python获取csv文件中某一列或者某些列 |
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把三个csv文件中的feature值整合到一个文件中，同时添加相应的label。 # -*-coding:utf-8 -*- import csv; label1 = '1' label2 = '2' label3 = '3' a = "feature1,feature2,feature3,feature4,feature5,feature6,feature7,feature8,feature9,feature10,label" + "\n" with open("./dataset/dataTime2.csv", 'a') as rfile: rfile.writelines(a) with open("./dataset/f02.csv", 'rb') as file: a = file.readline().strip() while a: a = a + ',' + label1 + "\n" #a = label1 + ',' + a + "\n" with open("./dataset/dataTime2.csv", 'a') as rfile: rfile.writelines(a) a = file.readline().strip() with open("./dataset/g03.csv", 'rb') as file: a = file.readline().strip() while a: a = a + ',' + label2 + "\n" #a = label2 + ',' + a + "\n" with open("./dataset/dataTime2.csv", 'a') as rfile: rfile.writelines(a) a = file.readline().strip() with open("./dataset/normal05.csv", 'rb') as file: a = file.readline().strip() while a: a = a + ',' + label3 + "\n" #a = label3 + ',' + a + "\n" with open("./dataset/dataTime2.csv", 'a') as rfile: rfile.writelines(a) a = file.readline().strip()获取csv文件中某一列，下面可以获得label为表头的列中对应的所有数值。 filename = "./dataset/dataTime2.csv" list1 = [] with open(filename, 'r') as file: reader = csv.DictReader(file) column = [row['label'] for row in reader]获取csv文件中某些列，下面可以获得除label表头的对应列之外所有数值。 import pandas as pd odata = pd.read_csv(filename) y = odata['label'] x = odata.drop(['label'], axis=1) #除去label列之外的所有feature值也可以处理成list[np.array]形式的数据。 filename = "./dataset/dataTime2.csv" list1 = [] with open(filename, 'r') as file: a = file.readline() while a: c = np.array(a.strip("\n").split(",")) list1.append(c)也可以处理成tensor格式数据集 # -*-coding:utf-8 -*- import tensorflow as tf # 读取的时候需要跳过第一行 filename = tf.train.string_input_producer(["./dataset/dataTime.csv"]) reader = tf.TextLineReader(skip_header_lines=1) key, value = reader.read(filename) record_defaults = [[1.], [1.], [1.], [1.], [1.], [1.], [1.], [1.], [1.], [1.], tf.constant([], dtype=tf.int32)] col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11= tf.decode_csv( value, record_defaults=record_defaults) features = tf.stack([col1, col2, col3, col4, col5, col6, col7, col8, col9, col10]) with tf.Session() as sess: # Start populating the filename queue. coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) trainx = [] trainy = [] for i in range(81000): # Retrieve a single instance: example, label = sess.run([features, col11]) trainx.append(example) trainy.append(label) coord.request_stop() coord.join(threads) #最后长度是81000，trainx是10个特征
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