python獲取csv檔案中某一列或者某些列

2021-09-12 20:07:17 字數 3023 閱讀 5503

把三個csv檔案中的feature值整合到乙個檔案中，同時新增相應的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()
獲取csv檔案中某一列，下面可以獲得label為表頭的列中對應的所有數值。

filename = "./dataset/datatime2.csv"


list1 = 


with open(filename, 'r') as file:


reader = csv.dictreader(file)


column = [row['label'] for row in reader]
獲取csv檔案中某些列，下面可以獲得除label表頭的對應列之外所有數值。

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
也可以處理成list[np.array]形式的資料。

filename = "./dataset/datatime2.csv"


list1 = 


with open(filename, 'r') as file:


a = file.readline()


while a:


c = np.array(a.strip("\n").split(","))
也可以處理成tensor格式資料集

# -*-coding:utf-8 -*-


import tensorflow as tf


# 讀取的時候需要跳過第一行


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])


coord.request_stop()


coord.join(threads)


#最後長度是81000，trainx是10個特徵

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