『Python核心技术与实战』Python将文本逐行写入到文件(csv,txt)(collection.Counter)

我就是我 2022-09-17 04:17 138阅读 0赞

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   <td><font>Python将文本逐行写入到文件(csv,txt)(collection.Counter)</font> </td> 
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### 文章目录 ###

*  一. 逐行写入到csv文件
 *  二. 逐行写入到txt文件
 *  三. 逐行写入到json文件
 *  四. collections包介绍(Counter)
 *   *  4.1. collection.Counter介绍
     *  4.2. 例子

# 一. 逐行写入到csv文件 #

>  *  **使用csv包： 如果一次写入数据量非常大，可能导致系统内存不足，下面是将数据逐行写入到文件中。**

import csv
    
    flag = False # 标志位，用来输出标题
    a = np.array([[1, 2, 3], [2, 3, 4], [4, 5, 6], [7, 8, 9]])
    with open("./one.csv", "w") as fw:
        writer = csv.writer(fw)
        for i in range(a.shape[0]):
            result = {
        "col1": a[i, 0],
                      "col2": a[i, 1],
                      "col3": a[i, 2]}
            print(result)
            if not flag:
                keys = result.keys()
                writer.writerow(keys)
                flag = True
            writer.writerow(result.values())

>  *  **输出结果**

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5biD6KGj5bCP5byg_size_20_color_FFFFFF_t_70_g_se_x_16][]

# 二. 逐行写入到txt文件 #

>  *  **下面是逐行写入到csv文件或者txt文件中！**

a = np.array([[1, 2, 3], [2, 3, 4], [4, 5, 6], [7, 8, 9]])
    with open("./out.txt", "w", encoding="utf-8") as fw: # 可以写入txt文件
    # with open("./out.csv", "w", encoding="utf-8") as fw:  # 可以写入csv文件
        for i in range(a.shape[0]):
    #         fw.write(str(a[i, 0]) + "\t" + str(a[i, 1]) + "\t" + str(a[i, 2]) + "\n")
            fw.write(str(a[i, 0]) + "," + str(a[i, 1]) + "," + str(a[i, 2]) + "\n")
        fw.close()

>  *  **输出结果**

![0d9e195257174b619426d153f9e2c32d.png][]

# 三. 逐行写入到json文件 #

>  *  **参考我之前的代码：**[https://blog.csdn.net/abc13526222160/article/details/111565523?spm=1001.2014.3001.5501][https_blog.csdn.net_abc13526222160_article_details_111565523_spm_1001.2014.3001.5501]

# 四. collections包介绍(Counter) #

## 4.1. collection.Counter介绍 ##

>  *  Counter(\{‘blue’: 3, ‘red’: 2, ‘green’: 1\})  
>     \{‘red’: 2, ‘blue’: 3, ‘green’: 1\}[https://blog.csdn.net/qwe1257/article/details/83272340][https_blog.csdn.net_qwe1257_article_details_83272340]
>  *  collections在python官方文档中的解释是High-performance container datatypes，直接的中文翻译解释**高性能容量数据类型**。它总共包含五种数据类型：

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5biD6KGj5bCP5byg_size_20_color_FFFFFF_t_70_g_se_x_16 1][]

>  *  其中 **Counter中文意思是计数器**，也就是我们常用于统计的一种数据类型，在使用Counter之后可以让我们的代码更加简单易读。

>  *  **我们先看一个简单的例子：**

#统计词频
    colors = ['red', 'blue', 'red', 'green', 'blue', 'blue']
    result = {
        }
    for color in colors:
    	# 方式1
        result[color] = result.get(color, 0) + 1 # get()函数返回指定键的值，如果值不在字典中返回默认值
        # 方式2
        # if result.get(color)==None:
        #     result[color]=1
        # else:
        #     result[color]+=1
    print (result)
    #{'red': 2, 'blue': 3, 'green': 1}

>  *  **下面我们看用Counter怎么实现：**

from collections import Counter
    
    colors = ['red', 'blue', 'red', 'green', 'blue', 'blue']
    c = Counter(colors)
    print(c)
    print (dict(c))
    # Counter({'blue': 3, 'red': 2, 'green': 1})
    # {'red': 2, 'blue': 3, 'green': 1}

>  *  **Counter(a).most\_common(数字)：** 经过Counter()方法排列后，获取数量最多的元素及准确数量，如果most\_common()的参数是2，则获取数量排在前两位的元素及具体数量。

from collections import Counter
    
    colors = ['red', 'blue', 'red', 'green', 'blue', 'blue']
    c = Counter(colors)
    print(c)
    # print (dict(c))
    c.most_common(2)
    
    # Counter({'blue': 3, 'red': 2, 'green': 1})
    # [('blue', 3), ('red', 2)]

## 4.2. 例子 ##

>  *  **例子：读文件统计词频并按照出现次数排序，文件是以空格隔开的单词的诸多句子：**

from collections import Counter
    lines = open("./data/input.txt","r").read().splitlines()
    lines = [lines[i].split(" ") for i in range(len(lines))]
    words = []
    for line in lines:
        words.extend(line)
    result = Counter(words)
    print (result.most_common(10))

>  *  **当需要统计的文件比较大，使用read()一次读不完的情况：**

from collections import Counter
    result = Counter()
    with open("./data/input.txt","r") as f:
        while True:
            lines = f.read(1024).splitlines()
            if lines==[]:
                break
            lines = [lines[i].split(" ") for i in range(len(lines))]
            words = []
            for line in lines:
                words.extend(line)
            tmp = Counter(words)
            result+=tmp
    
    print (result.most_common(10))

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5biD6KGj5bCP5byg_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/fef2a43e90834a3089f80e44d79d8e1e.png
[0d9e195257174b619426d153f9e2c32d.png]: /images/20220828/26df56b2f06748bf83c693163f535cc2.png
[https_blog.csdn.net_abc13526222160_article_details_111565523_spm_1001.2014.3001.5501]: https://blog.csdn.net/abc13526222160/article/details/111565523?spm=1001.2014.3001.5501
[https_blog.csdn.net_qwe1257_article_details_83272340]: https://blog.csdn.net/qwe1257/article/details/83272340
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5biD6KGj5bCP5byg_size_20_color_FFFFFF_t_70_g_se_x_16 1]: /images/20220828/65c846e48e224812892a55c129d6544f.png