I am already having Tkinter(someone said to install a tkinter)
code used:
imports are:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
if u want to view the data-set then it is :
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv")
code used to plot boxplot in jupyter notebook
fig, ax = plt.subplots(figsize = (20,20))
sns.boxplot(data = df,ax = ax)
)
I was supposed to add in my import's
%matplotlib inline
Related
using matplotlib, I have constructed a boxplot for a dataset read from excel. I want to include the individual data points alongside my boxplot. Any advice?
Here is the code I used for my boxplot:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Fast_G = pd.read_excel(r'C:\Users\Rex Reginald\Excel work\MFGP.xlsx')
plt.style.use('ggplot')
fig, ax = plt.subplots()
ax.boxplot((Fast_G.MIA, Fast_G.CTL), vert=True, showmeans=True, meanline=True, patch_artist=True,
labels=('MIA', 'CTL'),
medianprops={'linewidth': 2, 'color': 'purple'},
meanprops={'linewidth': 2, 'color': 'red'})
plt.title('MFGP')
plt.ylabel('MP')
plt.show()
I have the following snippet in the first cell of a Jupyter notebook:
import matplotlib.pyplot as plt
import pandas as pd
import ipywidgets as widgets
import numpy as np
out = widgets.Output()
data = pd.DataFrame(np.random.normal(size = 50))
plt.ioff()
with out:
fig, axes = plt.subplots()
data.hist(ax = axes)
display(fig)
plt.ion()
display(out)
If I restart the kernel and run this first cell, I see this output:
<Figure size 640x480 with 1 Axes>
However, if I run this first cell a second time, I see a matplotlib figure as I intended. This behavior also shows up if I move everything after the import of matplotlib to a second cell, restart the kernel, and rerun the entire notebook.
Is this difference in behavior intentional?
The code rearranging and adding magic command '%matplotlib notebook' work for me.
%matplotlib notebook
import matplotlib.pyplot as plt
import pandas as pd
import ipywidgets as widgets
import numpy as np
out = widgets.Output()
plt.ioff()
fig, axes = plt.subplots()
plt.ion()
data = pd.DataFrame(np.random.normal(size = 50))
data.hist(ax = axes)
display(out)
with out:
display(fig)
I typically don't have problems with matplotlib legend, but this is the first time I am using it with multiple seaborn plots, and the following does not work.
fig = plt.figure(figsize=(10,6))
a =sns.regplot(x='VarX', y='VarY1', data=data)
b = sns.regplot(x='VarX', y='VarY2', data=data)
c = sns.regplot(x='VarX', y='VarY3', data=data)
fig.legend(handles=[a, b, c],labels=['First','Second','Third'])
fig.show()
What am I doing wrong?
seaborn.regplot returns an axes. You cannot create a legend proxy handle from an axes. However this is not even necessary. Remove the handles from the legend and it should give the desired plot.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
import pandas as pd
import seaborn as sns
data=pd.DataFrame({"VarX" : np.arange(10),
'VarY1': np.random.rand(10),
'VarY2': np.random.rand(10),
'VarY3': np.random.rand(10)})
fig = plt.figure(figsize=(10,6))
sns.regplot(x='VarX', y='VarY1', data=data)
sns.regplot(x='VarX', y='VarY2', data=data)
sns.regplot(x='VarX', y='VarY3', data=data)
fig.legend(labels=['First','Second','Third'])
plt.show()
I'd like to change the spacing of the horizontal grid lines on a seaborn chart, I've tried setting the style with no luck:
seaborn.set_style("whitegrid", {
"ytick.major.size": 0.1,
"ytick.minor.size": 0.05,
'grid.linestyle': '--'
})
bar(range(len(data)),data,alpha=0.5)
plot(avg_line)
The gridlines are set automatically desipite me trying to overide the tick size
Any suggestions? Thanks!
you can set the tick locations explicitly later, and it will draw the grid at those locations.
The neatest way to do this is to use a MultpleLocator from the matplotlib.ticker module.
For example:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
sns.set_style("whitegrid", {'grid.linestyle': '--'})
fig,ax = plt.subplots()
ax.bar(np.arange(0,50,1),np.random.rand(50)*0.016-0.004,alpha=0.5)
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.005))
plt.show()
The OP asked about modifying tick distances in Seaborn.
If you are working in Seaborn and you use a plotting feature that returns an Axes object, then you can work with that just like any other Axes object in matplotlib. For example:
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from matplotlib.ticker import MultipleLocator
df = sm.datasets.get_rdataset("Guerry", "HistData").data
ax = sns.scatterplot('Literacy', 'Lottery', data=df)
ax.yaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_locator(MultipleLocator(10))
plt.show()
Put if you are working with one of the Seaborn processes that involve FacetGrid objects, you will see precious little help on how to modify the tick marks without manually setting them. You have dig out the Axes object from the numpy array inside FacetGrid.axes .
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator
tips = sns.load_dataset("tips")
g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, )
g.axes[0][0].yaxis.set_major_locator(MultipleLocator(3))
Note the double subscript required. g is a FacetGrid object, which holds a two-dimensional numpy array of dtype=object, whose entries are matplotlib AxesSubplot objects.
If you are working with a FacetGrid that has multiple axes, then each one will have to be extracted and modified.
I ran the following code to get two plots next to each other (it is a minimal working example that you can copy):
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
plt.show()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde")
Unfortunately, I then get the following image:
This is also an interesting layout, but I wanted the figures to be next to each other. What did I do wrong? How can I correct it?
For clarity, I could also use this:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
fig, axes = plt.subplots(2,2)
plt.show()
axes[0,0].hist(values, bins=100, alpha=0.5, color="r", normed=True) # Until here, it works. You get a half-finished correct image of what I was going for (though it is 2x2 here)
axes[0,1].plot(values, kind="kde") # This does not work
Unfortunately, in this approach axes[0,1] refers to the subplot that has a plot method but does not know kind="kde". Please take into consideration that the in the first version plot is executed on the pandas object, whereas in the second version plot is executed on the subplot, which does not work with the kind="kde" parameter.
use ax= argument to set which subplot object to plot:
import pandas as pd
import numpy as np
from matplotlib.pylab import plt
comp1 = np.random.normal(0,1,size=200)
values = pd.Series(comp1)
plt.close("all")
f = plt.figure()
sp1 = f.add_subplot(2,2,1)
values.hist(bins=100, alpha=0.5, color="r", normed=True, ax=sp1)
sp2 = f.add_subplot(2,2,2)
values.plot(kind="kde", ax=sp2)