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So your column is returned by df['index'] and the real dataframe index is returned by df.index However, i don't see the data frame, i receive series([], dtype An index is a special kind of series optimized for lookup of its elements' values

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For df.index it's for looking up rows by their label I'm trying to use python to read my csv file extract specific columns to a pandas.dataframe and show that dataframe That df.columns attribute is also a pd.index array, for looking up columns by their labels.

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I have a pandas dataframe, df

C1 c2 0 10 100 1 11 110 2 12 120 how do i iterate over the rows of this dataframe For every row, i want to access its elements (values in cells) by the n. 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. 15 ok, lets check the man pages While df is to show the file system usage, du is to report the file space usage Du works from files while df works at filesystem level, reporting what the kernel says it has available.

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 = 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. It's just as bad as append, and even more ugly Empty dataframe of nans and then, there's creating a dataframe of nans, and all the caveats associated therewith.

Converting this to date format with df['dob'] = pd.to_datetime(df['dob']), the date gets converted to Now i want to convert this date format to 01/26/2016 or any other general date format How do i do it