Python ValueError:找到具有0个样本（形状=（0,35））的数组，而StandardScaler要求最小值为1

我得到了这个错误。我该怎么办

这是代码

from __future__ import division

import numpy as np 
from sklearn import svm, preprocessing
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
from collections import Counter
from matplotlib import style
import statistics
style.use('ggplot')

premium_baseline = 0.06

features = ['DE Ratio',
            'Trailing P/E',
            'Price/Sales',
            'Price/Book',
            'Profit Margin',
            'Operating Margin',
            'Return on Assets',
            'Return on Equity',
            'Revenue Per Share',
            'Market Cap',
            'Enterprise Value',
            'Forward P/E',
            'PEG Ratio',
            'Enterprise Value/Revenue',
            'Enterprise Value/EBITDA',
            'Revenue',
            'Gross Profit',
            'EBITDA',
            'Net Income Avl to Common ',
            'Diluted EPS',
            'Earnings Growth',
            'Revenue Growth',
            'Total Cash',
            'Total Cash Per Share',
            'Total Debt',
            'Current Ratio',
            'Book Value Per Share',
            'Cash Flow',
            'Beta',
            'Held by Insiders',
            'Held by Institutions',
            'Shares Short (as of',
            'Short Ratio',
            'Short % of Float',
            'Shares Short (prior ']

def Premium(stock, sp500):
    difference = stock - sp500

    if difference > premium_baseline:
        return 1
    else:
        return 0

def Build_Data_Set():

    data_df = pd.read_csv('key_stats_acc_perf_WITH_NA.csv')
    data_df = data_df.fillna(0)
    data_df = data_df.reindex(np.random.permutation(data_df.index))
    data_df['premium'] = list(map(Premium, data_df['stock_p_change'], data_df['sp500_p_change']))

    X = data_df[features].values
    sc_X = StandardScaler()
    X = sc_X.fit_transform(X)
    # y = data_df['Status'].replace('underperform',0).replace('outperform',1).values
    y = data_df['premium'].values

    return X, y, data_df, sc_X

# def Randomizing():
#   df = pd.DataFrame({'D1':range(5),"D2":range(5)})
#   print df
#   df2 = df.reindex(np.random.permutation(df.index))
#   print df2


def Analysis():

    X, y, data_df, sc_X = Build_Data_Set()

    test_size = 1000
    invest_amount = 10000
    total_invests = 0
    if_market = 0
    if_strat = 0

    X_train = X[:-test_size]
    X_test = X[-test_size:]
    y_train = y[:-test_size]
    y_test = y[-test_size:]
    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

    clf = svm.SVC(kernel = "linear", C = 1.0)
    clf.fit(X_train, y_train)

    y_pred = clf.predict(X_test)

    accuracy = np.mean(y_pred == y_test)
    print (accuracy)

    cm = confusion_matrix(y_test, y_pred)
    print (cm)

    for i in range(y_pred.shape[0]):
        if y_pred[i] == 1:
            invest_return = invest_amount * (1 + data_df.loc[data_df.index[-(i+1)],'stock_p_change'])
            market_return = invest_amount * (1 + data_df.loc[data_df.index[-(i+1)],'sp500_p_change'])
            total_invests += 1
            if_strat += invest_return
            if_market += market_return

    print ("Total Trades:", total_invests)
    print ("Total Return with ML strategy trading", '$'+str(if_strat))
    print ("Total Return with sp500 basic market", '$'+str(if_market) )

    premium = round(((if_strat - if_market)/ if_market) * 100.0,2)

    do_nothing = total_invests * invest_amount
    avg_strat = round(((if_strat - do_nothing)/do_nothing) * 100.0,2)
    avg_market = round(((if_market - do_nothing)/do_nothing) * 100.0,2)

    print ("Compared to sp500 basic market, we earn", str(premium)+"% more.")
    print ("Average ML strategy trading return:", str(avg_strat) +"%.")
    print ("Average sp500 investment return:", str(avg_market) + "%.")

    sample_data_df = pd.read_csv('forward_sample_WITH_NA.csv')
    sample_data_df = sample_data_df.replace({'N/A</span>': np.nan}, regex=True)
    sample_data_df = sample_data_df.fillna(0)

    X_sample = sample_data_df[features].values
    X_sample = sc_X.transform(X_sample)

    invest_list = []

    for i in range(len(X_sample)):
        pred = clf.predict([X_sample[i]])[0]
        if pred == 1:
            invest_list.append(sample_data_df['Ticker'][i])

    print (len(invest_list), 'out of', len(X_sample))

    return invest_list


final_list = []

loops = 8

for x in range(loops):
    stock_list = Analysis()
    for i in stock_list:
        final_list.append(i)
    print (15*"_")

x = Counter(final_list)
print (x)

print (15*"_")
for each in x:
    if x[each] > loops - (loops/2.0):
        print (each)


    # w = clf.coef_[0]
    # a = -w[0] / w[1]

    # xx = np.linspace(min(X[:,0]), max(X[:,0]))
    # yy = a * xx - clf.intercept_[0] / w[1]

    # h0 = plt.plot(xx, yy, 'k-', label = "non weighted")

    # plt.scat      ter(X[:,0], X[:,1], c = y)
    # plt.ylabel('Trailing P/E')
    # plt.xlabel('DE Ratio')
    # plt.legend()
    # plt.show()


# High recall (989/989+40) but low accuracy (989/(989+748))
# 0.57127312296
# [[ 61 748]
#  [ 40 989]]

File "/home/shubh/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 586, in check_array
    context))
ValueError: Found array with 0 sample(s) (shape=(0, 35)) while a minimum of 1 is required by StandardScaler