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When I run the code below I notice that the heatmap does not have a square shape knowing that I have used square=True but it did not work! Any idea how can I print the heatmap in a square format? Thank you!
The code:
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib as plt
import os
import seaborn as sns
temp_hourly_A5_A7_AX_ASHRAE=pd.read_csv('C:\\Users\\cvaa4\\Desktop\\projects\\s\\temp_hourly_A5_A7_AX_ASHRAE.csv',index_col=0, parse_dates=True, dayfirst=True, skiprows=2)
sns.heatmap(temp_hourly_A5_A7_AX_ASHRAE,cmap="YlGnBu", vmin=18, vmax=27, square=True, cbar=False, linewidth=0.0001);
The result:
square=True should work to have square cells, below is a working example:
import pandas as pd
import numpy as np
import seaborn as sns
df = pd.DataFrame(np.tile([0,1], 15*15).reshape(-1,15))
sns.heatmap(df, square=True)
If you want a square shape of the plot however, you can use set_aspect and the shape of the data:
ax = sns.heatmap(df)
ax.set_aspect(df.shape[1]/df.shape[0]) # here 0.5 Y/X ratio
You can use matplotlib and set a figsize before plotting heatmap.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
rnd = np.random.default_rng(12345)
data = rnd.uniform(-100, 100, [100, 50])
plt.figure(figsize=(6, 5))
sns.heatmap(data, cmap='viridis');
Note that I used figsize=(6, 5) rather than a square figsize=(5, 5). This is because on a given figsize, seaborn also puts the colorbar, which might cause the heatmap to be squished a bit. You might want to change those figsizes too depending on what you need.
I want this plot's y-axis to be centered at 38, and the y-axis scaled such that the 'humps' disappear. How do I accomplish this?
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
s=['05/02/2019', '06/02/2019', '07/02/2019', '08/02/2019',
'09/02/2019', '10/02/2019', '11/02/2019', '12/02/2019',
'13/02/2019', '20/02/2019', '21/02/2019', '22/02/2019',
'23/02/2019', '24/02/2019', '25/02/2019']
df[0]=['38.02', '33.79', '34.73', '36.47', '35.03', '33.45',
'33.82', '33.38', '34.68', '36.93', '33.44', '33.55',
'33.18', '33.07', '33.17']
# Data for plotting
fig, ax = plt.subplots(figsize=(17, 2))
for i,j in zip(s,df[0]):
ax.annotate(str(j),xy=(i,j+0.8))
ax.plot(s, df[0])
ax.set(xlabel='Dates', ylabel='Latency',
title='Hongkong to sing')
ax.grid()
#plt.yticks(np.arange(min(df[p]), max(df[p])+1, 2))
fig.savefig("test.png")
plt.show()
I'm not entirely certain if this is what you're looking for but you can adjust the y-limits explicitly to change the scale, i.e.
ax.set_ylim([ax.get_ylim()[0], 42])
Which only sets the upper bound, leaving the lower limit unchanged, this would give you
you can supply any values you find appropriate, i.e.
ax.set_ylim([22, 52])
will give you something that looks like
Also note that the tick labels and general appearance of your plot will differ from what is shown here.
Edit - Here is the complete code as requested:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame()
s=['05/02/2019', '06/02/2019', '07/02/2019', '08/02/2019',
'09/02/2019', '10/02/2019', '11/02/2019', '12/02/2019',
'13/02/2019', '20/02/2019', '21/02/2019', '22/02/2019',
'23/02/2019', '24/02/2019', '25/02/2019']
df[0]=['38.02','33.79','34.73','36.47','35.03','33.45',
'33.82','33.38','34.68','36.93','33.44','33.55',
'33.18','33.07','33.17']
# Data for plotting
fig, ax = plt.subplots(figsize=(17, 3))
#for i,j in zip(s,df[0]):
# ax.annotate(str(j),xy=(i,j+0.8))
ax.plot(s, pd.to_numeric(df[0]))
ax.set(xlabel='Dates', ylabel='Latency',
title='Hongkong to sing')
ax.set_xticklabels(pd.to_datetime(s).strftime('%m.%d'), rotation=45)
ax.set_ylim([22, 52])
plt.show()
I have pandas series of complex numbers, which I would like to plot. Currently, I am looping through each point and assigning it a color. I would prefer to generate the plot without the need to loop over each point... Using Series.plot() would be preferable. Converting series to numpy is ok though.
Here is an example of what I currently have:
import pandas as pd
import numpy as np
from matplotlib import pyplot
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
cmap = pyplot.cm.viridis
for i, val in enumerate(s):
pyplot.plot(np.real(val), np.imag(val), 'o', ms=10, color=cmap(i/(len(s)-1)))
pyplot.show()
You can use pyplot.scatter, which allows coloring of points based on a value.
pyplot.scatter(np.real(s), np.imag(s), s=50, c=np.arange(len(s)), cmap='viridis')
Here, we set c to an increasing sequence to get the same result as in the question.
You can simply plot the real and imaginary part of the series without a loop.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.plot(s.values.real,s.values.imag, marker="o", ls="")
plt.show()
However, you need to use a scatter plot if you want to have different colors:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.scatter(s.values.real,s.values.imag, c = range(len(s)), cmap=plt.cm.viridis)
plt.show()
I'm trying to create a heatmap in seaborn (python) with certain squares colored with a different color, (these squares contain insignificant data - in my case it will be squares with values less than 1.3, which is -log of p-values >0.05). I couldn't find such function. Masking these squares also didn't work.
Here is my code:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import seaborn as sns; sns.set()
data = [[1.3531363408, 3.339479161, 0.0760855365], [5.1167382617, 3.2890920405, 2.4764601828], [0.0025058257, 2.3165128345, 1.6532714962], [0.2600549869, 5.8427407219, 6.6627226609], [3.0828581725, 16.3825494439, 12.6722666929], [2.3386307357, 13.7275065772, 12.5760972276], [1.224683813, 2.2213656372, 0.6300876451], [0.4163788387, 1.8128374089, 0.0013106046], [0.0277592882, 2.9286203949, 0.810978992], [0.0086613622, 0.6181261247, 1.8287878837], [1.0174519889, 0.2621290291, 0.1922637697], [3.4687429571, 4.0061981716, 0.5507951444], [7.4201304939, 3.881457516, 0.1294141768], [2.5227546319, 6.0526491816, 0.3814362442], [8.147538027, 14.0975727815, 7.9755706939]]
cmap2 = mpl.colors.ListedColormap(sns.cubehelix_palette(n_colors=20, start=0, rot=0.4, gamma=1, hue=0.8, light=0.85, dark=0.15, reverse=False))
ax = sns.heatmap(data, cmap=cmap2, vmin=0)
plt.show()
I want to add that I'm not very advanced programmer.
OK, so I can answer my question myself now :) Here is the code that solved the problem:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import seaborn as sns; sns.set()
data = np.array([[1.3531363408, 3.339479161, 0.0760855365],
[5.1167382617, 3.2890920405, 2.4764601828],
[0.0025058257, 2.3165128345, 1.6532714962],
[0.2600549869, 5.8427407219, 6.6627226609],
[3.0828581725, 16.3825494439, 12.6722666929],
[2.3386307357, 13.7275065772, 12.5760972276],
[1.224683813, 2.2213656372, 0.6300876451],
[0.4163788387, 1.8128374089, 0.0013106046],
[0.0277592882, 2.9286203949, 0.810978992],
[0.0086613622, 0.6181261247, 1.8287878837],
[1.0174519889, 0.2621290291, 0.1922637697],
[3.4687429571, 4.0061981716, 0.5507951444],
[7.4201304939, 3.881457516, 0.1294141768],
[2.5227546319, 6.0526491816, 0.3814362442],
[8.147538027, 14.0975727815, 7.9755706939]])
cmap1 = mpl.colors.ListedColormap(['c'])
fig, ax = plt.subplots(figsize=(8, 8))
sns.heatmap(data, ax=ax)
sns.heatmap(data, mask=data > 1.3, cmap=cmap1, cbar=False, ax=ax)
plt.show()
So the problem with masking which didn't work before was that it works only on arrays not on lists.
And another thing is just plotting the heatmap twice -second time with masking.
The only thing I still don't understand is that it masks opposite fields from what is written.. I want to mask values below 1.3, but then it colored values above 1.3.. So I wrote mask=data >1.3 and now it works...
Python (and matplotlib) newbie here coming over from R, so I hope this question is not too idiotic. I'm trying to make a loglog plot on a natural log scale. But after some googling I cannot somehow figure out how to force pyplot to use a base e scale on the axes. The code I have currently:
import matplotlib.pyplot as pyplot
import math
e = math.exp(1)
pyplot.loglog(range(1,len(degrees)+1),degrees,'o',basex=e,basey=e)
Where degrees is a vector of counts at each value of range(1,len(degrees)+1). For some reason when I run this code, pyplot keeps giving me a plot with powers of 2 on the axes. I feel like this ought to be easy, but I'm stumped...
Any advice is greatly appreciated!
When plotting using plt.loglog you can pass the keyword arguments basex and basey as shown below.
From numpy you can get the e constant with numpy.e (or np.e if you import numpy as np)
import numpy as np
import matplotlib.pyplot as plt
# Generate some data.
x = np.linspace(0, 2, 1000)
y = x**np.e
plt.loglog(x,y, basex=np.e, basey=np.e)
plt.show()
Edit
Additionally if you want pretty looking ticks you can use matplotlib.ticker to choose the format of your ticks, an example of which is given below.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
x = np.linspace(1, 4, 1000)
y = x**3
fig, ax = plt.subplots()
ax.loglog(x,y, basex=np.e, basey=np.e)
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
ax.xaxis.set_major_formatter(mtick.FuncFormatter(ticks))
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
It can also works for semilogx and semilogy to show them in e and also change their name.
import matplotlib.ticker as mtick
fig, ax = plt.subplots()
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
plt.semilogy(Time_Series, California_Pervalence ,'gray', basey=np.e )
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
Take a look at the image.