blocks|key|1495404|text|当然，与大多数Python对象一样，您可以将新属性附加到pandas.DataFrame|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1495405|import+pandas+as+pd
df+=+pd.DataFrame([])
df.instrument_name+=+'Binky'|code-block|syntax|javascript|1495406|但是，请注意，虽然可以将属性附加到DataFrame，但在DataFrame上执行的操作(例如groupby、pivot、join或loc仅举几个例子)可能会返回一个新的DataFrame，而不需要附加元数据。熊猫还没有一个强大的传播附加到DataFrames的元数据的方法。|1495407|保存文件中的元数据是可能的。您可以找到一个示例，说明如何在HDF5文件这里中存储元数据。|1495408|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pydata/pandas/issues/2485|1|https://stackoverflow.com/a/29130146/190597^0|S|G|0|0|1B|7|1J|5|1P|4|1U|3|39|H|0|0|Z|2|1|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|M|3|N|5|6|7|1E|8|@]|D|@$9|1F|A|1G|1|1H]]|E|$]]|$1|O|3|-4|5|6|7|1I|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]|W|$5|R|S|T|E|$U|X]]]]

Sure, like most Python objects, you can attach new attributes to a <code>pandas.DataFrame</code>:

<pre><code>import pandas as pd
df = pd.DataFrame([])
df.instrument_name = 'Binky'
</code></pre>

Note, however, that while you can attach attributes to a DataFrame, operations performed on the DataFrame (such as <code>groupby</code>, <code>pivot</code>, <code>join</code> or <code>loc</code> to name just a few) may return a new DataFrame without the metadata attached. Pandas does not yet have a robust method of propagating <a href="https://github.com/pydata/pandas/issues/2485" rel="noreferrer">metadata attached to DataFrames</a>.

Preserving the metadata in a file is possible. You can find an example of how to store metadata in an HDF5 file <a href="https://stackoverflow.com/a/29130146/190597">here</a>.

blocks|key|2286069|text|从熊猫1.0开始，可能更早的时候，这里就有了Dataframe.attrs的房产。这是实验性的，但这可能是你将来想要的。例如：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2286070|import+pandas+as+pd
df+=+pd.DataFrame([])
df.attrs['instrument_name']+=+'Binky'|code-block|syntax|javascript|2286071|在docs+这里中找到它。|2286072|用to_parquet和from_parquet尝试这一点，它似乎不会持久，所以请确保用您的用例来检查它。|2286073|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html^0|M|F|0|0|6|2|0|0|1|A|C|C|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|10|8|@]|D|@$9|11|A|12|1|13]]|E|$]]|$1|M|3|N|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|19|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

As of pandas 1.0, possibly earlier, there is now a <code>Dataframe.attrs</code> property. It is experimental, but this is probably what you'll want in the future.
For example:
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df = pd.DataFrame([])
df.attrs['instrument_name'] = 'Binky'
</code></pre>
Find it in the docs <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html" rel="noreferrer">here</a>.
Trying this out with <code>to_parquet</code> and then <code>from_parquet</code>, it doesn't seem to persist, so be sure you check that out with your use case.

blocks|key|2285940|text|我自己也遇到了这个问题。从熊猫0.13开始，DataFrames有一个_metadata属性，这个属性通过返回新DataFrames的函数持续存在。而且，序列化似乎也挺好的(我只试过json，但我认为hdf也包括在内)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2285941|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|$]]

Just ran into this issue myself. As of pandas 0.13, DataFrames have a _metadata attribute on them that does persist through functions that return new DataFrames. Also seems to survive serialization just fine (I've only tried json, but I imagine hdf is covered as well).

blocks|key|2285927|text|不怎么有意思。尽管您可以像@unutbu提到的那样向DataFrame类添加包含元数据的属性，但是许多DataFrame方法返回一个新的DataFrame，这样您的元数据就会丢失。如果需要操作数据，那么最好的选择是将元数据和dataframe包装到另一个类中。参见关于GitHub：https://github.com/pydata/pandas/issues/2485的讨论|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2285928|当前有一个开放的拉请求来添加一个MetaDataFrame对象，这将更好地支持元数据。|2285929|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pydata/pandas/issues/2485|1|https://github.com/pydata/pandas/pull/2695^0|3X|18|0|0|8|3|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

Not really. Although you could add attributes containing metadata to the DataFrame class as @unutbu mentions, many DataFrame methods return a new DataFrame, so your meta data would be lost. If you need to manipulate your dataframe, then the best option would be to wrap your metadata and DataFrame in another class. See this discussion on GitHub: <a href="https://github.com/pydata/pandas/issues/2485">https://github.com/pydata/pandas/issues/2485</a>

There is currently an open <a href="https://github.com/pydata/pandas/pull/2695">pull request</a> to add a MetaDataFrame object, which would support metadata better.

blocks|key|2286044|text|将任意属性附加到DataFrame对象的顶部答案是好的，但是如果使用字典、列表或元组，则会发出"Pandas不允许通过新属性名创建列“的错误。以下解决方案用于存储任意属性。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2286045|from+types+import+SimpleNamespace
df+=+pd.DataFrame()
df.meta+=+SimpleNamespace()
df.meta.foo+=+[1,2,3]|code-block|syntax|javascript|2286046|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The top answer of attaching arbitrary attributes to the DataFrame object is good, but if you use a dictionary, list, or tuple, it will emit an error of "Pandas doesn't allow columns to be created via a new attribute name". The following solution works for storing arbitrary attributes. 

<pre><code>from types import SimpleNamespace
df = pd.DataFrame()
df.meta = SimpleNamespace()
df.meta.foo = [1,2,3]
</code></pre>

blocks|key|2285993|text|正如其他回答和评论中提到的，_metadata不是公共API的一部分，所以在生产环境中使用它肯定不是一个好主意。但是你可能还是想在研究原型中使用它，如果它停止工作的话就更换它。现在，它与groupby/apply一起工作，这是很有帮助的。这是一个例子(我在其他答案中找不到)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2285994|df+=+pd.DataFrame([1,+2,+2,+3,+3],+columns=['val'])+
df.my_attribute+=+"my_value"
df._metadata.append('my_attribute')
df.groupby('val').apply(lambda+group:+group.my_attribute)|code-block|syntax|javascript|2285995|输出：|2285996|val
1++++my_value
2++++my_value
3++++my_value
dtype:+object|2285997|entityMap^0|E|9|2L|7|2T|5|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

As mentioned in other answers and comments, <code>_metadata</code> is not a part of public API, so it's definitely not a good idea to use it in a production environment. But you still may want to use it in a research prototyping and replace it if it stops working. And right now it works with <code>groupby</code>/<code>apply</code>, which is helpful. This is an example (which I couldn't find in other answers):

<pre><code>df = pd.DataFrame([1, 2, 2, 3, 3], columns=['val']) 
df.my_attribute = "my_value"
df._metadata.append('my_attribute')
df.groupby('val').apply(lambda group: group.my_attribute)
</code></pre>

Output:

<pre><code>val
1 my_value
2 my_value
3 my_value
dtype: object
</code></pre>

blocks|key|2286036|text|正如@choldgraf所提到的，我发现X阵列是在比较数据和在几个数据之间绘制结果时附加元数据的优秀工具。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2286037|在我的工作中，我们经常比较几个固件版本和不同测试场景的结果，并添加这样的信息：|2286038|df+=+pd.read_csv(meaningless_test)
metadata+=+{'fw':+foo,+'test_name':+bar,+'scenario':+sc_01}
ds+=+xr.Dataset.from_dataframe(df)
ds.attrs+=+metadata|code-block|syntax|javascript|2286039|entityMap|0|LINK|mutability|MUTABLE|url|http://xarray.pydata.org/en/stable/^0|K|3|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@]|C|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

As mentioned by @choldgraf I have found <a href="http://xarray.pydata.org/en/stable/" rel="noreferrer">xarray</a> to be an excellent tool for attaching metadata when comparing data and plotting results between several dataframes. 

In my work, we are often comparing the results of several firmware revisions and different test scenarios, adding this information is as simple as this:

<pre><code>df = pd.read_csv(meaningless_test)
metadata = {'fw': foo, 'test_name': bar, 'scenario': sc_01}
ds = xr.Dataset.from_dataframe(df)
ds.attrs = metadata
</code></pre>

blocks|key|2285965|text|在此之前，我认为如果需要元数据在I/O上持久化，这可能会很有帮助。我一直在使用一个名为h5io的相对新的包来完成这一任务。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2285966|它应该允许您对几种常见格式进行HDF5的快速读/写，其中一种是数据格式。因此，例如，您可以在字典中放置一个数据，并将元数据作为字段包含在字典中。例如：|2285967|save_dict+=+dict(data=my_df,+name='chris',+record_date='1/1/2016')
h5io.write_hdf5('path/to/file.hdf5',+save_dict)
in_data+=+h5io.read_hdf5('path/to/file.hdf5')
df+=+in_data['data']
name+=+in_data['name']
etc...|code-block|syntax|javascript|2285968|另一种选择是查看像X光片这样的项目，它在某些方面比较复杂，但我认为它确实允许您使用元数据，并且很容易转换为DataFrame。|2285969|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/h5io/h5io|1|https://github.com/pydata/xarray^0|17|4|0|0|0|0|9|3|1|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|6|7|10|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|11|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|M|3|-4|5|6|7|16|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]|U|$5|P|Q|R|C|$S|V]]]]

Coming pretty late to this, I thought this might be helpful if you need metadata to persist over I/O. There's a relatively new package called <a href="https://github.com/h5io/h5io" rel="nofollow">h5io</a> that I've been using to accomplish this.

It should let you do a quick read/write from HDF5 for a few common formats, one of them being a dataframe. So you can, for example, put a dataframe in a dictionary and include metadata as fields in the dictionary. E.g.:

<pre><code>save_dict = dict(data=my_df, name='chris', record_date='1/1/2016')
h5io.write_hdf5('path/to/file.hdf5', save_dict)
in_data = h5io.read_hdf5('path/to/file.hdf5')
df = in_data['data']
name = in_data['name']
etc...
</code></pre>

Another option would be to look into a project like <a href="https://github.com/pydata/xarray" rel="nofollow">xray</a>, which is more complex in some ways, but I think it does let you use metadata and is pretty easy to convert to a DataFrame.

blocks|key|1495682|text|我一直在寻找解决方案，并发现熊猫框架具有attrs属性。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1495683|pd.DataFrame().attrs.update({'your_attribute'+:+'value'})
frame.attrs['your_attribute']|code-block|syntax|javascript|1495684|每当您传递该属性时，该属性将始终坚持到您的帧上！|1495685|entityMap^0|K|5|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|N|$]]

I have been looking for a solution and found that pandas frame has the property <code>attrs</code>

<pre><code>pd.DataFrame().attrs.update({'your_attribute' : 'value'})
frame.attrs['your_attribute']
</code></pre>

This attribute will always stick to your frame whenever you pass it!

blocks|key|176117|text|我也遇到了同样的问题，并利用元数据从字典中创建了一个新的、较小的DF：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|176118|++++meta+=+{"name":+"Sample+Dataframe",+"Created":+"19/07/2019"}
++++dfMeta+=+pd.DataFrame.from_dict(meta,+orient='index')|code-block|syntax|javascript|176119|然后，可以将此dfMeta与原始DF一起保存在泡菜等中。|176120|有关使用泡菜保存和检索多个数据文件的最佳答案，请参见保存并加载泡菜文件中的多个对象？+(Lutz的答案)|offset|length|176121|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/20716812/saving-and-loading-multiple-objects-in-pickle-file^0|0|0|0|Q|G|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]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|X|8|@]|9|@$K|Y|L|Z|1|10]]|A|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

I was having the same issue and used a workaround of creating a new, smaller DF from a dictionary with the metadata:

<pre><code> meta = {"name": "Sample Dataframe", "Created": "19/07/2019"}
 dfMeta = pd.DataFrame.from_dict(meta, orient='index')
</code></pre>

This dfMeta can then be saved alongside your original DF in pickle etc

See <a href="https://stackoverflow.com/questions/20716812/saving-and-loading-multiple-objects-in-pickle-file">Saving and loading multiple objects in pickle file?</a> (Lutz's answer) for excellent answer on saving and retrieving multiple dataframes using pickle

blocks|key|176215|text|用熊猫添加原始属性(如df.my_metadata+=+"source.csv")并不是个好主意。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|176216|即使在最新版本(python3.8上的1.2.4)上，这样做也会在使用read_csv这样的简单操作时随机导致分段错误。调试将很困难，因为read_csv会正常工作，但稍后(似乎是随机的)您会发现数据已经从内存中释放了。|176217|与熊猫有关的cpython扩展似乎对dataframe的数据布局做出了非常明确的假设。|176218|attrs是目前使用元数据属性的唯一安全方法：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html|176219|例如：|176220|df.attrs.update({'my_metadata'+:+"source.csv"})|code-block|syntax|javascript|176221|在所有情况下，吸引人应该如何表现还没有得到充分的证实。您可以在以下问题中帮助提供有关attrs预期行为的反馈：https://github.com/pandas-dev/pandas/issues/28283|176222|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html|1|https://github.com/pandas-dev/pandas/issues/28283^0|B|T|0|Z|8|1X|8|0|0|0|5|N|2E|0|0|0|0|16|5|1J|1D|1|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|17|8|@$9|18|A|19|B|C]|$9|1A|A|1B|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1C|8|@]|D|@]|E|$]]|$1|J|3|K|5|6|7|1D|8|@$9|1E|A|1F|B|C]]|D|@$9|1G|A|1H|1|1I]]|E|$]]|$1|L|3|M|5|6|7|1J|8|@]|D|@]|E|$]]|$1|N|3|O|5|P|7|1K|8|@]|D|@]|E|$Q|R]]|$1|S|3|T|5|6|7|1L|8|@$9|1M|A|1N|B|C]]|D|@$9|1O|A|1P|1|1Q]]|E|$]]|$1|U|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]]|V|$W|$5|X|Y|Z|E|$10|11]]|12|$5|X|Y|Z|E|$10|13]]]]

Adding raw attributes with pandas (e.g. <code>df.my_metadata = &quot;source.csv&quot;</code>) is not a good idea.
Even on the latest version (1.2.4 on python 3.8), doing this will randomly cause segfaults when doing very simple operations with things like <code>read_csv</code>. It will be hard to debug, because <code>read_csv</code> will work fine, but later on (seemingly at random) you will find that the dataframe has been freed from memory.
It seems cpython extensions involved with pandas seem to make very explicit assumptions about the data layout of the dataframe.
<code>attrs</code> is the only safe way to use metadata properties currently:
<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.attrs.html</a>
e.g.
<pre><code>df.attrs.update({'my_metadata' : &quot;source.csv&quot;})
</code></pre>
How attrs should behave in all scenarios is not fully fleshed out. You can help provide feedback on the expected behaviors of <code>attrs</code> in this issue: <a href="https://github.com/pandas-dev/pandas/issues/28283" rel="nofollow noreferrer">https://github.com/pandas-dev/pandas/issues/28283</a>

blocks|key|2286125|text|对于那些希望将数据块存储在HDFStore中的人，根据pandas.pydata.org，推荐的方法是：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2286126|import+pandas+as+pd

df+=+pd.DataFrame(dict(keys=['a',+'b',+'c'],+values=['1',+'2',+'3']))
df.to_hdf('/tmp/temp_df.h5',+key='temp_df')
store+=+pd.HDFStore('/tmp/temp_df.h5')+
store.get_storer('temp_df').attrs.attr_key+=+'attr_value'
store.close()|code-block|syntax|javascript|2286127|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/dev/user_guide/cookbook.html^0|R|H|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

For those looking to store the datafram in an HDFStore, according to <a href="https://pandas.pydata.org/pandas-docs/dev/user_guide/cookbook.html" rel="nofollow noreferrer">pandas.pydata.org</a>, the recommended approach is:
<pre><code>import pandas as pd

df = pd.DataFrame(dict(keys=['a', 'b', 'c'], values=['1', '2', '3']))
df.to_hdf('/tmp/temp_df.h5', key='temp_df')
store = pd.HDFStore('/tmp/temp_df.h5') 
store.get_storer('temp_df').attrs.attr_key = 'attr_value'
store.close()
</code></pre>

Is it possible to add some meta-information/metadata to a pandas DataFrame?

For example, the instrument's name used to measure the data, the instrument responsible, etc.

One workaround would be to create a column with that information, but it seems wasteful to store a single piece of information in every row!

Adding meta-information/metadata to pandas DataFrame

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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能为熊猫DataFrame添加一些元信息/元数据吗？例如，用于测量数据的仪器名称、负责的仪器等。一种解决方法是使用该信息创建一个列，但是在每一行中存储一条信息似乎是浪费的！

问向熊猫DataFrame添加元信息/元数据
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