Python - "cannot perform reduce with flexible type" when trying to use numpy.mean

numpy, python-2.7, scipy

Solution

When you're working with structured arrays like that you lose some of the flexibility you'd otherwise have. You can take the mean after selecting the appropriate piece, though:

>>> ifile
array([(3.385, 44.5), (0.48, 15.5), (1.35, 8.1), (465.0, 423.0),
       (36.33, 119.5), (27.66, 115.0), (14.83, 98.2), (1.04, 5.5)], 
      dtype=[('brainwt', '<f8'), ('bodywt', '<f8')])
>>> ifile["brainwt"].mean()
68.759375000000006
>>> ifile["bodywt"].mean()
103.66249999999999

I use `numpy` almost every day, but when working with data of the sort where I want to name columns, I think the `pandas` library makes things much more convenient, and it interoperates very well. It's worth a look. Example:

>>> import pandas as pd
>>> df = pd.read_csv("brainandbody.csv", skipinitialspace=True)
>>> df
   Brain Weight  Body Weight
0         3.385         44.5
1         0.480         15.5
2         1.350          8.1
3       465.000        423.0
4        36.330        119.5
5        27.660        115.0
6        14.830         98.2
7         1.040          5.5
>>> df.mean()
Brain Weight     68.759375
Body Weight     103.662500
dtype: float64

Problem

I'm at my wit's end as I keep getting "cannot perform reduce with flexible type" when I try to compute the mean of a column, the file is read in just fine (no missing values in any rows/column) but when I put in the line: Brain_wt_mean = np.mean(ifile axis=0) then Python 2.7.5 does not like it. I am using this within the Spyder IDE. Thanks much for any help. ``` import os import numpy as np if __name__ == "__main__": try: curr_dir = os.getcwd() file_path = curr_dir + '\\brainandbody.csv' ifile = np.loadtxt('brainandbody.csv', delimiter=',', skiprows=1, dtype=[('brainwt', 'f8'), ('bodywt', 'f8')]) except IOError: print "The file does not exist, exiting gracefully" Brain_wt_mean = np.mean(ifile axis=0) ### BELOW is a sample of the csv file ###### Brain Weight Body Weight 3.385 44.5 0.48 15.5 1.35 8.1 465 423 36.33 119.5 27.66 115 14.83 98.2 1.04 5.5 ```

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