Question

尝试将方法从here应用到多索引数据框似乎不起作用。

采取数据框架：

import pandas as pd
import numpy as np

dates = pd.date_range('20070101',periods=3200)
df = pd.DataFrame(data=np.random.randint(0,100,(3200,1)), columns =list('A'))
df['A'][5,6,7, 8, 9, 10, 11, 12, 13] = np.nan #add missing data points
df['date'] = dates
df = df[['date','A']]

将季节功能应用于日期时间索引

def get_season(row):
    if row['date'].month >= 3 and row['date'].month <= 5:
        return '2'
    elif row['date'].month >= 6 and row['date'].month <= 8:
        return '3'
    elif row['date'].month >= 9 and row['date'].month <= 11:
        return '4'
    else:
        return '1'

应用功能

df['Season'] = df.apply(get_season, axis=1)

为索引创建“年份”列

df['Year'] = df['date'].dt.year

按年份和季节划分的多指数

df = df.set_index(['Year', 'Season'], inplace=False)

计算每个季节的数据点

count = df.groupby(level=[0, 1]).count()

在不到75天的时间里放下季节

count = count.drop(count[count.A < 75].index)

为超过75天的季节创建变量

complete = count[count['A'] >= 75].index

使用isin函数会对所有内容都显示为false，而我希望它选择“A”中有超过75天有效数据的所有季节

df = df.isin(complete)
df

每一个价值都是假的，我看不出原因。

我希望这很简洁，我需要使用季节来处理多索引，所以我把它包括在内！

修改

另一种基于多索引重建索引的方法（{3}}无法正常工作（也会产生空白数据帧）

df3 = df.reset_index().groupby('Year').apply(lambda x: x.set_index('Season').reindex(count,method='pad'))

编辑2

也尝试了这个

seasons = count[count['A'] >= 75].index

df = df[df['A'].isin(seasons)]

再次，空白输出

Answer 1

我认为您可以使用Index.isin：

complete = count[count['A'] >= 75].index 


idx = df.index.isin(complete)
print idx
[ True  True  True ..., False False False]

print df[idx]

                 date     A
Year Season                 
2007 1      2007-01-01  24.0
     1      2007-01-02  92.0
     1      2007-01-03  54.0
     1      2007-01-04  91.0
     1      2007-01-05  91.0
     1      2007-01-06   NaN
     1      2007-01-07   NaN
     1      2007-01-08   NaN
     1      2007-01-09   NaN
     1      2007-01-10   NaN
     1      2007-01-11   NaN
     1      2007-01-12   NaN
     1      2007-01-13   NaN
     1      2007-01-14   NaN
     1      2007-01-15  18.0
     1      2007-01-16  82.0
     1      2007-01-17  55.0
     1      2007-01-18  64.0
     1      2007-01-19  89.0
     1      2007-01-20  37.0
     1      2007-01-21  45.0
     1      2007-01-22   4.0
     1      2007-01-23  34.0
     1      2007-01-24  35.0
     1      2007-01-25  90.0
     1      2007-01-26  17.0
     1      2007-01-27  29.0
     1      2007-01-28  58.0
     1      2007-01-29   7.0
     1      2007-01-30  57.0
...                ...   ...
2015 3      2015-08-02  42.0
     3      2015-08-03   0.0
     3      2015-08-04  31.0
     3      2015-08-05  39.0
     3      2015-08-06  25.0
     3      2015-08-07   1.0
     3      2015-08-08   7.0
     3      2015-08-09  97.0
     3      2015-08-10  38.0
     3      2015-08-11  59.0
     3      2015-08-12  28.0
     3      2015-08-13  84.0
     3      2015-08-14  43.0
     3      2015-08-15  63.0
     3      2015-08-16  68.0
     3      2015-08-17   0.0
     3      2015-08-18  19.0
     3      2015-08-19  61.0
     3      2015-08-20  11.0
     3      2015-08-21  84.0
     3      2015-08-22  75.0
     3      2015-08-23  37.0
     3      2015-08-24  40.0
     3      2015-08-25  66.0
     3      2015-08-26  50.0
     3      2015-08-27  74.0
     3      2015-08-28  37.0
     3      2015-08-29  19.0
     3      2015-08-30  25.0
     3      2015-08-31  15.0

[3106 rows x 2 columns]

从多索引数据框（pandas）中删除不完整的季节

1 个答案: