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I have a pandas dataframe, df Df = df.drop([x for x in candidates if x in df.columns], axis=1) it has the benefit of readability and (with a small tweak to the code) the ability to record exactly which columns existed/were dropped when. C1 c2 0 10 100 1 11 110 2 12 120 how do i iterate over the rows of this dataframe
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For every row, i want to access its elements (values in cells) by the n. Empty dataframe of nans and then, there's creating a dataframe of nans, and all the caveats associated therewith. So your column is returned by df['index'] and the real dataframe index is returned by df.index
An index is a special kind of series optimized for lookup of its elements' values
For df.index it's for looking up rows by their label That df.columns attribute is also a pd.index array, for looking up columns by their labels. Question what are the differences between the following commands The book typically refers to columns of a dataframe as df['column'] however, sometimes without explanation the book uses df.column
I don't understand the difference between the two. That might work for your case, but in op's case,.loc[1,0] raises keyerror Maybe you meant.iloc instead, but then, doing df.isnull() on the whole dataframe is wasteful when you just want one value I just updated the question to say that btw.
0 df.values is gives us dataframe values as numpy array object
Df.values [:, 1:] is a way of accessing required values with indexing it means all the rows and all columns except 0th index column in dataframe. # `in` operation df[np.isin(df['countries'], c1)] countries 1 uk 4 china # `not in` operation df[np.isin(df['countries'], c1, invert=true)] countries 0 us 2 germany 3 nan why is it worth considering Numpy functions are usually a bit faster than their pandas equivalents because of lower overhead Since this is an elementwise operation that does not depend on index alignment, there are very few.
It's just as bad as append, and even more ugly
