Python数据分析(三)pandas resample 重采样
下方是pandas中resample方法的定义,帮助文档http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling中有更加详细的解释。
def resample(self, rule, how=None, axis=0, fill_method=None, closed=None,
label=None, convention='start', kind=None, loffset=None,
limit=None, base=0, on=None, level=None):
"""
Convenience method for frequency conversion and resampling of time
series. Object must have a datetime-like index (DatetimeIndex,
PeriodIndex, or TimedeltaIndex), or pass datetime-like values
to the on or level keyword.(数据重采样和频率转换,数据必须有时间类型的索引列) Parameters
----------
rule : string
the offset string or object representing target conversion(代表目标转换的偏移量)
axis : int, optional, default 0(操作的轴信息)
closed : {'right', 'left'}
Which side of bin interval is closed. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.(哪一个方向的间隔是关闭的,)
label : {'right', 'left'}
Which bin edge label to label bucket with. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.(区间的哪一个方向的边界标签保留)
convention : {'start', 'end', 's', 'e'}
For PeriodIndex only, controls whether to use the start or end of
`rule`
kind: {'timestamp', 'period'}, optional
Pass 'timestamp' to convert the resulting index to a
``DateTimeIndex`` or 'period' to convert it to a ``PeriodIndex``.
By default the input representation is retained.
loffset : timedelta
Adjust the resampled time labels
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0
on : string, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like. .. versionadded:: 0.19.0 level : string or int, optional
For a MultiIndex, level (name or number) to use for
resampling. Level must be datetime-like. .. versionadded:: 0.19.0 Returns
-------
Resampler object Notes
-----
See the `user guide
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling>`_
for more. To learn more about the offset strings, please see `this link
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__. Examples
-------- Start by creating a series with 9 one minute timestamps.(新建频率为1min的时间序列) >>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: T, dtype: int64 Downsample the series into 3 minute bins and sum the values
of the timestamps falling into a bin.(下采样为三分钟) >>> series.resample('3T').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
value in the resampled bucket with the label ``2000-01-01 00:03:00``
does not include 3 (if it did, the summed value would be 6, not 3).
To include this value close the right side of the bin interval as
illustrated in the example below this one. >>> series.resample('3T', label='right').sum()(保留间隔的右侧标签,上一个结果是左侧标签)
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.(降采样为3分钟) >>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3T, dtype: int64 Upsample the series into 30 second bins.(生采样为30秒) >>> series.resample('30S').asfreq()[0:5] #select first 5 rows
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1.0
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
Freq: 30S, dtype: float64 Upsample the series into 30 second bins and fill the ``NaN``
values using the ``pad`` method.(向前0阶保持)
pad/ffill:用前一个非缺失值去填充该缺失值
backfill/bfill:用下一个非缺失值填充该缺失值
>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64 Upsample the series into 30 second bins and fill the
``NaN`` values using the ``bfill`` method.(向后0阶保持) >>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64 Pass a custom function via ``apply`` >>> def custom_resampler(array_like):
... return np.sum(array_like)+5 >>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3T, dtype: int64 For a Series with a PeriodIndex, the keyword `convention` can be
used to control whether to use the start or end of `rule`. >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
freq='A',
periods=2))
>>> s
2012 1
2013 2
Freq: A-DEC, dtype: int64 Resample by month using 'start' `convention`. Values are assigned to
the first month of the period. >>> s.resample('M', convention='start').asfreq().head()
2012-01 1.0
2012-02 NaN
2012-03 NaN
2012-04 NaN
2012-05 NaN
Freq: M, dtype: float64 Resample by month using 'end' `convention`. Values are assigned to
the last month of the period. >>> s.resample('M', convention='end').asfreq()
2012-12 1.0
2013-01 NaN
2013-02 NaN
2013-03 NaN
2013-04 NaN
2013-05 NaN
2013-06 NaN
2013-07 NaN
2013-08 NaN
2013-09 NaN
2013-10 NaN
2013-11 NaN
2013-12 2.0
Freq: M, dtype: float64 For DataFrame objects, the keyword ``on`` can be used to specify the
column instead of the index for resampling. >>> df = pd.DataFrame(data=9*[range(4)], columns=['a', 'b', 'c', 'd'])
>>> df['time'] = pd.date_range('1/1/2000', periods=9, freq='T')
>>> df.resample('3T', on='time').sum()
a b c d
time
2000-01-01 00:00:00 0 3 6 9
2000-01-01 00:03:00 0 3 6 9
2000-01-01 00:06:00 0 3 6 9 For a DataFrame with MultiIndex, the keyword ``level`` can be used to
specify on level the resampling needs to take place. >>> time = pd.date_range('1/1/2000', periods=5, freq='T')
>>> df2 = pd.DataFrame(data=10*[range(4)],
columns=['a', 'b', 'c', 'd'],
index=pd.MultiIndex.from_product([time, [1, 2]])
)
>>> df2.resample('3T', level=0).sum()
a b c d
2000-01-01 00:00:00 0 6 12 18
2000-01-01 00:03:00 0 4 8 12
Python数据分析(三)pandas resample 重采样的更多相关文章
- Python数据分析库pandas基本操作
Python数据分析库pandas基本操作2017年02月20日 17:09:06 birdlove1987 阅读数:22631 标签: python 数据分析 pandas 更多 个人分类: Pyt ...
- Python数据分析之pandas基本数据结构:Series、DataFrame
1引言 本文总结Pandas中两种常用的数据类型: (1)Series是一种一维的带标签数组对象. (2)DataFrame,二维,Series容器 2 Series数组 2.1 Series数组构成 ...
- Python 数据分析:Pandas 缺省值的判断
Python 数据分析:Pandas 缺省值的判断 背景 我们从数据库中取出数据存入 Pandas None 转换成 NaN 或 NaT.但是,我们将 Pandas 数据写入数据库时又需要转换成 No ...
- Python数据分析之Pandas操作大全
从头到尾都是手码的,文中的所有示例也都是在Pycharm中运行过的,自己整理笔记的最大好处在于可以按照自己的思路来构建矿建,等到将来在需要的时候能够以最快的速度看懂并应用=_= 注:为方便表述,本章设 ...
- Python数据分析之pandas学习
Python中的pandas模块进行数据分析. 接下来pandas介绍中将学习到如下8块内容:1.数据结构简介:DataFrame和Series2.数据索引index3.利用pandas查询数据4.利 ...
- python数据分析之pandas数据选取:df[] df.loc[] df.iloc[] df.ix[] df.at[] df.iat[]
1 引言 Pandas是作为Python数据分析著名的工具包,提供了多种数据选取的方法,方便实用.本文主要介绍Pandas的几种数据选取的方法. Pandas中,数据主要保存为Dataframe和Se ...
- Python数据分析之pandas
Python中的pandas模块进行数据分析. 接下来pandas介绍中将学习到如下8块内容:1.数据结构简介:DataFrame和Series2.数据索引index3.利用pandas查询数据4.利 ...
- Python数据分析之pandas学习(基础操作)
一.pandas数据结构介绍 在pandas中有两类非常重要的数据结构,即序列Series和数据框DataFrame.Series类似于numpy中的一维数组,除了通吃一维数组可用的函数或方法,而且其 ...
- python数据分析三个重要方法之:numpy和pandas
关于数据分析的组件之一:numpy ndarray的属性 4个必记参数:ndim:维度shape:形状(各维度的长度)size:总长度dtype:元素类型 一:np.array()产生n维 ...
随机推荐
- MySQL高可用之MGR安装测试(续)
Preface I've implemented the Group Replication with three servers yesterday,What a shame it ...
- LeetCode 删除链表倒数第N个节点
基本思路 定义两个指示指针a b 让a先行移动n+1个位置 若a指向了NULL的位置,则删除的是头节点(由于走过了n+1个节点刚好指在尾部的NULL上) 否则让b与a一起移动直至a->next, ...
- php后端跨域Header头
header("Access-Control-Allow-Origin: http://a.com"); // 允许a.com发起的跨域请求 //如果需要设置允许所有域名发起的跨域 ...
- nodeJs 对 Mysql 数据库的 curd
var mysql = require('mysql'); var connection = mysql.createConnection({ host : 'localhost', user : ' ...
- Python的scrapy之爬取妹子图片
闲来无事,做的一个小爬虫项目 爬虫主程序: import scrapy from ..items import MeiziItem class MztSpider(scrapy.Spider): na ...
- 官方yum源安装选择所需版本mysql数据库并初始化(yum默认安装的是最新版MySQL8.+)
在官网是找不到5.x系列的域名源的,系统默认是安装的oracle数据库,在安装前需要删除默认的 以下教程来源于官网说明 先去官网下载yum源,地址 https://dev.mysql.com/down ...
- PHP json_decode返回null解析失败原因
在PHP5.4之前 json_decode函数有两个参数json_decode有两个参数,第一个是待解析的字符串,第二个是是否解析为Array json_decode要求的字符串比较严格:(1)使用U ...
- c语言中 *p++ 和 (*p)++ 和 *(p++) 和 *(++p) 和++(*p)和 *(p--)和 *(--p)有什么区别?
*p++是指下一个地址; (*p)++是指将*p所指的数据的值加一; /******************解释**********************/ ->C编译器认为*和++是同优先级 ...
- Intellij 出现“Usage of API documented as @since 1.4+”的解决办法
https://blog.csdn.net/wust_lh/article/details/73277185
- ffmpeg安装配置以及库调用
参考https://blog.csdn.net/jayson_jang/article/details/52329508 cd ffmpeg ./configure --enable-shared - ...