正如Mohit Motwani建议的最快方法是将数据收集到字典中，然后将所有内容加载到数据帧中。下面是一些速度测量示例：
import pandas as pdimport numpy as npimport timeimport randomend_value = 10000
用于创建字典的度量，最后将所有内容加载到数据帧中
start_time = time.time()dictinary_list = []for i in range(0, end_value, 1): dictionary_data = {k: random.random() for k in range(30)} dictinary_list.append(dictionary_data)df_final = pd.Dataframe.from_dict(dictinary_list)end_time = time.time()print('Execution time = %.6f seconds' % (end_time-start_time))执行时间= 0.090153秒
将数据附加到列表中并连接到数据框中的度量：
start_time = time.time()appended_data = []for i in range(0, end_value, 1): data = pd.Dataframe(np.random.randint(0, 100, size=(1, 30)), columns=list('A'*30)) appended_data.append(data)appended_data = pd.concat(appended_data, axis=0)end_time = time.time()print('Execution time = %.6f seconds' % (end_time-start_time))执行时间= 4.183921秒
附加数据帧的测量：
start_time = time.time()df_final = pd.Dataframe()for i in range(0, end_value, 1): df = pd.Dataframe(np.random.randint(0, 100, size=(1, 30)), columns=list('A'*30)) df_final = df_final.append(df)end_time = time.time()print('Execution time = %.6f seconds' % (end_time-start_time))执行时间= 11.085888秒
使用loc的插入数据测量：
start_time = time.time()df = pd.Dataframe(columns=list('A'*30))for i in range(0, end_value, 1): df.loc[i] = list(np.random.randint(0, 100, size=30))end_time = time.time()print('Execution time = %.6f seconds' % (end_time-start_time))执行时间= 21.029176秒
