目录

在平时的需求开发中,经常涉及到利用Pandas处理日期相关类型字段的转换和操作,为此特地记录以下练习案例,帮助大家的同时,也便于日后的学习和复盘

案例1

问题:提取"W1|2022/7/28"字段中的年月日信息,取名为week_start,即一周开始的日期,并根据week_start计算出该周结束的具体日期week_end

(资料图)

import pandas as pd
import datetime
df1 = pd.DataFrame([[6,3],[6,3]], columns = ["Working day","W1|2022/7/28"])
# 一周开始的日期
# "2022/7/28"——>str类型
week_start = df1.columns[1].split("|")[1]
# 将start_day类型转换成date类型(2022-07-28)
week_start = datetime.datetime.strptime(week_start, "%Y/%m/%d").date()
# 一周结束的日期(2022-08-03)
week_end = week_start + datetime.timedelta(days=6)

df1

案例2

问题:根据"Date"字段生成"Date - 2"字段

import pandas as pd
from datetime import timedelta
from datetime import datetime
 
df2 = pd.DataFrame([[1,"20191031"],
                   [2,"20191106"],
                   [3,"20191106"]],columns=["Id","Date"])
# "Date"字段中的值减去2天,生成"Date - 2"字段
df2["Date - 2"] = df2["Date"].apply(lambda x:(datetime.strptime(x,"%Y%m%d") - timedelta(days=datetime.strptime(x,"%Y%m%d").weekday())).strftime("%Y%m%d"))

df2

案例3

问题:从字符串表示的日期时间中仅获取“年/月/日”

import pandas as pd
from datetime import datetime
 
df3 = pd.DataFrame([[1,"2017-01-02 00:00:00"],
                   [2,"2017-01-09 00:00:00"]
                   ],columns = ["Id","Wk"])

df3

错误写法

# 运行以下代码会报错"str" object has no attribute "strftime"
df3["new_wk"] = df3["Wk"].apply(lambda x:x.strftime("%Y%m%d"))

正确写法

# 先利用.strptime()将str格式的变量转化成datetime下的时间格式
# 然后再利用.strftime()获取“年/月/日”
df3["Wk"] = df3["Wk"].apply(lambda x:datetime.strptime(x,"%Y-%m-%d %H:%M:%S"))
df3["new_Wk"] = df3["Wk"].apply(lambda x:x.strftime("%Y/%m/%d"))

处理过后的df3

案例4

问题:将"月/日/年 时间"格式的值转换为"年月日"(10/11/19 05:28:27 => 20191011)

import pandas as pd
 
df4 = pd.DataFrame([["A","10/11/19 05:28:27","08/04/20 08:38:59"],
                   ["B","10/11/19 05:28:27",None],
                   ["C","10/11/19 05:28:27",None]
                  ],columns = ["site","creation_date","closure_date"])

df4

# 将"creation_date"栏位的值变形
# 10/11/19 05:28:27 => 20191011
df4["creation_date"] = df4["creation_date"].apply(lambda x:pd.to_datetime(x).strftime("%Y%m%d"))
 
# 将"closure_date"字段中nan值填充为0
df4["closure_date"] = df4["closure_date"].fillna(0)
# 筛选closure_date"字段中值为0的数据记录,取名为df4_na
df4_na = df4[df4["closure_date"].isin([0])]
# 筛选closure_date"字段中值不为0的数据记录,取名为df4
df4 = df4[~df4["closure_date"].isin([0])]
 
# 将"closure_date"栏位的值变形
# 08/04/20 08:38:59 => 20200804
df4["closure_date"] = df4["closure_date"].apply(lambda x:pd.to_datetime(x).strftime("%Y%m%d"))
 
df4 = pd.concat([df4, df4_na], ignore_index = True)

处理过后的df4

补充知识

我们通常使用pd.to_datetime()和s.astype("datetime64[ns]")来做时间类型转换

import pandas as pd
 
t = pd.Series(["20220720","20220724"])
# dtype: datetime64[ns]
new_t1 = pd.to_datetime(t)
new_t2 = t.astype("datetime64[ns]")

t

new_t1

new_t2

案例5

问题:添加字段"Week"，逐行递增

import pandas as pd
 
df5 = pd.DataFrame(columns=["Week","Materials"])
all_material = ["A32456","B78495"]
 
for row in range(0,3):
    week = row + 1
    datas = [week, all_material]
    df5.loc[row] = datas
"""
df5:
 
  Week         Materials
0    1  [A32456, B78495]
1    2  [A32456, B78495]
2    3  [A32456, B78495]
"""
print(df5)

案例6

问题:日期型转换为字符型

import datetime
today = datetime.date.today() # date类型 2022-07-28
today.strftime("%Y-%m-%d") # "2022-07-28"

import datetime
dt = datetime.datetime.now() # datetime类型 2022-07-28 22:46:20.528813
dt.strftime("%Y-%m-%d") # "2022-07-28"

import datetime
today = str(datetime.date.today()) # str类型 2022-07-28
today.replace("-","") # "20220728"

案例7

问题:文本型转日期型

#文本型日期转为日期型日期
import pandas as pd
from datetime import datetime
df7=pd.DataFrame({"销售日期":["2022-05-01","2022-05-02","2022-05-03","2022-05-04","2022-05-05","2022-05-06","2022-05-07","2022-05-08","2022-05-09","2022-05-10"],
                "城市":["兰州","白银","天水","武威","金昌","陇南","嘉峪关","酒泉","敦煌","甘南"]})

df7

文本型转为日期型可用datetime.strptime函数

# "%Y-%m-%d"表示将文本日期解析为年月日的日期格式
df7["日期"] = df7["销售日期"].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

文本型转为日期型也可用pd.to_datetime函数

# "%Y-%m-%d"表示将文本日期解析为年月日的日期格式
df7["日期"] = pd.to_datetime(df7["销售日期"],format="%Y-%m-%d")

处理过后的df7

案例8

问题:提取日期字段的年份、月份、日份和周数

import pandas as pd
from datetime import datetime
df8=pd.DataFrame({"销售日期":["2022-05-01","2022-05-02","2022-05-03","2022-05-04","2022-05-05","2022-05-06","2022-05-07","2022-05-08","2022-05-09","2022-05-10"],
                "城市":["兰州","白银","天水","武威","金昌","陇南","嘉峪关","酒泉","敦煌","甘南"]})
 
df8["日期"] = df8["销售日期"].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

df8

#由日期数据提取年
df8["年份"] = df8["日期"].apply(lambda x: x.year)
df8["年份"] =df8["年份"].astype(str)+"年"
 
#由日期数据提取月
df8["月份"] = df8["日期"].apply(lambda x: x.month)
df8["月份"] =df8["月份"].astype(str)+"月"
 
#由日期数据提取日
df8["日份"] = df8["日期"].apply(lambda x: x.day)
df8["日份"] =df8["日份"].astype(str)+"日"
 
# 日期中的周使用date.isocalendar()[1]提取
#根据日期返回周数，以周一为第一天开始
df8["周数"] = [date.isocalendar()[1] for date in df8["日期"].tolist()]
df8["周数"] = df8["周数"].astype(str)+"周"

处理后的df8

案例9

问题:借助offset时间偏移函数将日期加3天

import pandas as pd
from datetime import datetime
df9=pd.DataFrame({"销售日期":["2022-05-01","2022-05-02","2022-05-03","2022-05-04","2022-05-05","2022-05-06","2022-05-07","2022-05-08","2022-05-09","2022-05-10"],
                "城市":["兰州","白银","天水","武威","金昌","陇南","嘉峪关","酒泉","敦煌","甘南"]})
 
df9["日期"] = df9["销售日期"].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

df9

#借助offset时间偏移函数将日期加3天
from pandas.tseries.offsets import Day
df9["日期_3"]=df9["日期"]+Day(3)

处理后的df9

案例10

问题:将文本型日期转换为日期型日期

#文本型日期转为日期型日期
import pandas as pd
import datetime as dt
from datetime import datetime
df1=pd.DataFrame({"销售时间":["2022-05-01 00:00:00","2022-05-02 00:00:00","2022-05-03 00:00:00","2022-05-04 00:00:00","2022-05-05 00:00:00",
                         "2022-05-06 00:00:00","2022-05-07 00:00:00","2022-05-08 00:00:00","2022-05-09 00:00:00","2022-05-10 00:00:00",]})
#df["日期"]=df["销售日期"].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))
df1["日期_x"]=df1["销售时间"].str.split(" ",expand=True)[0]
df1["日期_y"]=pd.to_datetime(df1["销售时间"],format="%Y-%m-%d")
df1

df10

日期中带有时分秒"00:00:00",有如下方法将其处理为"%Y-%m-%d"形式

df10["日期"]=df10["销售时间"].str.split(" ",expand=True)[0]

处理后的df10

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关键词： 销售日期 时间类型 数据记录 日期格式