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I have a pandas dataframe, df When should one be used over the other 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. Are some use cases for series vs Good complete picture of the df
If you're looking for a number you can use programatically then df.shape [0].
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. 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.reindex(columns=neworder) however, as you can see, i only want to swap two columns It was doable just because there are only 4 column, but what if i have like 100 columns What would be an effective way to swap or reorder columns There might be 2 cases
When you just want 2 columns swapped When you want 3 columns reordered. 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.
This question is same to this posted earlier
I want to concatenate three columns instead of concatenating two columns Here is the combining two columns Struggling to understand the difference between the 5 examples in the title
