为啥运行通过了一直不能打印，也已经把文件放到根目录下面了

import pandas as pd
import numpy as np

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['FangSong']#指定默认字体
matplotlib.rcParams['axes.unicode_minus'] = False #解决保存图像是负号‘-’显示为方块问题
class GrayForecast():
#初始化
def int(self, data, datacolumn=None):

     if isinstance(data, pd.core.frame.DataFrame):
        self.data = data
        try:
            self.data.columns = ['数据']
        except:
            if not datacolumn:
                raise Exception('您传入的dataframe不止一列')
            else:
                self.data = pd.DataFrame(data[datacolumn])
                self.data.columns = ['数据']
     elif isinstance(data, pd.core.series.Series):
          self.data = pd.DataFrame(data, columns=['数据'])
     else:
          self.data = pd.DataFrame(data, columns=['数据'])

     self.forecast_list = self.data.copy()

     if datacolumn:
         self.datacolumn = datacolumn
     else:
         self.datacolumn = None
# save arg:
#         data             DataFrame     数据
#         forecast         DataFrame     预测序列
#         datacolumn       string        数据的含义

#级比校验
def level_check(self):
n = len(self.data)
lambda_k = np.zeros(n - 1)
for i in range(n - 1):
lambda_k[i] = self.data.ix[i]['数据'] / self.data.ix[i + 1]['数据']
if lambda_k[i] < np.exp(-2 / (n + 1)) or lambda_k[i] > np.exp(2 / (n + 2)):
flag = False
else:
flag = True

          self.lambda_k = lambda_k

          if not flag:
              print("级比校验失败，请对x(0)做平移变换")
              return False
          else:
              print("级比校验成功，请继续")
              return True

#GM(1,1)建模
def GM_11_build_model(self, forecast = 5):
if forecast > len(self.data):
raise Exception('您的数据行不够')
X_0 = np.array(self.forecast_list['数据'].tail(forecast))
#1-AGO
X_1 = np.zeros(X_0.shape)
for i in range(X_0.shape[0]):
X_1[i] = np.sum(X_0[0:i+1])
#紧邻均值生成数列
Z_1 = np.zeros(X_1.shape[0]-1)
for i in range(1,X_1.shape[0]):
Z_1[i-1] = -0.5*(X_1[i]+X_1[i-1])

      B = np.append(np.array(np.mat(Z_1).T), np.ones(Z_1.shape).reshape((Z_1.shape[0], 1)), axis=1)
      Yn = X_0[1:].reshape((X_0[1:].shape[0], 1))

      B = np.mat(B)
      Yn = np.mat(Yn)
      a_ = (B.T*B)**-1*B.T*Yn
      a, b = np.array(a_.T)[0]
      X_ = np.zeros(X_0.shape[0])
      def f(k):
          return (X_0[0]-b/a)*(1-np.exp(a))*np.exp(-a*(k))

      self.forecast_list.loc[len(self.forecast_list)] = f(X_.shape[0])

#预测
def forecast(self, time = 5, forecast_data_len = 5):
for i in range(time):
self.GM_11_build_model(forecast=forecast_data_len)

#打印日记
def log(self):
res = self.forecast_list.copy()
if self.datacolumn:
res.columns = [self.datacolumn]
return res
#重置
def reset(self):
self.forecast_list = self.data.copy()
#作图
def plot(self):
self.forecast_list.plot()
if self.datacolumn:
plt.ylabel(self.datacolumn)
plt.legend([self.datacolumn])
f = open("教育支出.csv", encoding="utf8")
df = pd.read_csv(f)
df.tail()

      gf = GrayForecast(df, '教育支出')
      gf.forecast(20)
      gf.log()
      gf.plot()

大哥，你这代码太多了，缩进还是乱的。
简化一下吧

初始化的名字写错了，应该是init

img