使用 seaborn 绘制密度图 [英] Density plot using seaborn

问题描述

我正在尝试绘制每小时需求的密度图:

夜间对自行车的需求似乎较低......但很明显，它们可能用于通勤，高峰时间为上午 8 点和下午 5-6 点.

I'm trying to make a density plot of the hourly demand: data

The 'hr' means different hours, 'cnt' means demand.

I know how to make a density plot such as:

sns.kdeplot(bike['hr'])

However, this only works when the demand for different hours is unknown. Thus I can count each hour as its demand. Now I know the demand count of each hour, how I can make a density plot of such data?

解决方案

A density plot aims to show an estimate of a distribution. To make a graph showing the density of hourly demand, we would really expect to see many iid samples of demand, with time-stamps, i.e. one row per sample. Then a density plot would make sense.

But in the type of data here, where the demand ('cnt') is sampled regularly and aggregated over that sample period (the hour), a density plot is not directly meaningful. But a bar graph as a histogram does make sense, using the hours as the bins.

Below I show how to use pandas functions to produce such a plot -- really simple. For reference I also show how we might produce a density plot, through a sort of reconstruction of "original" samples.

df = pd.read_csv("../data/hour.csv") # load dataset, inc cols hr, cnt, no NaNs

# using the bar plotter built in to pandas objects
fig, ax = plt.subplots(1,2)
df.groupby('hr').agg({'cnt':sum}).plot.bar(ax=ax[0]) 

# reconstructed samples - has df.cnt.sum() rows, each one containing an hour of a rental.
samples = np.hstack([ np.repeat(h, df.cnt.iloc[i]) for i, h in enumerate(df.hr)])

# plot a density estimate
sns.kdeplot(samples, bw=0.5, lw=3, c="r", ax=ax[1])
    
# to make a useful comparison with a density estimate, we need to have our bar areas 
# sum up to 1, so we use groupby.apply to divide by the total of all counts.
tot = float(df.cnt.sum())
df.groupby('hr').apply(lambda x: x['cnt'].sum()/tot).plot.bar(ax=ax[1], color='C0')  

Demand for bikes seems to be low during the night... But it is also apparent that they are probably used for commuting, with peaks at hours 8am and 5-6pm.

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