Develop Reference

Boxplot image combining

I am working now on plot my dataset by boxplot as in below code
plt.figure(figsize=(8,5))
fig = plt.figure()
num_list=Final_dataset.columns.values.tolist()
for i in range(len(num_list)):
column=num_list[i]
sns.boxplot(x="label", y=column, data=Final_dataset, palette='Set2')
plt.savefig('{}.png'. format(i))
plt.show()
I need to produce one image that combine all attributes figures as in this figure rather than several figures.
how Ican fix it?
thanks, a lot

Matplotlib Subplot axes sharing: Apply to every other plot?

I am trying to find a way to apply the shared axes parameters of subplot() to every other plot in a series of subplots.
I've got the following code, which uses data from RPM4, based on rows in fpD
fig, ax = plt.subplots(2*(fpD['name'].count()), sharex=True, figsize=(6,fpD['name'].count()*2),
gridspec_kw={'height_ratios':[5,1]*fpD['name'].count()})
for i, r in fpD.iterrows():
RPM4[RPM4['name'] == RPM3.iloc[i,0]].plot(x='date', y='RPM', ax=ax[(2*i)], legend=False)
RPM4[RPM4['name'] == RPM3.iloc[i,0]].plot(kind='area', color='lightgrey', x='date', y='total', ax=ax[(2*i)+1],
legend=False,)
ax[2*i].set_title('test', fontsize=12)
plt.tight_layout()
Which produces an output that is very close to what I need. It loops through the 'name' column in a table and produces two plots for each, and displays them as subplots:
As you can see, the sharex parameter works fine for me here, since I want all the plots to share the same axis.
However, what I'd really like is for all the even-numbered (bigger) plots to share the same y axis, and for the odd-numbered (small grey) plots to all share a different y axis.
Any help on accomplishing this is much appreciated, thanks!

python how to set a plot on the left of the first plot

im trying to plot 2 different plot, one at the left of the other, i try to use sublot from matplot but it is putting the seccond plot down the other i supose i use it wrong the subplot, this is my code
# Create bars
from matplotlib.pyplot import figure
bar = plt.figure(figsize=(10,5))
plt.subplot(121)
plt.barh(plottl['Nombres'] ,plottl['Probas'])
presunto= plt.figure(figsize=(10,10))
presunto = plt.subplot(122)
img=mpimg.imread((predict+names[0]+ '/'+ onlyfiles[0]))
mgplot = plt.imshow(img)
plt.show()
predictions=[]
now here is a pic. of what is happening
i was hopping you can helpme to solve this, thank you all in advance
edit: i put here the asked picture

You are creating 2 figures, instead of one with 2 subplots. remove the line presunto= plt.figure(figsize=(10,10)) and it should work.

You are creating 2 figures instead of 2 subplots, although it's better to use gridspec when you want to draw subplots with different sizes. look at this link
from matplotlib.pyplot import figure
bar = plt.figure(figsize=(10,5))
plt.subplot(121)
plt.barh(plottl['Nombres'] ,plottl['Probas'])
presunto = plt.subplot(122)
img=mpimg.imread((predict+names[0]+ '/'+ onlyfiles[0]))
mgplot = plt.imshow(img,aspect="auto")
plt.show()

How to combine 2 dataframe histograms in 1 plot?

I would like to use a code that shows all histograms in a dataframe. That will be df.hist(bins=10). However, I would like to add another histograms which shows CDF df_hist=df.hist(cumulative=True,bins=100,density=1,histtype="step")
I tried separating their matplotlib axes by using fig=plt.figure() and
plt.subplot(211). But this df.hist is actually part of pandas function, not matplotlib function. I also tried setting axes and adding ax=ax1 and ax2 options to each histogram but it didn't work.
How can I combine these histograms together?
Any help?
Histograms that I want to combine are like these. I want to show them side by side or put the second one on tip of the first one.
Sorry that I didn't care to make them look good.

It is possible to draw them together:
# toy data frame
df = pd.DataFrame(np.random.normal(0,1,(100,20)))
# draw hist
fig, axes = plt.subplots(5,4, figsize=(16,10))
df.plot(kind='hist', subplots=True, ax=axes, alpha=0.5)
# clone axes so they have different scales
ax_new = [ax.twinx() for ax in axes.flatten()]
df.plot(kind='kde', ax=ax_new, subplots=True)
plt.show()
Output:
It's also possible to draw them side-by-side. For example
fig, axes = plt.subplots(10,4, figsize=(16,10))
hist_axes = axes.flatten()[:20]
df.plot(kind='hist', subplots=True, ax=hist_axes, alpha=0.5)
kde_axes = axes.flatten()[20:]
df.plot(kind='kde', subplots=True, ax=kde_axes, alpha=0.5)
will plot hist on top of kde.

You can find more info here: Multiple histograms in Pandas (possible duplicate btw) but apparently Pandas cannot handle multiple histogram on same graphs.
It's ok because np.histogram and matplotlib.pyplot can, check the above link for a more complete answer.

Solution for overlapping histograms with df.hist with any number of subplots
You can combine two dataframe histogram figures by creating twin axes using the grid of axes returned by df.hist. Here is an example of normal histograms combined with cumulative step histograms where the size of the figure and the layout of the grid of subplots are taken care of automatically:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
# Create sample dataset stored in a pandas dataframe
rng = np.random.default_rng(seed=1) # random number generator
letters = [chr(i) for i in range(ord('A'), ord('G')+1)]
df = pd.DataFrame(rng.exponential(1, size=(100, len(letters))), columns=letters)
# Set parameters for figure dimensions and grid layout
nplots = df.columns.size
ncols = 3
nrows = int(np.ceil(nplots/ncols))
subp_w = 10/ncols # 10 is the total figure width in inches
subp_h = 0.75*subp_w
bins = 10
# Plot grid of histograms with pandas function (with a shared y-axis)
grid = df.hist(grid=False, sharey=True, figsize=(ncols*subp_w, nrows*subp_h),
layout=(nrows, ncols), bins=bins, edgecolor='white', linewidth=0.5)
# Create list of twin axes containing second y-axis: note that due to the
# layout, the grid object may contain extra unused axes that are not shown
# (here in the H and I positions). The ax parameter of df.hist only accepts
# a number of axes that corresponds to the number of numerical variables
# in df, which is why the flattened array of grid axes is sliced here.
grid_twinx = [ax.twinx() for ax in grid.flat[:nplots]]
# Plot cumulative step histograms over normal histograms: note that the grid layout is
# preserved in grid_twinx so no need to set the layout parameter a second time here.
df.hist(ax=grid_twinx, histtype='step', bins=bins, cumulative=True, density=True,
color='tab:orange', linewidth=2, grid=False)
# Adjust space between subplots after generating twin axes
plt.gcf().subplots_adjust(wspace=0.4, hspace=0.4)
plt.show()
Solution for displaying histograms of different types side-by-side with matplotlib
To my knowledge, it is not possible to show the different types of plots side-by-side with df.hist. You need to create the figure from scratch, like in this example using the same dataset as before:
# Set parameters for figure dimensions and grid layout
nvars = df.columns.size
plot_types = 2 # normal histogram and cumulative step histogram
ncols_vars = 2
nrows = int(np.ceil(nvars/ncols_vars))
subp_w = 10/(plot_types*ncols_vars) # 10 is the total figure width in inches
subp_h = 0.75*subp_w
bins = 10
# Create figure with appropriate size
fig = plt.figure(figsize=(plot_types*ncols_vars*subp_w, nrows*subp_h))
fig.subplots_adjust(wspace=0.4, hspace=0.7)
# Create subplots by adding a new axes per type of plot for each variable
# and create lists of axes of normal histograms and their y-axis limits
axs_hist = []
axs_hist_ylims = []
for idx, var in enumerate(df.columns):
axh = fig.add_subplot(nrows, plot_types*ncols_vars, idx*plot_types+1)
axh.hist(df[var], bins=bins, edgecolor='white', linewidth=0.5)
axh.set_title(f'{var} - Histogram', size=11)
axs_hist.append(axh)
axs_hist_ylims.append(axh.get_ylim())
axc = fig.add_subplot(nrows, plot_types*ncols_vars, idx*plot_types+2)
axc.hist(df[var], bins=bins, density=True, cumulative=True,
histtype='step', color='tab:orange', linewidth=2)
axc.set_title(f'{var} - Cumulative step hist.', size=11)
# Set shared y-axis for histograms
for ax in axs_hist:
ax.set_ylim(max(axs_hist_ylims))
plt.show()

Creating a matrix of plots with sns distplot

I am plotting 20+ features like so:
for col in dsd_mod["ae_analysis"].columns[:len(dsd_mod["ae_analysis"].columns)]:
if col != "sae_flag":
sns.distplot(dsd_mod["ae_analysis"].loc[(dsd_mod["ae_analysis"]['sae_flag'] == 1),col],
color='r',
kde=True,
hist=False,
label='sae_ae = 1')
sns.distplot(dsd_mod["ae_analysis"].loc[(dsd_mod["ae_analysis"]['sae_flag'] == 0),col],
color='y',
kde=True,
hist=False,
label='sae_ae = 0')
Which creates a separate graph for each feature. How can I put these all on a matrix? Or like how pair plots outputs?
Right now I get 30 graphs like this all in one column:
How can I modify this so that I can get 6 rows and 5 columns ?
Thanks in advance!

displot can use whatever axes object you want to draw the plot. So you just need to create your axes with the desired geometry, and pass the relevant axes to your functions.
fig, axs = plt.subplots(6,5)
# axs is a 2D array with shape (6,5)
# you can keep track of counters in your for-loop to place the resulting graphs
# using ax=axs[i,j]
# or an alternative is to use a generator that you can use to get the next axes
# instance at every step of the loop
ax_iter = iter(axs.flat)
for _ in range(30):
ax = next(ax_iter)
sns.distplot(np.random.normal(loc=0, size=(1000,)), ax=ax)
sns.distplot(np.random.normal(loc=1, size=(1000,)), ax=ax)