creating and transforming dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'name,mask,weapon\\nRaphael,red,sai\\nDonatello,purple,bo staff\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    ">>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],\n",
    "...                    'mask': ['red', 'purple'],\n",
    "...                    'weapon': ['sai', 'bo staff']})\n",
    ">>> df.to_csv(index=False)\n",
    "'name,mask,weapon\\nRaphael,red,sai\\nDonatello,purple,bo staff\\n'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "compression_opts = dict(method='zip',\n",
    "...                         archive_name='out.csv')  \n",
    ">>> df.to_csv('out.zip', index=False,\n",
    "...           compression=compression_opts)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(df,columns =[\"name\",\"surname\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.mean of         name    mask    weapon\n",
       "0    Raphael     red       sai\n",
       "1  Donatello  purple  bo staff>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "mu = 0\n",
    "std = 1\n",
    "x = np.linspace(start=-4, stop=4, num=100)\n",
    "y = stats.norm.pdf(x, mu, std) \n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>surname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Raphael</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donatello</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "        name  surname\n",
       "0    Raphael      NaN\n",
       "1  Donatello      NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "',name,mask,weapon\\n0,Raphael,red,sai\\n1,Donatello,purple,bo staff\\n'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv(index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Raphael</td>\n",
       "      <td>red</td>\n",
       "      <td>sai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donatello</td>\n",
       "      <td>purple</td>\n",
       "      <td>bo staff</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name    mask    weapon\n",
       "0    Raphael     red       sai\n",
       "1  Donatello  purple  bo staff"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_flip = df1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0          1\n",
       "name     Raphael  Donatello\n",
       "surname      NaN        NaN"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_flip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_flip = df3_flip.rename(columns = {\n",
    "    df3_flip.columns[0]:\"00\"\n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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   "execution_count": null,
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   "outputs": [],
   "source": [
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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