(轴表示方向)|147238|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|147239|entityMap^0|0|0|2|7|0|0|2|8|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@$I|X|J|Y|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|10|8|@$I|11|J|12|K|L]]|9|@]|A|$]]|$1|Q|3|R|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|S|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|T|$]]

Another way to explain:

<pre><code>// 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
</code></pre>

About <code>df.drop</code> (axis means the position)

<pre><code>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 &gt;&gt; df.drop("idx3", axis=1)

// Result
---------------------------------------1
| idx1 idx2 idx4
| idx1 1 1 1
| idx2 2 2 2
| idx3 3 3 3
0
</code></pre>

About <code>df.apply</code> (axis means direction)

<pre><code>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 &gt;&gt; df.apply(lambda x: x.sum(), axis=0)

// Result
idx1 6
idx2 6
idx3 6
idx4 6
</code></pre>

blocks|key|2729542|text|已经有了正确的答案，但我给你另一个>2维的例子。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2729543|将参数axis轴表示为changed.时，+to+be+|offset|length|style|BOLD|CODE|2729544|例如，假设有一个维度为a+x+b+x+c的数据帧。|2729545|2729546|df.mean(axis=1)返回维度为a+x+1+x+c的数据帧。|unordered-list-item|2729547|df.drop("col4",+axis=1)返回维度为a+x+(b-1)+x+c的数据帧。|2729548|2729549|在这里，axis=1指的是第二个轴，即b，因此在这些示例中，b值将被更改。|2729550|entityMap^0|0|3|4|I|1|L|7|3|4|0|0|P|0|0|0|Y|0|F|0|0|1A|0|N|0|0|0|11|4|6|J|1|U|1|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|W|8|@$D|X|E|Y|F|G]|$D|Z|E|10|F|G]|$D|11|E|12|F|G]|$D|13|E|14|F|H]]|9|@]|A|$]]|$1|I|3|J|5|6|7|15|8|@$D|16|E|17|F|G]]|9|@]|A|$]]|$1|K|3|-4|5|6|7|18|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|19|8|@$D|1A|E|1B|F|G]|$D|1C|E|1D|F|H]]|9|@]|A|$]]|$1|O|3|P|5|N|7|1E|8|@$D|1F|E|1G|F|G]|$D|1H|E|1I|F|H]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|1J|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|1K|8|@$D|1L|E|1M|F|G]|$D|1N|E|1O|F|H]|$D|1P|E|1Q|F|H]|$D|1R|E|1S|F|H]]|9|@]|A|$]]|$1|T|3|-4|5|6|7|1T|8|@]|9|@]|A|$]]]|U|$]]

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

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

<ul>
<li><code>df.mean(axis=1)</code> returns a dataframe with dimenstion a x 1 x c. </li>
<li><code>df.drop("col4", axis=1)</code> returns a dataframe with dimension a x (b-1) x c.</li>
</ul>

Here, <code>axis=1</code> means the second axis which is <code>b</code>, so <code>b</code> value will be changed in these examples.

blocks|key|2942113|text|应该更广为人知的是，可以使用字符串别名'index'和‘列’来代替整数0/1。这些别名更加明确，可以帮助我记住计算是如何发生的。“index”的另一个别名是“”。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2942114|当使用axis='index'时，计算是沿着列进行的，这是令人困惑的。但是，我记得它得到的结果与另一行的大小相同。|CODE|2942115|让我们在屏幕上获取一些数据，看看我在说什么：|2942116|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|code-block|syntax|javascript|2942117|当我们想要取所有列的平均值时，我们使用axis='index'来获得以下内容：|2942118|df.mean(axis='index')
a++++0.562664
b++++0.478956
c++++0.410046
d++++0.546366
dtype:+float64|2942119|可以通过以下方式获得相同的结果：|2942120|df.mean()+#+default+is+axis=0
df.mean(axis=0)
df.mean(axis='rows')|2942121|要在行上从左到右使用操作，请使用axis=‘column’。我之所以记住这一点，是因为我认为可能会在我的DataFrame中添加一个附加列：|2942122|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|2942123|2942124|df.mean(axis=1)|2942125|2942126|添加一个轴=0/索引/行的新行|2942127|让我们使用这些结果来添加额外的行或列来完成解释。因此，每当使用axis+=+0/index/rows时，就像获得了DataFrame的新行。让我们添加一行：|2942128|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|2942129|2942130|添加轴=1/列的新列|2942131|类似地，当axis=1/columns时，它将创建数据，这些数据可以很容易地放入自己的列中：|2942132|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|2942133|看起来您可以看到具有以下私有变量的所有别名：|2942134|df._AXIS_ALIASES
{'rows':+0}

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

df._AXIS_NAMES
{0:+'index',+1:+'columns'}|2942135|entityMap^0|J|7|R|3|27|1|0|3|C|0|0|0|J|C|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1O|8|@$9|1P|A|1Q|B|C]|$9|1R|A|1S|B|C]|$9|1T|A|1U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1V|8|@$9|1W|A|1X|B|H]]|D|@]|E|$]]|$1|I|3|J|5|6|7|1Y|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|1Z|8|@]|D|@]|E|$N|O]]|$1|P|3|Q|5|6|7|20|8|@$9|21|A|22|B|H]]|D|@]|E|$]]|$1|R|3|S|5|M|7|23|8|@]|D|@]|E|$N|O]]|$1|T|3|U|5|6|7|24|8|@]|D|@]|E|$]]|$1|V|3|W|5|M|7|25|8|@]|D|@]|E|$N|O]]|$1|X|3|Y|5|6|7|26|8|@]|D|@]|E|$]]|$1|Z|3|10|5|M|7|27|8|@]|D|@]|E|$N|O]]|$1|11|3|U|5|6|7|28|8|@]|D|@]|E|$]]|$1|12|3|13|5|M|7|29|8|@]|D|@]|E|$N|O]]|$1|14|3|-4|5|6|7|2A|8|@]|D|@]|E|$]]|$1|15|3|16|5|6|7|2B|8|@]|D|@]|E|$]]|$1|17|3|18|5|6|7|2C|8|@]|D|@]|E|$]]|$1|19|3|1A|5|M|7|2D|8|@]|D|@]|E|$N|O]]|$1|1B|3|-4|5|6|7|2E|8|@]|D|@]|E|$]]|$1|1C|3|1D|5|6|7|2F|8|@]|D|@]|E|$]]|$1|1E|3|1F|5|6|7|2G|8|@]|D|@]|E|$]]|$1|1G|3|1H|5|M|7|2H|8|@]|D|@]|E|$N|O]]|$1|1I|3|1J|5|6|7|2I|8|@]|D|@]|E|$]]|$1|1K|3|1L|5|M|7|2J|8|@]|D|@]|E|$N|O]]|$1|1M|3|-4|5|6|7|2K|8|@]|D|@]|E|$]]]|1N|$]]

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'.

When <code>axis='index'</code> 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.

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

<pre><code>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
</code></pre>

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

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

The same result would be gotten by:

<pre><code>df.mean() # default is axis=0
df.mean(axis=0)
df.mean(axis='rows')
</code></pre>

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:

<pre><code>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
</code></pre>

The same result would be gotten by:

<pre><code>df.mean(axis=1)
</code></pre>

<hr>

<h3>Add a new row with axis=0/index/rows</h3>

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:

<pre><code>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
</code></pre>

<hr>

<h3>Add a new column with axis=1/columns</h3>

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

<pre><code>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
</code></pre>

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

<pre><code>df._AXIS_ALIASES
{'rows': 0}

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

df._AXIS_NAMES
{0: 'index', 1: 'columns'}
</code></pre>

blocks|key|2942141|text|当axis='+rows‘或axis=0时，它表示在行的方向上从上到下访问元素。如果沿着axis=0应用sum，它会给出每一列的总和。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2942142|当axis='+columns‘或axis=1时，它表示在列的方向上从左到右访问元素。如果沿着axis=1应用sum，我们将得到每行的总和。|2942143|还是让人迷惑！但上面的内容让我的工作变得更容易了。|2942144|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

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.

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.

Still confusing! But the above makes it a bit easier for me.

blocks|key|151281|text|我记得通过维度的改变，如果axis=0，行改变，列不变，如果axis=1，列改变，行不变。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|151282|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I remembered by the change of dimension, if axis=0, row changes, column unchanged, and if axis=1, column changes, row unchanged.

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:

<pre><code>&gt;&gt;&gt; df = pd.DataFrame([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], columns=["col1", "col2", "col3", "col4"])
&gt;&gt;&gt; df
 col1 col2 col3 col4
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
</code></pre>

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

<pre><code>&gt;&gt;&gt; df.mean(axis=1)
0 1
1 2
2 3
</code></pre>

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

<pre><code>&gt;&gt;&gt; df.drop("col4", axis=1)
 col1 col2 col3
0 1 1 1
1 2 2 2
2 3 3 3
</code></pre>

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

A side note, <code>DataFrame.mean</code> just might be defined wrong. It says in the documentation for <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html"><code>DataFrame.mean</code></a> that <code>axis=1</code> is supposed to mean a mean over the columns, not the rows...

Ambiguity in Pandas Dataframe / Numpy Array "axis" definition

我一直很困惑python轴是如何定义的，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"])>>> ...

问Pandas Dataframe / Numpy数组“轴”定义中的多义性
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