Sharing Y-axis in a matplotlib subplots - python

I have been trying to create a matplotlib subplot (1 x 3) with horizontal bar plots on either side of a lineplot.
It looks like this:
The code for generating the above plot -
u_list = [2, 0, 0, 0, 1, 5, 0, 4, 0, 0]
n_list = [0, 0, 1, 0, 4, 3, 1, 1, 0, 6]
arr_ = list(np.arange(10, 11, 0.1))
data_ = pd.DataFrame({
'points': list(np.arange(0, 10, 1)),
'value': [10.4, 10.5, 10.3, 10.7, 10.9, 10.5, 10.6, 10.3, 10.2, 10.4][::-1]
})
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 8))
ax1 = plt.subplot(1, 3, 1)
sns.barplot(u_list, arr_, orient="h", ax=ax1)
ax2 = plt.subplot(1, 3, 2)
x = data_['points'].tolist()
y = data_['value'].tolist()
ax2.plot(x, y)
ax2.set_yticks(arr_)
plt.gca().invert_yaxis()
ax3 = plt.subplot(1, 3, 3, sharey=ax1, sharex=ax1)
sns.barplot(n_list, arr_, orient="h", ax=ax3)
fig.tight_layout()
plt.show()
Edit
How do I share the y-axis of the central line plot with the other horizontal bar plots?


I would set the limits of all y-axes to the same range, set the ticks in all axes and than set the ticks/tick-labels of all but the most left axis to be empty. Here is what I mean:
from matplotlib import pyplot as plt
import numpy as np
u_list = [2, 0, 0, 0, 1, 5, 0, 4, 0, 0]
n_list = [0, 0, 1, 0, 4, 3, 1, 1, 0, 6]
arr_ = list(np.arange(10, 11, 0.1))
x = list(np.arange(0, 10, 1))
y = [10.4, 10.5, 10.3, 10.7, 10.9, 10.5, 10.6, 10.3, 10.2, 10.4]
fig, axs = plt.subplots(1, 3, figsize=(20, 8))
axs[0].barh(arr_,u_list,height=0.1)
axs[0].invert_yaxis()
axs[1].plot(x, y)
axs[1].invert_yaxis()
axs[2].barh(arr_,n_list,height=0.1)
axs[2].invert_yaxis()
for i in range(1,len(axs)):
axs[i].set_ylim( axs[0].get_ylim() ) # align axes
axs[i].set_yticks([]) # set ticks to be empty (no ticks, no tick-labels)
fig.tight_layout()
plt.show()
This is a minimal example and for the sake of conciseness, I refrained from mixing matplotlib and searborn. Since seaborn uses matplotlib under the hood, you can reproduce the same output there (but with nicer bars).

Related

Putting minor ticks in both top and bottom x-axis when using set_xlim command

For visual appeal I was trying to put minor tick labels on both the upper and lower x axis. I used axs[i].xaxis.set_minor_locator(AutoMinorLocator(10)) as well as axs[0].set_xticks([2, 1, 0, -1, -2], minor=True). just using autominorlocator is not producing any minor tick labels in the 2nd subplot when I put minor=True in set_xticks, I am getting the minor ticks but the labels also changes to include 1, 1.5, 2, 2.5 instead of 1, 2.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import AutoMinorLocator
x1 = np.linspace(2, -2, 5)
y1 = np.linspace(-2, 2, 5)
x2 = np.linspace(1, -5, 7)
y2 = np.linspace(-5, 1, 7)
fig, axs = plt.subplots(2, 1, figsize=(14, 10))
fig.suptitle('With minor x ticks')
# plot 1 (a)
axs[0].plot(x1, y1)
axs[0].set_ylim(-2, 2)
axs[0].set_xlim(2, -2)
axs[0].set_xticks([2, 1, 0, -1, -2], minor=True)
axs[0].set_yticks([-2, -1, 0, 1, 2])
axs[0].set_box_aspect(1)
# plot 1 (b)
axs[1].plot(x2, y2)
axs[1].set_ylim(-5, 1)
axs[1].set_ylim(1, -5)
axs[1].set_xticks([1, 0, -1, -2, -3, -4, -5], minor=True)
axs[1].set_yticks([-5, -4, -3, -2, -1, 0, 1])
axs[1].set_box_aspect(1)
for i in range(2):
axs[i].yaxis.set_minor_locator(AutoMinorLocator(10))
axs[i].xaxis.set_minor_locator(AutoMinorLocator(10))
axs[i].yaxis.set_ticks_position('both')
axs[i].xaxis.set_ticks_position('both')
axs[i].tick_params(axis='both', which='both', direction='in')
plt.show()
fig.savefig("fig1_with minor.pdf")
)

How to generate a histogram with the list below?

How to generate a histogram with the list below?
[[0, 0, 0, 19, 7], [0, 0, 0, 21, 7], [0, 0, 0, 21, 7], [0, 0, 0, 29, 0]]
Explaining the list: [0, 0, 0, 19, 7]
First value = repetition average between 0-20
Second value = repetition average between 20-40
Third value = repetition average between 40-60
Fourth value = average repetition between 60-80
Fifth value = repetition average between 80-100
These sublists within the list can grow exponentially, I would like each sub-list to have a distance between each other, to better interpret the graph
What I have achieved so far:
result = [[[0, 0, 0, 19, 7], [0, 0, 0, 21, 7], [0, 0, 0, 21, 7], [0, 0, 0, 29, 0]]]
fig, ax = plt.subplots(figsize=(10,6))
for i in range(len(result)):
data = np.array(result[i])
x=np.arange(len(data)) + i*6
# draw means
ax.bar(x-0.2, data[:,0], color='blue', width=0.4)
ax.bar(x+0.2, data[:,1], color='green', width=0.4)
ax.bar(x-0.2, data[:,2], color='yellow', width=0.4)
ax.bar(x+0.2, data[:,3], color='orange', width=0.4)
ax.bar(x+0.2, data[:,4], color='red', width=0.4)
# separation line
ax.axvline(4.75)
# turn off xticks
ax.set_xticks([])
ax.legend(labels=['0-20', '20-40', '40-60', '60-80', '80-100'])
leg = ax.get_legend()
leg.legendHandles[0].set_color('blue')
leg.legendHandles[1].set_color('green')
leg.legendHandles[2].set_color('yellow')
leg.legendHandles[3].set_color('orange')
leg.legendHandles[4].set_color('red')
plt.title("Histogram")
plt.ylabel('Consume')
plt.xlabel('Percent')
plt.show()
Any suggetions?

Here is an approach to draw the described plot. Note that normally matplotlib only sets one legend entry for a complete bar graph. To have an entry for individual bars, a label needs to be set to each of them explicitly. In the code below such a label is added to each bar in the first set.
(Note that I left out one set of square parenthesis for result as in the original post it is a 3D list. If such a 3D list would be necessary, you could write the loop as for i, data in enumerate(result[0])).
import numpy as np
import matplotlib.pyplot as plt
result = [[0, 0, 0, 19, 7], [0, 0, 0, 21, 7], [0, 0, 0, 21, 7], [0, 0, 0, 29, 0]]
colors = ['blue', 'green', 'yellow', 'orange', 'red']
labels = ['0-20', '20-40', '40-60', '60-80', '80-100']
fig, ax = plt.subplots(figsize=(10, 6))
for i, data in enumerate(result):
x = np.arange(len(data)) + i*6
bars = ax.bar(x, data, color=colors, width=0.4)
if i == 0:
for bar, label in zip(bars, labels):
bar.set_label(label)
if i < len(result) - 1:
# separation line after each part, but not after the last
ax.axvline(4.75 + i*6, color='black', linestyle=':')
ax.set_xticks([])
ax.legend()
ax.set_title("Histogram")
ax.set_ylabel('Consume')
ax.set_xlabel('Percent')
plt.show()

Histogram Bars not Centred over xticks in pyplot.hist

I guess I just didn't use the right keywords, because this probably has been asked before, but I didn't find a solution. Anyway, I have a problem where the the bars of a histogram do not line up with the xticks. I want the bars to be centred over the xticks they correspond to, but they get placed between ticks to fill the space in-between evenly.
import matplotlib.pyplot as plt
data = [1, 1, 1, 1.5, 2, 4, 4, 4, 4, 4.5, 5, 6, 6.5, 7, 9,9, 9.5]
bins = [x+n for n in range(1, 10) for x in [0.0, 0.5]]+[10.0]
plt.hist(data, bins, rwidth = .3)
plt.xticks(bins)
plt.show()

Note that what you are plotting here is not a histogram. A histogram would be
import matplotlib.pyplot as plt
data = [1, 1, 1, 1.5, 2, 4, 4, 4, 4, 4.5, 5, 6, 6.5, 7, 9,9, 9.5]
bins = [x+n for n in range(1, 10) for x in [0.0, 0.5]]+[10.0]
plt.hist(data, bins, edgecolor="k", alpha=1)
plt.xticks(bins)
plt.show()
Here, the bars range between the bins as expected. E.g. you have 3 values in the interval 1 <= x < 1.5.
Conceptually what you want to do here is get a bar plot of the counts of data values. This would not require any bins at all and could be done as follows:
import numpy as np
import matplotlib.pyplot as plt
data = [1, 1, 1, 1.5, 2, 4, 4, 4, 4, 4.5, 5, 6, 6.5, 7, 9,9, 9.5]
u, inv = np.unique(data, return_inverse=True)
counts = np.bincount(inv)
plt.bar(u, counts, width=0.3)
plt.xticks(np.arange(1,10,0.5))
plt.show()
Of course you can "misuse" a histogram plot to get a similar result. This would require to move the center of the bar to the left bin edge, plt.hist(.., align="left").
import matplotlib.pyplot as plt
data = [1, 1, 1, 1.5, 2, 4, 4, 4, 4, 4.5, 5, 6, 6.5, 7, 9,9, 9.5]
bins = [x+n for n in range(1, 10) for x in [0.0, 0.5]]+[10.0]
plt.hist(data, bins, align="left", rwidth = .6)
plt.xticks(bins)
plt.show()
This results in the same plot as above.

How to annotate a stacked bar plot and add legend labels

In short:
Height of bars does not match the numbers.
Labels seem to be placed on the wrong height. (should be right in the middle of each bar)
On the very bottom I also see the '0' labels which I really don't want to see in the graph.
Explained:
I'm trying to make a stacked bar chart and label each bar with it's appropriate value in it. But for some reason the height of the bars is completely wrong. Like for the first week the green bar should be 20 points long but it is only 10. And the red bar should be 10 points long but it is only 8 or so. And week 17 should have multiple bars in it but instead has only one (the white one)
I am guessing that because of the wrong bar heights the labels are misplaced too. I have no idea why the 0's on the very bottom are also showing but that's a problem too.
I don't know if these are all separate questions and should be asked in separate posts, but I feel like they are all connected and that there is an answer that solves them all.
import matplotlib.pyplot as plt
import numpy as np
newYearWeek =[201613, 201614, 201615, 201616, 201617, 201618, 201619, 201620, 201621, 201622]
uniqueNames = ['Word1', 'Word2', 'Word3', 'Word4', 'Word5', 'Word6',
'Word7', 'Word8', 'Word9', 'Word10', 'Word11']
#Each column in the multiarray from top to bottom represents 1 week
#Each row from left to right represents the values of that word.
#So that makes 11 rows and 10 columns.
#And yes the multidimensional array have to be like this with the 0's in it.
keywordsMuliarray = [
[20, 3, 1, 0, 0, 1, 6, 3, 1, 2],
[10, 1, 0, 0, 3, 1, 3, 1, 0, 2],
[2, 2, 5, 3, 5, 4, 5, 4, 3, 2],
[0, 4, 3, 3, 1, 0, 2, 7, 1, 2],
[0, 0, 2, 0, 1, 1, 1, 0, 1, 3],
[0, 0, 3, 2, 0, 0, 0, 1, 0, 0],
[1, 0, 1, 0, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 1, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 7, 6, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 2, 0, 1]]
fig = plt.figure(figsize=(8.5, 5.5))
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
N = len(newYearWeek)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
colors = ['seagreen', 'indianred', 'steelblue', 'darkmagenta', 'wheat',
'orange', 'mediumslateblue', 'silver',
'whitesmoke', 'black', 'darkkhaki', 'dodgerblue', 'crimson',
'sage', 'navy', 'plum', 'darkviolet', 'lightpink']
def autolabel(rects, values):
# Attach some text labels.
for (rect, value) in zip(rects, values):
ax.text(rect.get_x() + rect.get_width() / 2.,
rect.get_y() + rect.get_height() / 2.,
'%d'%value,
ha = 'center',
va = 'center')
left = np.zeros(len(uniqueNames)) # left alignment of data starts at zero
helpingNumber = 0
for i in range(0, len(newYearWeek)):
rects1 = plt.bar(ind, keywordsMuliarray[helpingNumber][:],width, color=colors[helpingNumber], label=uniqueNames[helpingNumber])
autolabel(rects1, keywordsMuliarray[helpingNumber][:])
helpingNumber = helpingNumber+1
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 1, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', fontsize=9, bbox_to_anchor=(1, 0.5))
#plt.ylabel('Scores')
plt.xticks(ind + width/2., newYearWeek, fontsize=8)
#plt.yticks(np.arange(0, 81, 10))
plt.margins(x=0.02)
plt.tight_layout(rect=[0,0,0.8,1])
plt.show()
This is how the graph looks now:

To make what you want you have to sum heights of all previous bars in current column (list bot_heights), like here:
import matplotlib.pyplot as plt
import numpy as np
newYearWeek =[201613, 201614, 201615, 201616, 201617, 201618, 201619, 201620, 201621, 201622]
uniqueNames = ['Word1', 'Word2', 'Word3', 'Word4', 'Word5', 'Word6',
'Word7', 'Word8', 'Word9', 'Word10', 'Word11']
#Each column in the multiarray from top to bottom represents 1 week
#Each row from left to right represents the values of that word.
#So that makes 11 rows and 10 columns.
#And yes the multidimensional array have to be like this with the 0's in it.
keywordsMuliarray = [
[20, 3, 1, 0, 0, 1, 6, 3, 1, 2],
[10, 1, 0, 0, 3, 1, 3, 1, 0, 2],
[2, 2, 5, 3, 5, 4, 5, 4, 3, 2],
[0, 4, 3, 3, 1, 0, 2, 7, 1, 2],
[0, 0, 2, 0, 1, 1, 1, 0, 1, 3],
[0, 0, 3, 2, 0, 0, 0, 1, 0, 0],
[1, 0, 1, 0, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 1, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 7, 6, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 2, 0, 1]]
fig = plt.figure(figsize=(8.5, 5.5))
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
N = len(newYearWeek)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
colors = ['seagreen', 'indianred', 'steelblue', 'darkmagenta', 'wheat',
'orange', 'mediumslateblue', 'silver',
'whitesmoke', 'black', 'darkkhaki', 'dodgerblue', 'crimson',
'sage', 'navy', 'plum', 'darkviolet', 'lightpink']
def autolabel(rects, values):
# Attach some text labels
for (rect, value) in zip(rects, values):
if value > 0:
ax.text(rect.get_x() + rect.get_width() / 2.,
rect.get_y() + rect.get_height() / 2.,
'%d'%value, ha = 'center', va = 'center', size = 9)
left = np.zeros(len(uniqueNames)) # left alignment of data starts at zero
# plot the first bars
rects1 = plt.bar(ind, keywordsMuliarray[0][:],width,
color=colors[0], label=uniqueNames[0])
autolabel(rects1, keywordsMuliarray[0][:])
# put other bars on previuos
bot_heights = [0.] * len(keywordsMuliarray[0][:])
for i in xrange(1,N):
bot_heights = [bot_heights[j] + keywordsMuliarray[i-1][j] for j in xrange(len(bot_heights))]
rects1 = plt.bar(ind, keywordsMuliarray[i][:],width,
color=colors[i], label=uniqueNames[i],
bottom=bot_heights)
autolabel(rects1, keywordsMuliarray[i][:])
# Shrink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 1, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', fontsize=9, bbox_to_anchor=(1, 0.5))
#plt.ylabel('Scores')
plt.xticks(ind + width/2., newYearWeek, fontsize=8)
plt.yticks(np.arange(0, 41, 5))
plt.margins(x=0.02)
plt.tight_layout(rect=[0,0,0.8,1])
plt.show()
To prevent overlapping of bar labels I recommend you do not add a label if a value is zero (look to modified autolabel function). As a result I get:

The other answer doesn't plot data for 'Word11'
Lists and arrays of data can most easily be plotted by loading them into pandas
Plot the dataframe with pandas.DataFrame.plot and kind='bar'
When plotting data from pandas, the index values become the axis tick labels and the column names are the segment labels
matplotlib.pyplot.bar_label can be used to add annotations
See Adding value labels on a matplotlib bar chart for more options using .bar_label.
Tested in pandas 1.3.1, python 3.81., and matplotlib 3.4.21.
Minimum version required
labels = [f'{v.get_height():0.0f}' if v.get_height() > 0 else '' for v in c ] without the assignment expression (:=).
import pandas as pd
import matplotlib.pyplot as plt
# create a dataframe from the data in the OP and transpose it with .T
df = pd.DataFrame(data=keywordsMuliarray, index=uniqueNames, columns=newYearWeek).T
# display(df.head())
Word1 Word2 Word3 Word4 Word5 Word6 Word7 Word8 Word9 Word10 Word11
201613 20 10 2 0 0 0 1 0 0 0 0
201614 3 1 2 4 0 0 0 0 1 0 0
201615 1 0 5 3 2 3 1 0 0 0 0
201616 0 0 3 3 0 2 0 1 0 0 0
201617 0 3 5 1 1 0 1 0 7 0 0
colors = ['seagreen', 'indianred', 'steelblue', 'darkmagenta', 'wheat', 'orange', 'mediumslateblue', 'silver', 'whitesmoke', 'black', 'darkkhaki']
# plot the dataframe
ax = df.plot(kind='bar', stacked=True, figsize=(9, 6), color=colors, rot=0, ec='k')
# Put a legend to the right of the current axis
ax.legend(loc='center left', fontsize=9, bbox_to_anchor=(1, 0.5))
# add annotations
for c in ax.containers:
# customize the label to account for cases when there might not be a bar section
labels = [f'{h:0.0f}' if (h := v.get_height()) > 0 else '' for v in c ]
# set the bar label
ax.bar_label(c, labels=labels, label_type='center', fontsize=8)
plt.show()

How to place lines below markers in Python?

I have to plot multiple lines and their curve fit lines on a single plot. All these lines are plotted using a for loop. Since it is plot using loops the curve fit lines of the succeeding step is plotted over its predecessor as shown in figure.
The reproducible code:
import matplotlib.pyplot as plt
import numpy as np
x = np.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
y = np.array([[4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],
[6, 5.2, 8.5, 9.1, 13.4, 15.1, 16.1, 18.3, 20.4, 22.1, 23.7]])
m, n = x.shape
figure = plt.figure(figsize=(5.15, 5.15))
figure.clf()
plot = plt.subplot(111)
for i in range(m):
poly = np.polyfit(x[i, :], y[i, :], deg =1)
plt.plot(poly[0] * x[i, :] + poly[1], linestyle = '-')
plt.plot(x[i, :], y[i, :], linestyle = '', marker = 'o', markersize = 20)
plot.set_ylabel('Y', labelpad = 6)
plot.set_xlabel('X', labelpad = 6)
plt.show()
I can fix this using another loop as:
import matplotlib.pyplot as plt
import numpy as np
x = np.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
y = np.array([[4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],
[6, 5.2, 8.5, 9.1, 13.4, 15.1, 16.1, 18.3, 20.4, 22.1, 23.7]])
m, n = x.shape
figure = plt.figure(figsize=(5.15, 5.15))
figure.clf()
plot = plt.subplot(111)
for i in range(m):
poly = np.polyfit(x[i, :], y[i, :], deg =1)
plt.plot(poly[0] * x[i, :] + poly[1], linestyle = '-')
for i in range(m):
plt.plot(x[i, :], y[i, :], linestyle = '', marker = 'o', markersize = 20)
plot.set_ylabel('Y', labelpad = 6)
plot.set_xlabel('X', labelpad = 6)
plt.show()
which gives me all the fit lines below the markers.
But is there any built-in function in Python/matplotlib to do this without using two loops?
Update
Only as an example I have used n = 2, n can be greater than 2, i.e. the loop would be run multiple times.
Update 2 after answer
Can I do this for the same line also? As an example:
plt.plot(x[i, :], y[i, :], linestyle = ':', marker = 'o', markersize = 20)
Can I give the linestyle a zorder = 1 and the markers a zorder = 3?

Editing just your plotting lines:
plt.plot(poly[0] * x[i, :] + poly[1], linestyle = '-',
zorder=-1)
plt.plot(x[i, :], y[i, :], linestyle = '', marker = 'o', markersize = 20,
zorder=3)
now the markers are all in front of the lines, though within marker/line groups they're still order-of-plotting.
Update answer
No. One call to plot, one zorder argument.
If you want to match the color and style of markers and line in each pass through the loop, set up an iterator or generator for colors and get current_color on each pass, then use that as an argument for plot calls.

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