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How can I iterate over rows in a Pandas DataFrame? 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
How do I get the row count of a Pandas DataFrame? could use df info () so you get row count (# entries), number of non-null entries in each column, dtypes and memory usage Good complete picture of the df If you're looking for a number you can use programatically then df shape [0]
How to get set a pandas index column title or name? 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:
python - Renaming column names in Pandas - Stack Overflow To focus on the need to rename of replace column names with a pre-existing list, I'll create a new sample dataframe df with initial column names and unrelated new column names
How to iterate over columns of a pandas dataframe 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 Now that isn't very helpful if you want to iterate over all the columns But it comes in handy when you want to iterate over columns of your choosing only
python - How to check if particular value (in cell) is NaN in pandas . . . >>> df iloc[1,0] nan So, why is the second option not working? Is it possible to check for NaN values using iloc? Editor's note: This question previously used pd np instead of np and ix in addition to iloc, but since these no longer exist, they have been edited out to keep it short and clear
Creating an empty Pandas DataFrame, and then filling it df loc[len(df)] = [a, b, c] As before, you have not pre-allocated the amount of memory you need each time, so the memory is re-grown each time you create a new row 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