<p>直接摘自韦斯·麦金尼的<a href="http://shop.oreilly.com/product/0636920023784.do" rel="noreferrer">Python for Data Analysis</a>书,第132页(我强烈推荐这本书):</p>
<blockquote>
<p>Another frequent operation is applying a function on 1D arrays to each column or row. DataFrame’s apply method does exactly this:</p>
</blockquote>
<pre><code>In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
In [117]: frame
Out[117]:
b d e
Utah -0.029638 1.081563 1.280300
Ohio 0.647747 0.831136 -1.549481
Texas 0.513416 -0.884417 0.195343
Oregon -0.485454 -0.477388 -0.309548
In [118]: f = lambda x: x.max() - x.min()
In [119]: frame.apply(f)
Out[119]:
b 1.133201
d 1.965980
e 2.829781
dtype: float64
</code></pre>
<blockquote>
<p>Many of the most common array statistics (like sum and mean) are DataFrame methods,
so using apply is not necessary.</p>
<p>Element-wise Python functions can be used, too. Suppose you wanted to compute a formatted string from each floating point value in frame. You can do this with applymap:</p>
</blockquote>
<pre><code>In [120]: format = lambda x: '%.2f' % x
In [121]: frame.applymap(format)
Out[121]:
b d e
Utah -0.03 1.08 1.28
Ohio 0.65 0.83 -1.55
Texas 0.51 -0.88 0.20
Oregon -0.49 -0.48 -0.31
</code></pre>
<blockquote>
<p>The reason for the name applymap is that Series has a map method for applying an element-wise function:</p>
</blockquote>
<pre><code>In [122]: frame['e'].map(format)
Out[122]:
Utah 1.28
Ohio -1.55
Texas 0.20
Oregon -0.31
Name: e, dtype: object
</code></pre>
<p>总之,<code>apply</code>在数据帧的行/列基础上工作,<code>applymap</code>在数据帧上按元素工作,<code>map</code>在序列上按元素工作。</p>