According to the pandas documentation, Series.nonzero() is replaced by Series.to_numpy().nonzero().

Instead of

df.iloc[0].nonzero()

Try

df.iloc[0].to_numpy().nonzero()

Check the version of your pandas library:

import pandas
print(pandas.__version__)

If your version is less than 0.24.1:

pip install --upgrade pandas

I had to update my Fbprophet version. It was 0.2. I updated it to 0.6 and that solved the issue.

The method .str is defined for data Series (eg. columns), but not DataFrames (tables). It requires an attribute series to be defined. In your case, in line 5 you slice the DataFrame with the 2nd column:

split_df[[2]]

This syntax returns a DataFrame with one column. Instead, you should be indexing and accessing the 2nd series:

split_df[2]

This returns a Series-type object, which has a series attribute and thus can be converted to string. This is also why df["Maths Formula"] works - it returns a Series object.

As stated in another answer, the as_matrix method is deprecated since 0.23.0, so you should use to_numpy instead. However, I want to highlight the fact that as_matrix and to_numpy have different signatures: as_matrix takes a list of column names as one of its parameter, in case you want to limit the conversion to a subset of the original DataFrame; to_numpy does not accept such a parameter. As a consequence, the two methods are completely interchangeable only if you want to convert the DataFrame in full. If you (as in my case) need to convert a subset of the matrix, the usage would be quite different in the two use cases.

For example let's assume we only need to convert the subset ['col1', 'col2', 'col4'] of our original DataFrame to a Numpy array. In that case you might have some legacy code relying on as_matrix to convert, which looks more or less like:

df.as_matrix(['col1', 'col2', 'col4'])

While converting the above code to to_numpy you cannot simply replace the function name like in:

df.to_numpy(['col1', 'col2', 'col4'])  # WRONG

because to_numpy does not accept a subset of columns as parameter. The solution in that case would be to do the selection first, and apply to_numpy to the result, as in:

df[['col1', 'col2', 'col4']].to_numpy()  # CORRECT