对象的增删改查¶
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import pandas as pd
series结构的增删改查¶
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data =[10,11,12]
index=['a','b','c']
s=pd.Series(data=data,index=index)
s
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- 查操作
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s[0]
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s[0:2]
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mask=[True,False,True]
s[mask]
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s.loc['b']
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s.iloc[1]
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- 改操作
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s1=s.copy()
s1['a']=100
s1
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s1.replace(to_replace=100,value=101,inplace=True)#inplace=True时s1原来的值会变,反之为False则不会变
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s1
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s1.index
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s1.index=['a','b','d']#修改index的值
s1
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s1.rename(index={'a':'A'},inplace=True)#inplace=True可以改变原来s1的index值
s1
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- 增操作
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data=[11,110]
index=['h','k']
s2=pd.Series(data=data,index=index)
s2
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s3=s1.append(s2)#增加一整个Series
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s3['j']=500#单独增加一个
s3
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s1.append(s2,ignore_index=False)#ignore_index=False:不对序列进行重新排序,保留原来的排序
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s1.append(s2,ignore_index=True)#ignore_index=True:对原来的序列进行重新排序
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- 删操作
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s3
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del s3['A']#指定删除1行
s3
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s3.drop(['b','d'],inplace=True)#多行删除
s3
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DataFrame结构的增删改查¶
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data=[[1,2,3],[4,5,6]]
index=['a','b']
columns=['A','B','C']
df=pd.DataFrame(data=data,index=index,columns=columns)
df
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- 查操作
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df['A']
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df.loc['a']
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df.iloc[1]
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- 改操作
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df.loc['a']['A']
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df.loc['a']['A']=150#直接在原数据上修改值
df
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df.index=['f','g']#改索引
df
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df.rename(index={'f':'F'},inplace=True)#改索引
df
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- 增操作
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df.loc['c']=[1,2,3]#增加1行
df
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data=[[1,2,3],[4,5,6]]
index=['j','k']
columns=['A','B','C']
df2=pd.DataFrame(data=data,index=index,columns=columns)
df2
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df3=pd.concat([df,df2],axis=0)#把两个DataFrame连接
df3
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df2['Tang']=[10,11]#增加一列
df2
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df4=pd.DataFrame([[10,11],[12,13]],index=['j','k'],columns=['D','E'])
df4
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df5=pd.concat([df2,df4],axis=1)
df5
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- 删操作
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df5.drop(['j'],axis=0,inplace=True)
df5
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del df5['Tang']#删1列
df5
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df5.drop(['A','B','C'],axis=1,inplace=True)
df5
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