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# coding=utf-8
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df = pd.read_csv("./911.csv")
df["timeStamp"] = pd.to_datetime(df["timeStamp"])
df.set_index("timeStamp",inplace=True)
#统计出911数据中不同月份电话次数的
count_by_month = df.resample("M").count()["title"]
print(count_by_month)
#画图
_x = count_by_month.index
_y = count_by_month.values
# for i in _x:
#     print(dir(i))
#     break
_x = [i.strftime("%Y%m%d") for i in _x]
plt.figure(figsize=(20,8),dpi=80)
plt.plot(range(len(_x)),_y)
plt.xticks(range(len(_x)),_x,rotation=45)
plt.show()

# coding=utf-8
#911数据中不同月份不同类型的电话的次数的变化情况
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
#把时间字符串转为时间类型设置为索引
df = pd.read_csv("./911.csv")
df["timeStamp"] = pd.to_datetime(df["timeStamp"])
#添加列，表示分类
temp_list = df["title"].str.split(": ").tolist()
cate_list = [i[0] for i in temp_list]
# print(np.array(cate_list).reshape((df.shape[0],1)))
df["cate"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)))
df.set_index("timeStamp",inplace=True)
print(df.head(1))
plt.figure(figsize=(20, 8), dpi=80)
#分组
for group_name,group_data in df.groupby(by="cate"):
    #对不同的分类都进行绘图
    count_by_month = group_data.resample("M").count()["title"]
    # 画图
    _x = count_by_month.index
    print(_x)
    _y = count_by_month.values
    _x = [i.strftime("%Y%m%d") for i in _x]
    plt.plot(range(len(_x)), _y, label=group_name)
plt.xticks(range(len(_x)), _x, rotation=45)
plt.legend(loc="best")
plt.show()