英文:
Convert Weeknumber to date using Python and Pandas
问题
我需要帮助从我的数据框中提取周日期：
df = pd.DataFrame({
'weeknum':[12,24,36,48,50],
'year':[2023,2023,2023,2023,2023]
})
我期望的输出是有一个新列名为"weekDate"，该列将以"yyyy-MM-dd"格式提取特定周的第一个星期一的日期：
例如，对于周：12，年：2023 ====> "2023-03-20"（这是2023年第12周的第一个星期一的日期）
非常感谢提前帮助。
英文:
I need help extracting the weekdate from my df:
df = pd.DataFrame({
'weeknum':[12,24,36,48,50],
'year':[2023,2023,2023,2023,2023]
})
my desired output is to have a new column named "weekDate" which will pull the date of the first Monday of the specific week in a "yyyy-MM-dd" format:
E.g. for Week: 12, Year: 2023 ====> "2023-03-20" (which is the first Monday of week 12 in 2023)
Many thanks in advance,
答案1
得分: 1
To convert week numbers to dates, you can use the datetime module with the pandas library.
import pandas as pd
from datetime import datetime, timedelta
df = pd.DataFrame({
'weeknum':[12,24,36,48,50],
'year':[2023,2023,2023,2023,2023]
})
# Function to get the date of the first Monday of a given week and year
def get_first_monday(year, week):
date_str = f'{year}-W{week}'
date_obj = datetime.strptime(date_str + '-1', "%Y-W%W-%w")
return date_obj.strftime('%Y-%m-%d')
# Apply the function to create the 'weekDate' column
df['weekDate'] = df.apply(lambda row: get_first_monday(row['year'], row['weeknum']), axis=1)
print(df)
Output
weeknum year weekDate
0 12 2023 2023-03-20
1 24 2023 2023-06-12
2 36 2023 2023-09-04
3 48 2023 2023-11-27
4 50 2023 2023-12-11
英文:
To convert week numbers to dates, you can use the datetime module with the pandas library.
import pandas as pd
from datetime import datetime, timedelta
df = pd.DataFrame({
'weeknum':[12,24,36,48,50],
'year':[2023,2023,2023,2023,2023]
})
# Function to get the date of the first Monday of a given week and year
def get_first_monday(year, week):
date_str = f'{year}-W{week}'
date_obj = datetime.strptime(date_str + '-1', "%Y-W%W-%w")
return date_obj.strftime('%Y-%m-%d')
# Apply the function to create the 'weekDate' column
df['weekDate'] = df.apply(lambda row: get_first_monday(row['year'], row['weeknum']), axis=1)
print(df)
Output
weeknum year weekDate
0 12 2023 2023-03-20
1 24 2023 2023-06-12
2 36 2023 2023-09-04
3 48 2023 2023-11-27
4 50 2023 2023-12-11
答案2
得分: 0
你可以连接这些列并使用to_datetime以'%Y-%W-%w'格式（更多信息请看这里)：
df['weekDate'] = pd.to_datetime(
df['year'].astype(str)+'-'+df['weeknum'].astype(str)+'-1',
format='%Y-%W-%w') # ^
# 1 is for Monday
# or # |
df['weekDate'] = pd.to_datetime( # v
df['year'].astype(str)+df['weeknum'].astype(str)+'1',
format='%Y%W%w')
输出:
weeknum year weekDate
0 12 2023 2023-03-20
1 24 2023 2023-06-12
2 36 2023 2023-09-04
3 48 2023 2023-11-27
4 50 2023 2023-12-11
英文:
You can concatenate the columns and use to_datetime with the '%Y-%W-%w' format (more info here):
df['weekDate'] = pd.to_datetime(
df['year'].astype(str)+'-'+df['weeknum'].astype(str)+'-1',
format='%Y-%W-%w') # ^
# 1 is for Monday
# or # |
df['weekDate'] = pd.to_datetime( # v
df['year'].astype(str)+df['weeknum'].astype(str)+'1',
format='%Y%W%w')
Output:
weeknum year weekDate
0 12 2023 2023-03-20
1 24 2023 2023-06-12
2 36 2023 2023-09-04
3 48 2023 2023-11-27
4 50 2023 2023-12-11
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