# coding=utf-8
import pandas as pd
from matplotlib import pyplot as plt
file_path = "./books.csv"
df = pd.read_csv(file_path)
df.head()

df[pd.notnull(df["original_publication_year"])]

# print(df.head(2))
#
# print(df.info())

data1 = df[pd.notnull(df["original_publication_year"])]

grouped = data1.groupby(by="original_publication_year").count()["title"]
grouped.head()

#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()

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


df = pd.read_csv("./911.csv")
df.head()

#获取分类
# print()df["title"].str.split(": ")
temp_list = df["title"].str.split(": ").tolist()
cate_list = list(set([i[0] for i in temp_list]))
cate_list

#构造全为0的数组
zeros_df = pd.DataFrame(np.zeros((df.shape[0],len(cate_list))),columns=cate_list)
#赋值
for cate in cate_list:
    zeros_df[cate][df["title"].str.contains(cate)] = 1
    # break
# print(zeros_df)
sum_ret = zeros_df.sum(axis=0)
print(sum_ret)

df.head(5)

df["title"].str.split(": ").head()

#获取分类
temp_list = df["title"].str.split(": ").tolist()
cate_list = [i[0] for i in temp_list]
df["cate"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)))
# print(df.head(5))
df.groupby(by="cate").count()["title"]