python中读取csv文件中的某些列

python中读取csv文件中的某些列

下面小编就为大家分享一篇使用python读取csv文件中的某些列的方法，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

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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