.

fastai/Dockerfile

@@ -1,32 +0,0 @@
-# -------------------------------------------------------
-# Relies on JUPYTER_PORT being set in docker-compose.yml
-# -------------------------------------------------------
-FROM pytorch/pytorch:latest
-
-# RUN useradd -m -s /bin/bash oleg
-
-# Install packages 
-# --- this version of jupyter is required to work with the widgets
-RUN pip install jupyterlab==3.6.7 
-#
-RUN pip install ipywidgets
-RUN pip install pandas numpy plotly
-#
-RUN pip install fastbook
-
-# Install additional applications
-RUN apt update
-RUN apt-get install graphviz -y
-
-# Set environment variables, etc.
-#ENV MY_ENV_VAR=myvalue
-
-# Set the working directory
-WORKDIR /workspace
-
-# Expose the port for JupyterLab
-EXPOSE ${JUPYTER_PORT}
-
-# Command to run JupyterLab
-CMD ["jupyter-lab", "--ip=0.0.0.0", "--port=7777", "--no-browser", "--allow-root", "--NotebookApp.token=''"]
-

fastai/docker-compose.yml

@@ -7,10 +7,12 @@ version: '3.8'
 #==================================================
 services:
   jupyter:
-    build: ${ROOT_DIR}
-    image: cvtt/fastai:v0.0.1 
+    # build: ${ROOT_DIR}
+    # image: cvtt/fastai:v0.0.1
+    image: cloud21.cvtt.vpn:5500/fastai_jupyter:latest
     container_name: fastai_jupyter
     runtime: nvidia
+    restart: unless-stopped
     environment:
       - JUPYTER_ENABLE_LAB=yes
     volumes:
@@ -18,7 +20,7 @@ services:
       - ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
       - ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
       - ${ROOT_DIR}/.cache/torch:/root/.cache/torch
-      # - /opt/jupyter_gpu/data:/workspace/data
     ports:
       - "${JUPYTER_PORT}:${JUPYTER_PORT}"
     shm_size: "8gb"
+

fastai/notebooks/.gitignore

@@ -1,2 +1,3 @@
+fastbook
 bears
 *.jpg

fastai/notebooks/fastbook

@@ -1 +0,0 @@
-Subproject commit 054cd8a62c7b6c11f0c36265149ccbaf95d91034

load_market_data.sh

@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-
-date=${1}
-if [ "${date}" == "" ] ; then
-    date="yesterday"
-fi
-
-SourceHost=cryptoval3.cvtt.vpn
-SourceUser=cvtt
-SourceRootDir=/localdisk/cvtt/archive/md_archive
-SourceFile=$(date -d ${date} "+%Y%m%d.mktdata.db")
-SourceFileZip="${SourceFile}.gz"
-SourceFilePath=$(date -d ${date} "+${SourceRootDir}/%Y/%m/${SourceFileZip}")
-
-TargetDir="/opt/jupyter_gpu/data/crypto_md"
-TargetFile=$(date -d ${date} "+%Y%m%d.mktdata.ohlcv.db")
-TargetFilePath="${TargetDir}/${TargetFile}"
-
-echo ${SourceFile}
-tmp_dir=$(mktemp -d)
-
-function cleanup {
-    cd ${HOME}
-    rm -rf ${tmp_dir}
-}
-trap cleanup EXIT
-
-function download_file {
-
-    Cmd="rsync"
-    Cmd="${Cmd} -ahv"
-    if tty -s; then    
-        Cmd="${Cmd} --progress=info2"
-    fi
-    Cmd="${Cmd} ${SourceUser}@${SourceHost}:${SourceFilePath} ${tmp_dir}/"
-    echo ${Cmd}
-    eval ${Cmd}
-    ls -lh ${tmp_dir}
-    Cmd="gunzip ${tmp_dir}/${SourceFileZip}"
-    echo ${Cmd} && eval ${Cmd}
-    ls -lh ${tmp_dir}
-
-    rm -f ${TargetFilePath}
-    touch ${TargetFilePath}
-
-    for table in bnbfut_ohlcv_1min bnbspot_ohlcv_1min coinbase_ohlcv_1min
-    do
-        Cmd="sqlite3 ${tmp_dir}/${SourceFile} \".dump ${table}\" | sqlite3 ${TargetFilePath}"
-        echo ${Cmd}
-        eval ${Cmd}
-    done
-    chmod 600 ${TargetFilePath}
-    ls -lh ${TargetFilePath}
-
-
-}
-download_file

pytorch/.env

@@ -1,2 +1,5 @@
 ROOT_DIR=/opt/jupyter_gpu/pytorch
+DATA_DIR=/opt/jupyter_gpu/data
 JUPYTER_PORT=8888
+# PUID=1000
+# PGID=1000

pytorch/Dockerfile

@@ -18,6 +18,7 @@ RUN apt-get install graphviz -y
 
 # Set environment variables, etc.
 #ENV MY_ENV_VAR=myvalue
+COPY cvttpy /cvttpy
 
 # Set the working directory
 WORKDIR /workspace

pytorch/docker-compose.yml

@@ -3,14 +3,18 @@ version: '3.8'
 #==================================================
 # Relies on the file `.env` content for varables:
 #    ROOT_DIR
+#    DATA_DIR
 #    JUPYTER_PORT
+#    UID
+#    GID
 #==================================================
 services:
   jupyter:
-    build: ${ROOT_DIR}
-    image: cvtt/jupyter_gpu_pt:v1.1.3
+    # user: "${UID}:${GID}"
+    image: cloud21.cvtt.vpn:5500/jupyter_gpu_pytorch:latest
     container_name: jupyter_gpu_pt
     runtime: nvidia
+    restart: unless-stopped
     environment:
       - JUPYTER_ENABLE_LAB=yes
       - PYTHONPATH=/cvtt/prod
@@ -19,8 +23,8 @@ services:
       - ${ROOT_DIR}/jupyter_settings/user-settings:/root/.jupyter/lab/user-settings
       - ${ROOT_DIR}/jupyter_settings/workspaces:/root/.jupyter/lab/workspaces
       - ${ROOT_DIR}/.cache/torch:/root/.cache/torch
-      - /opt/jupyter_gpu/data:/workspace/data
-      - ${ROOT_DIR}/cvtt:/cvtt
+
+      - ${DATA_DIR}:/workspace/data
     ports:
       - "${JUPYTER_PORT}:${JUPYTER_PORT}"
     shm_size: "8gb"

pytorch/notebooks/Mean Reversion Studies.ipynb

@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7dac550-c0ed-4ec7-846e-8edb2086c9cc",
+   "metadata": {},
+   "source": [
+    "# Augmented Dickey-Fuller Test (ADF)\n",
+    "Stationarity Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "778b9362-37e3-40e0-a20a-1ca5e2cddf05",
+   "metadata": {},
+   "source": [
+    "## Preparing The data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "998ecc54-aaba-4761-bb98-1eda5c9fa091",
+   "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>id</th>\n",
+       "      <th>tstamp</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722470400000000000</td>\n",
+       "      <td>64640.679892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722470460000000000</td>\n",
+       "      <td>64652.991289</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722470520000000000</td>\n",
+       "      <td>64660.005093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722470580000000000</td>\n",
+       "      <td>64653.482847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722470640000000000</td>\n",
+       "      <td>64687.458279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1372</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722556500000000000</td>\n",
+       "      <td>65439.307663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1373</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722556560000000000</td>\n",
+       "      <td>65445.733114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1374</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722556620000000000</td>\n",
+       "      <td>65446.371741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1375</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722556680000000000</td>\n",
+       "      <td>65420.879478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1376</th>\n",
+       "      <td>PAIR-BTC-USDT</td>\n",
+       "      <td>1722556740000000000</td>\n",
+       "      <td>65377.032222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1377 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id               tstamp        target\n",
+       "0     PAIR-BTC-USDT  1722470400000000000  64640.679892\n",
+       "1     PAIR-BTC-USDT  1722470460000000000  64652.991289\n",
+       "2     PAIR-BTC-USDT  1722470520000000000  64660.005093\n",
+       "3     PAIR-BTC-USDT  1722470580000000000  64653.482847\n",
+       "4     PAIR-BTC-USDT  1722470640000000000  64687.458279\n",
+       "...             ...                  ...           ...\n",
+       "1372  PAIR-BTC-USDT  1722556500000000000  65439.307663\n",
+       "1373  PAIR-BTC-USDT  1722556560000000000  65445.733114\n",
+       "1374  PAIR-BTC-USDT  1722556620000000000  65446.371741\n",
+       "1375  PAIR-BTC-USDT  1722556680000000000  65420.879478\n",
+       "1376  PAIR-BTC-USDT  1722556740000000000  65377.032222\n",
+       "\n",
+       "[1377 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from statsmodels.tsa.stattools import adfuller\n",
+    "import numpy as np\n",
+    "\n",
+    "def demo_example_data() -> pd.Series:\n",
+    "    # Generate example time series data\n",
+    "    # np.random.seed(0)\n",
+    "    time_series_data = np.random.randn(100)  # Random data for demonstration\n",
+    "    \n",
+    "    # Create a pandas Series\n",
+    "    data = pd.Series(time_series_data)\n",
+    "    \n",
+    "    # Optionally, you can add a datetime index if you have time-indexed data\n",
+    "    dates = pd.date_range(start='2020-01-01', periods=len(time_series_data), freq='D')\n",
+    "    data = pd.Series(time_series_data, index=dates)\n",
+    "\n",
+    "    # Display the first few rows of the data\n",
+    "    print(data.head())\n",
+    "    return data\n",
+    "\n",
+    "def load_df_from_db(file: str, query: str) -> pd.DataFrame:\n",
+    "    import sqlite3 \n",
+    "    \n",
+    "    conn = sqlite3.connect(file)\n",
+    "    df = pd.read_sql_query(query, conn)\n",
+    "    df['timestamp'] = pd.to_datetime(df['tstamp'])\n",
+    "    df.set_index('timestamp', inplace=True)\n",
+    "    return df\n",
+    "\n",
+    "file_path = \"/workspace/data/crypto_md/20240801.mktdata.ohlcv.db\"\n",
+    "instrument_id='PAIR-BTC-USDT'\n",
+    "query = f\"\"\"\n",
+    "select \n",
+    "    instrument_id as id, \n",
+    "    tstamp, \n",
+    "    vwap \n",
+    "from bnbspot_ohlcv_1min \n",
+    "where instrument_id = '{instrument_id}'\n",
+    "\"\"\"\n",
+    "\n",
+    "df = load_df_from_db(file=file_path, query=query)\n",
+    "df.rename(columns={'vwap': 'target'}, inplace=True)\n",
+    "# df[\"tstamp2\"] = df.index\n",
+    "df = df.reset_index()\n",
+    "df = df.drop([\"timestamp\"], axis=1) \n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43e43154-5a04-4a1d-977c-7f930d62f241",
+   "metadata": {},
+   "source": [
+    "## Running Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b97d357f-787b-4cff-849f-b91b1ec35e7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2020-01-01   -1.652908\n",
+      "2020-01-02   -0.157302\n",
+      "2020-01-03   -1.396187\n",
+      "2020-01-04    0.150374\n",
+      "2020-01-05    1.048603\n",
+      "Freq: D, dtype: float64\n",
+      "ADF Statistic: -9.985535987881171\n",
+      "p-value: 2.060269774403535e-17\n",
+      "Critical Values: {'1%': -3.498198082189098, '5%': -2.891208211860468, '10%': -2.5825959973472097}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from statsmodels.tsa.stattools import adfuller\n",
+    "\n",
+    "# Example time series data\n",
+    "data = demo_example_data()\n",
+    "\n",
+    "# Perform the ADF test\n",
+    "result = adfuller(data)\n",
+    "\n",
+    "# Extract and print the results\n",
+    "print('ADF Statistic:', result[0])\n",
+    "print('p-value:', result[1])\n",
+    "print('Critical Values:', result[4])\n"
+   ]
+  }
+ ],
+ "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pytorch/notebooks/Testing GPU.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "6b269e64-be58-43b5-ad60-0fbd1d37861a",
    "metadata": {},
    "outputs": [
@@ -19,7 +19,8 @@
      "output_type": "stream",
      "text": [
       "CUDA is available. Number of GPUs: 1\n",
-      "GPU Name: NVIDIA GeForce RTX 3060\n"
+      "GPU Name: NVIDIA GeForce RTX 3060\n",
+      "GPU Capability: 8\n"
      ]
     }
    ],
@@ -30,6 +31,7 @@
     "if torch.cuda.is_available():\n",
     "    print(f\"CUDA is available. Number of GPUs: {torch.cuda.device_count()}\")\n",
     "    print(f\"GPU Name: {torch.cuda.get_device_name(0)}\")\n",
+    "    print(f\"GPU Capability: {torch.cuda.get_device_capability()[0]}\")\n",
     "else:\n",
     "    print(\"CUDA is not available. No GPU detected.\")\n"
    ]
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
    "metadata": {},
    "outputs": [
@@ -53,7 +55,7 @@
       "text/plain": [
        "('3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]',\n",
        " environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
-       "         'HOSTNAME': '6c24ffc52409',\n",
+       "         'HOSTNAME': '30c1a1a6daca',\n",
        "         'JUPYTER_ENABLE_LAB': 'yes',\n",
        "         'PYTHONPATH': '/cvtt/prod',\n",
        "         'NVIDIA_VISIBLE_DEVICES': 'all',\n",
@@ -75,14 +77,18 @@
        "         'CUDA_MODULE_LOADING': 'LAZY'})"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import sys\n",
-    "sys.version,os.environ"
+    "import os\n",
+    "\n",
+    "sys.path.append(\"/\")\n",
+    "sys.version,os.environ\n",
+    "\n"
    ]
   },
   {
@@ -90,12 +96,12 @@
    "id": "943ac637-42c7-4a69-a6c4-94c382e22653",
    "metadata": {},
    "source": [
-    "### Test packages avai"
+    "### Test packages availability"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
    "metadata": {},
    "outputs": [],
@@ -114,21 +120,29 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "566016ad-3f13-4a61-b460-39738ae3bf4c",
+   "metadata": {},
+   "source": [
+    "### Jupyter widgets"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "eb38de31-fc19-4515-b08d-9cd7607ea958",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2b2a98cdd61d477b811279a6753630a2",
+       "model_id": "54e9ddbe3f4349ca9ebbee5aaec14477",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(IntSlider(value=5, description='x', max=10), Output()), _dom_classes=('widget-interact',…"
+       "interactive(children=(IntSlider(value=5, description='x', max=1000), Output()), _dom_classes=('widget-interact…"
       ]
      },
      "metadata": {},
@@ -144,10 +158,10 @@
     {
      "data": {
       "text/plain": [
-       "1717721060059418080"
+       "1723585308075979805"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,17 +174,34 @@
     "def f(x):\n",
     "    return x\n",
     "\n",
-    "interact(f, x=IntSlider(min=0, max=10, step=1, value=5))\n",
+    "interact(f, x=IntSlider(min=0, max=1000, step=1, value=5))\n",
     "print(\"Done\")\n",
     "tu.current_nanoseconds()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "f46e46a7-9b57-44aa-9bc9-dcbcf643bc88",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "jupyter-events==0.10.0\n",
+      "jupyter-lsp==2.2.5\n",
+      "jupyter_client==8.6.2\n",
+      "jupyter_core==5.7.2\n",
+      "jupyter_server==2.14.2\n",
+      "jupyter_server_terminals==0.5.3\n",
+      "jupyterlab==4.2.4\n",
+      "jupyterlab_pygments==0.3.0\n",
+      "jupyterlab_server==2.27.3\n",
+      "jupyterlab_widgets==3.0.11\n"
+     ]
+    }
+   ],
    "source": [
     "!pip freeze | grep jupyter"
    ]

tensorflow/.env

@@ -1,2 +1,3 @@
 ROOT_DIR=/opt/jupyter_gpu/tensorflow
+DATA_DIR=/opt/jupyter_gpu/data
 JUPYTER_PORT=9999

tensorflow/Dockerfile

@@ -17,6 +17,15 @@ RUN pip install jupyterlab
 RUN pip install pandas
 RUN pip install numpy
 RUN pip install plotly
+RUN pip install seaborn
+RUN pip install statsmodels
+
+RUN pip install scikit-learn
+RUN pip install Arch
+RUN pip install Quantlib
+RUN pip install Backtrader
+RUN pip install pyfolio
+RUN pip install XGBoost
 
 # Install additional applications
 RUN apt update

tensorflow/docker-compose.yml

@@ -3,24 +3,25 @@ version: '3.8'
 #==================================================
 # Relies on the file `.env` content for varables:
 #    ROOT_DIR
+#    DATA_DIR
 #    JUPYTER_PORT
 #==================================================
 
 services:
   jupyter:
-    # user: "oleg"
-    build: ${ROOT_DIR}
-    image: cvtt/jupyter_gpu_tf:v1.0.5
+    image: cloud21.cvtt.vpn:5500/jupyter_gpu_tensorflow:latest
     container_name: jupyter_gpu_tf
     runtime: nvidia
+    restart: unless-stopped
     environment:
       - JUPYTER_ENABLE_LAB=yes
+      #- PYTHONPATH=/cvtt/prod
       - TF_CACHE_DIR=/workspace/tf_cache
     volumes:
       - ${ROOT_DIR}/notebooks:/workspace
       - ${ROOT_DIR}/jupyter_settings/user-settings:/home/oleg/.jupyter/lab/user-settings
       - ${ROOT_DIR}/jupyter_settings/workspaces:/home/oleg/.jupyter/lab/workspaces
-      - /opt/jupyter_gpu/data:/workspace/data
+      - ${DATA_DIR}:/workspace/data
     ports:
       - "${JUPYTER_PORT}:${JUPYTER_PORT}"
     shm_size: "8gb"

tensorflow/notebooks/Loading Market Data Example.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "89251b34-9738-4fa3-b2db-7d6a7dde7d48",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "eb24b805-ee73-4db4-b1de-0c43bea7d86f",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "id": "8dcb0ec1-4e55-4d13-91fc-57c24fac6460",
    "metadata": {},
    "outputs": [
@@ -246,7 +246,7 @@
        "[5760 rows x 9 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }