目录 1、pandas中6个时间相关的类 1、pandas中6个时间相关的类 在多数情况下,对时间类型数据进行分析的前提就是将原本为字符串的时间转换为标准时间类型。 2、Timestamp类
1)查看时间列,是str字符串列,还是时间格式列import pandas as pd df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'].head() type(df['lock_time'][0]) pd.to_datetime(df['lock_time']).head() 结果如下: 2)使用pd.to_datetime()将字符串,转换为日期格式import pandas as pd df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'] = pd.to_datetime(df['lock_time']) df['lock_time'].head() 结果如下: 3)Timestamp类只能表示1677年-2262年的时间pd.Timestamp.min pd.Timestamp.max 结果如下: 4)Timestamp类常用属性
import pandas as pd df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'] = pd.to_datetime(df['lock_time']) df["年"] = df['lock_time'].apply(lambda x:x.year) df["年"].head() 结果如下: 5)利用strftime()方法提取指定格式日期df[‘lock_time’][0] 结果如下: 3、DatetimeIndex与PeriodIndex函数:类似于to_datetime()函数
import pandas as pd df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'] = pd.DatetimeIndex(df['lock_time']) df['lock_time'][0] --------------------------------------------------------------- df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'] = pd.PeriodIndex(df['lock_time'],freq="S") df['lock_time'][0] 结果如下: 4、Timedelta类
1)日期前移、后移一天import pandas as pd df = pd.read_csv(r"E:\电脑视频录制软件\视频下载安装路径\Python数据分析与应用人邮版\data\meal_order_info.csv", engine="python", encoding="gbk") df['lock_time'] = pd.to_datetime(df['lock_time']) df['lock_time'][0] # 后移一天 df['lock_time'][0] + pd.Timedelta(days=1) # 前移一天 df['lock_time'][0] + pd.Timedelta(days=-1) 结果如下: 2)两个时间做差
df['lock_time'][0] pd.to_datetime("2020-3-13") - df['lock_time'][0] a = pd.to_datetime("2020-3-13") - df['lock_time'][0] a.days 结果如下: |
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