I plotted a scatterplot with seaborn library and I want to change the legend text but dont know how to do that.
example:
The following is iris dataset with species columns encoded in 0/1/2 as per species.
plt.figure(figsize=(8,8))
pl = sns.scatterplot(x='petal_length', y ='petal_width', hue='Species', data=data, s=40,
palette='Set1', legend='full')
I want to change the legends text from [0, 1, 2] to ['setosa', 'versicolor', 'virginica'].
can anybody help.
First, Seaborn (and Matplotlib) usually picks up the labels to put into the legend for hue from the unique values of the array you provide as hue. So as a first step, check that the column Species in your dataframe actually contains the values "setosa", "versicolor", "virginica". If not, one solution is to temporarily map them to other values, for the purpose of plotting:
legend_map = {0: 'setosa',
1: 'versicolor',
2: 'virginica'}
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x=data['petal_length'], y =data['petal_width'], hue=data['species'].map(legend_map),
s=40, palette='Set1', legend='full')
plt.show()
Alternatively, if you want to directly manipulate the plot information and not the underlying data, you can do by accessing the legend names directly:
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x='petal_length', y ='petal_width', hue='species', data=data, s=40,
palette='Set1', legend='full')
l = ax.legend()
l.get_texts()[0].set_text('Species') # You can also change the legend title
l.get_texts()[1].set_text('Setosa')
l.get_texts()[2].set_text('Versicolor')
l.get_texts()[3].set_text('Virginica')
plt.show()
This methodology allows you to also change the legend title, if need be.
Related
I am trying to create visualizations for recent commonwealth medal tally dataset.
I would like to create a grouped bar chart of top ten countries by total number of medals won.
Y axis = total
x axis = Country name
How can I divide totals into three bars consisting of no of :
gold, Silver,Bronze medals won by each country?
I created one using excel, but don't know how to do it using seaborn
P.S. I have already tried using a list of columns for hue.
df_10 = df.head(10)
sns.barplot(data = df_10, x = 'team' , y = 'total' , hue = df_10[["gold" ,
"silver","bronze"]].apply(tuple , axis = 1) )
Here is the chart that I created using excel:
enter image description here
To plot the graph, you will need to change the dataframe to the format that will allow for easy plotting. One of the ways to do this is using dataframe.melt(). The method used by you may not work... Once the data is in a format that seaborn understands easily, plotting will become simple. As you have not provided the format for df_10, I have assumed the data to have 4 columns - Country, Gold, Silver and Bronze. Below is the code...
## Use melt using Country as ID and G, S, B as the rows for values
df_10 = pd.melt(df_10, id_vars=['Country'], value_vars=['Gold', 'Silver', 'Bronze'])
df_10.rename(columns={'value':'Count', 'variable':'Medals'}, inplace=True) ##Rename so the plot has informative texts
fig, ax=plt.subplots(figsize=(12, 7)) ## Set figure size
ax=sns.barplot(data=df_10, x='Country', y='Count', hue='Medals') ## Plot the graph
My dataframe has a column 'rideable_type' which has 3 unique values:
1.classic_bike
2.docked_bike
3.electric_bike
While plotting a barplot using the following code:
g = sns.FacetGrid(electric_casual_type_week, col='member_casual', hue='rideable_type', height=7, aspect=0.65)
g.map(sns.barplot, 'day_of_week', 'number_of_rides').add_legend()
I only get a plot showing 2 unique 'rideable_type' values.
Here is the plot:
As you can see only 'electric_bike' and 'classic_bike' are seen and not 'docked_bike'.
The main problem is that all the bars are drawn on top of each other. Seaborn's barplots don't easily support stacked bars. Also, this way of creating the barplot doesn't support the default "dodging" (barplot is called separately for each hue value, while it would be needed to call it in one go for dodging to work).
Therefore, the recommended way is to use catplot, a special version of FacetGrid for categorical plots.
g = sns.catplot(kind='bar', data=electric_casual_type_week, x='day_of_week', y='number_of_rides',
col='member_casual', hue='rideable_type', height=7, aspect=0.65)
Here is an example using Seaborn's 'tips' dataset:
import seaborn as sns
tips = sns.load_dataset('tips')
g = sns.FacetGrid(data=tips, col='time', hue='sex', height=7, aspect=0.65)
g.map_dataframe(sns.barplot, x='day', y='total_bill')
g.add_legend()
When comparing with sns.catplot, the coinciding bars are clear:
g = sns.catplot(kind='bar', data=tips, x='day', y='total_bill', col='time', hue='sex', height=7, aspect=0.65)
I have a dataframe 'Spreads' where one of the columns is 'HY_OAS'. My goal is to draw a horizontal line (basically representing a range of values for 'HY_OAS') and plot the column mean on that line. In addition, I wanted the x axis min/max to be the min/max for that column and I'd like to include text boxes annotating the min/max. The problem is I'm not sure how to proceed because all I have is the below. Thanks for any and all help. The goal is the second image and the current code is the first image.
fig8 = px.scatter(x=[Spreads['HY_OAS'].mean()], y=[0])
fig8.update_xaxes(visible=True,showticklabels=False,range=[Spreads['HY_OAS'].min(),Spreads['HY_OAS'].max()])
fig8.update_yaxes(visible=True,showticklabels=False, range=[0,0])
Following what you describe and what you have coded
generate some sample data in a dataframe
scatter values along x-axis and use constant for y-axis
add mean marker
format figure
add required annotations
import numpy as np
import plotly.express as px
import pandas as pd
# simulate some data
Spreads = pd.DataFrame({"HY_OAS": np.sin(np.random.uniform(0, np.pi * 2, 50))})
# scatter values along x-axis and and larger point for mean
fig = px.scatter(Spreads, x="HY_OAS", y=np.full(len(Spreads), 0)).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
# fix up figure config
fig.update_layout(
xaxis_visible=False,
yaxis_visible=False,
showlegend=False,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
)
# finally required annootations
fig.add_annotation(x=Spreads["HY_OAS"].mean(), y=0, text=Spreads["HY_OAS"].mean().round(4))
fig.add_annotation(x=Spreads["HY_OAS"].min(), y=0, text=Spreads["HY_OAS"].min().round(2), showarrow=False, xshift=-20)
fig.add_annotation(x=Spreads["HY_OAS"].max(), y=0, text=Spreads["HY_OAS"].max().round(2), showarrow=False, xshift=20)
straight line
build base figure as follows
then same code to add annotations and configure layout
fig = px.line(x=[Spreads["HY_OAS"].min(), Spreads["HY_OAS"].max()], y=[0,0]).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
Here is a code snippet
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g = g.map(plt.hist, "tip")
with the following output
I want to introduce despine offset to these plots while keeping the rest unchanged. Therefore, I inserted the despine function in the existing code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
which results in the following plots
As a result, the offset is applied to the axes. However, the ytick labels on the right plot are back, which I don't want.
Could anyone help me on this?
To remove the yaxis tick labels, you can use the code below:
The libs:
import seaborn as sns
sns.set_style('ticks')
The adjusted code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
# IMPORTANT: I assume that you use colwrap=None in FacetGrid constructor
# loop over the non-left axes:
for ax in g.axes[:, 1:].flat:
# get the yticklabels from the axis and set visibility to False
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
A bit more general, image you now have a 2x2 FacetGrid, you want to despine with an offset, but the x- and yticklabels return:
Remove them all using this code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time', row='sex')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
# IMPORTANT: I assume that you use colwrap=None in FacetGrid constructor
# loop over the non-left axes:
for ax in g.axes[:, 1:].flat:
# get the yticklabels from the axis and set visibility to False
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
# loop over the top axes:
for ax in g.axes[:-1, :].flat:
# get the xticklabels from the axis and set visibility to False
for label in ax.get_xticklabels():
label.set_visible(False)
ax.xaxis.offsetText.set_visible(False)
UPDATE:
for completeness, mwaskom (ref to github issue) gave an explanation why this issue is occuring:
So this happens because matplotlib calls axis.reset_ticks() internally when moving the spine. Otherwise, the spine gets moved but the ticks stay in the same place. It's not configurable in matplotlib and, even if it were, I don't know if there is a public API for moving individual ticks. Unfortunately I think you'll have to remove the tick labels yourself after offsetting the spines.
I have with 5 different values and I would like to create a legend ?
These are continuous data, I need small coloured squares !
How to add legend in imagesc plot in matlab Something like this but with squares, I tried replacing "line" by "rectangle" but that's not the trick apparently !
Thank you
I just used your linked example code and modified it a little:
N=4; % # of data types, hence legend entries
Data = randi(N,30,30); % generate fake data
imagesc(Data) % image it
cmap = jet(N); % assigen colormap
colormap(cmap)
hold on
markerColor = mat2cell(cmap,ones(1,N),3);
L = plot(ones(N), 'LineStyle','none','marker','s','visible','off');
set(L,{'MarkerFaceColor'},markerColor,{'MarkerEdgeColor'},markerColor);
legend('A','B','C','D')
The trick is to use markers instead of the line itself.
it returns: