title('Predicted: ' + str(predicted_class[i]) + ', True: ' + str(true_class[i])) plt.show() DNN分类算法实现了输入图片特征向量X,输出Y(范围0~1)预测X的分类。 第一步,得到关于X线性回归函数 可以通过线性回归得到WX + b,其中W是权重,b是偏差值。但不能用本式表述预测的值,因为输出Y的值...
df_data#使用:wb = load_workbook(r'表格名字xlsx') #打开表格,打开表格不需要指定sheet,workbook = wb['sheet1']dataframe =transform_type(workbook)这样就将数据改成dataframe类型了 ok,通过上面可以将openpyxl 生成的数据的每个需要的sheet都转化为pandas可以处理的dataframe数据,下面需要写一个可以进行搜索的方法...
scored = pd.DataFrame(index=X_test.index) scored['Loss_mae'] = np.mean(np.abs(X_pred-X_test), axis =1) scored['Threshold'] = threshod scored['Anomaly'] = scored['Loss_mae'] > scored['Threshold'] scored.head() 七、基于...
13、转换dataframe列的值 1、基本属性 df= pd.DataFrame() 创建一个DataFrame对象 df.values 返回ndarray类型的对象 df.index 获取行索引 df.columns 获取列索引 df.axes 获取行及列索引 df.T 行与列对调 df. info() 打印DataFrame对象的信息 df.head(i) 显示前 i 行数据 df.tail(i) 显示后 i 行数据 ...
<class 'pandas.core.groupby.generic.DataFrameGroupBy'> <pandas.core.groupby.generic.DataFrameGroupBy object at 0x127112df0> 1. 2. grouped的类型是DataFrameGroupBy,直接尝试输出,打印是内存地址,不太直观,这里写一个函数来展示(可以这么写的原理,后面会介绍) ...
class Agg(object): def buffer(self): return [0.0, 0] def __call__(self, buffer, val): buffer[0] += val buffer[1] += 1 def merge(self, buffer, pbuffer): buffer[0] += pbuffer[0] buffer[1] += pbuffer[1] def getvalue(self, buffer): if buffer[1] == 0: return 0.0 re...
DataFrame.xs(key[, axis, level, drop_level])Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. DataFrame.isin(values)是否包含数据框中的元素 DataFrame.where(cond[, other, inplace, …])条件筛选 DataFrame.mask(cond[, other, inplace, axis, …])Return an object of...
DataFrame.xs(key[, axis, level, drop_level])Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. DataFrame.isin(values)是否包含数据框中的元素 DataFrame.where(cond[, other, inplace, …])条件筛选 DataFrame.mask(cond[, other, inplace, axis, …])Return an object of...
(rel_class, key=lambda x:x[1], reverse=True))rel_class = sorted(rel_class, key=lambda x:x[1], reverse=True)[0][0]return rel_class ...# 调用sklearn直接实现from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import K...
# 查看数据的基本信息 dataframe.info() 输出结果: <class 'pandas.core.frame.DataFrame'> RangeIndex: 10000000 entries, 0 to 9999999 Data columns (total 5 columns): userid int64 itemid int64 categoryid int64 type object timestamp int64 dtypes: int64(4), object(1) memory usage: 381.5+ MB...