Python data analysis-data processing

Data import:

Import CSV fromPandasImportREAD_CSV;DF= Read_csv ('D://pa//4.1//1.csv') to import text, to be converted to UTF-8 No BOM format: fromPandasImportREAD_TABLE;DF= Read_table ('D://pa//4.1//2.txt') Import Excle fromPandasImportREAD_EXCEL;DF= Read_excel ('c:/pa/4.1/3.xlsx')

View CodeData export: Export of data:

 fromPandasImportDATAFRAME;DF=DataFrame ({' Age': [21, 22, 23],     'name': ['KEN','John','JIMI']});d F.to_csv ("C:/pa/4.1/df.csv");#do not import ordinalDf.to_csv ("C:/pa/4.1/df.csv", Index=false);

View CodeDuplicate value processing:

 from Import  = read_csv ('c:/pa/4.1/data.csv'= Df.drop_duplicates ();

View CodeMissing value handling:

 from Import  = read_csv ('c:/pa/4.4/data.csv'= Df.dropna ();

View CodeWhitespace value processing:

 from Import  = read_csv ('c:/pa/4.5/data.csv'= df["name"  ].str.strip ();d f["name"]=newdf;

View CodeField Extraction:

Astype (str) is converted into character data for easy processing.

 fromPandasImportREAD_CSV;DF= Read_csv ('C:/pa/4.6/data.csv') df["Tel"]=df["Tel"].astype (str); bands=df["Tel"].str.slice (0,3); Areas=df["Tel"].str.slice (3,7); Numbs=df["Tel"].str.slice (7,11);

View CodeField split:

 from Import read_csv;df=read_csv ("c:/pa/4.7/data.csv"); NEWDF=df[" name "]. Str.split ("", 1, True); Newdf.columns=["band", " name "];

View CodeRecord extraction:

Import pandas;  from Import read_csv;df=read_csv ("c:/pa/4.8/data.csv", sep="| " );d f[df.comments>1000];d f[df.comments.between (1000,10000)];d f[pandas.isnull ( Df.title)];d f[df.title.str.contains (" Taiwan Power ", na=False)];d f[( Df.comments>=1000) & (df.comments<=10000)]

View CodeRandom sampling:

Import NumPy;  from Import read_csv;df=read_csv ("c:/pa/4.9/data.csv"); R=numpy.random.randint (0,10,3);d f.loc[r,:];

View CodeRecord Merge:

Importpandas; fromPandasImportREAD_CSV;DF1=read_csv ("C:/pa/4.10/data1.csv", sep="|");d F2=read_csv ("C:/pa/4.10/data2.csv", sep="|");d F3=read_csv ("C:/pa/4.10/data3.csv", sep="|");d F=pandas.concat ([DF1,DF2,DF3])

View CodeField Merge:

 fromPandasImportREAD_CSV;DF=Read_csv ("C:/pa/4.11/data.csv", Sep=" ", Names=['band',' Area','Num']);d f=Df.astype (str); tel= df['band'] + df[' Area'] + df['Num']

View CodeField match:

Importpandas; fromPandasImportRead_csv;item=Read_csv ("C:/pa/4.12/data1.csv", Sep="|", Names=["ID","Comments","title"]);p rices=Read_csv ("C:/pa/4.12/data1.csv", Sep="|", Names=["ID","Oldprice","Newprice"]) itemprices=Pandas.merge (item, Prices, left_on="ID", right_on="ID" );

View CodeSimple calculation:

Import pandas;  from Import read_csv;df=read_csv ("c:/pa/4.13/data.csv", sep="| " result=df.price*df.numdf["sum"]=result

View CodeData normalization:

Import pandas;  from Import read_csv;df=read_csv ("c:/pa/4.14/data.csv");scale = ( Df.score-df.score.min ())/(Df.score.max ()-df.score.min ())

View CodeData grouping:

Importpandas; fromPandasImportREAD_CSV;DF= Read_csv ("C:\\pa\\4.15\\data.csv", sep='|'); Bins= [min (df.cost)-1, +, +1, Max (df.cost).];labels= ['20 or less','20 to','40 to','60 to','80 to','100 or more'];p andas.cut (df.cost, bins) pandas.cut (df.cost, bins, right=False) pandas.cut (df.cost, bins, right=false, Labels=labels)

View CodeDate conversion:

Import pandas;  from Import read_csv;  from Import  = read_csv ("c:\\pa\\4.16\\data.csv", encoding="utf-8 " );d F_dt=to_datetime (DF. Registration time, format="%y/%m/%d");

View CodeDate formatting:

Importpandas; fromPandasImportread_csv; fromPandasImportTO_DATETIME;DF= Read_csv ("C:\\pa\\4.16\\data.csv", encoding="Utf-8");d F_dt=to_datetime (DF. Registration Time, format="%y/%m/%d");d F_dt_str=df_dt.apply (LambdaX:datatime.strftime (x,"%d-%m-%y"))

View CodeDate Extraction:

 import   pandas;  from  pandas import   read_csv;  from  pandas import   to_datetime;df  = read_csv ( " c:\\pa\\4.18\\data.csv  " , encoding=  " utf-8   " );d F_dt  =to_datetime (DF. Registration time, Format= Span style= "COLOR: #800000" >%y/%m/%d   " ); Df_dt.dt.year;df_dt.dt.second;df_dt.dt.minute;df_dt.dt.hour;df_dt.dt.day;df_dt.dt.month;df_dt.dt.weekday;  

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Python data analysis-data processing