我的玩具示例如下：
import numpy as np
from sklearn.datasets import load_iris
import pandas as pd
### prepare data
Xy = np.c_[load_iris(return_X_y=True)]
mycol = ['x1','x2','x3','x4','group']
df = pd.DataFrame(data=Xy, columns=mycol)
dat = df.iloc[:100,:] #only consider two species
dat['group'] = dat.group.apply(lambda x: 1 if x ==0 else 2) #two species means two groups
dat.shape
dat.head()
### Linear discriminant analysis procedure
G1 = dat.iloc[:50,:-1]; x1_bar = G1.mean(); S1 = G1.cov(); n1 = G1.shape[0]
G2 = dat.iloc[50:,:-1]; x2_bar = G2.mean(); S2 = G2.cov(); n2 = G2.shape[0]
Sp = (n1-1)/(n1 n2-2)*S1 (n2-1)/(n1 n2-2)*S2
a = np.linalg.inv(Sp).dot(x1_bar-x2_bar); u_bar = (x1_bar x2_bar)/2
m = a.T.dot(u_bar); print("Linear discriminant boundary is {} ".format(m))
def my_lda(x):
y = a.T.dot(x)
pred = 1 if y >= m else 2
return y.round(4), pred
xx = dat.iloc[:,:-1]
xxa = xx.agg(my_lda, axis=1)
xxa.shape
type(xxa)
我們有xxa一個pandas.core.series.Series帶形狀的(100,)。請注意，括號中有兩列xxa，我想轉換xxa為pd.DataFramewith100 rows x 2 columns并嘗試
xxa_df1 = pd.DataFrame(data=xxa, columns=['y','pred'])
這給出了ValueError: Shape of passed values is (100, 1), indices imply (100, 2). 然后我繼續嘗試
xxa2 = xxa.to_frame()
# xxa2 = pd.DataFrame(xxa) #equals `xxa.to_frame()`
xxa_df2 = pd.DataFrame(data=xxa2, columns=['y','pred'])
并以. xxa_df2_ 接下來我該怎么辦？NaN100 rows x 2 columns
uj5u.com熱心網友回復：
我們試試看Series.tolist()
xxa_df1 = pd.DataFrame(data=xxa.tolist(), columns=['y','pred'])
print(xxa_df1)
y pred
0 42.0080 1
1 32.3859 1
2 37.5566 1
3 31.0958 1
4 43.5050 1
.. ... ...
95 -56.9613 2
96 -61.8481 2
97 -62.4983 2
98 -38.6006 2
99 -61.4737 2
[100 rows x 2 columns]
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