I’m trying to reproduce some plots from this video with up-to-date data and superimposing points on the lines for measures taken by governments. Using pandas for the data, and to call the plot commands.
wI have no trouble plotting the lines and appropriate legends. I then add superimposed points, for which I defined these properties:
point_opts = lambda marker, color: {'label': '', 'color': 'w', 'marker': marker, 'markeredgecolor': color, 'markeredgewidth': 3, 'linestyle': None}
I would like to only add those to the legend once, instead of once per country, hence the empty label.
I then try to modify the legend as follows:
handles, labels = ax.get_legend_handles_labels()
for props in ({**point_opts(marker, 'black'), 'label': measure} for measure, marker in points.items()):
handles.append(matplotlib.lines.Line2D([], [], **props))
labels.append(props['label'])
ax.legend(handles=handles, labels=labels)
However this does not change the axis legends (and no error messages are shown). The values seem right however. For example, if I add a second plot, on the Figure:
fig.legend(handles=handles, labels=labels, loc='center left')
I then get the result below.
Why is this happening? How can I actually modify my plot axis? Using python 3.7.3 and matplotlib 3.1.3 on OpenSuse x64, if that’s of any relevance.
Ugh alright, I’ve found it… I was, somewhere later, moving the legend around with:
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
Apparently that resets the legend content to whatever the plot commands put there, erasing andy manual additions.
Related
I've been trying to plot a shapefile over a basemap. My issue here is the placement of the legend. I wanted it to be placed outside (next to) the map.
Specifically, I am plotting the "Ecoregion" column of the shapefile which basically labels each polygon with a colour (I figured this was better than actually putting the names on each polygon). I've tried the following code and receive an error:
pip install geopandas
pip install contextily
import geopandas as gpd
import contextily as ctx
data = gpd.read_file("icemap.shp")
plt.rcParams.update({'font.size': 14})
ax = data.plot(
figsize=(12, 10),
column="Ecoregion",
cmap="tab10",
)
map = Basemap(
llcrnrlon=-50,
llcrnrlat=30,
urcrnrlon=70.0,
urcrnrlat=85.0,
resolution="i",
lat_0=39.5,
lon_0=1,
)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
map.fillcontinents(color="lightgreen")
map.drawcoastlines()
map.drawparallels(np.arange(10,90,20),labels=[1,1,1,1])
map.drawmeridians(np.arange(-180,180,30),labels=[1,1,0,1])
plt.title("Map", fontsize=16)
The error is -
WARNING:matplotlib.legend:No handles with labels found to put in legend.
So I tried adding
legend-True
in the "ax" parentheses and removed the "ax.legend(...)", but then the legend appears on top of the map as the picture below.
Does "handle" refer to the column that is plotted? If so, I'm confused as to why I get this error. Or do I need to add another line of code?
I'd be grateful to receive some help in this.
(Attached file link: https://drive.google.com/file/d/1OfOAstBbbxiqSybpl_CQf-o47YgpbY7D/view?usp=sharing)
I would also consider Cartopy, since Basemap has been end-of-life for a long time. The resolution of your vector is also well beyond what's plotted on screen, so you could really increase performance by simplifying it a little.
But you can pass the legend keywords along when plotting the Geodataframe.
ax = data.plot(
figsize=(10, 8),
column="Ecoregion",
cmap="tab10",
legend=True,
legend_kwds=dict(bbox_to_anchor=(1.05, 1), loc='upper left'),
)
I am trying to make a simple line plot using plotly express, of a data frame:
diccionary = {'Bet_type': ['dozen', 'column', 'even_odd', 'red_black'],
'Max_money': [216, 329, 377,193],
'times_played':[51,83,594,202]}
df = pd.DataFrame(diccionary)
df
when I try to plot all the Bet_type in a single graph, the graph appears with correct names but no lines
fig = px.line(df, x="times_played", y="Max_money", color ='Bet_type')
fig.show()
If I remove color it works but plots something that I am not interested in, In the same notebook I've made a similar plot with a different data frame and it works perfectly, so I assume doesn't have to do with the libraries.
If someone have any idea why is this happening would be really appreciated. thanks
I am plotting a relplot with Seaborn, but getting the legend (and an empty axis plot) printed under the main plot.
Here is how it looks like (in 2 photos, as my screen isn't that big):
Here is the code I used:
fig, axes = plt.subplots(1, 1, figsize=(12, 5))
clean_df['tax_class_at_sale'] = clean_df['tax_class_at_sale'].apply(str)
sns.relplot(x="sale_price_millions", y='gross_sqft_thousands', hue="neighborhood", data=clean_df, ax=axes)
fig.suptitle('Sale Price by Neighborhood', position=(.5,1.05), fontsize=20)
fig.tight_layout()
fig.show()
Does someone has an idea how to fix that, so that the legend (maybe much smaller, but it's not a problem) is printed next to the plot, and the empty axis disappears?
Here is my dataset form (in 2 screenshot, to capture all columns. "sale_price_millions" is the target column)
Since you failed to provide a Minimal, Complete, and Verifiable example, no one can give you a final working answer because we can't reproduce your figure. Nevertheless, you can try specifying the location for placing the legend as following and see if it works as you want
sns.relplot(x="sale_price_millions", y='gross_sqft_thousands', hue="neighborhood", data=clean_df, ax=axes)
plt.legend(loc=(1.05, 0.5))
I can't figure out how to rotate the text on the X Axis. Its a time stamp, so as the number of samples increase, they get closer and closer until they overlap. I'd like to rotate the text 90 degrees so as the samples get closer together, they aren't overlapping.
Below is what I have, it works fine with the exception that I can't figure out how to rotate the X axis text.
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 8}
matplotlib.rc('font', **font)
values = open('stats.csv', 'r').readlines()
time = [datetime.datetime.fromtimestamp(float(i.split(',')[0].strip())) for i in values[1:]]
delay = [float(i.split(',')[1].strip()) for i in values[1:]]
plt.plot(time, delay)
plt.grid(b='on')
plt.savefig('test.png')
This works for me:
plt.xticks(rotation=90)
Many "correct" answers here but I'll add one more since I think some details are left out of several. The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well; otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90).
Easiest / Least Code
Option 1
plt.xticks(rotation=45, ha='right')
As mentioned previously, that may not be desirable if you'd rather take the Object Oriented approach.
Option 2
Another fast way (it's intended for date objects but seems to work on any label; doubt this is recommended though):
fig.autofmt_xdate(rotation=45)
fig you would usually get from:
fig = plt.gcf()
fig = plt.figure()
fig, ax = plt.subplots()
fig = ax.figure
Object-Oriented / Dealing directly with ax
Option 3a
If you have the list of labels:
labels = ['One', 'Two', 'Three']
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, rotation=45, ha='right')
In later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks([1, 2, 3], labels, rotation=45, ha='right')
Option 3b
If you want to get the list of labels from the current plot:
# Unfortunately you need to draw your figure first to assign the labels,
# otherwise get_xticklabels() will return empty strings.
plt.draw()
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
As above, in later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')
Option 4
Similar to above, but loop through manually instead.
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
Option 5
We still use pyplot (as plt) here but it's object-oriented because we're changing the property of a specific ax object.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
Option 6
This option is simple, but AFAIK you can't set label horizontal align this way so another option might be better if your angle is not 90.
ax.tick_params(axis='x', labelrotation=45)
Edit:
There's discussion of this exact "bug" but a fix hasn't been released (as of 3.4.0):
https://github.com/matplotlib/matplotlib/issues/13774
Easy way
As described here, there is an existing method in the matplotlib.pyplot figure class that automatically rotates dates appropriately for you figure.
You can call it after you plot your data (i.e.ax.plot(dates,ydata) :
fig.autofmt_xdate()
If you need to format the labels further, checkout the above link.
Non-datetime objects
As per languitar's comment, the method I suggested for non-datetime xticks would not update correctly when zooming, etc. If it's not a datetime object used as your x-axis data, you should follow Tommy's answer:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
Try pyplot.setp. I think you could do something like this:
x = range(len(time))
plt.xticks(x, time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)
Appart from
plt.xticks(rotation=90)
this is also possible:
plt.xticks(rotation='vertical')
I came up with a similar example. Again, the rotation keyword is.. well, it's key.
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.bar( [0,1,2], [1,3,5] )
ax.set_xticks( [ 0.5, 1.5, 2.5 ] )
ax.set_xticklabels( ['tom','dick','harry'], rotation=45 ) ;
If you want to apply rotation on the axes object, the easiest way is using tick_params. For example.
ax.tick_params(axis='x', labelrotation=90)
Matplotlib documentation reference here.
This is useful when you have an array of axes as returned by plt.subplots, and it is more convenient than using set_xticks because in that case you need to also set the tick labels, and also more convenient that those that iterate over the ticks (for obvious reasons)
If using plt:
plt.xticks(rotation=90)
In case of using pandas or seaborn to plot, assuming ax as axes for the plot:
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Another way of doing the above:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
My answer is inspired by cjohnson318's answer, but I didn't want to supply a hardcoded list of labels; I wanted to rotate the existing labels:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
The simplest solution is to use:
plt.xticks(rotation=XX)
but also
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=X.XX)
e.g for dates I used rotation=45 and bottom=0.20 but you can do some test for your data
import pylab as pl
pl.xticks(rotation = 90)
To rotate the x-axis label to 90 degrees
for tick in ax.get_xticklabels():
tick.set_rotation(45)
It will depend on what are you plotting.
import matplotlib.pyplot as plt
x=['long_text_for_a_label_a',
'long_text_for_a_label_b',
'long_text_for_a_label_c']
y=[1,2,3]
myplot = plt.plot(x,y)
for item in myplot.axes.get_xticklabels():
item.set_rotation(90)
For pandas and seaborn that give you an Axes object:
df = pd.DataFrame(x,y)
#pandas
myplot = df.plot.bar()
#seaborn
myplotsns =sns.barplot(y='0', x=df.index, data=df)
# you can get xticklabels without .axes cause the object are already a
# isntance of it
for item in myplot.get_xticklabels():
item.set_rotation(90)
If you need to rotate labels you may need change the font size too, you can use font_scale=1.0 to do that.
I'm plotting a map with legends using the GeoPandas plotting function. When I plot, my legends appear in the upper right corner of the figure. Here is how it looks like:
I wanted to move the legends to the lower part of the graph. I would normally would have done something like this for a normal matplotlib plot:
fig, ax = plt.subplots(1, figsize=(4.5,10))
lima_bank_num.plot(ax=ax, column='quant_cuts', cmap='Blues', alpha=1, legend=True)
ax.legend(loc='lower left')
However, this modification is not taken into account.
This could be done using the legend_kwds argument:
df.plot(column='values', legend=True, legend_kwds={'loc': 'lower right'});
You can access the legend defined on the ax instance with ax.get_legend(). You can then update the location of the legend using the method set_bbox_to_anchor. This doesn't provide the same ease of use as the loc keyword when creating a legend from scratch, but does give control over placement. So, for your example, something like:
leg = ax.get_legend()
leg.set_bbox_to_anchor((0., 0., 0.2, 0.2))
A bit of documentation of set_bbox_to_anchor, though I don't find it extraordinarily helpful.
If you have a horizontal legend and you're trying to simply reduce the gap between the legend and plot, I recommend the colorbar approach detailed at https://gis.stackexchange.com/a/330175/32531 along with passing the pad legend_kwd argument:
legend_kwds={"orientation": "horizontal", "pad": 0.01}