python - how to convert pandas data frame into numpy data frame -

python - how to convert pandas data frame into numpy data frame -

i have 1 simple info set class label , stored "mydata.csv",

ga_id pn_id pc_id mbp_id gr_id ap_id class 0.033 6.652 6.681 0.194 0.874 3.177 0 0.034 9.039 6.224 0.194 1.137 3.177 0 0.035 10.936 10.304 1.015 0.911 4.9 1 0.022 10.11 9.603 1.374 0.848 4.566 1

i utilize given code convert info numpy array can utilize info set predictions , machine learning modeling due header error has been raised "valueerror: not convert string float: " when removed header file method work me :

import numpy np #from sklearn import metrics #from sklearn.linear_model import logisticregression sklearn.svm import svc raw_data = open("/home/me/desktop/scklearn/data.csv") dataset = np.loadtxt(raw_data, delimiter=",") x = dataset[:,0:5] y = dataset[:,6]

i tried skip header error occurs:

dataset = np.loadtxt(raw_data, delimiter=",")[1:]

then moved pandas , able import info method:

raw_data = pandas.read_csv("/home/me/desktop/scklearn/data.csv")

but here sucked 1 time again when tried convert numpy array showing error previous.

is there method available in pandas can : save heathers list :

header_list = ('ga_id','pn_id','pc_id' ,'mbp_id' ,'gr_id' , 'ap_id','class')

last column class label , remaining part(1:4,0:5) numpy array model building:

i have write downwards code column list

clm_list = [] raw_data = pandas.read_csv("/home/me/desktop/scklearn/data.csv") clms = raw_data.columns() clm in clms: clm_list.append(clm) print clm_list ## produces column list

after reading lot achieved want , implemented info on scikit-learn, code convert csv info scikit-learn compatible form given bellow.

import pandas pd r = pd.read_csv("/home/zebrafish/desktop/ex.csv") print r.values clm_list = [] column in r.columns: clm_list.append(column) x = r[clm_list[0:len(clm_list)-1]].values y = r[clm_list[len(clm_list)-1]].values print clm_list print x print y

out come of code want :

['ga_id', 'pn_id', 'pc_id', 'mbp_id', 'gr_id', 'ap_id', 'class'] [[ 0.033 6.652 6.681 0.194 0.874 3.177] [ 0.034 9.039 6.224 0.194 1.137 3.177] [ 0.035 10.936 10.304 1.015 0.911 4.9 ] [ 0.022 10.11 9.603 1.374 0.848 4.566]] [0 0 1 1]

python csv numpy pandas