I have a dataframe with 95 columns where are Max(), Min() and Avg() values of different measures, I want to plot their histograms on a FacetGrid of 3 columns and 32 rows where 1st column is max value, 2nd is avg value and 3rd is min value, and the rows are the measure type.
I have this code right now:
fig, axes = plt.subplots(nrows=32, ncols=3, figsize=(20, 96))
columnas_numeric = df_agg_new.select_dtypes(include=['float64', 'int64']).columns
columnas_numeric = columnas_numeric.drop('season')
for i, colum in enumerate(columns):
if colum.endswith('Max'):
sns.histplot(
data = df_agg_new,
x = colum,
stat = "count",
kde = True,
line_kws = {'linewidth': 2},
alpha = 0.3,
ax = axes[int(i/3)][0]
)
axes[int(i/3)][0].set_title(colum, fontsize = 7, fontweight = "bold")
axes[int(i/3)][0].tick_params(labelsize = 6)
axes[int(i/3)][0].set_xlabel("")
elif colum.endswith('Avg'):
sns.histplot(
data = df_agg_new,
x = colum,
stat = "count",
kde = True,
line_kws = {'linewidth': 2},
alpha = 0.3,
ax = axes[int(i/3)][1]
)
axes[int(i/3)][1].set_title(colum, fontsize = 7, fontweight = "bold")
axes[int(i/3)][1].tick_params(labelsize = 6)
axes[int(i/3)][1].set_xlabel("")
else:
sns.histplot(
data = df_agg_new,
x = colum,
stat = "count",
kde = True,
line_kws = {'linewidth': 2},
alpha = 0.3,
ax = axes[int(i/3)][2]
)
axes[int(i/3)][2].set_title(colum, fontsize = 7, fontweight = "bold")
axes[int(i/3)][2].tick_params(labelsize = 6)
axes[int(i/3)][2].set_xlabel("")
fig.tight_layout()
plt.subplots_adjust(top = 0.97)
fig.suptitle('Distribution plots', fontsize = 10, fontweight = "bold");
But don't work because some measure values go to other rows.
this is my list of columns:
Index(['Var1_phase2_Avg', 'Var1_phase2_Max',
'Var1_phase2_Min', 'Var1_phase3_Avg',
'Var1_phase3_Max', 'Var1_phase3_Min',
'Var1_phase1_Avg', 'Var1_phase1_Max',
'Var1_phase1_Min', 'Var1_phase4_Avg',
'Var1_phase4_Max', 'Var1_phase4_Min',
'Var2_phase2_Avg', 'Var2_phase2_Max',
'Var2_phase3_Avg', 'Var2_phase3_Max',
'Var2_phase1_Avg', 'Var2_phase1_Max',
'Var2_phase4_Avg', 'Var2_phase4_Max',
'Var3_phase2_Avg', 'Var3_phase2_Max',
'Var3_phase2_Min', 'Var3_phase3_Avg',
'Var3_phase3_Max', 'Var3_phase3_Min',
'Var3_phase1_Avg', 'Var3_phase1_Max',
'Var3_phase1_Min', 'Var3_phase4_Avg',
'Var3_phase4_Max', 'Var3_phase4_Min',
'Var4_phase2_Avg', 'Var4_phase2_Max',
'Var4_phase3_Avg', 'Var4_phase3_Max',
'Var4_phase1_Avg', 'Var4_phase1_Max',
'Var4_phase4_Avg', 'Var4_phase4_Max',
'Var5_phase2_Avg', 'Var5_phase2_Max',
'Var5_phase2_Min', 'Var5_phase3_Avg',
'Var5_phase3_Max', 'Var5_phase3_Min',
'Var5_phase1_Avg', 'Var5_phase1_Max',
'Var5_phase1_Min', 'Var5_phase4_Avg',
'Var5_phase4_Max', 'Var5_phase4_Min',
'Var6_phase2_Avg', 'Var6_phase2_Max',
'Var6_phase2_Min', 'Var6_phase3_Avg',
'Var6_phase3_Max', 'Var6_phase1_Avg',
'Var6_phase1_Max', 'Var7_phase2_Avg',
'Var7_phase2_Max', 'Var7_phase2_Min',
'Var7_phase3_Avg', 'Var7_phase3_Max',
'Var7_phase3_Min', 'Var7_phase1_Avg',
'Var7_phase1_Max', 'Var7_phase1_Min',
'Var7_phase4_Avg', 'Var7_phase4_Max',
'Var7_phase4_Min', 'Var8_phase2_Avg',
'Var8_phase2_Max', 'Var8_phase2_Min',
'Var8_phase3_Avg', 'Var8_phase3_Max',
'Var8_phase3_Min', 'Var8_phase1_Avg',
'Var8_phase1_Max', 'Var8_phase1_Min',
'Var8_phase4_Avg', 'Var8_phase4_Max',
'Var8_phase4_Min'],
dtype='object')
And this is how i want to get represented (with their histplot):
'Var1_phase2_Avg', 'Var1_phase2_Max', 'Var1_phase2_Min',
'Var1_phase3_Avg', 'Var1_phase3_Max', 'Var1_phase3_Min',
'Var1_phase1_Avg', 'Var1_phase1_Max', 'Var1_phase1_Min',
'Var1_phase4_Avg', 'Var1_phase4_Max', 'Var1_phase4_Min',
'Var2_phase2_Avg', 'Var2_phase2_Max',
'Var2_phase3_Avg', 'Var2_phase3_Max',
'Var2_phase1_Avg', 'Var2_phase1_Max',
'Var2_phase4_Avg', 'Var2_phase4_Max',
'Var3_phase2_Avg', 'Var3_phase2_Max', 'Var3_phase2_Min',
'Var3_phase3_Avg', 'Var3_phase3_Max', 'Var3_phase3_Min',
'Var3_phase1_Avg', 'Var3_phase1_Max', 'Var3_phase1_Min',
'Var3_phase4_Avg', 'Var3_phase4_Max', 'Var3_phase4_Min',
'Var4_phase2_Avg', 'Var4_phase2_Max',
'Var4_phase3_Avg', 'Var4_phase3_Max',
'Var4_phase1_Avg', 'Var4_phase1_Max',
'Var4_phase4_Avg', 'Var4_phase4_Max',
'Var5_phase2_Avg', 'Var5_phase2_Max', 'Var5_phase2_Min',
'Var5_phase3_Avg', 'Var5_phase3_Max', 'Var5_phase3_Min',
'Var5_phase1_Avg', 'Var5_phase1_Max', 'Var5_phase1_Min',
'Var5_phase4_Avg', 'Var5_phase4_Max', 'Var5_phase4_Min',
'Var6_phase2_Avg', 'Var6_phase2_Max', 'Var6_phase2_Min',
'Var6_phase3_Avg', 'Var6_phase3_Max',
'Var6_phase1_Avg', 'Var6_phase1_Max',
'Var7_phase2_Avg', 'Var7_phase2_Max', 'Var7_phase2_Min',
'Var7_phase3_Avg', 'Var7_phase3_Max', 'Var7_phase3_Min',
'Var7_phase1_Avg', 'Var7_phase1_Max', 'Var7_phase1_Min',
'Var7_phase4_Avg', 'Var7_phase4_Max', 'Var7_phase4_Min',
'Var8_phase2_Avg', 'Var8_phase2_Max', 'Var8_phase2_Min',
'Var8_phase3_Avg', 'Var8_phase3_Max', 'Var8_phase3_Min',
'Var8_phase1_Avg', 'Var8_phase1_Max', 'Var8_phase1_Min',
'Var8_phase4_Avg', 'Var8_phase4_Max', 'Var8_phase4_Min'
Each column is for Avg, Max and Min values, and each row if for each phase of the day and later for each different measure.
Your use of axes[int(i/3)][0] supposes that the list of columns comes in nice groups of 3, which is not the case.
Given how your columns are named, you could:
'Var1_phase2' for 'Var1_phase2_Avg')nrows=len(filas)Optionally, you could share the x and/or y axes of the plots, which makes them easier to compare. These are parameters in plt.subplots(..., sharex=True, sharey=True. This would also skip the repeated labeling of the axes, which saves some space (but could be unwanted if you have many rows; if so you can enable them again via axes[i, j].tick_params(..., labelbottom=True)).
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
columnas_numeric = df_agg_new.select_dtypes(include=['float64', 'int64']).columns
columnas_numeric = columnas_numeric.drop('season')
# columnas_numeric = ['Var1_phase2_Avg', 'Var1_phase2_Max', 'Var1_phase2_Min', 'Var1_phase3_Avg', 'Var1_phase3_Max', 'Var1_phase3_Min', 'Var1_phase1_Avg', 'Var1_phase1_Max', 'Var1_phase1_Min', 'Var1_phase4_Avg', 'Var1_phase4_Max', 'Var1_phase4_Min', 'Var2_phase2_Avg', 'Var2_phase2_Max', 'Var2_phase3_Avg', 'Var2_phase3_Max', 'Var2_phase1_Avg', 'Var2_phase1_Max', 'Var2_phase4_Avg', 'Var2_phase4_Max', 'Var3_phase2_Avg', 'Var3_phase2_Max', 'Var3_phase2_Min', 'Var3_phase3_Avg', 'Var3_phase3_Max', 'Var3_phase3_Min', 'Var3_phase1_Avg', 'Var3_phase1_Max', 'Var3_phase1_Min', 'Var3_phase4_Avg', 'Var3_phase4_Max', 'Var3_phase4_Min', 'Var4_phase2_Avg', 'Var4_phase2_Max', 'Var4_phase3_Avg', 'Var4_phase3_Max', 'Var4_phase1_Avg', 'Var4_phase1_Max', 'Var4_phase4_Avg', 'Var4_phase4_Max', 'Var5_phase2_Avg', 'Var5_phase2_Max', 'Var5_phase2_Min', 'Var5_phase3_Avg', 'Var5_phase3_Max', 'Var5_phase3_Min', 'Var5_phase1_Avg', 'Var5_phase1_Max', 'Var5_phase1_Min', 'Var5_phase4_Avg', 'Var5_phase4_Max', 'Var5_phase4_Min', 'Var6_phase2_Avg', 'Var6_phase2_Max', 'Var6_phase2_Min', 'Var6_phase3_Avg', 'Var6_phase3_Max', 'Var6_phase1_Avg', 'Var6_phase1_Max', 'Var7_phase2_Avg', 'Var7_phase2_Max', 'Var7_phase2_Min', 'Var7_phase3_Avg', 'Var7_phase3_Max', 'Var7_phase3_Min', 'Var7_phase1_Avg', 'Var7_phase1_Max', 'Var7_phase1_Min', 'Var7_phase4_Avg', 'Var7_phase4_Max', 'Var7_phase4_Min', 'Var8_phase2_Avg', 'Var8_phase2_Max', 'Var8_phase2_Min', 'Var8_phase3_Avg', 'Var8_phase3_Max', 'Var8_phase3_Min', 'Var8_phase1_Avg', 'Var8_phase1_Max', 'Var8_phase1_Min', 'Var8_phase4_Avg', 'Var8_phase4_Max', 'Var8_phase4_Min']
filas = [colum[:-4] for colum in columnas_numeric if colum.endswith('_Avg')]
filas = sorted(filas) # optionally sort the list for the rows
fig, axes = plt.subplots(nrows=len(filas), ncols=3, figsize=(20, 96))
for ax_row, fila in zip(axes, filas):
for ax, suffix in zip(ax_row, ['Max', 'Avg', 'Min']):
colum = fila + '_' + suffix
if not colum in columnas_numeric:
ax.remove() # remove empty subplot when there is no data
else:
sns.histplot(
data=df_agg_new,
x=colum,
stat="count",
kde=True,
line_kws={'linewidth': 2},
alpha=0.3,
ax=ax
)
ax.set_title(colum, fontsize=7, fontweight="bold")
ax.tick_params(labelsize=6, labelbottom=True)
ax.set_xlabel("")
fig.tight_layout()
plt.subplots_adjust(top=0.97)
fig.suptitle('Distribution plots', fontsize=10, fontweight="bold");
plt.show()