问题
I have a dataset of 5000 products with 50 features. One of the column is 'colors' and there are more than 100 colors in the column. I'm trying to plot a bar chart to show only the top 10 colors and how many products there are in each color.
top_colors = df.colors.value_counts()
top_colors[:10].plot(kind='barh')
plt.xlabel('No. of Products');
Using Seaborn:
sns.factorplot("colors", data=df , palette="PuBu_d");
1) Is there a better way to do this?
2) How can i replicate this with Seaborn?
3) How do i plot such that the highest count is at the top (i.e black at the very top of the bar chart)
回答1:
An easy trick might be to invert the y axis of your plot, rather than futzing with the data:
s = pd.Series(np.random.choice(list(string.uppercase), 1000))
counts = s.value_counts()
ax = counts.iloc[:10].plot(kind="barh")
ax.invert_yaxis()
Seaborn barplot doesn't currently support horizontally oriented bars, but if you want to control the order the bars appear in you can pass a list of values to the x_order param. But I think it's easier to use the pandas plotting methods here, anyway.
回答2:
If you want to use pandas then you can first sort:
top_colors[:10].sort(ascending=0).plot(kind='barh')
Seaborn already styles your pandas plots, but you can also use:
sns.barplot(top_colors.index, top_colors.values)
来源：https://stackoverflow.com/questions/28022227/sorted-bar-charts-with-pandas-matplotlib-or-seaborn