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這篇文章主要介紹“Pandas時(shí)間類型轉(zhuǎn)換與處理如何實(shí)現(xiàn)”,在日常操作中,相信很多人在Pandas時(shí)間類型轉(zhuǎn)換與處理如何實(shí)現(xiàn)問題上存在疑惑,小編查閱了各式資料,整理出簡(jiǎn)單好用的操作方法,希望對(duì)大家解答”Pandas時(shí)間類型轉(zhuǎn)換與處理如何實(shí)現(xiàn)”的疑惑有所幫助!接下來,請(qǐng)跟著小編一起來學(xué)習(xí)吧!
問題: 提取'W1|2022/7/28'字段中的年月日信息,取名為week_start,即一周開始的日期,并根據(jù)week_start計(jì)算出該周結(jié)束的具體日期week_end
import pandas as pd import datetime df1 = pd.DataFrame([[6,3],[6,3]], columns = ['Working day','W1|2022/7/28']) # 一周開始的日期 # '2022/7/28'——>str類型 week_start = df1.columns[1].split('|')[1] # 將start_day類型轉(zhuǎn)換成date類型(2022-07-28) week_start = datetime.datetime.strptime(week_start, '%Y/%m/%d').date() # 一周結(jié)束的日期(2022-08-03) week_end = week_start + datetime.timedelta(days=6)
df1
問題: 根據(jù)'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'字段中的值減去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
問題:從字符串表示的日期時(shí)間中僅獲取“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
錯(cuò)誤寫法
# 運(yùn)行以下代碼會(huì)報(bào)錯(cuò)'str' object has no attribute 'strftime' df3['new_wk'] = df3['Wk'].apply(lambda x:x.strftime("%Y%m%d"))
正確寫法
# 先利用.strptime()將str格式的變量轉(zhuǎn)化成datetime下的時(shí)間格式 # 然后再利用.strftime()獲取“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"))
處理過后的df3
問題:將'月/日/年 時(shí)間'格式的值轉(zhuǎn)換為'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
# 將'creation_date'欄位的值變形 # 10/11/19 05:28:27 => 20191011 df4['creation_date'] = df4['creation_date'].apply(lambda x:pd.to_datetime(x).strftime("%Y%m%d")) # 將'closure_date'字段中nan值填充為0 df4['closure_date'] = df4['closure_date'].fillna(0) # 篩選closure_date'字段中值為0的數(shù)據(jù)記錄,取名為df4_na df4_na = df4[df4['closure_date'].isin([0])] # 篩選closure_date'字段中值不為0的數(shù)據(jù)記錄,取名為df4 df4 = df4[~df4['closure_date'].isin([0])] # 將'closure_date'欄位的值變形 # 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)
處理過后的df4
我們通常使用pd.to_datetime()和s.astype('datetime64[ns]')來做時(shí)間類型轉(zhuǎn)換
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
問題: 添加字段'Week',逐行遞增
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)
問題:日期型轉(zhuǎn)換為字符型
import datetime today = datetime.date.today() # date類型 2022-07-28 today.strftime('%Y-%m-%d') # '2022-07-28'
import datetime dt = datetime.datetime.now() # datetime類型 2022-07-28 22:46:20.528813 dt.strftime('%Y-%m-%d') # '2022-07-28'
import datetime today = str(datetime.date.today()) # str類型 2022-07-28 today.replace("-","") # '20220728'
問題:文本型轉(zhuǎn)日期型
#文本型日期轉(zhuǎn)為日期型日期 import pandas as pd from datetime import datetime df7=pd.DataFrame({'銷售日期':['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':['蘭州','白銀','天水','武威','金昌','隴南','嘉峪關(guān)','酒泉','敦煌','甘南']})
df7
文本型轉(zhuǎn)為日期型可用datetime.strptime函數(shù)
# "%Y-%m-%d"表示將文本日期解析為年月日的日期格式 df7['日期'] = df7['銷售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))
文本型轉(zhuǎn)為日期型也可用pd.to_datetime函數(shù)
# "%Y-%m-%d"表示將文本日期解析為年月日的日期格式 df7['日期'] = pd.to_datetime(df7['銷售日期'],format='%Y-%m-%d')
處理過后的df7
問題:提取日期字段的年份、月份、日份和周數(shù)
import pandas as pd from datetime import datetime df8=pd.DataFrame({'銷售日期':['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':['蘭州','白銀','天水','武威','金昌','隴南','嘉峪關(guān)','酒泉','敦煌','甘南']}) df8['日期'] = df8['銷售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))
df8
#由日期數(shù)據(jù)提取年 df8['年份'] = df8['日期'].apply(lambda x: x.year) df8['年份'] =df8['年份'].astype(str)+'年' #由日期數(shù)據(jù)提取月 df8['月份'] = df8['日期'].apply(lambda x: x.month) df8['月份'] =df8['月份'].astype(str)+'月' #由日期數(shù)據(jù)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 #根據(jù)日期返回周數(shù),以周一為第一天開始 df8['周數(shù)'] = [date.isocalendar()[1] for date in df8['日期'].tolist()] df8['周數(shù)'] = df8['周數(shù)'].astype(str)+'周'
處理后的df8
問題:借助offset時(shí)間偏移函數(shù)將日期加3天
import pandas as pd from datetime import datetime df9=pd.DataFrame({'銷售日期':['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':['蘭州','白銀','天水','武威','金昌','隴南','嘉峪關(guān)','酒泉','敦煌','甘南']}) df9['日期'] = df9['銷售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))
df9
#借助offset時(shí)間偏移函數(shù)將日期加3天 from pandas.tseries.offsets import Day df9['日期_3']=df9['日期']+Day(3)
處理后的df9
問題:將文本型日期轉(zhuǎn)換為日期型日期
#文本型日期轉(zhuǎn)為日期型日期 import pandas as pd import datetime as dt from datetime import datetime df1=pd.DataFrame({'銷售時(shí)間':['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['銷售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d")) df1['日期_x']=df1['銷售時(shí)間'].str.split(' ',expand=True)[0] df1['日期_y']=pd.to_datetime(df1['銷售時(shí)間'],format='%Y-%m-%d') df1
df10
日期中帶有時(shí)分秒'00:00:00',有如下方法將其處理為'%Y-%m-%d'形式
df10['日期']=df10['銷售時(shí)間'].str.split(' ',expand=True)[0]
處理后的df10
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