i am having some trouble with a seaborn pointplot.
I am to plot the Temperature vs. growth rate of four kinds of bacteria, so that each type has its own graph, but all four are in the same plot. The thing is, i cannot connect the individual points, i can only get the individual points.
My code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats, integrate
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
dataSorted=data.sort_values(['Temperature','Growth_rate'],ascending=[True,True])
plt.subplots()
ax2=sns.pointplot(x='Temperature',y='Growth_rate', hue='Bacteria' ,data=dataSorted,scale=0.7,join=True)
axes2=ax2.axes
axes2.set_xlim(10,60)
axes2.set_ylim(0,1.5)
axes2.set_xticks(np.arange(1,7)*10)
axes2.set_xticklabels(np.arange(1,7)*10)
The output is exactly as specified, apart from the lines between points:
My plot - without lines
I have no idea how to fix this, i have even set the "join" parameter manually, even though it is set as True by default.
Related
I want to add an additional variable to the plot listed below. At the moment I have a different colour of marker corresponding to a different metal. But for every metal, there is a different geometry, so I would like to add a marker for every colour (e.g. red dot and red square). When I add "style=POM" I get this error message:
ValueError: Could not interpret value POM for parameter style
Any help on this would be appreciated!
Example .csv file:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.pyplot as plt
dfdata=pd.read_csv('C:/Users/2586376t/conda/BOTH.csv')
sns.jointplot(data=dfdata, x="MO", y="ORB", hue="METAL")
plt.show()
style=POM tries to set the parameter style to POM. In seaborn jointplot, the style parameter does not exist
Ref : https://seaborn.pydata.org/generated/seaborn.jointplot.html
If you want 1 plot with different colors corresponding to different metals and different markers corresponding to different POM values then your solution is to use a scatterplot like this :
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.pyplot as plt
dfdata=pd.read_csv('C:/Users/2586376t/conda/BOTH.csv')
plot = sns.scatterplot(data=dfdata, x="MO", y="ORB", hue="METAL" , style=dfdata['POM'])
plt.show()
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt("traffic.csv", delimiter=',', encoding="utf8",dtype=None)
plt.hist(data[1:,2])
plt.show()
So, basically it overlaps, and I want it to be simplified.
csv link
You can use MaxNLocator to set the maximum number of ticks that will fit on nicely.
I am creating a joyplot using joypy.
All my data is between[0,1].
But I get a big range of negative values in the graph:
import joypy
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import cm
import matplotlib.ticker as ticker
import matplotlib
matplotlib.use('TkAgg')
iris = pd.read_csv("1_5.csv")
fig, axes = joypy.joyplot(iris)
x = [0,0.25,0.5,0.75,1]
plt.xticks(x)
plt.show()
It isn't clear that your xticks are in any way tied to the actual joyplot itself (ie, you've created arbitrary x-ticks and placed them on the plot).
Are tick marks not represented on the plot originally (similar plots I've seen all have them by default)?
If I use the following code I end up with an overcrowded x-axis. I would like to show only every 10th number on the x axis. Meaning [0,10,...].
Any idea how to do this?
import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
a = pd.DataFrame({'y':np.random.randn(100)})
a['time']=a.index
ax = sns.pointplot(x='time', y="y", data=a)
plt.show()
You may decide not to use a pointplot at all. A usual lineplot seems to suffice.
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
a = pd.DataFrame({'y':np.random.randn(100)})
plt.plot(a.index, a.y)
plt.show()
Now this gives ticks at steps of 20. The easiest option here would be to use
plt.xticks(range(0,101,10))
to get the steps of 10. Or equally possible,
plt.gca().locator_params(nbins=11)
to devide the axis into 11 bins.
Of course the use of an appropriate locator would be equally possible.
I try to plot group wise median values using seaborn's pointlot on top of a swarmplot. Even though I call pointPlot second, the point plot ends up behind the swarmplot. How can I change the 'layer order' such that the point plot is in front of the swarmplot?
datDf=pd.DataFrame({'values':np.random.randint(0,100,100)})
datDf['group']=np.random.randint(0,5,100)
sns.swarmplot(data=datDf,x='group',y='values')
sns.pointplot(data=datDf,x='group',y='values',estimator=np.median,join=False)
Use zorder property to set proper drawing order.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
datDf=pd.DataFrame({'values':np.random.randint(0,100,100)})
datDf['group']=np.random.randint(0,5,100)
sns.swarmplot(data=datDf,x='group',y='values',zorder=1)
sns.pointplot(data=datDf,x='group',y='values',estimator=np.median,join=False, zorder=100)
plt.show()