{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"\n",
"
Prepared by Asif Bhat
\n",
" \n",
"
Data Visualization With Seaborn
\n",
"\n",
"
\n",
"\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style(\"darkgrid\")\n",
"fig1 , axes = plt.subplots(nrows=2,ncols=2 , figsize = (14,14))\n",
"\n",
"x = np.random.normal(1,10,1000)\n",
"\n",
"#Simple KDE Plot\n",
"axes[0,0].set_title(\"Simple KDE Plot\")\n",
"sns.kdeplot(x,ax=axes[0,0])\n",
"\n",
"\n",
"# Shade under the density curve using the \"shade\" parameter\n",
"axes[0,1].set_title(\"KDE Plot (Shaded Area Under the Curve)\")\n",
"sns.kdeplot(x,shade=True,ax=axes[0,1])\n",
"\n",
"\n",
"# Shade under the density curve using the \"shade\" parameter and use a different color.\n",
"axes[1,0].set_title(\"KDE Plot (Different Color)\")\n",
"sns.kdeplot(x,ax=axes[1,0],color = 'r',shade=True,cut=0)\n",
"\n",
"#Plotting the density on the vertical axis\n",
"axes[1,1].set_title(\"KDE Plot (Density on Vertical Axis)\")\n",
"sns.kdeplot(x,vertical=True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 282,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,8))\n",
"x = np.linspace(0, 10, 100) \n",
"y = np.sin(x)\n",
"sns.kdeplot(x,y,shade=True,cmap=\"Reds\", shade_lowest=False)"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" sex | \n",
" bmi | \n",
" children | \n",
" smoker | \n",
" region | \n",
" charges | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 19 | \n",
" female | \n",
" 27.900 | \n",
" 0 | \n",
" yes | \n",
" southwest | \n",
" 16884.92400 | \n",
"
\n",
" \n",
" | 1 | \n",
" 18 | \n",
" male | \n",
" 33.770 | \n",
" 1 | \n",
" no | \n",
" southeast | \n",
" 1725.55230 | \n",
"
\n",
" \n",
" | 2 | \n",
" 28 | \n",
" male | \n",
" 33.000 | \n",
" 3 | \n",
" no | \n",
" southeast | \n",
" 4449.46200 | \n",
"
\n",
" \n",
" | 3 | \n",
" 33 | \n",
" male | \n",
" 22.705 | \n",
" 0 | \n",
" no | \n",
" northwest | \n",
" 21984.47061 | \n",
"
\n",
" \n",
" | 4 | \n",
" 32 | \n",
" male | \n",
" 28.880 | \n",
" 0 | \n",
" no | \n",
" northwest | \n",
" 3866.85520 | \n",
"
\n",
" \n",
"
\n",
"