我有一个包含日期和某些值的pandas dataframe,类似于以下内容:
原始数据:
list = [('2018-10-29', 6.1925), ('2018-10-29', 6.195), ('2018-10-29', 1.95833333333333),
('2018-10-29', 1.785), ('2018-10-29', 3.05), ('2018-10-29', 1.30666666666667),
('2018-10-29', 1.6325), ('2018-10-30', 1.765), ('2018-10-30', 1.265),
('2018-10-30', 2.1125), ('2018-10-30', 2.16714285714286), ('2018-10-30', 1.485),
('2018-10-30', 1.72), ('2018-10-30', 2.754), ('2018-10-30', 1.79666666666667),
('2018-10-30', 1.27833333333333), ('2018-10-30', 3.48), ('2018-10-30', 6.19),
('2018-10-30', 6.235), ('2018-10-30', 6.11857142857143), ('2018-10-30', 6.088),
('2018-10-30', 4.3), ('2018-10-30', 7.80666666666667),
('2018-10-30', 7.78333333333333), ('2018-10-30', 10.9766666666667),
('2018-10-30', 2.19), ('2018-10-30', 1.88)]
加载到pandas之后
df = pd.DataFrame(list)
0 1
0 2018-10-29 6.192500
1 2018-10-29 6.195000
2 2018-10-29 1.958333
3 2018-10-29 1.785000
4 2018-10-29 3.050000
5 2018-10-29 1.306667
6 2018-10-29 1.632500
7 2018-10-30 1.765000
8 2018-10-30 1.265000
9 2018-10-30 2.112500
10 2018-10-30 2.167143
11 2018-10-30 1.485000
12 2018-10-30 1.720000
13 2018-10-30 2.754000
14 2018-10-30 1.796667
15 2018-10-30 1.278333
16 2018-10-30 3.480000
17 2018-10-30 6.190000
18 2018-10-30 6.235000
19 2018-10-30 6.118571
20 2018-10-30 6.088000
21 2018-10-30 4.300000
22 2018-10-30 7.806667
23 2018-10-30 7.783333
24 2018-10-30 10.976667
25 2018-10-30 2.190000
26 2018-10-30 1.880000
这是我如何加载数据框的方式
df = pd.DataFrame(list)
df[0] = pd.to_datetime(df[0], errors='coerce')
df.set_index(0, inplace=True)
现在我想找到slope
(斜率)。通过在互联网上的研究,我发现这就是需要做的事情,以获得slope
。
trend_coord = list(map(list, zip(df.index.strftime('%Y-%m-%d'), sm.tsa.seasonal_decompose(df.iloc[:,0].values).trend.interpolate(method='linear',axis=0).fillna(0).values)))
results = sm.OLS(np.asarray(sm.tsa.seasonal_decompose(df.iloc[:,0].values).trend.interpolate(method='linear', axis=0).fillna(0).values), sm.add_constant(np.array([i for i in range(len(trend_coord))])), missing='drop').fit()
slope = results.params[1]
print(slope)
但我收到以下错误
Traceback (most recent call last):
File "/home/souvik/Music/UI_Server2/test35.py", line 11, in <module>
trend_coord = list(map(list, zip(df.index.strftime('%Y-%m-%d'), sm.tsa.seasonal_decompose(df.iloc[:,0].values).trend.interpolate(method='linear',axis=0).fillna(0).values)))
File "/home/souvik/django_test/webdev/lib/python3.5/site-packages/statsmodels/tsa/seasonal.py", line 127, in seasonal_decompose
raise ValueError("You must specify a freq or x must be a "
ValueError: You must specify a freq or x must be a pandas object with a timeseries index with a freq not set to None
现在,如果我向seasonal_decompose方法添加一个freq
参数,例如
trend_coord = list(map(list, zip(df.index.strftime('%Y-%m-%d'), sm.tsa.seasonal_decompose(df.iloc[:,0].values, freq=1).trend.interpolate(method='linear',axis=0).fillna(0).values)))
然后我会收到类似的错误信息:
Traceback (most recent call last):
File "/home/souvik/Music/UI_Server2/test35.py", line 11, in <module>
trend_coord = list(map(list, zip(df.index.strftime('%Y-%m-%d'), sm.tsa.seasonal_decompose(df.iloc[:,0].values, freq=1).trend.interpolate(method='linear',axis=0).fillna(0).values)))
AttributeError: 'numpy.ndarray' object has no attribute 'interpolate'
但是,如果我摆脱任何关于数据的细节,如interpolate
等,并执行以下操作:
trend_coord = sm.tsa.seasonal_decompose(df.iloc[:,0].values, freq=1, model='additive').trend
results = sm.OLS(np.asarray(trend_coord),
sm.add_constant(np.array([i for i in range(len(trend_coord))])), missing='drop').fit()
slope = results.params[1]
print(">>>>>>>>>>>>>>>> slope", slope)
然后我得到了一个斜率值为0.13668559218559242
。
但是我不确定这是否是找出斜率的正确方法,甚至这个值是否正确。
有没有更好的方法来找出斜率呢?
list,dict,tuple,set,max,min,abs,sum,...
作为变量名是一个坏习惯。 - Patrick Artner