问题：将Pandas列转换为DateTime
我在以字符串格式导入的pandas DataFrame中有一个字段。它应该是日期时间变量。如何将其转换为datetime列，然后根据日期进行过滤。
例：
- 数据框名称：raw_data
- 列名称：Mycol
- 列中的值格式：“ 05SEP2014：00：00：00.000”
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:
- DataFrame Name: raw_data
- Column Name: Mycol
- Value
Format in Column: ’05SEP2014:00:00:00.000′
回答 0
使用该to_datetime函数，指定一种格式以匹配您的数据。
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
Use the to_datetime function, specifying a format to match your data.
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
回答 1
您可以使用DataFrame方法.apply()对Mycol中的值进行操作：
>>> 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,'%d%b%Y:%H:%M:%S.%f'))
>>> df
Mycol
0 2014-09-05
You can use the DataFrame method .apply() to operate on the values in Mycol:
>>> 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,'%d%b%Y:%H:%M:%S.%f'))
>>> df
Mycol
0 2014-09-05
回答 2
如果要转换的列不止一个，则可以执行以下操作：
df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)
If you have more than one column to be converted you can do the following:
df[["col1", "col2", "col3"]] = df[["col1", "col2", "col3"]].apply(pd.to_datetime)
回答 3
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
可以，但是会导致Python警告：试图在DataFrame的切片副本上设置一个值。尝试.loc[row_indexer,col_indexer] = value改用
我猜这是由于一些链接索引。
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], format='%d%b%Y:%H:%M:%S.%f')
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 .loc[row_indexer,col_indexer] = value instead
I would guess this is due to some chaining indexing.
回答 4
使用pandas to_datetime函数将列解析为DateTime。另外，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。
import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)
Use the pandas to_datetime function to parse the column as DateTime. Also, by using infer_datetime_format=True, it will automatically detect the format and convert the mentioned column to DateTime.
import pandas as pd
raw_data['Mycol'] = pd.to_datetime(raw_data['Mycol'], infer_datetime_format=True)
