244-1、语法

# 244、pandas.Series.asfreq方法

pandas.Series.asfreq(freq, method=None, how=None, normalize=False, fill_value=None)

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this Series/DataFrame is a PeriodIndex, the new index is the result of transforming the original index with PeriodIndex.asfreq (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see pandas.date_range()). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The resample() method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters:

freq

DateOffset or str

Frequency DateOffset or string.

method

{‘backfill’/’bfill’, ‘pad’/’ffill’}, default None

Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

‘pad’ / ‘ffill’: propagate last valid observation forward to next valid

‘backfill’ / ‘bfill’: use NEXT valid observation to fill.

how

{‘start’, ‘end’}, default end

For PeriodIndex only (see PeriodIndex.asfreq).

normalize

bool, default False

Whether to reset output index to midnight.

fill_value

scalar, optional

Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns:

Series/DataFrame

Series/DataFrame object reindexed to the specified frequency.

244-2、参数

244-2-1、freq(必须)：字符串或DataOffset对象，表示指定目标频率，常见的频率字符串包括：

'D'： 每日'M'： 每月'A'： 每年'H'： 每小时'T'或'min'： 每分钟'S'： 每秒

244-2-2、method(可选，默认值为None)：字符串('pad','ffill','backfill','bfill')或None，指定当重新采样时如何填充缺失的值。

'pad'或'ffill'：用前一个有效值填充缺失值。'backfill'或'bfill'：用下一个有效值填充缺失值。

244-2-3、how(可选，默认值为None)：字符串，在较新的版本中已经被移除，可以忽略此参数。

244-2-4、normalize(可选，默认值为False)：布尔值，如果为True，则将时间戳规范化到午夜时间。

244-2-5、fill_value(可选，默认值为None)：标量值，指定用于填充缺失值的标量值。

244-3、功能

        用于将时间序列重新采样为指定的频率，可能会填充或不填充缺失值，具体取决于method和fill_value参数。

244-4、返回值

        返回一个新的Series，其索引为指定频率的时间戳，数据根据指定的填充方法处理。

244-5、说明

        无

244-6、用法

244-6-1、数据准备

无

244-6-2、代码示例

# 244、pandas.Series.asfreq方法

import pandas as pd

# 创建一个时间序列

rng = pd.date_range('2024-01-01', periods=6, freq='2D')code>

ts = pd.Series(range(6), index=rng)

# 将时间序列转换为每日频率，使用前向填充方法

ts_daily_ffill = ts.asfreq('D', method='ffill')code>

# 将时间序列转换为每日频率，不填充缺失值

ts_daily_no_fill = ts.asfreq('D')

# 将时间序列转换为每日频率，填充缺失值为0

ts_daily_fill_value = ts.asfreq('D', fill_value=0)

print("原始时间序列:")

print(ts)

print("\n转换为每日频率，使用前向填充方法:")

print(ts_daily_ffill)

print("\n转换为每日频率，不填充缺失值:")

print(ts_daily_no_fill)

print("\n转换为每日频率，填充缺失值为0:")

print(ts_daily_fill_value)

244-6-3、结果输出