The main thing is to summarize the processing time problems that you have encountered,会一直更新的

字符串转时间

Time split in two ways,xx.str.slice() 或者 Extract with regular expressions

提取时间

实例：

re.findall(r'(.*)T.*','2022-05-12T07:56:13')[0]

# '2022-05-12'

方法一：df_data[‘year’] = pd.to_datetime(df_data[‘date’]).dt.year

# 转换为时间类型
df["date"] = pd.to_datetime(df["date"], format='%Y-%m-%d')
# 获取年
df["year"] = pd.to_datetime(df["date"]).dt.year
# 获取月
df["month"] = pd.to_datetime(df["date"]).dt.month
# 获取日
df["day"] = pd.to_datetime(df["date"]).dt.day
# 获取周
df["week"] = pd.to_datetime(df["date"]).dt.week
print(df)
print(df.dtypes)

结果：

        date  year  month  day  week
0 2019-12-09  2019     12    9    50
1 2019-12-02  2019     12    2    49
date     datetime64[ns]
year              int64
month             int64
day               int64
week              int64
dtype: object

方法二：df_data[‘year’] = df_data[‘date’].apply(lambda x:x.strftime(“%Y”))

# 转换为时间
df["date"] = pd.to_datetime(df["date"])
# 获取年月日
df["year-month-day"] = df["date"].apply(lambda x: x.strftime("%Y-%m-%d"))
# 获取年
df["year"] = df["date"].apply(lambda x: x.strftime("%Y"))
# 获取月
df["month"] = df["date"].apply(lambda x: x.strftime("%m"))
# 获取日
df["day"] = df["date"].apply(lambda x: x.strftime("%d"))
# 获取月日
df["month-day"] = df["date"].apply(lambda x: x.strftime("%Y-%m"))
# 获取周
df['week'] = df['date'].apply(lambda x: x.strftime('%W'))
print(df)
print(df.dtypes)