import time

print(time.time())
print(time.localtime())
print(time.strftime('%Y-%m-%d %X',time.localtime()))

绘图显示中文配置

import matplotlib.pyplot as plt

a = [1,1,2,3]
b = [2,2,2,2]
plt.plot(a,b)
plt.title("天生自然")
plt.show()

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv")
print(df.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
df.to_csv("E:\\temp\\taobao_price_data.csv", columns=["宝贝","价格"],index=False,header=True)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df[0:3])

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
cols = df[["宝贝","价格"]]
print(cols.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.ix[0:3,["宝贝","价格"]]
print(a)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
df["销售量"] = df["价格"]*df["成交量"]
print(df.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[(df["价格"]<100)&(df["成交量"]<10000)]
print(a)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df.head())
df1 = df.set_index("位置")
print(df1.head())
df2 = df1.sort_index()
print(df2.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
df1 = df.set_index(["位置","卖家"])
print(df1.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
df1 = df.set_index(["位置","卖家"]).sortlevel(0)
print(df1.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.drop(["宝贝","卖家"],axis=1)
print(a.head())

b = df.drop(["宝贝","卖家"],axis=1).groupby("位置")
print(b.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.drop(["宝贝","卖家"],axis=1).groupby("位置").mean()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.drop(["宝贝","卖家"],axis=1).groupby("位置").mean().sort_values("成交量",ascending=False)
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.drop(["宝贝","卖家"],axis=1).groupby("位置").sum().sort_values("成交量",ascending=False)
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df.info())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df.describe())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
print(df.describe(include=["object"]))

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df["成交量"].groupby(df["位置"])
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df["成交量"].groupby(df["位置"]).mean()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df["成交量"].groupby([df["位置"],df["卖家"]]).mean()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.groupby("位置").mean()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.groupby(["位置","卖家"]).mean()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df.groupby(["位置","卖家"]).size()
print(a.head())

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[30:35][["位置","卖家"]]
print(a)

b = df[90:95][["卖家","成交量"]]
print(b)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[30:35][["位置","卖家"]]
b = df[30:35][["卖家","成交量"]]
c = pd.merge(a,b)
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[30:35][["位置","卖家"]]
b = df[30:35][["卖家","成交量"]]
c = pd.merge(a,b,on="卖家")
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[10:20][["位置","卖家"]]
b = df[30:40][["卖家","成交量"]]
c = pd.merge(a,b,how="outer")
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[10:20][["位置","卖家"]]
b = df[30:40][["卖家","成交量"]]
c = pd.merge(a,b,how="left")
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[10:20][["位置","卖家"]]
b = df[30:40][["卖家","成交量"]]
c = pd.merge(a,b,how="right")
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:10][["位置","卖家"]]
print(a)
b = df[:10][["卖家","成交量"]]
print(b)
c = pd.merge(a,b,how="right")
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:10][["位置","卖家"]]
b = df[:10][["卖家","成交量"]]
c = pd.merge(a,b,left_index=True,right_index=True)
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:10][["位置","卖家"]]
b = df[:10][["价格","成交量"]]
c = pd.merge(a,b,left_index=True,right_index=True)
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:10][["位置","卖家"]]
b = df[:10][["价格","成交量"]]
c = a.join(b)
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:5]["宝贝"]
b = df[5:10]["宝贝"]
c = df[10:15]["宝贝"]
d = pd.concat([a,b,c])
print(d)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:5]["宝贝"]
print(a)
b = df[:5]["价格"]
print(b)
c = df[:5]["成交量"]
print(c)
d = pd.concat([a,b,c],axis=1)
print(d)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:5][["位置","卖家"]]
print(a)
b = df[:5][["价格","成交量"]]
print(b)
c = pd.concat([a,b])
print(c)

import pandas as pd

df = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\taobao_data.csv",delimiter=",",encoding="utf8",header=0)
a = df[:5][["位置","卖家"]]
print(a)
b = df[:5][["价格","成交量"]]
print(b)
c = pd.concat([a,b],axis=1)
print(c)

吴裕雄 python 数据处理(1)的更多相关文章

  1. 吴裕雄 python 数据处理(3)

    import time a = time.time()print(a)b = time.localtime()print(b)c = time.strftime("%Y-%m-%d %X&q ...

  2. 吴裕雄 python 数据处理(2)

    import pandas as pd data = pd.read_csv("F:\\python3_pachongAndDatareduce\\data\\pandas data\\hz ...

  3. 吴裕雄 python 神经网络——TensorFlow 输入数据处理框架

    import tensorflow as tf files = tf.train.match_filenames_once("E:\\MNIST_data\\output.tfrecords ...

  4. 吴裕雄 python神经网络 花朵图片识别(10)

    import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skim ...

  5. 吴裕雄 python神经网络 花朵图片识别(9)

    import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skim ...

  6. 吴裕雄 python 神经网络——TensorFlow pb文件保存方法

    import tensorflow as tf from tensorflow.python.framework import graph_util v1 = tf.Variable(tf.const ...

  7. 吴裕雄 python 神经网络——TensorFlow 花瓣分类与迁移学习(4)

    # -*- coding: utf-8 -*- import glob import os.path import numpy as np import tensorflow as tf from t ...

  8. 吴裕雄 python 神经网络——TensorFlow 花瓣分类与迁移学习(3)

    import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platfor ...

  9. 吴裕雄 python 神经网络——TensorFlow 花瓣分类与迁移学习(2)

    import glob import os.path import numpy as np import tensorflow as tf from tensorflow.python.platfor ...

随机推荐

  1. webpack入门认知

    webpack 是什么? 本质上,webpack 是一个现代 JavaScript 应用程序的静态模块打包器(module bundler).当 webpack 处理应用程序时,它会递归地构建一个依赖 ...

  2. 大数据hadoop与spark的区别

    学习hadoop已经有很长一段时间了,好像是二三月份的时候朋友给了一个国产Hadoop发行版下载地址,因为还是在学习阶段就下载了一个三节点的学习版玩一下.在研究.学习hadoop的朋友可以去找一下看看 ...

  3. paramiko不能通过cd改变路径分析

    原文: 意思就是 每次执行execute_command()会重新创建一个新的会话,而新会话的当前路径为缺省目录. (这和linux中每次终端登录类似) 解决方法: .execute_command( ...

  4. 在chrome中安装基于REST的web服务客户端

    基于REST的Web服务客户端的使用方法 点击转到基于REST的Web服务客户端下载页面 chrome浏览器如果安装扩展程序点击chrome浏览器右上角,选择“设置在“设置”对话框里选择“扩展程序”然 ...

  5. WDA编译失败问题

    1.放假回来,wda编译失败,报错如下 2018-09-25 10:03:09.020964+0800 WebDriverAgentRunner-Runner[335:33309] +[CATrans ...

  6. PHPExcel导入导出 若在thinkPHP3.2中使用(无论实例还是静态调用(如new classname或classname::function)都必须加反斜杠,因3.2就命名空间,如/classname

    php利用PHPExcel类导出导入Excel用法 来源:   时间:2013-09-05 19:26:56   阅读数: 分享到: 16 [导读] PHPExcel类是php一个excel表格处理插 ...

  7. vlc的应用之二:vlc的ActiveX及cab

    请移步https://higoge.github.io/,所有下载资料在那个博客都能找到.谢谢. http://jeremiah.blog.51cto.com/ 2009-05-14补充:8. Act ...

  8. MYSQL中只知表名查询属于哪个SCHEMA

    只知道表名XXX查该表属于哪个schema.以及该表有哪些列等信息 SELECT * from information_schema.columns WHERE table_name = 'xxx'; ...

  9. Windows下MySQL免安装版的安装、卸载

    一.安装 1.下载 到MySQL官网http://dev.mysql.com/downloads/mysql/ 下载mysql-5.6.15-win32.zip. 2.拷贝 将mysql-5.6.15 ...

  10. MongoDB对Javascript的支持

    在项目中MongoDB的Map-Reduce功能做了许多统计任务,在重构代码的时候修改了_id对象里面的属性字段名称,当用db.collection.update({$rename:{"_i ...