一、表格創(chuàng)建的幾種方法

import pandas as pdimport numpy as np#1.pd.read_csv方法data=pd.read_csv(r"G:\大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)庫\3.大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)\meal_order_info.csv",encoding="GBK")#2.pd.read_sql_table方法import sqlalchemyconn=sqlalchemy.create_engine(r"sqlite:///G:\大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)庫\0.engine數(shù)據(jù)庫\tesss.db")pd.read_sql_table("meal_order_detail1",conn)#3.pd.DataFrame方法t3=pd.DataFrame([],columns=["a","b","c"],index=[1,2,3])for i in t3.columns:    for j in t3.index:        t3.loc[j,i]=0print(t3)

二、表格合并的幾種方法

1.concat方法

import pandas as pdimport numpy as nptx=pd.DataFrame([],columns=list("ABCD"),index=range(1,5))for i in tx.columns:    for j in tx.index:        tx.loc[j,i]=i+str(j)ty=pd.DataFrame([],columns=list("BDF"),index=[2,4,6,8])for i in ty.columns:    for j in ty.index:        ty.loc[j,i]=i+str(j)#1.axis=0  表格上下連接  inner交集  outer并集t1=pd.concat([tx,ty],axis=0,join="inner",sort=True)  t2=pd.concat([tx,ty],axis=0,join="outer",sort=True)  #2.axis=1  表格左右連接  t3=pd.concat([tx,ty],axis=1,join="inner",sort=True)  t4=pd.concat([tx,ty],axis=1,join="outer",sort=True)  

2.append方法（上下連接）

import pandas as pdimport numpy as nppx1=tx.iloc[:,2:]px2=pd.DataFrame([],columns=["A","B"],index=[5])px1.append(px2)

3.merge方法（相同關(guān)鍵字合并）

import pandas as pdimport numpy as np#1.關(guān)鍵字不在同一行合并tx=pd.DataFrame([],columns=["A","B","Key"],index=range(1,5))for i in tx.columns:    for j in tx.index:        tx.loc[j,i]=i+str(j)tx2=pd.DataFrame([],columns=["C","D","Key"],index=range(1,5))for i in tx2.columns:    for j in tx2.index:        tx2.loc[j,i]=i+str(j)pd.merge(tx,tx2,left_on="Key",right_on="Key")#pd.merge(tx2,tx,left_on="Key",right_on="Key")

4.combine方法（數(shù)據(jù)補(bǔ)充）

t8=pd.DataFrame([[3,5],[4.6,np.nan]],columns=[0,1],index=[0,1])t9=pd.DataFrame([[np.nan,np.nan],[3,4.0]],columns=[0,1],index=[0,1])t10=t8.combine_first(t9)print(t8)print("**************")print(t9)print("**************")print(t10)

三、loc和iloc的區(qū)別

import pandas as pdimport numpy as npdata=pd.read_csv(r"G:\大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)庫\3.大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)\meal_order_info.csv",encoding="GBK")#1.loc用行列的名稱表示表格數(shù)據(jù)data.loc[3,"info_id"]data.loc[3:5,["info_id","emp_id"]]#2.用（x，y）純數(shù)字表示表格數(shù)據(jù)data.iloc[1,1]data.iloc[:,:2]

四、去重

import pandas as pdimport numpy as npdata=pd.read_csv(r"G:\大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)庫\3.大數(shù)據(jù)實(shí)驗(yàn)數(shù)據(jù)\meal_order_info.csv",encoding="GBK")print(len(data["info_id"]))#1.drop_duplicates去重print(len(data["info_id"].drop_duplicates()))#2.set去重（數(shù)據(jù)的順序會改變）print(len(set(data["info_id"]))) #3.多列去重print(len(data.drop_duplicates(subset=["info_id","emp_id"])))

補(bǔ)充：fillna方法（當(dāng)里面沒有數(shù)，默認(rèn)填寫）

tx=pd.DataFrame([],columns=list("ABCD"),index=range(1,5))tx.fillna(99)