I have difficulty with setting two different data plots inside one axis in tkinter canvas. Currently is being displayed only last plot, second is hidden.
Update:
Test example is working, but not in my original setup.
Problem is moved here.
Test example:
from tkinter import Tk, Canvas
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
window = Tk()
window.geometry('800x600')
df = pd.DataFrame()
df['x'] = np.linspace(1, 10, 10)
df['y'] = np.random.randint(1, 10, 10)
df2 = pd.DataFrame()
df2['x'] = np.linspace(1, 10, 50)
df2['y'] = np.random.randint(1, 10, 50)
fig, ax = plt.subplots()
df.plot(x='x', y='y', kind='bar', ax=ax, width=1., figsize=(3, 2.5), legend=None)
df2.plot(x='x', y='y', kind='line', ax=ax, legend=None)
Canvas(window, background='white') # create canvas field
canvas_plot = FigureCanvasTkAgg(fig, window) # Draw area
canvas_plot.get_tk_widget().grid(column=1, row=6, padx=1, pady=10, rowspan=2, columnspan=3)
canvas_plot.draw() # draw canvas
window.mainloop()
Question:
How to make this plots to be displayed in one figure?
Related
I have 2 sets of boxplots, one set in blue color and another in red color. I want the legend to show the label for each set of boxplots, i.e.
Legend:
-blue box- A, -red box- B
Added labels='A' and labels='B' within sns.boxplot(), but didn't work with error message "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument". How do I add the labels?
enter image description here
code for the inserted image:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = list(range(1,13))
n = 40
index = [item for item in x for i in range(n)]
np.random.seed(123)
df = pd.DataFrame({'A': np.random.normal(30, 2, len(index)),
'B': np.random.normal(10, 2, len(index))},
index=index)
red_diamond = dict(markerfacecolor='r', marker='D')
blue_dot = dict(markerfacecolor='b', marker='o')
plt.figure(figsize=[10,5])
ax = plt.gca()
ax1 = sns.boxplot( x=df.index, y=df['A'], width=0.5, color='red', \
boxprops=dict(alpha=.5), flierprops=red_diamond, labels='A')
ax2 = sns.boxplot( x=df.index, y=df['B'], width=0.5, color='blue', \
boxprops=dict(alpha=.5), flierprops=blue_dot, labels='B')
plt.ylabel('Something')
plt.legend(loc="center", fontsize=8, frameon=False)
plt.show()
Here are the software versions I am using: seaborn version 0.11.2. matplotlib version 3.5.1. python version 3.10.1
The following approach sets a label via the boxprops, and creates a legend using part of ax.artists. (Note that ax, ax1 and ax2 of the question's code are all pointing to the same subplot, so here only ax is used.)
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
x = np.arange(1, 13)
index = np.repeat(x, 40)
np.random.seed(123)
df = pd.DataFrame({'A': np.random.normal(30, 2, len(index)),
'B': np.random.normal(10, 2, len(index))},
index=index)
red_diamond = dict(markerfacecolor='r', marker='D')
blue_dot = dict(markerfacecolor='b', marker='o')
plt.figure(figsize=[10, 5])
ax = sns.boxplot(data=df, x=df.index, y='A', width=0.5, color='red',
boxprops=dict(alpha=.5, label='A'), flierprops=red_diamond)
sns.boxplot(data=df, x=df.index, y='B', width=0.5, color='blue',
boxprops=dict(alpha=.5, label='B'), flierprops=blue_dot, ax=ax)
ax.set_ylabel('Something')
handles, labels = ax.get_legend_handles_labels()
handles = [h for h, lbl, prev in zip(handles, labels, [None] + labels) if lbl != prev]
ax.legend(handles=handles, loc="center", fontsize=8, frameon=False)
plt.show()
Alternative approaches could be:
pd.melt the dataframe to long form, so hue could be used; a problem here is that then the legend wouldn't take the alpha from the boxprops into account; also setting different fliers wouldn't be supported
create a legend from custom handles
I am making a set of figures with subplots in Jupyter Notebook using matplotlib and geopandas. The top plots (A & B) have geospatial data and use various basemaps (aerial imagery, shaded relief, etc.).
How can I rotate the top two plots 90-degrees, so that they are elongated?
(I will need to redo gridspec layout of course, but that is easy; what I don't know how to do is: rotate the plots but keep the geographic information for basemap plotting.)
Repeatable code is below.
import pandas as pd
import geopandas as gpd
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import contextily as ctx
from shapely.geometry import Point
plt.style.use('seaborn-whitegrid')
### DUMMY DATA
long, lat = [(-118.155, -118.051, -118.08), (38.89, 39.512, 39.1)]
q, t = [(0, 70500, 21000), (0, 8000, -1200)]
df = pd.DataFrame(list(zip(q, t, lat, long)), columns =['q', 't', 'lat', 'long'])
gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['long'], df['lat']))
gdf.crs = "EPSG:4326"
### PLOTTING
fig = plt.figure(figsize=(10,7.5), constrained_layout=True)
gs = fig.add_gridspec(3, 2)
ax1 = fig.add_subplot(gs[0:2, 0])
ax2 = fig.add_subplot(gs[0:2, 1], sharex = ax1, sharey = ax1)
ax3 = fig.add_subplot(gs[-1, :])
### PlotA
gdf.plot(ax = ax1)
ctx.add_basemap(ax1, crs='epsg:4326', source=ctx.providers.Esri.WorldShadedRelief)
ax1.set_aspect('equal')
ax1.set_title('Plot-A')
ax1.tick_params('x', labelrotation=90)
### PlotB
gdf.plot(ax = ax2)
ctx.add_basemap(ax2, crs='epsg:4326', source=ctx.providers.Esri.WorldImagery, alpha=0.5)
ax2.set_aspect('equal')
ax2.set_title('Plot-B')
ax2.tick_params('x', labelrotation=90)
### PlotC
ax3.scatter(df.q, df.t)
ax3.set_aspect('equal')
ax3.set_title('Plot-C')
ax3.set_xlabel('q')
ax3.set_ylabel('t')
Im new to matplotlib and wont to use the graphics in Latex.
There ist a visual output as a graphic but:
Why is there no pdf output?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os #to remove a file
import datetime
from matplotlib.backends.backend_pdf import PdfPages
#######################
Val1 = [1,2,3,4,5,6,7,8,9,9,5,5] # in kWh
Val2 = [159,77,1.716246,2,4,73,128,289,372,347,354,302] #in m³
index = ['Apr', 'Mai', 'Jun', 'Jul','Aug','Sep','Okt','Nov','Dez','Jan', 'Feb', 'Mrz']
df = pd.DataFrame({'Val1': Val1,'Val2': Val2}, index=index)
with PdfPages('aas2s.pdf') as pdf:
plt.rc('text', usetex=True)
params = {'text.latex.preamble' : [r'\usepackage{siunitx}', r'\usepackage{amsmath}']}
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Liberation'
plt.rcParams.update(params)
plt.figure(figsize=(8, 6))
plt.rcParams.update({'font.size': 12})
ax = df[['Val1','Val2']].plot.bar(color=['navy','maroon'])
plt.xlabel('X Achse m')
plt.ylabel('Y Achse Taxi quer ')
plt.legend(loc='upper left', frameon=False)
plt.title('Franz jagt im komplett verwahrlosten Taxi quer durch Bayern')
plt.show()
pdf.savefig()
plt.close()
The error is called: ValueError: No such figure: None
And how do i get a second "Y" axis for the second value?
In general, savefig should be called before show. See e.g.
Matplotlib (pyplot) savefig outputs blank image
How come pyplot from Matplotlib doesn't allow you to save an image after you show it? (with some more explanation)
Second, you want to produce the plot inside the created figure, not create a new one, hence use
fig, ax = plt.subplots(figsize=...)
df.plot(..., ax=ax)
and later call the methods of the axes (object-oriented style).
In total,
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
#######################
Val1 = [1,2,3,4,5,6,7,8,9,9,5,5] # in kWh
Val2 = [159,77,1.716246,2,4,73,128,289,372,347,354,302] #in m³
index = ['Apr', 'Mai', 'Jun', 'Jul','Aug','Sep','Okt','Nov','Dez','Jan', 'Feb', 'Mrz']
df = pd.DataFrame({'Val1': Val1,'Val2': Val2}, index=index)
with PdfPages('aas2s.pdf') as pdf:
plt.rc('text', usetex=True)
params = {'text.latex.preamble' : [r'\usepackage{siunitx}', r'\usepackage{amsmath}']}
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Times New Roman'
plt.rcParams.update(params)
fig, ax = plt.subplots(figsize=(8, 6))
plt.rcParams.update({'font.size': 12})
df[['Val1','Val2']].plot.bar(color=['navy','maroon'], ax=ax)
ax.set_xlabel('X Achse m')
ax.set_ylabel('Y Achse Taxi quer ')
ax.legend(loc='upper left', frameon=False)
ax.set_title('Franz jagt im komplett verwahrlosten Taxi quer durch Bayern')
pdf.savefig()
plt.show()
plt.close()
Now if you still need to save the figure after is it being shown, you can do so by specifically using it as argument to savefig
plt.show()
pdf.savefig(fig)
As an exercise, I'm reproducing a plot from The Economist with matplotlib
So far, I can generate a random data and produce two plots independently. I'm struggling now with putting them next to each other horizontally.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
df1.plot.bar(stacked=True)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
plt.figure()
fig, ax1 = plt.subplots()
ax1.plot(df2['avg_hour'])
ax2 = ax1.twinx()
ax2.bar(left=range(2009,2016),height=df2['usage'])
plt.show()
You should try using subplots. First you create a figure by plt.figure(). Then add one subplot(121) where 1 is number of rows, 2 is number of columns and last 1 is your first plot. Then you plot the first dataframe, note that you should use the created axis ax1. Then add the second subplot(122) and repeat for the second dataframe. I changed your axis ax2 to ax3 since now you have three axis on one figure. The code below produces what I believe you are looking for. You can then work on aesthetics of each plot separately.
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
ax1 = fig.add_subplot(121)
df1.plot.bar(stacked=True,ax=ax1)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
ax2 = fig.add_subplot(122)
ax2.plot(df2['avg_hour'])
ax3 = ax2.twinx()
ax3.bar(left=range(2009,2016),height=df2['usage'])
plt.show()
I would like to produce a figure like this one using matplotlib:
(source: peltiertech.com)
My data are in a pandas DataFrame, and I've gotten as far as a regular stacked barchart, but I can't figure out how to do the part where each category is given its own y-axis baseline.
Ideally I would like the vertical scale to be exactly the same for all the subplots and move the panel labels off to the side so there can be no gaps between the rows.
I haven't exactly replicated what you want but this should get you pretty close.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
#create dummy data
cols = ['col'+str(i) for i in range(10)]
ind = ['ind'+str(i) for i in range(10)]
df = pd.DataFrame(np.random.normal(loc=10, scale=5, size=(10, 10)), index=ind, columns=cols)
#create plot
sns.set_style("whitegrid")
axs = df.plot(kind='bar', subplots=True, sharey=True,
figsize=(6, 5), legend=False, yticks=[],
grid=False, ylim=(0, 14), edgecolor='none',
fontsize=14, color=[sns.xkcd_rgb["brownish red"]])
plt.text(-1, 100, "The y-axis label", fontsize=14, rotation=90) # add a y-label with custom positioning
sns.despine(left=True) # get rid of the axes
for ax in axs: # set the names beside the axes
ax.lines[0].set_visible(False) # remove ugly dashed line
ax.set_title('')
sername = ax.get_legend_handles_labels()[1][0]
ax.text(9.8, 5, sername, fontsize=14)
plt.suptitle("My panel chart", fontsize=18)