I want to create a graph that has multiple y-axis on both left and right side.
The code below produces a graph with multiple y axis on the right side. I've tried changing the rspline setting to 'left', but it doesn't work.
How do I add y4 on the left side?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import rcParams
%matplotlib inline
x = np.random.rand(20)
y1 = x*5
y2 = x*5 + 0.2
y3 = x*x*3.5 + 0.2*x
y4 = x*5 + 0.2*x
yLimMin = 0
yLimMax = 2.1
lineWidth = 1.0
fontSize = 24
subTitle = ""
plt.rcParams.update({'axes.labelsize': 'small'})
fig = plt.figure(figsize=(21,29.7))
ax11 = fig.add_subplot(411)
subplotAdjustRight = 0.90
mks = 19 # marker step
ax11.plot(x,y1, linestyle='-', linewidth=lineWidth, color = 'k',
marker='*', markevery=11*mks,
label="CO")
ax11.set_ylabel('CO [%]', color='k')
plt.ylim((0,5))
fig.suptitle(subTitle, fontsize = fontSize)
ax11.yaxis.grid()
plt.locator_params(axis='y', nbins=10)
ax12=ax11.twinx()
ax12.plot(x,y2,linestyle='-', linewidth=lineWidth, color='r',
marker='*', markevery=11*mks,
label="CO22")
ax12.set_ylabel('NO [%]', color='r')
plt.ylim((0,10))
plt.locator_params(axis='y', nbins=10)
ax13= ax11.twinx()
rspine = ax13.spines['right']
rspine.set_position(('axes', 1.05))
ax13.set_frame_on(True)
ax13.plot(x,y3,linestyle='-', linewidth=lineWidth, color='m',
marker='*', markevery=11*mks,
label="CO222")
ax13.set_ylabel('O [%] ', color='m')
plt.ylim((0,20))
plt.locator_params(axis='y', nbins=10)
The logic is always the same. You create a twin axes and offset the spine. The only difference when creating an axes which should appear at the left side of the plot is that you need to tell matplotlib to set the ticks and the ticklabel to the left (because by default a twin axes has them set to the right).
ax14= ax11.twinx()
ax14.yaxis.tick_left()
rspine = ax14.spines['left']
rspine.set_position(('axes', -0.15))
ax14.set_ylabel('blubb ', color='limegreen')
ax14.yaxis.set_label_position("left")
Related
I am trying to create an axis plot. I was trying to loop over it as I am plotting the same variable for two different categories. Currently, I have written code two times but I am looking for a smarter way with looping, if possible. Any other suggestion will also be helpful.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
if zone[i] == zone[0]:
ax0.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax0.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax00 = ax0.twinx()
ax00.bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
elif zone[i] == zone[1]:
ax1.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax1.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax01 = ax1.twinx()
ax01.bar(df0['datetime'], df0['hedge'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
i = i + 1
I want to check if something like below can be done with axis plots or not.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
ax{''}.format(i).plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax{''}.format(i).plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax0{''}.format(i) = ax{''}.format(i).twinx()
ax0{''}.format(i).bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
It did not work for me. Any leads to execute axis plot with loop will be helpful.
Here are some ways:
Simply loop over the list of axes
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,1)
x = np.linspace(0,5,21)
for ax in axes:
ax.plot(x,np.sin(x))
plt.show()
Works also with index:
for i in range(len(axes)):
axes[i].plot(x,np.sin(x))
For a grid of plot you can use a similar approach:
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for i in range(len(axes)):
for j in range(len(axes[0])):
axes[i][j].plot(x,np.sin(x))
plt.show()
If you don't like double-loops, you can flatten the array with np.ravel()
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for ax in np.ravel(axes):
ax.plot(x,np.sin(x))
plt.show()
When I run the following lines, I get a plot with a large space at the top and the bottom with no bars.
How can I remove this extra space?
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
random.seed(1)
df = pd.DataFrame(np.random.randn(50, 1), columns=["parameter"])
df["standard_error"]= ((df.parameter**2)**0.5)/2
name = "plot"
x = ["A"+str(x) for x in df.index.tolist()]
y1 = df.parameter
yerr1 = df.standard_error
fig, ax = plt.subplots()
fig.set_figheight(len(x))
plt.rc('axes', labelsize=22)
plt.grid(b=True, which='major', color='#666666', linestyle='-', alpha=0.2)
trans1 = Affine2D().translate(-0.1, 0.0) + ax.transData
trans2 = Affine2D().translate(+0.1, 0.0) + ax.transData
er1 = ax.errorbar(y1, x, xerr=yerr1, marker="o", linestyle="none", transform=trans1)
ax.axvline(x=0, color="black")
plt.savefig(name + '.png', bbox_inches='tight')
If you mean the extra space below and above your smallest and largest data points along the y-axis then you can simply use plt.ylim, e.g:
plt.ylim(0, 50)
Which will change the extent of the y-axis to the range 0 - 50. Similarly for the x-axis there's plt.xlim
I would like to display the following dataframe in barchart but with double y axis, I want to show areas columns on left side and prices columns on right side:
area1 area2 price1 price2
level
first 263.16 906.58 10443.32 35101.88
second 6879.83 14343.03 2077.79 4415.53
third 31942.75 60864.24 922.87 1774.47
I tried with code below, it works but only display left side.
import matplotlib.pyplot as plt
df.plot(kind='bar')
plt.xticks(rotation=45, fontproperties="SimHei")
plt.xlabel("")
plt.legend()
Thank you.
If I understood you correctly, one way could be this, but you have to "play" a bit with the values of width and position of the ticks:
import pandas as pd
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)
ax2 = ax.twinx()
width = 0.1
df.area1.plot(kind='bar', color='red', ax=ax, width=width, position=0 )
df.area2.plot(kind='bar', color='orange', ax=ax, width=width, position=1)
df.price1.plot(kind='bar', color='blue', ax=ax2, width=width, position=2)
df.price2.plot(kind='bar', color='green', ax=ax2, width=width, position=3)
ax.set_ylabel('Area')
ax2.set_ylabel('Price')
ax.legend(["Area1", "Area2"], bbox_to_anchor=(0.8,1.0))
ax2.legend(["Price1", "Price2"], bbox_to_anchor=(0.9,1.0))
plt.show()
Another way is this:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
ax2 = ax.twinx()
# ax.set_xticklabels(ax.get_xticklabels(),rotation=45) # Rotation 45 degrees
width = 0.1
ind = np.arange(len(df))
ax.set_ylabel('Area')
ax2.set_ylabel('Price')
ax.set_xlabel('Level')
ax.bar(ind, df.area1, width, color='red', label='area1')
ax.bar(ind + width, df.area2, width, color='orange', label='area2')
ax2.bar(ind + 2*width, df.price1, width, color='blue', label='price1')
ax2.bar(ind + 3*width, df.price2, width, color='green', label='price2')
ax.set(xticks=(ind + 1.5*width), xticklabels=df.index, xlim=[2*width - 1, len(df)])
ax.legend(["Area1", "Area2"], bbox_to_anchor=(1,1))
ax2.legend(["Price1", "Price2"], bbox_to_anchor=(1,0.87))
plt.show()
i wanted to know how to make a plot with two y-axis so that my plot that looks like this :
to something more like this by adding another y-axis :
i'm only using this line of code from my plot in order to get the top 10 EngineVersions from my data frame :
sns.countplot(x='EngineVersion', data=train, order=train.EngineVersion.value_counts().iloc[:10].index);
I think you are looking for something like:
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.bar(x, y)
ax2.plot(x, y1, 'o-', color="red" )
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show()
Output:
#gdubs If you want to do this with Seaborn's library, this code set up worked for me. Instead of setting the ax assignment "outside" of the plot function in matplotlib, you do it "inside" of the plot function in Seaborn, where ax is the variable that stores the plot.
import seaborn as sns # Calls in seaborn
# These lines generate the data to be plotted
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots() # initializes figure and plots
ax2 = ax1.twinx() # applies twinx to ax2, which is the second y axis.
sns.barplot(x = x, y = y, ax = ax1, color = 'blue') # plots the first set of data, and sets it to ax1.
sns.lineplot(x = x, y = y1, marker = 'o', color = 'red', ax = ax2) # plots the second set, and sets to ax2.
# these lines add the annotations for the plot.
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show(); # shows the plot.
Output:
Seaborn output example
You could try this code to obtain a very similar image to what you originally wanted.
import seaborn as sb
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
x = ['1.1','1.2','1.2.1','2.0','2.1(beta)']
y = [1000,2000,500,8000,3000]
y1 = [3,4,1,8,5]
g = sb.barplot(x=x, y=y, color='blue')
g2 = sb.lineplot(x=range(len(x)), y=y1, color='orange', marker='o', ax=g.axes.twinx())
g.set_xticklabels(g.get_xticklabels(), rotation=-30)
g.set_xlabel('EngineVersion')
g.set_ylabel('Counts')
g2.set_ylabel('Detections rate')
g.legend(handles=[Rectangle((0,0), 0, 0, color='blue', label='Nontouch device counts'), Line2D([], [], marker='o', color='orange', label='Detections rate for nontouch devices')], loc=(1.1,0.8))
I'm trying to visualize a name co-occurrence matrix. This version works okay:
import pandas as pd
import numpy as np
import string
import matplotlib.pyplot as plt
n = 10
names = ['Long Name ' + suffix for suffix in string.ascii_uppercase[:n]]
df = pd.DataFrame(np.random.randint(0, 100, size=(n,n)),
columns=names, index=names)
fig = plt.figure()
ax = plt.gca()
im = ax.matshow(df, interpolation='none')
fig.colorbar(im)
ax.set_xticks(np.arange(n))
ax.set_xticklabels(names)
ax.set_yticks(np.arange(n))
ax.set_yticklabels(names)
ax.xaxis.set_ticks_position("bottom")
plt.setp(ax.get_xticklabels(), rotation=45,
ha="right", rotation_mode="anchor")
for (i,j), z in np.ndenumerate(df):
if z != 0:
ax.text(j, i, str(z), ha="center", va="center")
ax.set_title("Name Co-Occurrences")
fig.tight_layout()
plt.show()
The problem is that the actual matrix I have is fairly large, so I would like to display the names both on the top and the bottom. I've tried to do so by using twiny:
import pandas as pd
import numpy as np
import string
import matplotlib.pyplot as plt
n = 10
names = ['Long Name ' + suffix for suffix in string.ascii_uppercase[:n]]
df = pd.DataFrame(np.random.randint(0, 100, size=(n,n)),
columns=names, index=names)
fig = plt.figure()
botax = plt.gca()
im = botax.matshow(df, interpolation='none')
fig.colorbar(im)
topax = botax.twiny()
for ax, ha, pos in zip([topax, botax], ["left", "right"], ["top", "bottom"]):
ax.set_xticks(np.arange(n))
ax.set_xticklabels(names)
ax.set_yticks(np.arange(n))
ax.set_yticklabels(names)
ax.xaxis.set_ticks_position(pos)
plt.setp(ax.get_xticklabels(), rotation=45,
ha=ha, va="center", rotation_mode="anchor")
for (i,j), z in np.ndenumerate(df):
if z != 0:
botax.text(j, i, str(z), ha="center", va="center")
botax.set_title("Name Co-Occurrences")
fig.tight_layout()
plt.show()
Unfortunately the top labels aren't aligned correctly and I don't know why:
In order to label both, bottom and top of an axes, there is no need for a twin axes. This may make this all a bit easier. You can instead just turn the bottom and top ticks and labels on, and then rotate and align them separately.
import pandas as pd
import numpy as np
import string
import matplotlib.pyplot as plt
n = 10
names = ['Long Name ' + suffix for suffix in string.ascii_uppercase[:n]]
df = pd.DataFrame(np.random.randint(0, 100, size=(n,n)),
columns=names, index=names)
fig = plt.figure()
ax = plt.gca()
im = ax.matshow(df, interpolation='none')
fig.colorbar(im)
ax.set_xticks(np.arange(n))
ax.set_xticklabels(names)
ax.set_yticks(np.arange(n))
ax.set_yticklabels(names)
# Set ticks on both sides of axes on
ax.tick_params(axis="x", bottom=True, top=True, labelbottom=True, labeltop=True)
# Rotate and align bottom ticklabels
plt.setp([tick.label1 for tick in ax.xaxis.get_major_ticks()], rotation=45,
ha="right", va="center", rotation_mode="anchor")
# Rotate and align top ticklabels
plt.setp([tick.label2 for tick in ax.xaxis.get_major_ticks()], rotation=45,
ha="left", va="center",rotation_mode="anchor")
ax.set_title("Name Co-Occurrences", pad=55)
fig.tight_layout()
plt.show()
You will have to first set the aspect ratio of the upper x-axis to be the same as that of the lower x-axis. How to do this has been answered here. Then you can use y=1.3 to lift the title a bit upward so that it does not overlap with the upper x-axis tick labels.
topax = botax.twiny()
aspect0 = botax.get_aspect()
if aspect0 == 'equal':
aspect0 = 1.0
dy = np.abs(np.diff(botax.get_ylim()))
dx = np.abs(np.diff(botax.get_xlim()))
aspect = aspect0 / (float(dy) / dx)
topax.set_aspect(aspect)
for ax, ha, pos in zip([topax, botax], ["left", "right"], ["top", "bottom"]):
ax.set_xticks(np.arange(n))
.....
.....
botax.set_title("Name Co-Occurrences", y=1.3)