1：尋找list中最大值、最小值位置

轉載自：https://blog.csdn.net/fengjiexyb/article/details/77435676

c = [-10,-5,0,5,3,10,15,-20,25]

print c.index(min(c))  # 返回最小值
print c.index(max(c)) # 返回最大值

2：報錯 Reshape your data either using array.reshape(-1, 1)

訓練數劇維度和模型要求不一致會報這個錯誤，我是在講一個特徵進行訓練時得到這個錯誤，rf模型，提示要加reshape（-1,1），最新版本要求values.reshape()

rf.fit(X_train.iloc[:, tmp_list].values.reshape(-1, 1), y_train)   

3:python 列印格式話輸出的時候，替換部分要加（）

“%s %s” % （value1，value2）

    for i,s in enumerate(feature_indexs):
        print("%s  %s %s \n" % (s, pd_data.columns.values[s],final_score[i]))  

4：pandas 深度copy

DataFrame.copy(deep=True)

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: int64
Shallow copy versus default (deep) copy:

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.

>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True
Deep copy has own copy of data and index.

>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False
Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged.

>>> s[0] = 3
>>> shallow[1] = 4
>>> s
a    3
b    4
dtype: int64
>>> shallow
a    3
b    4
dtype: int64
>>> deep
a    1
b    2
dtype: int64