缺失值NaN的处理

可以丢弃dropna 整行或列去除

也可以填充固定值或插值 fillna

df1.dropna()

df1.fillna(value=2))

# Set
s1=pd.Series(list(range(10,18)),index=pd.date_range("20170301",periods=8))
df["F"]=s1
print(df)
df.at[dates[0],"A"]=0
print(df)
df.iat[1,1]=1
df.loc[:,"D"]=np.array([4]*len(df))
print(df)
df2=df.copy()
df2[df2>0]=-df2
print(df2)

# Select切片
print(df["A"])
print(type(df["A"]))
print(df[:3])
print(df["20170301":"20170304"])
print(df.loc[dates[0]])
print(df.loc["20170301":"20170304",["B","D"]])
print(df.at[dates[0],"C"])
print(df.at["20170301","C"])

print(df.iloc[1:3,2:4])
print(df.iloc[1,4])
print(df.iat[1,4])

print(df[df.B>0][df.A<0])
print(df[df>0.1])
print(df[df["E"].isin([1,2])])

本节代码 df1=df.reindex(index=dates[:4],columns = list("ABCD")+["G"])    df1.loc[dates[0]:dates[1],"G"]=1    print (df1)    #丢弃数据    print(df1.dropna())    #填充数据    print(df1.fillna(value=1))