特征锦囊：常用的统计图在Python里怎么画?

# 导入一些常用包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
plt.style.use('fivethirtyeight')

#解决中文显示问题，Mac
%matplotlib inline
from matplotlib.font_manager import FontProperties


# 引入第 1 个数据集 Salary_Ranges_by_Job_Classification
salary_ranges = pd.read_csv('./data/Salary_Ranges_by_Job_Classification.csv')

# 引入第 2 个数据集 GlobalLandTemperaturesByCity
climate = pd.read_csv('./data/GlobalLandTemperaturesByCity.csv')
# 移除缺失值
climate.dropna(axis=0, inplace=True)
# 只看中国
# 日期转换, 将dt 转换为日期，取年份, 注意map的用法
climate['dt'] = pd.to_datetime(climate['dt'])
climate['year'] = climate['dt'].map(lambda value: value.year)
climate_sub_china = climate.loc[climate['Country'] == 'China']
climate_sub_china['Century'] = climate_sub_china['year'].map(lambda x:int(x/100 +1))

# 设置显示的尺寸
plt.rcParams['figure.figsize'] = (4.0, 4.0) # 设置figure_size尺寸
plt.rcParams['image.interpolation'] = 'nearest' # 设置 interpolation style
plt.rcParams['image.cmap'] = 'gray' # 设置 颜色 style
plt.rcParams['savefig.dpi'] = 100 #图片像素
plt.rcParams['figure.dpi'] = 100 #分辨率
plt.rcParams['font.family'] = ['Arial Unicode MS'] #正常显示中文

# 绘制条形图
salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(10).plot(kind='bar')
# 绘制饼图
salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(5).plot(kind='pie')
# 绘制箱体图
salary_ranges['Union Code'].value_counts().sort_values(ascending=False).head(5).plot(kind='box')
# 绘制直方图
climate['AverageTemperature'].hist()
# 绘制散点图
x = climate_sub_china['year']
y = climate_sub_china['AverageTemperature']
fig, ax = plt.subplots(figsize=(10,5))
ax.scatter(x, y)
plt.show()