pandas groupby对数据进行处理 并可视化

# coding=utf-8
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib import font_manager

my_font = font_manager.FontProperties(fname="/Library/Fonts/Songti.ttc")

file_path = "./starbucks_store_worldwide.csv"

df = pd.read_csv(file_path)
df = df[df["Country"]=="CN"]

#使用matplotlib呈现出店铺总数排名前10的国家
#准备数据
data1 = df.groupby(by="City").count()["Brand"].sort_values(ascending=False)[:25]

_x = data1.index
_y = data1.values

#画图
plt.figure(figsize=(20,12),dpi=80)

# plt.bar(range(len(_x)),_y,width=0.3,color="orange")
plt.barh(range(len(_x)),_y,height=0.3,color="orange")

plt.yticks(range(len(_x)),_x,fontproperties=my_font)

plt.show()

# coding=utf-8
import pandas as pd
from matplotlib import pyplot as plt


file_path = "./books.csv"

df = pd.read_csv(file_path)
# print(df.head(2))
#
# print(df.info())

# data1 = df[pd.notnull(df["original_publication_year"])]
#
# grouped = data1.groupby(by="original_publication_year").count()["title"]


#不同年份书的平均评分情况
#去除original_publication_year列中nan的行
data1 = df[pd.notnull(df["original_publication_year"])]

grouped = data1["average_rating"].groupby(by=data1["original_publication_year"]).mean()

# print(grouped)

_x = grouped.index
_y = grouped.values

#画图
plt.figure(figsize=(20,8),dpi=80)
plt.plot(range(len(_x)),_y)
print(len(_x))

plt.xticks(list(range(len(_x)))[::10],_x[::10].astype(int),rotation=45)
plt.show()