Develop Reference

Legend in subplots() for vertical lines Matplotlib.pyplot Python

I am trying to do EDA with the Kaggle dataset link
I made a plot with 3 subplots and have plotted 3 vertical lines on the basis of mean, median and mode. is there any way to show these 3 lines in a legend?
This is my code
def plott(data):
fig, axes = plt.subplots(3, sharex=True, figsize=(15, 15),gridspec_kw={"height_ratios": (1, 0.2, 0.6)})
fig.suptitle('Spread of Data for ' + data.name, fontsize=20, fontweight='bold')
sns.histplot(data, kde=True, binwidth=1, ax=axes[0])
sns.boxplot(x=data, orient='h', ax=axes[1])
sns.violinplot(x=data, ax=axes[2])
axes[0].set_xlabel('')
axes[1].set_xlabel('')
axes[2].set_xlabel('')
axes[0].axvline(data.mean(), color='r', linewidth=2, linestyle='solid')
axes[0].axvline(data.median(), color='r', linewidth=2, linestyle='dashed')
axes[0].axvline(data.mode()[0], color='r', linewidth=2, linestyle='dotted')
axes[1].axvline(data.mean(), color='r', linewidth=2, linestyle='solid')
axes[1].axvline(data.median(), color='r', linewidth=2, linestyle='dashed')
axes[1].axvline(data.mode()[0], color='r', linewidth=2, linestyle='dotted')
axes[2].axvline(data.mean(), color='r', linewidth=2, linestyle='solid')
axes[2].axvline(data.median(), color='r', linewidth=2, linestyle='dashed')
axes[2].axvline(data.mode()[0], color='r', linewidth=2, linestyle='dotted')
axes[0].tick_params(axis='both', which='both', labelsize=10, labelbottom=True)
axes[1].tick_params(axis='both', which='both', labelsize=10, labelbottom=True)
axes[2].tick_params(axis='both', which='both', labelsize=10, labelbottom=True)
plott(df['Age'])
This is the resulting plot
Is there a way to add the legend in here in accordance to the 3 vertical lines
like this with each line type denoting the value?
Also, how to add more values in x axis of all three graphs?
like make it interval of 5 or 2 years apart?
Thanks

Give the axvlines a "label" value, then call plt.legend after plotting it.
Example:
import matplotlib.pyplot as plt
plt.plot([1,2,3],[1,2,3],label="Test")
plt.axvline(x=0.22058956, label="Test2", color="red")
plt.legend()
Output:

X-axis minor gridlines still not showing even after trying all solutions

My x-axis minor gridlines are not showing, this is my code
ax = plt.gca()
ax.minorticks_on()
plt.semilogx(data_x1,data_y1,"red")
plt.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
plt.xlabel("frequency(Hz)")
plt.ylabel("Iramp(dB)")
plt.show()
enter image description here

Either I'm not sure of what you want, or your code is actually working correctly. The minor grid lines are those between the powers of 10. I made a little example to show a comparison of your plot with the minor grid lines on and off.
import numpy as np
import matplotlib.pyplot as plt
data_x1 = np.linspace(0,2,10)
data_x2 = np.linspace(0,4,10)
data_y1 = np.random.rand(10)
data_y2 = np.random.rand(10)
fig, axall =plt.subplots(1,2, figsize=(10,5))
# your code with some changes
ax = axall[0]
ax.minorticks_on()
ax.semilogx(data_x1,data_y1,"red")
ax.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
ax.set_xlabel("frequency(Hz)")
ax.set_ylabel("Iramp(dB)")
# code to make the plot on the right.
ax = axall[1]
ax.minorticks_on()
ax.semilogx(data_x1,data_y1,"red")
ax.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
# ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
ax.set_xlabel("frequency(Hz)")
ax.set_ylabel("Iramp(dB)")
plt.show()
Note how I commented out your minor grid lines.

Matplotlib can't plot log grid if use gca() parameters

I'm trying to customize my plot, everything works fine except plotting the log grid. Any idea how to fix the issue?
Fig_Z21 = plt.figure()
plt.loglog(Z21['Frequency']/10e5, Z21['Z21_Mag'], label='Z21')
plt.gca().set_axis_bgcolor('white')
plt.grid(True, which='majorminor', color='k', linestyle='-', linewidth=0.4)
plt.gca().spines['bottom'].set_color('black')
plt.gca().spines['top'].set_color('black')
plt.gca().spines['left'].set_color('black')
plt.gca().spines['right'].set_color('black')
plt.gca().yaxis.label.set_color('black')
plt.gca().xaxis.label.set_color('black')
plt.gca().tick_params(axis='x', colors='black')
plt.gca().tick_params(axis='y', colors='black')
plt.rcParams['text.usetex'] = False #Let TeX do the typsetting
plt.xlabel('Frequency [MHz]', fontsize=12)
plt.ylabel('Magnitude', fontsize=12)
plt.show()

Try to use both as value of which in plt.grid:
plt.grid(True, which='both', color='k', linestyle='-', linewidth=0.4)

figure/subplot confusion with regard to x/y limits

I am trying to set the x and y limits on a subplot but am having difficultly. I suspect that the difficultly stems from my fundamental lack of understanding of how figures and subplots work. I have read these two questions:
question 1
question 2
I tried to use that approach, but neither had any effect on the x and y limits. Here's my code:
fig = plt.figure(figsize=(9,6))
ax = plt.subplot(111)
ax.hist(sub_dict['b'], bins=30, color='r', alpha=0.3)
ax.set_ylim=([0,200])
ax.set_xlim=([0,100])
plt.xlabel('x')
plt.ylabel('y')
plt.title('title')
plt.show()
I am confused as whether to apply commands to fig or ax? For instance .xlabel and .title don't seem to be available for ax. Thanks

Why don't you do:
Ax = fig.add_subplot(111)
import matplotlib.pyplot as plt
import numpy as np
mu, sigma = 100, 15
x = mu + sigma*np.random.randn(100)
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)
ax.hist(x, bins=30, color='r', alpha=0.3)
ax.set_ylim=(0, 200)
ax.set_xlim=(0, 100)
plt.xlabel('x')
plt.ylabel('y')
plt.title('title')
plt.show()
I've run your code on some sample code, and I'm attaching the screenshot. I'm not sure this is the desired result but this is what I got.

For a multiplot, where you have subplots in a single figure, you can have several xlabel and one title
fig.title("foobar")
ax.set_xlabel("x")
This is explained in great detail here on the Matplotlib website.
You in your case, use a subplot for just a single plot. This is possible, just doesn't make a lot of sense. Plots like the one below are supposed to be created with the subplot feature:
To answer your question: you can set the x- and y-limits on a per-subplot and per-axis basis by simply addressing the respective subplot directly (ax for subplot 1) and them calling the set_xlabel member function to set the label on the x-axis.
EDIT
For your updated question:
Use this code as inspiration, I had to generate some data on my own so no guarantees:
import matplotlib.pyplot as plt
plt.hist(sub_dict['b'], bins=30, color='r', alpha=0.3)
plt.ylim(0,200)
plt.xlim(0,100)
plt.xlabel('x')
plt.ylabel('y')
plt.title('title')
plt.show()

Bit more googling and I got the following that has worked:
sub_dict = subset(data_dict, 'b', 'a', greater_than, 10)
fig = plt.figure(figsize=(9,6))
ax = fig.add_subplot(111)
ax.hist(sub_dict['b'], bins=30, color='r', alpha=0.3)
plt.ylim(0,250)
plt.xlim(0,100)
plt.xlabel('x')
plt.ylabel('y')
plt.title('title')
plt.show()

Why I get additional empty plot in matplotlib?

I have the following code in my IPython notebook:
import matplotlib.pyplot as plt
plt.setp(plt.xticks()[1], rotation=45)
plt.figure(figsize=(17, 10)) # <--- This is the problematic line!!!!!!!!!!!!!
plt.plot_date(df['date'],df['x'], color='black', linestyle='-')
plt.plot_date(df['date'],df['y'], color='red', linestyle='-')
plt.plot_date(df['date'],df['z'], color='green', linestyle='-')
In the above example df is pandas data frame.
Without the marked line (containig figsize) the plot is too small. With the mentioned line I have an increased image as I want but before it I have an additional empty plot.
Does anybody know why it happens an how this problem can be resolved?

Try reversing the first two lines after the import. plt.setp is opening a figure.

here's how I would do this:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(17, 10))
plt.setp(plt.xticks()[1], rotation=45)
ax.plot_date(df['date'],df['x'], color='black', linestyle='-')
ax.plot_date(df['date'],df['y'], color='red', linestyle='-')
ax.plot_date(df['date'],df['z'], color='green', linestyle='-')
It's a good practice to explicitly create and operate on your your Figure and Axes objects.