When I tried to plot this boxplot figure , legend of age group was shown as below.
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
plt.figure(figsize=(14,7))
sns.set(style="white", palette="Blues", color_codes=True)
f = sns.boxplot(x="RIAGENDRtxt", y="BPXSY1", hue="agegrp", data=df)
plt.savefig("out.png",pad_inches=0.5)
plt.show()
But when I tried to customize the legend my code is
plt.figure(figsize=(14,7))
sns.set(style="white", palette="Blues", color_codes=True)
f = sns.boxplot(x="RIAGENDRtxt", y="BPXSY1", hue="agegrp", data=df)
f.set_xlabel("Sex")
f.set_ylabel("Systolic Blood Pressure")
legend_label = ["(18, 30)", "(30, 40)", "(40, 50)", "(50, 60)", "(60, 70)", "(70, 80)"]
f.legend(title="Age Group", labels=legend_label)
plt.savefig("out.png",pad_inches=0.5)
plt.show()
This f.legend(title="Age Group", labels=legend_label) line was able to customize the title and labels but it caused errors in the markers. I need to set the markers to the color pallet as it was in the previous figure.
As of seaborn 0.10.1, the legend label is stored in ax.legend_.texts[0], where ax is the matplotlib Axes returned by sns.boxplot(). This means that you can edit the legend label without changing anything else about the legend as follows.
g = sns.boxplot(...)
new_legend_label = 'Age Group'
g.legend_.texts[0].set_text(new_legend_label)
Depending on what version of seaborn you're using, the method might be different. See these answers from 2017 and 2019 for slightly different syntax with older versions.
Thank You Emerson Harkin. Your solution was useful. I just iterate over list of labels to update all. Here is my updated code and figure:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
plt.figure(figsize=(14,7))
sns.set(style="white", palette="Blues", color_codes=True)
f = sns.boxplot(x="RIAGENDRtxt", y="BPXSY1", hue="agegrp", data=df)
f.set_xlabel("Sex")
f.set_ylabel("Systolic Blood Pressure")
legend_label = ["(18, 30)", "(30, 40)", "(40, 50)", "(50, 60)", "(60, 70)", "(70, 80)"]
f.legend(title="Age Group")
n = 0
for i in legend_label:
f.legend_.texts[n].set_text(i)
n += 1
plt.show()
Updated Figure
Related
I'm trying to plot two data sets with Seaborn, this is my code.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.axes_style("ticks")
ss_data = pd.read_csv('A.csv')
ks_data = pd.read_csv('B.csv')
g = sns.lineplot(data=ks_data, x="K", y="pd", dashes=False)
sns.lineplot(data=ss_data, x="K", y="pd", dashes=False)
g.set_xticks(range(0,22,4))
plt.legend(labels=["A", "B"])
plt.savefig("test.png", dpi=500)
But this is the graph I am getting, as you can see, the legend doesn't correctly show the line colour for B.
I think it's probably due to the way that I am adding the second lineplot to the graph, but I couldn't make it work any other way.
Use the label parameter (passed to matplotlib.axes.Axes.plot()), and no need for plt.legend().
sns.lineplot(
data=ks_data, x="K", y="pd",
label='A', errobar=None)
sns.lineplot(
data=ss_data, x="K", y="pd",
label='B', errorbar=None)
Importantly, pass errorbar=None (or for seaborn versions prior to 0.12.0, ci=None), to turn off plotting of the confidence interval.
Maybe a matplotlib / seaborn version issue?
I'm not able to reproduce your graph. With some dummy data I get the expected results:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.axes_style("ticks")
data1 = {"K":[1,5,10,15,20], "pd":[2,10,20,30,40]}
data2 = {"K":[1,5,10,15,20], "pd":[1.5,9,18,16,35]}
ss_data = pd.DataFrame(data=data1)
ks_data = pd.DataFrame(data=data2)
g = sns.lineplot(data=ks_data, x="K", y="pd", dashes=False)
sns.lineplot(data=ss_data, x="K", y="pd", dashes=False)
g.set_xticks(range(0,22,4))
plt.legend(labels=["A", "B"])
I have seaborn == 0.11.2 and matplotlib==3.5.0
I'm doing a jointplot with a basemap, the problem is that when I add the basemap the main plot doesn't have the same size of the marginal plots. I've tried with different parameters without luck. Does anyone have an idea?
import seaborn as sns
import matplotlib.pyplot as plt
import contextily as ctx
import pandas as pd
##exaplme of the data
coords={'longitud':[-62.2037376443, -62.1263309099, -62.1111660957, -62.2094232682, -62.2373117384, -62.4837603464,
-62.4030570833, -62.3975699059, -62.7017114116, -62.7830883096, -62.7786038141, -62.7683234105, -62.7490101452,
-62.7709656745, -63.1002199219, -63.1890252191, -63.1183018549, -63.069960016, -62.7957745659, -63.1715687622,
-63.2156105034, -63.0634381954, -63.2243260588, -63.1153871895, -63.1068292891, -63.103945266, -63.046202785,
-63.1002257551, -63.2076065143, -62.9766391316, -62.9639256604, -62.9911452446, -62.9819984159, -62.9693649898,
-63.066770885, -62.9867441519, -62.9566360192, -62.962616287, -62.835080907, -63.0704805194, -62.8796906301,
-63.0725050601, -63.2224345145, -63.1609069526, -63.0614466072, -62.8847887504, -63.1093652381, -62.822694115,
-63.211982035, -63.1689040153],
'latitud':[8.54644405234, 8.54344899107, 8.54223724187, 8.54290207992, 8.49122679072, 8.48386575122, 8.46450360179,
8.46404720757, 8.35310083084, 8.31701565261, 8.30258604829, 8.29974870902, 8.29281679496, 8.28939264064, 8.28785272804,
8.28221439317, 8.27978694565, 8.27864159366, 8.27634987807, 8.27619269053, 8.27236343925, 8.27258932351, 8.26833993531,
8.267530064, 8.26446669791, 8.26266392333, 8.2641092051, 8.26208837315, 8.26034269744, 8.26123972942, 8.25789799656,
8.25825378832, 8.25833002805, 8.25914612933, 8.2540499893, 8.25347956867, 8.2540932736, 8.25405171513, 8.2478564527,
8.24561857662, 8.2440865055, 8.24256528837, 8.24089278, 8.23877286416, 8.23782626443, 8.23865421655, 8.23733824299,
8.23477115627, 8.23552604027, 8.24327920905]}
df = pd.DataFrame(coords)
OSM_C = 'http://c.tile.openstreetmap.org/{z}/{x}/{y}.png'
joint_axes = sns.jointplot(
x='longitud', y='latitud', data=df, ec="r", s=5)
ctx.add_basemap(joint_axes.ax_joint,crs=4326,attribution=False,url=OSM_C)
adjust(hspace=0, wspace=0)
#plt.tight_layout()
plt.show()
Here is an approach that:
removes the axes sharing in the y-direction to be able to change the aspect to 'datalim'
sets the aspect to 'equal', 'datalim'
sets the y data limits of the marginal plot to be the same as the joint plot; this seems to need a redraw
The following code shows the idea (using imshow, as I don't have contextily installed):
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
coords = {'longitud' : [-62.2037376443, -62.1263309099, -62.1111660957, -62.2094232682, -62.2373117384, -62.4837603464, -62.4030570833, -62.3975699059, -62.7017114116, -62.7830883096, -62.7786038141, -62.7683234105, -62.7490101452, -62.7709656745, -63.1002199219, -63.1890252191, -63.1183018549, -63.069960016, -62.7957745659, -63.1715687622, -63.2156105034, -63.0634381954, -63.2243260588, -63.1153871895, -63.1068292891, -63.103945266, -63.046202785, -63.1002257551, -63.2076065143, -62.9766391316, -62.9639256604, -62.9911452446, -62.9819984159, -62.9693649898, -63.066770885, -62.9867441519, -62.9566360192, -62.962616287, -62.835080907, -63.0704805194, -62.8796906301, -63.0725050601, -63.2224345145, -63.1609069526, -63.0614466072, -62.8847887504, -63.1093652381, -62.822694115, -63.211982035, -63.1689040153],
'latitud' : [8.54644405234, 8.54344899107, 8.54223724187, 8.54290207992, 8.49122679072, 8.48386575122, 8.46450360179, 8.46404720757, 8.35310083084, 8.31701565261, 8.30258604829, 8.29974870902, 8.29281679496, 8.28939264064, 8.28785272804, 8.28221439317, 8.27978694565, 8.27864159366, 8.27634987807, 8.27619269053, 8.27236343925, 8.27258932351, 8.26833993531, 8.267530064, 8.26446669791, 8.26266392333, 8.2641092051, 8.26208837315, 8.26034269744, 8.26123972942, 8.25789799656, 8.25825378832, 8.25833002805, 8.25914612933, 8.2540499893, 8.25347956867, 8.2540932736, 8.25405171513, 8.2478564527, 8.24561857662, 8.2440865055, 8.24256528837, 8.24089278, 8.23877286416, 8.23782626443, 8.23865421655, 8.23733824299, 8.23477115627, 8.23552604027, 8.24327920905]}
df = pd.DataFrame(coords)
g = sns.jointplot(data=df, x='longitud', y='latitud')
ctx.add_basemap(g.ax_joint,crs=4326,attribution=False,url=OSM_C)
# g.ax_joint.imshow(np.random.rand(20, 10), cmap='spring', interpolation='bicubic',
# extent=[df['longitud'].min(), df['longitud'].max(), df['latitud'].min(), df['latitud'].max()])
for axes in g.ax_joint.get_shared_y_axes():
for ax in axes:
g.ax_joint.get_shared_y_axes().remove(ax)
g.ax_joint.set_aspect('equal', 'datalim')
g.fig.canvas.draw()
g.ax_marg_y.set_ylim(g.ax_joint.get_ylim())
plt.show()
You can still combine this approach with changing the figure's width or height, or adding more whitespace on top or below.
Can you please help me adding the median to each of the seaborn plots ? We have here one plot per specialty, and this is allowed by the following line of code:
sns.displot(data=all_df, x="exp", hue="sexe", col='specialite', kind="kde"), which gives:
Thank you very much
You could add the medians manually, e.g. using .map_dataframe(). Here is some example code using the penguins dataset:
import matplotlib.pyplot as plt
import seaborn as sns
def plot_medians(data, color):
for hue, color in zip(hue_order, palette):
plt.axvline(data[data['sex'] == hue][x_colummn].median(), color=color, ls=':')
penguins = sns.load_dataset('penguins').dropna()
hue_order = penguins['sex'].unique()
palette = ['dodgerblue', 'crimson']
x_colummn = "bill_length_mm"
g = sns.displot(data=penguins, x=x_colummn,
hue="sex", hue_order=hue_order, palette=palette,
col="species", kind="kde")
g.map_dataframe(plot_medians)
plt.show()
I can plot multiple histograms in a single plot using pandas but there are few things missing:
How to give the label.
I can only plot one figure, how to change it to layout=(3,1) or something else.
Also, in figure 1, all the bins are filled with solid colors, and its kind of difficult to know which is which, how to fill then with different markers (eg. crosses,slashes,etc)?
Here is the MWE:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7,label='species')
plt.legend()
Output:
To change layout I can use by keyword, but can't give them colors
HOW TO GIVE DIFFERENT COLORS?
df.hist('sepal_length',by='species',layout=(3,1))
plt.tight_layout()
Gives:
You can resolve to groupby:
fig,ax = plt.subplots()
hatches = ('\\', '//', '..') # fill pattern
for (i, d),hatch in zip(df.groupby('species'), hatches):
d['sepal_length'].hist(alpha=0.7, ax=ax, label=i, hatch=hatch)
ax.legend()
Output:
In pandas version 1.1.0 you can simply set the legend keyword to true.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7, legend = True)
output image
It's more code, but using pure matplotlib will always give you more control over the plots. For your second case:
import matplotlib.pyplot as plt
import numpy as np
from itertools import zip_longest
# Dictionary of color for each species
color_d = dict(zip_longest(df.species.unique(),
plt.rcParams['axes.prop_cycle'].by_key()['color']))
# Use the same bins for each
xmin = df.sepal_length.min()
xmax = df.sepal_length.max()
bins = np.linspace(xmin, xmax, 20)
# Set up correct number of subplots, space them out.
fig, ax = plt.subplots(nrows=df.species.nunique(), figsize=(4,8))
plt.subplots_adjust(hspace=0.4)
for i, (lab, gp) in enumerate(df.groupby('species')):
ax[i].hist(gp.sepal_length, ec='k', bins=bins, color=color_d[lab])
ax[i].set_title(lab)
# same xlim for each so we can see differences
ax[i].set_xlim(xmin, xmax)
I'm facing a problem on ticks since hours now.
I would like to plot a boxplot with log scaling but it seems there is 2 issues :
Data has no positive values, and therefore cannot be log-scaled.
And
Attempted to set non-positive xlimits for log-scale axis; invalid limits will be ignored.
Her is my code :
# Import librairies
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
# Undersampling the DataFrame through columns because too big
sampling_factor = 50
all_frame_inverted_undersampled = all_frame_inverted.take([x for x in range(0,all_frame_inverted.shape[1], sampling_factor)], axis=1)
# Settings the canvas
fig, ax= plt.subplots(figsize=(18, 6))
ax.set_xlabel('Cycles')
ax.set_ylabel('Measures')
# setting log axis
ax.set_xscale('log')
# Plotting
g = sns.boxplot(data=all_frame_inverted_undersampled , palette='rainbow', orient="v", ax=ax)
# Showing
plt.tight_layout()
Here is the DataFrame's for the 3 first lines :
And here, what I get :
I've tried many things like setting :
ax.xaxis.set_major_formatter(xmajor_formatter) with LogFormatter
I've tried symlog, the error message disapear but the results is quite the same.
I tried also : ax.set_xscale('symlog', linthreshy=1e3)
EDIT
I'm trying to get, on the x axis, ticks formatted as in the plot below :
EDIT 2 : Here is a minimal exemple of my code :
# Preprocessing and Cleaning data
import numpy as np
import pandas as pd
# Data Visualization
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
%matplotlib inline
# Data Analysis & Visualization
import seaborn as sns
# Data
data= [[181.08, 180.23, 181.60, 178.05, 172.33, 175.50, 169.81, 167.10, 166.92, 166.10, 159.69],
[144.94,140.30,140.15,146.07,143.90,143.06,139.74,139.97,144.06,145.13,146.34],
[176.25,195.43,217.50,183.75,174.74,169.11,166.81,161.82,164.08,162.25,166.72],
[198.31,221.16,214.19,209.06,202.08,180.08,185.79,181.73,178.95,179.53,189.08],
[167.81,166.28,144.18,138.22,139.48,144.66,141.34,141.60,146.53,145.84,155.20]]
# Data to Dataframe
df = pd.DataFrame(data=data, index=['M01', 'M02', 'M03', 'M04', 'M05'],
columns=[8796, 60501505, 142252576, 224057457, 305801670, 387546170, 487628661, 609874323, 732114489, 854385341, 1190477590])
# Boxplot calculated for each cycle through all structures Mxx
fig, ax= plt.subplots(figsize=(18, 6))
ax.set_xlabel('Cycles')
ax.set_ylabel('Measures')
ax.set_xscale('symlog')
plt.xticks(rotation=45)
sns.boxplot(data=df, palette='rainbow', orient="v", ax=ax)
# Showing plot
plt.tight_layout()
Here is what I can do with Plotly, I want this result using boxplot
please :
# Interactive Data Visualization needs : pip install plotly & pip install cufflinks
from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
df.iplot(kind='box', xTitle='Cycles', yTitle='Measures', logx=True)
Thank you for your help