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+~~NOTOC~~
+|<100% 25% - >|
+^  \\ DATA ANALYTICS REFERENCE DOCUMENT\\ \\   ^^
+^  Document Title:|Python Data Analytics references|
+^  Document No.:|1541783112|
+^  Author(s):|Gerhard van der Linde|
+^  Contributor(s):|  |
+**REVISION HISTORY**
+|< 100% 10% - - 10% 17% 10% >|
+^  \\  Revision\\  \\  ^\\ Details of Modification(s)^\\ Reason for modification^  \\ Date  ^  \\ By  ^
+|  [[:doku.php?id=python&do=revisions|0]]  |Draft release|A Rererence summary to the libabries and commands |  2018/11/09 17:05  |  Gerhard van der Linde  |
+----
+====== Python Reference ======
+There's thousands of tutorial and references out there, so this is not another one, rather just a reference page to provide quick samples to analytics essentials and links to the detailed summaries instead.
+===== Pandas =====
+Query a dataframe for a string in a field
+<code python df_query.py>
+df[df['Description'].str.contains('[a-z_,: ]6mm')]
+</code>
+The string in the sample is over complicated and includes a regulate expression, so can be a simple string, but has the ability to include regular expressions too.
+<code python>
+#read a csv into a dataframe
+csv=pd.read_csv('joblog.csv')
+df=pd.read_csv("HF250-210C POR 020519.csv",skiprows=[1,2],encoding='ansi')
+h5=pd.read_hdf('joblog_20180502.h5')
+csv['Time Mark']=pd.to_datetime(csv['Time Mark'])
+csv['RunTimeRecipe']=pd.to_timedelta(csv['RunTimeRecipe'])
+csv['RunTimeRecipe']=csv['RunTimeRecipe']/pd.to_timedelta('1 hour')
+h5.append(csv)
+new=h5.append(csv)
+new=new.reset_index(drop=True)
+new.to_hdf('joblog_20180724.h5', key='index')
+#merging dataframes on column
+b2=pd.merge(bom,baan, on='PartNo', how='left')
+#vlookup for dataframes
+df5.loc[df5.Item.isin(dfu.ms),['WhereUsed']]=dfu[103:104]['usage'].item()
+db=pd.read_hdf('stat/joblog_20181121.h5')
+# db=pd.read_hdf('stat/joblog_20180502.h5')
+#db=pd.read_csv('stat/joblog.csv')
+#specify column names
+db.columns=['UniqueJobIdentifier', 'JobName', 'TimeMark', 'UserName',\
+'SampleTime', 'EntryAngle', 'ExitAngle', 'LoadedWafers', 'ToolID',\
+'Recipename', 'RunTimeRecipe', 'VacuumMedian', 'VacuumElement',\
+'TempMax', 'FieldMax', 'TempDeltaMax', 'LogFilename',\
+'LogFilePath']
+# Filer on tool ID
+db=db[db['ToolID'].str.contains('ABCO-06')]
+# Filter on Job ID - B4300 - A3225
+db=db[db['JobName'].str.contains('B4300')]
+# remove the runtime outliers
+db=db[abs(db['RunTimeRecipe']-db['RunTimeRecipe'].mean())<db['RunTimeRecipe'].std()*TimeOutLierFilter]
+# remove the runtime outliers
+db=db[abs(db['TempMax']-db['TempMax'].mean())<db['TempMax'].std()*OutLierFilter]
+# remove the runtime outliers
+db=db[abs(db['TempDeltaMax']-db['TempDeltaMax'].mean())<db['TempDeltaMax'].std()*OutLierFilter]
+#remove times before jan2017
+db=db[db['TimeMark']>pd.to_datetime('2016-01-01')]
+#remove times greater than now
+db=db[db['TimeMark']<pd.to_datetime(time.asctime())]
+</code>