I'm using Matplotlib and Pandas to plot x by y, grouped by z. So I have the following:
x = df['ColumnA']
y = df['ColumnB']
fig, ax = plt.subplots(figsize=(20, 10))
for key, grp in df.groupby(['ColumnC']):
plt.plot(grp['ColumnA'], grp['ColumnB'].rolling(window=30).mean(), label=key)
I also want to highlight 2 specific values from the total amount of values that will be plotted:
ax.legend(('Value1', 'Value2'))
plt.show()
This works fine. I just have the 2 values in my legend, but all values are actually plotted. What I actually want, is to be able to specify the colors for the 2 Values above. i.e. red and blue and have all the other values from Column C show on the plot as one color. The objective is to highlight how Value 1 & 2 are performing compared to everything else.
First, change the colours of the lines of interest.
lines_to_highlight = {
'Value1': 'red',
'Value2': 'blue'
}
DEFAULT_COLOR = 'gray'
legend_entries = [] # Lines to show in legend
for line in ax.lines:
if line.get_label() in lines_to_highlight:
line.set_color(lines_to_highlight[line.get_label()])
legend_entries.append(line)
else:
line.set_color(DEFAULT_COLOR)
Second, create your legend.
ax.legend(
legend_entries,
[entry.get_label() for entry in legend_entries]
)
Notes:
ax.legend(('Value1', 'Value2')) doesn't do what you expect. It simply resets the labels for the first two lines you plotted. It doesn't restrict the legend to lines you created with those labels. (The matplotlib docs themselves say that this mistake is easy to make.)
You must call ax.legend(...) after setting the line colours. Otherwise, the colours in the legend might not match the ones in the plot.
Example
import matplotlib.pyplot as plt
ax = plt.subplot(111)
ax.plot([1, 1, 1], label='one')
ax.plot([2, 2, 2], label='two')
ax.plot([3, 3, 3], label='three')
lines_to_highlight = {
'one': 'red',
'three': 'blue'
}
DEFAULT_COLOR = 'gray'
legend_entries = [] # Lines to show in legend
for line in ax.lines:
if line.get_label() in lines_to_highlight:
line.set_color(lines_to_highlight[line.get_label()])
legend_entries.append(line)
else:
line.set_color(DEFAULT_COLOR)
ax.legend(
legend_entries, [entry.get_label() for entry in legend_entries]
)
plt.show()
In addition to the solution posted in this link I would also like if I can also add the Hue Parameter, and add the Median Values in each of the plots.
The Current Code:
testPlot = sns.boxplot(x='Pclass', y='Age', hue='Sex', data=trainData)
m1 = trainData.groupby(['Pclass', 'Sex'])['Age'].median().values
mL1 = [str(np.round(s, 2)) for s in m1]
p1 = range(len(m1))
for tick, label in zip(p1, testPlot.get_xticklabels()):
print(testPlot.text(p1[tick], m1[tick] + 1, mL1[tick]))
Gives a Output Like:
I'm working on the Titanic Dataset which can be found in this link.
I'm getting the required values, but only when I do a print statement, how do I include it in my Plot?
Place your labels manually according to hue parameter and width of bars for every category in a cycle of all xticklabels:
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
trainData = pd.read_csv('titanic.csv')
testPlot = sns.boxplot(x='pclass', y='age', hue='sex', data=trainData)
m1 = trainData.groupby(['pclass', 'sex'])['age'].median().values
mL1 = [str(np.round(s, 2)) for s in m1]
ind = 0
for tick in range(len(testPlot.get_xticklabels())):
testPlot.text(tick-.2, m1[ind+1]+1, mL1[ind+1], horizontalalignment='center', color='w', weight='semibold')
testPlot.text(tick+.2, m1[ind]+1, mL1[ind], horizontalalignment='center', color='w', weight='semibold')
ind += 2
plt.show()
This answer is nearly copy & pasted from here but fit more to your example code. The linked answer is IMHO a bit missplaced there because that question is just about labeling a boxplot and not about a boxplot using the hue argument.
I couldn't use your Train dataset because it is not available as Python package. So I used Titanic instead which has nearly the same column names.
#!/usr/bin/env python3
import pandas as pd
import matplotlib
import matplotlib.patheffects as path_effects
import seaborn as sns
def add_median_labels(ax, fmt='.1f'):
"""Credits: https://stackoverflow.com/a/63295846/4865723
"""
lines = ax.get_lines()
boxes = [c for c in ax.get_children() if type(c).__name__ == 'PathPatch']
lines_per_box = int(len(lines) / len(boxes))
for median in lines[4:len(lines):lines_per_box]:
x, y = (data.mean() for data in median.get_data())
# choose value depending on horizontal or vertical plot orientation
value = x if (median.get_xdata()[1] - median.get_xdata()[0]) == 0 else y
text = ax.text(x, y, f'{value:{fmt}}', ha='center', va='center',
fontweight='bold', color='white')
# create median-colored border around white text for contrast
text.set_path_effects([
path_effects.Stroke(linewidth=3, foreground=median.get_color()),
path_effects.Normal(),
])
df = sns.load_dataset('titanic')
plot = sns.boxplot(x='pclass', y='age', hue='sex', data=df)
add_median_labels(plot)
plot.figure.show()
Als an alternative when you create your boxplot with a figure-based function. In that case you need to give the axes parameter to add_median_labels().
# imports and add_median_labels() unchanged
df = sns.load_dataset('titanic')
plot = sns.catplot(kind='box', x='pclass', y='age', hue='sex', data=df)
add_median_labels(plot.axes[0][0])
plot.figure.show()
The resulting plot
This solution also works with more then two categories in the column used for the hue argument.
The code below achieves what I want to do, but does so in a very roundabout way. I have looked around for a succinct way to produce a single legend for a figure that includes multiple subplots that takes into account their labels, to no avail. plt.figlegend() requires you to pass in labels and lines, and plt.legend() requires only handles (slightly better).
My example below illustrates what I want. I have 9 vectors, each with one of 3 categories. I want to plot each vector on a separate sub plot, label it, and plot a legend which indicates (using colour) what the label means; this is the automatic behaviour on a single plot.
Do you know of a better way of achieving the plot below?
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
nr_lines = 9
nr_cats = 3
np.random.seed(1337)
# Data
X = np.random.randn(nr_lines, 100)
labels = ['Category {}'.format(ii) for ii in range(nr_cats)]
y = np.random.choice(labels, nr_lines)
# Ideally wouldn't have to manually pick colours
clrs = matplotlib.rcParams['axes.prop_cycle'].by_key()['color']
clrs = [clrs[ii] for ii in range(nr_cats)]
lab_clr = {k: v for k, v in zip(labels, clrs)}
fig, ax = plt.subplots(3, 3)
ax = ax.flatten()
for ii in range(nr_lines):
ax[ii].plot(X[ii,:], label=y[ii], color=lab_clr[y[ii]])
lines = [a.lines[0] for a in ax]
l_labels = [l.get_label() for l in lines]
# the hack - get a single occurance of each label
idx_list = [l_labels.index(lab) for lab in labels]
lines_ = [lines[idx] for idx in idx_list]
#l_labels_ = [l_labels[idx] for idx in idx_list]
plt.legend(handles=lines_, bbox_to_anchor=[2, 2.5])
plt.tight_layout()
plt.savefig('/home/james/Downloads/stack_figlegend_example.png',
bbox_inches='tight')
You could use a dictionary to collect them using the label as a key. For example:
handles = {}
for ii in range(nr_lines):
l1, = ax[ii].plot(X[ii,:], label=y[ii], color=lab_clr[y[ii]])
if y[ii] not in handles:
handles[y[ii]] = l1
plt.legend(handles=handles.values(), bbox_to_anchor=[2, 2.5])
You only add a handle to the dictionary if the category isn't already present.
I am plotting multiple dataframes as point plot using seaborn. Also I am plotting all the dataframes on the same axis.
How would I add legend to the plot ?
My code takes each of the dataframe and plots it one after another on the same figure.
Each dataframe has same columns
date count
2017-01-01 35
2017-01-02 43
2017-01-03 12
2017-01-04 27
My code :
f, ax = plt.subplots(1, 1, figsize=figsize)
x_col='date'
y_col = 'count'
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_3,color='red')
This plots 3 lines on the same plot. However the legend is missing. The documentation does not accept label argument .
One workaround that worked was creating a new dataframe and using hue argument.
df_1['region'] = 'A'
df_2['region'] = 'B'
df_3['region'] = 'C'
df = pd.concat([df_1,df_2,df_3])
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df,hue='region')
But I would like to know if there is a way to create a legend for the code that first adds sequentially point plot to the figure and then add a legend.
Sample output :
I would suggest not to use seaborn pointplot for plotting. This makes things unnecessarily complicated.
Instead use matplotlib plot_date. This allows to set labels to the plots and have them automatically put into a legend with ax.legend().
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
date = pd.date_range("2017-03", freq="M", periods=15)
count = np.random.rand(15,4)
df1 = pd.DataFrame({"date":date, "count" : count[:,0]})
df2 = pd.DataFrame({"date":date, "count" : count[:,1]+0.7})
df3 = pd.DataFrame({"date":date, "count" : count[:,2]+2})
f, ax = plt.subplots(1, 1)
x_col='date'
y_col = 'count'
ax.plot_date(df1.date, df1["count"], color="blue", label="A", linestyle="-")
ax.plot_date(df2.date, df2["count"], color="red", label="B", linestyle="-")
ax.plot_date(df3.date, df3["count"], color="green", label="C", linestyle="-")
ax.legend()
plt.gcf().autofmt_xdate()
plt.show()
In case one is still interested in obtaining the legend for pointplots, here a way to go:
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df1,color='blue')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df2,color='green')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df3,color='red')
ax.legend(handles=ax.lines[::len(df1)+1], labels=["A","B","C"])
ax.set_xticklabels([t.get_text().split("T")[0] for t in ax.get_xticklabels()])
plt.gcf().autofmt_xdate()
plt.show()
Old question, but there's an easier way.
sns.pointplot(x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(x=x_col,y=y_col,data=df_3,color='red')
plt.legend(labels=['legendEntry1', 'legendEntry2', 'legendEntry3'])
This lets you add the plots sequentially, and not have to worry about any of the matplotlib crap besides defining the legend items.
I tried using Adam B's answer, however, it didn't work for me. Instead, I found the following workaround for adding legends to pointplots.
import matplotlib.patches as mpatches
red_patch = mpatches.Patch(color='#bb3f3f', label='Label1')
black_patch = mpatches.Patch(color='#000000', label='Label2')
In the pointplots, the color can be specified as mentioned in previous answers. Once these patches corresponding to the different plots are set up,
plt.legend(handles=[red_patch, black_patch])
And the legend ought to appear in the pointplot.
This goes a bit beyond the original question, but also builds on #PSub's response to something more general---I do know some of this is easier in Matplotlib directly, but many of the default styling options for Seaborn are quite nice, so I wanted to work out how you could have more than one legend for a point plot (or other Seaborn plot) without dropping into Matplotlib right at the start.
Here's one solution:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# We will need to access some of these matplotlib classes directly
from matplotlib.lines import Line2D # For points and lines
from matplotlib.patches import Patch # For KDE and other plots
from matplotlib.legend import Legend
from matplotlib import cm
# Initialise random number generator
rng = np.random.default_rng(seed=42)
# Generate sample of 25 numbers
n = 25
clusters = []
for c in range(0,3):
# Crude way to get different distributions
# for each cluster
p = rng.integers(low=1, high=6, size=4)
df = pd.DataFrame({
'x': rng.normal(p[0], p[1], n),
'y': rng.normal(p[2], p[3], n),
'name': f"Cluster {c+1}"
})
clusters.append(df)
# Flatten to a single data frame
clusters = pd.concat(clusters)
# Now do the same for data to feed into
# the second (scatter) plot...
n = 8
points = []
for c in range(0,2):
p = rng.integers(low=1, high=6, size=4)
df = pd.DataFrame({
'x': rng.normal(p[0], p[1], n),
'y': rng.normal(p[2], p[3], n),
'name': f"Group {c+1}"
})
points.append(df)
points = pd.concat(points)
# And create the figure
f, ax = plt.subplots(figsize=(8,8))
# The KDE-plot generates a Legend 'as usual'
k = sns.kdeplot(
data=clusters,
x='x', y='y',
hue='name',
shade=True,
thresh=0.05,
n_levels=2,
alpha=0.2,
ax=ax,
)
# Notice that we access this legend via the
# axis to turn off the frame, set the title,
# and adjust the patch alpha level so that
# it closely matches the alpha of the KDE-plot
ax.get_legend().set_frame_on(False)
ax.get_legend().set_title("Clusters")
for lh in ax.get_legend().get_patches():
lh.set_alpha(0.2)
# You would probably want to sort your data
# frame or set the hue and style order in order
# to ensure consistency for your own application
# but this works for demonstration purposes
groups = points.name.unique()
markers = ['o', 'v', 's', 'X', 'D', '<', '>']
colors = cm.get_cmap('Dark2').colors
# Generate the scatterplot: notice that Legend is
# off (otherwise this legend would overwrite the
# first one) and that we're setting the hue, style,
# markers, and palette using the 'name' parameter
# from the data frame and the number of groups in
# the data.
p = sns.scatterplot(
data=points,
x="x",
y="y",
hue='name',
style='name',
markers=markers[:len(groups)],
palette=colors[:len(groups)],
legend=False,
s=30,
alpha=1.0
)
# Here's the 'magic' -- we use zip to link together
# the group name, the color, and the marker style. You
# *cannot* retreive the marker style from the scatterplot
# since that information is lost when rendered as a
# PathCollection (as far as I can tell). Anyway, this allows
# us to loop over each group in the second data frame and
# generate a 'fake' Line2D plot (with zero elements and no
# line-width in our case) that we can add to the legend. If
# you were overlaying a line plot or a second plot that uses
# patches you'd have to tweak this accordingly.
patches = []
for x in zip(groups, colors[:len(groups)], markers[:len(groups)]):
patches.append(Line2D([0],[0], linewidth=0.0, linestyle='',
color=x[1], markerfacecolor=x[1],
marker=x[2], label=x[0], alpha=1.0))
# And add these patches (with their group labels) to the new
# legend item and place it on the plot.
leg = Legend(ax, patches, labels=groups,
loc='upper left', frameon=False, title='Groups')
ax.add_artist(leg);
# Done
plt.show();
Here's the output:
I'm plotting multiple hysteresis loops from multiple files in the same axis using a for loop, and I'm wondering how to have a different label and colour for each loop. Each loop is actually made up of two lines. The relevant code is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
filelist = ['file1', 'file2', 'file3']
fig = plt.figure()
axes = fig.add_subplot(111)
for item in filelist:
filename = item
#import hyst files
up = pd.read_csv(filename)
up.columns = ['Field','Moment','Temperature']
upcut = up2[:cut_loc -1] #cuts the file in 2
down = up2[cut_loc +1:]
upcutsrt = (upcut.sort(['Field']))
upcutsrt.plot(x='Field', y='Moment', ax=axes)
down.plot(x='Field', y='Moment', ax=axes)
ax1.set_xlabel('Magnetic Field', fontsize = 20, labelpad = 15)
ax1.set_ylabel('Magnetic Moment', fontsize=20, labelpad = 15)
plt.show()
So this plots 3 different hysteresis curves on the same axes with the same axes labels and scale and everything. Unfortunately though, all the lines are the same colour. I would like to have each curve (up and down from file) to have a different colour and a different label but I'm at a loss as to how to do it.
Well first you need a list of colors, one per file:
colors = ['red', 'green', 'blue']
then modify the for loop:
for item,col in zip(filelist,colors):
and finally plot using the colors:
upcutsrt.plot(x='Field', y='Moment', ax=axes, color=col)
down.plot(x='Field', y='Moment', ax=axes, color=col)