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Is there a way that I can set two different scales at the x-axis in a python plot?
I have following code:
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
data=pd.read_csv(file, names=['Wavenumber', 'Intensity'])
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(data['Wavenumber'], data['Intensity'])
ax.invert_xaxis()
ax.set_xticks([4000,3000,2000,1600,1200,800,400])
plt.show()
This gives:
But I would like to have equal spacing between the ticks, so a linear scaling from 4000 to 2000 in steps of 1000, and then again linear scaling from 2000 to 400 in steps of 400. This should look like this:
Creating a custom scale in matplotlib can be quite an effort. As you only need two different linear scales, it is easier to use a workaround consisting of joining two subplots together. With many data points located near the boundary between the two scales (as in your case), the jump from one to the other will not cause any irregular space between the ticks around the boundary if you were to show many tick marks (contrary to here). All you need is to find the data point closest to the boundary to seamlessly connect both subplots, as illustrated in the following example:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
# Create sample dataset
rng = np.random.default_rng(seed=1)
x = np.linspace(4000, 400, num=50)
y = 1 - rng.exponential(scale=0.1, size=x.size)
df = pd.DataFrame(dict(Wavenumber=x, Intensity=y))
# Select data for each subplot by using a boundary point
x_boundary = min(df['Wavenumber'], key=lambda x: abs(x-2000))
df1 = df[df['Wavenumber'] >= x_boundary]
df2 = df[df['Wavenumber'] <= x_boundary]
# Select x-axis ticks for each subplot
ticks = np.array([4000, 3000, 2000, 1600, 1200, 800, 400])
tk1 = ticks[ticks >= x_boundary]
tk2 = ticks[ticks <= x_boundary]
# Create figure with 2 Axes side-by-side with no space in between
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharey=True,
gridspec_kw=dict(wspace=0))
# Loop through both Axes to plot data, adjust x-axis limits and remove boundary spines
for ax, data, spine, tk in zip((ax1, ax2), (df1, df2), ('right','left'), (tk1, tk2)):
data.plot(x='Wavenumber', xlabel='', ax=ax, legend=None)
ax.set_xlim(data['Wavenumber'].iloc[[0,-1]])
ax.spines[spine].set_visible(False)
ax.set_xticks(tk)
# Additional formatting
ax2.tick_params(axis='y', length=0)
ax1.set_xlabel('Wavenumber', x=1, labelpad=10, size=12)
ax1.set_ylabel('Intensity', labelpad=10, size=12)
fig.suptitle('Plot with two linear x-axis scales joined together', size=16, y=0.95);
I am having an issue trying to superimpose plots with seaborn. I am able to generate the two plots separetly as
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
The output looks like this:
But when i try to put both plots superimposed, but assiging both to the same ax object.
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
I am not able to identify with the X axis in the Lineplot changes when superimposing both plots (both plots X axis go from 0 to 0.069).
My goal is for both plots to be superimposed, while keeping the same X axis range.
Seaborn's boxplot creates categorical x-axis, with all boxes nicely with the same distance. Internally the x-axis is numbered as 0, 1, 2, ... but externally it gets the labels from 0 to 0.069.
To combine a line plot with a boxplot, matplotlib's boxplot can be addressed directly, so that positions and widths can be set explicitly. When patch_artist=True, a rectangle is created (instead of just lines), for which a facecolor can be given. manage_ticks=False prevents that boxplot changes the x ticks and their limits. Optionally notch=True would accentuate the median a bit more, but depending on the data, the confidence interval might be too large and look weird.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data1 = pd.DataFrame({'pct_gc': np.linspace(0, 0.069, 200), 'MSE': np.random.normal(0.02, 0.1, 200).cumsum()})
data1['pct_range'] = pd.cut(data1['pct_gc'], 10)
fig, ax1 = plt.subplots(ncols=1, figsize=(20, 7))
sns.lineplot(data=data1, y='MSE', x='pct_gc', ax=ax1)
for interval, color in zip(np.unique(data1['pct_range']), plt.cm.tab10.colors):
ax1.boxplot(data1[data1['pct_range'] == interval]['MSE'],
positions=[interval.mid], widths=0.4 * interval.length,
patch_artist=True, boxprops={'facecolor': color},
notch=False, medianprops={'color':'yellow', 'linewidth':2},
manage_ticks=False)
plt.show()
I have a Pandas DataFrame with a column called "AXLES", which can take an integer value between 3-12. I am trying to use Seaborn's countplot() option to achieve the following plot:
left y axis shows the frequencies of these values occurring in the data. The axis extends are [0%-100%], tick marks at every 10%.
right y axis shows the actual counts, values correspond to tick marks determined by the left y axis (marked at every 10%.)
x axis shows the categories for the bar plots [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
Annotation on top of the bars show the actual percentage of that category.
The following code gives me the plot below, with actual counts, but I could not find a way to convert them into frequencies. I can get the frequencies using df.AXLES.value_counts()/len(df.index) but I am not sure about how to plug this information into Seaborn's countplot().
I also found a workaround for the annotations, but I am not sure if that is the best implementation.
Any help would be appreciated!
Thanks
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
for p in ax.patches:
ax.annotate('%{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))
EDIT:
I got closer to what I need with the following code, using Pandas' bar plot, ditching Seaborn. Feels like I'm using so many workarounds, and there has to be an easier way to do it. The issues with this approach:
There is no order keyword in Pandas' bar plot function as Seaborn's countplot() has, so I cannot plot all categories from 3-12 as I did in the countplot(). I need to have them shown even if there is no data in that category.
The secondary y-axis messes up the bars and the annotation for some reason (see the white gridlines drawn over the text and bars).
plt.figure(figsize=(12,8))
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
ax = (dfWIM.AXLES.value_counts()/len(df)*100).sort_index().plot(kind="bar", rot=0)
ax.set_yticks(np.arange(0, 110, 10))
ax2 = ax.twinx()
ax2.set_yticks(np.arange(0, 110, 10)*len(df)/100)
for p in ax.patches:
ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))
You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move the axis labels
You can then set the ticks using the matplotlib.ticker module, specifically ticker.MultipleLocator and ticker.LinearLocator.
As for your annotations, you can get the x and y locations for all 4 corners of the bar with patch.get_bbox().get_points(). This, along with setting the horizontal and vertical alignment correctly, means you don't need to add any arbitrary offsets to the annotation location.
Finally, you need to turn the grid off for the twinned axis, to prevent grid lines showing up on top of the bars (ax2.grid(None))
Here is a working script:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.ticker as ticker
# Some random data
dfWIM = pd.DataFrame({'AXLES': np.random.normal(8, 2, 5000).astype(int)})
ncount = len(dfWIM)
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
# Make twin axis
ax2=ax.twinx()
# Switch so count axis is on right, frequency on left
ax2.yaxis.tick_left()
ax.yaxis.tick_right()
# Also switch the labels over
ax.yaxis.set_label_position('right')
ax2.yaxis.set_label_position('left')
ax2.set_ylabel('Frequency [%]')
for p in ax.patches:
x=p.get_bbox().get_points()[:,0]
y=p.get_bbox().get_points()[1,1]
ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y),
ha='center', va='bottom') # set the alignment of the text
# Use a LinearLocator to ensure the correct number of ticks
ax.yaxis.set_major_locator(ticker.LinearLocator(11))
# Fix the frequency range to 0-100
ax2.set_ylim(0,100)
ax.set_ylim(0,ncount)
# And use a MultipleLocator to ensure a tick spacing of 10
ax2.yaxis.set_major_locator(ticker.MultipleLocator(10))
# Need to turn the grid on ax2 off, otherwise the gridlines end up on top of the bars
ax2.grid(None)
plt.savefig('snscounter.pdf')
I got it to work using core matplotlib's bar plot. I didn't have your data obviously, but adapting it to yours should be straight forward.
Approach
I used matplotlib's twin axis and plotted the data as bars on the second Axes object. The rest ist just some fiddeling around to get the ticks right and make annotations.
Hope this helps.
Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
tot = np.random.rand( 1 ) * 100
data = np.random.rand( 1, 12 )
data = data / sum(data,1) * tot
df = pd.DataFrame( data )
palette = sns.husl_palette(9, s=0.7 )
### Left Axis
# Plot nothing here, autmatically scales to second axis.
fig, ax1 = plt.subplots()
ax1.set_ylim( [0,100] )
# Remove grid lines.
ax1.grid( False )
# Set ticks and add percentage sign.
ax1.yaxis.set_ticks( np.arange(0,101,10) )
fmt = '%.0f%%'
yticks = matplotlib.ticker.FormatStrFormatter( fmt )
ax1.yaxis.set_major_formatter( yticks )
### Right Axis
# Plot data as bars.
x = np.arange(0,9,1)
ax2 = ax1.twinx()
rects = ax2.bar( x-0.4, np.asarray(df.loc[0,3:]), width=0.8 )
# Set ticks on x-axis and remove grid lines.
ax2.set_xlim( [-0.5,8.5] )
ax2.xaxis.set_ticks( x )
ax2.xaxis.grid( False )
# Set ticks on y-axis in 10% steps.
ax2.set_ylim( [0,tot] )
ax2.yaxis.set_ticks( np.linspace( 0, tot, 11 ) )
# Add labels and change colors.
for i,r in enumerate(rects):
h = r.get_height()
r.set_color( palette[ i % len(palette) ] )
ax2.text( r.get_x() + r.get_width()/2.0, \
h + 0.01*tot, \
r'%d%%'%int(100*h/tot), ha = 'center' )
I think you can first set the y major ticks manually and then modify each label
dfWIM = pd.DataFrame({'AXLES': np.random.randint(3, 10, 1000)})
total = len(dfWIM)*1.
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
for p in ax.patches:
ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))
#put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe
ax.yaxis.set_ticks(np.linspace(0, total, 11))
#adjust the ticklabel to the desired format, without changing the position of the ticks.
_ = ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))
I want to plot categorical plots with the Seaborn pointplot, but data points that are not adjacent are not connected with a line in the plot. I would like to interpolate between non adjacent points, and connect them in the same way as adjacent points are connected, how can I do this?
An example: In the left and middle images, the blue and green points should be connected with a curve, respectively, but now they are separated into small parts. How can I plot the left and middle images just like the right one?
fig, axs = plt.subplots(ncols=3, figsize=(10,5))
exp_methods = ['fMRI left', 'fMRI right', 'MEG']
for i in range(3):
experiment = exp_methods[i]
dataf = df[df['data']==experiment]
sns.pointplot(x='number_of_subjects', y='accuracy', hue='training_size', data=dataf,
capsize=0.2, size=6, aspect=0.75, ci=95, legend=False, ax=axs[i])
I don't think there is an option to interpolate where there are missing data points, and hence the line stops instead. This question on the same topic from 2016 remains unanswered.
Instead, you could use plt.errorbar as suggested in the comments, or add the lines afterwards using plt.plot while still using seaborn to plot the means and error bars:
import seaborn as sns
tips = sns.load_dataset('tips')
# Create a gap in the data and plot it
tips.loc[(tips['size'] == 4) & (tips['sex'] == 'Male'), 'size'] = 5
sns.pointplot('size', 'total_bill', 'sex', tips, dodge=True)
# Fill gap with manual line plot
ax = sns.pointplot('size', 'total_bill', 'sex', tips, dodge=True, join=False)
# Loop over the collections of point in the axes and the grouped data frame
for points, (gender_name, gender_slice) in zip(ax.collections, tips.groupby('sex')):
# Retrieve the x axis positions for the points
x_coords = [coord[0] for coord in points.get_offsets()]
# Manually calculate the mean y-values to use with the line
means = gender_slice.groupby(['size']).mean()['total_bill']
ax.plot(x_coords, means, lw=2)
I have a Pandas DataFrame with a column called "AXLES", which can take an integer value between 3-12. I am trying to use Seaborn's countplot() option to achieve the following plot:
left y axis shows the frequencies of these values occurring in the data. The axis extends are [0%-100%], tick marks at every 10%.
right y axis shows the actual counts, values correspond to tick marks determined by the left y axis (marked at every 10%.)
x axis shows the categories for the bar plots [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
Annotation on top of the bars show the actual percentage of that category.
The following code gives me the plot below, with actual counts, but I could not find a way to convert them into frequencies. I can get the frequencies using df.AXLES.value_counts()/len(df.index) but I am not sure about how to plug this information into Seaborn's countplot().
I also found a workaround for the annotations, but I am not sure if that is the best implementation.
Any help would be appreciated!
Thanks
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
for p in ax.patches:
ax.annotate('%{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))
EDIT:
I got closer to what I need with the following code, using Pandas' bar plot, ditching Seaborn. Feels like I'm using so many workarounds, and there has to be an easier way to do it. The issues with this approach:
There is no order keyword in Pandas' bar plot function as Seaborn's countplot() has, so I cannot plot all categories from 3-12 as I did in the countplot(). I need to have them shown even if there is no data in that category.
The secondary y-axis messes up the bars and the annotation for some reason (see the white gridlines drawn over the text and bars).
plt.figure(figsize=(12,8))
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
ax = (dfWIM.AXLES.value_counts()/len(df)*100).sort_index().plot(kind="bar", rot=0)
ax.set_yticks(np.arange(0, 110, 10))
ax2 = ax.twinx()
ax2.set_yticks(np.arange(0, 110, 10)*len(df)/100)
for p in ax.patches:
ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))
You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move the axis labels
You can then set the ticks using the matplotlib.ticker module, specifically ticker.MultipleLocator and ticker.LinearLocator.
As for your annotations, you can get the x and y locations for all 4 corners of the bar with patch.get_bbox().get_points(). This, along with setting the horizontal and vertical alignment correctly, means you don't need to add any arbitrary offsets to the annotation location.
Finally, you need to turn the grid off for the twinned axis, to prevent grid lines showing up on top of the bars (ax2.grid(None))
Here is a working script:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.ticker as ticker
# Some random data
dfWIM = pd.DataFrame({'AXLES': np.random.normal(8, 2, 5000).astype(int)})
ncount = len(dfWIM)
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
# Make twin axis
ax2=ax.twinx()
# Switch so count axis is on right, frequency on left
ax2.yaxis.tick_left()
ax.yaxis.tick_right()
# Also switch the labels over
ax.yaxis.set_label_position('right')
ax2.yaxis.set_label_position('left')
ax2.set_ylabel('Frequency [%]')
for p in ax.patches:
x=p.get_bbox().get_points()[:,0]
y=p.get_bbox().get_points()[1,1]
ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y),
ha='center', va='bottom') # set the alignment of the text
# Use a LinearLocator to ensure the correct number of ticks
ax.yaxis.set_major_locator(ticker.LinearLocator(11))
# Fix the frequency range to 0-100
ax2.set_ylim(0,100)
ax.set_ylim(0,ncount)
# And use a MultipleLocator to ensure a tick spacing of 10
ax2.yaxis.set_major_locator(ticker.MultipleLocator(10))
# Need to turn the grid on ax2 off, otherwise the gridlines end up on top of the bars
ax2.grid(None)
plt.savefig('snscounter.pdf')
I got it to work using core matplotlib's bar plot. I didn't have your data obviously, but adapting it to yours should be straight forward.
Approach
I used matplotlib's twin axis and plotted the data as bars on the second Axes object. The rest ist just some fiddeling around to get the ticks right and make annotations.
Hope this helps.
Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
tot = np.random.rand( 1 ) * 100
data = np.random.rand( 1, 12 )
data = data / sum(data,1) * tot
df = pd.DataFrame( data )
palette = sns.husl_palette(9, s=0.7 )
### Left Axis
# Plot nothing here, autmatically scales to second axis.
fig, ax1 = plt.subplots()
ax1.set_ylim( [0,100] )
# Remove grid lines.
ax1.grid( False )
# Set ticks and add percentage sign.
ax1.yaxis.set_ticks( np.arange(0,101,10) )
fmt = '%.0f%%'
yticks = matplotlib.ticker.FormatStrFormatter( fmt )
ax1.yaxis.set_major_formatter( yticks )
### Right Axis
# Plot data as bars.
x = np.arange(0,9,1)
ax2 = ax1.twinx()
rects = ax2.bar( x-0.4, np.asarray(df.loc[0,3:]), width=0.8 )
# Set ticks on x-axis and remove grid lines.
ax2.set_xlim( [-0.5,8.5] )
ax2.xaxis.set_ticks( x )
ax2.xaxis.grid( False )
# Set ticks on y-axis in 10% steps.
ax2.set_ylim( [0,tot] )
ax2.yaxis.set_ticks( np.linspace( 0, tot, 11 ) )
# Add labels and change colors.
for i,r in enumerate(rects):
h = r.get_height()
r.set_color( palette[ i % len(palette) ] )
ax2.text( r.get_x() + r.get_width()/2.0, \
h + 0.01*tot, \
r'%d%%'%int(100*h/tot), ha = 'center' )
I think you can first set the y major ticks manually and then modify each label
dfWIM = pd.DataFrame({'AXLES': np.random.randint(3, 10, 1000)})
total = len(dfWIM)*1.
plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')
for p in ax.patches:
ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))
#put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe
ax.yaxis.set_ticks(np.linspace(0, total, 11))
#adjust the ticklabel to the desired format, without changing the position of the ticks.
_ = ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))