The float
function can do this:
>>> float('1.31E+01')
13.1
or for a list:
>>> map(float, ['3.76E+00', '1.31E+01', '1.14E+01'])
[3.76, 13.1, 11.4]
質問
I'm trying to import a large .csv file containing text and numbers using genfromtxt
in numpy. I'm only interested in two columns. I have most of the import sorted out with:
def importfile(root):
data = root.entry.get()
atw = np.genfromtxt(data, delimiter=",",
skip_header=1,
skip_footer=2,
autostrip=True,
usecols=(25,26),
dtype=("|S10"))
elem = atw[:,0]
concs = atw[:,1]
print(elem)
print(concs)
With output for elem
and concs
respectively:
['Na2O' 'MgO' 'Al2O3' 'SiO2' 'P2O5' 'SO3' 'Cl' 'K2O' 'CaO' 'TiO2' 'Cr2O3'
'MnO' 'FeO' 'NiO' 'Cu2O' 'ZnO' 'Ga2O3' 'SrO' 'Y2O3']
['3.76E+00' '1.31E+01' '1.14E+01' '4.04E+01' '1.24E+00' '5.89E-02'
'2.43E-02' '1.53E+00' '1.49E+01' '2.87E+00' '6.05E-02' '1.96E-01'
'1.17E+01' '3.69E-02' '8.73E-03' '1.39E-02' '1.93E-03' '1.88E-01'
'5.58E-03']
I have tried many different things for converting the concs
string into a float, but it doesn't seem to like the fact that the concs are in scientific notation... is there a way to turn the concs
values into a float?
解決
The float
function can do this:
>>> float('1.31E+01')
13.1
or for a list:
>>> map(float, ['3.76E+00', '1.31E+01', '1.14E+01'])
[3.76, 13.1, 11.4]
他のヒント
with open( datafile,'r' ) as inData:
for line in inData:
j = list( map( float, filter( None , [ x for x in line.strip().split(',') ] )) )
Just mentioned generally, as it solves a similar problem that brought me to this page.
MAybe that will be helpful for anybody, I had similar problem and I've found on stackoverflow about applying pandas to_numeric to DataFrame columns including replacing commas with dots
import re
import pandas as pd
atw[cc] = pd.to_numeric(atw[cc].apply(lambda x: re.sub(',', '.', str(x))))
Having such list of scientific notations, you can also do this:
1. a = [9.0181446e-01, 1.3179450e-02, 4.3021311e-04, 2.3546994e-03, 3.6531375e-03, 7.8567989e-02]
2. max(a)
Output will be: 0.90181446