I have a dataframe from which I remove some rows. As a result, I get a dataframe in which index is something like that: [1,5,6,10,11] and I would like to reset it to [0,1,2,3,4]. How can I do it? The following seems to work: df = df.reset_index() del df['index'] The following does not work: df … Read more
Recently began branching out from my safe place (R) into Python and and am a bit confused by the cell localization/selection in Pandas. I’ve read the documentation but I’m struggling to understand the practical implications of the various localization/selection options.
I’m confused about the rules Pandas uses when deciding that a selection from a dataframe is a copy of the original dataframe, or a view on the original.
I have DataFrame with column Sales.
I am curious as to why df[2] is not supported, while df.ix[2] and df[2:3] both work.
I have a list:
Can I insert a column at a specific column index in pandas?
Given a DataFrame with a column “BoolCol”, we want to find the indexes of the DataFrame in which the values for “BoolCol” == True
From the pandas documentation, I’ve gathered that unique-valued indices make certain operations efficient, and that non-unique indices are occasionally tolerated.
Can someone point me to a link or provide an explanation of the benefits of indexing in pandas? I routinely deal with tables and join them based on columns, and this joining/merging process seems to re-index things anyway, so it’s a bit cumbersome to apply index criteria considering I don’t think I need to.