M3ExploratoryDataAnalysis-lab.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n","    </a>\n","</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["# **Exploratory Data Analysis Lab**\n"]},{"cell_type":"markdown","metadata":{},"source":["Estimated time needed: **30** minutes\n"]},{"cell_type":"markdown","metadata":{},"source":["In this module you get to work with the cleaned dataset from the previous module.\n","\n","In this assignment you will perform the task of exploratory data analysis.\n","You will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Objectives\n"]},{"cell_type":"markdown","metadata":{},"source":["In this lab you will perform the following:\n"]},{"cell_type":"markdown","metadata":{},"source":["-   Identify the distribution of data in the dataset.\n","\n","-   Identify outliers in the dataset.\n","\n","-   Remove outliers from the dataset.\n","\n","-   Identify correlation between features in the dataset.\n"]},{"cell_type":"markdown","metadata":{},"source":["* * *\n"]},{"cell_type":"markdown","metadata":{},"source":["## Hands on Lab\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the pandas module.\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"markdown","metadata":{},"source":["Load the dataset into a dataframe.\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["## Distribution\n"]},{"cell_type":"markdown","metadata":{},"source":["### Determine how the data is distributed\n"]},{"cell_type":"markdown","metadata":{},"source":["The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n","\n","This assumes 12 working months and 50 working weeks.\n"]},{"cell_type":"markdown","metadata":{},"source":["Plot the distribution curve for the column `ConvertedComp`.\n"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["converted_comp = list(df['ConvertedComp'].dropna())\n","plt.figure(figsize=(10, 6))\n","plt.ticklabel_format(style='plain')\n","\n","# Plot a density plot (smoothed histogram - KDE)\n","sns.kdeplot(converted_comp, color=\"blue\")\n","\n","# Add titles and labels\n","plt.title('Distribution Curve')\n","plt.xlabel('Converted annual Salary in USD')\n","plt.ylabel('Density')\n","\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["Plot the histogram for the column `ConvertedComp`.\n"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# your code goes here\n","plt.figure(figsize=(10, 6))\n","sns.histplot(converted_comp, bins=20)\n","plt.ticklabel_format(style='plain')\n","plt.title('Histogram for Converted Compensation')\n","plt.xlabel('Converted annual Salary in USD')\n","plt.ylabel('Density')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["What is the median of the column `ConvertedComp`?\n"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[{"data":{"text/plain":["57745.0"]},"execution_count":52,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df['ConvertedComp'].median()"]},{"cell_type":"code","execution_count":64,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Man', 'Woman',\n","       'Non-binary, genderqueer, or gender non-conforming', nan,\n","       'Woman;Non-binary, genderqueer, or gender non-conforming',\n","       'Woman;Man',\n","       'Woman;Man;Non-binary, genderqueer, or gender non-conforming',\n","       'Man;Non-binary, genderqueer, or gender non-conforming'],\n","      dtype=object)"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["df['Gender'].unique()"]},{"cell_type":"markdown","metadata":{},"source":["How many responders identified themselves only as a **Man**?\n"]},{"cell_type":"code","execution_count":69,"metadata":{},"outputs":[{"data":{"text/plain":["10480"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df['Gender'].value_counts()['Man']"]},{"cell_type":"markdown","metadata":{},"source":["Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"data":{"text/plain":["57708.0"]},"execution_count":72,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df[df['Gender']=='Woman']['ConvertedComp'].median()"]},{"cell_type":"markdown","metadata":{},"source":["Give the five number summary for the column `Age`?\n"]},{"cell_type":"markdown","metadata":{},"source":["**Double click here for hint**.\n","\n","<!--\n","min,q1,median,q3,max of a column are its five number summary.\n","-->\n"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"data":{"text/plain":["count    11111.000000\n","mean        30.778895\n","std          7.393686\n","min         16.000000\n","25%         25.000000\n","50%         29.000000\n","75%         35.000000\n","max         99.000000\n","Name: Age, dtype: float64"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df['Age'].describe()"]},{"cell_type":"code","execution_count":88,"metadata":{},"outputs":[{"data":{"text/plain":["29.0"]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["df['Age'].median()"]},{"cell_type":"markdown","metadata":{},"source":["Plot a histogram of the column `Age`.\n"]},{"cell_type":"code","execution_count":77,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# your code goes here\n","plt.figure(figsize=(10, 6))\n","sns.histplot(df['Age'].dropna(), bins=30)\n","plt.ticklabel_format(style='plain')\n","plt.title('Age distribution')\n","plt.xlabel('Age')\n","plt.ylabel('Density')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["## Outliers\n"]},{"cell_type":"markdown","metadata":{},"source":["### Finding outliers\n"]},{"cell_type":"markdown","metadata":{},"source":["Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"]},{"cell_type":"code","execution_count":105,"metadata":{},"outputs":[{"data":{"text/plain":["26"]},"execution_count":105,"metadata":{},"output_type":"execute_result"}],"source":["df['ConvertedComp'].value_counts()[0]"]},{"cell_type":"code","execution_count":92,"metadata":{},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":92,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# your code goes here\n","plt.figure(figsize=(10,6))\n","sns.boxplot(converted_comp)"]},{"cell_type":"markdown","metadata":{},"source":["Find out the Inter Quartile Range for the column `ConvertedComp`.\n"]},{"cell_type":"code","execution_count":107,"metadata":{},"outputs":[{"data":{"text/plain":["73132.0"]},"execution_count":107,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","Q3 = df['ConvertedComp'].quantile(0.75)\n","Q1 = df['ConvertedComp'].quantile(0.25)\n","\n","inter_quantile_range = Q3- Q1\n","inter_quantile_range"]},{"cell_type":"markdown","metadata":{},"source":["Find out the upper and lower bounds.\n"]},{"cell_type":"code","execution_count":111,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["209698.0 , -82830.0\n"]}],"source":["# your code goes here\n","lower_bound = Q1 - 1.5 * inter_quantile_range\n","upper_bound = Q3 + 1.5 * inter_quantile_range\n","\n","print(upper_bound, ',', lower_bound)"]},{"cell_type":"markdown","metadata":{},"source":["Identify how many outliers are there in the `ConvertedComp` column.\n"]},{"cell_type":"code","execution_count":112,"metadata":{},"outputs":[{"data":{"text/plain":["879"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","outliers = df[(df['ConvertedComp'] > upper_bound) | (df['ConvertedComp'] < lower_bound)]\n","len(outliers)"]},{"cell_type":"markdown","metadata":{},"source":["Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"]},{"cell_type":"code","execution_count":118,"metadata":{},"outputs":[],"source":["# your code goes here\n","inlier = df[(df['ConvertedComp'] <= upper_bound) & (df['ConvertedComp'] >= lower_bound)]"]},{"cell_type":"code","execution_count":119,"metadata":{},"outputs":[{"data":{"text/plain":["<Axes: ylabel='ConvertedComp'>"]},"execution_count":119,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10,6))\n","sns.boxplot(inlier['ConvertedComp'].dropna())"]},{"cell_type":"code","execution_count":120,"metadata":{},"outputs":[{"data":{"text/plain":["52704.0"]},"execution_count":120,"metadata":{},"output_type":"execute_result"}],"source":["inlier['ConvertedComp'].median()"]},{"cell_type":"code","execution_count":122,"metadata":{},"outputs":[{"data":{"text/plain":["59883.20838915799"]},"execution_count":122,"metadata":{},"output_type":"execute_result"}],"source":["inlier['ConvertedComp'].mean()"]},{"cell_type":"code","execution_count":121,"metadata":{},"outputs":[{"data":{"text/plain":["<Axes: ylabel='Age'>"]},"execution_count":121,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAGKCAYAAADqqIAWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAi/UlEQVR4nO3dfXBU5d3/8U8S8oBAFsJdNllIIHGgSICKKCRA+alEkQLKSHmw6a0oJQiJFAVRKs8CQQa1IgFE0VAJBRlHCxRomTCQIgkCUqLRAtoIKXEjLbLLQwkh2d8f3JyyBTRokrO58n7N7JC9zpXNF0dyPnud714nyOfz+QQAAGCoYLsLAAAAqE2EHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0RrZXUAgqKqqUmlpqZo1a6agoCC7ywEAANXg8/l0+vRpuVwuBQdff/2GsCOptLRUsbGxdpcBAAC+h5KSErVp0+a6xwk7kpo1aybp0n+syMhIm6sBAADV4fV6FRsba53Hr4ewI1mXriIjIwk7AADUM9/VgkKDMgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAo9kadvLy8jR48GC5XC4FBQXp/fff9zvu8/k0Y8YMxcTEqHHjxkpJSdGRI0f85pw8eVKpqamKjIxU8+bNNXr0aJ05c6YO/xYAACCQ2Rp2zp49q5/85CfKysq65vGFCxdq8eLFWr58ufbs2aMmTZqof//+On/+vDUnNTVVRUVF2rZtmzZt2qS8vDylpaXV1V8BAAAEuCCfz+ezuwjp0mfk33vvPQ0ZMkTSpVUdl8ulSZMmafLkyZIkj8cjp9Op7OxsjRw5Up999pk6deqkvXv36vbbb5ckbd26VT/72c/0j3/8Qy6Xq1o/2+v1yuFwyOPxsM8OAAD1RHXP3wHbs1NcXCy3262UlBRrzOFwqGfPnsrPz5ck5efnq3nz5lbQkaSUlBQFBwdrz549133t8vJyeb1evwcA81RWVurAgQPKzc3VgQMHVFlZaXdJAGwQsDsou91uSZLT6fQbdzqd1jG3261WrVr5HW/UqJGioqKsOdeSmZmp2bNn13DFAAJJXl6eli5d6ve7IDo6WuPHj1ffvn1trAxAXQvYlZ3aNHXqVHk8HutRUlJid0kAalBeXp5mzpyphIQEZWVlafPmzcrKylJCQoJmzpypvLw8u0sEUIcCNuxER0dLksrKyvzGy8rKrGPR0dH6+uuv/Y5fvHhRJ0+etOZcS3h4uHUfLO6HBZilsrJSS5cuVXJysubOnavExETddNNNSkxM1Ny5c5WcnKxly5ZxSQtoQAI27MTHxys6Olq5ubnWmNfr1Z49e5ScnCxJSk5O1qlTp7R//35rzvbt21VVVaWePXvWec0A7FdYWCi3263U1FQFB/v/igsODlZqaqq++uorFRYW2lQhgLpma8/OmTNn9Pnnn1vPi4uL9de//lVRUVGKi4vTxIkTNXfuXLVv317x8fGaPn26XC6X9YmtW265Rffdd5/GjBmj5cuXq6KiQhkZGRo5cmS1P4kFwCwnT56UdOkN07VcHr88D4D5bA07+/bt01133WU9f+qppyRJjzzyiLKzszVlyhSdPXtWaWlpOnXqlPr06aOtW7cqIiLC+p6cnBxlZGSoX79+Cg4O1tChQ7V48eI6/7sACAxRUVGSLr15SkxMvOp4cXGx3zwA5guYfXbsxD47gDkqKyuVmpqqhIQEzZ071+9SVlVVlaZNm6bi4mKtXr1aISEhNlYK4Ieq9/vsAMD3ERISovHjxys/P1/Tpk1TUVGRzp07p6KiIk2bNk35+fkaN24cQQdoQFjZESs7gImutc9OTEyMxo0bxz47gCGqe/4m7IiwA5iqsrJShYWFOnnypKKiotS1a1dWdACDVPf8HbA7KAPADxUSEqJu3brZXQYAm9GzAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYL6LBTWVmp6dOnKz4+Xo0bN9bNN9+s559/Xj6fz5rj8/k0Y8YMxcTEqHHjxkpJSdGRI0dsrBoAAASSgA47L7zwgpYtW6YlS5bos88+0wsvvKCFCxfq1VdfteYsXLhQixcv1vLly7Vnzx41adJE/fv31/nz522sHAAABIog35XLJAFm0KBBcjqdWrlypTU2dOhQNW7cWKtXr5bP55PL5dKkSZM0efJkSZLH45HT6VR2drZGjhxZrZ/j9XrlcDjk8XgUGRlZK38XAABQs6p7/g7olZ1evXopNzdXhw8fliQdPHhQu3bt0oABAyRJxcXFcrvdSklJsb7H4XCoZ8+eys/Pv+7rlpeXy+v1+j0AAICZGtldwLd59tln5fV61bFjR4WEhKiyslLz5s1TamqqJMntdkuSnE6n3/c5nU7r2LVkZmZq9uzZtVc4AAAIGAG9svPOO+8oJydHa9as0UcffaRVq1Zp0aJFWrVq1Q963alTp8rj8ViPkpKSGqoYAAAEmoBe2Xn66af17LPPWr03Xbp00dGjR5WZmalHHnlE0dHRkqSysjLFxMRY31dWVqZbb731uq8bHh6u8PDwWq0dAAAEhoBe2Tl37pyCg/1LDAkJUVVVlSQpPj5e0dHRys3NtY57vV7t2bNHycnJdVorAAAITAG9sjN48GDNmzdPcXFxSkxM1IEDB/TSSy/psccekyQFBQVp4sSJmjt3rtq3b6/4+HhNnz5dLpdLQ4YMsbd4AAAQEAI67Lz66quaPn26xo8fr6+//loul0tjx47VjBkzrDlTpkzR2bNnlZaWplOnTqlPnz7aunWrIiIibKwcAAAEioDeZ6eusM8OAAD1jxH77AAAAPxQhB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACM1sjuAgCgtlRWVqqwsFAnT55UVFSUunbtqpCQELvLAlDHCDsAjJSXl6elS5fK7XZbY9HR0Ro/frz69u1rY2UA6hqXsQAYJy8vTzNnzlRCQoKysrK0efNmZWVlKSEhQTNnzlReXp7dJQKoQ0E+n89ndxF283q9cjgc8ng8ioyMtLscAD9AZWWlUlNTlZCQoLlz5yo4+D/v6aqqqjRt2jQVFxdr9erVXNIC6rnqnr9Z2QFglMLCQrndbqWmpurixYtav369XnnlFa1fv14XL15UamqqvvrqKxUWFtpdKoA6Qs8OAKOcPHlSkrR9+3ZNmDBBlZWV1rHly5dryJAhfvMAmI+wA8AoUVFRkqR3331XLVq00OjRo5WcnKz8/HytXLlS7777rt88AOYj7AAwSseOHSVJoaGhWrduncLCwiRJgwYN0r333quBAweqoqLCmgfAfPTsADDKpk2bJEkVFRWaNWuWioqKdO7cORUVFWnWrFmqqKjwmwfAfKzsADBKaWmpJOnpp5/W22+/rfT0dOtYTEyMJk+erEWLFlnzAJiPsAPAKC6XS5Lk8/mUk5Nz1Q7Kmzdv9psHwHzssyP22QFMcuHCBQ0YMECRkZFav369GjX6z3u6ixcvatiwYfJ6vdqyZYvVzwOgfqru+ZuVHQBGCQsL07Bhw7R27Vr9/Oc/17333iuXy6XS0lL9+c9/1qlTpzRy5EiCDtCAEHYAGOfxxx9XSUmJPvjgA73zzjt+x3r37q3HH3/cpsoA2IGwA8A4eXl52r17t5KSktS6dWtduHBBYWFhOn78uHbv3q28vDxuBgo0IIQdAEaprKzU0qVLlZycfN17Yy1btky9e/fm3lhAA8E+OwCMcuW9sa4MOpIUHBzMvbGABoiwA8Aol+95FR8ff83jl8e5NxbQcBB2ABjl8j2viouLr3n88jj3xgIaDsIOAKN07dpV0dHRysnJUVVVld+xqqoq5eTkKCYmRl27drWpQgB1jQZlAEYJCQnR+PHjNXPmTD333HPq0aOHwsPDVV5erg8//FAFBQWaPXs2zclAA0LYAWCcvn37asSIEVq/fr3y8/Ot8ZCQEI0YMYKPnQMNDGEHgHHy8vK0bt06JSUlXbWys27dOnXq1InAAzQg3BtL3BsLMEllZaVSU1OVkJBw3X12iouLtXr1ai5lAfVcdc/fNCgDMMqV++z4fD4dOHBAubm5OnDggHw+H/vsAA0Ql7EAGOXy/jmlpaV6/vnn5Xa7rWPR0dEaPXq03zwA5iPsADDK5f1z5s+fr+TkZE2fPl3x8fEqLi5WTk6O5s+f7zcPgPm4jAXAKImJiQoJCVHz5s01Z84cJSYm6qabblJiYqLmzJmj5s2bKyQkRImJiXaXCqCOsLIDwChFRUWqrKzUN998o2nTpql169YqLy9XeHi4jh8/rm+++caa161bN5urBVAXCDsAjHK5FycpKUkFBQVXHb88Ts8O0HAQdgAY5XIvTkFBgVq0aKF77rlHLpdLpaWl2rZtmxWA6NkBGg7CDgCjdOzYUZIUGhqqdevWKSwszDr2q1/9SgMHDlRFRYU1D4D5aFAGYJRNmzZJkioqKjRr1iwVFRXp3LlzKioq0qxZs1RRUeE3D4D5WNkBYJTS0lJJ0tNPP623335b6enp1rGYmBhNnjxZixYtsuYBMB9hB4BRXC6XJMnn8yknJ0eFhYU6efKkoqKi1LVrV23evNlvHgDzcW8scW8swCQXLlzQgAEDFBkZqfXr16tRo/+8p7t48aKGDRsmr9erLVu2+PXzAKh/qnv+ZmUHgFHCwsI0bNgwrV27Vg8++KAkWfvsSJd+OY4cOZKgAzQgrOyIlR3ARPfcc4/VjHyl0NBQbdu2zYaKANQ07noOoMEaNGiQFXRatGihpKQktWjRQtKlT2kNGjTIzvIA1DEuYwEwyokTJ3TmzBlJ0oYNG/ze7Xm9Xt1///06c+aMTpw4oR/96Ed2lQmgDrGyA8AoGRkZkqR27drJ5/MpIyNDw4YNU0ZGhnw+n9q2bes3D4D5WNkBYBSPxyPp0irOAw88YI2fOHFCDzzwgHU56/I8AOZjZQeAURwOh6T/3BC0R48eWrJkiXr06CFJ1l3PL88DYD4+jSU+jQWY5Msvv9SoUaMkSWvXrlV0dLR1zO12a+TIkZKk7OxstWvXzoYKAdSU6p6/CTsi7AAmycjI0CeffPKd8zp37qwlS5bUQUUAagsfPQfQIJWVldXoPAD1H2EHgFGcTudVY8HBV/+qu9Y8AGbi01gAjDJhwgSlpaVJkn73u98pLi7OOnbs2DE9/PDD1jwADQM9O6JnBzDJfffdp/Pnz1vPY2Nj1bZtWx09elQlJSXWeEREhLZu3WpHiQBqiDE9O8ePH9cvf/lLtWzZUo0bN1aXLl20b98+67jP59OMGTMUExOjxo0bKyUlRUeOHLGxYgB2Ki8vlyTrbuclJSXatWuXFXRCQkL85gEwX0CHnW+++Ua9e/dWaGiotmzZok8//VQvvviitSmYJC1cuFCLFy/W8uXLtWfPHjVp0kT9+/f3e2cHoOG4fHfzixcvqmnTpnK5XIqKipLL5VLTpk1VWVnpNw+A+QL6Mtazzz6rDz74QH/5y1+uedzn88nlcmnSpEmaPHmypEu7ojqdTmVnZ1v7aXwXLmMB5mCfHaDhqO75+3s3KF+4cEHFxcW6+eabreXimrZhwwb1799fw4YN086dO9W6dWuNHz9eY8aMkSQVFxfL7XYrJSXF+h6Hw6GePXsqPz//umGnvLzcbwnb6/XWSv0A6t7evXutr7/tDc/evXsJO0ADccOXsc6dO6fRo0frpptuUmJioo4dOyZJeuKJJ7RgwYIaLe7vf/+7li1bpvbt2+tPf/qTxo0bpwkTJmjVqlWSLr1Lk67+CKnT6bSOXUtmZqYcDof1iI2NrdG6AdintLS0RucBqP9uOOxMnTpVBw8e1I4dOxQREWGNp6SkaN26dTVaXFVVlW677TbNnz9f3bp1U1pamsaMGaPly5f/oNedOnWqPB6P9bjyExoA6jeXy1Wj8wDUfzd8/en999/XunXrlJSUpKCgIGs8MTFRX3zxRY0WFxMTo06dOvmN3XLLLXr33XclyboWX1ZWppiYGGtOWVmZbr311uu+bnh4OM2JgKG6d+9ufb1mzRq/UFNaWqpf/OIXV80DYLYbDjsnTpxQq1atrho/e/asX/ipCb1799ahQ4f8xg4fPqy2bdtKkuLj4xUdHa3c3Fwr3Hi9Xu3Zs0fjxo2r0VoA1A9jx461vn700UdVUVGhqqoqBQcHKzQ01G/etm3b7CgRQB274ctYt99+u/74xz9azy8HnDfeeEPJyck1V5mkJ598UgUFBZo/f74+//xzrVmzRitWrFB6err1sydOnKi5c+dqw4YN+vjjj/Xwww/L5XJpyJAhNVoLgPqhoqLC+rq8vFxVVVWSLl0Wv/KDCVfOA2C2G17ZmT9/vgYMGKBPP/1UFy9e1CuvvKJPP/1Uu3fv1s6dO2u0uDvuuEPvvfeepk6dqjlz5ig+Pl6//e1vlZqaas2ZMmWKzp49q7S0NJ06dUp9+vTR1q1b/fqJADQcoaGhfkEmJCRE7du315EjR6w9di7PA9AwfK99dr744gstWLBABw8e1JkzZ3TbbbfpmWeeUZcuXWqjxlrHPjuAOT788ENNmTJFkrR48WJ17drVOlZYWGjdE2vhwoXq0aOHLTUCqBnVPX8H9KaCdYWwA5ijX79+fis41xMSEqLc3Nw6qAhAbam1TQWvtwFfUFCQwsPDFRYWdqMvCQA1pjpB50bmAaj/bjjsNG/e/Fs/ddWmTRuNGjVKM2fOVHBwQN96C4CBQkJCqr2yA6BhuOGwk52dreeee06jRo2yrnd/+OGHWrVqlaZNm6YTJ05o0aJFCg8P129+85saLxgAvk1mZma1enYyMzNtqQ9A3bvhnp1+/fpp7NixGj58uN/4O++8o9dee025ubl6++23NW/ePP3tb3+r0WJrCz07gDnuueeean2sPDQ0lH12gHquuufvG77OtHv3bnXr1u2q8W7duik/P1+S1KdPH+ueWQBQl6q7fw777AANxw2HndjYWK1cufKq8ZUrV1o31PzXv/6lFi1a/PDqAOAGVXf/HPbZARqOG+7ZWbRokYYNG6YtW7bojjvukCTt27dPn332mXXPqr1792rEiBE1WykAVMP06dM1Y8YMSVfvpXPlHjzTp0+3pT4Ade977bPz5Zdfavny5Tp8+LAk6cc//rHGjh2rM2fOqHPnzjVeZG2jZwcwx5133lntuTt27Ki1OgDUvlrbZ0eS2rVrpwULFlg/6Pe//71GjBihffv2sXcFAAAIKN97I5y8vDw98sgjcrlcevHFF3XXXXepoKCgJmsDAAD4wW5oZcftdis7O1srV66U1+vV8OHDVV5ervfff1+dOnWqrRoBoNpmzJihOXPmSLp04+JevXpZx3bv3m3t/3W5rweA+ardszN48GDl5eVp4MCBSk1N1X333aeQkBCFhobq4MGD9Trs0LMDmOPuu+9WVVWV9TwoKEiDBw/Wxo0bdeWvu+DgYG3fvt2OEgHUkBrv2dmyZYsmTJigcePGqX379jVSJADUtCuDjiT5fD5t2LDhO+cBMFe1e3Z27dql06dPq3v37urZs6eWLFmif/7zn7VZGwDcsOrek4979wENR7X/tSclJen111/XV199pbFjx2rt2rVyuVyqqqrStm3bdPr06dqsEwCq5Y033rhq7NZbb63WPABmuuG3Nk2aNNFjjz2mXbt26eOPP9akSZO0YMECtWrVSvfff39t1AgA1VZaWur3PDQ0VF26dLlqx+T/ngfAXN9rU8H/VllZqY0bN+rNN9+85rXxQEeDMmAONhUEGo5auxHotYSEhGjIkCH1MugAAACz0aEHwHgJCQl2lwDARoQdAEYZNGiQ9fWYMWO0Y8cOvfnmm9qxY4fGjBlzzXkAzFYjPTv1HT07gDno2QEajjrt2QEAAAhUhB0AAGA0wg4Ao/z0pz+1vh4+fLh27NhhPYYPH37NeQDMRs+O6NkBTELPDtBw0LMDAAAgwg4AADAcYQeAUa68jPXQQw/59ew89NBD15wHwGz07IieHcAk1woxl/99/zd6doD6jZ4dAPg/1wo6ABoOwg4AY6WlpX3rcwANA2EHgFFmzJhhfd2uXTu/np127dpdcx4AszWyuwDANOfPn9exY8fsLqPBatOmjfX1b37zG0lS69atdfz48avmHT58uE5rwyVxcXGKiIiwuww0IDQoiwZl1KzDhw9zuQT4FitWrFCHDh3sLgMGqO75m5UdoIbFxcVpxYoVdpcBSbt371Z2drb1fNSoUerVq5d9BUHSpX8jQF0i7AA1LCIignetAaJDhw7q1auX0tLSWE0AGjAalAEAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0epV2FmwYIGCgoI0ceJEa+z8+fNKT09Xy5Yt1bRpUw0dOlRlZWX2FQkAAAJKvQk7e/fu1WuvvaauXbv6jT/55JPauHGj1q9fr507d6q0tFQPPvigTVUCAIBAUy/CzpkzZ5SamqrXX39dLVq0sMY9Ho9Wrlypl156SXfffbe6d++ut956S7t371ZBQYGNFQMAgEBRL8JOenq6Bg4cqJSUFL/x/fv3q6Kiwm+8Y8eOiouLU35+/nVfr7y8XF6v1+8BAADM1MjuAr7L2rVr9dFHH2nv3r1XHXO73QoLC1Pz5s39xp1Op9xu93VfMzMzU7Nnz67pUgEAQAAK6JWdkpIS/frXv1ZOTo4iIiJq7HWnTp0qj8djPUpKSmrstQEAQGAJ6LCzf/9+ff3117rtttvUqFEjNWrUSDt37tTixYvVqFEjOZ1OXbhwQadOnfL7vrKyMkVHR1/3dcPDwxUZGen3AAAAZgroy1j9+vXTxx9/7Df26KOPqmPHjnrmmWcUGxur0NBQ5ebmaujQoZKkQ4cO6dixY0pOTrajZAAAEGACOuw0a9ZMnTt39htr0qSJWrZsaY2PHj1aTz31lKKiohQZGaknnnhCycnJSkpKsqNkAAAQYAI67FTHyy+/rODgYA0dOlTl5eXq37+/li5dandZAAAgQNS7sLNjxw6/5xEREcrKylJWVpY9BQEAgIAW0A3KAAAAPxRhBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGq3c3AsX1lZWVyePx2F0GEFCOHj3q9yeASxwOh5xOp91l1Ikgn8/ns7sIu3m9XjkcDnk8HkVGRtpdzvdSVlamX/7vw6q4UG53KQCAeiA0LFyr3/5dvQ481T1/s7JjCI/Ho4oL5fp3wv9TVYTD7nIAAAEs+LxH+vtOeTyeeh12qouwY5iqCIeqmvyP3WUAABAwaFAGAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgtEZ2F4CaFfzvU3aXAAAIcA3tXEHYMUzj4jy7SwAAIKAQdgzz7/i+qmrc3O4yAAABLPjfpxrUm2PCjmGqGjdXVZP/sbsMAAACBg3KAADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNG4EaJvi8x+4SAAABrqGdKwg7hnA4HAoNC5f+vtPuUgAA9UBoWLgcDofdZdQJwo4hnE6nVr/9O3k8DSutA9/l6NGjmjdvnp577jm1bdvW7nKAgOFwOOR0Ou0uo04QdgzidDobzP+4wI1q27atOnToYHcZAGxAgzIAADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAowV02MnMzNQdd9yhZs2aqVWrVhoyZIgOHTrkN+f8+fNKT09Xy5Yt1bRpUw0dOlRlZWU2VQwAAAJNQIednTt3Kj09XQUFBdq2bZsqKip077336uzZs9acJ598Uhs3btT69eu1c+dOlZaW6sEHH7SxagAAEEga2V3At9m6davf8+zsbLVq1Ur79+9X37595fF4tHLlSq1Zs0Z33323JOmtt97SLbfcooKCAiUlJdlRNgAACCABvbLz3zwejyQpKipKkrR//35VVFQoJSXFmtOxY0fFxcUpPz//uq9TXl4ur9fr9wAAAGaqN2GnqqpKEydOVO/evdW5c2dJktvtVlhYmJo3b+431+l0yu12X/e1MjMz5XA4rEdsbGxtlg4AAGxUb8JOenq6PvnkE61du/YHv9bUqVPl8XisR0lJSQ1UCAAAAlFA9+xclpGRoU2bNikvL09t2rSxxqOjo3XhwgWdOnXKb3WnrKxM0dHR13298PBwhYeH12bJAAAgQAT0yo7P51NGRobee+89bd++XfHx8X7Hu3fvrtDQUOXm5lpjhw4d0rFjx5ScnFzX5QIAgAAU0Cs76enpWrNmjf7whz+oWbNmVh+Ow+FQ48aN5XA4NHr0aD311FOKiopSZGSknnjiCSUnJ/NJLAAAICnAw86yZcskSXfeeaff+FtvvaVRo0ZJkl5++WUFBwdr6NChKi8vV//+/bV06dI6rhQAAASqgA47Pp/vO+dEREQoKytLWVlZdVARAACobwK6ZwcAAOCHIuwAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIwW0JsKAvXR+fPndezYMbvLwP85evSo35+wX1xcnCIiIuwuAw0IYQeoYceOHVNaWprdZeC/zJs3z+4S8H9WrFihDh062F0GGhDCDlDD4uLitGLFCrvLAAJWXFyc3SWggSHsADUsIiKCd60AEEBoUAYAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNO56Lsnn80mSvF6vzZUAAIDqunzevnwevx7CjqTTp09LkmJjY22uBAAA3KjTp0/L4XBc93iQ77viUANQVVWl0tJSNWvWTEFBQXaXA6AGeb1excbGqqSkRJGRkXaXA6AG+Xw+nT59Wi6XS8HB1+/MIewAMJrX65XD4ZDH4yHsAA0UDcoAAMBohB0AAGA0wg4Ao4WHh2vmzJkKDw+3uxQANqFnBwAAGI2VHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaP8fTpwj05EXRUgAAAAASUVORK5CYII=","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.boxplot(df['Age'])"]},{"cell_type":"markdown","metadata":{},"source":["## Correlation\n"]},{"cell_type":"markdown","metadata":{},"source":["### Finding correlation\n"]},{"cell_type":"markdown","metadata":{},"source":["Find the correlation between `Age` and all other numerical columns.\n"]},{"cell_type":"code","execution_count":123,"metadata":{},"outputs":[{"data":{"text/plain":["Respondent       0.004041\n","CompTotal        0.006970\n","ConvertedComp    0.105386\n","WorkWeekHrs      0.036518\n","CodeRevHrs      -0.020469\n","Age              1.000000\n","dtype: float64"]},"execution_count":123,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","import numpy as np\n","num_df = df.select_dtypes(include=[np.number])\n","correlation = num_df.corrwith(num_df['Age'])\n","correlation"]},{"cell_type":"markdown","metadata":{},"source":["## Authors\n"]},{"cell_type":"markdown","metadata":{},"source":["Ramesh Sannareddy\n"]},{"cell_type":"markdown","metadata":{},"source":["### Other Contributors\n"]},{"cell_type":"markdown","metadata":{},"source":["Rav Ahuja\n"]},{"cell_type":"markdown","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","metadata":{},"source":["| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n","| ----------------- | ------- | ----------------- | ---------------------------------- |\n","| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"]},{"cell_type":"markdown","metadata":{},"source":[" Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.9.17"}},"nbformat":4,"nbformat_minor":4}