改写编码words=[] df = pd.read_excel("EcustNew.xls").astype(str) df.head() df.dropna() # 通过for循环遍历来进行所有标题的分词 for i, row in df.iterrows(): words.append(' '.join(jieba.cut(row['标题']))) # 将标题转换为数值型向量,建立词频矩阵 vect = CountVectorizer() X = vect.fit_transform(wor

ds)

words=[]
df = pd.read_excel("EcustNew.xls").astype(str)
df = df.dropna() # 去除缺失值

# 通过for循环遍历来进行所有标题的分词
for i, row in df.iterrows():
words.append(' '.join(jieba.cut(row['标题'])))

# 将标题转换为数值型向量,建立词频矩阵
vect = CountVectorizer()
X = vect.fit_transform(words)

相关问题

根据上述代码，改写下列程序try: df1 = pd.read_csv('image_3_2.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') df2 = pd.read_csv('image_6.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') tree = KDTree(df2.iloc[:, :2].values) k = 4 distances, indices = tree.query(df1.iloc[:, :2].values, k=k) values = df2.iloc[indices[:, 1:], 2].values mean_values = values.mean(axis=1) df1.iloc[:, 2] = mean_values df1.to_csv('4.txt', sep='\t', header=None, index=None) except Exception as e: print("Error: ", e)

根据上述代码，改写后的程序如下：

try:
    points3D = pd.read_csv('points3D.txt', sep='\t', header=None, error_bad_lines=False, na_values='?')
    skeleton = cv2.imread('skeleton.jpg', cv2.IMREAD_GRAYSCALE)
    tree = KDTree(points3D.iloc[:, :2].values)
    with open("./voc/skelon/3d/result.txt", 'w', encoding='utf-8') as f1:
        for y in range(skeleton.shape[0]):
            for x in range(skeleton.shape[1]):
                if skeleton[y][x] == 255:
                    x_axis_coord, y_axis_coord = transform_xy(x, y)
                    query_point = [x_axis_coord, y_axis_coord]
                    dist, index = tree.query(query_point)
                    f1.write(str(points3D.iloc[index, 0]) + " " +
                             str(points3D.iloc[index, 1]) + " " +
                             str(points3D.iloc[index, 2]) + " " + '\n')
except Exception as e:
    print("Error: ", e)

这段代码与原始代码类似，不过是将输入的数据文件和输出的文件名、格式都进行了修改以适应新的应用场景。同时，由于我们无法直接读取图像文件，需要使用cv2库里的imread函数获取二维骨架图像。最后，将得到的三维坐标写入到txt文件中。

改写成python伪代码：result = df[df['label'] == -1] result.to_excel('E:/project\\bird_data_process\\flight direction\\-1.xlsx', index=False)

result = df[df['label'] == -1]
result.to_excel('E:/project\\bird_data_process\\flight direction\\-1.xlsx', index=False)

可以改写为python伪代码：

# 根据label列筛选出标签为-1的行
result = df[df['label'] == -1]

# 将结果保存到Excel文件中，不包含索引列
result.to_excel('E:/project\\bird_data_process\\flight direction\\-1.xlsx', index=False)