blocks|key|4135485|text|您可以使用df.index访问索引对象，然后使用df.index.tolist()获取列表中的值。类似地，您可以对系列使用df['col'].tolist()。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4135486|entityMap^0|5|8|O|H|1P|I|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|O|8|@]|D|@]|E|$]]]|G|$]]

You can use <code>df.index</code> to access the index object and then get the values in a list using <code>df.index.tolist()</code>. Similarly, you can use <code>df['col'].tolist()</code> for Series.

blocks|key|688925|text|要获取NumPy数组，应使用values属性：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|688926|In+[1]:+df+=+pd.DataFrame({'A':+[1,+2,+3],+'B':+[4,+5,+6]},+index=['a',+'b',+'c']);+df
+++A++B
a++1++4
b++2++5
c++3++6

In+[2]:+df.index.values
Out[2]:+array(['a',+'b',+'c'],+dtype=object)|code-block|syntax|javascript|688927|这将访问数据已经存储的方式，因此不需要进行转换。|688928|注意:此属性也可用于许多其他熊猫的对象。|688929|In+[3]:+df['A'].values
Out[3]:+Out[16]:+array([1,+2,+3])|688930|688931|要获取列表形式的索引，请调用tolist|688932|In+[4]:+df.index.tolist()
Out[4]:+['a',+'b',+'c']|688933|同样，对于列也是如此。|688934|entityMap^0|E|6|0|0|0|0|0|0|E|6|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|14|8|@]|D|@]|E|$]]|$1|O|3|P|5|H|7|15|8|@]|D|@]|E|$I|J]]|$1|Q|3|-4|5|6|7|16|8|@]|D|@]|E|$]]|$1|R|3|S|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|T|3|U|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|V|3|W|5|6|7|1B|8|@]|D|@]|E|$]]|$1|X|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]]|Y|$]]

To get a NumPy array, you should use the <code>values</code> attribute:

<pre><code>In [1]: df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['a', 'b', 'c']); df
 A B
a 1 4
b 2 5
c 3 6

In [2]: df.index.values
Out[2]: array(['a', 'b', 'c'], dtype=object)
</code></pre>

This accesses how the data is already stored, so there's no need for a conversion. 
Note: This attribute is also available for many other pandas' objects.

<pre><code>In [3]: df['A'].values
Out[3]: Out[16]: array([1, 2, 3])
</code></pre>

<hr>

To get the index as a list, call <code>tolist</code>:

<pre><code>In [4]: df.index.tolist()
Out[4]: ['a', 'b', 'c']
</code></pre>

And similarly, for columns.

blocks|key|4135510|text|从pandas+v0.13开始，您还可以使用get_values|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4135511|df.index.get_values()|code-block|syntax|javascript|4135512|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.get_values.html^0|M|A|M|A|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

Since pandas v0.13 you can also use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.get_values.html" rel="nofollow noreferrer"><code>get_values</code></a>:

<pre><code>df.index.get_values()
</code></pre>

blocks|key|1830882|text|如果您正在处理多索引数据帧，您可能会对仅提取多索引的一个名称的列感兴趣。您可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1830883|df.index.get_level_values('name_sub_index')|code-block|syntax|javascript|1830884|当然，name_sub_index必须是FrozenList+df.index.names的一个元素|offset|length|style|CODE|1830885|entityMap^0|0|0|3|E|K|A|V|E|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]|$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|N|$]]

If you are dealing with a multi-index dataframe, you may be interested in extracting only the column of one name of the multi-index. You can do this as

<pre><code>df.index.get_level_values('name_sub_index')
</code></pre>

and of course <code>name_sub_index</code> must be an element of the <code>FrozenList</code> <code>df.index.names</code>

blocks|key|2010698|text|我将pandas+dataframe转换为list，然后使用基本的list.index()。如下所示：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2010699|dd+=+list(zone[0])+#Where+zone[0]+is+some+specific+column+of+the+table
idx+=+dd.index(filename[i])|code-block|syntax|javascript|2010700|您的索引值为idx。|2010701|entityMap^0|9|9|L|4|X|C|0|0|6|3|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|N|$]]

I converted the pandas <code>dataframe</code> to <code>list</code> and then used the basic <code>list.index()</code>. Something like this:

<pre><code>dd = list(zone[0]) #Where zone[0] is some specific column of the table
idx = dd.index(filename[i])
</code></pre>

You have you index value as <code>idx</code>.

blocks|key|4135662|text|pandas+>=+0.24|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4135663|不推荐使用.values来支持这些方法！|offset|length|style|CODE|4135664|从v0.24.0开始，我们将有两种全新的、首选的方法来从Index、Series和DataFrame对象中获取NumPy数组：to_numpy()，和.array.关于用法，文档提到：|BOLD|4135665|4135666|我们尚未删除或弃用Series.values或DataFrame.values，但强烈建议您使用+.array+或。|blockquote|4135667|4135668|4135669|有关详细信息，请参阅this+section+of+the+v0.24.0+release+notes。|4135670|4135671|+|4135672|df.index.to_numpy()
#+array(['a',+'b'],+dtype=object)

df['A'].to_numpy()
#++array([1,+4])|code-block|syntax|javascript|4135673|默认情况下，返回一个视图。所做的任何修改都会影响原始文件。|4135674|v+=+df.index.to_numpy()
v[0]+=+-1

df
++++A++B
-1++1++2
b+++4++5|4135675|如果您需要副本，请使用to_numpy(copy=True)；|4135676|v+=+df.index.to_numpy(copy=True)
v[-1]+=+-123

df
+++A++B
a++1++2
b++4++5|4135677|请注意，此函数也适用于DataFrames+(而.array不适用)。|4135678|4135679|+|4135680|此属性返回一个支持索引/系列的ExtensionArray对象。|4135681|pd.__version__
#+'0.24.0rc1'

#+Setup.
df+=+pd.DataFrame([[1,+2],+[4,+5]],+columns=['A',+'B'],+index=['a',+'b'])
df

+++A++B
a++1++2
b++4++5|4135682|​|4135683|df.index.array++++
#+<PandasArray>
#+['a',+'b']
#+Length:+2,+dtype:+object

df['A'].array
#+<PandasArray>
#+[1,+4]
#+Length:+2,+dtype:+int64|4135684|在这里，可以使用list获取列表|4135685|list(df.index.array)
#+['a',+'b']

list(df['A'].array)
#+[1,+4]|4135686|或者，直接调用.tolist()|4135687|df.index.tolist()
#+['a',+'b']

df['A'].tolist()
#+[1,+4]|4135688|关于返回的内容，文档中提到，|4135689|4135690|对于由普通NumPy阵列支持的Series和Indexes，Series.array将返回一个新的arrays.PandasArray，它是numpy.ndarray的精简(无拷贝)包装。arrays.PandasArray本身并不是特别有用，但它确实提供了与pandas或第三方库中定义的任何扩展数组相同的接口。|4135691|4135692|4135693|因此，总而言之，.array将返回|4135694|4135695|支持索引/系列的现有ExtensionArray，或者|ordered-list-item|4135696|如果有支持该系列的NumPy数组，则会创建一个新的ExtensionArray对象，作为底层数组的瘦包装。|4135697|4135698|4135699|添加两个新方法的基本原理|4135700|这些函数是在两个GitHub问题GH19954和GH23623下讨论后添加的。|4135701|具体地说，文档提到了理由：|4135702|4135703|+...对于.values，不清楚返回值是实际数组、转换后的数组，还是熊猫自定义数组(如Categorical)。例如，对于PeriodIndex，.values每次都会生成period对象的新ndarray。..。|4135704|4135705|4135706|这两个函数旨在提高API的一致性，这是朝着正确方向迈出的重要一步。|4135707|最后，.values在当前版本中不会被弃用，但我预计这可能会在未来的某个时候发生，所以我强烈建议用户尽快迁移到更新的API。|4135708|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.24.0.html#accessing-the-values-in-a-series-or-index|1|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.to_numpy.html|2|http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html|3|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.array.html#pandas.Series.array|4|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html|5|https://github.com/pandas-dev/pandas/issues/19954|6|https://github.com/pandas-dev/pandas/issues/23623^0|0|5|7|0|S|5|Y|6|15|9|1R|A|23|6|1R|A|23|6|0|0|9|D|N|G|1D|6|1D|6|1K|2|0|0|0|A|15|0|0|0|0|1|1|0|0|0|0|B|I|0|0|O|6|B|A|2|0|0|0|1|3|0|F|E|F|E|4|0|0|0|0|8|4|0|0|7|9|0|0|0|0|F|6|M|5|U|C|1D|I|1Y|D|2M|I|0|0|0|8|6|0|0|A|E|0|P|E|0|0|0|8|4|0|0|13|G|7|5|O|7|6|0|0|D|0|0|0|30|6|7|18|B|1Q|B|22|7|2P|7|0|0|0|0|X|0|0|1Q|3|7|0^^$0|@$1|2|3|4|5|6|7|39|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|3A|8|@$D|3B|E|3C|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|3D|8|@$D|3E|E|3F|F|G]|$D|3G|E|3H|F|G]|$D|3I|E|3J|F|G]|$D|3K|E|3L|F|G]|$D|3M|E|3N|F|G]|$D|3O|E|3P|F|J]|$D|3Q|E|3R|F|J]]|9|@]|A|$]]|$1|K|3|-4|5|6|7|3S|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|3T|8|@$D|3U|E|3V|F|G]|$D|3W|E|3X|F|G]|$D|3Y|E|3Z|F|G]|$D|40|E|41|F|J]|$D|42|E|43|F|J]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|44|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|45|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|46|8|@]|9|@$D|47|E|48|1|49]]|A|$]]|$1|S|3|-4|5|6|7|4A|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|4B|8|@]|9|@$D|4C|E|4D|1|4E]]|A|$]]|$1|V|3|W|5|X|7|4F|8|@]|9|@]|A|$Y|Z]]|$1|10|3|11|5|6|7|4G|8|@]|9|@]|A|$]]|$1|12|3|13|5|X|7|4H|8|@]|9|@]|A|$Y|Z]]|$1|14|3|15|5|6|7|4I|8|@$D|4J|E|4K|F|G]]|9|@]|A|$]]|$1|16|3|17|5|X|7|4L|8|@]|9|@]|A|$Y|Z]]|$1|18|3|19|5|6|7|4M|8|@$D|4N|E|4O|F|G]]|9|@$D|4P|E|4Q|1|4R]]|A|$]]|$1|1A|3|-4|5|6|7|4S|8|@]|9|@]|A|$]]|$1|1B|3|1C|5|6|7|4T|8|@]|9|@$D|4U|E|4V|1|4W]]|A|$]]|$1|1D|3|1E|5|6|7|4X|8|@$D|4Y|E|4Z|F|G]]|9|@$D|50|E|51|1|52]]|A|$]]|$1|1F|3|1G|5|X|7|53|8|@]|9|@]|A|$Y|Z]]|$1|1H|3|1I|5|6|7|54|8|@]|9|@]|A|$]]|$1|1J|3|1K|5|X|7|55|8|@]|9|@]|A|$Y|Z]]|$1|1L|3|1M|5|6|7|56|8|@$D|57|E|58|F|G]]|9|@]|A|$]]|$1|1N|3|1O|5|X|7|59|8|@]|9|@]|A|$Y|Z]]|$1|1P|3|1Q|5|6|7|5A|8|@$D|5B|E|5C|F|G]]|9|@]|A|$]]|$1|1R|3|1S|5|X|7|5D|8|@]|9|@]|A|$Y|Z]]|$1|1T|3|1U|5|6|7|5E|8|@]|9|@]|A|$]]|$1|1V|3|-4|5|6|7|5F|8|@]|9|@]|A|$]]|$1|1W|3|1X|5|N|7|5G|8|@$D|5H|E|5I|F|G]|$D|5J|E|5K|F|G]|$D|5L|E|5M|F|G]|$D|5N|E|5O|F|G]|$D|5P|E|5Q|F|G]|$D|5R|E|5S|F|G]]|9|@]|A|$]]|$1|1Y|3|-4|5|6|7|5T|8|@]|9|@]|A|$]]|$1|1Z|3|-4|5|6|7|5U|8|@]|9|@]|A|$]]|$1|20|3|21|5|6|7|5V|8|@$D|5W|E|5X|F|G]]|9|@]|A|$]]|$1|22|3|-4|5|6|7|5Y|8|@]|9|@]|A|$]]|$1|23|3|24|5|25|7|5Z|8|@$D|60|E|61|F|G]]|9|@]|A|$]]|$1|26|3|27|5|25|7|62|8|@$D|63|E|64|F|G]]|9|@]|A|$]]|$1|28|3|-4|5|6|7|65|8|@]|9|@]|A|$]]|$1|29|3|-4|5|6|7|66|8|@]|9|@]|A|$]]|$1|2A|3|2B|5|6|7|67|8|@$D|68|E|69|F|J]]|9|@]|A|$]]|$1|2C|3|2D|5|6|7|6A|8|@$D|6B|E|6C|F|J]]|9|@$D|6D|E|6E|1|6F]|$D|6G|E|6H|1|6I]]|A|$]]|$1|2E|3|2F|5|6|7|6J|8|@$D|6K|E|6L|F|J]]|9|@]|A|$]]|$1|2G|3|-4|5|6|7|6M|8|@]|9|@]|A|$]]|$1|2H|3|2I|5|N|7|6N|8|@$D|6O|E|6P|F|J]|$D|6Q|E|6R|F|G]|$D|6S|E|6T|F|G]|$D|6U|E|6V|F|G]|$D|6W|E|6X|F|G]|$D|6Y|E|6Z|F|G]]|9|@]|A|$]]|$1|2J|3|-4|5|6|7|70|8|@]|9|@]|A|$]]|$1|2K|3|-4|5|6|7|71|8|@]|9|@]|A|$]]|$1|2L|3|2M|5|6|7|72|8|@$D|73|E|74|F|J]]|9|@]|A|$]]|$1|2N|3|2O|5|6|7|75|8|@$D|76|E|77|F|J]|$D|78|E|79|F|G]]|9|@]|A|$]]|$1|2P|3|-4|5|6|7|7A|8|@]|9|@]|A|$]]]|2Q|$2R|$5|2S|2T|2U|A|$2V|2W]]|2X|$5|2S|2T|2U|A|$2V|2Y]]|2Z|$5|2S|2T|2U|A|$2V|30]]|31|$5|2S|2T|2U|A|$2V|32]]|33|$5|2S|2T|2U|A|$2V|34]]|35|$5|2S|2T|2U|A|$2V|36]]|37|$5|2S|2T|2U|A|$2V|38]]]]

<h2>pandas >= 0.24</h2>

<h3>Deprecate your usage of <code>.values</code> in favour of these methods!</h3>

From v0.24.0 onwards, we will have two brand spanking new, preferred methods for obtaining NumPy arrays from <code>Index</code>, <code>Series</code>, and <code>DataFrame</code> objects: they are <code>to_numpy()</code>, and <code>.array</code>. Regarding usage, the docs mention:

<blockquote>
 We haven’t removed or deprecated <code>Series.values</code> or
 <code>DataFrame.values</code>, but we highly recommend and using <code>.array</code> or
 <code>.to_numpy()</code> instead.
</blockquote>

See <a href="https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.24.0.html#accessing-the-values-in-a-series-or-index" rel="noreferrer">this section of the v0.24.0 release notes</a> for more information. 

<hr>

<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.to_numpy.html" rel="noreferrer"><code>to_numpy()</code> Method</a>

<pre><code>df.index.to_numpy()
# array(['a', 'b'], dtype=object)

df['A'].to_numpy()
# array([1, 4])
</code></pre>

By default, a view is returned. Any modifications made will affect the original.

<pre><code>v = df.index.to_numpy()
v[0] = -1

df
 A B
-1 1 2
b 4 5
</code></pre>

If you need a copy instead, use <code>to_numpy(copy=True</code>);

<pre><code>v = df.index.to_numpy(copy=True)
v[-1] = -123

df
 A B
a 1 2
b 4 5
</code></pre>

Note that this function also works for <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html" rel="noreferrer">DataFrames</a> (while <code>.array</code> does not).

<hr>

<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.array.html#pandas.Series.array" rel="noreferrer"><code>array</code> Attribute</a> 
This attribute returns an <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html" rel="noreferrer"><code>ExtensionArray</code></a> object that backs the Index/Series. 

<pre><code>pd.__version__
# '0.24.0rc1'

# Setup.
df = pd.DataFrame([[1, 2], [4, 5]], columns=['A', 'B'], index=['a', 'b'])
df

 A B
a 1 2
b 4 5
</code></pre>



<pre><code>df.index.array 
# &lt;PandasArray&gt;
# ['a', 'b']
# Length: 2, dtype: object

df['A'].array
# &lt;PandasArray&gt;
# [1, 4]
# Length: 2, dtype: int64
</code></pre>

From here, it is possible to get a list using <code>list</code>:

<pre><code>list(df.index.array)
# ['a', 'b']

list(df['A'].array)
# [1, 4]
</code></pre>

or, just directly call <code>.tolist()</code>:

<pre><code>df.index.tolist()
# ['a', 'b']

df['A'].tolist()
# [1, 4]
</code></pre>

Regarding what is returned, the docs mention,

<blockquote>
 For <code>Series</code> and <code>Index</code>es backed by normal NumPy arrays, <code>Series.array</code>
 will return a new <code>arrays.PandasArray</code>, which is a thin (no-copy)
 wrapper around a <code>numpy.ndarray</code>. <code>arrays.PandasArray</code> isn’t especially
 useful on its own, but it does provide the same interface as any
 extension array defined in pandas or by a third-party library.
</blockquote>

So, to summarise, <code>.array</code> will return either

<ol>
<li>The existing <code>ExtensionArray</code> backing the Index/Series, or </li>
<li>If there is a NumPy array backing the series, a new <code>ExtensionArray</code> object is created as a thin wrapper over the underlying array. </li>
</ol>

<hr>

Rationale for adding TWO new methods 
These functions were added as a result of discussions under two GitHub issues <a href="https://github.com/pandas-dev/pandas/issues/19954" rel="noreferrer">GH19954</a> and <a href="https://github.com/pandas-dev/pandas/issues/23623" rel="noreferrer">GH23623</a>. 

Specifically, the docs mention the rationale:

<blockquote>
 [...] with <code>.values</code> it was unclear whether the returned value would be the
 actual array, some transformation of it, or one of pandas custom
 arrays (like <code>Categorical</code>). For example, with <code>PeriodIndex</code>, <code>.values</code>
 generates a new <code>ndarray</code> of period objects each time. [...]
</blockquote>

These two functions aim to improve the consistency of the API, which is a major step in the right direction.

Lastly, <code>.values</code> will not be deprecated in the current version, but I expect this may happen at some point in the future, so I would urge users to migrate towards the newer API, as soon as you can.

blocks|key|687165|text|下面是将dataframe列转换为numpy数组的简单方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687166|df+=+pd.DataFrame(somedict)+
ytrain+=+df['label']
ytrain_numpy+=+np.array([x+for+x+in+ytrain['label']])|code-block|syntax|javascript|687167|ytrain_numpy是一个numpy数组。|687168|我尝试使用to.numpy()，但它给了我以下错误:+TypeError:不支持类型的转换：(dtype('O')，)，同时使用线性SVC进行二进制相关性分类。to.numpy()将dataFrame转换为numpy数组，但内部元素的数据类型为list，因此观察到上述错误。|offset|length|style|CODE|687169|entityMap^0|0|0|0|5|A|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

Below is a simple way to convert dataframe column into numpy array.

<pre><code>df = pd.DataFrame(somedict) 
ytrain = df['label']
ytrain_numpy = np.array([x for x in ytrain['label']])
</code></pre>

ytrain_numpy is a numpy array.

I tried with <code>to.numpy()</code> but it gave me the below error:
TypeError: no supported conversion for types: (dtype('O'),) while doing Binary Relevance classfication using Linear SVC.
to.numpy() was converting the dataFrame into numpy array but the inner element's data type was list because of which the above error was observed.

blocks|key|687185|text|一种更新的方法是使用.to_numpy()函数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687186|如果我有一个包含'price‘列的dataframe，我可以按如下方式转换它：|687187|priceArray+=+df['price'].to_numpy()|code-block|syntax|javascript|687188|您还可以将数据类型作为函数的参数进行传递|687189|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|O|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

A more recent way to do this is to use the .to_numpy() function.

If I have a dataframe with a column 'price', I can convert it as follows:

<pre><code>priceArray = df['price'].to_numpy()
</code></pre>

You can also pass the data type, such as float or object, as an argument of the function

Do you know how to get the index or column of a DataFrame as a NumPy array or python list?

How do I convert a pandas Series or index to a Numpy array?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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您知道如何以NumPy数组或python列表的形式获取DataFrame的索引或列吗？

问如何将pandas序列或索引转换为Numpy数组？
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