Let's say I want to plot four sets of axes on the same plot that all share from each other as follows:
ax2 shares a y-axis with ax1. Therefore, I do ax2 = ax1.twiny()
ax3 shares a x-axis with ax1. So, I do ax3 = ax1.twinx()
Now, ax4 needs to share its x-axis with ax2, but its y-axis with ax3. I know that if it shared both axes with, say, ax2 I could do ax4 = ax2.twinx().twiny(), but what do I do where I need it to share each with a different axis?
Related
I am producing a plot and I want to make the secondary y-axes 10 times smaller than the main y-axes.
ax2 = ax.twinx()
sns.lineplot(data=df["A"], ax=ax)
sns.lineplot(data=df["B"], ax=ax2)
is it possible to do define ax2 = ax.twinx()/10? how can specifcy that ax2 should be 10 times lower than ax? for example if ax goes from 0 to 100, ax2 should go from 0 to 1.
IIUC, use get_ylim and set_ylim to re-adjust the limit of ax2 :
ax2 = ax.twinx()
sns.lineplot(data=df["A"], ax=ax)
sns.lineplot(data=df["B"], ax=ax2)
ax2.set_ylim(0, ax.get_ylim()[1]/100) # <- add this line
NB : If you need a view limit from 0 to 1, you need to divide by 100 and not 10.
Output :
I'm new to python and attempting to chart some time series data. I'm using pyplot to create 3 stacked line charts which have the same x-axis (dates), but a different scale for the y-axes. However, each y-axis, as well as the x-axis for the bottom chart, have overlapping labels. There are labels generated from 0 to 1, as well as axis labels from my data set. How do I turn 'off' the auto-generated 0 to 1 labels on the y-axes and the bottom x-axis?
fig, ax = plt.subplots(3,1,sharex='all', squeeze=False, figsize=(12,8))
ax = fig.add_subplot(3,1,1)
plt.plot(df1['date'], df1['value'])
ax2 = fig.add_subplot(3,1,2)
plt.plot(df2['date'], df2['value'])
ax3 = fig.add_subplot(3,1,3)
plt.plot(df3['date'], df3['value'])
plt.show()
You can see the issue in the below picture. Any help is greatly appreciated!
You have already created subplots with all the axes in the initial assignment
fig, ax = plt.subplots(3,1,sharex='all', squeeze=False, figsize=(12,8))
therefore the following assignements of
ax = fig.add_subplot(3,1,1)
ax2 = fig.add_subplot(3,1,2)
ax3 = fig.add_subplot(3,1,3)
are not only unnecessary, but they seem to overlap the already created subplots (if you change it to add_subplot(2,1,1) you will notice it just starts dividing figure again and overlaying axes on top of each other).
What you want to do, is access the axes created in plt.subplots() call:
fig, ax = plt.subplots(3,1,sharex='all', squeeze=False, figsize=(12,8))
ax[0].plot(df1['date'], df1['value'])
ax[1].plot(df2['date'], df2['value'])
ax[2].plot(df3['date'], df3['value'])
plt.show()
Simulated Output:
Data from seaborn tips dataset
I have two plots that I generated from my data:
Here the second plot shows the distribution of results from the first one.
What I want is to plot them side-by-side so you could see both the data and the distribution on the same plot. And I want plots to share y-axis as well.
I tried to do the following:
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(40, 15), sharey=True)
ax1 = sns.lineplot(plotting_df.index, plotting_df.error, color=('#e65400'), lw=2, label='random forest residual error')
ax1 = sns.lineplot(plotting_df.index, plotting_df.val, color=('#9b9b9b'), lw=1, label='current model residual error')
ax1 = sns.lineplot(plotting_df.index, 0, color=('#2293e3'), lw=1)
ax1.xaxis.set_visible(False)
ax1.set_ylabel('Residual Fe bias', fontsize=16)
ax1.set_title('Models residual error comparison', fontsize=20, fontweight='bold')
sns.despine(ax=ax1, top=True, bottom=True, right=True)
ax2 = sns.distplot(results_df.error, hist=True, color=('#e65400'), bins=81,
label='Random forest model', vertical=True)
ax2 = sns.distplot(plotting_df.val, hist=True, color=('#9b9b9b'),
bins=81, label='Rolling averages model', vertical=True)
ax2.set_title('Error distribution comparison between models', fontsize=20, fontweight='bold')
sns.despine(ax=ax2, top=True, right=True)
fig.savefig("blabla.png", format='png')
But when I do run it I get strange results - the first chart is in the second column, whereas I wanted it on the left and the second chart is completely blank. Not sure what I did wrong here.
Both lineplot and distplot accept a matplotlib axes object as an argument, which tells it which axes to plot onto. If no axes is passed into it, then the plot is placed onto the current axes.
You create a figure and 2 axes using :
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(40, 15), sharey=True)
Therefore, ax2 will be the current axes. So your distplot is being plotted on top of your lineplot, both in ax2.
You need to pass the axes into the seaborn plotting functions.
sns.lineplot(..., ax=ax1)
sns.distplot(..., ax=ax2)
I am trying to get the values of xticks from one plot and then use these values for another plot but set the new ticks as 10 to the power of the other plot's ticks. The following lines doesn't do the job I am aiming for
labels=[item for item in ax1.get_xticklabels()]
ax2.set_xticklabels(['$10^{{{:d}}}$'.format(int(i)) for i in labels])
I will appreciate for any suggestion.
What about sharing axes ? This will fix the same limits and number of ticks for ax1 and ax2 :
fig, ax = plt.subplots(1, 2, sharex=True)
ax1 = ax[0]
ax2 = ax[1]
Then your code will do the trick since you are sure that both subplots have the same xticks
labels = [item for item in ax2.get_xticklabels()]
ax2.set_xticklabels(['$10^{{{:d}}}$'.format(int(i)) for i in labels])
I am plotting 4 subplots (i.e 2 rows 2 columns) in this way:
fig1= plt.figure(figsize=(8,6))
ax1 = fig1.add_subplot(221)
ax1.errorbar((r1),(xi1),fmt='',yerr=(low_err_1,upp_err_1),ls='none',color='black')
ax1.scatter((r1),(xi1),c='red',marker="o",s=30,label= r'$\xi(r)$ $0.0<z<0.5$')
ax1.plot((r1),(curve_y_1),'--',label='fit $0.0<z<0.5$')
ax1.set_xscale('log')
ax1.set_yscale('log')
ax2 = fig1.add_subplot(222)
ax2.errorbar((r2),(xi2),fmt='',yerr=(low_err_2,upp_err_2),ls='none',color='black')
ax2.scatter((r2),(xi2),c='blue',marker="o",s=30,label=r'$\xi(r)$ $0.5<z<1.0$')
ax2.plot((r2),(curve_y_2),'--',label='fit $0.5<z<1.0$')
ax2.set_xscale('log')
ax2.set_yscale('log')
ax3 = fig1.add_subplot(223)
ax3.errorbar((r3),(xi3),fmt='',yerr=(low_err_3,upp_err_3),ls='none',color='black')
ax3.scatter((r3),(xi3),c='yellow',marker="o",s=30,label=r'$\xi(r)$ $1.0<z<1.5$')
ax3.plot((r3),(curve_y_3),'--',label='fit $1.0<z<1.5$')
ax3.set_xscale('log')
ax3.set_yscale('log')
ax4 = fig1.add_subplot(224)
ax4.errorbar((r4),(xi4),fmt='',yerr=(low_err_4,upp_err_4),ls='none',color='black')
ax4.scatter((r4),(xi4),c='black',marker="o",s=30,label=r'$\xi(r)$ $1.5<z<2.0$')
ax4.plot((r4),(curve_y_4),'--',label='fit $1.5<z<2.0$')
ax4.set_xscale('log')
ax4.set_yscale('log')
My questions are:
Is there a way to add legends to all these subplots using a single (common) command, instead of typing ax1.legend(loc = 'best'), ax2.legend(loc = 'best') and so on separately for each subplot?
I would like to set log-scaling for each subplot using a single (common) command. As you can see, now I am setting the axis-scales separately to log for each subplot.
Just define a axes formatting function:
def style_ax(ax):
ax.legend(loc='best')
ax.set_yscale('log')
ax.set_xscale('log')
And than call it when finished:
for ax in [ax1, ax2, ax3, ax4]:
style_ax(ax)