Hey I'm using rainbow text function, which can be found in here
in order to make y axis label have colors that match closest colors of the conosle names on y axis.
So currently I've came up with this code:
fig, ax= plt.subplots(figsize=(5,6)) #used to take care of the size
sns.barplot(x=gbyplat,y=gbyplat.index, palette='husl') #creating barplot
ax.set_ylabel('Publisher', color='deepskyblue', size=15, alpha=0.8) #setting labels
ax.set_xlabel('Number of titles published', color='slateblue', size=15, alpha=0.7)
ax.set_title('Titles per platform ranking', color='deeppink', size=17, alpha=0.6)
ax.set_xlim(0,2350) #setting limit for the plot
ax.set_xticks(np.arange(0, max(gbyplat), 250)) #ticks frequency
ax.annotate('newest', size=12, xy=(390, 13), xytext=(700, 13.3),
arrowprops=dict(arrowstyle="fancy")) #annotations on plot
ax.annotate('max', size=9, xy=(2230,0.3), bbox=dict(boxstyle="round", fc="w", alpha=0.5))
ax.plot(2161,0, 'o', color='cyan') #creating the cricle highlight for PS2 max
p = sns.color_palette("husl", len(gbyplat))
for i, label in enumerate(ax.get_yticklabels()):
label.set_color(p[i])
rainbow_text(0,5, "Pub lis her".split(),
[p[10],p[11],p[12]],
size=10)
However, the issue is that I have to manually set coordinates for newly produced 'Publisher' label. According to the function code i can pass ax argument which would automatically fit the label to the y axis (if I understood correctly). So how can I do that? And second question, is there a way to access ylabel coordinates (of the current y axis label 'Publisher')?
Thanks
One can set the text at the position where the ylabel would normally reside by first drawing the ylabel, obtaining its coordinates and then setting it to an empty string. One can then adapt the example rainbow text function to use the obtained coordinates.
It will still be very tricky to select the colors and coordinates such that the text will have exactly the color of the bars next to it. This probably involves a lot a trial and error.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import transforms
import seaborn as sns
l =list("ABCDEFGHIJK")
x = np.arange(1,len(l)+1)[::-1]
f, ax=plt.subplots(figsize=(7,4.5))
sns.barplot(x=x,y=l, palette='husl', ax=ax)
plt.xlabel('Number of titles published', color='slateblue', size=15, alpha=0.7)
p = sns.color_palette("husl", len(l))
for i, label in enumerate(ax.get_yticklabels()):
label.set_color(p[i])
def rainbow_text(x, y, strings, colors, ax=None, **kw):
if ax is None:
ax = plt.gca()
canvas = ax.figure.canvas
lab = ax.set_ylabel("".join(strings))
canvas.draw()
labex = lab.get_window_extent()
t = ax.transAxes
labex_data = t.inverted().transform((labex.x0, labex.y0- labex.height/2.))
ax.set_ylabel("")
for s, c in zip(strings, colors):
text = ax.text(labex_data[0]+x, labex_data[1]+y, s, color=c, transform=t,
rotation=90, va='bottom', ha='center', **kw)
text.draw(canvas.get_renderer())
ex = text.get_window_extent()
t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
rainbow_text(0, 0.06, ["Pub", "lish", "er"],[p[6], p[5],p[4]],size=15)
plt.show()
Related
I have a long bar chart with lots of bars and I wanna improve its reability from axis to the bars.
Suppose I have the following graph:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
y = np.linspace(1,-1,20)
x = np.arange(0,20)
labels = [f'Test {i}' for i in x]
fig, ax = plt.subplots(figsize=(12,8))
sns.barplot(y = y, x = x, ax=ax )
ax.set_xticklabels(labels, rotation=90)
which provides me the following:
All I know is how to change the label position globally across the chart. How can I change the axis layout to be cantered in the middle and change its label position based on a condition (in this case, being higher or lower than 0)? What I want to achieve is:
Thanks in advance =)
You could remove the existing x-ticks and place texts manually:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
y = np.linspace(1,-1,20)
x = np.arange(0,20)
labels = [f'Test {i}' for i in x]
fig, ax = plt.subplots(figsize=(12,8))
sns.barplot(y = y, x = x, ax=ax )
ax.set_xticks([]) # remove existing ticks
for i, (label, height) in enumerate(zip(labels, y)):
ax.text(i, 0, ' '+ label+' ', rotation=90, ha='center', va='top' if height>0 else 'bottom' )
ax.axhline(0, color='black') # draw a new x-axis
for spine in ['top', 'right', 'bottom']:
ax.spines[spine].set_visible(False) # optionally hide spines
plt.show()
Here is another approach, I'm not sure whether it is "more pythonic".
move the existing xaxis to y=0
set the tick marks in both directions
put the ticks behind the bars
prepend some spaces to the labels to move them away from the axis
realign the tick labels depending on the bar value
fig, ax = plt.subplots(figsize=(12, 8))
sns.barplot(y=y, x=x, ax=ax)
ax.spines['bottom'].set_position('zero')
for spine in ['top', 'right']:
ax.spines[spine].set_visible(False)
ax.set_xticklabels([' ' + label for label in labels], rotation=90)
for tick, height in zip(ax.get_xticklabels(), y):
tick.set_va('top' if height > 0 else 'bottom')
ax.tick_params(axis='x', direction='inout')
ax.set_axisbelow(True) # ticks behind the bars
plt.show()
I'm trying to use Seaborn Pair Grid to make a correlogram with scatterplots in one half, histograms on the diagonal and the pearson coefficient on the other half. I've managed to put together the following code which does what I need, but I'm really struggling with further customization
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
df = sns.load_dataset('iris')
def reg_coef(x,y,label=None,color=None,**kwargs):
ax = plt.gca()
r,p = pearsonr(x,y)
ax.annotate('{:.2f}'.format(r), xy=(0.5,0.5), xycoords='axes fraction', ha='center',fontsize=30,
bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 20})
ax.set_axis_off()
sns.set(font_scale=1.5)
sns.set_style("white")
g = sns.PairGrid(df)
g.map_diag(plt.hist)
g.map_lower(plt.scatter)
g.map_upper(reg_coef)
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle('Iris Correlogram', fontsize=30)
plt.show()
This is the result
What I'd like to do:
Change the font used for the whole plot and assign my own defined rgb colour to the font and axes (same one)
Remove the X & Y tick labels
Change the colour of the scatter dots and histogram bars to my own defined rgb colour (same one)
Set a diverging colour map for the background of the pearson number to highlight the degree and type of correlation, again using my own defined rgb colours.
I know Im asking a lot but Ive spent hours going round in circles trying to figure this out!!
The color can be set as extra parameter in g.map_diag(plt.hist, color=...) and
g.map_lower(plt.scatter, color=...). The function reg_coef can be modified to take a colormap into account.
The font color and family can be set via the rcParams. The ticks can be removed via plt.setp(g.axes, xticks=[], yticks=[]). Instead of subplot_adjust, g.fig.tight_layout() usually fits all elements nicely into the plot. Here is an example:
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
def reg_coef(x, y, label=None, color=None, cmap=None, **kwargs):
ax = plt.gca()
r, p = pearsonr(x, y)
norm = plt.Normalize(-1, 1)
cmap = cmap if not cmap is None else plt.cm.coolwarm
ax.annotate(f'{r:.2f}', xy=(0.5, 0.5), xycoords='axes fraction', ha='center', fontsize=30,
bbox={'facecolor': cmap(norm(r)), 'alpha': 0.5, 'pad': 20})
ax.set_axis_off()
df = sns.load_dataset('iris')
sns.set(font_scale=1.5)
sns.set_style("white")
for param in ['text.color', 'axes.labelcolor', 'xtick.color', 'ytick.color']:
plt.rcParams[param] = 'cornflowerblue'
plt.rcParams['font.family'] = 'cursive'
g = sns.PairGrid(df, height=2)
g.map_diag(plt.hist, color='turquoise')
g.map_lower(plt.scatter, color='fuchsia')
g.map_upper(reg_coef, cmap=plt.get_cmap('PiYG'))
plt.setp(g.axes, xticks=[], yticks=[])
g.fig.suptitle('Iris Correlogram', fontsize=30)
g.fig.tight_layout()
plt.show()
Can someone please help me plot x axis labels in percentages given the following code of my horizontal bar chart?
Finding it difficult to find as I want a more simplistic chart without x axis labels and ticks.
[Horizontal Bar Chart][1]
# Plot the figure size
plt.figure(figsize= (8,6))
# New variable and plot the question of the data frame in a normalized in a horizontal bar chat.
ax1 = df[q1].value_counts(normalize=True).sort_values().plot(kind="barh", color='#fd6a02', width=0.75, zorder=2)
# Draw vague vertical axis lines and set lines to the back of the order
vals = ax1.get_xticks()
for tick in vals:
ax1.axvline(x=tick, linestyle='dashed', alpha=0.4, color = '#d3d3d3', zorder=1)
# Tot3als to produce a composition ratio
total_percent = df[q1].value_counts(normalize=True) *100
# Remove borders
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.spines['bottom'].set_visible(False)
# Set the title of the graph inline with the Y axis labels.
ax1.set_title(q1, weight='bold', size=14, loc = 'left', pad=20, x = -0.16)
# ax.text(x,y,text,color)
for i,val in enumerate(total):
ax1.text(val - 1.5, i, str("{:.2%}".format(total_percent), color="w", fontsize=10, zorder=3)
# Create axis labels
plt.xlabel("Ratio of Responses", labelpad=20, weight='bold', size=12)
Each time I get a EOF error. Can someone help?
It's not based on your code, but I'll customize the answer from the official reference.
The point is achieved with ax.text(), which is a looping process.
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.rcdefaults()
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
ax.barh(y_pos, performance, align='center')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')
# Totals to produce a composition ratio
total = sum(performance)
# ax.text(x,y,text,color)
for i,val in enumerate(performance):
ax.text(val - 1.5, i, str("{:.2%}".format(val/total)), color="w", fontsize=10)
plt.show()
I would like to plot circles with matplotlib (patches), and annotate them. The annotation would be a word, and it needs to be in the centre of the circle.
So far, I can plot a circle and annotate it:
But the annotation is not centred, neither horizontally or vertically. In order to do that, I would need access to the dimensions of the text.
Is there a way to access the dimensions of the text in "the coordinate systems" ?. For example, if the circle has a radius of 15 (15 something, not pixels), the text would have a length of 12 something (not pixels).
I'm open to any other suggestion on how to do that.
Here is my code so far:
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
fig, ax = plt.subplots()
ax = fig.add_subplot(111)
x = 0
y = 0
circle = plt.Circle((x, y), radius=1)
ax.add_patch(circle)
label = ax.annotate("cpicpi", xy=(x, y), fontsize=30)
ax.axis('off')
ax.set_aspect('equal')
ax.autoscale_view()
plt.show()
You need to set the horizontal alignment in ax.annotate using ha="center". The same thing can be done for the vertical direction if necessary using the argument va="center"
fig, ax = plt.subplots()
ax = fig.add_subplot(111)
x = 0
y = 0
circle = plt.Circle((x, y), radius=1)
ax.add_patch(circle)
label = ax.annotate("cpicpi", xy=(x, y), fontsize=30, ha="center")
ax.axis('off')
ax.set_aspect('equal')
ax.autoscale_view()
plt.show()
You can add two additional arguments to the annotate() call:
label = ax.annotate(
"cpicpi",
xy=(x, y),
fontsize=30,
verticalalignment="center",
horizontalalignment="center"
)
(See the docs for the arguments of annotate and of Text – whose constructor is called by annotate)
I want to plot a bar chart or a histogram using matplotlib. I don't want a stacked bar plot, but a superimposed barplot of two lists of data, for instance I have the following two lists of data with me:
Some code to begin with :
import matplotlib.pyplot as plt
from numpy.random import normal, uniform
highPower = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,1419.34,
1415.13,1182.70,1165.17]
lowPower = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,1138.70,
1104.12,1012.95,1000.36]
plt.hist(highPower, bins=10, histtype='stepfilled', normed=True,
color='b', label='Max Power in mW')
plt.hist(lowPower, bins=10, histtype='stepfilled', normed=True,
color='r', alpha=0.5, label='Min Power in mW')
I want to plot these two lists against the number of values in the two lists such that I am able to see the variation per reading.
You can produce a superimposed bar chart using plt.bar() with the alpha keyword as shown below.
The alpha controls the transparency of the bar.
N.B. when you have two overlapping bars, one with an alpha < 1, you will get a mixture of colours. As such the bar will appear purple even though the legend shows it as a light red. To alleviate this I have modified the width of one of the bars, this way even if your powers should change you will still be able to see both bars.
plt.xticks can be used to set the location and format of the x-ticks in your graph.
import matplotlib.pyplot as plt
import numpy as np
width = 0.8
highPower = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,
1419.34,1415.13,1182.70,1165.17]
lowPower = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,
1138.70,1104.12,1012.95,1000.36]
indices = np.arange(len(highPower))
plt.bar(indices, highPower, width=width,
color='b', label='Max Power in mW')
plt.bar([i+0.25*width for i in indices], lowPower,
width=0.5*width, color='r', alpha=0.5, label='Min Power in mW')
plt.xticks(indices+width/2.,
['T{}'.format(i) for i in range(len(highPower))] )
plt.legend()
plt.show()
Building on #Ffisegydd's answer, if your data is in a Pandas DataFrame, this should work nicely:
def overlapped_bar(df, show=False, width=0.9, alpha=.5,
title='', xlabel='', ylabel='', **plot_kwargs):
"""Like a stacked bar chart except bars on top of each other with transparency"""
xlabel = xlabel or df.index.name
N = len(df)
M = len(df.columns)
indices = np.arange(N)
colors = ['steelblue', 'firebrick', 'darksage', 'goldenrod', 'gray'] * int(M / 5. + 1)
for i, label, color in zip(range(M), df.columns, colors):
kwargs = plot_kwargs
kwargs.update({'color': color, 'label': label})
plt.bar(indices, df[label], width=width, alpha=alpha if i else 1, **kwargs)
plt.xticks(indices + .5 * width,
['{}'.format(idx) for idx in df.index.values])
plt.legend()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if show:
plt.show()
return plt.gcf()
And then in a python command line:
low = [1000.95, 1233.37, 1198.97, 1198.01, 1214.29, 1130.86, 1138.70, 1104.12, 1012.95, 1000.36]
high = [1184.53, 1523.48, 1521.05, 1517.88, 1519.88, 1414.98, 1419.34, 1415.13, 1182.70, 1165.17]
df = pd.DataFrame(np.matrix([high, low]).T, columns=['High', 'Low'],
index=pd.Index(['T%s' %i for i in range(len(high))],
name='Index'))
overlapped_bar(df, show=False)
It is actually simpler than the answers all over the internet make it appear.
a = range(1,10)
b = range(4,13)
ind = np.arange(len(a))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(x=ind, height=a, width=0.35,align='center')
ax.bar(x=ind, height=b, width=0.35/3, align='center')
plt.xticks(ind, a)
plt.tight_layout()
plt.show()