介绍
您是否曾经需要分析非结构化的文本数据？ Python可以帮助您做到这一点。在本文中，我们将研究使用Python在MongoDB中解析非结构化文本数据的基本示例。
让我们写代码
首先，让我们连接到mongoDB客户端，然后从数据库中的集合中检索所有文档：
import pymongo
import re
from matplotlib import pyplot as plt
# Connect to a MongoDB client
client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["your_db"]
col = db["your_collection"]
接下来，我们将创建一个空字符串，该字符串将包含每个文档中的所有报价详细信息：
# Create an empty string that contains all texts
all_details_string = ''
# Iterate over all documents in your MongoDB instance
for doc in col.find():
all_details_string = all_details_string + doc.get('offer_details').upper()
# Create a list containing all words
doc_general = re.split(" |/|\n", all_details_string)
# Get all words count
all_words_count = len(doc_general)
print('Total Occurrences:', all_words_count)
# Set technologies to analise
tec_list = ['Java', 'C#', 'Angular', 'React']
count_list = []
在我们要分析的技术列表中，遍历每个技术，计算串联字符串中的发生数量，并将计数添加到我们的列表中：
for tec in tec_list:
count_list.append(doc_general.count(tec.upper()))
print(tec, 'Total Occurrences:', doc_general.count(tec.upper()))
在我的环境中，我得到了以下结果：
最后，我们将使用matplotlib库创建一个条形图，其技术在X轴上及其各自的Y轴计数：
# Create a bar chart
labels = tec_list
values = count_list
fig, ax = plt.subplots()
ax.bar(labels, values)
# Add labels and title
ax.set_ylabel('Occurrence')
ax.set_xlabel('Technology')
ax.set_title('Occurrences of the chosen technologies')
# Show the chart
plt.show()
完整的代码和最终想法
通过分析非结构化的文本数据，我们可以深入了解最常见的单词和主题。这对于广泛的应用程序（例如情感分析，主题建模等）可能很有用。
在这里完整代码：
import pymongo
import re
from matplotlib import pyplot as plt
# Connect to a MongoDB client
client = pymongo.MongoClient(“mongodb://localhost:27017/”)
db = client[“your_db”]
col = db[“your_collection”]
# Create an empty string that contains all texts
all_details_string = ‘’
list_detail_offer = []
# Iterate over all documents in your MongoDB instance
for doc in col.find():
all_details_string = all_details_string + doc.get(‘offer_details’).upper()
# Create a list containing all words
doc_general = re.split(“ |/|\n”, all_details_string)
# Get all words count
all_words_count = len(doc_general)
print(‘Total Occurrences:’, all_words_count)
# Set technologies to analise
tec_list = [‘Java’, ‘C#’, ‘Angular’, ‘React’]
count_list = []
for tec in tec_list:
count_list.append(doc_general.count(tec.upper()))
print(tec, ‘Total Occurrences:’, doc_general.count(tec.upper()))
# Create a bar chart
labels = tec_list
values = count_list
fig, ax = plt.subplots()
ax.bar(labels, values)
# Add labels and title
ax.set_ylabel(‘Occurrence’)
ax.set_xlabel(‘Technology’)
ax.set_title(‘Occurrences of the chosen technologies’)
# Show the chart
plt.show()
谢谢！