概述
目录:
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 15 20:51:04 2021
@author: 62669
"""
# In[1]:
import pandas as pd
import datetime
import re
import numpy as np
from presto_cli import presto_client
import win32com.client as win32
import time as tm
from datetime import datetime
import datetime
#处理工作中遇到的
# In[1]:
#字段类型转换问题
wo['放款时效'] = wo['放款时效'].astype('float64')
wo['give_time'] = pd.to_datetime(wo['give_time'],format='%Y-%m-%d',errors='coerce')
#日期格式处理问题
today = datetime.date.today()
current_month_first= today.replace(day=1)
last_month_last = current_month_first - datetime.timedelta(days=1)
last_month_first = last_month_last.replace(day=1)
print(last_month_first.strftime("%Y-%m"))
def make_week(dt):
if dt.day <= 7 :
rn = str(dt.year) + str(dt.month) + '第1周'
elif dt.day >7 and dt.day<=14:
rn = str(dt.year) + str(dt.month) + '第2周'
elif dt.day >14 and dt.day<=21:
rn = str(dt.year) + str(dt.month) + '第3周'
else :
rn = str(dt.year) + str(dt.month) + '第4周'
return rn
wo['give_time_day'] = wo['give_time'].map(lambda x:x.date())
wo['give_time_month'] =wo['give_time'].map(lambda x:x.date().replace(day=1))
#wo['give_time'] =xs[i].map(lambda x:x.date()).map(make_week)
#groupby问题
wo["fk_shixiao_0"] = wo['放款时效'].apply( lambda x: 1 if x<=0.5 else 0 )
wo3 = wo.groupby(['give_time_month'])['fk_shixiao_0'].mean().reset_index()
tmp1 = xs.groupby([col_2])['shenhe_shixiao'].quantile(0.5).reset_index()
#merge问题
fk = pd.merge(fk,wo4,how='left',left_on=['give_time_month'],right_on=['give_time_month'])
import numpy as np
import pandas as pd
import datetime
today = datetime.date.today()
type(today)
a=str(today)
now = datetime.datetime.now()
print(type(now))
t1 = datetime.datetime(2019,10,22)
t2 = datetime.datetime(2019,10,22,11,45,36)
t2-t1
print( t2-t1 )
tx = datetime.timedelta(100)
print(t2-tx)
from dateutil.parser import parse
date ='20170501'
date2 ='21/12/2019'
print(parse(date),type(parse(date)))
########pandas timestamp
date2 ='21/12/2019'
t1 = pd.Timestamp(date2)
t2 = pd.to_datetime(date2)
date3 =['21/12/2019','22/12/2019','23/12/2019','aa']
a = pd.to_datetime(date3,errors='ignore')
print(pd.to_datetime(date3,errors='ignore'))
print(pd.to_datetime(date3,errors='coerce'))
print(pd.to_datetime(date3,errors='ignore'))
ex = pd.date_range('2000-1-1', periods=1000, freq='M')
print(ex)
ex.date
#6字符串转换成datetime格式: strptime
df_data1 = pd.DataFrame(columns=['date','values'])
df_data1['date'] = ['2019-01-01','2019-01-02','2019-01-03','2019-01-04','2019-01-05']
df_data1['values'] = np.random.randn(5)
type(df_data1['date'][2])
df_data1['date1'] = df_data1['date'].map(lambda x:datetime.datetime.strptime(x,'%Y-%m-%d'))
df_data1
df_data1.info()
#6datetime变回string格式: strftime
df_data = pd.DataFrame(columns=['date','values'])
df_data['date'] = pd.date_range('2019/01/01',periods=5)
df_data['values'] = np.random.randn(5)
df_data
df_data.info()
df_data['date1'] = df_data['date'].apply(lambda x:x.strftime('%Y-%m')) #datetime格式转成str
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 11 11:55:42 2021
@author: 62669
"""
import pandas as pd
import numpy as np
pt = r'F:a_houdashi_workbbb_python学习pandas操作清洗1.xlsx'
t = pd.read_excel(pt,skiprows=1)
t.isnull()
t['分数'].isnull()
t.head()
t.dropna(axis="columns",how="all",inplace=True)
t.dropna(axis="index",how="all",inplace=True)
t.fillna({'分数':0})
t.loc[:,'分数'] = t['分数'].fillna(0)
t['分数'] = t['分数'].fillna(0)
t.loc[:,'姓名'] = t['姓名'].fillna(method='ffill')
t['姓名'] = t['姓名'].fillna(method='ffill')
#设置筛选条件
con= t['姓名']=='小明'
t.loc[con,'新分数']=t['分数']*2
t[con].head()
tt = t[con].copy()
tt['新分数']=tt['分数']*2
#排序
t['分数'].sort_values()
t.sort_values(by=['分数'],ascending=False)
t.sort_values(by=['分数'],ascending=True)
t.sort_values(by=['分数','姓名'],ascending=True)
最后
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