我正在尝试计算Y = CA列的线性回归，x = [＆＃39; Plate X＆＃39;，＆＃39; Plate Y＆＃39;＆＃39; Field X＆＃39;]和用Drum和Plate对这些值进行分组。其他问题 - 如何将结果保存为文件，csv更可取。 pandas包是否足以完成此任务或其他所需的包。 谢谢 有我的数据集：
DF = {'A': {0: 305.03277000000003,
1: 304.42513500000001,
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'B': {0: 311.10912000000002,
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'C': {0: 305.72721000000001,
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'C-A': {0: 0.69443999999999995,
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'Drum': {0: 'LAAA',
1: 'LAAA',
2: 'LAAA',
3: 'LAAA',
4: 'LAAA',
5: 'LAAA',
6: 'LAAA',
7: 'LAAA',
8: 'LAAA',
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90: 'LAAA',
91: 'LAAA',
92: 'LAAA',
93: 'LAAA',
94: 'LAAA',
95: 'LAAA',
96: 'LAAA',
97: 'LAAA',
98: 'LAAA',
99: 'LAAA'},
'FIELD X': {0: 4.7949800000000007,
1: -5.5198839999999993,
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3: 4.7949800000000007,
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'FIELD Y': {0: 1.8893500000000001,
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'Plate': {0: 72,
1: 72,
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80: 131,
81: 131,
82: 131,
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27: -30.985420000000001,
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31: -30.985420000000001,
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57: 51.533491999999995,
58: 51.533491999999995,
59: 51.533491999999995,
60: 51.533491999999995,
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63: 72.163219999999995,
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65: 72.163219999999995,
66: 72.163219999999995,
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68: 92.792947999999996,
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70: 92.792947999999996,
71: 92.792947999999996,
72: 113.422676,
73: 113.422676,
74: 113.422676,
75: 113.422676,
76: 113.422676,
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28: 27.092099999999999,
29: 54.144300000000001,
30: 108.2487,
31: 135.30090000000001,
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98: 98,
99: 99}}
答案 0 :(得分：2)
从您的问题来看，它听起来并不像您想要多元回归（即多个Y＆＃39; s）。如果你只是从多个X预测单个Y，你可以用pandas这样做，然后将结果保存到txt文件：
import pandas as pd
df = pd.DataFrame(DF)
res = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
file = open("C:/Users/Simon/Desktop/results.txt", "w")
file.write(str(res))
file.close()
您在问题中提到要按Drum和Plate分组分析。但是，Drum行的每个值都相同。但是，如果要按Plate分组，然后在每个子组上运行OLS，则可以执行以下操作：
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
results.append( pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']]))
df.groupby('Plate').apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.close()
如果您还希望按鼓分组，并记下每个分析所针对的鼓/盘组合，您可以执行类似的操作，只需在结果文件中添加一些额外的txt：
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
curCombo = "plate:" + str(df["Plate"].mean()) + ", drum:" + str(df["Drum"].unique())
regression_results = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
results.append([curCombo, regression_results])
df.groupby(['Plate', 'Drum']).apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.write("\n\n")
file.close()