"Data analysis using Python" reading notes--seventh. Data normalization: Cleanup, transformation, merger, remodeling (II.)

3. Data Conversion

After the reflow of the data is introduced, the following describes the filtering, cleanup, and other conversion work for the data.

• Go heavy

#-*-encoding:utf-8-*-ImportNumPy as NPImportPandas as PDImportMatplotlib.pyplot as Plt fromPandasImportSeries,dataframe#Dataframe to Heavydata = DataFrame ({'K1':[' One']*3 + [' Both'] * 4,    'K2': [1,1,2,3,3,4,4,]})#Print DataPrintData.duplicated ()#Returns a Boolean series, repeating true, not repeating false#after getting the dataframe, you should realize that it is very common.PrintData.drop_duplicates (). Reset_index (drop =True)#You can select the columns that need to be weighedPrintData.drop_duplicates (['K1'])#default preserves rows that appear firstPrintData.drop_duplicates (['K1'],take_last = True)#set the last occurrence of the row to be preserved

"Data analysis using Python" reading notes--seventh. Data normalization: Cleanup, transformation, merger, remodeling (II.)