seaborn 绘制dataframe histogram 直方图

我们经常需要绘制直方图来可视化某些特征或变量的分布。
如何快速获得有用的情节并完成工作？ 如果我们关心的是每个值的频率，seaborn 提供 一个方便的方法，countplot（）函数，不用自己统计数据就可以得到图，迅速完成工作。

参考下面的示例：

get the data and do a count plot

%matplotlib inline
import seaborn as sns


titanic = sns.load_dataset("titanic")
titanic['class'] = titanic['class'].astype('str')
display(titanic)

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

sns.set_theme(style="darkgrid")
ax = sns.countplot(x="embark_town", data=titanic)

what if we have too many values for the feature, and we can’t plot all of their distributions in the histogram?

# get the distinct values first, then choose the top n values we want to present; here we choose 2 as an example

sub_index = titanic['class'].value_counts().index[:2]
sub_data = titanic[titanic['class'].isin(sub_index)]
sub_data = sub_data.reset_index(drop=True)

ax = sns.countplot(x="class", data=sub_data)

# we can also explicitly require the order to be ascending
ax = sns.countplot(x="class", data=sub_data,order=sub_index[::-1])

now how to show the value counts for two categorical variables?

ax = sns.countplot(x="class", hue="who", data=titanic)

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