v2ex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据来源\n",
    "\n",
    "过去将近七百天的连续签到数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"test.xlsx\")\n",
    "df1 = df[df[u'类型'] == u\"每日登录奖励\"][[u\"时间\", u\"数额\"]]\n",
    "df1[u'时间'] = df1.apply(lambda x: x[u'时间'].split(\" \")[0], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 平均值，方差，标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26.371720116618075, 211.96532269717551, 14.559028906392607)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.mean(),dataset.var(),dataset.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = df1[u'数额'].values\n",
    "total_size = len(dataset)\n",
    "dict_count = {}\n",
    "dict_prob = {}\n",
    "def action(x):\n",
    "    dict_count[x] = dict_count.get(x, 0) + 1\n",
    "    dict_prob[x] = dict_count[x] * 1.0 / total_size\n",
    "map(action, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计散点图\n",
    "``横轴是签到得到的数额，纵轴是出现次数``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb4597e06d0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WaSb6/T47dvQYhjnAXq68sku/36+vKEkGukbX6/V4/vk+sFosWeWFF07T6/XqK0qSga7R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\nT10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb459bbe990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lists = sorted(dict_count.items()) \n",
    "x, y = zip(*lists)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 概率分布\n",
    "``横轴是签到得到的数额，纵轴是出现频率``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb45984e1d0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZM5bEEBG7IuJURLwSEQ8s0+bhiDgdESciYnvP+oMRcSEiXhq0nZrNgqs0eyrXGCJiHfAK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb459c18b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lists = sorted(dict_prob.items()) \n",
    "x, y = zip(*lists)\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 时间分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb459b2dfd0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lc/NKzp27Nxsbq0kWsvvzLJcu3W1QYqKIVQA4GufMyadoEHOj01nvG4ySZCEbG6vpdNbH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\nEeCAmiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb459b02c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df1.iloc[0:50, :]\n",
    "a.plot(x=u'时间', y=u'数额', style=\"o\", figsize=(15,5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "widgets": {
   "state": {},
   "version": "1.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}