我有一个dataframe,有两个columns,code和count,我需要by code去统计每个code对应的count栏位的数值加总,生成一个新dataframe
时间: 2024-03-10 07:44:26 浏览: 22
好的,你可以使用`groupby()`和`sum()`方法来实现这个需求。以下是示例代码:
```python
import pandas as pd
# 假设你的原始数据存储在df变量中
# 创建一个新的DataFrame,按code分组并计算count列的总和
new_df = pd.DataFrame(df.groupby('code')['count'].sum())
# 输出新的DataFrame
print(new_df)
```
这段代码会按照`code`列进行分组,并计算每个组中`count`列的总和,最终生成一个新的DataFrame。你可以将代码中的`df`替换为你实际使用的DataFrame对象。
相关问题
基于grouplens的Book-Crossing数据集 写一个python书籍推荐系统
好的,以下是一个基于GroupLens的Book-Crossing数据集的Python书籍推荐系统的示例代码:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import cosine_similarity
# 加载数据集
ratings = pd.read_csv('BX-Book-Ratings.csv', delimiter=';', error_bad_lines=False, encoding="latin-1")
books = pd.read_csv('BX-Books.csv', delimiter=';', error_bad_lines=False, encoding="latin-1")
users = pd.read_csv('BX-Users.csv', delimiter=';', error_bad_lines=False, encoding="latin-1")
# 过滤掉评分次数少于50次的书籍
book_ratings_count = pd.DataFrame(ratings.groupby('ISBN')['Book-Rating'].count())
book_ratings_count = book_ratings_count[book_ratings_count['Book-Rating'] >= 50]
book_ratings_count.reset_index(inplace=True)
# 合并数据集
ratings = pd.merge(ratings, book_ratings_count, on='ISBN', how='inner')
ratings.drop('Book-Rating_y', axis=1, inplace=True)
ratings.rename(columns={'Book-Rating_x': 'Book-Rating'}, inplace=True)
# 过滤掉评分次数少于50次的用户
user_ratings_count = pd.DataFrame(ratings.groupby('User-ID')['Book-Rating'].count())
user_ratings_count = user_ratings_count[user_ratings_count['Book-Rating'] >= 50]
user_ratings_count.reset_index(inplace=True)
# 合并数据集
ratings = pd.merge(ratings, user_ratings_count, on='User-ID', how='inner')
ratings.drop('Book-Rating_y', axis=1, inplace=True)
ratings.rename(columns={'Book-Rating_x': 'Book-Rating'}, inplace=True)
# 构建评分矩阵
ratings_matrix = ratings.pivot_table(index='User-ID', columns='ISBN', values='Book-Rating')
# 填充缺失值
ratings_matrix.fillna(0, inplace=True)
# 计算相似度矩阵
cosine_sim = cosine_similarity(ratings_matrix)
# 定义函数:获取相似的书籍
def get_similar_books(book_title, cosine_sim=cosine_sim):
idx = books[books['Book-Title'] == book_title].index[0]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:11]
book_indices = [i[0] for i in sim_scores]
return books['Book-Title'].iloc[book_indices]
# 测试
book_title = 'The Da Vinci Code'
print('对于书籍:', book_title, '的推荐如下:')
print(get_similar_books(book_title))
```
这个示例代码使用了基于内容的推荐算法,通过计算书籍的相似度来进行推荐。具体实现中,首先加载了Book-Crossing数据集,并过滤掉了评分次数过少的书籍和用户。然后,构建了评分矩阵,并计算了相似度矩阵。最后,定义了一个函数,通过输入书籍名称,返回相似度最高的10本书籍。
需要注意的是,这个示例代码只是一个基础的框架,实际使用时需要根据具体业务需求进行调整和优化。
'DataFrame' object has no attribute 'append' File "D:\chapter12\demo\code\123.py", line 24, in <module> hit_count_7 = hit_count_7.append([{'用户数': time}], ignore_index=True) AttributeError: 'DataFrame' object has no attribute 'append'
Based on the error message you provided, it seems that you are trying to use the `append()` method on a DataFrame object, but this method does not exist for DataFrames. Instead, you can use the `concat()` method to concatenate DataFrames vertically.
Here's an example of how you can use `concat()` to append a row to a DataFrame:
```
import pandas as pd
# create an empty DataFrame
df = pd.DataFrame(columns=['用户数'])
# append a row to the DataFrame
time = 10
new_row = pd.DataFrame({'用户数': [time]})
df = pd.concat([df, new_row], ignore_index=True)
# print the updated DataFrame
print(df)
```
This should create a new DataFrame with one row containing the value of `time` in the `用户数` column. You can modify this code to fit your specific use case.