在Python Pandas 中从日期中提取周数
很多时候,在处理一些包含日期的数据时,我们可能需要从一个特定的日期中提取周数。在Python中,在pandas的帮助下,这可以很容易地完成。
示例 1:
# importing pandas as pd
import pandas as pd
# creating a dictionary containing a date
dict = {'Date':["2015-06-17"]}
# converting the dictionary to a dataframe
df = pd.DataFrame.from_dict(dict)
# converting the date to the required format
df['Date'] = pd.to_datetime(df['Date'], errors ='coerce')
df.astype('int64').dtypes
# extracting the week from the date
weekNumber = df['Date'].dt.week
print(weekNumber)
输出:
0 25
Name: Date, dtype: int64
例子2:我们也可以通过在’Date’对象中添加更多的日期,对多个日期做同样的处理。
# importing pandas as pd
import pandas as pd
# creating a dictionary containing a date
dict = {'Date':["2020-06-17", "2020-01-14",
"2020-09-20", "2020-08-15"]}
# converting the dictionary to a
# dataframe
df = pd.DataFrame.from_dict(dict)
# converting the date to the required
# format
df['Date'] = pd.to_datetime(df['Date'],
errors ='coerce')
df.astype('int64').dtypes
# extracting the week from the date
weekNumber = df['Date'].dt.week
print(weekNumber)
输出:
例子3:使用date_range()和to_series()从多个日期中提取星期数。
- pandas.data_range()。它生成了从开始日期到结束日期的所有日期
语法:pandas.date_range(start, end, periods, freq, tz, normalize, name, closed)
- pandas.to_series()。它创建了一个索引和值都等于索引键的系列。
语法:
Index.to_series(self, index, name)
# importing pandas as pd
import pandas as pd
# generating all dates in given range
# with increment by days
allDates = pd.date_range('2020-06-27', '2020-08-03', freq ='W')
# converting dates to series
series = allDates.to_series()
series.dt.week
输出:
例子4:在这个例子中,我们将使用pandas.Series()来生成日期,并使用不同的方式将系列转换为数据框架。
pandas.Series():用于创建一个带有轴标签的一维ndarray。
语法:
pandas.Series(data, index, dtype, name, copy, fastpath)
# importing pandas as pd
import pandas as pd
# generating the series
dates = pd.Series(pd.date_range('2020-2-10',
periods = 5,
freq ='M'))
# converting to dataframe
df = pd.DataFrame({'date_given': dates})
# extracting the week number
df['week_number'] = df['date_given'].dt.week
df
输出: