英文:
How to assign new columns based on chaining in pandas
问题
我正在尝试在pandas中使用链式操作创建一个新的数据框。
result = (df.drop(['Day_of_year', 'Month', 'Week_of_year'], axis='columns'),pd.to_datetime(df['timestamp']),.assign("random" = 0))# 访问元组的第一个元素updated_df = result[0]updated_df如果我注释掉最后一行，元组中的代码可以正常工作，但我想要分配新的列。我该如何做到这一点？
英文:
I'm trying to create a new dataframe using chaining in pandas.
result = (df.drop(['Day_of_year', 'Month', 'Week_of_year'], axis='columns'),pd.to_datetime(df['timestamp']),.assign("random" = 0))# Access the first element of the tupleupdated_df = result[0]updated_df
If I comment out the last line the code in the tuple work but I want to assign new columns.
How do I do this?
答案1
得分: 1
尝试这个：
updated_df = (df.drop(columns=['Day_of_year', 'Month', 'Week_of_year']).assign(**{"random": 0, 'timestamp': pd.to_datetime(df['timestamp'])}))
测试
在一个虚拟数据框上评估上述代码：
import pandas as pd# 创建一个日期范围date_range = pd.date_range(start='2023-07-01', end='2023-07-10')# 创建数据框df = pd.DataFrame()df['timestamp'] = date_rangedf['Day_of_year'] = df['timestamp'].dt.dayofyeardf['Month'] = df['timestamp'].dt.monthdf['Week_of_year'] = df['timestamp'].dt.isocalendar().weekupdated_df = (df.drop(columns=['Day_of_year', 'Month', 'Week_of_year']).assign(**{"random": 0, 'timestamp': pd.to_datetime(df['timestamp'])}))print(updated_df)# 打印：## timestamp random# 0 2023-07-01 0# 1 2023-07-02 0# 2 2023-07-03 0# 3 2023-07-04 0# 4 2023-07-05 0# 5 2023-07-06 0# 6 2023-07-07 0# 7 2023-07-08 0# 8 2023-07-09 0# 9 2023-07-10 0
英文:
Try this:
updated_df = (df.drop(columns=['Day_of_year', 'Month', 'Week_of_year']).assign(**{"random": 0, 'timestamp': pd.to_datetime(df['timestamp'])}))
Testing
Evaluating the above code on a dummy dataframe:
import pandas as pd# Create a date rangedate_range = pd.date_range(start='2023-07-01', end='2023-07-10')# Create the DataFramedf = pd.DataFrame()df['timestamp'] = date_rangedf['Day_of_year'] = df['timestamp'].dt.dayofyeardf['Month'] = df['timestamp'].dt.monthdf['Week_of_year'] = df['timestamp'].dt.isocalendar().weekupdated_df = (df.drop(columns=['Day_of_year', 'Month', 'Week_of_year']).assign(**{"random": 0, 'timestamp': pd.to_datetime(df['timestamp'])}))print(updated_df)# Prints:## timestamp random# 0 2023-07-01 0# 1 2023-07-02 0# 2 2023-07-03 0# 3 2023-07-04 0# 4 2023-07-05 0# 5 2023-07-06 0# 6 2023-07-07 0# 7 2023-07-08 0# 8 2023-07-09 0# 9 2023-07-10 0
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