绘制 pandas 中的groupby操作的结果 [英] Plot the result of a groupby operation in pandas
问题描述
我有这个样本表:
ID日期天日成交量/日
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
我想通过ID和日期进行分组后,让我的表最终表如下所示。
ID日期CumDays交易量/日
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
接下来,我希望能够分机记录每个ID的每日体积/日期,所有CumDays值以及每个ID和日期的所有体积/日期值的第一个值。因此,我可以将它们用于进一步计算并绘制Volume / Day vs CumDays。 ID为111的例子,Volume / Day的第一个值将只有50,ID:112,它将只有55. ID:111的所有CumDays值将是20,45 ...和ID:112,它会是31,57 ...对于所有卷/天--- ID:111,将是50,40 ...和ID:112将是55,45 ...
我的解决方案:
def get_time_rate(grp_df):
t = grp_df ['Days']。cumsum ()'b $ br = grp_df ['Volume / Day']
return t,r
vals = df.groupby(['ID','Date'])。apply get_time_rate)
vals
这样做,累积计算完全不起作用。它返回原始的Days值。这不允许我进一步提取Volume / Day的第一个值,所有CumDays值和我需要的所有Volume / Day值。任何意见或帮助如何去解决它将不胜感激。谢谢
获取 groupby
对象。
g = df.groupby('ID')
使用 transform
计算列:
df ['CumDays'] = g.Days.transform('cumsum')
df ['First Volume / Day'] = g ['Volume / Day']。transform('first')
df
ID日期天日成交量/日CumDays第一成交量/日
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
如果您想分组绘图,可以按照 ID
进行分组, 。要绘制,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()
I have this sample table:
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.
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. So I can use them for further computation and plotting Volume/Day vs CumDays. 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...
My solution:
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. It returns the original Days value. 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. Any advice or help on how to go about it will be appreciated. Thanks
Get a groupby
object.
g = df.groupby('ID')
Compute columns with transform
:
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
. To plot, first set index and call 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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