C06. Jupyter Notebook Widgets.ipynb

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    "<img src=\"logo.png\">"
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   "source": [
    "# Installation:\n",
    "!pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install\n",
    "!pip install autopep8\n",
    "!pip install yapf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Widgets Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install ipywidgets"
   ]
  },
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   "source": [
    "Escribir en terminal:\n",
    "\n",
    "``jupyter nbextension enable --py widgetsnbextension``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "def f(x):\n",
    "    return np.sin(x)+0.5\n",
    "\n",
    "interact(f, x=1.0,);"
   ]
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   "cell_type": "code",
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   "source": [
    "def f1(x):\n",
    "    return x\n",
    "interact(f1, x='Hola a todos');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Date picker\n",
    "widgets.DatePicker(\n",
    "    description='Escoge una fecha',\n",
    "    disabled=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Color picker\n",
    "widgets.ColorPicker(\n",
    "    concise=False,\n",
    "    description='Escoge un color',\n",
    "    value='blue',\n",
    "    disabled=False\n",
    ")"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieving the content of a widget:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "output = widgets.DatePicker(\n",
    "    description='Escoge una fecha',\n",
    "    disabled=False\n",
    ")\n",
    "display(output)"
   ]
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    "output.value"
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   "cell_type": "markdown",
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   "source": [
    "## Q grid: dar una salida de tipo hoja de cálculo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install qgrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``\n",
    "jupyter nbextension enable --py --sys-prefix qgrid\n",
    "jupyter nbextension enable --py --sys-prefix widgetsnbextension``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/hp master/Documents/SciData/Py_CDD/data/iris_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qgrid\n",
    "qgrid_view = qgrid.show_grid(df, show_toolbar=True)\n",
    "qgrid_view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wines = pd.read_csv('C:/Users/hp master/Documents/SciData/Py_CDD/data/wines.csv')\n",
    "df_wines.head()"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "@interact\n",
    "def show_feature(column='quality', x=5):\n",
    "    return df_wines.loc[df_wines[column] > x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_feature(\"quality\",5)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@interact\n",
    "def show_features_more_than(column=['pH', 'alcohol', 'quality'], x=(2, 9, 0.5)): # format start, stop, step \n",
    "    return df_wines.loc[df_wines[column] > x]"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "@interact\n",
    "def correlation(col1 = list(df_wines.select_dtypes('number').columns),\n",
    "                col2 = list(df_wines.select_dtypes('number').columns)):\n",
    "    print(f\"Correlación: {df_wines[col1].corr(df_wines[col2])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install cufflinks\n",
    "import cufflinks as cf\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline(connected=True)\n",
    "cf.set_config_file(colorscale='plotly', world_readable=True)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "@interact\n",
    "def scatter_plot(x=list(df_wines.select_dtypes('number').columns), \n",
    "                 y=list(df_wines.select_dtypes('number').columns)[1:]):\n",
    "    df_wines.iplot(kind='scatter', x=x, y=y, mode='markers', \n",
    "             xTitle=x.title(), yTitle=y.title(), title=f'{y.title()} vs {x.title()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.figure_factory as ff\n",
    "\n",
    "corrs = df_wines.corr()\n",
    "cscales = ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n",
    "            'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n",
    "            'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']\n",
    "\n",
    "@interact_manual\n",
    "def plot_corrs(colorscale=cscales):\n",
    "    figure = ff.create_annotated_heatmap(z = corrs.round(2).values, \n",
    "                                     x =list(corrs.columns), \n",
    "                                     y=list(corrs.index), \n",
    "                                     colorscale=colorscale,\n",
    "                                     annotation_text=corrs.round(2).values)\n",
    "    iplot(figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@interact\n",
    "def plot_spread(column1=['pH', 'density', 'sulphates', 'citric acid'], \n",
    "                 column2=['density', 'pH', 'sulphates', 'citric acid']):\n",
    "    df_wines.iplot(kind='ratio', y=column1, secondary_y=column2,\n",
    "                    title=f'{column1.title()} and {column2.title()} Spread Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference:\n",
    "\n",
    "https://ipywidgets.readthedocs.io/en/stable/index.html"
   ]
  }
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