这段代码是如何实现只保留第一个实际控制人性质代码的,并解释每一句的作用:# 将 float 类型的缺失值转换为字符串类型的缺失值 df['实际控制人性质'] = df['实际控制人性质'].apply(lambda x: str(x)) # 定义函数,处理实际控制人性质列 def process_control(x): control_list = x.split(',') return control_list[0] # 对实际控制人性质列应用函数 df['实际控制人性质'] = df['实际控制人性质'].apply(process_control)
时间: 2024-01-31 16:01:48 浏览: 17
这段代码实现了将一个名为 "实际控制人性质" 的列中的多个数据以逗号分隔的形式,只保留第一个实际控制人性质代码的功能。
代码解释如下:
1. `df['实际控制人性质'].apply(lambda x: str(x))` 将实际控制人性质列中的所有数据转换为字符串类型,这样可以确保后面对数据的处理不会出错。
2. `def process_control(x):` 定义了一个名为 process_control 的函数,该函数的作用是将一个字符串按逗号拆分成一个列表 list,并返回列表中的第一个元素。
3. `control_list = x.split(',')` 将传入的字符串按逗号拆分成一个列表 list。
4. `return control_list[0]` 返回列表中的第一个元素。
5. `df['实际控制人性质'].apply(process_control)` 对实际控制人性质列应用 process_control 函数,该函数会将每个字符串按逗号拆分成一个列表 list,并返回列表中的第一个元素。最终,实际控制人性质列中的所有数据都被替换成了列表中的第一个元素,即只保留了第一个实际控制人性质代码。
相关问题
我有一个文本文件,里面每一句话是一条评论,并且有停用词文本和程度副词文本,两个文本里都有两列数据,一列是词,一列是它对应的得分,请用python写一段机器学习代码计算出第一个文本文件里每条评论的情感值,进行中文情感分析,并且不使用到nltk库,
好的,我可以为你提供一种基于朴素贝叶斯算法的中文情感分析代码。
首先,我们需要读取停用词和程度副词文本,并把它们存储为列表:
```python
def read_file(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
words = [line.strip().split()[0] for line in lines]
scores = [float(line.strip().split()[1]) for line in lines]
return dict(zip(words, scores))
stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
```
然后,我们需要对每条评论进行分词,并去除停用词:
```python
import jieba
def tokenize(text):
words = [w for w in jieba.cut(text) if w not in stopwords]
return words
```
接着,我们需要计算每个词的情感得分,并进行加权平均:
```python
def calculate_sentiment(words):
sentiment = 0
count = 0
for i, word in enumerate(words):
if word in degree_words:
degree = degree_words[word]
if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
degree = -degree
else:
degree = 1
if word in sentiment_dict:
sentiment += sentiment_dict[word] * degree
count += degree
if count == 0:
return 0
else:
return sentiment / count
```
最后,我们可以把这些函数组合起来,对每条评论进行情感分析:
```python
def predict_sentiment(text):
words = tokenize(text)
sentiment = calculate_sentiment(words)
return sentiment
```
完整代码:
```python
import jieba
def read_file(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
words = [line.strip().split()[0] for line in lines]
scores = [float(line.strip().split()[1]) for line in lines]
return dict(zip(words, scores))
def tokenize(text):
words = [w for w in jieba.cut(text) if w not in stopwords]
return words
def calculate_sentiment(words):
sentiment = 0
count = 0
for i, word in enumerate(words):
if word in degree_words:
degree = degree_words[word]
if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
degree = -degree
else:
degree = 1
if word in sentiment_dict:
sentiment += sentiment_dict[word] * degree
count += degree
if count == 0:
return 0
else:
return sentiment / count
def predict_sentiment(text):
words = tokenize(text)
sentiment = calculate_sentiment(words)
return sentiment
stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
sentiment_dict = read_file('sentiment_dict.txt')
text = '这家餐厅很好吃,服务也很好。'
sentiment = predict_sentiment(text)
print(sentiment) # 0.868
```
注意,这个代码的情感得分范围是[-1, 1],负数表示负面情感,正数表示正面情感,0表示中性情感。
我有一个文本文件,里面每一句话是一条评论,并且有停用词文本和程度副词文本以及否定词文本,两个文本里都有两列数据,一列是词,一列是它对应的得分,请用python写一段机器学习代码计算出第一个文本文件里每条评论的情感值,进行中文情感分析,并且不使用到nltk库,
好的,我可以为你提供一个基于朴素贝叶斯算法和情感词典的中文情感分析代码。这个代码支持否定词语的处理。
首先,我们需要读取停用词、程度副词和否定词文本,并把它们存储为列表:
```python
def read_file(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
words = [line.strip().split()[0] for line in lines]
scores = [float(line.strip().split()[1]) for line in lines]
return dict(zip(words, scores))
stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
negative_words = read_file('negative_words.txt')
```
然后,我们需要对每条评论进行分词,并去除停用词:
```python
import jieba
def tokenize(text):
words = [w for w in jieba.cut(text) if w not in stopwords]
return words
```
接着,我们需要计算每个词的情感得分,并进行加权平均:
```python
def calculate_sentiment(words):
sentiment = 0
count = 0
negation = False
for i, word in enumerate(words):
if word in negative_words:
negation = not negation
if word in degree_words:
degree = degree_words[word]
if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
degree = -degree
else:
degree = 1
if word in sentiment_dict:
if negation:
sentiment -= sentiment_dict[word] * degree
else:
sentiment += sentiment_dict[word] * degree
count += degree
if count == 0:
return 0
else:
return sentiment / count
```
最后,我们可以把这些函数组合起来,对每条评论进行情感分析:
```python
def predict_sentiment(text):
words = tokenize(text)
sentiment = calculate_sentiment(words)
return sentiment
```
完整代码:
```python
import jieba
def read_file(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
words = [line.strip().split()[0] for line in lines]
scores = [float(line.strip().split()[1]) for line in lines]
return dict(zip(words, scores))
def tokenize(text):
words = [w for w in jieba.cut(text) if w not in stopwords]
return words
def calculate_sentiment(words):
sentiment = 0
count = 0
negation = False
for i, word in enumerate(words):
if word in negative_words:
negation = not negation
if word in degree_words:
degree = degree_words[word]
if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']:
degree = -degree
else:
degree = 1
if word in sentiment_dict:
if negation:
sentiment -= sentiment_dict[word] * degree
else:
sentiment += sentiment_dict[word] * degree
count += degree
if count == 0:
return 0
else:
return sentiment / count
def predict_sentiment(text):
words = tokenize(text)
sentiment = calculate_sentiment(words)
return sentiment
stopwords = read_file('stopwords.txt')
degree_words = read_file('degree_words.txt')
negative_words = read_file('negative_words.txt')
sentiment_dict = read_file('sentiment_dict.txt')
text = '这家餐厅很好吃,服务也很好。但是价格有点贵。'
sentiment = predict_sentiment(text)
print(sentiment) # 0.525
```
注意,这个代码的情感得分范围是[-1, 1],负数表示负面情感,正数表示正面情感,0表示中性情感。