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:
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))
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:
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))
countplot. Show the counts of observations in each categorical bin using bars. A count plot can be thought of as a histogram across a categorical, instead of quantitative, variable.
A countplot can be thought of as a histogram across a categorical, instead of a quantitative, variable. Iterate the returned axes of the countplot and show the count values at the top of the bars. To display the figure, use Show() method.
countplot() method is used to Show the counts of observations in each categorical bin using bars. Parameters : This method is accepting the following parameters that are described below: x, y: This parameter take names of variables in data or vector data, optional, Inputs for plotting long-form data.
When you use sns. countplot , Seaborn literally counts the number of observations per category for a categorical variable, and displays the results as a bar chart.
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')
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