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 tuple
updated_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_range
df['Day_of_year'] = df['timestamp'].dt.dayofyear
df['Month'] = df['timestamp'].dt.month
df['Week_of_year'] = df['timestamp'].dt.isocalendar().week
updated_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 range
date_range = pd.date_range(start='2023-07-01', end='2023-07-10')
# Create the DataFrame
df = pd.DataFrame()
df['timestamp'] = date_range
df['Day_of_year'] = df['timestamp'].dt.dayofyear
df['Month'] = df['timestamp'].dt.month
df['Week_of_year'] = df['timestamp'].dt.isocalendar().week
updated_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
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论