从 pandas groupby 中提取列作为向量

Question

I created this table using pandas' groupby function and want to extract each column as vector/array
df_duration_means = df.groupby('Duration').mean()

	Interest	Loan amount	LTV
Duration
6	0.107500	274000.000000	0.652500
9	0.112500	510500.000000	0.580000
12	0.105345	276632.758621	0.595517
15	0.080000	81000.000000	0.678000
18	0.109167	516557.666667	0.455867
24	0.101500	374500.000000	0.554800

Now I want to extract a vector for each of the 4 columns (including duration). But I was not able to do it, even checking pandas documentation and all possible similar threads.

dur = df_duration_means.index
print(dur)
interest_mx = df_duration_means['Loan amount']
print(interest_mx)

So that I can plot each column vector vs the duration vector:

fig =  plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.plot(dur,in,color=)

Answer 1

Try .reset_index() first, and then use method .tolist() . Like this:

df_duration_means = df_duration_means.reset_index()
duration = df_duration_means.index.tolist()
interest = df_duration_means['Interest'].tolist()
loan_amount = df_duration_means['Loan amount'].tolist()
ltv = df_duration_means['LTV'].tolist()

And then, use duration , interest , loan_amount , and ltv as an input into plotly

EDIT: There is simpler solution using plotly.express:

import plotly.express as px 
df_duration_means = df_duration_means.reset_index()
fig = px.line(df_duration_means, x=df_duration_means.index, y=['Interest', 'Loan amount', 'LTV'])