20210410.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% 莫烦python教程\n"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-4-d62f8797524d>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      5\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 7\u001B[1;33m \u001B[1;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mj\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mlist1\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mlist2\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      8\u001B[0m     \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mj\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mValueError\u001B[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "list1=[1,2,3]\n",
    "list2=[1,2,3]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i,j in list1,list2:\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "2"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = lambda x : x+1\n",
    "y(1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "[2, 3]"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "list(map(y,[1,2]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "(2, 3)\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.array([[1,2,3],\n",
    "                  [4,5,6]])\n",
    "print(array.ndim)\n",
    "print(array.shape)\n",
    "print(array.size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.1\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2. 3.]\n",
      " [4. 5. 6.]]\n",
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[[ 0.          0.52631579  1.05263158  1.57894737  2.10526316  2.63157895\n",
      "   3.15789474  3.68421053  4.21052632  4.73684211]\n",
      " [ 5.26315789  5.78947368  6.31578947  6.84210526  7.36842105  7.89473684\n",
      "   8.42105263  8.94736842  9.47368421 10.        ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "              [4,5,6]],dtype=np.float)\n",
    "print(a)\n",
    "b  = np.zeros((3,4))\n",
    "# np.empty()\n",
    "print(b)\n",
    "\n",
    "# c = np.arange(10,20,2)\n",
    "c = np.arange(12).reshape(3,4)\n",
    "print(c)\n",
    "\n",
    "# d = np.linspace(0,10,20)\n",
    "d = np.linspace(0,10,20).reshape(2,10)\n",
    "print(d)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.2\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ True False False]\n",
      " [False False False]]\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "              [4,5,6]],dtype=np.float)\n",
    "b = np.array([[1,2,3],\n",
    "              [4,5,6]],dtype=np.float)\n",
    "# c = a - b\n",
    "# c = a + b\n",
    "# c = b**2\n",
    "# c = np.sin(a)\n",
    "# c = np.cos(a)\n",
    "# c = a*b\n",
    "# c_dot = np.dot(a,b)\n",
    "# c_dot = a.dot(b)\n",
    "print(a<2)\n",
    "print(c)\n",
    "\n",
    "d = np.random.randn((2,4))\n",
    "print(d)\n",
    "# np.sum()\n",
    "# np.sum(d,axis=1)\n",
    "# np.min()\n",
    "# np.max()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.3\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],\n",
    "              [4,5,6]],dtype=np.float)\n",
    "b = np.array([[1,2,3],\n",
    "              [4,5,6]],dtype=np.float)\n",
    "\n",
    "# np.argmin(a)\n",
    "# np.argmax(a)\n",
    "# np.mean(a)\n",
    "# np.mean(a,axis=0)\n",
    "# a.mean()\n",
    "# np.average(a)\n",
    "# np.median(a)\n",
    "# np.cumsum(a)\n",
    "# np.diff(a)\n",
    "# np.nonzero(a)\n",
    "# np.sort(a)\n",
    "# np.transpose(a)\n",
    "# a.T\n",
    "# np.clip(a,5,9)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.4\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[ 4  8 12]\n",
      "[3 4 5 6]\n",
      "[ 7  8  9 10]\n",
      "[11 12 13 14]\n",
      "[ 3  7 11]\n",
      "[ 4  8 12]\n",
      "[ 5  9 13]\n",
      "[ 6 10 14]\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "[ 3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(3,15).reshape(3,4)\n",
    "print(a[0][0])\n",
    "print(a[:,1])\n",
    "\n",
    "for row in a:\n",
    "    print(row)\n",
    "\n",
    "for column in a.T:\n",
    "    print(column)\n",
    "\n",
    "for item in a.flat:\n",
    "    print(item)\n",
    "\n",
    "b = a.flatten()\n",
    "print(b)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.5\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]\n",
      " [ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]]\n",
      "[[ 3  4  5  6  3  4  5  6]\n",
      " [ 7  8  9 10  7  8  9 10]\n",
      " [11 12 13 14 11 12 13 14]]\n",
      "[[ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]\n",
      " [ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]\n",
      " [ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]\n",
      " [ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]]\n",
      "[[1]\n",
      " [1]\n",
      " [1]]\n",
      "[[1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(3,15).reshape(3,4)\n",
    "b = np.arange(3,15).reshape(3,4)\n",
    "\n",
    "print(np.vstack((a,b)))\n",
    "print(np.hstack((a,b)))\n",
    "\n",
    "print(np.concatenate((a,b,b,a),axis=0))\n",
    "\n",
    "c = np.array([1,1,1])\n",
    "print(c[:,np.newaxis])\n",
    "print(c[np.newaxis,:])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.6\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 3,  4],\n",
      "       [ 7,  8],\n",
      "       [11, 12]]), array([[ 5,  6],\n",
      "       [ 9, 10],\n",
      "       [13, 14]])]\n",
      "[array([[ 3,  4],\n",
      "       [ 7,  8],\n",
      "       [11, 12]]), array([[ 5],\n",
      "       [ 9],\n",
      "       [13]]), array([[ 6],\n",
      "       [10],\n",
      "       [14]])]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(3,15).reshape(3,4)\n",
    "print(np.split(a,2,axis=1))\n",
    "# print(np.array_split(a,3,axis=1)) 不等分裂\n",
    "np.vsplit(a,3)\n",
    "np.hsplit(a,2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.7\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  4  5  6  7  8  9 10 11 12 13 14]\n",
      "[ 1  4  5  6  7  8  9 10 11 12 13 14]\n",
      "[ 1  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    },
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(3,15)\n",
    "b = a\n",
    "c = a\n",
    "d = b\n",
    "a[0] = 1\n",
    "print(a)\n",
    "print(b)\n",
    "print(d)\n",
    "b is a\n",
    "b = a.copy()\n",
    "b is a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%2.8\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1.0\n",
      "1     3.0\n",
      "2     6.0\n",
      "3     NaN\n",
      "4    44.0\n",
      "5     1.0\n",
      "dtype: float64\n",
      "DatetimeIndex(['2021-04-10', '2021-04-11', '2021-04-12', '2021-04-13',\n",
      "               '2021-04-14', '2021-04-15'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "                   a         b         c         d\n",
      "2021-04-10  0.330302  0.512858  0.889856  0.027446\n",
      "2021-04-11 -0.456969 -1.056727 -0.307113  0.274279\n",
      "2021-04-12 -1.079527 -0.100775  0.294301  1.281248\n",
      "2021-04-13 -0.410675  1.656470 -1.646020 -2.310514\n",
      "2021-04-14  1.121217  0.410480 -0.070944  0.012767\n",
      "2021-04-15  0.835262 -0.220576  0.042342  0.049499\n"
     ]
    },
    {
     "data": {
      "text/plain": "                   a         b         c         d\n2021-04-11 -0.456969 -1.056727 -0.307113  0.274279\n2021-04-15  0.835262 -0.220576  0.042342  0.049499\n2021-04-12 -1.079527 -0.100775  0.294301  1.281248\n2021-04-14  1.121217  0.410480 -0.070944  0.012767\n2021-04-10  0.330302  0.512858  0.889856  0.027446\n2021-04-13 -0.410675  1.656470 -1.646020 -2.310514",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-04-11</th>\n      <td>-0.456969</td>\n      <td>-1.056727</td>\n      <td>-0.307113</td>\n      <td>0.274279</td>\n    </tr>\n    <tr>\n      <th>2021-04-15</th>\n      <td>0.835262</td>\n      <td>-0.220576</td>\n      <td>0.042342</td>\n      <td>0.049499</td>\n    </tr>\n    <tr>\n      <th>2021-04-12</th>\n      <td>-1.079527</td>\n      <td>-0.100775</td>\n      <td>0.294301</td>\n      <td>1.281248</td>\n    </tr>\n    <tr>\n      <th>2021-04-14</th>\n      <td>1.121217</td>\n      <td>0.410480</td>\n      <td>-0.070944</td>\n      <td>0.012767</td>\n    </tr>\n    <tr>\n      <th>2021-04-10</th>\n      <td>0.330302</td>\n      <td>0.512858</td>\n      <td>0.889856</td>\n      <td>0.027446</td>\n    </tr>\n    <tr>\n      <th>2021-04-13</th>\n      <td>-0.410675</td>\n      <td>1.656470</td>\n      <td>-1.646020</td>\n      <td>-2.310514</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([1,3,6,np.nan,44,1])\n",
    "print(s)\n",
    "\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "print(dates)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "\n",
    "# df = pd.DataFrame(np.arange(12).reshape((3,4)))\n",
    "print(df)\n",
    "df.dtypes\n",
    "df.index\n",
    "df.columns\n",
    "df.values\n",
    "df.describe()\n",
    "df.T\n",
    "df.sort_index(axis=0,ascending=False)\n",
    "df.sort_values(by='b')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.1\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -0.567864\n",
      "b   -0.832057\n",
      "c   -0.572294\n",
      "d    0.551917\n",
      "Name: 2021-04-10 00:00:00, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([1,3,6,np.nan,44,1])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "\n",
    "# number\n",
    "# print(df['a'],df.a)\n",
    "# print(df[0:3])\n",
    "# print(df['20210410':'20210413'])\n",
    "\n",
    "# loc\n",
    "# print(df.loc['20210410'])\n",
    "# print(df.loc[:,['a','b']])\n",
    "# print(df.loc['20210410',['a','b']])\n",
    "\n",
    "# iloc\n",
    "# print(df.iloc[3:5,:])\n",
    "# print(df.iloc[[1,3,5],:])\n",
    "\n",
    "# # .ix is deprecated\n",
    "# print(df.ix[:3,['a','b']])\n",
    "\n",
    "# print(df[df.a<0])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.2\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a         b            c         d   f  e\n",
      "2021-04-10  0.000000  2.241519    -0.421715 -0.357219 NaN  1\n",
      "2021-04-11 -0.395553  0.532326    -0.831499 -0.556132 NaN  2\n",
      "2021-04-12 -1.068869  0.421623  1111.000000  0.973207 NaN  3\n",
      "2021-04-13  0.000000 -0.901519     0.211040  0.833294 NaN  4\n",
      "2021-04-14 -0.472545 -1.611837     0.427712  1.902838 NaN  5\n",
      "2021-04-15 -0.213866  0.978141     0.635621 -1.168295 NaN  6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([1,3,6,np.nan,44,1])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "\n",
    "df.iloc[2,2] = 1111\n",
    "df.loc['20210410','a'] = 1111\n",
    "# df[df.a>0] = 0\n",
    "df.a[df.a>0] = 0\n",
    "df['f'] = np.nan\n",
    "df['e'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('20210410',periods=6))\n",
    "print(df)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.3\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([1,3,6,np.nan,44,1])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "\n",
    "df.iloc[0,1] = np.nan\n",
    "df.iloc[1,2] = np.nan\n",
    "# df = df.dropna(axis=0,how='any') #how={'any','all'}\n",
    "# df = df.fillna(value=0)\n",
    "df = df.isnull()\n",
    "df = np.any(df.isnull()) == True\n",
    "print(df)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.4\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Glc_Long  Glc_Lati\n",
      "name                                    \n",
      "Hailuogou               101.92     29.58\n",
      "Parlung No.94            96.97     29.38\n",
      "Qiyi                     97.76     39.24\n",
      "Xiaodongkemadi           92.00     33.00\n",
      "Muztagh No.15            75.22     38.23\n",
      "Meikuang                 94.18     35.67\n",
      "NM551                    90.00     31.00\n",
      "Kangwure                 85.82     28.47\n",
      "Urumqi No.1              86.82     43.12\n",
      "Baishui Glacier No.1    100.20     27.10\n",
      "Ts.Tuyuksuyskiy          77.10     43.10\n",
      "No.125                   87.70     50.10\n",
      "Maliy Aktru              87.70     50.00\n",
      "Leviy Aktru              87.70     50.10\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([1,3,6,np.nan,44,1])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "\n",
    "data = pd.read_csv('position.csv',index_col='name')\n",
    "print(data)\n",
    "data.to_pickle('position.pickle')\n",
    "data = pd.read_pickle('position.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.5\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          a         b         c         d\n",
      "0 -0.686535 -0.032749 -1.767900  0.655543\n",
      "1 -0.494465  0.212911 -1.271909 -1.349967\n",
      "2 -1.506161  0.939994 -0.658577  1.864453\n",
      "3  0.890464  0.434122 -0.955453  2.970846\n",
      "4  1.034722 -0.730178  0.646463  1.522345\n",
      "5 -1.220496 -0.140690  2.302037 -0.678449\n",
      "6  1.000000  3.000000  6.000000  1.000000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# concatenating\n",
    "s = pd.Series([1,3,6,1],index=['a','b','c','d'])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df1 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "df2 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "df3 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','e'])\n",
    "# res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)\n",
    "# print(res)\n",
    "\n",
    "#join,['inner','outer']\n",
    "# res = pd.concat([df1,df3],join='inner',ignore_index=True)\n",
    "# print(res)\n",
    "\n",
    "#join_axes\n",
    "# res = pd.concat([df1,df3],join_axes=[df1.index],join='inner')\n",
    "\n",
    "#append\n",
    "# res = df1.append(df2,ignore_index=True)\n",
    "res = df1.append(s,ignore_index=True)\n",
    "print(res)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.6\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          a         b         c   d         e   indicator\n",
      "0 -1.216894  1.592524 -0.758134 NaN -0.817994  right_only\n",
      "1  1.003281  0.474827 -0.079961 NaN -0.534497  right_only\n",
      "2  0.346510  0.196888  1.458720 NaN  0.277879  right_only\n",
      "3 -1.503612 -1.268718  1.351216 NaN  0.718625  right_only\n",
      "4  0.524971 -2.005343 -1.986081 NaN -0.518431  right_only\n",
      "5 -0.978838 -1.769183 -0.339419 NaN -0.404863  right_only\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# concatenating\n",
    "s = pd.Series([1,3,6,1],index=['a','b','c','d'])\n",
    "dates = pd.date_range('20210410',periods=6)\n",
    "df1 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "# df2 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])\n",
    "df3 = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','e'])\n",
    "\n",
    "res = pd.merge(left=df1,right=df3,on=['a','b','c'],how='right',indicator='indicator')\n",
    "print(res)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.7\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name Hailuogou Parlung No.94    Qiyi Xiaodongkemadi Muztagh No.15 Meikuang  \\\n",
      "1957       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "1958       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "1959       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "1960       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "1961       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "...        ...           ...     ...            ...           ...      ...   \n",
      "2013   -274.54        -119.5 -151.78        -582.27        -45.61  -274.46   \n",
      "2014       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "2015       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "2016       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "2017       NaN           NaN     NaN            NaN           NaN      NaN   \n",
      "\n",
      "name NM551 Kangwure Urumqi No.1  Baishui Glacier No.1 Ts．Tuyuksuyskiy  \\\n",
      "1957   NaN      NaN          NaN                  NaN        -164.844   \n",
      "1958   NaN      NaN          NaN                  NaN         322.274   \n",
      "1959   NaN      NaN           87                  NaN        -429.408   \n",
      "1960   NaN      NaN         -188                  NaN        -113.898   \n",
      "1961   NaN      NaN          -33                  NaN        -568.839   \n",
      "...    ...      ...          ...                  ...             ...   \n",
      "2013 -1652      NaN         -536                -1872        -352.541   \n",
      "2014   NaN      NaN         -185                -1590         -1093.5   \n",
      "2015   NaN      NaN         -815                -1490             NaN   \n",
      "2016   NaN      NaN        -1017                -1872             NaN   \n",
      "2017   NaN      NaN          NaN                -1713             NaN   \n",
      "\n",
      "name No． 125 ( Vodopadniy)  Maliy Aktru  Leviy Aktru   \n",
      "1957                    NaN          NaN          NaN  \n",
      "1958                    NaN          NaN          NaN  \n",
      "1959                    NaN          NaN          NaN  \n",
      "1960                    NaN          NaN          NaN  \n",
      "1961                    NaN          NaN          NaN  \n",
      "...                     ...          ...          ...  \n",
      "2013                    NaN          NaN          NaN  \n",
      "2014                    NaN          NaN          NaN  \n",
      "2015                    NaN          NaN          NaN  \n",
      "2016                    NaN          NaN          NaN  \n",
      "2017                    NaN          NaN          NaN  \n",
      "\n",
      "[61 rows x 14 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x26bf51dfcc0>"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 65294 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "e:\\python\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 65294 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Series\n",
    "# date = pd.Series(np.random.randn(1000),index=np.arange(1000))\n",
    "# date = date.cumsum()\n",
    "# date.plot()\n",
    "\n",
    "#DataFrame\n",
    "data = pd.read_csv('glacier_MB.csv')\n",
    "data = data.T\n",
    "data.columns = data.iloc[0]\n",
    "data = data.drop(['name'])\n",
    "# data = data.drop(index='name',axis=0)\n",
    "print(data)\n",
    "data.plot()\n",
    "# data.iloc[11].plot()\n",
    "# data.iloc[12].plot()\n",
    "# data.iloc[13].plot()\n",
    "# plt.legend()\n",
    "# data.plot(x=column,y='Qiyi')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%3.8\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}