python - 如何引用列表绘制 Pandas 数据框的值(索引问题)？

Question

我正在寻找一种聪明的方法来制作这样一个相当幼稚的例子的情节:

使用这样的源数据:

days = ['Monday','Tuesday','Wednesday','Thursday','Friday']


   Feature                   Values                             observed on
0        1  [5.5, 14.3, 12.0, 11.8]  [Tuesday, Wednesday, Thursday, Friday]
1        2        [6.1, 14.6, 12.7]            [Monday, Tuesday, Wednesday]
2        3             [15.2, 13.3]                       [Tuesday, Friday]
3        4       [14.9, 14.3, 17.0]              [Monday, Thursday, Friday]
4        5  [13.0, 13.1, 13.5, 10.3]     [Monday, Tuesday, Thursday, Friday]
5        6              [12.5, 7.0]                     [Wednesday, Friday]

换句话说，对于此数据框的每一行，我想绘制/连接获取它们的“天数”的值。 (请注意这里的日子只是为了说明我的问题，使用 datetime 不是解决方案。)但是我迷失了索引。

这就是我准备图形的方式(即每天都有垂直黑线)

for count, log in enumerate(days):
    plt.plot(np.ones(len(allvalues))*count,np.array(allvalues),'k',linestyle='-',linewidth=1.)
    plt.xticks(np.arange(0,5,1),['M','T','W','T','F'])

这很有效，我得到了垂直线和标签。 (稍后我可能想绘制其他数据集而不是那些垂直线，但现在，垂直线更具说明性)但是现在，我该如何绘制每一天的值？

for index, group in observations.iterrows():
    whichdays= group['observed on']
    values = group['Values']
    for d in whichdays:
        plt.plot(days[np.where(days==d)],values)

但这会产生 TypeError: list indices must be integers, not tuple

Answer 1

一个可能的解决方案是从lists、pivot 和 plot 中拉平值:

from  itertools import chain

df2 = pd.DataFrame({
        "Feature": np.repeat(df.Feature.values, df.Values.str.len()),
        "Values": list(chain.from_iterable(df.Values)),
        "observed on": list(chain.from_iterable(df['observed on']))})
print (df2)
    Feature  Values observed on
0         1     5.5     Tuesday
1         1    14.3   Wednesday
2         1    12.0    Thursday
3         1    11.8      Friday
4         2     6.1      Monday
5         2    14.6     Tuesday
6         2    12.7   Wednesday
7         3    15.2     Tuesday
8         3    13.3      Friday
9         4    14.9      Monday
10        4    14.3    Thursday
11        4    17.0      Friday
12        5    13.0      Monday
13        5    13.1     Tuesday
14        5    13.5    Thursday
15        5    10.3      Friday
16        6    12.5   Wednesday
17        6     7.0      Friday

---

df = df2.pivot(index='observed on', columns='Feature', values='Values')
df.index.name = None
df.columns.name = None
print (df)
              1     2     3     4     5     6
Friday     11.8   NaN  13.3  17.0  10.3   7.0
Monday      NaN   6.1   NaN  14.9  13.0   NaN
Thursday   12.0   NaN   NaN  14.3  13.5   NaN
Tuesday     5.5  14.6  15.2   NaN  13.1   NaN
Wednesday  14.3  12.7   NaN   NaN   NaN  12.5

df.plot(linestyle='-',linewidth=1.)