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)
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
I have the following scatter plot created with seaborn.scatterplot:
where I used the code:
seaborn.scatterplot(x=X1, y=Y2, s=5, color=".15")
Now I want to add a 2D histogram generated from related data but described by the same reference frame. The histogram looks like the following when calculated independently:
where I have used:
seaborn.jointplot(x=X2, y=Y2,kind="hex",marginal_kws=dict(bins=100))
So, how can I merge both plots?
sns.jointplot() returns a JointGrid which has attributes ax_joint, ax_marg_x, and ax_marg_y which can be used to modify the plot.
sns.scatterplot() can accept an ax as a parameter on which to draw the scatter plot.
A combined operation could look like:
import seaborn as sns
# ... read in or generate data
g = sns.jointplot(x=X2, y=Y2, kind="hex", marginal_kws=dict(bins=100))
sns.scatterplot(x=X1, y=Y2, s=5, color=".15", ax=g.ax_joint)
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.
I'm using Bokeh to plot the results of ~700 simulations against another set of results using a scatter plot. I'd like to use the hover tool to qualitatively determine patterns in the data by assigning a custom index that identifies the simulation parameters.
In the code below, x and y are the columns from a Pandas DataFrame which has the simulation IDs for the index. I've been able to assign this index to an array using <DataFrameName>.index.values but I haven't found any documentation on how to assign an index to the hover tool.
# Bokeh Plotting
h = 500
w = 500
default_tools = "pan, box_zoom, resize, wheel_zoom, save, reset"
custom_tools = ", hover"
fig = bp.figure(x_range=xr, y_range=yr, plot_width=w, plot_height=h, tools=default_tools+custom_tools)
fig.x(x, y, size=5, color="red", alpha=1)
bp.show(fig)
The documentation for configuring the hover tool has an example of how to do this that worked for me. Here's the code I used:
from bokeh.models import ColumnDataSource, HoverTool
cds = ColumnDataSource(
data=dict(
x=xdata,
y=ydata,
desc=sim
)
)
hover = HoverTool()
hover.tooltips = [
("Index", "$index"),
("(2z,1z)", "($x, $y)"),
("ID", "#desc")
]
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: