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 Dijkstra 算法实现原理

 https://www.jianshu.com/p/ff6db00ad866

gtest

https://blog.csdn.net/pbe_sedm/article/details/42240885

# -*- coding:utf-8 -*-
import calendar
import pandas as pd
from datetime import datetime
import warnings
import matplotlib.pyplot as plt
import seaborn as sn


#pd.options.mode.chained_assignment = None
warnings.filterwarnings("ignore", category=DeprecationWarning)

#设置选项，防止head()出现省略号
pd.set_option('display.width',None)

dailyData = pd.read_csv("d:/train.csv", encoding='gbk')

print(dailyData.shape)
print(dailyData.head())
print(dailyData.columns.tolist())
#2011-01-01 00:00:00       1        0           0        1  9.84  14.395        81        0.0       3          13     16
dailyData["date"] = dailyData.datetime.apply(lambda x : x.split()[0])
dailyData["hour"] = dailyData.datetime.apply(lambda x : x.split()[1].split(":")[0])
dailyData["weekday"] = dailyData.date.apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d").weekday()])
dailyData["month"] = dailyData.date.apply(lambda dateString : calendar.month_name[datetime.strptime(dateString,"%Y-%m-%d").month])
dailyData["season"] = dailyData.season.map({1: "Spring", 2 : "Summer", 3 : "Fall", 4 :"Winter" })
dailyData["weather"] = dailyData.weather.map({1: " Clear + Few clouds + Partly cloudy + Partly cloudy",\
                                        2 : " Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist ", \
                                        3 : " Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds", \
                                        4 :" Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog " })


categoryVariableList = ["hour","weekday","month","season","weather","holiday","workingday"]
for var in categoryVariableList:
    dailyData[var] = dailyData[var].astype("category")

dailyData  = dailyData.drop(["datetime"],axis=1)
dailyData.head() 

dataTypeDf = pd.DataFrame(dailyData.dtypes.value_counts()).reset_index().rename(columns={"index":"variableType",0:"count"})

print(dataTypeDf)
#------------------------------------------------------------------
new_dic = {}
for i in range(dataTypeDf.shape[0]):
    temp = dataTypeDf.loc[i, "variableType"]
    tempType = temp.name
    if tempType in new_dic:
        new_dic[tempType] = new_dic[tempType] + dataTypeDf.loc[i, "count"]
    else:
        new_dic[tempType] = dataTypeDf.loc[i, "count"]
print(new_dic)

mylist = list()
for key in new_dic.keys():
    mylist.append([key, new_dic[key]])
print(mylist)

dataTypeDf = pd.DataFrame(mylist, columns=list(dataTypeDf))
#--------------------------------------------------------------------------
print(dataTypeDf)
fig,ax = plt.subplots()
fig.set_size_inches(12,5)
sn.barplot(data=dataTypeDf,x="variableType",y="count",ax=ax)####
ax.set(xlabel='variableType', ylabel='Count',title="Variables DataType Count")
plt.show()

 

Dijkstra 算法实现原理