Brtdku

I am using JetBrains Pycharm (Windows OS) for python coding. In my code, I read a csv file using pd.read_csv(dataset). Then do few moderation on some of the table columns & finally want to see the updated table, so at the end give the command --> print (dataset).

Now when the modified dataset is printed at the bottom window, all the elements of the dataset are printed in floating point exponential format.

e.g.,
[ 0.00000000e+00 0.00000000e+00 1.00000000e+00 1.65349200e+05
1.36897800e+05 4.71784100e+05]

This is where I need the help. I need the dataset to be printed in flaoting point decimal format or simple decimal format.

How that is possible in the easiest way ?
Also, is there any option in Pycharm, to configure the output format (exponential, decimal etc..) from any setting/configuration window, instead of hard-coding ?

Use {n}. format ()

For ex: '{:n}'.format(1234))

ds_x=[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,1.65349200e+05, 1.36897800e+05, 4.71784100e+05]

# For Numpy arrays use '.astype'

# Copy of the array 'ds_x', cast to a specified type 'float'.

# Note: Try type 'float32' OR 'float64' if the below fails to provide correct precision.

data_set = ds_x.astype(float)

for i in data_set:

    print("{:.16f}".format(float(i)))

# More about better precision can be found here

Output:

0.0000000000000000

0.0000000000000000

1.0000000000000000

165349.2000000000116415

136897.7999999999883585

471784.0999999999767169

Or you can use the alternative way:

"%.16f" % (float( 00,1.65349200e+05))

The second question regarding PyCharm. You may want to look into formatting markers . Complete details on reformatting .

Update:

Here is a updated code sample with the dataset that was provided by you.

import numpy as np



ds = np.array([[ 0.00000000e+00, 1.00000000e+00, 0.00000000e+00, 2.86637600e+04, 1.27056210e+05, 2.01126820e+05],

                [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 1.44372410e+05, 1.18671850e+05, 3.83199620e+05]])



for data_x in iter(ds):

    print()

    for data_y in data_x:

        print("%.16f" % float(data_y))

output:

0.0000000000000000

1.0000000000000000

0.0000000000000000

28663.7599999999983993

127056.2100000000064028

201126.8200000000069849



0.0000000000000000

0.0000000000000000

1.0000000000000000

144372.4100000000034925

118671.8500000000058208

383199.6199999999953434

Regarding the error:

TypeError: only size-1 arrays can be converted to Python scalars

This is because the dataset that you are using is a Python matrix OR a list of a list / two arrays. The first code sample in this post is using one loop which works with one array; however, since you are using a larger dataset containing two arrays on the first loop it will pass the entire array causing the above error to be outputted. To correct this I simply added a second loop to iterate through each value for each array.