I have three pandas Series, called: Col_data, C_PV_data and C_elec_data. Each one has these values:

 Col_data:
 0    625814.205486
1    782267.756857
2    938721.308229
Name: 7, dtype: object

 C_PV_data:
 0    2039032.206909
1    2548790.258636
2    3058548.310363
Name: 3, dtype: object

 C_elec_data:
 0    1337523.743009
1    1671904.678761
2    2006285.614513
Name: 0, dtype: object

I would like to aggregate them into a single DataFrame, to export that DataFrame to a .xlsx file, in which each column is called as the variable. For instance:

Finally, I would like to represent each column with a graph in which the central value is a line, and two dots over that line, for the lowest and hights value. For instance, the graph would be something like this:

CodePudding user response：

Sure, here you go:

Init

Col_data = pd.Series([
    625814.205486,
    782267.756857,
    938721.308229])
C_PV_data = pd.Series([
    2039032.206909,
    2548790.258636,
    3058548.310363])
C_elec_data = pd.Series([
    1337523.743009,
    1671904.678761,
    2006285.614513])

As a df

df = pd.concat(
    [Col_data, C_PV_data, C_elec_data], axis=1,
    keys=['Col_data', 'C_PV_data', 'C_elec_data'])
>>> df
        Col_data     C_PV_data   C_elec_data
0  625814.205486  2.039032e 06  1.337524e 06
1  782267.756857  2.548790e 06  1.671905e 06
2  938721.308229  3.058548e 06  2.006286e 06

Side note: I always dislike repeats. The following alternative to the above is DRY (Don't Repeat Yourself), but less clear perhaps:

keys = ['Col_data', 'C_PV_data', 'C_elec_data']
d = locals()  # just for DRY...
df = pd.concat([d[k] for k in keys], axis=1, keys=keys)

To xlsx

Assuming you have openpyxl installed:

df.to_excel('foo.xlsx', index=False)

Box plot

df.loc[[0,1,1,1,2]].plot.box()