Data-Analysis.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Data-Analysis","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNPwwqgc2GUlIEJKeD2ts6B"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"DUI3yHncC8zN","colab_type":"text"},"source":["## Importing Required Libraries"]},{"cell_type":"code","metadata":{"id":"Mdlhx3aa1TPA","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595166635068,"user_tz":-330,"elapsed":1275,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}}},"source":["import pandas as pd\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","mpl.style.use('ggplot')"],"execution_count":51,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GGxynjjnBBdZ","colab_type":"text"},"source":["# Languages"]},{"cell_type":"markdown","metadata":{"id":"rpGkTjGJBQU0","colab_type":"text"},"source":["### Preparing languages dataframe"]},{"cell_type":"code","metadata":{"id":"LxqGKQUU74NT","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":111},"executionInfo":{"status":"ok","timestamp":1595168373458,"user_tz":-330,"elapsed":1492,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"7ac700d0-6f4d-4ab7-d368-aab932315a9b"},"source":["# initialize list of languages \n","data_lan = [['java', 0.123, 1.179 ], ['scala', 10.172, 3.528]] \n","  \n","# Create the pandas DataFrame \n","df_languages = pd.DataFrame(data_lan, columns = ['languages', 'small','big']) \n","  \n","# print dataframe. \n","df_languages"],"execution_count":114,"outputs":[{"output_type":"execute_result","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>languages</th>\n","      <th>small</th>\n","      <th>big</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>java</td>\n","      <td>0.123</td>\n","      <td>1.179</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>scala</td>\n","      <td>10.172</td>\n","      <td>3.528</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  languages   small    big\n","0      java   0.123  1.179\n","1     scala  10.172  3.528"]},"metadata":{"tags":[]},"execution_count":114}]},{"cell_type":"code","metadata":{"id":"hZYYKhc3BZRk","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":142},"executionInfo":{"status":"ok","timestamp":1595168377360,"user_tz":-330,"elapsed":1442,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"9c1428ef-28c9-409d-92af-e7c4221d6563"},"source":["# Appending Data, Use this method in the future when trying to add more languages\n","\n","df_languages = df_languages.append({'languages':'python','small':0.006, 'big':1.154}, ignore_index=True)\n","df_languages"],"execution_count":115,"outputs":[{"output_type":"execute_result","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>languages</th>\n","      <th>small</th>\n","      <th>big</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>java</td>\n","      <td>0.123</td>\n","      <td>1.179</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>scala</td>\n","      <td>10.172</td>\n","      <td>3.528</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>python</td>\n","      <td>0.006</td>\n","      <td>1.154</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  languages   small    big\n","0      java   0.123  1.179\n","1     scala  10.172  3.528\n","2    python   0.006  1.154"]},"metadata":{"tags":[]},"execution_count":115}]},{"cell_type":"code","metadata":{"id":"ZnSujg8nHeCC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":173},"executionInfo":{"status":"ok","timestamp":1595168381060,"user_tz":-330,"elapsed":873,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"fdbd7764-5675-478b-ae13-eff91d617193"},"source":["# Set the languages as index for our x axis\n","df_languages.set_index('languages', inplace=True)\n","df_languages"],"execution_count":116,"outputs":[{"output_type":"execute_result","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>small</th>\n","      <th>big</th>\n","    </tr>\n","    <tr>\n","      <th>languages</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>java</th>\n","      <td>0.123</td>\n","      <td>1.179</td>\n","    </tr>\n","    <tr>\n","      <th>scala</th>\n","      <td>10.172</td>\n","      <td>3.528</td>\n","    </tr>\n","    <tr>\n","      <th>python</th>\n","      <td>0.006</td>\n","      <td>1.154</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            small    big\n","languages               \n","java        0.123  1.179\n","scala      10.172  3.528\n","python      0.006  1.154"]},"metadata":{"tags":[]},"execution_count":116}]},{"cell_type":"markdown","metadata":{"id":"7QXwP372bdh_","colab_type":"text"},"source":["### Plotting Languages Data"]},{"cell_type":"code","metadata":{"id":"Tqo5OfY8EXL3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":664},"executionInfo":{"status":"ok","timestamp":1595168463840,"user_tz":-330,"elapsed":1360,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"843e257b-28cf-445d-cd11-3677fea820f7"},"source":["ax = df_languages.plot(kind='bar',\n","                      figsize = (10,10)\n","                      )\n","\n","plt.xlabel('Languages')\n","plt.title('Execution times for each language for both small and large files')\n","\n","ax.set_facecolor('white')\n","ax.tick_params(axis='x', colors='black', labelsize=14)\n","ax.axhline(0, color='black')\n","ax.legend(facecolor='white',fontsize=14)\n","ax.tick_params(top=False, left=False, right=False, labelleft=False)\n","\n","for p in ax.patches:   #display the percentages above the bars\n","    width, height = p.get_width(), p.get_height()\n","    x, y = p.get_xy() \n","    ax.annotate('{} s'.format(height), (x, y + height + 0.1),fontsize=14)\n","\n","plt.show()"],"execution_count":124,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"2bnTWUn6eIu8","colab_type":"text"},"source":["### Observation for Languages\n"]},{"cell_type":"markdown","metadata":{"id":"i3ssnpPzd3Ns","colab_type":"text"},"source":["We have observed from the graphs that **Python** has the leat execution time for small and large files while **Scala** has the largest execution time."]},{"cell_type":"markdown","metadata":{"id":"Vwkjctq-boeU","colab_type":"text"},"source":["# Execution Engines\n"]},{"cell_type":"markdown","metadata":{"id":"ywgl90ONb4Np","colab_type":"text"},"source":["### Preparing Data for Execution Engines\n"]},{"cell_type":"code","metadata":{"id":"ZkpYxKpbbm8z","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":111},"executionInfo":{"status":"ok","timestamp":1595168509638,"user_tz":-330,"elapsed":1292,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"2feced8b-a1b7-4c0c-e1db-3f6ccd3d9671"},"source":["# initialize list of Execution Engines\n","\n","data_engine = [['hadoop', 3, 12], ['spark', 1, 3]]\n","  \n","# Create the pandas DataFrame \n","df_engines = pd.DataFrame(data_engine, columns = ['engine', 'small','big']) \n","  \n","# print dataframe. \n","df_engines"],"execution_count":125,"outputs":[{"output_type":"execute_result","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>engine</th>\n","      <th>small</th>\n","      <th>big</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>hadoop</td>\n","      <td>3</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>spark</td>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   engine  small  big\n","0  hadoop      3   12\n","1   spark      1    3"]},"metadata":{"tags":[]},"execution_count":125}]},{"cell_type":"code","metadata":{"id":"u5hWMj_jc7TS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":142},"executionInfo":{"status":"ok","timestamp":1595168534382,"user_tz":-330,"elapsed":1247,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"7beafe07-d9eb-4f3e-a155-e4e48ba51233"},"source":["# set engine as our x axis for graph\n","df_engines.set_index('engine', inplace=True)\n","df_engines"],"execution_count":126,"outputs":[{"output_type":"execute_result","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>small</th>\n","      <th>big</th>\n","    </tr>\n","    <tr>\n","      <th>engine</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>hadoop</th>\n","      <td>3</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>spark</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        small  big\n","engine            \n","hadoop      3   12\n","spark       1    3"]},"metadata":{"tags":[]},"execution_count":126}]},{"cell_type":"markdown","metadata":{"id":"zOous_WndjPd","colab_type":"text"},"source":["### Plotting Execution engines data"]},{"cell_type":"code","metadata":{"id":"CRVMwOh4dGLd","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":668},"executionInfo":{"status":"ok","timestamp":1595168608729,"user_tz":-330,"elapsed":1359,"user":{"displayName":"Thomas George Thomas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhRjmtno5yCmJOUjYiFNGHi_xP5YZBJ1ZpWGKsZ=s64","userId":"09561174857978505545"}},"outputId":"7e7ee75e-4f4d-469c-9d3b-d33acdd10b1c"},"source":["ax = df_engines.plot(kind='bar',\n","                      figsize = (10,10)\n","                      )\n","\n","plt.xlabel('Execution Engines')\n","plt.title('Execution times for each engine for both small and large files')\n","\n","ax.set_facecolor('white')\n","ax.tick_params(axis='x', colors='black', labelsize=14)\n","ax.axhline(0, color='black')\n","ax.legend(facecolor='white',fontsize=14)\n","ax.tick_params(top=False, left=False, right=False, labelleft=False)\n","\n","for p in ax.patches:   #display the percentages above the bars\n","    width, height = p.get_width(), p.get_height()\n","    x, y = p.get_xy() \n","    ax.annotate('{} s'.format(height), (x, y + height + 0.1),fontsize=14)\n","\n","plt.show()"],"execution_count":130,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"7hBOo_i3eXOC","colab_type":"text"},"source":["### Observation for execution engines\n"]},{"cell_type":"markdown","metadata":{"id":"DpFqHjm7ekS0","colab_type":"text"},"source":["We have observed that **Spark** has the least execution time while **Hadoop** has the highest execution time."]}]}