将系列重塑为python中的Dataframe矩阵

Question

我有一个305系列的系列，带有Datatime索引。数据看起来像这样

1992-01-31     1.123077 
1992-02-28    -2.174845 
1992-03-31    -3.884848
1992-04-30     8.682919
1992-05-29     1.312976
1992-06-30     7.851080
1992-07-31    -3.192788
1992-08-31    -7.351976
1992-09-30    -6.782217
1992-10-30   -17.182738
1992-11-30     3.898782
1992-12-31   -26.190414
1993-01-29     2.233359
1993-02-26     6.709006
continues with monthly data till December 2017

我想将数据重新整形为一个DataFrame，它具有行和行的所有年份以及适当填充的数据和数据

        January February  March     etc >>  December
2017    values  values    values    values  values  
2016    values  values    values    values  values  
2015    values  values    values    values  values  
etc \\// 
1992    values                  

我查看了其他帖子并尝试了重塑和asmatrix，但鉴于它是不均匀的系列我不断收到此错误。

ValueError：新数组的总大小必须保持不变。

我真正想要做的是，如果矩阵是奇数形状，则插入NaN以获取缺失值。因此，如果2017年没有11月或12月的价值，他们将是NaN

让我知道是否有人可以提供帮助

Answer 1

来源DF：

In [159]: df
Out[159]:
                  val
date
1992-01-31   1.123077
1992-02-28  -2.174845
1992-03-31  -3.884848
1992-04-30   8.682919
1992-05-29   1.312976
1992-06-30   7.851080
1992-07-31  -3.192788
1992-08-31  -7.351976
1992-09-30  -6.782217
1992-10-30 -17.182738
1992-11-30   3.898782
1992-12-31 -26.190414
1993-01-29   2.233359
1993-02-26   6.709006

解：

import calendar

In [158]: (df.assign(year=df.index.year, mon=df.index.month)
             .pivot(index='year', columns='mon', values='val')
             .rename(columns=dict(zip(range(13), calendar.month_name))))
Out[158]:
mon    January  February     March     April       May     June      July    August  September    October  November   December
year
1992  1.123077 -2.174845 -3.884848  8.682919  1.312976  7.85108 -3.192788 -7.351976  -6.782217 -17.182738  3.898782 -26.190414
1993  2.233359  6.709006       NaN       NaN       NaN      NaN       NaN       NaN        NaN        NaN       NaN        NaN

更新：或更好更短的version from @COLDSPEED：

In [164]: pd.pivot(df.index.year, df.index.month, df['val']) \
            .rename(columns=calendar.month_name.__getitem__)
Out[164]:
date   January  February     March     April       May     June      July    August  September    October  November   December
date
1992  1.123077 -2.174845 -3.884848  8.682919  1.312976  7.85108 -3.192788 -7.351976  -6.782217 -17.182738  3.898782 -26.190414
1993  2.233359  6.709006       NaN       NaN       NaN      NaN       NaN       NaN        NaN        NaN       NaN        NaN

Answer 2

s

1992-01-31     1.123077
1992-02-28    -2.174845
1992-03-31    -3.884848
1992-04-30     8.682919
1992-05-29     1.312976
1992-06-30     7.851080
1992-07-31    -3.192788
1992-08-31    -7.351976
1992-09-30    -6.782217
1992-10-30   -17.182738
1992-11-30     3.898782
1992-12-31   -26.190414
1993-01-29     2.233359
1993-02-26     6.709006
Name: 1, dtype: float64

type(s)
pandas.core.series.Series

如有必要，将索引转换为datetime -

s.index = pd.to_datetime(s.index, errors='coerce')

现在，使用pd.pivot -

x = pd.Series(s.index.strftime('%Y %B')).str.split()
y, m = x.str[0], x.str[1]

pd.pivot(y, m, s)

         April    August   December  February   January      July     June  \
1992  8.682919 -7.351976 -26.190414 -2.174845  1.123077 -3.192788  7.85108   
1993       NaN       NaN        NaN  6.709006  2.233359       NaN      NaN   

         March       May  November    October  September  
1992 -3.884848  1.312976  3.898782 -17.182738  -6.782217  
1993       NaN       NaN       NaN        NaN        NaN  

Answer 3

尝试类似的东西

#Give your series index a name so that we can reset index and have a new column
your_series.index = your_series.index.rename('Time')
df = your_series.toframe('Values').reset_index()

#Create variables for month and year
df['Month'] = df.Time.dt.month
df['Year'] = df.Time.dt.Year

#Assuming they are unique, create a pivot table
df.pivot('Year','Month','Values')

月份将是数字。如果你想要你必须要做的月份名称

import datetime as dt

df['Month'] = df.Time.date.apply(lambda x: dt.datetime.strftime(x,'%B'))

如果您的月份/年份对不是唯一的，那么请执行类似的操作

df.groupby(['Year','Month']).Values.sum().unstack()