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Python Geopandas: Single Legend for multiple plots

How do I use a single legend for multiple geopandas plots?
Right now I have a Figure like this:
This post explains how to set legend values to the same for each plot. Though, i would like to have single legend for all plots. Optimally it should be possible to have multiple legends for different df's that I want to plot. E.g. the lines you see in the pictures also have a description.
Here is my current code:
years = [2005, 2009, 2013]
# initialize figure
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 10), dpi=300, constrained_layout=True)
for i, year in enumerate(years):
# subset lines
lines_plot = lines[lines['year'] == year]
# subset controls plot
controls_plot = controls[controls['year'] == year]
# draw subfig
controls_plot.plot(column='pop_dens', ax=ax[i], legend=True, legend_kwds={'orientation': "horizontal"})
lines_plot.plot(ax=ax[i], color='red', lw=2, zorder=2)

Regarding the first of your questions 'How do I use a single legend for multiple geopandas plots?' you could make sure your plots all use the same colors (using the vmin and vmax args of the .plot() function) and then add a single colorbar to the figure like shown below. for the red lines you can just add another legend (the first thing is technically a colorbar not a legend).
import geopandas as gpd
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as mcolors
from matplotlib.lines import Line2D
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
f, ax = plt.subplots(nrows=1, ncols=3, figsize=(9, 4))
# define min and max values and colormap for the plots
value_min = 0
value_max = 1e7
cmap = 'viridis'
world.plot(ax=ax[0], column='pop_est', vmin=value_min, vmax=value_max, cmap=cmap)
world.plot(ax=ax[1], column='pop_est', vmin=value_min, vmax=value_max, cmap=cmap)
world.plot(ax=ax[2], column='pop_est', vmin=value_min, vmax=value_max, cmap=cmap)
# define a mappable based on which the colorbar will be drawn
mappable = cm.ScalarMappable(
norm=mcolors.Normalize(value_min, value_max),
cmap=cmap
)
# define position and extent of colorbar
cb_ax = f.add_axes([0.1, 0.1, 0.8, 0.05])
# draw colorbar
cbar = f.colorbar(mappable, cax=cb_ax, orientation='horizontal')
# add handles for the legend
custom_lines = [
Line2D([0], [0], color='r'),
Line2D([0], [0], color='b'),
]
# define labels for the legend
custom_labels = ['red line', 'blue line']
# plot legend, loc defines the location
plt.legend(
handles=custom_lines,
labels=custom_labels,
loc=(.4, 1.5),
title='2nd legend',
ncol=2
)
plt.tight_layout()
plt.show()

Twinx makes labels disappear

I have an issue with setting the x labels while using twinx function. My original data is a pandas dataframe, namely, df, which has 3 attributes, "name"=product name, "sold"=number of items sold, and "revenue". the name is a pandas series (like "2 shampoo"), but I can't set it to be x tick label (see pic below). How could I set the x labels to display the product's names?
fig = plt.figure() # Create matplotlib figure
ax = fig.add_subplot(111) # Create matplotlib axes
ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.
width = 0.4
df.sold.plot(kind='bar', color='red', ax=ax, width=width, position=1, rot=90)
df.revenue.plot(kind='bar', color='blue', ax=ax2, width=width, position=0, rot=90)
# print(type(df['name']), "\n", df['name'])
ax.set_ylabel('Sold')
ax2.set_ylabel('Revenue')
ax.legend(['Sold'], loc='upper left')
ax2.legend(['Revenue'], loc='upper right')
plt.show()

You will need to set the labels for X-axis using the set_xticklabels() to show the fields. Add this line after plotting the graph.
ax.set_xticklabels(df.Name)
and you will get the below plot.

Matplotlib How to change x and y ticks background color? [duplicate]

I'd like to Change the color of the axis, as well as ticks and value-labels for a plot I did using matplotlib and PyQt.
Any ideas?

As a quick example (using a slightly cleaner method than the potentially duplicate question):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(10))
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('red')
ax.xaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')
plt.show()
Alternatively
[t.set_color('red') for t in ax.xaxis.get_ticklines()]
[t.set_color('red') for t in ax.xaxis.get_ticklabels()]

If you have several figures or subplots that you want to modify, it can be helpful to use the matplotlib context manager to change the color, instead of changing each one individually. The context manager allows you to temporarily change the rc parameters only for the immediately following indented code, but does not affect the global rc parameters.
This snippet yields two figures, the first one with modified colors for the axis, ticks and ticklabels, and the second one with the default rc parameters.
import matplotlib.pyplot as plt
with plt.rc_context({'axes.edgecolor':'orange', 'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'}):
# Temporary rc parameters in effect
fig, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(range(10))
ax2.plot(range(10))
# Back to default rc parameters
fig, ax = plt.subplots()
ax.plot(range(10))
You can type plt.rcParams to view all available rc parameters, and use list comprehension to search for keywords:
# Search for all parameters containing the word 'color'
[(param, value) for param, value in plt.rcParams.items() if 'color' in param]

For those using pandas.DataFrame.plot(), matplotlib.axes.Axes is returned when creating a plot from a dataframe. Therefore, the dataframe plot can be assigned to a variable, ax, which enables the usage of the associated formatting methods.
The default plotting backend for pandas, is matplotlib.
See matplotlib.spines
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
import pandas as pd
# test dataframe
data = {'a': range(20), 'date': pd.bdate_range('2021-01-09', freq='D', periods=20)}
df = pd.DataFrame(data)
# plot the dataframe and assign the returned axes
ax = df.plot(x='date', color='green', ylabel='values', xlabel='date', figsize=(8, 6))
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes
seaborn axes-level plot
import seaborn as sns
# plot the dataframe and assign the returned axes
fig, ax = plt.subplots(figsize=(12, 5))
g = sns.lineplot(data=df, x='date', y='a', color='g', label='a', ax=ax)
# set the margines to 0
ax.margins(x=0, y=0)
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes
seaborn figure-level plot
# plot the dataframe and assign the returned axes
g = sns.relplot(kind='line', data=df, x='date', y='a', color='g', aspect=2)
# iterate through each axes
for ax in g.axes.flat:
# set the margins to 0
ax.margins(x=0, y=0)
# make the top and right spines visible
ax.spines[['top', 'right']].set_visible(True)
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes

motivated by previous contributors, this is an example of three axes.
import matplotlib.pyplot as plt
x_values1=[1,2,3,4,5]
y_values1=[1,2,2,4,1]
x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]
x_values3=[150,200,250,300,350]
y_values3=[-10,-20,-30,-40,-50]
fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)
ax3=fig.add_subplot(111, label="3", frame_on=False)
ax.plot(x_values1, y_values1, color="C0")
ax.set_xlabel("x label 1", color="C0")
ax.set_ylabel("y label 1", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")
ax2.scatter(x_values2, y_values2, color="C1")
ax2.set_xlabel('x label 2', color="C1")
ax2.xaxis.set_label_position('bottom') # set the position of the second x-axis to bottom
ax2.spines['bottom'].set_position(('outward', 36))
ax2.tick_params(axis='x', colors="C1")
ax2.set_ylabel('y label 2', color="C1")
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
ax2.tick_params(axis='y', colors="C1")
ax3.plot(x_values3, y_values3, color="C2")
ax3.set_xlabel('x label 3', color='C2')
ax3.xaxis.set_label_position('bottom')
ax3.spines['bottom'].set_position(('outward', 72))
ax3.tick_params(axis='x', colors='C2')
ax3.set_ylabel('y label 3', color='C2')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right')
ax3.spines['right'].set_position(('outward', 36))
ax3.tick_params(axis='y', colors='C2')
plt.show()

You can also use this to draw multiple plots in same figure and style them using same color palette.
An example is given below
fig = plt.figure()
# Plot ROC curves
plotfigure(lambda: plt.plot(fpr1, tpr1, linestyle='--',color='orange', label='Logistic Regression'), fig)
plotfigure(lambda: plt.plot(fpr2, tpr2, linestyle='--',color='green', label='KNN'), fig)
plotfigure(lambda: plt.plot(p_fpr, p_tpr, linestyle='-', color='blue'), fig)
# Title
plt.title('ROC curve')
# X label
plt.xlabel('False Positive Rate')
# Y label
plt.ylabel('True Positive rate')
plt.legend(loc='best',labelcolor='white')
plt.savefig('ROC',dpi=300)
plt.show();
Output:

Here is a utility function that takes a plotting function with necessary args and plots the figure with required background-color styles. You can add more arguments as necessary.
def plotfigure(plot_fn, fig, background_col = 'xkcd:black', face_col = (0.06,0.06,0.06)):
"""
Plot Figure using plt plot functions.
Customize different background and face-colors of the plot.
Parameters:
plot_fn (func): The plot functions with necessary arguments as a lamdda function.
fig : The Figure object by plt.figure()
background_col: The background color of the plot. Supports matlplotlib colors
face_col: The face color of the plot. Supports matlplotlib colors
Returns:
void
"""
fig.patch.set_facecolor(background_col)
plot_fn()
ax = plt.gca()
ax.set_facecolor(face_col)
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.grid(alpha=0.1)
ax.title.set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
A use case is defined below
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=50, n_classes=2, n_features=5, random_state=27)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=27)
fig=plt.figure()
plotfigure(lambda: plt.scatter(range(0,len(y)), y, marker=".",c="orange"), fig)

Matplotlib legend does not match seaborn bubble plot sizes

I have created a bubble plot using seaborn, and used matplotlib to draw the legend to the right of my seaborn plots. I specified the sizing of the bubbles in my seaborn code using sizes=(1,900) but the scaling on my matplotlib legend does not reflect what the plots show. The legend reads from 0 to 45 but the actual data in my plots range from 0 to 900
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(11,4))
sns.scatterplot(y="Min", x="Max",
size="Count", sizes=(1,900), alpha=0.5,
color='r', data=code1, ax=ax1, legend=False)
sns.scatterplot(y="Min", x="Max", alpha=0.5,
color='b', size="Count", sizes=(1,900),
data=code2, ax=ax2, legend=False)
sns.scatterplot(y="Min", x="Max", alpha=0.5,
color='g', size="Count", sizes=(1,900),
data=code3, ax=ax3)
ax3.legend(loc='upper right', bbox_to_anchor=(1.7,1), labelspacing=2,
fontsize=14, frameon=False, markerscale=1)
Here is my plot

I was unable to figure out how seaborn structures the legend output for ingestion by matplotlib. I did learn that my data (code1, code2, and code3) had different min and max values which should have been specified under seaborn's sizes argument. For code1, sizes=(1,900); for code2, sizes=(1,300); for code3, sizes=(1,45). Because I was using matplotlib to draw the legend to the right of code3's plot, the scaling was specific to the rightmost plot rather than for all 3 plots. In the end, I ended up using matplotlib's legend_elements as follows:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,4))
scatter = ax1.scatter(y=code1["Min"], x=code1["Max"],
s=code1["Count"],
color='r', alpha=0.5)
ax2.scatter(y=code2["Min"], x=code2["Max"],
color='b', s=code2["Count"], alpha=0.5)
ax3.scatter(y=code3["Min"], x=code3["Max"],
color='g', s=code3["Count"], alpha=0.5)
kw = dict(prop="sizes", num=[10,100,500,900])
legend = ax3.legend(*scatter.legend_elements(**kw), title="Count", fontsize=12,
loc='upper right', bbox_to_anchor=(1.5,1), labelspacing=2,
frameon=False)

How to change the color of the axis, ticks and labels for a plot

I'd like to Change the color of the axis, as well as ticks and value-labels for a plot I did using matplotlib and PyQt.
Any ideas?

As a quick example (using a slightly cleaner method than the potentially duplicate question):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(10))
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('red')
ax.xaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')
plt.show()
Alternatively
[t.set_color('red') for t in ax.xaxis.get_ticklines()]
[t.set_color('red') for t in ax.xaxis.get_ticklabels()]

If you have several figures or subplots that you want to modify, it can be helpful to use the matplotlib context manager to change the color, instead of changing each one individually. The context manager allows you to temporarily change the rc parameters only for the immediately following indented code, but does not affect the global rc parameters.
This snippet yields two figures, the first one with modified colors for the axis, ticks and ticklabels, and the second one with the default rc parameters.
import matplotlib.pyplot as plt
with plt.rc_context({'axes.edgecolor':'orange', 'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'}):
# Temporary rc parameters in effect
fig, (ax1, ax2) = plt.subplots(1,2)
ax1.plot(range(10))
ax2.plot(range(10))
# Back to default rc parameters
fig, ax = plt.subplots()
ax.plot(range(10))
You can type plt.rcParams to view all available rc parameters, and use list comprehension to search for keywords:
# Search for all parameters containing the word 'color'
[(param, value) for param, value in plt.rcParams.items() if 'color' in param]

For those using pandas.DataFrame.plot(), matplotlib.axes.Axes is returned when creating a plot from a dataframe. Therefore, the dataframe plot can be assigned to a variable, ax, which enables the usage of the associated formatting methods.
The default plotting backend for pandas, is matplotlib.
See matplotlib.spines
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
import pandas as pd
# test dataframe
data = {'a': range(20), 'date': pd.bdate_range('2021-01-09', freq='D', periods=20)}
df = pd.DataFrame(data)
# plot the dataframe and assign the returned axes
ax = df.plot(x='date', color='green', ylabel='values', xlabel='date', figsize=(8, 6))
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes
seaborn axes-level plot
import seaborn as sns
# plot the dataframe and assign the returned axes
fig, ax = plt.subplots(figsize=(12, 5))
g = sns.lineplot(data=df, x='date', y='a', color='g', label='a', ax=ax)
# set the margines to 0
ax.margins(x=0, y=0)
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes
seaborn figure-level plot
# plot the dataframe and assign the returned axes
g = sns.relplot(kind='line', data=df, x='date', y='a', color='g', aspect=2)
# iterate through each axes
for ax in g.axes.flat:
# set the margins to 0
ax.margins(x=0, y=0)
# make the top and right spines visible
ax.spines[['top', 'right']].set_visible(True)
# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both') # 'both' refers to minor and major axes

motivated by previous contributors, this is an example of three axes.
import matplotlib.pyplot as plt
x_values1=[1,2,3,4,5]
y_values1=[1,2,2,4,1]
x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]
x_values3=[150,200,250,300,350]
y_values3=[-10,-20,-30,-40,-50]
fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)
ax3=fig.add_subplot(111, label="3", frame_on=False)
ax.plot(x_values1, y_values1, color="C0")
ax.set_xlabel("x label 1", color="C0")
ax.set_ylabel("y label 1", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")
ax2.scatter(x_values2, y_values2, color="C1")
ax2.set_xlabel('x label 2', color="C1")
ax2.xaxis.set_label_position('bottom') # set the position of the second x-axis to bottom
ax2.spines['bottom'].set_position(('outward', 36))
ax2.tick_params(axis='x', colors="C1")
ax2.set_ylabel('y label 2', color="C1")
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
ax2.tick_params(axis='y', colors="C1")
ax3.plot(x_values3, y_values3, color="C2")
ax3.set_xlabel('x label 3', color='C2')
ax3.xaxis.set_label_position('bottom')
ax3.spines['bottom'].set_position(('outward', 72))
ax3.tick_params(axis='x', colors='C2')
ax3.set_ylabel('y label 3', color='C2')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right')
ax3.spines['right'].set_position(('outward', 36))
ax3.tick_params(axis='y', colors='C2')
plt.show()

You can also use this to draw multiple plots in same figure and style them using same color palette.
An example is given below
fig = plt.figure()
# Plot ROC curves
plotfigure(lambda: plt.plot(fpr1, tpr1, linestyle='--',color='orange', label='Logistic Regression'), fig)
plotfigure(lambda: plt.plot(fpr2, tpr2, linestyle='--',color='green', label='KNN'), fig)
plotfigure(lambda: plt.plot(p_fpr, p_tpr, linestyle='-', color='blue'), fig)
# Title
plt.title('ROC curve')
# X label
plt.xlabel('False Positive Rate')
# Y label
plt.ylabel('True Positive rate')
plt.legend(loc='best',labelcolor='white')
plt.savefig('ROC',dpi=300)
plt.show();
Output:

Here is a utility function that takes a plotting function with necessary args and plots the figure with required background-color styles. You can add more arguments as necessary.
def plotfigure(plot_fn, fig, background_col = 'xkcd:black', face_col = (0.06,0.06,0.06)):
"""
Plot Figure using plt plot functions.
Customize different background and face-colors of the plot.
Parameters:
plot_fn (func): The plot functions with necessary arguments as a lamdda function.
fig : The Figure object by plt.figure()
background_col: The background color of the plot. Supports matlplotlib colors
face_col: The face color of the plot. Supports matlplotlib colors
Returns:
void
"""
fig.patch.set_facecolor(background_col)
plot_fn()
ax = plt.gca()
ax.set_facecolor(face_col)
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')
ax.xaxis.label.set_color('white')
ax.yaxis.label.set_color('white')
ax.grid(alpha=0.1)
ax.title.set_color('white')
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
A use case is defined below
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(n_samples=50, n_classes=2, n_features=5, random_state=27)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=27)
fig=plt.figure()
plotfigure(lambda: plt.scatter(range(0,len(y)), y, marker=".",c="orange"), fig)