pandas基础及操作

r囧r小猫 2022-01-11 07:09 333阅读 0赞

import pandas as pd
    import numpy as np
    
    #加载数据
    detail=pd.read_excel('./meal_order_detail.xlsx')
    
    # print(detail.columns)
    #pandas 数据分析
    # print(detail['amounts'].max())
    # print(detail['amounts'].min())
    # print(detail['amounts'].var())
    # print(detail['amounts'].mode())
    # print(detail['amounts'].median())
    """
    178
    1
    1354.8693557124277
    0    35
    dtype: int64
    35.0
    
    """
    
    # df=pd.DataFrame([[1,2,3,4,5],[2,3,5,3,7]])
    # print(df)
    
    #统计dishes_name 众数  出现次数 非空值数量
    # print(detail['dishes_name'].mode())
    # print(detail['dishes_name'].count())
    
    
    #describe
    # print(detail['dishes_name'].describe())
    """
    count      2779
    unique      154
    top       白饭/大碗
    freq         92
    """
    #对于数值类型需要先转化成类型才可以进行统计众数次数
    
    
    # detail['amounts']=detail['amounts'].astype('category')
    # print(detail['amounts'].describe())
    
    #循环删除空行
    # columns=detail.columns
    # # print(columns)
    # for tmp in columns:
    #     # print(tmp)
    #     if detail[tmp].count()==0:
    #         detail.drop(labels=tmp,axis=1)
    # print(detail.shape)
    
    
    """
    pandas 默认支持的时间点Timestamp
    默认支持的时间序列类型 DatetimeIndex
    numpy  datetime[ns]
    
    """
    
    # place_order_time=detail['place_order_time']
    # print(place_order_time)
    #转化成pandas支持的时间
    # detail['place_order_time']=pd.to_datetime(place_order_time)
    # print(detail.dtypes)
    # detail['place_order_time']=pd.DatetimeIndex(place_order_time)
    # print(detail.dtypes)
    
    
    # df=pd.Series(['2019-06-19','2019-06-20'])
    # print(df)
    # df=pd.to_datetime(df)
    # print(df)
    
    
    #时间序列的操作
    # year=[i.year for i in detail['place_order_time']]
    # print(year)
    
    # weekday=[i.weekday_name for i in detail['place_order_time']]
    # print(weekday)
    
    #时间序列加减
    # a=detail['place_order_time']+pd.Timedelta(days=1)
    # print(a,detail['place_order_time'])
    
    #计算时间差
    # time=pd.to_datetime('2019-6-19')-pd.to_datetime('2019-6-10')
    # print(time)
    
    #计算机最早时间和最晚时间
    # print(pd.Timestamp.min)
    # print(pd.Timestamp.max)
    
    
    
    #分组与聚合
    
    #分组  按照amounts 分组   然后统计counts 的平均值
    # de=detail.groupby('amounts')['counts'].mean()
    # print(de)
    
    #按照order_id  和dishes_id  进行分组  统计单价amounts的均值
    # de=detail.groupby(by=['order_id','dishes_id'])['amounts'].mean()
    # print(de)
    
    
    #agg  1可以对多列数据同时进行多个统计分析
    # de=detail[['order_id','amounts']].agg([np.max,np.min])
    # print(de)
    
    #对order_id求max   对amounts求min
    # de=detail.agg({'order_id':np.max,'amounts':np.min})
    # print(de)
    
    
    #对order_id求mean  对amounts求min  max
    # de=detail.agg({'order_id':np.mean,'amounts':[np.min,np.max]})
    # print(de)
    
    
    #apply  transform
    # de=detail['order_id'].apply(lambda x:x+1)
    # print(de)
    
    # de=detail[['amounts','order_id']].transform(lambda x:x+1)
    # print(de)
    
    
    
    #数据透视表
    #创建数据透视表
    # data=detail[['order_id','dishes_id','dishes_name','amounts','counts']]
    #按照order_id进行行分组  统计amounts ，counts平均值  默认平均值
    # ret=pd.pivot_table(data,index='order_id',values=['amounts','counts'])
    # print(ret)
    #按照dishes_name进行分组  将其当做列
    # ret=pd.pivot_table(data,columns='dishes_name',values=['amounts','counts'])
    # print(ret)
    #aggfunc  默认均值  可以更改统计指标
    # ret=pd.pivot_table(data,columns='dishes_name',values=['amounts','counts'],aggfunc=max)
    # print(ret)
    #fill_value  空值填充   nan空值
    # ret=pd.pivot_table(data,columns='dishes_name',values=['amounts','counts'],aggfunc=max,fill_value=0)
    # print(ret)
    #margins 是否 求和
    # ret=pd.pivot_table(data,index='order_id',columns='dishes_name',values=['amounts','counts'],aggfunc=max,fill_value=0,margins=True)
    # print(ret)
    """
    类似分组聚合  比其功能更加强大
    """
    
    
    #单日营业额
    #获取数据
    # data=detail['place_order_time']
    # print(data)
    #将时间转化
    # detail['place_order_time']=pd.to_datetime(data)
    #获取日数据
    # day=[i.day for i in detail['place_order_time']]
    # print(day)
    #将数据保存到detail里
    # detail['day']=day
    
    #食品花费
    # cost=detail['amounts']*detail['counts']
    # detail['cost']=cost
    
    #按照day进行分组
    # ret=detail.groupby('day')['cost'].sum()
    # print(ret)
    
    #交叉表
    # ret=pd.crosstab(index=detail['counts'],columns=detail['amounts'],values=detail['dishes_name'],aggfunc=max,margins=True)
    # print(ret)
    
    
    #单列去重  对原来数据没有改变
    # detail=detail['counts'].drop_duplicates()
    # print(detail.shape)
    
    
    #多列去重
    # detail.drop_duplicates(subset=['counts','amounts'],inplace=True)
    # print(detail.shape)