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Python DataFrame.sort_values方法代碼示例

本文整理匯總了Python中pandas.core.frame.DataFrame.sort_values方法的典型用法代碼示例。如果您正苦於以下問題:Python DataFrame.sort_values方法的具體用法?Python DataFrame.sort_values怎麽用?Python DataFrame.sort_values使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在pandas.core.frame.DataFrame的用法示例。


在下文中一共展示了DataFrame.sort_values方法的2個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: zip

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import sort_values [as 別名]
    #print(shp)
    #print(sc)

    IDF = []
    for item_sc in sc:
        item_idf = math.log(item_sc)
        IDF.append(item_idf)
    #print(IDF)

    TFIDF = [i * j for i, j in zip(TF, IDF)]
    #print(TFIDF)
    #print(header)

    Dict_TFIDF={item_bank+'_hd':header, item_bank+'_ti':TFIDF}
    df_TFIDF = DataFrame(Dict_TFIDF)
    df_TFIDF1 = df_TFIDF.sort_values(item_bank+'_ti',ascending=False).reset_index(drop=True)
    df_Bank = pd.concat([df_Bank,df_TFIDF1], axis=1)

print(df_Bank)

sum(TF.isnull())
df_Bank.花旗_ti[~df_Bank.花旗_ti.isnull()]

'''

# decision tree
# 讀入
ptt_cont = df()
X = ptt_cont.data
Y = ptt_cont.target
開發者ID:TSR2,項目名稱:python,代碼行數:32,代碼來源:Code5_TFIDFTree.py

示例2: LineMesurer

# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import sort_values [as 別名]

#.........這裏部分代碼省略.........
                self.current_df = lines_dataframe
            
            self.line_to_Logdf()
        
        #Otherwise return the data from the fit
        else:
            return self.fit_dict

    def dazer_lineMeasuring(self, subWave, subFlux, wavelengths_list, lines_dataframe, Measuring_Method = 'lmfit'):
        
        #Clear the dictionaries with the line data
        self.fit_dict = Series(index = self.fitting_parameters, dtype=object)
         
        #Get indeces of line_regions in spectrum wavelength
        self.fit_dict.loc['idx0':'idx5'] = searchsorted(subWave, wavelengths_list)
        self.fit_dict.loc['Wave1':'Wave6'] = wavelengths_list
                
        #Calculation and plotting  parameters of adjacent continuum     
        self.continuum_Regions(subWave, subFlux)
                
        #Check if emission or absorption line as well as line mixture #WE SHOULD ONLY FEED HERE THE LINE REGION NOT ALL
        self.check_GaussianMixture(subWave, Measuring_Method, lines_dataframe, force_check=True)   

        #Convert emission lines to gaussian equivalents
        self.calculate_Intensity(subWave, subFlux, lines_dataframe)  
        
        #Save the fit:              
        if self.fit_dict.start_treatment:

            #Update the data frame with the new data
            self.line_to_Logdf()
            
            #Sort the data frame
            self.current_df.sort_values(['lambda_theo'], ascending=[True], inplace=True)
            
            #Store data to dataframe
            self.save_lineslog_dataframe(self.current_df, self.lineslog_df_address)
            
        return

    def continuum_Regions(self, subWave, subFlux, adjacent_continuum = True):
        
        #Indeces from the continuum regions
        idx1, idx2, idx3, idx4, idx5, idx6 = self.fit_dict[['idx0', 'idx1', 'idx2', 'idx3', 'idx4', 'idx5']]
        
        #In this case we use adjacent regions to compute the continuum level
        if adjacent_continuum == True:
        
            #Region resolution
            region_resolution = (subWave[-1] - subWave[0]) / len(subWave) #Should we save it?

            #We generate arrays containing the wavelength and flux values from blue and red continuum             
            FluxCont_BothSides = concatenate((subFlux[idx1:idx2],   subFlux[idx5:idx6]))
            WaveCont_BothSides = concatenate((subWave[idx1:idx2],  subWave[idx5:idx6]))

            self.fit_dict['zerolev_width'] = region_resolution * len(WaveCont_BothSides)
            
            #We generate and array with the standard deviation of the points on the left and the right  #WARNING no se si esto esta bien                     
            FluxError_BothSides = concatenate((std(subFlux[idx1:idx2]) * ones(len(subFlux[idx1:idx2])), std(subFlux[idx5:idx6]) * ones(len(subFlux[idx5:idx6]))))
            
            #We perform a linear regresion taking into consideration the error in the flux #WARNING no se si esto esta bien
            m_Continuum, n_continuum = Python_linfit(WaveCont_BothSides, FluxCont_BothSides, FluxError_BothSides, errors_output = False)
            
            #We calculate the wavelength and flux at the middle region of each continuums for graphical purposes
            self.fit_dict['Blue_wave_zerolev']  = (subWave[idx2] + subWave[idx1]) / 2
            self.fit_dict['Red_wave_zerolev']   = (subWave[idx6] + subWave[idx5]) / 2
開發者ID:Delosari,項目名稱:Dazer,代碼行數:70,代碼來源:DZ_LineMesurer.py


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