GVol <> Python Part 2 - Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "f9911f95",
   "metadata": {},
   "source": [
    "# GVol Python Part 2: Working with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bfb9407",
   "metadata": {},
   "source": [
    "#### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb0c3a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Pandas and Numpy\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e6ae4b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a variables data frame and create a random number set\n",
    "df = pd.DataFrame(np.random.randn(6,4), columns=[\"a\", \"b\", \"c\", \"d\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d203e832",
   "metadata": {},
   "source": [
    "####  Common ways to use Pandas to explore your data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "776c9287",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "      <th>d</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066882</td>\n",
       "      <td>0.300824</td>\n",
       "      <td>0.057481</td>\n",
       "      <td>-0.320479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.138771</td>\n",
       "      <td>0.937482</td>\n",
       "      <td>1.316672</td>\n",
       "      <td>0.776963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.061761</td>\n",
       "      <td>-1.334993</td>\n",
       "      <td>-2.207314</td>\n",
       "      <td>-1.788898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.008032</td>\n",
       "      <td>0.001804</td>\n",
       "      <td>-0.262922</td>\n",
       "      <td>-0.343238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.425451</td>\n",
       "      <td>0.497823</td>\n",
       "      <td>0.099525</td>\n",
       "      <td>-0.174269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.463629</td>\n",
       "      <td>0.912972</td>\n",
       "      <td>0.984455</td>\n",
       "      <td>0.095780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.288321</td>\n",
       "      <td>1.256470</td>\n",
       "      <td>1.457459</td>\n",
       "      <td>0.428324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              a         b         c         d\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.066882  0.300824  0.057481 -0.320479\n",
       "std    1.138771  0.937482  1.316672  0.776963\n",
       "min   -2.061761 -1.334993 -2.207314 -1.788898\n",
       "25%   -0.008032  0.001804 -0.262922 -0.343238\n",
       "50%    0.425451  0.497823  0.099525 -0.174269\n",
       "75%    0.463629  0.912972  0.984455  0.095780\n",
       "max    1.288321  1.256470  1.457459  0.428324"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pandas describe() function allows you to get a quick summary of the data in a data frame. \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34a923d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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>2</th>\n",
       "      <td>0.390310</td>\n",
       "      <td>0.416124</td>\n",
       "      <td>-2.207314</td>\n",
       "      <td>-1.788898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.061761</td>\n",
       "      <td>1.024122</td>\n",
       "      <td>-0.257767</td>\n",
       "      <td>-0.395987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.464642</td>\n",
       "      <td>-0.136303</td>\n",
       "      <td>1.457459</td>\n",
       "      <td>-0.184992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.140812</td>\n",
       "      <td>1.256470</td>\n",
       "      <td>1.160334</td>\n",
       "      <td>-0.163546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.288321</td>\n",
       "      <td>0.579523</td>\n",
       "      <td>-0.264640</td>\n",
       "      <td>0.182223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.460591</td>\n",
       "      <td>-1.334993</td>\n",
       "      <td>0.456817</td>\n",
       "      <td>0.428324</td>\n",
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      "text/plain": [
       "          a         b         c         d\n",
       "2  0.390310  0.416124 -2.207314 -1.788898\n",
       "0 -2.061761  1.024122 -0.257767 -0.395987\n",
       "4  0.464642 -0.136303  1.457459 -0.184992\n",
       "1 -0.140812  1.256470  1.160334 -0.163546\n",
       "5  1.288321  0.579523 -0.264640  0.182223\n",
       "3  0.460591 -1.334993  0.456817  0.428324"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort values in a column in descending order\n",
    "df.sort_values(by=\"d\", ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4ef4df",
   "metadata": {},
   "source": [
    "#### Using Pandas to Load a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c90fc53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in CSV. Set the index column to date\n",
    "df = pd.read_csv(\"ETH-OHLC.csv\", index_col=\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3425e1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we are adding a new column to our data frame which will be the Avg of the High & Low column\n",
    "df[\"mid\"] = (df[\"high\"] + df[\"low\"]) /2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "289adae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Another way to create an average is to define a function that does it. \n",
    "def average(a, b):\n",
    "    return (a+b)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8bb3f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying the function across the data frame using a lambdas. \n",
    "df[\"new_mid\"] = df.apply(lambda x: average(x[\"high\"], x[\"low\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2fa05647",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currency</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>mid</th>\n",
       "      <th>new_mid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-14</th>\n",
       "      <td>ETH</td>\n",
       "      <td>2516.83</td>\n",
       "      <td>2608.14</td>\n",
       "      <td>2503.88</td>\n",
       "      <td>2591.45</td>\n",
       "      <td>2556.010</td>\n",
       "      <td>2556.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-15</th>\n",
       "      <td>ETH</td>\n",
       "      <td>2591.45</td>\n",
       "      <td>2668.80</td>\n",
       "      <td>2512.00</td>\n",
       "      <td>2619.61</td>\n",
       "      <td>2590.400</td>\n",
       "      <td>2590.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-16</th>\n",
       "      <td>ETH</td>\n",
       "      <td>2619.61</td>\n",
       "      <td>2787.84</td>\n",
       "      <td>2607.23</td>\n",
       "      <td>2773.67</td>\n",
       "      <td>2697.535</td>\n",
       "      <td>2697.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-17</th>\n",
       "      <td>ETH</td>\n",
       "      <td>2775.15</td>\n",
       "      <td>2835.17</td>\n",
       "      <td>2750.42</td>\n",
       "      <td>2814.64</td>\n",
       "      <td>2792.795</td>\n",
       "      <td>2792.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-18</th>\n",
       "      <td>ETH</td>\n",
       "      <td>2814.64</td>\n",
       "      <td>2978.26</td>\n",
       "      <td>2771.37</td>\n",
       "      <td>2941.32</td>\n",
       "      <td>2874.815</td>\n",
       "      <td>2874.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-16</th>\n",
       "      <td>ETH</td>\n",
       "      <td>1236.89</td>\n",
       "      <td>1255.90</td>\n",
       "      <td>1050.94</td>\n",
       "      <td>1067.86</td>\n",
       "      <td>1153.420</td>\n",
       "      <td>1153.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-17</th>\n",
       "      <td>ETH</td>\n",
       "      <td>1067.10</td>\n",
       "      <td>1116.32</td>\n",
       "      <td>1050.30</td>\n",
       "      <td>1085.21</td>\n",
       "      <td>1083.310</td>\n",
       "      <td>1083.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-18</th>\n",
       "      <td>ETH</td>\n",
       "      <td>1085.21</td>\n",
       "      <td>1096.22</td>\n",
       "      <td>883.48</td>\n",
       "      <td>993.72</td>\n",
       "      <td>989.850</td>\n",
       "      <td>989.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-19</th>\n",
       "      <td>ETH</td>\n",
       "      <td>994.41</td>\n",
       "      <td>1155.62</td>\n",
       "      <td>934.96</td>\n",
       "      <td>1127.03</td>\n",
       "      <td>1045.290</td>\n",
       "      <td>1045.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-20</th>\n",
       "      <td>ETH</td>\n",
       "      <td>1127.03</td>\n",
       "      <td>1166.85</td>\n",
       "      <td>1051.76</td>\n",
       "      <td>1126.99</td>\n",
       "      <td>1109.305</td>\n",
       "      <td>1109.305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           currency     open     high      low    close       mid   new_mid\n",
       "date                                                                       \n",
       "2022-03-14      ETH  2516.83  2608.14  2503.88  2591.45  2556.010  2556.010\n",
       "2022-03-15      ETH  2591.45  2668.80  2512.00  2619.61  2590.400  2590.400\n",
       "2022-03-16      ETH  2619.61  2787.84  2607.23  2773.67  2697.535  2697.535\n",
       "2022-03-17      ETH  2775.15  2835.17  2750.42  2814.64  2792.795  2792.795\n",
       "2022-03-18      ETH  2814.64  2978.26  2771.37  2941.32  2874.815  2874.815\n",
       "...             ...      ...      ...      ...      ...       ...       ...\n",
       "2022-06-16      ETH  1236.89  1255.90  1050.94  1067.86  1153.420  1153.420\n",
       "2022-06-17      ETH  1067.10  1116.32  1050.30  1085.21  1083.310  1083.310\n",
       "2022-06-18      ETH  1085.21  1096.22   883.48   993.72   989.850   989.850\n",
       "2022-06-19      ETH   994.41  1155.62   934.96  1127.03  1045.290  1045.290\n",
       "2022-06-20      ETH  1127.03  1166.85  1051.76  1126.99  1109.305  1109.305\n",
       "\n",
       "[99 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ba208dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas gives us easy ways to apply a rolling average. Here we are creating a 10 day moving avg. \n",
    "df[\"SMA10\"] = df[\"open\"].rolling(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8538c937",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a 30 day moving averagee\n",
    "df[\"SMA30\"] = df[\"open\"].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56afe286",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a new column on our data frame that allows us to compare todays open vs. the previous days open. \n",
    "df[\"previous_day_open\"] = df[\"open\"].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "abc6b6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "#Creating some basic plotting for our moving averages. \n",
    "df[[\"close\", \"SMA10\", \"SMA30\"]].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
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