blocks|key|1643126|text|这将会起到作用。请注意，使用display()而不是print。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1643127|with+pd.option_context('display.max_rows',+5,+'display.max_columns',+None):+
++++display(my_df)|code-block|syntax|javascript|1643128|编辑：|1643129|需要使用display，因为pd.option_context设置仅适用于display，而不适用于print。|1643130|entityMap^0|E|9|0|0|0|4|7|E|H|11|7|1E|5|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|V|8|@$9|W|A|X|B|C]|$9|Y|A|Z|B|C]|$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|P|$]]

This will do the trick. Note the use of <code>display()</code> instead of print.

<pre><code>with pd.option_context('display.max_rows', 5, 'display.max_columns', None): 
 display(my_df)
</code></pre>

EDIT:

The use of <code>display</code> is required because <code>pd.option_context</code> settings only apply to <code>display</code> and not to <code>print</code>.

blocks|key|1857161|text|在交互式控制台中，很容易做到：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857162|data_all2.columns.tolist()|code-block|syntax|javascript|1857163|或者在脚本中这样做：|1857164|print(data_all2.columns.tolist())|1857165|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

In the interactive console, it's easy to do:

<pre><code>data_all2.columns.tolist()
</code></pre>

Or this within a script:

<pre><code>print(data_all2.columns.tolist())
</code></pre>

blocks|key|1857202|text|对我来说起作用的是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857203|pd.options.display.max_seq_items+=+None|code-block|syntax|javascript|1857204|也可以将其设置为大于列数的整数。|1857205|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

What worked for me was the following:

<pre><code>pd.options.display.max_seq_items = None
</code></pre>

You can also set it to an integer larger than your number of columns.

blocks|key|1643246|text|这不是一个传统的答案，但我猜您可以将数据帧转置为查看行而不是列。我使用这个是因为我发现查看行比查看列更“直观”：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1643247|data_all2.T|code-block|syntax|javascript|1643248|这将允许您查看所有行。此操作不是永久性的，它只是让您查看数据帧的转置版本。|1643249|如果行仍然被截断，只需使用print(data_all2.T)查看所有内容。|offset|length|style|CODE|1643250|entityMap^0|0|0|0|D|I|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|$]]

Not a conventional answer, but I guess you could transpose the dataframe to look at the rows instead of the columns. I use this because I find looking at rows more 'intuitional' than looking at columns:

<pre><code>data_all2.T
</code></pre>

This should let you view all the rows. This action is not permanent, it just lets you view the transposed version of the dataframe.

If the rows are still truncated, just use <code>print(data_all2.T)</code> to view everything.

blocks|key|1643262|text|我找到的最简单的方法就是|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1643263|list(df.columns)|code-block|syntax|javascript|1643264|就我个人而言，我不想更改全局变量，我并不是经常想看到所有列的名称。|1643265|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

The easiest way I've found is just
<pre><code>list(df.columns)
</code></pre>
Personally I wouldn't want to change the globals, it's not that often I want to see all the columns names.

blocks|key|1643215|text|你可以试试这个|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1643216|pd.pandas.set_option('display.max_columns',+None)|code-block|syntax|javascript|1643217|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|$]]

you can try this

<pre><code>pd.pandas.set_option('display.max_columns', None)
</code></pre>

blocks|key|1643295|text|被接受的答案导致我的列名被换行。要显示所有列名而不换行，请同时设置display.max_columns和display.width|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1643296|pandas.set_option('display.max_columns',+None)
pandas.set_option('display.width',+1000)|code-block|syntax|javascript|1643297|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/docs/reference/api/pandas.set_option.html^0|1H|D|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]]]]

The accepted answer caused my column names to wrap around. To show all the column names without wrapping, set both display.max_columns and the <a href="https://pandas.pydata.org/docs/reference/api/pandas.set_option.html" rel="nofollow noreferrer">display.width</a>:
<pre><code>pandas.set_option('display.max_columns', None)
pandas.set_option('display.width', 1000)
</code></pre>

blocks|key|1857096|text|要获得所有列名，可以遍历data_all2.columns。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1857097|columns+=+data_all2.columns
for+col+in+columns:
++++print+col|code-block|syntax|javascript|1857098|您将获得所有列名。或者，您可以将所有列名存储到另一个列表变量中，然后打印列表。|1857099|entityMap^0|C|H|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|$]]

To get all column name you can iterate over the <code>data_all2.columns</code>.

<pre><code>columns = data_all2.columns
for col in columns:
 print col
</code></pre>

You will get all column names.
Or you can store all column names to another list variable and then print list.

blocks|key|1857378|text|我知道这是一个重复，但我总是以复制粘贴和修改YOLO的答案而告终：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857379|pd.set_option('display.max_columns',+500)
pd.set_option('display.max_rows',+500)|code-block|syntax|javascript|1857380|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|$]]

I know it is a repetition but I always end up copy pasting and modifying YOLO's answer:

<pre><code>pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
</code></pre>

blocks|key|1856867|text|如果您只是想查看所有的列，您可以做一些这样的事情作为快速修复|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1856868|cols+=+data_all2.columns|code-block|syntax|javascript|1856869|现在cols将表现为一个可以索引的迭代变量。例如|1856870|cols[11:20]|1856871|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

If you just want to see all the columns you can do something of this sort as a quick fix

<pre><code>cols = data_all2.columns
</code></pre>

now cols will behave as a iterative variable that can be indexed. for example

<pre><code>cols[11:20]
</code></pre>

blocks|key|1856922|text|一个又快又脏的解决方案是将其转换为字符串|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1856923|print('\t'.join(data_all2.columns))|code-block|syntax|javascript|1856924|当然，这会导致所有名称都以制表符分隔打印出来，但请注意，由于有102个名称，所有名称都相当长，因此阅读起来会有些困难|1856925|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

A quick and dirty solution would be to convert it to a string

<pre><code>print('\t'.join(data_all2.columns))
</code></pre>

would cause all of them to be printed out separated by tabs
Of course, do note that with 102 names, all of them rather long, this will be a bit hard to read through

blocks|key|1857263|text|我有很多重复的列名，一旦我运行|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857264|df+=+df.loc[:,~df.columns.duplicated()]|code-block|syntax|javascript|1857265|我能够看到列的完整列表|1857266|图片来源：https://stackoverflow.com/a/40435354/5846417|offset|length|1857267|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/40435354/5846417^0|0|0|0|5|18|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 had lots of duplicate column names, and once I ran

<pre><code>df = df.loc[:,~df.columns.duplicated()]
</code></pre>

I was able to see the full list of columns

Credit:
<a href="https://stackoverflow.com/a/40435354/5846417">https://stackoverflow.com/a/40435354/5846417</a>

blocks|key|1857557|text|我可能说错了，但是我带着我发现的相同类型的问题来到这个线程，如果你想查看长长的列表和索引中的所有内容，这是一个简单的答案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857558|这是我在Spyder中使用的：|1857559|print(df.info())+|code-block|syntax|javascript|1857560|或者这是Jupyter所需要的：|1857561|df.info()|1857562|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

I may be off the mark but I came to this thread with the same type of problem I found this is the simple answer if you want to see everything in a long list and the index.
This is what I use in Spyder:
<pre><code>print(df.info()) 
</code></pre>
or this be what is needed in Jupyter:
<pre><code>df.info()
</code></pre>

blocks|key|1857598|text|你可以这样做|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1857599|df.info(show_counts=True)|code-block|syntax|javascript|1857600|它将显示所有列。将show_counts设置为True可显示not_null数据的计数。|offset|length|style|CODE|1857601|entityMap^0|0|0|9|B|N|4|U|8|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|$]]

You can do like this
<pre><code>df.info(show_counts=True)
</code></pre>
It will show all the columns. Setting <code>show_counts</code> to <code>True</code> shows the count of <code>not_null</code> data.

I have a dataframe that consist of hundreds of columns, and I need to see all column names.

What I did:

<pre><code>In[37]:
data_all2.columns
</code></pre>

The output is:

<pre><code>Out[37]:
Index(['customer_id', 'incoming', 'outgoing', 'awan', 'bank', 'family', 'food',
 'government', 'internet', 'isipulsa',
 ...
 'overdue_3months_feature78', 'overdue_3months_feature79',
 'overdue_3months_feature80', 'overdue_3months_feature81',
 'overdue_3months_feature82', 'overdue_3months_feature83',
 'overdue_3months_feature84', 'overdue_3months_feature85',
 'overdue_3months_feature86', 'loan_overdue_3months_total_y'],
 dtype='object', length=102)
</code></pre>

How do I show all columns, instead of a truncated list?

How to show all columns' names on a large pandas dataframe?

我有一个由数百列组成的数据框架，我需要看到所有的列名。我做了什么：In[37]:data_all2.columns输出为：Out[37]:Index(['customer_id', 'incoming', 'outgoing', 'awan', 'bank', 'family', 'food',       'government', 'internet', 'isipulsa',       .

问如何在大型pandas数据帧上显示所有列的名称？
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问如何在大型pandas数据帧上显示所有列的名称？EN

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问如何在大型pandas数据帧上显示所有列的名称？
EN