python

global全局变量

函数内部对全局变量有修改操作，必须先声明使用global变量

_TOP_LABEL_IDXS=[]
def knn_gbdt_tic_tac_toe_endgame(chooseds):global _TOP_LABEL_IDXS#if len(_TOP_LABEL_IDXS)==0:_TOP_LABEL_IDXS=GBDT(X,y)#pass

type类型

a=[1,2,3]
type(a)

反转list

a=[1,2,3]
b=a[::-1]

numpy

替换值

    y = y_features_df.values.ravel()y[y==2] = 0y[y==4] = 1

ndarray转list

a.tolist()

反转ndarray

a=numpy.array([1,2,3])
b=a[::-1]

argsort

# 按值升序，输出索引
sorted_idx = np.argsort(feature_importance)

ravel 多维拉成一维

#ravel 多维拉成一维
y = y_features_df.values.ravel()

pandas

常用类

DataFrame
Series

iloc获取list列

topnidx = _C_TOP_LABEL_IDXS[:topn]
X = x_features_df.iloc[:, topnidx].values

concat

# 按照行
data =pd.concat([data_train,data_test],axis=0)

DataFrame转ndarray

X = x_features_df.values

read_csv列不一致

# 指定names range>=最大列数
column_names=[i for i in range(20)]
braw_df = pd.read_csv(r"boston.data", header=None, sep="\\s+", skiprows=7, nrows =13, na_values="?",names=column_names)
feature_name=braw_df.values[:,0]
print("dddd")

读取指定行列

# skiprows=7, nrows =13
column_names=[i for i in range(20)]
braw_df = pd.read_csv(r"boston.data", header=None, sep="\\s+", skiprows=7, nrows =13, na_values="?",names=column_names)
feature_name=braw_df.values[:,0]
print("dddd")

matplotlib

sklearn

torch