Is there any possibility to do a bar plot without y-(x-)axis? In presentations all redundant informations have to be erased, so I would like to begin to delete the axis. I did not see helpful informations in the matplotlib documentation. Maybe you have better solutions than pyplot..?
Edit: I would like to have lines around the bars except the axis at the bottom. Is this possible
#!/usr/bin/env python
import matplotlib.pyplot as plt
ind = (1,2,3)
width = 0.8
fig = plt.figure(1)
p1 = plt.bar(ind,ind)
# plt.show()
fig.savefig("test.svg")
Edit: I did not see using plt.show()
that there is still the yaxis without ticks.
To make the axes not visible, try something like
import matplotlib.pyplot as plt
ind = (1,2,3)
width = 0.8
fig,a = plt.subplots()
p1 = a.bar(ind,ind)
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
plt.show()
Is this what you meant?
Here is the code I used at the end. It is not minimal anymore. Maybe it helps.
import matplotlib.pyplot as plt
import numpy as np
def adjust_spines(ax,spines):
for loc, spine in ax.spines.items():
if loc in spines:
spine.set_smart_bounds(True)
else:
spine.set_color('none') # don't draw spine
# turn off ticks where there is no spine
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
# no yaxis ticks
ax.yaxis.set_ticks([])
def nbar(samples, data, err, bWidth=0.4, bSafe=True, svgName='out'):
fig,a = plt.subplots(frameon=False)
if len(data)!=len(samples):
print("length(data) must be equal to length(samples)!")
return
ticks = np.arange(len(data))
p1 = plt.bar(ticks, data, bWidth, yerr=err)
plt.xticks(ticks+bWidth/2., samples )
adjust_spines(a,['bottom'])
a.xaxis.tick_bottom()
if bSafe:
fig.savefig(svgName+".svg")
samples = ('Sample1', 'Sample2','Sample3')
qyss = (91, 44, 59)
qysserr = (1,5,4)
nbar(samples,qyss,qysserr,svgName="test")
Thx to all contributors.
Related
I'm using this nice boxplot graph, answer from #Parfait.
I got an out of bound error on j and had to use range(i*5,i*5+5). Why?
I'd like to set the median to a particular color, let's say red. medianprops=dict(color="red") won't work. How to do it?
How to set the y-axis tick labels to the same color as the boxes?
Disclaimer: I don't know what I'm doing.
Here's the code using random data :
# import the required library
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
import matplotlib.colors as mc
import colorsys
# data
df = pd.DataFrame(np.random.normal(np.random.randint(5,15),np.random.randint(1,5),size=(100, 16)), columns=list(string.ascii_uppercase)[:16])
# Boxplot
fig, ax = plt.subplots(figsize=(9, 10))
medianprops=dict(color="red")
ax = sns.boxplot(data=df, orient="h", showfliers=False, palette = "husl")
ax = sns.stripplot(data=df, orient="h", jitter=True, size=7, alpha=0.5, palette = "husl") # show data points
ax.set_title("Title")
plt.xlabel("X label")
def lighten_color(color, amount=0.5):
# --------------------- SOURCE: #IanHincks ---------------------
try:
c = mc.cnames[color]
except:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
for i,artist in enumerate(ax.artists):
# Set the linecolor on the artist to the facecolor, and set the facecolor to None
col = lighten_color(artist.get_facecolor(), 1.2)
artist.set_edgecolor(col)
# Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
# Loop over them here, and use the same colour as above
for j in range(i*5,i*5+5):
line = ax.lines[j]
line.set_color(col)
line.set_mfc(col)
line.set_mec(col)
#line.set_linewidth(0.5)
To change the color of the median, you can use the medianprops in sns.boxplot(..., medianprops=...). If you also set a unique label, that label can be tested again when iterating through the lines.
To know how many lines belong to each boxplot, you can divide the number of lines by the number of artists (just after the boxplot has been created, before other elements have been added to the plot). Note that a line potentially has 3 colors: the line color, the marker face color and the marker edge color. Matplotlib creates the fliers as an invisible line with markers. The code below thus also changes these colors to make it more robust to different options and possible future changes.
Looping simultaneously through the boxes and the y tick labels allows copying the color. Making them a bit larger and darker helps for readability.
import matplotlib.pyplot as plt
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb, to_rgb
import seaborn as sns
import pandas as pd
import numpy as np
def enlighten(color, factor=0.5):
h, s, v = rgb_to_hsv(to_rgb(color))
return hsv_to_rgb((h, s, 1 - factor * (1 - v)))
def endarken(color, factor=0.5):
h, s, v = rgb_to_hsv(to_rgb(color))
return hsv_to_rgb((h, s, factor * v))
df = pd.DataFrame(np.random.normal(1, 5, size=(100, 16)).cumsum(axis=0),
columns=['Hydrogen', 'Helium', 'Lithium', 'Beryllium', 'Boron', 'Carbon', 'Nitrogen', 'Oxygen',
'Fluorine', 'Neon', 'Sodium', 'Magnesium', 'Aluminum', 'Silicon', 'Phosphorus', 'Sulfur'])
sns.set_style('white')
fig, ax = plt.subplots(figsize=(9, 10))
colors = sns.color_palette("husl", len(df.columns))
sns.boxplot(data=df, orient="h", showfliers=False, palette='husl',
medianprops=dict(color="yellow", label='median'), ax=ax)
lines_per_boxplot = len(ax.lines) // len(ax.artists)
for i, (box, ytick) in enumerate(zip(ax.artists, ax.get_yticklabels())):
ytick.set_color(endarken(box.get_facecolor()))
ytick.set_fontsize(20)
color = enlighten(box.get_facecolor())
box.set_color(color)
for lin in ax.lines[i * lines_per_boxplot: (i + 1) * lines_per_boxplot]:
if lin.get_label() != 'median':
lin.set_color(color)
lin.set_markerfacecolor(color)
lin.set_markeredgecolor(color)
sns.stripplot(data=df, orient="h", jitter=True, size=7, alpha=0.5, palette='husl', ax=ax)
sns.despine(ax=ax)
ax.set_title("Title")
ax.set_xlabel("X label")
plt.tight_layout()
plt.show()
I just answer point 2. of my question.
After tinkering, I found this to work :
# Each box has 5 associated Line2D objects (the whiskers and median)
# Loop over them here, and use the same colour as above
n=5 # this was for tinkering
for j in range(i*n,i*n+n):
if j != i*n+4 : line = ax.lines[j] # not the median
line.set_color(col)
Again, I don't know what I'm doing. So someone more knowledgeable may provide a more valuable answer.
I removed the stripplot for better clarity.
I have been trying to plot a smooth graph, and here is my code
import matplotlib.pyplot as plt
#fig,axes= plt.subplots(nrows=6, ncols=1, squeeze=False)
x = df["DOY"]
y = df["By"]
z = df["Bz"]
a = df["Vsw"]
b = df["Nsw"]
c = df["magnetopause_distance"]
d = df["reconnection_rate"]
And after that, I used the following logic to plot the same
#create a figure
fig=plt.figure()
#define subplots and define their position
plt1=fig.add_subplot(611)
plt2=fig.add_subplot(612)
plt3=fig.add_subplot(613)
plt4=fig.add_subplot(614)
plt5=fig.add_subplot(615)
plt6=fig.add_subplot(616)
plt1.plot(x,y,'black',linewidth=0.5,marker=None)
plt1.set_ylabel("By")
plt1.set_title("3-6 July 2003")
plt2.plot(x,z,'black',linewidth=0.5)
plt2.set_ylabel("Bz")
plt3.plot(x,a,'black',linewidth=0.5)
plt3.set_ylabel("Vsw")
plt4.plot(x,b,'black',linewidth=0.5)
plt4.set_ylabel("Nsw")
plt5.plot(x,c,'black',linewidth=0.5)
plt5.set_ylabel("MD")
plt6.plot(x,d,'black',linewidth=0.5)
plt6.set_ylabel("MRR")
plt.subplots_adjust(hspace = 2,wspace = 2)
#saving plot in .jpg format
plt.savefig('myplot01.jpg', format='jpeg',dpi=500, bbox_inches='tight')
Finally, I am getting a plot like this:
What I want is something like this:
Sorry for the typos. Thanks for your time :)
Use:
from scipy.interpolate import UnivariateSpline
import numpy as np
list_x_new = np.linspace(min(x), max(x), 1000)
list_y_smooth = UnivariateSpline(x, y, list_x_new)
plt.plot(list_x_new, list_y_smooth)
plt.show()
This is for one of the graphs, you can substitute the values in list_y_smooth in place of y according to the values you want to plot.
I want to create a secondary xaxis at the top which has an inverse relation with the major xaxis at the bottom. I followed the official tutorial here and have the following codes:
def forward(x):
return 10/x
def backward(y):
return 10/y
fig, ax = plt.subplots()
ax.set_xlim([0.14, 1.4])
secax = ax.secondary_xaxis('top', functions=(forward, backward))
secax.set_xticks(np.array([10,20,40,70])) # does not work!
plt.show()
The problem is that the xticks at the top are not at the right place. They are bunched together in the left due to the inverse function applied. How do I manually set the position of the xticks? (e.g. at 10,20,40,70)
Edit:
Just to make it more clear, the ticks are at the right place, but there are too many tickss as shown in the figure. In this case, I only want the ticks at 10, 20, 40, 70 (I don't want the ticks at 30, 50 and 60 as we can't see all the tick numbers clearly)
I believe either you missed import statement for numpy or you need to update you matplotlib. Below works fine for me -
import matplotlib.pyplot as plt
import numpy as np
def forward(x):
return 10/x
def backward(y):
return 10/y
fig, ax = plt.subplots()
ax.set_xlim([0.14, 1.4])
secax = ax.secondary_xaxis('top', functions=(forward, backward))
secax.set_xticks(np.array([10,20,40,70])) # does not work!
plt.show()
Check your version -
import matplotlib
print (matplotlib.__version__)
If above doesn't print 3.2.1. try below -
pip install matplotlib==3.2.1
It is not clear what you want to achieve.
If you want a linear relationship at the top, this might be relevant:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.set_xlim([0.14, 1.4])
secax = ax.secondary_xaxis('top', functions=(lambda x: 77 - 50 * x,
lambda y: (77 - y) / 50))
secax.set_xticks(np.array([10, 20, 40, 70]))
plt.show()
data = {'tenor': ['1w','1m','3m','6m','12m','1y','2y','3y','4y','5y','6y','7y','10y','15y','20y','25y','30y','40y','50y'],'rate_s': [0.02514, 0.026285, 0.0273, 0.0279, 0.029616, 0.026526, 0.026028, 0.024, 0.025958,0.0261375, 0.026355, 0.026, 0.026898, 0.0271745, 0.02741, 0.027, 0.0275, 0.0289,0.0284],'rate_t':[ 0.02314, 0.024285, 0.0253,0.0279, 0.028616, 0.026526,0.027028, 0.024, 0.025958,0.0271375, 0.02355, 0.026, 0.024898, 0.0271745, 0.02641,0.027, 0.0255, 0.0289,0.0284]}
I want to produce the chart in blue with the same format like below. I tried this piece of code but results are not satisfactory (chart in white). It also not showing all x-axis labels. Please suggest.
ax = plt.gca()
df.plot(kind='line',x='tenor',y='rate_s',marker='o',color='green',ax=ax)
df.plot(kind='line',x='tenor',y='rate_y',marker='o', color='red', ax=ax)
ax.minorticks_on()
ax.grid(which='major',linestyle='-', linewidth='0.5', color='blue')
ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
plt.show()
This is following the discussions in the comments.
There are a couple parts, the full example is at the bottom.
Style
One of your questions was how to change the style of the plot. This can be done with the following code:
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
there are many possible styles, and you can even create your own style if you wish. To see all possible styles see: the documentation. To list all styles use plt.style.available
Custom Ticker
For the custom tickers: you can use FixedLocator or if you know it is log or symlog, then matplotlib has a built-in locator. See the matplotlib doc for scales
You can use FixedLocator to set up the axis, to be separated. i.e. the following code will give you what you want.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
X = np.arange(0, 2000)
Y = np.arange(0, 2000)
def convert(date):
if 'w' in date:
return 7*int(date[:-1])
if 'm' in date:
return 30*int(date[:-1])
if 'y' in date:
return 30*int(date[:-1]) + 360
ticks = [convertdate(d) for d in tenor]
plt.style.use('seaborn-darkgrid')
ax = plt.axes()
t = ticker.FixedLocator(locs=ticks)
ax.xaxis.set_ticklabels(tenor)
ax.xaxis.set_major_locator(t)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(3))
plt.plot(X, Y, c = 'k')
plt.show()
Which produces:
Specific Case
For your specific case, you probably want the custom tickers to be on a specific interval (i.e. smallest of rate_t, biggest of rate_t).
Thus you would need to change the convert function to be as following:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
x = data['rate_t']
y = data['rate_s']
def get_indices(date):
if 'w' in date:
return 7*int(date[:-1])
if 'm' in date:
return 30*int(date[:-1])
if 'y' in date:
return 30*int(date[:-1]) + 360
def convert(indices):
x = np.linspace(min(data['rate_t']), max(data['rate_t']), indices[-1] + 1)
return x[indices]
indices = [get_indices(d) for d in tenor]
ticks = convert(indices)
plt.style.use('seaborn-darkgrid')
ax = plt.axes()
t = ticker.FixedLocator(locs=ticks)
ax.xaxis.set_ticklabels(tenor)
ax.xaxis.set_major_locator(t)
# ax.xaxis.set_minor_locator(ticker.MultipleLocator(3))
plt.plot(x, y, c = 'k')
plt.show()
(assuming the data['rate_s'] and data['rate_t'] are as is and without processing)
Which would produce this:
Let me know if you have any questions.
I'm trying to duplicate my y axis so that it appears on both the left and the right side of my graph (same scale on each side). I believe the correct way to do this is through the twiny method, but cannot get my head round it. Here is my current code:
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
def bar(data_df,
colour_df=None,
method='default',
ret_obj=False):
height = len(data_df.columns)*4
width = len(data_df.index)/4
ind = np.arange(len(data_df.index))
dat = data_df[data_df.columns[0]]
bar_width = 0.85
fig, ax = plt.subplots(figsize=(width,height))
ax1 = ax.bar(ind,dat,bar_width,color='y',log=True)
ax2 = ax1.twiny()
ax.tick_params(bottom='off', top='off', left='on', right='on')
plt.xticks(np.arange(len(data_df.index)) + bar_width,
data_df.index, rotation=67,ha='right')
ylab = 'Region Length (base pairs, log10)'
figname = 'bar' + method + '.png'
if ret_obj==False:
fig.savefig(figname,bbox_inches='tight',dpi=250)
print "Output figure:", figname
plt.close()
if ret_obj==True:
return fig
Which returns the following error when passed a dataframe:
AttributeError: 'BarContainer' object has no attribute 'twiny'
Having looked into it a bit further I believe that using the host/parasite methods would also work, but I'm a bit lost how I could fit it into my current code. Advice would be gratefully appreciated!
You don't have to use twiny in this case. It suffices to draw the labels on all sides:
bars = ax.bar(ind,dat,bar_width,color='y',log=True)
ax.tick_params(axis='both', which='both', labelbottom=True, labeltop=True,
labelleft=True, labelright=True)
I get following result with dummy data:
df = pd.DataFrame({"a": np.logspace(1,10,20)})
bar(df)