luoganttcc的博客

import pandas as pdimport numpy as npdf = pd.DataFrame({'name':['Jack','Alex','Bob','Nancy','Mary','Alice','Jerry','Wolf'], 'course':['Chinese','Math','Math','Chinese','Math','English','Chinese','English'], 'grade':[1,1,2,2,2

在做神经网络的房价分类时，发现一个奇怪的现象。模型预测误差较大的点大概率的落在房屋方向为“南北通透”这个类别之内，在特征选取时，我将‘’南北通透‘’与‘’南北”住宅并为一类；代码如下：        我以上海市浦东区，[“康桥半岛二期”,”康桥半岛三期”,”绿洲清水湾”,”中邦城市”]四个小区的389个样本做数据分析for i in dc['direction'].index: if ('南'

import pandas as pdimport numpy as npts1 = [0, 1, np.nan, np.nan, np.nan, np.nan]ts2 = [0, 2, np.nan, 3, np.nan, np.nan]d = {'X': ts1, 'Y': ts2, 'Z': ts2}df = pd.DataFrame(data=d)print('df=',df)dd

dfc = pd.DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})dfc.loc[0,'A'] = 11dfcOut[347]: A B0 11 11 bbb 22 ccc 3This can work at times, but is not guaranteed, and so should b

我曾经在图书馆里看到一本书，在那本书里作者提出了一种股票预测算法，即历史与未来中心对称于当下,我当时就花了一天时间写下了这个代码。# -*- coding: utf-8 -*-"""Created on Fri Apr 7 16:41:01 2017@author: Administrator"""import matplotlib.pyplot as pltimport

import numpy as npimport pandas as pddf = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"], "B": ["one", "one", "one",...

df = pd.DataFrame({'Animal' : ['Falcon', 'Falcon', 'Parrot', 'Parrot'], 'Max Speed' : [380., 370., 24., 26.]})df.groupby(['Animal']).mean() ...

s = pd.Series(range(5))0 01 12 23 34 4dtype: int64s.where(s >= 1, 10)大于等于1的值不动，小于1的值替换成100 101 12 23 34 4dtype: int64...

import pandas as pdleft = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'], 'key2': ['K0', 'K1', 'K0', 'K1'], 'A': ['A0', 'A1', 'A2', 'A3'], ...

# -*- coding: utf-8 -*-"""Spyder EditorThis is a temporary script file."""import google.authfrom google.cloud import bigqueryfrom google.cloud import bigquery_storage_v1beta1# Explicitly cr...

import pandas as pddf=pd.DataFrame([['a',2,5], ['b',3,6], ['c',5,8], ['a',7,3], ['c',6,9]],columns=['A','B','C'])...

import pandas as pdm=pd.DataFrame([[1,2,3],[4,5,6],[7,8,9],[10,11,12]],columns=['a','b','c'])print('m is \n',m)m is a b c0 1 2 31 4 5 62 7 8 93 10 11 12m_in=...

from dateutil.parser import parsea=20170825b=str(a)c=parse(b)print(c)2017-08-25 00:00:00

有行索引和列索引df=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four'])dfOut[14]: one two three foura 0 1 2 3b 4 5 6 7c

构造DataFramefrom

构造Seriesobj=Series([4,5,-7,7])objOut[140]: 0 41 52 -73 7dtype: int64obj.indexOut[141]: RangeIndex(start=0, stop=4, step=1)obj.valuesOut[142]: array([ 4, 5, -7, 7], dtype=int64)构造同时

建一个Seriesfrom pandas import Series,DataFrameobj=Series(range(3),index=['a','b','c'])obj

百度云代码连接：http://pan.baidu.com/s/1hr4QGgG 密码：puxwboxplot 箱形图catering_sale = '../data/catering_sale.xls' #餐饮数据data = pd.read_excel(catering_sale) #读取数据，指定“日期”列为索引列import matplotlib.pyplot as plt #导入图像库pl

画大饼import matplotlib.pyplot as pltlabels='uu','y','j','dd'sizes=[15,30,45,10]color=['yellow','gold','blue','red']explode=(0,0.1,0,0)plt.pie(size,explode=explode,labels=label,colors=colors,autopct='

建立Seriesfrom pandas import Series,DataFrameimport numpy as npimport pandas as pdobj=Series(np.arange(4),index=['a','b','c','d'])objOut[5]: a 0b 1c 2d 3dtype: int32    选取obj['b']Out

数据来源于公司的mongodb 数据库，由于公司保密需要，端口不能给出。大家看下吧。利用四个小区的历史交易训练模型，给出房价输出。# -*- coding: utf-8 -*-"""Created on Thu Aug 24 15:14:07 2017@author: Administrator"""import pymongofrom pymongo import MongoClient

df2=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four']) df2Out[10]: one two three foura 0 1 2 3b 4 5 6 7c 8

优雅的增加一行，一定要优雅！df.loc['new_raw'] = '3'dfOut[84]: one two three foura 0 1 2 3b 4 5 6 7c 8 9 10 11d 12 13 14 15new_raw 3 3

1：：： xgboost 2：：：随机森林 3：：：xgboost 线性模型 4：：：xgboost数据预处理               数据预处理2 5：：：文章集合 6：：：岭回归 7：：：xgboost资料1 xgboost资料2 8：：：keras资料              如何提高深度学习

from pandas import DataFrame import numpy as npdf=DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four']) dfOut[8]: one two three foura 0 1

1.0 删除Series项from pandas import Series,DataFrameimport numpy as npimport pandas as pdobj=Series(np.arange(5),index=['a','b','c','d','e'])objOut[82]: a 0b 1c 2d 3e 4dtype: int32