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In this following project we will plot different types of charts using python to draw conclusions for Recession Period vs Non Recession Period  Car sales using
Bar Chart
Line charts 
Scatter Charts
Pie charts

Author: LakshmiSKarumuru

1PieChart.ipynb

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+{
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "name": ""
+    }
+  },
+  "nbformat_minor": 4,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": "%pip install seaborn\n%pip install folium",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "import numpy as np\nimport pandas as pd\n%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport folium",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/historical_automobile_sales.csv\"\nresp = await fetch(URL)\ntext = io.BytesIO((await resp.arrayBuffer()).to_py())\nimport pandas as pd\ndf = pd.read_csv(text)\nprint('Data downloaded and read into a dataframe!')",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "df.describe()",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "df.columns",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "df_line = df.groupby(df['Year'])['Automobile_Sales'].mean()\n#create figure\nplt.figure(figsize=(10, 6))\ndf_line.plot(kind = 'line')\nplt.xlabel('g')\nplt.ylabel('lkg')\nplt.title('output')\nplt.show()",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}