Pandas DataFrame and Series Alignment Python Notes
This week we have a look at Data Alignment in Python
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import numpy as np import pandas as pd from pandas import Series, DataFrame |
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#create series ser1 = Series([0,1,3], index=['A','B','C']) ser1 |
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ser2 = Series([3,4,5,6], index=['A','B','C','D']) ser2 |
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#adding two series which have different lenghts ser1 + ser2 |
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#lets try and add dataframes #create dataframe dframe = DataFrame(np.arange(4).reshape((2,2)), columns=list('AB'), index=['LA','GA']) dframe |
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#create a second dataframe dframe2 = DataFrame(np.arange(9).reshape((3,3)), columns=list('ABC'), index=['LA','NYC','GA']) dframe2 |
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#add the dataframes up dframe + dframe2 |
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#add the 2 dataframe in such a way that we will not lose the values to NaNs dframe.add(dframe2, fill_value=0) # fill NaNs values to zero so the new dataframe can use the any original values of any of the frames |
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#make series out of dataframe ser3 = dframe2.ix[0] ser3 |
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#subtract series from a dframe dframe2 - ser3 |
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This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy.
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