I am trying to display a lineplot (using the FacetGrid version relplot(kind='line') and would like to customize the markers on the line. My problem is the color, size, and edge color of the markers but edgecolor does not seem to work here:
AttributeError: 'Line2D' object has no property 'edgecolor'
Apparently according to Seaborn docs, one can use a dictionary for the "markers" parameter but I haven't found any detailed documentation on that.
Does anyone have an idea on how to achieve this ?
This is what I tried, to no avail.
g = sns.relplot(kind='line', data=df2, x='field1', y='field2', hue='field3', marker='.', edgecolor='none')
g = sns.relplot(kind='line', data=df2, x='field1', y='field2', hue='field3', marker='.', edgecolor=None)
g = sns.relplot(kind='line', data=df2, x='field1', y='field2', hue='field3', marker='.', markers={'edgecolor': None})
g = sns.relplot(kind='line', data=df2, x='field1', y='field2', hue='field3', marker='.', markers={'edgecolor': 'none'})
none of these work
Related
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've followed this tutorial:
https://towardsdatascience.com/creating-beautiful-maps-with-python-6e1aae54c55c
and the one this above was derived from.
They pass a list of edge colors to the plot_graph function
like so:
fig, ax = ox.plot_graph(gdf, node_size=0, bbox = (north, south, east, west),figsize=(height, width),
dpi = 96,bgcolor = bgcolor,
save = False, edge_color=roadColors,
edge_linewidth=roadWidths, edge_alpha=1)
I don't think they're assigned the way that the tutorial indicates.
On the github I found get_edge_colors_by_attr which seems to take attributes into account.
How are the colors assigned?
Specifically I am asking because I'd like to plot "highways" in different colors based on their openstreetmap tag.
How does osmnx.plot_graph determine which edges get which colors?
You can see how it does it here. Essentially, it either applies a single color to all edges or, if you passed it a list of colors, it assigns the first color in the list to the first edge in the graph, the second to the second, the third to the third, and so on.
Specifically I am asking because I'd like to plot "highways" in different colors based on their openstreetmap tag.
You can create a list of colors based on the edges' highway attribute values:
import osmnx as ox
G = ox.graph_from_place('Piedmont, California, USA', network_type='drive')
# assign colors to edges based on "highway" value
hwy_color = {'residential': 'gray',
'secondary': 'r',
'tertiary': 'y',
'tertiary_link': 'b',
'unclassified': 'm'}
edges = ox.graph_to_gdfs(G, nodes=False)['highway']
ec = edges.replace(hwy_color)
# plot graph using these colors
fig, ax = ox.plot_graph(G, edge_color=ec)
Also, you mentioned get_edge_colors_by_attr but note that per the docs the attribute must be numeric.
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 tried to reproduce the solution from: How do I work with images in Bokeh (Python) , but it doesn't work. For that, I find an image on the net and put it in place of the 'url' field but the plot is just blank! From the original solution bokeh ask me to add up w and h params which I suppose are the width and height of the pic. Also I dropped x_range and y_range within figure() to wipe out the horizontal and vertical lines of the plot.
from bokeh.plotting import figure, show, output_notebook
output_notebook()
p = figure()
p.image_url( url=[ "http://pngimg.com/uploads/palm_tree/palm_tree_PNG2504.png"],
x=1, y=1, w=253, h=409)
show( p)
Anyone could tell me what's going on ?
Bokeh can't auto-range ImageURL it seems. So if there are no other glyphs, you need to provide explicit ranges. Additionlly, the default anchor is upper_left IIRC so it may be that our image is rendering off-canvas and you don't realize it. The code below works with Bokeh 0.12.5:
from bokeh.plotting import figure, show, output_file
output_file("foo.html")
p = figure(x_range=(0,500), y_range=(0,500))
p.image_url( url=[ "http://pngimg.com/uploads/palm_tree/palm_tree_PNG2504.png"],
x=1, y=1, w=253, h=409, anchor="bottom_left")
show(p)
Without the anchor set, the image plots blow the plot region (have to pan to see it)
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.