Largeur variable de l'histogramme de Matplotlib Bin

Question

Je crée une figure avec 3 sous-trames et certains des blocs de l'histogramme semblent être de tailles différentes, bien qu'ils soient tous égaux en largeur. Mon but est de créer un histogramme avec des barres de largeur égale.

Je suis en train de tracer des données à partir de trois trames de données différentes df1,df2,df3 et chacune a son propre axe. Les deux premières trames de données (df1,df2) ont 12 valeurs, tandis que la troisième (df3) a 21 valeurs. Un exemple de travail minimal:

import pandas as pd 
import matplotlib.pyplot as plt 
import numpy as np 

#Data 
df1 = pd.DataFrame(data={'Delta_Thick': {0: -0.10257269427388138,1: -0.39092250646203491,2:-0.23459561055233191,3: 0.68753181981137268,4: -0.86443211703287937,5: -0.46963178960649432,6: 0.14070311160589327,7: 0.1885440568340489,8: 0.64210565529921859,9: -0.81346415594104837,10: 0.68175896505459788,11: 0.33673654536030828}}) 
df2 = pd.DataFrame(data={'Delta_Thick':{0: -0.38775619399296002,1: -0.32367407025583783,2: -0.56055783338428344,3: 0.23824247437746471,4: -0.64925233000340721,5: -0.44120245730257612,6: 0.027222094241818928,7: -0.091069018106476163,8: 0.0066066466889458386,9: -0.60477189852646174,10: 0.12878952794346843,11: -0.0077463979905486591}}) 
df3 = pd.DataFrame(data={'Delta_Thick':{0: 0.28518349971907864,1: -0.06724843620619711,2: 0.32596222283195153,3: 0.44928934543390797,4: 0.20911991461399143,5: -0.036989014816141919,6: -0.21517978702947216, 7: -0.028429332303918198,8: 0.037553921139760305,9: 0.98813506475654656,10: 0.51938760439670373,11: 0.11348101736407434,12: 0.79676269452200232,13: 0.27961307494052506,14: -0.55282685608381399,15: 0.63549900861027275,16: -0.20869225741458663,17: 0.55296943711112945,18: 0.34448294335085694,19: 0.18268186220418725,20: 0.36422880308671302}}) 

fig, (ax,ax1,ax2) = plt.subplots(ncols=3) 

bins=[round(x,1)for x in np.linspace(-1,1,21)] 
counts, division = np.histogram(df1.loc[:,'Delta_Thick'],bins=bins) 
df1.loc[:,'Delta_Thick'].hist(ax=ax, bins=division,color='green',label='Thing',hatch='//') 
ax.xaxis.set_ticks(np.arange(-1, 1.5, 0.5)) 
ax.yaxis.set_ticks(np.arange(0, 5, 1)) 
ax.set_title('A. 1990-2016') 
ax.set_ylabel('Number of Sites') 
ax.legend(fontsize='x-small',loc=2) 

#Deficit 
bins=[round(x,1)for x in np.linspace(-1,0.6,16)] 
counts, division = np.histogram(df2.loc[:,'Delta_Thick'],bins=bins) 
df2.loc[:,'Delta_Thick'].hist(ax=ax1, bins=division,color='green',hatch='//') 
ax1.xaxis.set_ticks(np.arange(-1, 0.75, 0.5)) 
ax1.yaxis.set_ticks(np.arange(0, 5, 1)) 
ax1.set_title('B. 1990-2003') 
ax1.set_xlabel('X axis label') 

#Enrich 
bins=[round(x,1)for x in np.linspace(-1,0.6,16)] 
counts, division = np.histogram(df3.loc[:,'Delta_Thick'],bins=bins) 
df3.loc[:,'Delta_Thick'].hist(ax=ax2, bins=division,color='green',hatch='//') 
ax2.xaxis.set_ticks(np.arange(-1, 1.5, 0.5)) 
ax2.yaxis.set_ticks(np.arange(0, 5, 1)) 
ax2.set_title('C. 2003-2016') 
plt.tight_layout() 
plt.show() 

Dans la parcelle ci-dessus, la troisième intrigue secondaire ax2 a une barre d'histogramme qui semble avoir une largeur de bac de 0,2.

La longueur de la troisième trame de données peut-elle causer ce problème?

La variable division ne dicte-t-elle pas la largeur de la corbeille?

Answer 1

Je ne sais pas pourquoi, mais d'une façon ou d'une autre quand j'ajustais les ticks de x (c'est-à-dire ax2.xaxis.set_ticks) il a changé l'aspect des barres d'histogramme. Donc, la solution de travail est:

import pandas as pd 
import matplotlib.pyplot as plt 
import numpy as np 

#Data 
df1 = pd.DataFrame(data={'Delta_Thick': {0: -0.10257269427388138,1: -0.39092250646203491,2:-0.23459561055233191,3: 0.68753181981137268,4: -0.86443211703287937,5: -0.46963178960649432,6: 0.14070311160589327,7: 0.1885440568340489,8: 0.64210565529921859,9: -0.81346415594104837,10: 0.68175896505459788,11: 0.33673654536030828}}) 
df2 = pd.DataFrame(data={'Delta_Thick':{0: -0.38775619399296002,1: -0.32367407025583783,2: -0.56055783338428344,3: 0.23824247437746471,4: -0.64925233000340721,5: -0.44120245730257612,6: 0.027222094241818928,7: -0.091069018106476163,8: 0.0066066466889458386,9: -0.60477189852646174,10: 0.12878952794346843,11: -0.0077463979905486591}}) 
df3 = pd.DataFrame(data={'Delta_Thick':{0: 0.28518349971907864,1: -0.06724843620619711,2: 0.32596222283195153,3: 0.44928934543390797,4: 0.20911991461399143,5: -0.036989014816141919,6: -0.21517978702947216, 7: -0.028429332303918198,8: 0.037553921139760305,9: 0.98813506475654656,10: 0.51938760439670373,11: 0.11348101736407434,12: 0.79676269452200232,13: 0.27961307494052506,14: -0.55282685608381399,15: 0.63549900861027275,16: -0.20869225741458663,17: 0.55296943711112945,18: 0.34448294335085694,19: 0.18268186220418725,20: 0.36422880308671302}}) 

fig, (ax,ax1,ax2) = plt.subplots(ncols=3) 

bins=[round(x,1)for x in np.linspace(-1,1,21)] 
counts, division = np.histogram(df1.loc[:,'Delta_Thick'],bins=bins) 
df1.loc[:,'Delta_Thick'].hist(ax=ax, bins=division,color='green',hatch='//') 
ax.xaxis.set_ticks(np.arange(-1, 1.5, 0.5)) 
ax.yaxis.set_ticks(np.arange(0, 5, 1)) 
ax.set_title('A. 1990-2016') 
ax.set_ylabel('Number of Sites') 
ax.legend(fontsize='x-small',loc=2) 

#Deficit 
bins=[round(x,1)for x in np.linspace(-1,1,21)] 
counts, division = np.histogram(df2.loc[:,'Delta_Thick'],bins=bins) 
#ax1.hist(df2.loc[:,'Delta_Thick'],bins=counts.size) 
df2.loc[:,'Delta_Thick'].hist(ax=ax1, bins=division,color='green',hatch='//') 
ax1.xaxis.set_ticks(np.arange(-1, 1.5, 0.5)) 
ax1.yaxis.set_ticks(np.arange(0, 5, 1)) 
ax1.set_title('B. 1990-2003') 
ax1.set_xlabel('X axis label') 

#Enrich 

bins=[round(x,1)for x in np.linspace(-1,1,21)] 
counts, division = np.histogram(df3.loc[:,'Delta_Thick'],bins=bins) 
df3.loc[:,'Delta_Thick'].hist(ax=ax2, bins=division,color='green',hatch='//') 
ax2.xaxis.set_ticks(np.arange(-1, 2, 0.5)) 
ax2.yaxis.set_ticks(np.arange(0, 10, 1)) 
ax2.set_title('C. 2003-2016') 


plt.tight_layout() 
plt.show() 

Avis je changé ax2.xaxis.set_ticks(np.arange(-1, 1.5, 0.5)) à ax2.xaxis.set_ticks(np.arange(-1, 2, 0.5)).