中原地形图,清川虹子

新葡萄京娱乐场app 1

一个完完全全的神州地形图(各类省,市。还有国两制),谢谢大家。

突显整个83天 收起

# S!T!A!R!T“`python%matplotlib inlineimport numpy as npimport pandas as pdimport matplotlib.pyplot as plt“`## 1.define a Dataframe data“`pythondates = pd.date_range(‘20170129’, periods = 5, freq = ‘d’)df = pd.DataFrame(np.random.randn(5,4), columns = list(‘ABCD’), index = dates)“““pythondf“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`pythondf.plot(kind = ‘barh’)“`![png](output_5_1.png)##
2.having data types in ‘df’“`pythondf.dtypes“`    A    float64 
  B    float64    C    float64    D    float64    dtype: object## 3.
Choose indexs&cloumns&values“`pythonprint(‘-_’*33)print(‘The index
of df is :\n{0}’.format(df.index))print(‘-_’*33)print(‘The values of
df is :\n{0}’.format(df.values))print(‘-_’*33)print(‘The columns of
df is
:\n{0}’.format(df.columns))print(‘-_’*33)print(df.describe())print(‘-_’*33)“` 
 
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  The index of df is :    DatetimeIndex([‘2017-01-29’, ‘2017-01-30’,
‘2017-01-31’, ‘2017-02-01’,                  ‘2017-02-02′],           
      dtype=’datetime64[ns]’, freq=’D’)   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  The values of df is :    [[-0.87679455  0.62570401  0.8394737 
1.23924262]    [ 0.35142547  0.61910446  0.69015155  0.89032261]   
[ 0.48453389  2.18840161 -0.2584778  -0.83329982]    [ 1.49215272
-1.739632  -1.86894142 -0.01332174]    [ 0.67477899  0.31703409
-0.37920271 -0.07616798]]   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  The columns of df is :    Index([‘A’, ‘B’, ‘C’, ‘D’],
dtype=’object’)   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                A        B        C        D    count  5.000000 
5.000000  5.000000  5.000000    mean  0.425219  0.402122 -0.195399 
0.241355    std    0.852178  1.403764  1.083244  0.827270    min 
-0.876795 -1.739632 -1.868941 -0.833300    25%    0.351425  0.317034
-0.379203 -0.076168    50%    0.484534  0.619104 -0.258478 -0.013322   
75%    0.674779  0.625704  0.690152  0.890323    max    1.492153 
2.188402  0.839474  1.239243   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_##
4.Transposing your data“`pythonprint(df.T)“`      2017-01-29 
2017-01-30  2017-01-31  2017-02-01  2017-02-02    A  -0.876795   
0.351425    0.484534    1.492153    0.674779    B    0.625704   
0.619104    2.188402  -1.739632    0.317034    C    0.839474   
0.690152  -0.258478  -1.868941  -0.379203    D    1.239243    0.890323 
-0.833300  -0.013322  -0.076168## 5.Sort“`pythondf“`

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中原地形图,清川虹子。vertical-align: top;

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`pythonprint(‘-_’*33)print(df.sort_index(axis = 0, ascending =
False))print(‘-_’*33)print(df.sort_index(axis = 1, ascending =
False))print(‘-_’*33)print(df.sort_values(axis = 0, ascending = True
, by = ‘A’))print(‘-_’*33)“`   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    A        B        C        D    2017-02-02 
0.674779  0.317034 -0.379203 -0.076168    2017-02-01  1.492153 -1.739632
-1.868941 -0.013322    2017-01-31  0.484534  2.188402 -0.258478
-0.833300    2017-01-30  0.351425  0.619104  0.690152  0.890323   
2017-01-29 -0.876795  0.625704  0.839474  1.239243   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    D        C        B        A    2017-01-29 
1.239243  0.839474  0.625704 -0.876795    2017-01-30  0.890323 
0.690152  0.619104  0.351425    2017-01-31 -0.833300 -0.258478 
2.188402  0.484534    2017-02-01 -0.013322 -1.868941 -1.739632 
1.492153    2017-02-02 -0.076168 -0.379203  0.317034  0.674779   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    A        B        C        D    2017-01-29
-0.876795  0.625704  0.839474  1.239243    2017-01-30  0.351425 
0.619104  0.690152  0.890323    2017-01-31  0.484534  2.188402 -0.258478
-0.833300    2017-02-02  0.674779  0.317034 -0.379203 -0.076168   
2017-02-01  1.492153 -1.739632 -1.868941 -0.013322   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_##
6.Choose data### 6.1.Choose columns by single
label“`pythondf“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`pythonprint(‘-_’*33)print(df.A)print(‘-_’*33)print(df[‘B’])print(‘-_’*33)“` 
 
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  2017-01-29  -0.876795    2017-01-30    0.351425    2017-01-31   
0.484534    2017-02-01    1.492153    2017-02-02    0.674779    Freq: D,
Name: A, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  2017-01-29    0.625704    2017-01-30    0.619104    2017-01-31   
2.188402    2017-02-01  -1.739632    2017-02-02    0.317034    Freq: D,
Name: B, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_###
6.2.Choose rows by slice via'[ : ]’“`pythondf“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`python#not include the second numberdf[ :3]“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

### 6.3. Choose rows by index name via'[:]’“`python# include
the second labledf[‘20170129’: ‘2017-1-31’]“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

###新葡萄京娱乐场app , 6.4. Choose complex sections using ‘.loc( )’“`pythondf“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`pythonprint(df.loc[dates[0]])print(‘-_’*33)print(df.loc[‘20170201’])print(‘-_’*33)print(df.loc[dates[0]:dates[len(df)-1],
list(‘AB’)])print(‘-_’*33)print(df.loc[‘20170129’: ‘20170202’,
[‘A’, ‘C’, ‘D’]])print(‘-_’*33)print(df.loc[:,
list(‘CBA’)])print(‘-_’*33)print(df.loc[dates[0],
‘A’])print(‘-_’*33)print(df.loc[dates[0],
[‘A’]])print(‘-_’*33)##A’ can not be a
listprint(df.at[dates[0], ‘A’])“`    A  -0.876795    B   
0.625704    C    0.839474    D    1.239243    Name: 2017-01-29 00:00:00,
dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  A    1.492153    B  -1.739632    C  -1.868941    D  -0.013322    Name:
2017-02-01 00:00:00, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    A        B    2017-01-29 -0.876795  0.625704   
2017-01-30  0.351425  0.619104    2017-01-31  0.484534  2.188402   
2017-02-01  1.492153 -1.739632    2017-02-02  0.674779  0.317034   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    A        C        D    2017-01-29 -0.876795 
0.839474  1.239243    2017-01-30  0.351425  0.690152  0.890323   
2017-01-31  0.484534 -0.258478 -0.833300    2017-02-01  1.492153
-1.868941 -0.013322    2017-02-02  0.674779 -0.379203 -0.076168   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    C        B        A    2017-01-29  0.839474 
0.625704 -0.876795    2017-01-30  0.690152  0.619104  0.351425   
2017-01-31 -0.258478  2.188402  0.484534    2017-02-01 -1.868941
-1.739632  1.492153    2017-02-02 -0.379203  0.317034  0.674779   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  -0.876794550096   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  A  -0.876795    Name: 2017-01-29 00:00:00, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  -0.876794550096### 6.5. Choose complex sections using ‘.lioc(
)’“`pythondf“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`pythonprint(‘-_’*33)#single
elementprint(df.iloc[0,0])print(‘-_’*33)#complex
sectionsprint(df.iloc[:3,2:])print(‘-_’*33)#single
rowprint(df.iloc[0])print(‘-_’*33)#single
columnprint(df.iloc[:,0])print(‘-_’*33)“`   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  -0.876794550096   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
                    C        D    2017-01-29  0.839474  1.239243   
2017-01-30  0.690152  0.890323    2017-01-31 -0.258478 -0.833300   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  A  -0.876795    B    0.625704    C    0.839474    D    1.239243   
Name: 2017-01-29 00:00:00, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_ 
  2017-01-29  -0.876795    2017-01-30    0.351425    2017-01-31   
0.484534    2017-02-01    1.492153    2017-02-02    0.674779    Freq: D,
Name: A, dtype: float64   
-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_###

  1. Boolean indexing“`python#element in column ‘A’ > 0, return
    the DataFramedf[df.A > 0]“`

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ABCD

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

“`python#all element in ‘df’ which <=0 is np.nandf[df >
0]“`

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ABCD

2017-01-29NaN0.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402NaNNaN

2017-02-011.492153NaNNaNNaN

2017-02-020.6747790.317034NaNNaN

“`pythondf2=df.copy()df2[‘E’] = [‘fuck’, ‘shit’, ] * 2 +
[‘suck’]df2[df2.E.isin([‘fuck’, ‘suck’])]“`

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ABCDE

2017-01-29-0.8767950.6257040.8394741.239243fuck

2017-01-310.4845342.188402-0.258478-0.833300fuck

2017-02-020.6747790.317034-0.379203-0.076168suck

### 8.Missing Data“`pythondf3 = df[df > 0]df3“`

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ABCD

2017-01-29NaN0.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402NaNNaN

2017-02-011.492153NaNNaNNaN

2017-02-020.6747790.317034NaNNaN

“`pythondf3.dropna()“`

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ABCD

2017-01-300.3514250.6191040.6901520.890323

“`pythondf3.fillna(value = ‘Missing’)“`

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ABCD

2017-01-29Missing0.6257040.8394741.23924

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.1884MissingMissing

2017-02-011.49215MissingMissingMissing

2017-02-020.6747790.317034MissingMissing

“`pythondf4 = pd.isnull(df3)df4“`

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ABCD

2017-01-29TrueFalseFalseFalse

2017-01-30FalseFalseFalseFalse

2017-01-31FalseFalseTrueTrue

2017-02-01FalseTrueTrueTrue

2017-02-02FalseFalseTrueTrue

### 9. Merge“`pythona = df[  : 2]b = df[2 : 4]c = df[4 :
5]pieces = [a, b, c]pd.concat(pieces)“`

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ABCD

2017-01-29-0.8767950.6257040.8394741.239243

2017-01-300.3514250.6191040.6901520.890323

2017-01-310.4845342.188402-0.258478-0.833300

2017-02-011.492153-1.739632-1.868941-0.013322

2017-02-020.6747790.317034-0.379203-0.076168

### 10. Time Series### 10.1. define time series based
‘df’“`pythontime = pd.date_range(‘20170129’, periods = 1000, freq =
‘d’)col = list(‘ABCDEFG’)df_time =
pd.DataFrame(np.random.randn(len(time), len(col)), index = time, columns
= col)df_time.head(5)“`

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ABCDEFG

2017-01-29-2.442170-1.423526-0.2833060.4749680.2284170.8108171.918797

2017-01-300.3414900.611065-0.6841250.9758580.595433-1.2319040.027245

2017-01-31-0.448969-0.416562-1.026428-1.529416-0.5672462.305233-0.186806

2017-02-010.3858290.0555172.3483670.419194-0.3857280.0490750.613855

2017-02-02-0.2629490.3337900.750670-0.7366430.0635610.6028150.328664

### 10.2. resample ‘df’ by month“`pythondf_time2 =
df_time.resample(‘m’).mean()df_time2.head()“`

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ABCDEFG

2017-01-31-0.849883-0.409675-0.664620-0.0261960.0855350.6280490.586412

2017-02-28-0.0904390.0590900.069942-0.049585-0.075990-0.1410150.258787

2017-03-310.133955-0.0674840.157919-0.0252380.2037280.217022-0.109552

2017-04-30-0.1271820.1696820.065305-0.2698410.2872390.1400860.193535

2017-05-31-0.0336770.088312-0.284405-0.1335220.114316-0.551639-0.158488

“`pythondf_time3 =
df_time2.tz_localize(‘utc’)df_time3.head()“`

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ABCDEFG

2017-01-31
00:00:00+00:00-0.849883-0.409675-0.664620-0.0261960.0855350.6280490.586412

2017-02-28
00:00:00+00:00-0.0904390.0590900.069942-0.049585-0.075990-0.1410150.258787

2017-03-31
00:00:00+00:000.133955-0.0674840.157919-0.0252380.2037280.217022-0.109552

2017-04-30
00:00:00+00:00-0.1271820.1696820.065305-0.2698410.2872390.1400860.193535

2017-05-31
00:00:00+00:00-0.0336770.088312-0.284405-0.1335220.114316-0.551639-0.158488

“`pythondf_time3.tz_convert(‘US/Eastern’).head()“`

.dataframe thead tr:only-child th {

text-align: right;

}

.dataframe thead th {

text-align: left;

}

.dataframe tbody tr th {

vertical-align: top;

}

ABCDEFG

2017-01-30
19:00:00-05:00-0.849883-0.409675-0.664620-0.0261960.0855350.6280490.586412

2017-02-27
19:00:00-05:00-0.0904390.0590900.069942-0.049585-0.075990-0.1410150.258787

2017-03-30
20:00:00-04:000.133955-0.0674840.157919-0.0252380.2037280.217022-0.109552

2017-04-29
20:00:00-04:00-0.1271820.1696820.065305-0.2698410.2872390.1400860.193535

2017-05-30
20:00:00-04:00-0.0336770.088312-0.284405-0.1335220.114316-0.551639-0.158488

### 11.
Plotting“`pythondf.plot()“`![png](output_49_1.png)

### 12. Data I/O

“`python

df_time.to_csv(‘DataFrame_time_data.csv’)

“`

“`python

df_time.to_excel(‘DataFrame_time_data.xlsx’, sheet_name =
‘data_1’)

“`

“`python

df_time.to_html(‘DataFrame_time_data.html’)

“`

“`python

df_time.to_json(‘DataFrame_time_data.txt’)

“`

“`python

“`

关注 859

忘了网上哪位的范例,我加了些修改。

第1天
2012-03-29

献吻 0

 <html xmlns="http://www.w3.org/1999/xhtml">
 <head>
     <title>中国地图 (包括香港、台湾、澳门)</title>
     <style type="text/css">
         *{margin: 0;padding: 0      </style>
     <link href="jquery-jvectormap-1.2.2.css" media="screen" rel="stylesheet" type="text/css" />
     <script src="jquery-1.9.1.min.js" type="text/javascript"></script>
     <script src="jquery-jvectormap-1.2.2.min.js" type="text/javascript"></script>
     <script src="jquery-jvectormap-cn.js" type="text/javascript"></script>
     <script type="text/javascript">
         $(             $('#container'                 map: 'map_format_cn'                 backgroundColor: "#EEE"                 markersSelectable:                           fill: '#8d8d8d'

     </script>
 </head>
 <body>
     <div id="container"></div>
 </body>
 </html>

新葡萄京娱乐场app 2
新葡萄京娱乐场app 3

献花 0

以下文件可到.官网下载。 

喜欢2

清川虹子

<script type="text/javascript" src="http://www.cnblogs.com/ssjedcw/admin/jquery-1.9.1.min.js"></script>

<link href="jquery-jvectormap-1.2.2.css" media="screen" rel="stylesheet" type="text/css" />

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上海

英文名:

下面呢,就是大家中华地形图的多少了,是SVG的啊。

第109天
2012-07-15

Nijiko Kiyokawa

新葡萄京娱乐场app 4
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新葡萄京娱乐场app 5

性别:

忘了网上哪位的范例,我加了些修改。 html
xmlns=”” h…

第112天
2012-07-18

新葡萄京娱乐场app 6

民族:

第179天
2012-09-23

身高:

新葡萄京娱乐场app 7

生日:

第188天
2012-10-02

1912-11-24

新葡萄京娱乐场app 8
新葡萄京娱乐场app 9
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新葡萄京娱乐场app 11

体重:

第197天
2012-10-11

生肖:

新葡萄京娱乐场app 12

第198天
2012-10-12

国籍:

新葡萄京娱乐场app 13

日本

第201天
2012-10-15

星座:

新葡萄京娱乐场app 14
新葡萄京娱乐场app 15
新葡萄京娱乐场app 16
新葡萄京娱乐场app 17
新葡萄京娱乐场app 18
新葡萄京娱乐场app 19
新葡萄京娱乐场app 20
新葡萄京娱乐场app 21
新葡萄京娱乐场app 22
新葡萄京娱乐场app 23
新葡萄京娱乐场app 24

射手座

第202天
2012-10-16

出生地:

新葡萄京娱乐场app 25

血型:

第204天
2012-10-18

职 业:

新葡萄京娱乐场app 26

演员

第205天
2012-10-19

结业该校:

新葡萄京娱乐场app 27
新葡萄京娱乐场app 28
新葡萄京娱乐场app 29
新葡萄京娱乐场app 30

所属集团:

第210天
2012-10-24

代表小说:

新葡萄京娱乐场app 31
新葡萄京娱乐场app 32

第216天
2012-10-30

新葡萄京娱乐场app 33
新葡萄京娱乐场app 34
新葡萄京娱乐场app 35
新葡萄京娱乐场app 36

第217天
2012-10-31

新葡萄京娱乐场app 37
新葡萄京娱乐场app 38

第221天
2012-11-04

新葡萄京娱乐场app 39

第222天
2012-11-05

新葡萄京娱乐场app 40
新葡萄京娱乐场app 41
新葡萄京娱乐场app 42
新葡萄京娱乐场app 43

第226天
2012-11-09

新葡萄京娱乐场app 44
新葡萄京娱乐场app 45
新葡萄京娱乐场app 46

第231天
2012-11-14

新葡萄京娱乐场app 47

第232天
2012-11-15

新葡萄京娱乐场app 48
新葡萄京娱乐场app 49

第242天
2012-11-25

新葡萄京娱乐场app 50

第243天
2012-11-26

新葡萄京娱乐场app 51
新葡萄京娱乐场app 52

第244天
2012-11-27

新葡萄京娱乐场app 53

第245天
2012-11-28

新葡萄京娱乐场app 54
新葡萄京娱乐场app 55

第247天
2012-11-30

新葡萄京娱乐场app 56
新葡萄京娱乐场app 57
新葡萄京娱乐场app 58
新葡萄京娱乐场app 59
新葡萄京娱乐场app 60

第248天
2012-12-01

新葡萄京娱乐场app 61
新葡萄京娱乐场app 62
新葡萄京娱乐场app 63

第252天
2012-12-05

新葡萄京娱乐场app 64

第253天
2012-12-06

新葡萄京娱乐场app 65
新葡萄京娱乐场app 66
新葡萄京娱乐场app 67

第254天
2012-12-07

新葡萄京娱乐场app 68
新葡萄京娱乐场app 69

第255天
2012-12-08

新葡萄京娱乐场app 70
新葡萄京娱乐场app 71
新葡萄京娱乐场app 72
新葡萄京娱乐场app 73

第256天
2012-12-09

新葡萄京娱乐场app 74

第257天
2012-12-10

新葡萄京娱乐场app 75
新葡萄京娱乐场app 76
新葡萄京娱乐场app 77
新葡萄京娱乐场app 78
新葡萄京娱乐场app 79
新葡萄京娱乐场app 80
新葡萄京娱乐场app 81

第259天
2012-12-12

新葡萄京娱乐场app 82
新葡萄京娱乐场app 83
新葡萄京娱乐场app 84
新葡萄京娱乐场app 85

第263天
2012-12-16

新葡萄京娱乐场app 86
新葡萄京娱乐场app 87

第264天
2012-12-17

新葡萄京娱乐场app 88
新葡萄京娱乐场app 89
新葡萄京娱乐场app 90
新葡萄京娱乐场app 91

第267天
2012-12-20

新葡萄京娱乐场app 92
新葡萄京娱乐场app 93

第268天
2012-12-21

新葡萄京娱乐场app 94
新葡萄京娱乐场app 95
新葡萄京娱乐场app 96
新葡萄京娱乐场app 97
新葡萄京娱乐场app 98
新葡萄京娱乐场app 99
新葡萄京娱乐场app 100
新葡萄京娱乐场app 101
新葡萄京娱乐场app 102
新葡萄京娱乐场app 103

第275天
2012-12-28

新葡萄京娱乐场app 104
新葡萄京娱乐场app 105
新葡萄京娱乐场app 106
新葡萄京娱乐场app 107
新葡萄京娱乐场app 108

第277天
2012-12-30

新葡萄京娱乐场app 109

第279天
2013-01-01

新葡萄京娱乐场app 110

第280天
2013-01-02

新葡萄京娱乐场app 111

第281天
2013-01-03

新葡萄京娱乐场app 112

第282天
2013-01-04

新葡萄京娱乐场app 113
新葡萄京娱乐场app 114
新葡萄京娱乐场app 115

第287天
2013-01-09

新葡萄京娱乐场app 116
新葡萄京娱乐场app 117
新葡萄京娱乐场app 118
新葡萄京娱乐场app 119
新葡萄京娱乐场app 120
新葡萄京娱乐场app 121
新葡萄京娱乐场app 122
新葡萄京娱乐场app 123
新葡萄京娱乐场app 124
新葡萄京娱乐场app 125
新葡萄京娱乐场app 126
新葡萄京娱乐场app 127
新葡萄京娱乐场app 128
新葡萄京娱乐场app 129
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新葡萄京娱乐场app 134
新葡萄京娱乐场app 135
新葡萄京娱乐场app 136
新葡萄京娱乐场app 137
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新葡萄京娱乐场app 139
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新葡萄京娱乐场app 245
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新葡萄京娱乐场app 247
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新葡萄京娱乐场app 254
新葡萄京娱乐场app 255
新葡萄京娱乐场app 256
新葡萄京娱乐场app 257

第288天
2013-01-10

新葡萄京娱乐场app 258
新葡萄京娱乐场app 259
新葡萄京娱乐场app 260
新葡萄京娱乐场app 261
新葡萄京娱乐场app 262
新葡萄京娱乐场app 263
新葡萄京娱乐场app 264
新葡萄京娱乐场app 265

第289天
2013-01-11

新葡萄京娱乐场app 266

第296天
2013-01-18

新葡萄京娱乐场app 267
新葡萄京娱乐场app 268
新葡萄京娱乐场app 269
新葡萄京娱乐场app 270

第303天
2013-01-25

新葡萄京娱乐场app 271

第312天
2013-02-03

新葡萄京娱乐场app 272

第314天
2013-02-05

新葡萄京娱乐场app 273

第321天
2013-02-12

新葡萄京娱乐场app 274

第323天
2013-02-14

新葡萄京娱乐场app 275

第324天
2013-02-15

新葡萄京娱乐场app 276
新葡萄京娱乐场app 277

第325天
2013-02-16

新葡萄京娱乐场app 278

第344天
2013-03-07

新葡萄京娱乐场app 279
新葡萄京娱乐场app 280

第351天
2013-03-14

新葡萄京娱乐场app 281

第354天
2013-03-17

新葡萄京娱乐场app 282
新葡萄京娱乐场app 283

第364天
2013-03-27

新葡萄京娱乐场app 284
新葡萄京娱乐场app 285

第394天
2013-04-26

新葡萄京娱乐场app 286

第396天
2013-04-28

新葡萄京娱乐场app 287
新葡萄京娱乐场app 288

第407天
2013-05-09

新葡萄京娱乐场app 289
新葡萄京娱乐场app 290
新葡萄京娱乐场app 291
新葡萄京娱乐场app 292
新葡萄京娱乐场app 293
新葡萄京娱乐场app 294
新葡萄京娱乐场app 295

第414天
2013-05-16

新葡萄京娱乐场app 296

第416天
2013-05-18

新葡萄京娱乐场app 297
新葡萄京娱乐场app 298
新葡萄京娱乐场app 299
新葡萄京娱乐场app 300
新葡萄京娱乐场app 301

第421天
2013-05-23

新葡萄京娱乐场app 302

第422天
2013-05-24

新葡萄京娱乐场app 303
新葡萄京娱乐场app 304

第435天
2013-06-06

新葡萄京娱乐场app 305

第437天
2013-06-08

新葡萄京娱乐场app 306

第443天
2013-06-14

新葡萄京娱乐场app 307

第448天
2013-06-19

新葡萄京娱乐场app 308
新葡萄京娱乐场app 309

第451天
2013-06-22

新葡萄京娱乐场app 310
新葡萄京娱乐场app 311
新葡萄京娱乐场app 312
新葡萄京娱乐场app 313
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新葡萄京娱乐场app 325
新葡萄京娱乐场app 326

第453天
2013-06-24

新葡萄京娱乐场app 327

第460天
2013-07-01

新葡萄京娱乐场app 328
新葡萄京娱乐场app 329
新葡萄京娱乐场app 330
新葡萄京娱乐场app 331
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新葡萄京娱乐场app 341
新葡萄京娱乐场app 342
新葡萄京娱乐场app 343

第461天
2013-07-02

新葡萄京娱乐场app 344

第468天
2013-07-09

新葡萄京娱乐场app 345

第469天
2013-07-10

新葡萄京娱乐场app 346
新葡萄京娱乐场app 347

第470天
2013-07-11

新葡萄京娱乐场app 348

第480天
2013-07-21

新葡萄京娱乐场app 349
新葡萄京娱乐场app 350

第487天
2013-07-28

新葡萄京娱乐场app 351
新葡萄京娱乐场app 352

第490天
2013-07-31

新葡萄京娱乐场app 353

第491天
2013-08-01

新葡萄京娱乐场app 354

第493天
2013-08-03

新葡萄京娱乐场app 355
新葡萄京娱乐场app 356
新葡萄京娱乐场app 357
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新葡萄京娱乐场app 360
新葡萄京娱乐场app 361
新葡萄京娱乐场app 362

第494天
2013-08-04

新葡萄京娱乐场app 363
新葡萄京娱乐场app 364
新葡萄京娱乐场app 365
新葡萄京娱乐场app 366
新葡萄京娱乐场app 367
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新葡萄京娱乐场app 389
新葡萄京娱乐场app 390
新葡萄京娱乐场app 391
新葡萄京娱乐场app 392
新葡萄京娱乐场app 393
新葡萄京娱乐场app 394
新葡萄京娱乐场app 395
新葡萄京娱乐场app 396
新葡萄京娱乐场app 397
新葡萄京娱乐场app 398
新葡萄京娱乐场app 399
新葡萄京娱乐场app 400
新葡萄京娱乐场app 401
新葡萄京娱乐场app 402
新葡萄京娱乐场app 403
新葡萄京娱乐场app 404
新葡萄京娱乐场app 405
新葡萄京娱乐场app 406
新葡萄京娱乐场app 407
新葡萄京娱乐场app 408
新葡萄京娱乐场app 409
新葡萄京娱乐场app 410
新葡萄京娱乐场app 411
新葡萄京娱乐场app 412
新葡萄京娱乐场app 413
新葡萄京娱乐场app 414
新葡萄京娱乐场app 415
新葡萄京娱乐场app 416
新葡萄京娱乐场app 417
新葡萄京娱乐场app 418
新葡萄京娱乐场app 419
新葡萄京娱乐场app 420
新葡萄京娱乐场app 421
新葡萄京娱乐场app 422
新葡萄京娱乐场app 423
新葡萄京娱乐场app 424
新葡萄京娱乐场app 425
新葡萄京娱乐场app 426

第495天
2013-08-05

新葡萄京娱乐场app 427
新葡萄京娱乐场app 428
新葡萄京娱乐场app 429
新葡萄京娱乐场app 430
新葡萄京娱乐场app 431
新葡萄京娱乐场app 432
新葡萄京娱乐场app 433
新葡萄京娱乐场app 434
新葡萄京娱乐场app 435
新葡萄京娱乐场app 436
新葡萄京娱乐场app 437
新葡萄京娱乐场app 438
新葡萄京娱乐场app 439
新葡萄京娱乐场app 440
新葡萄京娱乐场app 441
新葡萄京娱乐场app 442
新葡萄京娱乐场app 443
新葡萄京娱乐场app 444
新葡萄京娱乐场app 445
新葡萄京娱乐场app 446
新葡萄京娱乐场app 447
新葡萄京娱乐场app 448
新葡萄京娱乐场app 449
新葡萄京娱乐场app 450
新葡萄京娱乐场app 451
新葡萄京娱乐场app 452
新葡萄京娱乐场app 453
新葡萄京娱乐场app 454
新葡萄京娱乐场app 455
新葡萄京娱乐场app 456
新葡萄京娱乐场app 457
新葡萄京娱乐场app 458
新葡萄京娱乐场app 459
新葡萄京娱乐场app 460
新葡萄京娱乐场app 461
新葡萄京娱乐场app 462
新葡萄京娱乐场app 463
新葡萄京娱乐场app 464
新葡萄京娱乐场app 465
新葡萄京娱乐场app 466
新葡萄京娱乐场app 467
新葡萄京娱乐场app 468
新葡萄京娱乐场app 469
新葡萄京娱乐场app 470
新葡萄京娱乐场app 471
新葡萄京娱乐场app 472
新葡萄京娱乐场app 473
新葡萄京娱乐场app 474
新葡萄京娱乐场app 475
新葡萄京娱乐场app 476
新葡萄京娱乐场app 477
新葡萄京娱乐场app 478
新葡萄京娱乐场app 479
新葡萄京娱乐场app 480
新葡萄京娱乐场app 481
新葡萄京娱乐场app 482
新葡萄京娱乐场app 483
新葡萄京娱乐场app 484
新葡萄京娱乐场app 485
新葡萄京娱乐场app 486
新葡萄京娱乐场app 487
新葡萄京娱乐场app 488
新葡萄京娱乐场app 489
新葡萄京娱乐场app 490
新葡萄京娱乐场app 491
新葡萄京娱乐场app 492
新葡萄京娱乐场app 493
新葡萄京娱乐场app 494
新葡萄京娱乐场app 495
新葡萄京娱乐场app 496
新葡萄京娱乐场app 497
新葡萄京娱乐场app 498
新葡萄京娱乐场app 499
新葡萄京娱乐场app 500
新葡萄京娱乐场app 501

第1037天
2015-01-29

新葡萄京娱乐场app 502
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