本文介绍了 pandas 重新采样到季度,并显示开始和结束月份的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我的df看起来像这样:
My df looks like this:
Total
language Julia Python R SQLite
date
2015-03-01 NaN NaN 17.0 NaN
2015-04-01 NaN 156.0 189.0 NaN
2015-05-01 13.0 212.0 202.0 NaN
该指数是按月计算,我希望它是每季度一次:
The index is on a monthly basis and I want it to be quarterly:
df.resample("Q").sum()
给我这个:
Total
language Julia Python R SQLite
date
2015-03-31 NaN NaN 17.0 NaN
2015-06-30 22.0 677.0 594.0 26.0
2015-09-30 37.0 1410.0 1250.0 146.0
但是我想显示这样的索引,而不是结束日期.所需的df:
But I would like to show the index like this Start month - End month 2017
instead of the end date. Desired df:
Total
language Julia Python R SQLite
Jan - Mar, 2015 NaN NaN 17.0 NaN
Apr - Jun, 2015 22.0 677.0 594.0 26.0
Jul - Sep, 2015 37.0 1410.0 1250.0 146.0
有没有做到这一点的大熊猫方法?我是这样做的,但是它很脏,我敢肯定有更好的方法可以做到(示例中缺少docs中的resample方法...):
Is there a pandas way of doing it? I did it like this but it is quite dirty and I am sure there is a better way to do it (the resample method in docs is lacking in examples...):
def quarterlyMonthNmaes(x):
start_date = x.name - pd.offsets.MonthBegin(3)
final_date = str(start_date.strftime('%b')) + " - " + str(x.name.strftime('%b, %Y'))
return final_date
df["Total"].apply(quarterlyMonthNmaes, axis=1)
推荐答案
使用时段:
idx = df.index.to_period('Q')
df.index = ['{0[0]}-{0[1]}'.format(x) for x in zip(idx.asfreq('M', 's').strftime('%b'),
idx.asfreq('M', 'e').strftime('%b %Y'))]
print (df)
Total
language Julia Python R SQLite
Jan-Mar 2015 NaN NaN 17.0 NaN NaN
Apr-Jun 2015 22.0 677.0 594.0 26.0 NaN
Jul-Sep 2015 37.0 1410.0 1250.0 146.0 NaN
或更简单:
idx2 = df.index.strftime('%b %Y')
idx1 = (df.index - pd.offsets.MonthBegin(3)).strftime('%b')
df.index = ['{0[0]}-{0[1]}'.format(x) for x in zip(idx1, idx2)]
这篇关于 pandas 重新采样到季度,并显示开始和结束月份的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!