Here is an example code to demonstrate my problem:

import numpy as np
import pandas as pd

np.random.seed(10)

df = pd.DataFrame(np.random.randint(0,10,size=(100, 2)), columns=list('xy'))

df

    x   y
0   9   4
1   0   1
2   9   0
3   1   8
4   9   0
... ... ...
95  0   4
96  6   4
97  9   8
98  0   7
99  1   7

groups = df.groupby(['x'])

groups.size()

x
0    11
1    12
2    15
3    13
4    14
5     5
6     6
7     9
8     5
9    10
dtype: int64

How can I access the x-values as a column and the aggregated y-values as a second column to plot x versus y?

CodePudding user response：

Two options.

1. Use reset_index():

groups = df.groupby(['x']).size().reset_index(name='size')

2. Add as_index=False to groupby:

groups = df.groupby(['x'], as_index=False).size()

Output for both:

>>> groups
   x  size
0  0    16
1  1     9
2  2     9
3  3     5
4  4     7
5  5    10
6  6    10
7  7     7
8  8    12
9  9    15

CodePudding user response：

IIUC, use as_index=False:

groups = df.groupby(['x'], as_index=False)
out = groups.size()
out.plot(x='x', y='size')

If you only want to plot, you can also keep the x as index:

df.groupby(['x']).size().plot()

output:

   x  size
0  0    16
1  1     9
2  2     9
3  3     5
4  4     7
5  5    10
6  6    10
7  7     7
8  8    12
9  9    15