df_count_city=df_count_city.rename(columns={'index':'city','city':'count'})
时间: 2024-01-06 07:05:40 浏览: 24
这段代码的作用是将 `df_count_city` 数据框中的列名进行重命名。具体地说,它将列名 `'index'` 修改为 `'city'`,将列名 `'city'` 修改为 `'count'`。通过使用 `rename()` 方法,并传递一个包含原始列名和新列名对应关系的字典,可以实现对列名的重命名操作。这样做可以使数据框的列名更具有描述性,增加代码的可读性和理解性。
相关问题
import pandas as pd import math as mt import numpy as np from sklearn.model_selection import train_test_split from Recommenders import SVDRecommender triplet_dataset_sub_song_merged = triplet_dataset_sub_song_mergedpd triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index() triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True) triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df) triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged small_set = triplet_dataset_sub_song_merged user_codes = small_set.user.drop_duplicates().reset_index() song_codes = small_set.song.drop_duplicates().reset_index() user_codes.rename(columns={'index':'user_index'}, inplace=True) song_codes.rename(columns={'index':'song_index'}, inplace=True) song_codes['so_index_value'] = list(song_codes.index) user_codes['us_index_value'] = list(user_codes.index) small_set = pd.merge(small_set,song_codes,how='left') small_set = pd.merge(small_set,user_codes,how='left') mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']] data_array = mat_candidate.fractional_play_count.values row_array = mat_candidate.us_index_value.values col_array = mat_candidate.so_index_value.values data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float) K=50 urm = data_sparse MAX_PID = urm.shape[1] MAX_UID = urm.shape[0] recommender = SVDRecommender(K) U, S, Vt = recommender.fit(urm) Compute recommendations for test users uTest = [1,6,7,8,23] uTest_recommended_items = recommender.recommend(uTest, urm, 10) Output recommended songs in a dataframe recommendations = pd.DataFrame(columns=['user','song', 'score','rank']) for user in uTest: rank = 1 for song_index in uTest_recommended_items[user, 0:10]: song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0] # Get song details recommendations = recommendations.append({'user': user, 'song': song['title'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True) rank += 1 display(recommendations)这段代码报错了,为什么?给出修改后的 代码
代码中的错误是使用了未定义的模块和类。需要先安装相应的模块并导入相应的类。
以下是修改后的代码:
```python
import pandas as pd
import numpy as np
from scipy.sparse import coo_matrix
from sklearn.model_selection import train_test_split
from Recommenders.SVDRecommender import SVDRecommender # 导入SVDRecommender类
triplet_dataset_sub_song_merged = pd.read_csv('triplet_dataset_sub_song_merged.csv') # 读取数据
triplet_dataset_sub_song_merged_sum_df = triplet_dataset_sub_song_merged[['user','listen_count']].groupby('user').sum().reset_index()
triplet_dataset_sub_song_merged_sum_df.rename(columns={'listen_count':'total_listen_count'},inplace=True)
triplet_dataset_sub_song_merged = pd.merge(triplet_dataset_sub_song_merged,triplet_dataset_sub_song_merged_sum_df)
triplet_dataset_sub_song_merged['fractional_play_count'] = triplet_dataset_sub_song_merged['listen_count']/triplet_dataset_sub_song_merged['total_listen_count']
small_set = triplet_dataset_sub_song_merged[['user','song','fractional_play_count']]
user_codes = small_set.user.drop_duplicates().reset_index()
song_codes = small_set.song.drop_duplicates().reset_index()
user_codes.rename(columns={'index':'user_index'}, inplace=True)
song_codes.rename(columns={'index':'song_index'}, inplace=True)
song_codes['so_index_value'] = list(song_codes.index)
user_codes['us_index_value'] = list(user_codes.index)
small_set = pd.merge(small_set,song_codes,how='left')
small_set = pd.merge(small_set,user_codes,how='left')
mat_candidate = small_set[['us_index_value','so_index_value','fractional_play_count']]
data_array = mat_candidate.fractional_play_count.values
row_array = mat_candidate.us_index_value.values
col_array = mat_candidate.so_index_value.values
data_sparse = coo_matrix((data_array, (row_array, col_array)),dtype=float)
K=50
urm = data_sparse
MAX_PID = urm.shape[1]
MAX_UID = urm.shape[0]
recommender = SVDRecommender(K) # 创建SVD推荐器
U, S, Vt = recommender.fit(urm) # 训练推荐器
uTest = [1,6,7,8,23]
uTest_recommended_items = recommender.recommend(uTest, urm, 10)
# 输出推荐结果
recommendations = pd.DataFrame(columns=['user','song', 'score','rank'])
for user in uTest:
rank = 1
for song_index in uTest_recommended_items[user, 0:10]:
song = small_set.loc[small_set['so_index_value'] == song_index].iloc[0]
recommendations = recommendations.append({'user': user, 'song': song['song'], 'score': song['fractional_play_count'], 'rank': rank}, ignore_index=True)
rank += 1
display(recommendations)
```
修改报错的语句:words_count=df_all_words.groupby(by=['all_words'])['all_words'].agg({"count":numpy.size}) print(words_count) words_count=words_count.reset_index().sort_values(by=["count"],ascending=False) words_count.head()
您好,根据您提供的代码,可能会提示以下报错:
```
FutureWarning: Using a dict on a groupby is deprecated and will be removed in a future version of pandas.
```
这是因为 `agg` 函数的参数应该是一个函数或函数列表,而不是字典。为了避免这个警告和报错,您可以将字典改为函数 `numpy.size`,如下所示:
```
words_count = df_all_words.groupby(by=['all_words'])['all_words'].agg(numpy.size).rename('count')
print(words_count)
words_count = words_count.reset_index().sort_values(by=["count"], ascending=False)
words_count.head()
```
这样就可以正确地计算每个单词出现的次数并按照出现次数从大到小排序了。