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I have a pandas dataframe, df But it comes in handy when you want to iterate over columns of your choosing only. 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. Now that isn't very helpful if you want to iterate over all the columns Only, when the size of the dataframe approaches million rows, many of the methods tend to take ages when using df[df['col']==val]
I wanted to have all possible values of another_column that correspond to specific values in some_column (in this case in a dictionary).
Question what are the differences between the following commands To just get the index column names df.index.names will work for both a single index or multiindex as of the most recent version of pandas As someone who found this while trying to find the best way to get a list of index names + column names, i would have found this answer useful: 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. I import a dataframe via read_csv, but for some reason can't extract the year or month from the series df['date'], trying that gives attributeerror 'series' object has no attribute 'year' Df.values returns a numpy array with the underlying data of the dataframe, without any index or columns names
[:, 1:] is a slice of that array, that returns all rows and every column starting from the second column
(the first column is index 0) 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.
66 this answer is to iterate over selected columns as well as all columns in a df Df.columns gives a list containing all the columns' names in the df
