So far, I have managed to spawn a legend box and have managed to put it outside the chart. But it is showing the same colours for both the labels (white and white) whereas I would prefer it to show white and gray.
import missingno as msno
msno.matrix(X_train, figsize=(15,10), sparkline=False, p=0);
plt.legend(['missing','not missing'],loc='center left', bbox_to_anchor=(1, 0.5))
You'll have to craft the legend by hand. matplotlib has a legend guide showing how you can do this. The section describing "proxy artists" in particular is relevant to your use case. I haven't tested it, but the following should work:
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import missingno as msno
msno.matrix(...your data...)
gray_patch = mpatches.Patch(color='gray', label='Data present')
white_patch = mpatches.Patch(color='white', label='Data absent ')
plt.legend(handles=[gray_patch, white_patch])
plt.show()
Related
I am creating shot plots for NHL games and I have succeeded in making the plot, but I would like to draw the lines that you see on a hockey rink on it. I basically just want to draw two circles and two lines on the plot like this.
Let me know if this is possible/how I could do it
Pandas plot is in fact matplotlib plot, you can assign it to variable and modify it according to your needs ( add horizontal and vertical lines or shapes, text, etc)
# plot your data, but instead diplaying it assing Figure and Axis to variables
fig, ax = df.plot()
ax.vlines(x, ymin, ymax, colors='k', linestyles='solid') # adjust to your needs
plt.show()
working code sample
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
df = seaborn.load_dataset('tips')
ax = df.plot.scatter(x='total_bill', y='tip')
ax.vlines(x=40, ymin=0, ymax=20, colors='red')
patches = [Circle((50,10), radius=3)]
collection = PatchCollection(patches, alpha=0.4)
ax.add_collection(collection)
plt.show()
I want to get rid of the white border when I save my image to a png in python.
I tried plt.box(on=None), plt.axis('off'). I tried setting the figure's 'frameon' parameter to false.
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
figure(num=None, figsize=(7.965,7.965), dpi=80,facecolor='none',clear=True)
plt.box(on=None)
plt.axis('off')
plt.imshow(Data, cmap='Greys_r', norm=Norm,origin='lower',aspect='auto',interpolation='nearest')
plt.savefig(locationFITSfolder+fitsFile[:-5],transparent=False,bbox=False)
I want there to be no white border to my image. Transparent.
If you change the parameters to the savefig function, you will get the desired output.
Specifically, you must use transparent=True. Note that bbox=False and frameon=False are optional, and only change the width of transparent space around your image.
Adapting from your sample code:
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
#create sample data
import numpy as np
Data = np.random.random([4,4])
figure(num=None, figsize=(7.965,7.965), dpi=80,facecolor='none',clear=True)
plt.box(on=None)
plt.axis('off')
plt.imshow(Data, cmap='Greys_r',origin='lower',aspect='auto',interpolation='nearest')
plt.savefig(locationFITSfolder+fitsFile[:-5],transparent=True)
(sidenote -- you may wish to use os.path.join, .split, and .splitext for file I/O, instead of slicing string names)
This yields the expected image output: (note that the image has transparent borders when you open it in a new tab or download it).
When I run the code below in a Jupyter Notebook,
I get a map of the world, colored in red.
There are fine white-ish lines between the countries.
Is there a way to plot the world so that all countries
are solid and there's no line in between?
I'm asking, because my real world usecase is a fine grid that
behaves just like the world map: Each grid shape has a fine outline
which I do not want to have in the plot. (Update, since this was asked: The grid shapes will not have the same fill color.
)
import geopandas as gpd
import geoplot as gplt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world['total'] = 1
world.plot(column='total', cmap='Set1')
For the grid example, the grid files are at https://opendata-esri-de.opendata.arcgis.com/datasets/3c1f46241cbb4b669e18b002e4893711_0
A simplified example that shows the problem.
sf = 'Hexagone_125_km/Hexagone_125_km.shp'
shp = gpd.read_file(sf)
shp.crs = {'init': 'epsg:4326'}
shp['sum'] = 1 # for example, fill sum with something
shp.plot(figsize=(20,20), column='sum', cmap='gnuplot', alpha=1, legend=True)
The white lines are due to antialiasing. This usually makes the visual more smooth, but leads to white lines in between different shapes. You can turn off anialiasing via
antialiased=False
That has the inevitable drawback of the plot looking pixelated.
An alternative is to give the patches an edge with a certain linewidth. The edges should probably have the same color as the faces, so
edgecolor="face", linewidth=0.4
would be an option. This removes the white lines, but introduces a slight "searing" effect (You'll notice mainly looking at islands like Indonesia or Japan). This will be the more noticable, the smaller the features, so it may be irrelevant for showing a hexbin plot. Still, playing a bit with the linewidth might improve the result further.
Code for reproduction:
import numpy as np; np.random.seed(42)
import geopandas as gpd
import matplotlib.pyplot as plt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world['total'] = np.random.randint(0,10, size=len(world))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, figsize=(7,10))
world.plot(column='total', cmap='Set1', ax=ax1)
world.plot(column='total', cmap='Set1', ax=ax2, antialiased=False)
world.plot(column='total', cmap='Set1', ax=ax3, edgecolor="face", linewidth=0.4)
ax1.set_title("original")
ax2.set_title("antialiased=False")
ax3.set_title("edgecolor='face', linewidth=0.4")
plt.tight_layout()
plt.savefig("world.png")
plt.show()
I have plotted a bar chart using the code below:
dffinal['CI-noCI']='Cognitive Impairement'
nocidffinal['CI-noCI']='Non Cognitive Impairement'
res=pd.concat([dffinal,nocidffinal])
sns.barplot(x='6month',y='final-formula',data=res,hue='CI-noCI')
plt.xticks(fontsize=8, rotation=45)
plt.show()
the result is as below:
I want to change the color of them to red and green.
How can I do?
just as information, this plot is reading two different data frame.
the links I have gone through were with the case the dataframe was only one data frame so did not apply to my case.
Thanks :)
You can use matplotlib to overwrite Seaborn's default color cycling to ensure the hues it uses are red and green.
import matplotlib.pyplot as plt
plt.rcParams['axes.prop_cycle'] = ("cycler('color', 'rg')")
Example:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({'date': [1,2,3,4,4,5],
'value': [10,15,35,14,18,4],
'hue_v': [1,1,2,1,2,2]})
# The normal seaborn coloring is blue and orange
sns.barplot(x='date', y='value', data=df, hue='hue_v')
# Now change the color cycling and re-make the same plot:
plt.rcParams['axes.prop_cycle'] = ("cycler('color', 'rg')")
sns.barplot(x='date', y='value', data=df, hue='hue_v')
This will now impact all of the other figures you make, so if you want to restore the seaborn defaults for all other plots you need to then do:
sns.reset_orig()
I want the text to appear beside the box instead of inside it:
Here is what I did:
import matplotlib as mpl
import matplotlib.pyplot as plt
from custombox import MyStyle
fig = plt.figure(figsize=(10,10))
legend_ax = plt.subplot(111)
legend_ax.annotate("Text",xy=(0.5,0.5),xycoords='data',xytext=(0.5, 0.5),textcoords= ('data'),ha="center",rotation = 180,bbox=dict(boxstyle="angled, pad=0.5", fc='white', lw=4, ec='Black'))
legend_ax.text(0.6,0.5,"Text", ha="center",size=15)
Here is what it gives me:
Note: custombox is similar to the file that is written in this link:
http://matplotlib.org/1.3.1/users/annotations_guide.html
My ultimate aim is to make it look legend like where the symbol (angled box) appears beside the text that represents it.
EDIT 1: As suggested by Ajean I have annotated text separately but I can't turn of the text within the arrow
One way to do it would be to separate the text and the bbox (which you can reproduce using an arrow). The following gives me something close to what you want, I think.
import matplotlib.pyplot as plt
from matplotlib import patches
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
ax.annotate("Text", (0.4,0.5))
bb = patches.FancyArrow(0.5,0.5,0.1,0.0,length_includes_head=True, width=0.05,
head_length=0.03, head_width=0.05, fc='white', ec='black',
lw=4)
ax.add_artist(bb)
plt.show()
You can futz with the exact placement as needed. I'm not an expert on all the kwargs, so this may not be the best solution, but it will work.