Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.array([[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5],\n",
       "       [1, 2, 3, 4, 5]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 2],\n",
       "       [3, 3],\n",
       "       [4, 4],\n",
       "       [5, 5]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(array.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1\n",
       "0  1  1\n",
       "1  2  2\n",
       "2  3  3\n",
       "3  4  4\n",
       "4  5  5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe3faf83d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "from_bounds() argument after * must be an iterable, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-afd445d5adb6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mfigure\u001b[1;34m(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[0mdpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m72\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 540\u001b[1;33m         figManager = new_figure_manager(num, figsize=figsize,\n\u001b[0m\u001b[0;32m    541\u001b[0m                                         \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    542\u001b[0m                                         \u001b[0mfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mnew_figure_manager\u001b[1;34m(cls, num, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3334\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFigure\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3335\u001b[0m         \u001b[0mfig_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'FigureClass'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3336\u001b[1;33m         \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig_cls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3337\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_figure_manager_given_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, figsize, dpi, facecolor, edgecolor, linewidth, frameon, subplotpars, tight_layout, constrained_layout)\u001b[0m\n\u001b[0;32m    349\u001b[0m             raise ValueError('figure size must be positive finite not '\n\u001b[0;32m    350\u001b[0m                              f'{figsize}')\n\u001b[1;32m--> 351\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi_scale_trans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: from_bounds() argument after * must be an iterable, not int"
     ]
    }
   ],
   "source": [
    "plt.figure(5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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