Migrate drawing to pandas

[bdrum]

Now I use such constructions for drawing:

bw, = ax.plot(np.linspace(x.min(), x.max(),1000), fit.bw(x=np.linspace(x.min(), x.max(),1000), M = result.best_values['bw_M'], G=result.best_values['bw_G'], amp=result.best_values['bw_amp'] ), 'g--',label=r'Breit-Wigner')
bkg, = plt.plot(np.linspace(x.min(), x.max(),1000), fit.polyn(np.linspace(x.min(), x.max(),1000), result.best_values['bckg_c0'], result.best_values['bckg_c1'], result.best_values['bckg_c2'], result.best_values['bckg_c3'], result.best_values['bckg_c4'],0,0,0, result.best_values['bckg_amp']), 'b--', label='Bckgr fit')
# plt.plot(bcg_x, bcg_y, 'r+', label='bkg data')
result.plot_fit(datafmt='ko',fitfmt='r-',numpoints=100,data_kws={'xerr':0.022})
ax.set_title('')
_ = ax.set_ylabel(r'Counts per 20 Mev/$c^2$')
_ = ax.set_xlabel(r'M$(\pi^+\pi^-\pi^+\pi^-)$ (GeV/$c^2$)')
_ = plt.legend([dat,ft, bw, bkg],['Data','Fit','Breit-Wigner','Bkg'])

Instead of this, I can combine data to DataFrames and use pandas plot for drawing purposes

[bdrum]

E.g. this one

# data part
xx =  np.linspace(x.min(),x.max(),100)
ff =  result.eval(x=xx)
fbw1 = fit.bw(x=xx, M = result.best_values['bw1_M'],G=result.best_values['bw1_G'], amp=result.best_values['bw1_amp'] )
fbw2 = fit.bw(x=xx, M = result.best_values['bw2_M'],G=result.best_values['bw2_G'], amp=result.best_values['bw2_amp'] )
fbckg = fit.polyn(xx, result.best_values['bckg_c0'], result.best_values['bckg_c1'], result.best_values['bckg_c2'], result.best_values['bckg_c3'], result.best_values['bckg_c4'],0,0,0, result.best_values['bckg_amp'])
df=pd.DataFrame([xx,ff,fbw1,fbw2,fbckg]).T
df.columns=['x', 'fit', 'bw1','bw2', 'bkgr']

# drawing part
fig = plt.figure()
ax = fig.add_subplot()

dt, = hep.histplot(np.histogram(mass_events(kinematics.GetTracksWithPtCut(four_tracks_zq_after_all_cuts)), bins=b, range=r),ax=ax, yerr=True, xerr=0.022, color='black', histtype="errorbar",label='data')

ll = df.plot(x='x', y=['fit', 'bw1','bw2', 'bkgr'],ax=ax, style=['r-', 'g-', 'm-', 'b--'], xlabel=r'M($\pi^+\pi^-\pi^+\pi^-$) (GeV/$c^2$)', ylabel=r'Counts per 45 MeV/$c^2$')
_ = ax.legend([dt] + ll.get_children()[4:8], ['Data', 'Fit', 'Breit-Wigner', 'Breit-Wigner', 'Bkgr'])