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发送短消息 UID 222 精华 0 查看公共资料搜索主题搜索帖子 TUhjnbcbe TUhjnbcbe 组别新手上路生日帖子1 积分11 性别注册时间2020-03-27	1^# 字体大小: t T 发表于 2023-07-04 22:41 \|只看楼主北京中科医院假 https://m-mip.39.net/czk/mipso_5153159.html 刚刚接触pandas的朋友，想了解数据结构，就一定要认识DataFrame，接下来给大家详细介绍！初识DataFrame importnumpyasnp importpandasaspd data={name:[Jack,Tom,LiSa], age:[20,21,18], city:[BeiJing,TianJin,ShenZhen]} print(data) print() frame=pd.DataFrame(data)#创建DataFrame print(frame) print() print(frame.index)#查看行索引 print() print(frame.columns)#查看列索引 print() print(frame.values)#查看值 {name:[Jack,Tom,LiSa],age:[20,21,18],city:[BeiJing,TianJin,ShenZhen]} agecityname BeiJingJack TianJinTom ShenZhenLiSa RangeIndex(start=0,stop=3,step=1) Index([age,city,name],dtype=object) [[20BeiJingJack] [21TianJinTom] [18ShenZhenLiSa]] 创建DataFrame 方法一:由字典创建字典的key是列索引值可以是1.列表2.ndarray3.Series #值是列表 data1={a:[1,2,3], b:[4,5,6], c:[7,8,9] } print(data1) print() print(pd.DataFrame(data1))#创建DataFrame print() #注意:index是可以给行索引重新命名columns是给列索引重新指定顺序如果没有该列那么产生NaN值 print(pd.DataFrame(data1,index=list(mnp),columns=list(bcad))) print() {a:[1,2,3],b:[4,5,6],c:[7,8,9]} abc bcad mNaN nNaN pNaN #值是ndarray注意:用ndarray创建DataFrame值的个数必须相同否则报错 data2={one:np.random.rand(3), two:np.random.rand(3) } print(data2) print() print(pd.DataFrame(data2)) {one:array([0.,0.,0.]),two:array([0.,0.,0.])} onetwo 00.. 10.. 20.. #值是Series--带有标签的一维数组注意:用Series创建DataFrame值的个数可以不同少的值用Nan填充 data3={one:pd.Series(np.random.rand(4)), two:pd.Series(np.random.rand(5)) } print(data3) print() df3=pd.DataFrame(data3) print(df3) print() {one:00. 10. 20. 30. dtypeloat64,two:00. 10. 20. 30. 40. dtypeloat64} onetwo 00.0. 10.0. 20.0. 30.0. 4NaN0. 方法二:通过二维数组直接创建 arr=np.random.rand(12).reshape(3,4) print(arr) print() df1=pd.DataFrame(arr) print(df1) print() df2=pd.DataFrame(arr,index=list(abc),columns=[one,two,three,four])#通过index和columns指定行索引和列索引 print(df2) [[0....] [0.569183...] [0....]] 00.... 10.... 20.... onetwothreefour a0.... b0.... c0.... 方法三:由字典组成的列表创建DataFrame data=[{one:1,two:2},{one:5,two:10,three:15}]#每一个字典在DataFrame里就是一行数据 print(data) print() df1=pd.DataFrame(data) print(df1) print() df2=pd.DataFrame(data,index=list(ab),columns=[one,two,three,four]) print(df2) [{one:1,two:2},{one:5,two:10,three:15}] onethreetwo 01NaN2 . onetwothreefour a12NaNNaN b.0NaN 创建方法四:由字典组成的字典 #columns为字典的keyindex为子字典的key data={Jack:{age:1,country:China,sex:man}, LiSa:{age:18,country:America,sex:women}, Tom:{age:20,country:English}} df1=pd.DataFrame(data) print(df1) print() #注意:这里的index并不能给子字典的key(行索引)重新命名但可以给子字典的key重新排序若出现原数组没有的index那么就填充NaN值 df2=pd.DataFrame(data,index=[sex,age,country]) print(df2) print() df3=pd.DataFrame(data,index=list(abc)) print(df3) print() #columns给列索引重新排序若出现原数组没有的列索引填充NaN值 df4=pd.DataFrame(data,columns=[Tom,LiSa,Jack,TangMu]) print(df4) JackLiSaTom age countryChinaAmericaEnglish sexmanwomenNaN JackLiSaTom sexmanwomenNaN age countryChinaAmericaEnglish JackLiSaTom aNaNNaNNaN bNaNNaNNaN cNaNNaNNaN TomLiSaJackTangMu ageNaN countryEnglishAmericaChinaNaN sexNaNwomenmanNaN DataFrame索引选择行与列选择列直接用df[列标签] df=pd.DataFrame(np.random.rand(12).reshape(3,4), index=[one,two,three],columns=[a,b,c,d]) print(df) print() print(df[a],,type(df[a]))#取一列 print() print(df[[a,c]],,type(df[[a,c]]))#取多列 abcd one92.... two91.... three3.... one92. two91. three3. Name:a,dtypeloat64classpandas.core.series.Series ac one92.19. two91.4. three3.14.classpandas.core.frame.DataFrame 选择行不能通过标签索引df[one]来选择行要用df.loc[one],loc就是针对行来操作的 print(df) print() print(df.loc[one],,type(df.loc[one]))#取一行 print() print(df.loc[[one,three]],,type(df.loc[[one,three]]))#取不连续的多行 print() abcd one92.... two91.... three3.... a92. b11. c19. d77. Namene,dtypeloat64classpandas.core.series.Series abcd one92.... three3....classpandas.core.frame.DataFrame loc支持切片索引--针对行并包含末端df.loc[one:three] df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four], columns=[a,b,c,d]) print(df) print() print(df.loc[one:three]) print() print(df[:3])#切片表示取连续的多行(尽量不用免得混淆) abcd one65.89419... two31.... three54.... four45.... abcd one65.89419... two31.... three54.... abcd one65.89419... two31.... three54.... iloc也是对行来操作的只不过把行标签改成了行索引并且是不包含末端的 print(df) print() print(df.iloc[0])#取一行 print() print(df.iloc[[0,2]])#取不连续的多行 print() print(df.iloc[0:3])#不包含末端 abcd one65.89419... two31.... three54.... four45.... a65.894 b19. c31. d41. Namene,dtypeloat64 abcd one65.89419... three54.... abcd one65.89419... two31.... three54.... 布尔型索引 df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four], columns=[a,b,c,d]) print(df) print() d1=df50#d1为布尔型索引 print(d1) print() print(df[d1])#df根据d1只返回True的值False的值对应为NaN print() abcd one91.... two49.... three78.... four79.... abcd oneTrueTrueTrueTrue twoFalseFalseFalseTrue threeTrueTrueFalseTrue fourTrueTrueFalseFalse abcd one91.... twoNaNNaNNaN69. three78..NaN93. four79..NaNNaN 选取某一列作为布尔型索引返回True所在行的所有列注意:不能选取多列作为布尔型索引 df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four], columns=[a,b,c,d],dtype=np.int64) print(df) print() d2=df50 print(d2) print() print(df[d2]) abcd one two three four oneFalse twoFalse threeTrue fourFalse Name:b,dtype:bool abcd three 选取多列作为布尔型索引返回True所对应的值False对应为NaN没有的列全部填充为NaN *df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four], columns=[a,b,c,d],dtype=np.int64) print(df) print() d3=df[[a,c]]50 print(d3) print() print(df[d3]) abcd one two three four91677 ac oneFalseFalse twoTrueFalse threeFalseTrue fourFalseFalse abcd oneNaNNaNNaNNaN two78.0NaNNaNNaN threeNaNNaN84.0NaN fourNaNNaNNaNNaN 多重索引 print(df) abcd one two three four91677 print(df[a].loc[[one,three]])#取列再取行 print() print(df[[a,c]].iloc[0:3]) one49 three6 Name:a,dtype:int64 ac one two three print(df.loc[[one,three]][[a,c]])#取行再取列 ac one three print(df50) print() print(df[df50]) print() print(df[df50][[a,b]]) abcd oneFalseTrueFalseFalse twoTrueFalseFalseTrue threeFalseTrueTrueTrue fourFalseTrueFalseTrue abcd oneNaN82.0NaNNaN two78.0NaNNaN84.0 threeNaN84...0 fourNaN89.0NaN77.0 ab oneNaN82.0 two78.0NaN threeNaN84.0 fourNaN89.0 DataFrame基本技巧 importnumpyasnp importpandasaspd arr=np.random.rand(16).reshape(8,2)10* #print(arr) print() print(len(arr)) print() df=pd.DataFrame(arr,index=[chr(i)foriinrange(97,97+len(arr))],columns=[one,two]) print(df) 8 onetwo a2.. b8.6320. c6.262. d6.. e6.. f2.. g6.. h9.. 查看数据 print(df) print() print(df.head(2))#查看头部数据默认查看5条 print() print(df.tail(3))#查看末尾数据默认查看5条 onetwo a2.. b8.6320. c6.262. d6.. e6.. f2.. g6.. h9.. onetwo a2.. b8.6320. onetwo f2.. g6.. h9.. 转置 print(df) onetwo a2.. b8.6320. c6.262. d6.. e6.. f2.. g6.. h9.. print(df.T) abcdefg\ one2..6326..... two1.8270.3.9.3.6.7. h one9. two3. 添加与修改 df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four],columns=[a,b,c,d]) print(df) print() df.loc[five]=#增加一行 print(df) print() df[e]=10#增加一列 print(df) print() df[e]=#修改一列 print(df) print() df.loc[five]=#修改一行 print(df) print() abcd one0.7810... two0.... three0.... four0..5490.. abcd one0.7810... two0.... three0.... four0..5490.. five.... abcde one0.7810...99007 two0....10 three0....10 four0..5490..10 five....00000 abcde one0.7810...990071 two0.... three0.... four0..5490.. five....000001 abcde one0.7810...990071 two0.... three0.... four0..5490.. five.... 删除del(删除行)/drop(删除列指定axis=1删除行) df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four],columns=[a,b,c,d]) print(df) print() deldf[a]#删除列改变原数组 print(df) abcd one0.... two0..1670.. three0.... four0.... bcd one0... two0.1670.. three0... four0... df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four],columns=[a,b,c,d]) print(df) print() d1=df.drop(one)#删除行并返回新的数组不改变原数组 print(d1) print() print(df) abcd one0.... two0.4260... three0.... four0.... abcd two0.4260... three0.... four0.... abcd one0.... two0.4260... three0.... four0.... df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[one,two,three,four],columns=[a,b,c,d]) print(df) print() d2=df.drop(a,axis=1)#删除列返回新的数组不会改变原数组 print(d2) print() print(df) abcd one0..6130.. two0.... three0...1760. four0.... bcd one0.6130.. two0... three0..1760. four0... abcd one0..6130.. two0.... three0...1760. four0.... 排序根据指定列的列值排序同时列值所在的行也会跟着移动.sort_values([列]) #单列 df=pd.DataFrame(np.random.rand(16).reshape(4,4),columns=[a,b,c,d]) print(df) print() print(df.sort_values([a]))#默认升序 print() print(df.sort_values([a],ascending=False))#降序 abcd 00.... 10.... 20.... 30...9910. abcd 10.... 30...9910. 00.... 20.... abcd 20.... 00.... 30...9910. 10.... 根据索引排序.sort_index() df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[2,1,3,0],columns=[a,b,c,d]) print(df) print() print(df.sort_index())#默认升序 print() print(df.sort_index(ascending=False))#降序 abcd 20.6110... 10.... 30..3540.. 00...1990. abcd 00...1990. 10.... 20.6110... 30..3540.. abcd 30..3540.. 20.6110... 10.... 00...1990. df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[x,z,y,t],columns=[a,b,c,d]) print(df) print() print(df.sort_index())#根据字母顺序表排序 abcd x0.... z0...2. y0.... t0.... abcd t0.... x0.... y0.... z0...2. df=pd.DataFrame(np.random.rand(16).reshape(4,4),index=[three,one,four,two],columns=[a,b,c,d]) print(df) print() print(df.sort_index())#根据单词首字母排序 abcd three0.... one0...9160. four0.... two0.... abcd four0.... one0...9160. three0.... two0....
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