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Python数据可视化

花匠小妙招
2024-11-06 15:39

详细介绍可以看seaborn官方API和example galler。

1 set_style( ) set( )

set_style( )是用来设置主题的，Seaborn有五个预设好的主题： darkgrid , whitegrid , dark , white ,和 ticks 默认： darkgrid

import matplotlib.pyplot as plt import seaborn as sns sns.set_style("whitegrid") plt.plot(np.arange(10)) plt.show()

set( )通过设置参数可以用来设置背景，调色板等，更加常用。

import seaborn as sns import matplotlib.pyplot as plt sns.set(style="white", palette="muted", color_codes=True) plt.plot(np.arange(10)) plt.show()

2 distplot( ) kdeplot( )

distplot( )为hist加强版，kdeplot( )为密度曲线图

import matplotlib.pyplot as plt import seaborn as sns df_iris = pd.read_csv('../input/iris.csv') fig, axes = plt.subplots(1,2) sns.distplot(df_iris['petal length'], ax = axes[0], kde = True, rug = True) sns.kdeplot(df_iris['petal length'], ax = axes[1], shade=True) plt.show()

import numpy as np import seaborn as sns import matplotlib.pyplot as plt sns.set( palette="muted", color_codes=True) rs = np.random.RandomState(10) d = rs.normal(size=100) f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True) sns.distplot(d, kde=False, color="b", ax=axes[0, 0]) sns.distplot(d, hist=False, rug=True, color="r", ax=axes[0, 1]) sns.distplot(d, hist=False, color="g", kde_kws={"shade": True}, ax=axes[1, 0]) sns.distplot(d, color="m", ax=axes[1, 1]) plt.show()

3 箱型图 boxplot( ) import matplotlib.pyplot as plt import seaborn as sns df_iris = pd.read_csv('../input/iris.csv') sns.boxplot(x = df_iris['class'],y = df_iris['sepal width']) plt.show()

import matplotlib.pyplot as plt import seaborn as sns tips = pd.read_csv('../input/tips.csv') sns.set(style="ticks") sns.boxplot(x="day", y="total_bill", hue="sex", data=tips, palette="PRGn") plt.show()

4 联合分布jointplot( ) tips = pd.read_csv('../input/tips.csv') sns.jointplot("total_bill", "tip", tips) plt.show()

tips = pd.read_csv('../input/tips.csv') sns.jointplot("total_bill", "tip", tips, kind='reg') plt.show()

5 热点图heatmap( )

internal_chars = ['full_sq', 'life_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'kitch_sq', 'state', 'price_doc']
corrmat = train[internal_chars].corr()

f, ax = plt.subplots(figsize=(10, 7))
plt.xticks(rotation='90')
sns.heatmap(corrmat, square=True, linewidths=.5, annot=True)
plt.show()

6 散点图scatter( )

f, ax = plt.subplots(figsize=(10, 7))
plt.scatter(x=train['full_sq'], y=train['price_doc'], c='r')
plt.xlim(0,500)
plt.show()

7.pointplot画出变量间的关系

grouped_df = train_df.groupby('floor')['price_doc'].aggregate(np.median).reset_index()

plt.figure(figsize=(12,8))

sns.pointplot(grouped_df.floor.values, grouped_df.price_doc.values, alpha=0.8, color=color[2])

plt.ylabel('Median Price', fontsize=12)

plt.xlabel('Floor number', fontsize=12)

plt.xticks(rotation='vertical') plt.show()

8 pairplot( ) import matplotlib.pyplot as plt import seaborn as sns data = pd.read_csv("../input/iris.csv") sns.set() sns.pairplot(data,hue="class") plt.show()

import seaborn as sns import matplotlib.pyplot as plt iris = pd.read_csv('../input/iris.csv') sns.pairplot(iris, vars=["sepal width", "sepal length"],hue='class',palette="husl") plt.show()

9 FacetGrid( ) import seaborn as sns import matplotlib.pyplot as plt tips = pd.read_csv('../input/tips.csv') g = sns.FacetGrid(tips, col="time", row="smoker") g = g.map(plt.hist, "total_bill", color="r") plt.show()

10 barplot( )

f, ax=plt.subplots(figsize=(12,20))

#orient='h'表示是水平展示的，alpha表示颜色的深浅程度
sns.barplot(y=group_df.sub_area.values, x=group_df.price_doc.values,orient='h', alpha=0.8, color='red')

#设置y轴、X轴的坐标名字与字体大小
plt.ylabel('price_doc', fontsize=16)
plt.xlabel('sub_area', fontsize=16)

#设置X轴的各列下标字体是水平的
plt.xticks(rotation='horizontal')

#设置Y轴下标的字体大小
plt.yticks(fontsize=15)
plt.show()

注：如果orient='v'表示成竖直显示的话，一定要记得y=group_df.sub_area.values, x=group_df.price_doc.values调换一下坐标轴，否则报错

11.bar图

import matplotlib.pyplot as plt
import numpy as np
plt.rc('font', family='SimHei', size=13)

num = np.array([13325, 9403, 9227, 8651])
ratio = np.array([0.75, 0.76, 0.72, 0.75])
men = num * ratio
women = num * (1-ratio)
x = ['聊天','支付','团购n优惠券','在线视频']

width = 0.5
idx = np.arange(len(x))
plt.bar(idx, men, width, color='red', label='男性用户')
plt.bar(idx, women, width, bottom=men, color='yellow', label='女性用户') #这一块可是设置bottom,top，如果是水平放置的，可以设置right或者left。
plt.xlabel('应用类别')
plt.ylabel('男女分布')
plt.xticks(idx+width/2, x, rotation=40)

#bar图上显示数字

for a,b in zip(idx,men):

plt.text(a, b+0.05, '%.0f' % b, ha='center', va= 'bottom',fontsize=12)
for a,b,c in zip(idx,women,men):
plt.text(a, b+c+0.5, '%.0f' % b, ha='center', va= 'bottom',fontsize=12)

plt.legend()
plt.show()

12、双Y轴绘图

本例主要用dataframe的两个列进行双Y轴画图

eng_name,chn_name,GDP,rate
a, 中国,100,0.6
b,美国,180,0.3
c,日本,80,0.2
d,瑞典,65,0.15
f,荷兰,56,0.23

#读取的时候，讲索引列变为chn_name,这样画图时候X轴自动为索引
df=pd.read_csv('b.csv',index_col='chn_name') df.index.name='国家'#这样x轴的label就变成‘国家了’。
plt.rc('font', family='SimHei', size=13) plt.figure() df['GDP'].plot(kind='bar') plt.ylabel('GDP') plt.title('国家发展情况对比') p = df['rate'] p.plot(color='black',secondary_y=True,style='--o',linewidth=2) #style--表示虚线，-表示实线 plt.ylabel('增长速度')

　x=[0,1,2,3,4]#因为x轴是汉字，所以默认对应的数值是从0开始的
　for a,b in zip(x,p):
　　plt.text(a+0.1, b+0.02, '%.2f' % b, ha='center', va= 'bottom',fontsize=12)