python数据分析-03pandas库

#Seriesimport numpy as npimport pandas as pd

# s1 = pd.Series([1,2,3,4])# print(s1)# # 0    1# # 1    2# # 2    3# # 3    4# # dtype: int64# print(s1.values) #[1 2 3 4]# print(s1.index) #RangeIndex(start=0, stop=4, step=1)

#传入数组# s2 = pd.Series(np.arange(10))# print(s2)# 0    0# 1    1# 2    2# 3    3# 4    4# 5    5# 6    6# 7    7# 8    8# 9    9# dtype: int32

#传入字典# s3 = pd.Series({"1":1,"2":2,"3":3})# print(s3)# # 1    1# # 2    2# # 3    3# # dtype: int64# print(s3.values) #[1 2 3]# print(s3.index) #Index([‘1‘, ‘2‘, ‘3‘], dtype=‘object‘)

# s4 = pd.Series([1,2,3,4],index=[‘A‘,‘B‘,‘C‘,‘D‘])# print(s4)# # A    1# # B    2# # C    3# # D    4# # dtype: int64# print(s4["A"]) #1# print(s4[s4>2])# # C    3# # D    4# # dtype: int64# print(s4.to_dict()) #{‘A‘: 1, ‘B‘: 2, ‘C‘: 3, ‘D‘: 4}

# s4 = pd.Series([1,2,3,4],index=[‘A‘,‘B‘,‘C‘,‘D‘])# print(s4)# A    1# B    2# C    3# D    4# dtype: int64# index_1 = [‘A‘,‘B‘,‘C‘,‘D‘,‘E‘]# s5 = pd.Series(s4,index=index_1)#print(s5)# A    1.0# B    2.0# C    3.0# D    4.0# E    NaN# dtype: float64#print(s5.isnull())# A    False# B    False# C    False# D    False# E     True# dtype: bool# print(s5.notnull())# A     True# B     True# C     True# D     True# E    False# dtype: bool

# s5.name = ‘demo‘# print(s5)# A    1.0# B    2.0# C    3.0# D    4.0# E    NaN# Name: demo, dtype: float64# s5.index.name = "demo index"# print(s5)# Name: demo, dtype: float64# demo index# A    1.0# B    2.0# C    3.0# D    4.0# E    NaN# Name: demo, dtype: float64

from pandas import Series,DataFrame

# import webbrowser# link = "https://www.tiobe.com/tiobe-index/"# webbrowser.open(link) #打开这个网页，然后使用鼠标进行复制操作# df = pd.read_clipboard()#这里是读取鼠标复制的数据#print(df)#     Apr 2019  Apr 2018  Change  Programming Language  Ratings Change.1# 0          1         1     NaN                  Java  15.035%   -0.74%# 1          2         2     NaN                     C  14.076%   +0.49%# 2          3         3     NaN                   C++   8.838%   +1.62%# 3          4         4     NaN                Python   8.166%   +2.36%# 4          5         6  change     Visual Basic .NET   5.795%   +0.85%# 5          6         5  change                    C#   3.515%   -1.75%# 6          7         8  change            JavaScript   2.507%   -0.99%# 7          8         9  change                   SQL   2.272%   -0.38%# 8          9         7  change                   PHP   2.239%   -1.98%# 9         10        14  change     Assembly language   1.710%   +0.05%# 10        11        18  change           Objective-C   1.505%   +0.25%# 11        12        17  change                MATLAB   1.285%   -0.17%# 12        13        10  change                  Ruby   1.277%   -0.74%# 13        14        16  change                  Perl   1.269%   -0.26%# 14        15        11  change  Delphi/Object Pascal   1.264%   -0.70%# 15        16        12  change                     R   1.181%   -0.63%# 16        17        13  change          Visual Basic   1.060%   -0.74%# 17        18        19  change                    Go   1.009%   -0.17%# 18        19        15  change                 Swift   0.978%   -0.56%# 19        20        68  change                Groovy      NaN      NaN#print(type(df)) #<class ‘pandas.core.frame.DataFrame‘>#print(df.columns)# Index([‘Apr 2019‘, ‘Apr 2018‘, ‘Change‘, ‘Programming Language‘, ‘Ratings‘,#        ‘Change.1‘],#       dtype=‘object‘)#print(df.Ratings) #直接获取Ratings列 或者print(df["Ratings"])# 0    15.035%# 1    14.076%# 2     8.838%# 3     8.166%# 4     5.795%# 5     3.515%# 6     2.507%# 7     2.272%# 8     2.239%# Name: Ratings, dtype: object#print(DataFrame(df,columns=["Programming Language","Ratings"]))#获取多列#   Programming Language  Ratings# 0                 Java  15.035%# 1                    C  14.076%# 2                  C++   8.838%# 3               Python   8.166%# 4    Visual Basic .NET   5.795%# 5                   C#   3.515%# 6           JavaScript   2.507%# 7                  SQL   2.272%# 8                  PHP   2.239%

#填充一列新列：Apr 2020，数据是用NaN填充的#df_new = DataFrame(df,columns=["Programming Language","Ratings","Apr 2020"])# print(df_new)#   Programming Language  Ratings  Apr 2020# 0                 Java  15.035%       NaN# 1                    C  14.076%       NaN# 2                  C++   8.838%       NaN# 3               Python   8.166%       NaN# 4    Visual Basic .NET   5.795%       NaN# 5                   C#   3.515%       NaN# 6           JavaScript   2.507%       NaN# 7                  SQL   2.272%       NaN# 8                  PHP   2.239%       NaN

# df_new["Apr 2020"] = range(9)# print(df_new)#给新的一列赋值#   Programming Language  Ratings  Apr 2020# 0                 Java  15.035%         0# 1                    C  14.076%         1# 2                  C++   8.838%         2# 3               Python   8.166%         3# 4    Visual Basic .NET   5.795%         4# 5                   C#   3.515%         5# 6           JavaScript   2.507%         6# 7                  SQL   2.272%         7# 8                  PHP   2.239%         8

#把数组赋值过来# df_new["Apr 2020"] = np.arange(9)# print(df_new)#   Programming Language  Ratings  Apr 2020# 0                 Java  15.035%         0# 1                    C  14.076%         1# 2                  C++   8.838%         2# 3               Python   8.166%         3# 4    Visual Basic .NET   5.795%         4# 5                   C#   3.515%         5# 6           JavaScript   2.507%         6# 7                  SQL   2.272%         7# 8                  PHP   2.239%         8

#由于其本身每列都是Series，所以可以用Series赋值# df_new["Apr 2020"] = pd.Series(np.arange(9))# print(df_new)#   Programming Language  Ratings  Apr 2020# 0                 Java  15.035%         0# 1                    C  14.076%         1# 2                  C++   8.838%         2# 3               Python   8.166%         3# 4    Visual Basic .NET   5.795%         4# 5                   C#   3.515%         5# 6           JavaScript   2.507%         6# 7                  SQL   2.272%         7# 8                  PHP   2.239%         8

#只赋值给某一行# df_new["Apr 2020"] = pd.Series([100,200],index=[1,2])# print(df_new)#   Programming Language  Ratings  Apr 2020# 0                 Java  15.035%       NaN# 1                    C  14.076%     100.0# 2                  C++   8.838%     200.0# 3               Python   8.166%       NaN# 4    Visual Basic .NET   5.795%       NaN# 5                   C#   3.515%       NaN# 6           JavaScript   2.507%       NaN# 7                  SQL   2.272%       NaN# 8                  PHP   2.239%       NaN

data = {‘country‘:[‘belgium‘,‘India‘,‘Brazil‘],        ‘Capital‘:[‘Brussels‘,‘New Delhi‘,‘Brasilia‘],        ‘Population‘:[11190846,1303171035,207847528]}# s1 = pd.Series(data[‘country‘])# print(s1)# 0    belgium# 1      India# 2     Brazil# dtype: object# print(s1.values)#[‘belgium‘ ‘India‘ ‘Brazil‘]# print(s1.index)#RangeIndex(start=0, stop=3, step=1)# s1 = pd.Series(data[‘country‘],index=[‘A‘,‘B‘,‘C‘])# print(s1)# A    belgium# B      India# C     Brazil# dtype: object

#DataFrame# df1 = DataFrame(data)# print(df1)#    country    Capital  Population# 0  belgium   Brussels    11190846# 1    India  New Delhi  1303171035# 2   Brazil   Brasilia   207847528# cou = df1["country"]# print(type(cou)) #<class ‘pandas.core.series.Series‘># print(df1.iterrows()) #<generator object DataFrame.iterrows at 0x000000000B6F4DE0># for row in df1.iterrows():#     print(row)#     # (0, country        belgium#     # Capital       Brussels#     # Population    11190846#     # Name: 0, dtype: object)#     print(row[0])#0#     print(row[1])#     # country belgium#     # Capital Brussels#     # Population  11190846#     # Name: 0, dtype: object#     print(type(row[1])) #<class ‘pandas.core.series.Series‘>#     break

# s1 = pd.Series(data["country"])# s2 = pd.Series(data["Capital"])# s3 = pd.Series(data["Population"])#print(s1)# 0    belgium# 1      India# 2     Brazil# dtype: object# df_new = DataFrame([s1,s2,s3],index=[‘country‘,‘Capital‘,‘Population‘])# df_new = df_new.T# print(df_new)#    country    Capital  Population# 0  belgium   Brussels    11190846# 1    India  New Delhi  1303171035# 2   Brazil   Brasilia   207847528

#DataFrame的IO操作# import webbrowser# link = "http://pandas.pydata.org/pandas-docs/version/0.20/io.html"# webbrowser.open(link)## df1 = pd.read_clipboard()#print(df1)#   Format Type      Data Description          Reader        Writer# 0         text                   CSV        read_csv        to_csv# 1         text                  JSON       read_json       to_json# 2         text                  HTML       read_html       to_html# 3         text       Local clipboard  read_clipboard  to_clipboard# 4       binary              MS Excel      read_excel      to_excel# 5       binary           HDF5 Format        read_hdf        to_hdf# 6       binary        Feather Format    read_feather    to_feather# 7       binary               Msgpack    read_msgpack    to_msgpack# 8       binary                 Stata      read_stata      to_stata# 9       binary                   SAS        read_sas# 10      binary  Python Pickle Format     read_pickle     to_pickle# 11         SQL                   SQL        read_sql        to_sql# 12         SQL      Google Big Query        read_gbq        to_gbq

# df1.to_csv("df1.csv",index=False)#去掉前面的index# df2 = pd.read_csv("df1.csv")# print(df2)  # Format Type      Data Description          Reader        Writer# 0         text                   CSV        read_csv        to_csv# 1         text                  JSON       read_json       to_json# 2         text                  HTML       read_html       to_html# 3         text       Local clipboard  read_clipboard  to_clipboard# 4       binary              MS Excel      read_excel      to_excel# 5       binary           HDF5 Format        read_hdf        to_hdf# 6       binary        Feather Format    read_feather    to_feather# 7       binary               Msgpack    read_msgpack    to_msgpack# 8       binary                 Stata      read_stata      to_stata# 9       binary                   SAS        read_sas# 10      binary  Python Pickle Format     read_pickle     to_pickle# 11         SQL                   SQL        read_sql        to_sql# 12         SQL      Google Big Query        read_gbq        to_gbq

# print(df1.to_json())# {"Format":{"0":"text","1":"text","2":"text","3":"text","4":"binary","5":"binary","6":"binary","7":"binary","8":"binary","9":"binary","10":"binary","11":"SQL","12":"SQL"},"Type":{"0":"CSV","1":"JSON","2":"HTML","3":"Local","4":"MS","5":"HDF5","6":"Feather","7":"Msgpack","8":"Stata","9":"SAS","10":"Python","11":"SQL","12":"Google"},"Data":{"0":"read_csv","1":"read_json","2":"read_html","3":"clipboard","4":"Excel","5":"Format","6":"Format","7":"read_msgpack","8":"read_stata","9":"read_sas","10":"Pickle","11":"read_sql","12":"Big"},"Description":{"0":"to_csv","1":"to_json","2":"to_html","3":"read_clipboard","4":"read_excel","5":"read_hdf","6":"read_feather","7":"to_msgpack","8":"to_stata","9":null,"10":"Format","11":"to_sql","12":"Query"},"Reader":{"0":null,"1":null,"2":null,"3":"to_clipboard","4":"to_excel","5":"to_hdf","6":"to_feather","7":null,"8":null,"9":null,"10":"read_pickle","11":null,"12":"read_gbq"},"Writer":{"0":null,"1":null,"2":null,"3":null,"4":null,"5":null,"6":null,"7":null,"8":null,"9":null,"10":"to_pickle","11":null,"12":"to_gbq"}}

# print(pd.read_json(df1.to_json()))#    Format Type      Data Description          Reader        Writer# 0         text                   CSV        read_csv        to_csv# 1         text                  JSON       read_json       to_json# 10      binary  Python Pickle Format     read_pickle     to_pickle# 11         SQL                   SQL        read_sql        to_sql# 12         SQL      Google Big Query        read_gbq        to_gbq# 2         text                  HTML       read_html       to_html# 3         text       Local clipboard  read_clipboard  to_clipboard# 4       binary              MS Excel      read_excel      to_excel# 5       binary           HDF5 Format        read_hdf        to_hdf# 6       binary        Feather Format    read_feather    to_feather# 7       binary               Msgpack    read_msgpack    to_msgpack# 8       binary                 Stata      read_stata      to_stata# 9       binary                   SAS        read_sas

#假设有movie_metadata.csv 文件# imbd = pd.read_csv("movie_metadata.csv")# print(imbd.shape)#(5043,28)# print(imbd.head())# print(imbd[["color","director_name"]])#q取出两列数据

# sub_df = imbd["director_name","movie_title","imbd_score"]# print(sub_df.head(5))# print(sub_df.iloc[10:20,:])# print(sub_df.iloc[10:20,0:2])# print(sub_df.loc[10:20,:])#和iloc类似，只是多了第20行# print(sub_df.loc[10:20,:"director_name"]) #可以使使用key键

#Series Reindex#s1 = Series([1,2,3,4],index=[‘A‘,‘B‘,‘C‘,‘D‘])# print(s1)# A    1# B    2# C    3# D    4# dtype: int64

#print(s1.reindex(index=[‘A‘,‘B‘,‘C‘,‘D‘,‘E‘]))# A    1.0# B    2.0# C    3.0# D    4.0# E    NaN# dtype: float64

#print(s1.reindex(index=[‘A‘,‘B‘,‘C‘,‘D‘,‘E‘],fill_value=10))# A     1# B     2# C     3# D     4# E    10# dtype: int64

#s2 = Series([‘A‘,‘B‘,‘C‘],index=[1,5,10])#print(s2)# 1     A# 5     B# 10    C# dtype: object

#print(s2.reindex(index=range(15)))# 1     A# 5     B# 10    C# dtype: object# 0     NaN# 1       A# 2     NaN# 3     NaN# 4     NaN# 5       B# 6     NaN# 7     NaN# 8     NaN# 9     NaN# 10      C# 11    NaN# 12    NaN# 13    NaN# 14    NaN# dtype: object

#print(s2.reindex(index=range(15),method="ffill"))# 0     NaN# 1       A# 2       A# 3       A# 4       A# 5       B# 6       B# 7       B# 8       B# 9       B# 10      C# 11      C# 12      C# 13      C# 14      C# dtype: object

#Reindex dataframe# df1 = DataFrame(np.random.rand(25).reshape(5,5))# print(df1)#           0         1         2         3         4# 0  0.150685  0.741189  0.642348  0.625132  0.318640# 1  0.781998  0.793684  0.434840  0.053550  0.076352# 2  0.657116  0.261819  0.089875  0.298170  0.035670# 3  0.408057  0.550972  0.298262  0.734598  0.920229# 4  0.707607  0.163687  0.861138  0.553325  0.439473

# df2 = DataFrame(np.random.rand(25).reshape(5,5),index=[‘A‘,‘B‘,‘D‘,‘E‘,‘F‘],columns=[‘c1‘,‘c2‘,‘c3‘,‘c4‘,‘c5‘])# print(df2)#         c1        c2        c3        c4        c5# A  0.096956  0.687012  0.242486  0.106347  0.951611# B  0.534206  0.555345  0.743860  0.156659  0.228296# D  0.963385  0.648523  0.603671  0.904279  0.161911# E  0.549797  0.987869  0.048364  0.706606  0.820717# F  0.003817  0.923006  0.611485  0.986054  0.160444

# print(df2.reindex(index=[‘A‘,‘B‘,‘D‘,‘C‘,‘E‘,‘F‘]))#          c1        c2        c3        c4        c5# A  0.745011  0.621461  0.288680  0.177793  0.013119# B  0.431538  0.170305  0.780363  0.007156  0.139781# D  0.663396  0.807862  0.732135  0.347896  0.959864# C       NaN       NaN       NaN       NaN       NaN# E  0.145247  0.191087  0.811372  0.648703  0.697846# F  0.742532  0.439197  0.612185  0.114661  0.221951

# print(df2.reindex(columns=[‘c1‘,‘c2‘,‘c3‘,‘c4‘,‘c5‘,‘c6‘]))#          c1        c2        c3        c4        c5  c6# A  0.287383  0.910655  0.418470  0.613704  0.200391 NaN# B  0.942793  0.389105  0.619344  0.076861  0.474860 NaN# D  0.945629  0.308200  0.165710  0.152989  0.552817 NaN# E  0.876477  0.138687  0.838985  0.656992  0.773661 NaN# F  0.866165  0.539998  0.500313  0.540542  0.002450 NaN

# print(df2.reindex(index=[‘A‘,‘B‘,‘D‘,‘C‘,‘E‘,‘F‘],columns=[‘c1‘,‘c2‘,‘c3‘,‘c4‘,‘c5‘,‘c6‘]))#          c1        c2        c3        c4        c5  c6# A  0.978832  0.807321  0.366297  0.148317  0.308838 NaN# B  0.905668  0.114278  0.368676  0.428269  0.162910 NaN# D  0.930796  0.963658  0.902773  0.584296  0.295554 NaN# C       NaN       NaN       NaN       NaN       NaN NaN# E  0.101119  0.000268  0.301075  0.697321  0.121599 NaN# F  0.402271  0.660168  0.477529  0.590062  0.459596 NaN

# print(df2.reindex(index=[‘A‘,‘B‘]))#          c1        c2        c3        c4        c5# A  0.855483  0.462398  0.282791  0.454249  0.027320# B  0.223694  0.827418  0.368981  0.867265  0.471167

# print(df2.drop("A"))#          c1        c2        c3        c4        c5# B  0.047756  0.880659  0.744061  0.012340  0.216161# D  0.603093  0.769085  0.526477  0.187897  0.991472# E  0.159034  0.909088  0.765743  0.428868  0.972190# F  0.239292  0.982104  0.802697  0.848463  0.503050

# print(df2.drop("A",axis=0))#         c1        c2        c3        c4        c5# B  0.474883  0.859859  0.594369  0.077369  0.616871# D  0.562033  0.190256  0.882217  0.810458  0.855765# E  0.545617  0.872125  0.406509  0.544556  0.718795# F  0.944125  0.268808  0.070181  0.351121  0.040010

# print(df2.drop("c1",axis=1))#          c2        c3        c4        c5# A  0.404537  0.646484  0.319498  0.818558# B  0.231232  0.132706  0.851948  0.061789# D  0.067037  0.789874  0.368729  0.761373# E  0.176873  0.294302  0.818214  0.284220# F  0.378809  0.835109  0.124004  0.857353

原文地址：https://www.cnblogs.com/nikecode/p/11130876.html

时间： 2024-10-17 16:24:20

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