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@sheilnaik
Created June 2, 2020 04:03
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Question 1

Question

I tried to find the number of rows and columns in my DataFrame but got a weird error: "TypeError: 'tuple' object is not callable". What does this mean?

Answer

Try Googling the exact error message you receive followed by a brief description of what you're trying to do. In the above example, Googling "TypeError: 'tuple' object is not callable" dataframe rows and columns returns a search result from Stack Overview titled array.shape() giving error tuple not callable. The first response says:

shape is just an attribute, not a method. Just use y_pred.shape (no parentheses).

It turns out, I had .shape() in my code when I should've just used .shape

Sample error message

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-9-0e566b70f572> in <module>()
----> 1 df.shape()

TypeError: 'tuple' object is not callable

Question 2

Question

I tried to export my DataFrame to a CSV, but when I did so, I noticed that the first column was just numbered 0 to the number of rows in my DataFrame. What is this column and how do I get rid of it?

Answer

Searching for export csv first column numbers isn't very helpful. If the question is pandas or Python-specific, try starting your search with "pandas". Searching for pandas export csv first column numbers is much more helpful.

The first link brings us to the pandas documentation. If we click it, we can see that index is a parameter that writes the row names to the CSV file, and by default, it's set as True. If we change this to False, the first column in our exported file is removed.

If that wasn't immediately obvious, the third link down from Stack Overflow is titled "Remove index column while saving csv in pandas". The first response tells us:

What you are seeing is the index column. Just set index=False:

which is the solution to the problem. Stack Overflow is definitely helpful, but as you practice with pandas more often, you'll get better at reading the official documentation and you'll find it to be the more straightforward source for an answer to your question. However, either way works. Choose whichever you are most comfortable with!

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