data_com1 = pd.merge(data1, df1[[index, 'name','Alkene (C=C)','Aldehyde (-CHO)','Ketone (C=O)','Hydroxyl (-OH)']], on=index, how='left')帮我修改
时间: 2024-10-09 09:06:28 浏览: 23
这个Python代码片段是在使用pandas库进行数据合并(merge),它将"data1" DataFrame 与 "df1" 中指定的列[index、'name'、'Alkene (C=C)'、'Aldehyde (-CHO)'、'Ketone (C=O)'、'Hydroxyl (-OH)']基于索引(index)进行左连接(left join)。"on" 参数指定了连接的关键字,这里是index。"how='left'"表示保留data1的所有记录,如果df1中没有匹配的数据,则对应列会被填充NaN值。
如果你想确保所有列都被合并,并且不想丢失任何数据,可以将`how='left'`改为`how='inner'`来进行内连接(只保留两个DataFrame中都有的行);如果你想要在df1中没有匹配时填充特定值而非NaN,可以在merge函数之后使用`.fillna()`或者直接设置`{'column_name': value}`代替`NaN`。
修改后的代码示例如下:
```python
data_com1 = pd.merge(data1, df1[['index', 'name','Alkene (C=C)','Aldehyde (-OH)']], on='index', how='inner' or 'left')
# 如果想填充特定值 fillna('your_value')
data_com1.fillna(value={'column_name': 'your_value'}, inplace=True)
```
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)
```
df_merged = pd.merge(df1, data2, on='value',how='inner')
这段代码使用了 Pandas 库中的 merge 函数来将两个 DataFrame 进行合并。其中,
- `df1` 和 `data2` 是要进行合并的两个 DataFrame;
- `on='value'` 表示以 `value` 列为键来进行合并,即只有在两个 DataFrame 中都存在的 `value` 值才会被保留;
- `how='inner'` 表示使用内连接方式,即只保留两个 DataFrame 中都存在的行。
合并后的结果存放在新的 DataFrame `df_merged` 中。
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