[英]Plot the result of a groupby operation in pandas
I have this sample table: 我有此示例表:
ID Date Days Volume/Day
0 111 2016-01-01 20 50
1 111 2016-02-01 25 40
2 111 2016-03-01 31 35
3 111 2016-04-01 30 30
4 111 2016-05-01 31 25
5 111 2016-06-01 30 20
6 111 2016-07-01 31 20
7 111 2016-08-01 31 15
8 111 2016-09-01 29 15
9 111 2016-10-01 31 10
10 111 2016-11-01 29 5
11 111 2016-12-01 27 0
0 112 2016-01-01 31 55
1 112 2016-02-01 26 45
2 112 2016-03-01 31 40
3 112 2016-04-01 30 35
4 112 2016-04-01 31 30
5 112 2016-05-01 30 25
6 112 2016-06-01 31 25
7 112 2016-07-01 31 20
8 112 2016-08-01 30 20
9 112 2016-09-01 31 15
10 112 2016-11-01 29 10
11 112 2016-12-01 31 0
I'm trying to make my table final table look like this below after grouping by ID and Date. 在按ID和日期分组后,我试图使我的表最终表如下所示。
ID Date CumDays Volume/Day
0 111 2016-01-01 20 50
1 111 2016-02-01 45 40
2 111 2016-03-01 76 35
3 111 2016-04-01 106 30
4 111 2016-05-01 137 25
5 111 2016-06-01 167 20
6 111 2016-07-01 198 20
7 111 2016-08-01 229 15
8 111 2016-09-01 258 15
9 111 2016-10-01 289 10
10 111 2016-11-01 318 5
11 111 2016-12-01 345 0
0 112 2016-01-01 31 55
1 112 2016-02-01 57 45
2 112 2016-03-01 88 40
3 112 2016-04-01 118 35
4 112 2016-05-01 149 30
5 112 2016-06-01 179 25
6 112 2016-07-01 210 25
7 112 2016-08-01 241 20
8 112 2016-09-01 271 20
9 112 2016-10-01 302 15
10 112 2016-11-01 331 10
11 112 2016-12-01 362 0
Next, I want to be able to extract the first value of Volume/Day per ID, all the CumDays values and all the Volume/Day values per ID and Date. 接下来,我希望能够提取每个ID的“体积/天”的第一个值,所有CumDays值以及每个ID和“日期”的所有“体积/天”的值。 So I can use them for further computation and plotting Volume/Day vs CumDays.
因此,我可以使用它们进行进一步的计算,并绘制“体积/天”与“累积日”的关系。 Example for ID:111, the first value of Volume/Day will be only 50 and ID:112, it will be only 55. All CumDays values for ID:111 will be 20,45... and ID:112, it will be 31,57...For all Volume/Day --- ID:111, will be 50, 40... and ID:112 will be 55,45...
以ID:111为例,“交易量/天”的第一个值将仅为50,而ID:112的第一个值将仅为55。ID:111的所有CumDays值将分别为20,45 ...和ID:112,将是31,57 ...对于所有交易量/天-ID:111将是50、40 ...而ID:112将是55,45 ...
My solution: 我的解决方案:
def get_time_rate(grp_df):
t = grp_df['Days'].cumsum()
r = grp_df['Volume/Day']
return t,r
vals = df.groupby(['ID','Date']).apply(get_time_rate)
vals
Doing this, the cumulative calculation doesn't take effect at all. 这样做,累积计算根本不起作用。 It returns the original Days value.
它返回原始的天数值。 This didn't allow me move further in extracting the first value of Volume/Day, all the CumDays values and all the Volume/Day values I need.
这不允许我进一步提取第一个“体积/天”值,所有CumDays值和所有我需要的“体积/天”值。 Any advice or help on how to go about it will be appreciated.
任何建议或帮助如何进行将不胜感激。 Thanks
谢谢
Get a groupby
object. 获取一个
groupby
对象。
g = df.groupby('ID')
Compute columns with transform
: 用
transform
计算列:
df['CumDays'] = g.Days.transform('cumsum')
df['First Volume/Day'] = g['Volume/Day'].transform('first')
df
ID Date Days Volume/Day CumDays First Volume/Day
0 111 2016-01-01 20 50 20 50
1 111 2016-02-01 25 40 45 50
2 111 2016-03-01 31 35 76 50
3 111 2016-04-01 30 30 106 50
4 111 2016-05-01 31 25 137 50
5 111 2016-06-01 30 20 167 50
6 111 2016-07-01 31 20 198 50
7 111 2016-08-01 31 15 229 50
8 111 2016-09-01 29 15 258 50
9 111 2016-10-01 31 10 289 50
10 111 2016-11-01 29 5 318 50
11 111 2016-12-01 27 0 345 50
0 112 2016-01-01 31 55 31 55
1 112 2016-01-02 26 45 57 55
2 112 2016-01-03 31 40 88 55
3 112 2016-01-04 30 35 118 55
4 112 2016-01-05 31 30 149 55
5 112 2016-01-06 30 25 179 55
6 112 2016-01-07 31 25 210 55
7 112 2016-01-08 31 20 241 55
8 112 2016-01-09 30 20 271 55
9 112 2016-01-10 31 15 302 55
10 112 2016-01-11 29 10 331 55
11 112 2016-01-12 31 0 362 55
If you want grouped plots, you can iterate over each groups after grouping by ID
. 如果要对图进行分组,可以在按
ID
分组后遍历每个组。 To plot, first set index and call plot
. 要进行绘图,首先设置索引并调用
plot
。
fig, ax = plt.subplots(figsize=(8,6))
for i, g in df2.groupby('ID'):
g.plot(x='CumDays', y='Volume/Day', ax=ax, label=str(i))
plt.show()
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