Python load_from_csv Examples

Example #1

#Preparing signal and background data for classifier
data_sig = data_use[labels == 1]
data_bck = data_use[labels == 0]

#save signal and background data for classifier
data_sig.to_csv('../../Cern_Time_Series/Classification/data_sig_rolling_mean_original_classifier_10_13.csv')
data_bck.to_csv('../../Cern_Time_Series/Classification/data_bck_rolling_mean_original_classifier_10_13.csv')

# <codecell>

# Convert data to DataStorage
from cern_utils import converter_csv

#Load signal and background data
signal_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/data_sig_rolling_mean_original_classifier_10_13.csv', sep=',')
bck_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/data_bck_rolling_mean_original_classifier_10_13.csv', sep=',')

# <codecell>

# Get train and test data
signal_train, signal_test = signal_data.get_train_test(train_size=0.8)
bck_train, bck_test = bck_data.get_train_test(train_size=0.8)

# <codecell>

columns = signal_data.columns
print columns

#select variables for classifier
"""

Example #2

File: history.py Project: vikasgupta1812/Time_Series_Analysis

    else:
        b = np.percentile(data_res_sel[i].values, 90)
        
    plt.hist(data_sig[i].values, bins = 20, label='signal', alpha = 0.4, color = 'r',range= (a,b))
    plt.hist(data_bck[i].values, bins = 20, label='bck', alpha = 0.4, color = 'b', range= (a,b))
    plt.title(i)
    plt.legend(loc = 'best')
    plt.show()

# <codecell>

# Convert data to DataStorage
from cern_utils import converter_csv

#Load signal and background data
signal_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/data_sig_res_1year_6month_cumulative_10_15.csv', sep=',')
bck_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/data_bck_res_1year_6month_cumulative_10_15.csv', sep=',')

# <codecell>

# Get train and test data
signal_train, signal_test = signal_data.get_train_test(train_size=0.5)
bck_train, bck_test = bck_data.get_train_test(train_size=0.5)

# <codecell>

columns = signal_data.columns
print columns

#select variables for classifier
variables = [ 'Neg_Prob', 'slope', 'slope_std', 'y_0', 'y_0_std', 'y_1', 'y_1_std', 'y_f', 'y_f_std',

Example #3

# <codecell>

#Preparing signal and background data for classifier
df_sig = df[y_true == 1]
df_bck = df[y_true == 0]

#save signal and background data for classifier
df_sig.to_csv('../../Cern_Time_Series/Classification/df_sig_binary_10_23.csv')
df_bck.to_csv('../../Cern_Time_Series/Classification/df_bck_binary_10_23.csv')

# Convert data to DataStorage
from cern_utils import converter_csv

#Load signal and background data
signal_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/df_sig_binary_10_23.csv', sep=',')
bck_data = converter_csv.load_from_csv('../../Cern_Time_Series/Classification/df_bck_binary_10_23.csv', sep=',')

# Get train and test data
signal_train, signal_test = signal_data.get_train_test(train_size=0.5)
bck_train, bck_test = bck_data.get_train_test(train_size=0.5)

# <codecell>

#select variables for classifier
columns = signal_data.columns
print columns
print '****************************************************'

variables = ['last-zeros', 'mass_center', 'inter_max', 'nb_peaks', u'inter_mean', u'inter_std', u'inter_rel',
             u'mass_moment', u'r_moment',u'FileType',

Example #4

# <codecell>

#Preparing signal and background data for classifier
df_sig = df[y_true == 1]
df_bck = df[y_true == 0]

#save signal and background data for classifier
df_sig.to_csv('/home/mikhail91/Documents/LBox/Cern_Time_Series/Classification/df_sig_binary_10_23.csv')
df_bck.to_csv('/home/mikhail91/Documents/LBox/Cern_Time_Series/Classification/df_bck_binary_10_23.csv')

# Convert data to DataStorage
from cern_utils import converter_csv

#Load signal and background data
signal_data = converter_csv.load_from_csv('/home/mikhail91/Documents/LBox/Cern_Time_Series/Classification/df_sig_binary_10_23.csv', sep=',')
bck_data = converter_csv.load_from_csv('/home/mikhail91/Documents/LBox/Cern_Time_Series/Classification/df_bck_binary_10_23.csv', sep=',')

# Get train and test data
signal_train, signal_test = signal_data.get_train_test(train_size=0.5)
bck_train, bck_test = bck_data.get_train_test(train_size=0.5)

# <codecell>

#Preparing signal and background data for classifier
df_sig = df[y_true == 1]
df_bck = df[y_true == 0]

#save signal and background data for classifier
df_sig.to_csv('../../Cern_Time_Series/Classification/df_sig_binary_10_23.csv')
df_bck.to_csv('../../Cern_Time_Series/Classification/df_bck_binary_10_23.csv')