Develop Reference

Show median and quantiles on Seaborn pairplot (Python)

I am making a corner plot using Seaborn. I would like to display lines on each diagonal histogram showing the median value and quantiles. Example shown below.
I usually do this using the Python package 'corner', which is straightforward. I want to use Seaborn just because it has better aesthetics.
The seaborn plot was made using this code:
import seaborn as sns
df = pd.DataFrame(samples_new, columns = ['r1', 'r2', 'r3'])
cornerplot = sns.pairplot(df, corner=True, kind='kde',diag_kind="hist", diag_kws={'color':'darkslateblue', 'alpha':1, 'bins':10}, plot_kws={'color':'darkslateblue', 's':10, 'alpha':0.8, 'fill':False})

Seaborn provides test data sets that come in handy to explain something you want to change to the default behavior. That way, you don't need to generate your own test data, nor to supply your own data that can be complicated and/or sensitive.
To update the subplots in the diagonal, there is g.map_diag(...) which will call a given function for each individual column. It gets 3 parameters: the data used for the x-axis, a label and a color.
Here is an example to add vertical lines for the main quantiles, and change the title. You can add more calculations for further customizations.
import matplotlib.pyplot as plt
import seaborn as sns
def update_diag_func(data, label, color):
for val in data.quantile([.25, .5, .75]):
plt.axvline(val, ls=':', color=color)
plt.title(data.name, color=color)
iris = sns.load_dataset('iris')
g = sns.pairplot(iris, corner=True, diag_kws={'kde': True})
g.map_diag(update_diag_func)
g.fig.subplots_adjust(top=0.97) # provide some space for the titles
plt.show()

Seaborn is built ontop of matplotlib so you can try this:
import seaborn as sns
from matplotlib import pyplot as plt
df = pd.DataFrame(samples_new, columns = ['r1', 'r2', 'r3'])
cornerplot = sns.pairplot(df, corner=True, kind='kde',diag_kind="hist", diag_kws={'color':'darkslateblue', 'alpha':1, 'bins':10}, plot_kws={'color':'darkslateblue', 's':10, 'alpha':0.8, 'fill':False})
plt.text(300, 250, "An annotation")
plt.show()

How to make a distplot for each column in a pandas dataframe

I 'm using Seaborn in a Jupyter notebook to plot histograms like this:
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('CTG.csv', sep=',')
sns.distplot(df['LBE'])
I have an array of columns with values that I want to plot histogram for and I tried plotting a histogram for each of them:
continous = ['b', 'e', 'LBE', 'LB', 'AC']
for column in continous:
sns.distplot(df[column])
And I get this result - only one plot with (presumably) all histograms:
My desired result is multiple histograms that looks like this (one for each variable):
How can I do this?

Insert plt.figure() before each call to sns.distplot() .
Here's an example with plt.figure():
Here's an example without plt.figure():
Complete code:
# imports
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [6, 2]
%matplotlib inline
# sample time series data
np.random.seed(123)
df = pd.DataFrame(np.random.randint(-10,12,size=(300, 4)), columns=list('ABCD'))
datelist = pd.date_range(pd.datetime(2014, 7, 1).strftime('%Y-%m-%d'), periods=300).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
df.iloc[0]=0
df=df.cumsum()
# create distplots
for column in df.columns:
plt.figure() # <==================== here!
sns.distplot(df[column])

Distplot has since been deprecated in seaborn versions >= 0.14.0. You can, however, use sns.histplot() to plot histogram distributions of the entire dataframe (numerical features only) in the following way:
fig, axes = plt.subplots(2,5, figsize=(15, 5))
ax = axes.flatten()
for i, col in enumerate(df.columns):
sns.histplot(df[col], ax=ax[i]) # histogram call
ax[i].set_title(col)
# remove scientific notation for both axes
ax[i].ticklabel_format(style='plain', axis='both')
fig.tight_layout(w_pad=6, h_pad=4) # change padding
plt.show()
If, you specifically want a way to estimate the probability density function of a continuous random variable using the Kernel Density Function (mimicing the default behavior of sns.distplot()), then inside the sns.histplot() function call, add kde=True, and you will have curves overlaying the histograms.

Also works when looping with plt.show() inside:
for column in df.columns:
sns.distplot(df[column])
plt.show()

How do i get a horizontal violin plot or boxplot? (default is vertical in hvplot holoviews)

Hvplot plots default a vertical violinplot or boxplot. See example below.
How do I get this to be a horizontal plot? So basically I would like to rotate this plot.
import numpy as np
import pandas as pd
import hvplot
import hvplot.pandas
df = pd.DataFrame(np.random.normal(size=[100, 2]), columns=['col1', 'col2'])
plot_hvplot = df.hvplot(kind='box')

You can do this by adding argument invert=True, like this:
plot_hvplot = df.hvplot(kind='box', invert=True)
or by using method .opts(invert_axes=True):
plot_hvplot = df.hvplot(kind='box').opts(invert_axes=True)

Different point size based on hue argument in seaborn

I am trying to have different point sizes on a seaboard scatterplot depending on the value on the "hue" column of my dataframe.
sns.scatterplot(x="X", y="Y", data=df, hue='value',style='value')
value can take 3 different values (0,1 and 2) and I would like points which value is 2 to be bigger on the graph.
I tried the sizes argument :
sizes=(1,1,4)
But could not get it done this way.

Let's use the s parameter and pass a list of sizes using a function of df['value'] to scale the point sizes:
df = pd.DataFrame({'X':[1,2,3],'Y':[1,4,9],'value':[1,0,2]})
import seaborn as sns
_ = sns.scatterplot(x='X',y='Y', data=df, s=df['value']*50+10)
Output:

Using seaborn scatterplots arguments:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({'X':[1,2,3,4,5],'Y':[1,2,3,4,5],
'value':[1,1,0,2,2]})
df["size"] = np.where(df["value"] == 2, "Big", "Small")
sns.scatterplot(x="X", y="Y", hue='value', size="size",
data=df, size_order=("Small", "Big"), sizes=(160, 40))
plt.show()
Note that the order of sizes needs to be reveresed compared to the size_order. I have no idea why that would make sense, though.

How to plot a Python Dataframe with category values like this picture?

How can I achieve that using matplotlib?

Here is my code with the data you provided. As there's no class [they are all different, despite your first example in your question does have classes], I gave colors based on the numbers. You can definitely start alone from here, whatever result you want to achieve. You just need pandas, seaborn and matplotlib:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# import xls
df=pd.read_excel('data.xlsx')
# exclude Ranking values
df1 = df.ix[:,1:-1]
# for each element it takes the value of the xls cell
df2=df1.applymap(lambda x: float(x.split('\n')[1]))
# now plot it
df_heatmap = df2
fig, ax = plt.subplots(figsize=(15,15))
sns.heatmap(df_heatmap, square=True, ax=ax, annot=True, fmt="1.3f")
plt.yticks(rotation=0,fontsize=16);
plt.xticks(fontsize=12);
plt.tight_layout()
plt.savefig('dfcolorgraph.png')
Which produces the following picture.