How To Remove E Scientific Notation From Mean Method Of Numpy Lib In Python

I'm new to python and numpy library.I'm doing PCA on my custom dataset. I calculate the mean of each row of my dataframe from pandas but I get below result as mean array: [ 7.433

Solution 1:

Are these large numbers realistic, and, if so how do you want to display them?

Copy and paste from your question:

In [1]: x=np.array([7.433148e+46,7.433148e+47])

The default numpy display adds a few decimal pts.

In [2]: x
Out[2]: array([  7.43314800e+46,   7.43314800e+47])

changing precision doesn't change much

In [5]: np.set_printoptions(precision=6)
In [6]: np.set_printoptions(suppress=True)

In [7]: x
Out[7]: array([  7.433148e+46,   7.433148e+47])

suppress does less. It supresses small floating point values, not large ones

suppress : bool, optional
Whether ornot suppress printing of small floating point values using       
scientific notation(default False).

The default python display for one of these numbers - also scientific:

In[8]: x[0]Out[8]: 7.4331480000000002e+46

With a formatting command we can display it in it's 46+ character glory (or gory detail):

In [9]: '%f'%x[0]
Out[9]: '74331480000000001782664341808476383296708673536.000000'

If that was a real value I'd prefer to see the scientific notation.

In [11]: '%.6g'%x[0]
Out[11]: '7.43315e+46'

To illustrate what suppress does, print the inverse of this array:

In[12]: 1/xOut[12]: array([ 0.,  0.])

In[13]: np.set_printoptions(suppress=False)

In[14]: 1/xOut[14]: array([  1.345325e-47,   1.345325e-48])

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I'm not that familiar with pandas, but I wonder if your mean calculation makes sense. What does pandas print for df.iloc[:,15]? For the mean to be this large, the original data has to have values of similar size. How does the source display them? I wonder if most of your values are smaller, normal values, and your have a few excessively large ones (outliers) that 'distort' the mean.

I think you can simplify the array extraction with values:

mean_vector = np.array(df.iloc[:,15],dtype=np.float64)
mean_vector = df.iloc[:,15].values