blocks|key|2934355|text|使用to_datetime函数，指定一个与您的数据匹配的format。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2934356|raw_data['Mycol']+=++pd.to_datetime(raw_data['Mycol'],+format='%25d%25b%25Y:%25H:%25M:%25S.%25f')|code-block|syntax|javascript|2934357|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html|1|http://strftime.org/^0|2|B|2|B|0|S|6|1|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]|$9|10|A|11|1|12]]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]|S|$5|N|O|P|E|$Q|T]]]]

Use the <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="noreferrer"><code>to_datetime</code></a> function, specifying a <a href="http://strftime.org/" rel="noreferrer">format</a> to match your data.

<pre><code>raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
</code></pre>

blocks|key|2722164|text|如果要转换多个列，可以执行以下操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2722165|df[["col1",+"col2",+"col3"]]+=+df[["col1",+"col2",+"col3"]].apply(pd.to_datetime)|code-block|syntax|javascript|2722166|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|$]]

If you have more than one column to be converted you can do the following:

<pre><code>df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)
</code></pre>

blocks|key|147399|text|您可以使用DataFrame方法.apply()对mycol值进行操作：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|147400|>>>+df+=+pd.DataFrame(['05SEP2014:00:00:00.000'],columns=['Mycol'])
>>>+df
++++++++++++++++++++Mycol
0++05SEP2014:00:00:00.000
>>>+import+datetime+as+dt
>>>+df['Mycol']+=+df['Mycol'].apply(lambda+x:+
++++++++++++++++++++++++++++++++++++dt.datetime.strptime(x,'%25d%25b%25Y:%25H:%25M:%25S.%25f'))
>>>+df
+++++++Mycol
0+2014-09-05|code-block|syntax|javascript|147401|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.apply.html^0|G|8|G|8|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]]]]

You can use the DataFrame method <a href="http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.apply.html" rel="noreferrer"><code>.apply()</code></a> to operate on the values in Mycol:

<pre><code>&gt;&gt;&gt; df = pd.DataFrame(['05SEP2014:00:00:00.000'],columns=['Mycol'])
&gt;&gt;&gt; df
 Mycol
0 05SEP2014:00:00:00.000
&gt;&gt;&gt; import datetime as dt
&gt;&gt;&gt; df['Mycol'] = df['Mycol'].apply(lambda x: 
 dt.datetime.strptime(x,'%d%b%Y:%H:%M:%S.%f'))
&gt;&gt;&gt; df
 Mycol
0 2014-09-05
</code></pre>

blocks|key|2722179|text|使用pandas+to_datetime函数将列解析为DateTime。此外，通过使用infer_datetime_format=True，它将自动检测格式并将提到的列转换为DateTime。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2722180|import+pandas+as+pd
raw_data['Mycol']+=++pd.to_datetime(raw_data['Mycol'],+infer_datetime_format=True)|code-block|syntax|javascript|2722181|entityMap^0|9|B|17|Q|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Use the pandas <code>to_datetime</code> function to parse the column as DateTime. Also, by using <code>infer_datetime_format=True</code>, it will automatically detect the format and convert the mentioned column to DateTime.

<pre><code>import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)
</code></pre>

blocks|key|2934378|text|raw_data['Mycol']+=++pd.to_datetime(raw_data['Mycol'],+format='%25d%25b%25Y:%25H:%25M:%25S.%25f')|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2934379|工作，但是它会导致Python警告:A值正试图在DataFrame中切片的副本上设置。请尝试使用.loc[row_indexer,col_indexer]+=+value|unstyled|offset|length|style|CODE|2934380|我猜这是由于一些链式索引造成的。|2934381|entityMap^0|0|1C|11|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|P|8|@$G|Q|H|R|I|J]]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|F|7|T|8|@]|9|@]|A|$]]]|N|$]]

<pre><code>raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
</code></pre>

works, however it results in a Python warning of 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using <code>.loc[row_indexer,col_indexer] = value</code> instead

I would guess this is due to some chaining indexing.

blocks|key|2722260|text|省时工具：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2722261|raw_data['Mycol']+=++pd.to_datetime(raw_data['Mycol'])|code-block|syntax|javascript|2722262|entityMap^0|0|5|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

Time Saver:
<pre><code>raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'])
</code></pre>

blocks|key|145849|text|需要注意的是，pandas.to_datetime几乎永远不会返回datetime.datetime。来自the+docs|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|145850|145851|区块引用|blockquote|145852|145853|145854|Returns+datetime
If+parsing+succeeded.+Return+type+depends+on+input:

list-like:+DatetimeIndex
Series:+Series+of+datetime64+dtype
scalar:+Timestamp

In+case+when+it+is+not+possible+to+return+designated+types+(e.g.+when+any+element+
of+input+is+before+Timestamp.min+or+after+Timestamp.max)+return+will+have+
datetime.datetime+type+(or+corresponding+array/Series).|code-block|syntax|javascript|145855|145856|区块报价|145857|145858|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html^0|1H|8|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]]|C|$]]|$1|D|3|-4|5|6|7|14|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|15|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|16|8|@]|9|@]|C|$]]|$1|I|3|-4|5|6|7|17|8|@]|9|@]|C|$]]|$1|J|3|K|5|L|7|18|8|@]|9|@]|C|$M|N]]|$1|O|3|-4|5|6|7|19|8|@]|9|@]|C|$]]|$1|P|3|Q|5|G|7|1A|8|@]|9|@]|C|$]]|$1|R|3|-4|5|6|7|1B|8|@]|9|@]|C|$]]|$1|S|3|-4|5|6|7|1C|8|@]|9|@]|C|$]]]|T|$U|$5|V|W|X|C|$Y|Z]]]]

It is important to note that pandas.to_datetime will almost never return a datetime.datetime. From <a href="https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html" rel="nofollow noreferrer">the docs</a>
<blockquote>
Blockquote
</blockquote>
<pre><code>Returns datetime
If parsing succeeded. Return type depends on input:

list-like: DatetimeIndex
Series: Series of datetime64 dtype
scalar: Timestamp

In case when it is not possible to return designated types (e.g. when any element 
of input is before Timestamp.min or after Timestamp.max) return will have 
datetime.datetime type (or corresponding array/Series).
</code></pre>
<blockquote>
Blockquote
</blockquote>

I have one field in a pandas DataFrame that was imported as string format. 
It should be a datetime variable.
How do I convert it to a datetime column and then filter based on date.

Example:

<ul>
<li>DataFrame Name: raw_data </li>
<li>Column Name: Mycol </li>
<li>Value
Format in Column: '05SEP2014:00:00:00.000'</li>
</ul>

Convert Pandas Column to DateTime

我在pandas DataFrame中有一个字段是以字符串格式导入的。它应该是datetime变量。如何将其转换为日期时间列，然后根据日期进行筛选。示例：DataFrame名称：raw_data 列名：Mycol 列中的值格式:Mycol

问将Pandas列转换为DateTime
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回答 7

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