由于代码中注释已经非常的清晰，文章中就不过多叙述了，直接上代码。

代码如下:

# 在开始之前先导入所需要的包

import warnings                                  # `do not disturbe` mode
warnings.filterwarnings('ignore')

import numpy as np                               # vectors and matrices
import pandas as pd                              # tables and data manipulations
import matplotlib.pyplot as plt                  # plots
import seaborn as sns                            # more plots

from dateutil.relativedelta import relativedelta # working with dates with style
from scipy.optimize import minimize              # for function minimization

import statsmodels.formula.api as smf            # statistics and econometrics
import statsmodels.tsa.api as smt
import statsmodels.api as sm
import scipy.stats as scs

from itertools import product                    # some useful functions
from tqdm import tqdm_notebook

from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error
from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error

%matplotlib inline

# 准备好数据，读取文件
# 注意:这里的文件路径的斜杠与windows相反

ads = pd.read_csv('C:/Users/xxxx/Desktop/ads.csv', index_col=['Time'], parse_dates=['Time'])
currency = pd.read_csv('C:/Users/xxxx/Desktop/currency.csv', index_col=['Time'], parse_dates=['Time'])

#  这里使用的pandas的read_csv

#  查看导入的ads文件

ads.head()

运行结果如下:

#  查看导入的currency文件

ads.head()

 运行结果如下:

#  查看ads的统计学描述,可以查看共有多少数据、均值、标准差、最小值、25%、50%、75%、最大值等等

ads.describe()

 运行结果如下:

#  利用plt画图

plt.figure(figsize=(15, 7))
plt.plot(ads.Ads)
plt.title('Ads watched (hourly data)')
plt.grid(True)
plt.show()

 

#  同理

plt.figure(figsize=(15, 7))
plt.plot(currency.GEMS_GEMS_SPENT)
plt.title('In-game currency spent (daily data)')
plt.grid(True)
plt.show()