blocks|key|4694498|text|如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4694499|import+pandas+as+pd
df+=+pd.DataFrame({"pear":+[1,2,3],+"apple":+[2,3,4],+"orange":+[3,4,5]})

len(df.columns)
3|code-block|syntax|javascript|4694500|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|$]]

Like so:

<pre><code>import pandas as pd
df = pd.DataFrame({"pear": [1,2,3], "apple": [2,3,4], "orange": [3,4,5]})

len(df.columns)
3
</code></pre>

blocks|key|4694532|text|替代方案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4694533|df.shape[1]|code-block|syntax|javascript|4694534|(df.shape[0]为行数)|offset|length|style|CODE|4694535|entityMap^0|0|0|1|B|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]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Alternative: 

<pre><code>df.shape[1]
</code></pre>

(<code>df.shape[0]</code> is the number of rows)

blocks|key|188605|text|如果保存数据帧的变量称为df，则：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|188606|len(df.columns)|code-block|syntax|javascript|188607|给出列数。|188608|对于那些想要行数的人：|188609|len(df.index)|188610|对于同时包含行数和列数的元组：|188611|df.shape|188612|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|V|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|W|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|6|7|X|8|@]|9|@]|A|$]]|$1|O|3|P|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

If the variable holding the dataframe is called df, then:

<pre><code>len(df.columns)
</code></pre>

gives the number of columns.

And for those who want the number of rows:

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

For a tuple containing the number of both rows and columns:

<pre><code>df.shape
</code></pre>

blocks|key|188626|text|这对我来说很有效，len(list(df))。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|188627|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|$]]

This worked for me len(list(df)).

blocks|key|4694568|text|df.info()函数会给出类似下面这样的结果。如果您使用的是不带sep参数的Pandas或带"，“的sep的read_csv方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4694569|raw_data+=+pd.read_csv("a1:\aa2/aaa3/data.csv")
raw_data.info()
<class+'pandas.core.frame.DataFrame'>
RangeIndex:+5144+entries,+0+to+5143
Columns:+145+entries,+R_fighter+to+R_age|code-block|syntax|javascript|4694570|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|$]]

df.info() function will give you result something like as below. 
If you are using read_csv method of Pandas without sep parameter or sep with ",".

<pre><code>raw_data = pd.read_csv("a1:\aa2/aaa3/data.csv")
raw_data.info()
&lt;class 'pandas.core.frame.DataFrame'&gt;
RangeIndex: 5144 entries, 0 to 5143
Columns: 145 entries, R_fighter to R_age
</code></pre>

blocks|key|188659|text|令人惊讶的是，我还没有看到这一点，所以不再赘述，这里是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|188660|df.columns.size|offset|length|188661|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.size.html?highlight=size#pandas.Index.size^0|0|0|F|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

Surprised I haven't seen this yet, so without further ado, here is:
<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.size.html?highlight=size#pandas.Index.size" rel="nofollow noreferrer">df.columns.size</a>

blocks|key|188710|text|为了在您的总形状中包含行索引“列”的数量，我会亲自将列数df.columns.size与属性pd.Index.nlevels/pd.MultiIndex.nlevels相加|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|188711|设置虚拟数据|188712|import+pandas+as+pd

flat_index+=+pd.Index([0,+1,+2])
multi_index+=+pd.MultiIndex.from_tuples([("a",+1),+("a",+2),+("b",+1),+names=["letter",+"id"])

columns+=+["cat",+"dog",+"fish"]

data+=+[[1,+2,+3],+[4,+5,+6],+[7,+8,+9]]
flat_df+=+pd.DataFrame(data,+index=flat_index,+columns=columns)
multi_df+=+pd.DataFrame(data,+index=multi_index,+columns=columns)

#+Show+data
#+-----------------
#+3+columns,+4+including+the+index
print(flat_df)
++++cat++dog++fish
id++++++++++++++++
0+++++1++++2+++++3
1+++++4++++5+++++6
2+++++7++++8+++++9

#+-----------------
#+3+columns,+5+including+the+index
print(multi_df)
+++++++++++cat++dog++fish
letter+id++++++++++++++++
a++++++1+++++1++++2+++++3
+++++++2+++++4++++5+++++6
b++++++1+++++7++++8+++++9|code-block|syntax|javascript|188713|将我们的流程写成一个函数：|188714|def+total_ncols(df,+include_index=False):
++++ncols+=+df.columns.size
++++if+include_index+is+True:
++++++++ncols+%2B=+df.index.nlevels
++++return+ncols

print("Ignore+the+index:")
print(total_ncols(flat_df),+total_ncols(multi_df))

print("Include+the+index:")
print(total_ncols(flat_df,+include_index=True),+total_ncols(multi_df,+include_index=True))|188715|这将打印：|188716|Ignore+the+index:
3+3

Include+the+index:
4+5|188717|如果您想在索引是pd.MultiIndex的情况下只包含索引的数量，那么可以在定义的函数中抛出一个isinstance检查。|188718|作为另一种选择，您可以使用df.reset_index().columns.size来实现相同的结果，但这不会有很好的性能，因为我们暂时将新列插入到索引中，并在获得列数之前创建新的索引。|188719|entityMap^0|S|F|1A|G|1R|L|0|0|0|0|0|0|0|8|D|1D|A|0|D|T|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|17|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|18|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|19|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|1A|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|1B|8|@]|D|@]|E|$]]|$1|S|3|T|5|J|7|1C|8|@]|D|@]|E|$K|L]]|$1|U|3|V|5|6|7|1D|8|@$9|1E|A|1F|B|C]|$9|1G|A|1H|B|C]]|D|@]|E|$]]|$1|W|3|X|5|6|7|1I|8|@$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|1L|8|@]|D|@]|E|$]]]|Z|$]]

In order to include the number of row index &quot;columns&quot; in your total shape I would personally add together the number of columns <code>df.columns.size</code> with the attribute <code>pd.Index.nlevels</code>/<code>pd.MultiIndex.nlevels</code>:
Set up dummy data
<pre><code>import pandas as pd

flat_index = pd.Index([0, 1, 2])
multi_index = pd.MultiIndex.from_tuples([(&quot;a&quot;, 1), (&quot;a&quot;, 2), (&quot;b&quot;, 1), names=[&quot;letter&quot;, &quot;id&quot;])

columns = [&quot;cat&quot;, &quot;dog&quot;, &quot;fish&quot;]

data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat_df = pd.DataFrame(data, index=flat_index, columns=columns)
multi_df = pd.DataFrame(data, index=multi_index, columns=columns)

# Show data
# -----------------
# 3 columns, 4 including the index
print(flat_df)
 cat dog fish
id 
0 1 2 3
1 4 5 6
2 7 8 9

# -----------------
# 3 columns, 5 including the index
print(multi_df)
 cat dog fish
letter id 
a 1 1 2 3
 2 4 5 6
b 1 7 8 9
</code></pre>
Writing our process as a function:
<pre><code>def total_ncols(df, include_index=False):
 ncols = df.columns.size
 if include_index is True:
 ncols += df.index.nlevels
 return ncols

print(&quot;Ignore the index:&quot;)
print(total_ncols(flat_df), total_ncols(multi_df))

print(&quot;Include the index:&quot;)
print(total_ncols(flat_df, include_index=True), total_ncols(multi_df, include_index=True))
</code></pre>
This prints:
<pre><code>Ignore the index:
3 3

Include the index:
4 5
</code></pre>
If you want to only include the number of indices if the index is a <code>pd.MultiIndex</code>, then you can throw in an <code>isinstance</code> check in the defined function.
As an alternative, you could use <code>df.reset_index().columns.size</code> to achieve the same result, but this won't be as performant since we're temporarily inserting new columns into the index and making a new index before getting the number of columns.

blocks|key|4694643|text|#use+a+regular+expression+to+parse+the+column+count
#https://docs.python.org/3/library/re.html

buffer+=+io.StringIO()
df.info(buf=buffer)
s+=+buffer.getvalue()
pat=re.search(r"total\s{1}[0-9]\s{1}column",s)
print(s)
phrase=pat.group(0)
value=re.findall(r'[0-9]%2B',phrase)[0]
print(int(value))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4694644|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>#use a regular expression to parse the column count
#https://docs.python.org/3/library/re.html

buffer = io.StringIO()
df.info(buf=buffer)
s = buffer.getvalue()
pat=re.search(r&quot;total\s{1}[0-9]\s{1}column&quot;,s)
print(s)
phrase=pat.group(0)
value=re.findall(r'[0-9]+',phrase)[0]
print(int(value))
</code></pre>

blocks|key|4694678|text|import+pandas+as+pd
df+=+pd.DataFrame({"pear":+[1,2,3],+"apple":+[2,3,4],+"orange":+[3,4,5]})


print(len(list(df.iterrows())))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4694679|给出行的长度|unstyled|4694680|3

[Program+finished]|4694681|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|L|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$B|C]]|$1|I|3|-4|5|F|7|N|8|@]|9|@]|A|$]]]|J|$]]

<pre><code>import pandas as pd
df = pd.DataFrame({&quot;pear&quot;: [1,2,3], &quot;apple&quot;: [2,3,4], &quot;orange&quot;: [3,4,5]})


print(len(list(df.iterrows())))
</code></pre>
gives length of rows
<pre><code>3

[Program finished]
</code></pre>

How do you programmatically retrieve the number of columns in a pandas dataframe? I was hoping for something like:

<pre><code>df.num_columns
</code></pre>

How do I retrieve the number of columns in a Pandas data frame?

如何以编程方式检索pandas数据帧中的列数？我希望是这样的：df.num_columns

问如何检索Pandas数据框中的列数？
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