TF-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-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01\" target=\"_blank\">\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":1,"metadata":{},"outputs":[],"source":["import pandas as pd"]},{"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":6,"metadata":{},"outputs":[{"data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x1eb157b6530>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAApyklEQVR4nO3deZRcZ3nn8e9TS1cv1a1dspFsy6uMDXgTYGNCwGxOSCAhJMAAwQzEMwxhIBshnEwGcmbOJCFDICEkmCWsscHGOMQDNgYMBmKM5Q1LxsaWLNuSF3Vr66261mf+uPe2yq1eartVpa7f55w+Xcutuu/tVv/01nPf+77m7oiISPslOt0AEZFepQAWEekQBbCISIcogEVEOkQBLCLSIQpgEZEO6boANrPPmtk+M9veovc70cy+bWY/N7P7zGxzK95XRKRZXRfAwOeAS1v4fl8APuzuzwSeB+xr4XuLiDSs6wLY3W8BDlQ/ZmanmtkNZnaHmf3QzM6s5b3M7Cwg5e43he896e7TrW+1iEj9ui6AF3AF8G53vwD4Y+ATNb7uDOCQmV1rZneZ2YfNLBlbK0VE6pDqdAOWYmZZ4AXA1WYWPZwJn3st8JfzvGyvu7+S4Ph+CTgPeBT4CnAZ8Jl4Wy0isrSuD2CCXvohdz937hPufi1w7SKv3QPc7e67AMzsOuBCFMAi0gW6vgTh7uPAw2b22wAWOKfGl98OrDSzdeH9S4D7YmimiEjdui6AzexK4FZgi5ntMbO3A28C3m5m9wA7gNfU8l7uXiaoGX/XzO4FDPhUPC0XEamPaTpKEZHO6LoesIhIr+iqk3CXXnqp33DDDZ1uhohIq9l8D3ZVD3hsbKzTTRARaZuuCmARkV4SawCb2Uozu8bM7g8nw7kozv2JiBxL4q4Bfwy4wd1fZ2Z9wGDM+xMROWbEFsBmtgJ4EcGlv7h7ASjEtT8RkWNNnCWIk4FR4F/CiXA+bWZDczcys8vNbJuZbRsdHY2xOSIi3SXOAE4B5wP/5O7nAVPA++du5O5XuPtWd9+6bt26uU+LiCxbcQbwHmCPu98W3r+GIJBFRIQYA9jdnwQeM7Mt4UMvRRPhiIjMinsUxLuBL4cjIHYBb4t5fyIix4xYA9jd7wa2xrkPEZFjla6EExHpEAXwAtyd3WNTaLpOEYmLAngBP3pojBf/7ff5jX/8MQ8+NdHp5ojIMqQAXsC9ew8DsHN0io9998EOt0ZElqOumg+4m+zcN8WGkQxnHjfCw2NTnW6OiCxD6gEvYNfYJKeszXLy2iEe2T+tWrCItJwCeB7uzq7RKU5dP8RJawaZzJcYm9Q8QiLSWgrgeeyfKnA4V+SUtVk2rw3mD9q9X2UIEWktBfA8do0GYXvq+iwnrwkDWHVgEWkxnYSbx87RSQBOWTvEcSv6SSZMPWARaTkF8Dx2jU6SSSXYuHKARMI4YdUAu8emO90sEVlmVIKYx8NjU5y8dohEIlhJevPaIfWARaTlFMDzGJ3Is2Gkf/b+5jVDuixZRFpOATyPg9NFVg2mZ+9vWjXAVKHMeK7UwVaJyHKjAJ7HwekCKwf7Zu+vyQa390/lO9UkEVmGFMBzFMsVJmZKrKoK4NVDGQAOTOliDBFpHQXwHIemiwCsHjpSglgdhrECWERaSQE8x6HpIGSrSxCrswpgEWk9BfAcUchWlyDWDEU1YAWwiLSOAniOg2EJYlVVCaI/nWSwL6kesIi0lAJ4jqgEUd0Dju4fVACLSAspgOc4sEAAr8n2qQQhIi2lAJ7j0HSR/nSCgb7k0x5fPdSnEoSItJQCeI6DU4Wjer8QDEVTAItIKymA55h7FVxEPWARaTUF8BwHp4tPuwgjsjrbR65YJlcod6BVIrIcKYDnWKgHfGQssOaDEJHWUADPEdSAj+4BR3Xhg1PFdjdJRJYpBXCVSsU5nCvOzv1QTTOiiUirKYCrjM8UqTgLnITTjGgi0loK4CrRZcgr5ylBrB7ShDwi0loK4CqTM8GKF8P9RwfwcCaFGYznVAMWkdZQAFeZyAfhms0cvVh0ImEMZ1KMz2hZIhFpDQVwlal8MMZ3vgAGGBlIMz6jHrCItIYCuMpk1APunz+Ah/vTWphTRFpGAVxlMuwBD2WS8z4/0p9SD1hEWkYBXGX2JFzm6JNwEJYgdBJORFpEAVxlKl8iYdCfnv/HMtKfZkIn4USkRRTAVSbzJbKZFGY27/PDKkGISAvNf7apRcxsNzABlIGSu2+Nc3/NigJ4ISMDaSbzJSoVJ5GYP6RFRGoVawCHXuLuY23YT9MmZ0oLjoCA4CScO0zkS6wYmL9OLCJSK5UgqkwVSgwt0QMGXQ0nIq0RdwA78G0zu8PMLo95X02bmFmiBBH2jlUHFpFWiLsE8UJ332tm64GbzOx+d7+leoMwmC8HOPHEE2NuzuKm8iWOX9G/4PMj4RwRGgkhIq0Qaw/Y3feG3/cBXweeN882V7j7Vnffum7dujibs6RaTsKBShAi0hqxBbCZDZnZcHQbeAWwPa79tcJkfokacNgD1oQ8ItIKcZYgNgBfD8fUpoB/dfcbYtxfU9ydqSV6wMNRDVg9YBFpgdgC2N13AefE9f6tliuWqfjCE/HAkQBWDVhEWkHD0ELRPBCLlSBSyQRDfUmNghCRllAAhybz0UQ8i38o0IQ8ItIqCuBQFMCL9YBB80GISOsogENRAC92Eg6CkRCalF1EWkEBHIpqwEsG8EB6du04EZFmKIBDU4UwgBcZBQFBCeKwasAi0gIK4NCRURDzL0cUGe5PzS7eKSLSDAVwKFoPbqHliCJDmdRsWIuINEMBHJrMFxddjigynElRKFfIl9QLFpHmKIBDU/kyQ4ssRxSJhqmpDCEizVIAh6YLJQb7Fq//wpFRElN5lSFEpDkK4FCuWGGwb+mpMaIA1nwQItIsBXAoVygxkK6hBxwOU4uGrYmINEoBHJoulBmooQQR1YA1EkJEmqUADuWK5ZpqwNFkPZOqAYtIkxTAoVyhTH8NJYghnYQTkRZRAIdq7QEPqQcsIi2iAA5NF2oL4KwCWERaRAEcqrUEkUwYA+mkTsKJSNMUwAQLctZagoBgKJqGoYlIsxTAQKFcoVzxmi7EgKAMoQsxRKRZCmCC8gNQUwkCggDWKAgRaZYCmGAEBFBzCWIok9RJOBFpmgKYYAQEUNOlyADZTHp2/mARkUYpgDlSgqjlUmSAbCbJpNaFE5EmKYCpvwSR1bJEItICCmDqL0FoWSIRaQUFMPWXIKJliQqlSpzNEpFlTgEM5IpBb7aeHjBoQh4RaY4CGMgVgp5sPRdigOaDEJHmKIAJ1oODekZBKIBFpHkKYKpqwLWOA+5XAItI8xTABMPQUgmjL1Xbj0NzAotIKyiAqX09uMiw1oUTkRZQABOUIGotP4BGQYhIayiAqX05oohqwCLSCgpgghJErVNRAgz1KYBFpHkKYGCmzh5wMmEM9mlZIhFpjgKYYBxwrRdhRIYyWpZIRJqjAKb+EgQEIyG0LJGINCP2ADazpJndZWbXx72vRtVbgoCwB6wasIg0oR094PcAP2/Dfho2XecwNAiWJdKcwCLSjFgD2Mw2Aa8CPh3nfpqVK9Z3IQYEyxJNqAcsIk2Iuwf8UeB9wIIT55rZ5Wa2zcy2jY6Oxtyc+eUK9ZcgspmkShAi0pTYAtjMfg3Y5+53LLadu1/h7lvdfeu6devias6CCqUKpYrXXYLI9qc0DlhEmhJnD/hi4NVmthu4CrjEzL4U4/4aEq0HV28JYiijABaR5sQWwO7+Z+6+yd03A28Avufub45rf42KpqKsdxzwcCZFoaRliUSkcT0/DvjIZOz1/Sg0IY+INKstAezu33f3X2vHvuo1W4JI19cD1qoYItKsnu8B17sickQBLCLNUgAXoxpw/aMgQAEsIo3r+QCernM9uIiWJRKRZvV8ADdagtCyRCLSLAVwgyUIjYIQkWb1fAA3WoJQDVhEmtXzATzT6JVwWpZIRJrU8wE8XSiRTBh9yfp+FNGyRCpBiEijFMDhXMBmVvdrNR+EiDSj5wN4poG5gCPDmRSTmpRdRBrU8wE83cBcwJGhTIrJmWKLWyQivaLnAzjXwHJEkWwmpWWJRKRhCuAmShDZ/pSWJRKRhvV8ADeyIGckqAGrBCEijen5AG5kPbhIUANWD1hEGqMALpYZqHM1jIjWhRORZvR8AE8XSgykG/sxZDMpimUnX9KJOBGpX88HcFCCaKwHPNyvGdFEpHEK4GKZ/gZPwmk+CBFpRk8HcLFcoVj2hk/CRTOiTagHLCIN6OkAbnQu4MiwVsUQkSb0dgCHcwE3WoKIesCaEU1EGqEApvEesFZGFpFm1BTAZnatmb3KzJZVYE+3KIBVAxaRRtQaqJ8A/hPwoJn9lZltibFNbRPVgJstQagHLCKNqCmA3f077v4m4HxgN/AdM/sPM3ubmaXjbGCcjpQgGhsHPJBOkjCNAxaRxtRcUjCzNcBlwDuAu4CPEQTyTbG0rA2mC0FwNjoZj5mR1aoYItKgmrp+ZvZ1YAvwReDX3f2J8KmvmNm2uBoXt1yDC3JWUwCLSKNq/ez9KXf/ZvUDZpZx97y7b42hXW3R7CgICCfkUQlCRBpQawnif83z2K2tbEgnRKMgGi1BgHrAItK4RXvAZnYcsBEYMLPzgGjp4BFgMOa2xa4lJYj+NIdzmpRdROq3VAnilQQn3jYBH6l6fAL4QExtaptcoYwZZFKND28ezqR4/FCuha0SkV6xaAC7++eBz5vZb7n719rUprbJFcsMppOY2dIbL2Aok1QNWEQaslQJ4s3u/iVgs5n94dzn3f0j87zsmDFdaHw1jEg2k1YNWEQaslT6DIXfs3E3pBNyhRIDfc1dXR0tS1SpOIlE4z1pEek9S5UgPhl+/1B7mtNeQQmiuR5wNCXlVKHEcP8xe1GgiHRArZPx/I2ZjZhZ2sy+a2ajZvbmuBsXt6AE0fgICAhWRgaYymtdOBGpT62fv1/h7uPArxHMBXEa8CdxNapdZorlpsYAQ/WEPBqKJiL1qTWAo8/prwKudvfDMbWnraYL5aaugoMjJQhNSSki9ao1gK83s/uBC4Dvmtk6YGaxF5hZv5n91MzuMbMdZtZ1deRcoUx/kwGsKSlFpFG1Tkf5fuAFwFZ3LwJTwGuWeFkeuMTdzwHOBS41swubaGvLReOAm5HNaFkiEWlMPUMAziQYD1z9mi8stLG7OzAZ3k2HX153C2PUihKEVsUQkUbVOh3lF4FTgbuB6HS/s0gAh69LAncQnLT7R3e/reGWxqAlJQitCyciDaq1B7wVOCvs1dbM3cvAuWa2Evi6mT3L3bdXb2NmlwOXA5x44on1vH1TSuUKhXKl6XHA0TA0XY4sIvWq9STcduC4Rnfi7oeAm4FL53nuCnff6u5b161b1+gu6nZkJrTmroTrSyXIpBLqAYtI3Wrt/q0F7jOznxKcXAPA3V+90AvCkRJFdz9kZgPAy4G/bqaxrXQkgJvrAYPmBBaRxtSaPh9s4L2PJ5hJLUnQ0/6qu1/fwPvEYnY1jCZHQcCR+SBEROpRUwC7+w/M7CTgdHf/jpkNAosml7v/DDivBW2MxexqGE2ehIOwB6wasIjUqda5IH4PuAb4ZPjQRuC6mNrUFq1YDSOSzaSYUA9YROpU6xmodwEXA+MA7v4gsD6uRrVDK0sQw1qYU0QaUGsA5929EN0JL8boqosq6pVrYQliKJNiqqAAFpH61BrAPzCzDxAszvly4Grg3+NrVvymi80vSR9RDVhEGlFrAL8fGAXuBf4L8E3gz+NqVDvkwh5rf4tGQagGLCL1qnUURMXMrgOuc/fReJvUHrM14BaMAx7OpCiUKuRLZTKp5gNdRHrDoj1gC3zQzMaAB4AHwtUw/qI9zYtPK0sQWhVDRBqxVAniDwhGPzzX3Ve7+2rg+cDFZvYHsbcuRrlCGTPIpJq7FBk0JaWINGap9HkL8EZ3fzh6wN13AW8GfjfOhsUtVwiWIzJrfiXj4X5NSSki9VsqgNPuPjb3wbAOfEwvATzdgvXgItlM8KPQ5cgiUo+lArjQ4HNdb6YFKyJHtDCniDRiqSEA55jZ+DyPG9AfQ3vaphWrYUSymeB9VIIQkXosGsDuvmzHVMVRgtAoCBGpR/NDAI5RKkGISKf1bABPF0stuQgDggl9zLQskYjUp3cDuNC6EkQiYWT7UowrgEWkDj0bwLkWnoSDYCywTsKJSD16NoBbOQoCYGQgzcSMasAiUrseDuASg5nW1IABRvrTjCuARaQOPRnAhVKFYtlbshpGZLg/xXhOJQgRqV1PBnArV8OIjAyoBywi9enJAJ4uBj3VoZaWIHQSTkTq05MBHF2xFsdJuErlmF4qT0TaqCcDuJWrYURG+tNUHC3OKSI168kAng5DstXjgAFdjCEiNevRAI6nBAFoLLCI1KzHA7i1JQhAQ9FEpGY9GcBTMZQgRgbCEkROPWARqU1PBnAujhJE1ANWCUJEatSTAXykB9y6EoQW5hSRevVkAEdL0venW3f4w7M1YPWARaQ2PRnA04VyOIl680vSR/pSCQbSSZUgRKRmPRrArZ0JLTIyoAl5RKR2PRrArZ0LODLcn2ZC68KJSI16MoCn8q1bjqjaiKakFJE69GQA54qlls6EFtGUlCJSj54M4Kl8PCWIkf60RkGISM16MoBbvSBnZGRAcwKLSO16MoCni6WWXoQRGe5PczhXxF1zAovI0nozgGMqQawcSFOq+OxkPyIii4ktgM3sBDO72czuM7MdZvaeuPZVr7iGoa0cDK6GO6Q6sIjUIM4ecAn4I3c/C7gQeJeZnRXj/mpSqTi5YpmBGEoQKwb6ADg0XWj5e4vI8hNbALv7E+5+Z3h7Avg5sDGu/dUqVwzKA0Mx9oAPT6sHLCJLa0sN2Mw2A+cBt83z3OVmts3Mto2OjsbeljjmAo6oBCEi9Yg9gM0sC3wNeK+7j8993t2vcPet7r513bp1cTcnlgU5I6sGoxKEAlhElhZrAJtZmiB8v+zu18a5r1rFsR5cZEW4LtxB1YBFpAZxjoIw4DPAz939I3Htp17RisgDMQRwfzpJfzrBYZUgRKQGcfaALwbeAlxiZneHX78a4/5qMpUPT8LFMBcEwMqBPo2CEJGaxJNCgLv/CGjdjOctMpUPesDZuAJ4MK0asIjUpOeuhJuIOYBXDKQ1CkJEatJzAdyOHrDGAYtILXougCfD2cpirQHnVAMWkaX1XgAXSvSlEvSl4jl01YBFpFa9F8AzpdjKDwArBtPkSxVmipoRTUQW13MBPJWPN4BXDuhqOBGpTc8F8GS+HFv9F6rng1AdWEQW14MBXCSbaf1VcJGV4eXI6gGLyFJ6LoCn8uXYa8CgOYFFZGk9F8CT+XiWpI9EM6IdVA9YRJbQkwE83B9/AB+YUg9YRBbXcwE8lS8xFMNcwJGBviSDfUkFsIgsqacCuByuWJyNsQcMsHqoTwEsIkvqqQCOliOK8yQcwJqhPsYm87HuQ0SOfT0VwNE8ELEHcDajHrCILKmnAjiaCS3OURCgEoSI1KanAngy5qkoI2uG+tg/WcDdY92PiBzbejOAYz4JtybbR6Fcmd2fiMh8eiqAZ0sQMQ5DA1g9lAE0FlhEFtdTATwRnoSL80IMCEoQAGOTCmARWVhPBXA7T8KBesAisrjeCuBCtCR9fLOhQVADBjgwpbHAIrKwngrgiZkSfckEmVTMARzWgFWCEJHF9FQAT+VLsfd+IZgPYiCt+SBEZHE9FcCT+VLsQ9Aia7K6GENEFtdTATwxUyKbSbdlX2uG+tivABaRRfRUAI/niqwYaE8PePVQH/s1IY+ILKKnAvhwrsiKgfb0gNcNZxidUACLyMIUwDHZMNLP2GSeUrnSlv2JyLFHARyT9SP9VBzVgUVkQT0TwIVShVyx3L4e8HAwFnjfuMoQIjK/ngngw7lgleKRNvaAAZ4an2nL/kTk2NNzAdy+GnDQA35qQgEsIvPruQBuVw94bTaDmUoQIrKwngng8Tb3gNPJBGuG+tinHrCILKBnArjdJQiA9cP9PKUesIgsQAEcow0jGZ2EE5EFKYBjtGGkn326Gk5EFtBTATzYlySdbN8hrx/O6Go4EVlQbGlkZp81s31mtj2ufdSjnVfBRdaP9OOuidlFZH5xdgc/B1wa4/vXZbwDAbxBF2OIyCJiC2B3vwU4ENf71+twrti2McCR41cEAfzE4Vxb9ysix4aO14DN7HIz22Zm20ZHR2PbTydKEJtWDQCw56ACWESO1vEAdvcr3H2ru29dt25dbPsZzxUZ6W9vAK8YSJPNpBTAIjKvjgdwu3SiB2xmbFo1wJ6D023dr4gcG3oigIvlClOF9k1FWS0IYPWAReRocQ5DuxK4FdhiZnvM7O1x7WspR+aBaM96cNU2rRpk78Ec7t72fYtId4stkdz9jXG9d72iVSlWZzNt3/emVQNM5EuM50qsGGx/D1xEuldPlCDGwsuB12b72r7vaCTEY6oDi8gcvRHAYQ94XUd6wIOAhqKJyNF6I4Bne8CdKUEAGgkhIkfpjQCezJNMWEdGQURjgfceUg9YRJ6uZwJ4zVAfiYS1fd/RWOBH96sHLCJP1xMBvH+y0JHyQ+TUdVl2jk52bP8i0p16IoDHJvOsHe5kAA/x6IFp8qVyx9ogIt2nRwK4wNqh9g9Bi5y6PkvFYfeYyhAicsSyD2B3Z7TjPeAsgMoQIvI0yz6AJ/MlCqVKRy7CiJyybgiAnfsUwCJyxLIP4Gg5oE6ehBvsS7Fx5QAPqQcsIlV6IICDizDWdDCAIagDqwQhItWWfwB3cB6IaqeuG2LnvikqFc2KJiKB5R/AHZwHotrp64fJFcuaE0JEZi37AB6dyGMGqzs4DA3g2RtXAHDv3sMdbYeIdI9lH8B7D+bYMNxPKtnZQz3juCx9yQQ/23uoo+0Qke6x7AP4sYPTszOSdVImleTM44e5d496wCISWPYBvPdgjhNWD3a6GUBQhrh372GdiBMRYJkHcLFc4YnDOU7ogh4wwHM2rWBipsQjB3RJsogs8wB+/FCOih9ZlaLTnr1xJQA/23Ooo+0Qke6wrAM4GvK1aXV39IBP35ClP53gzkcOdropItIFlnUAPxZ+1D+hS3rA6WSC5528hh89NNbppohIF1jeAXxwmmTCOH5Ff6ebMuuXTlvLztEpnjisCzJEet3yDuADOY5f0fkxwNVeePpaAH70oHrBIr2ue5IpBnsOTndN+SFy5nHDrM1m+LHKECI9b1kH8KMHcl1xEUY1M+OFp63hhw+OUSpXOt0cEemgZRvAY5N5xibznLFhuNNNOcqlzzqe/VMF/mPn/k43RUQ6aNkG8I7HxwF4VjgJTjd5yZnrGOlPcd1dezvdFBHpoGUbwNvDWcfOesZIh1tytEwqyaueczw37HiS6UKp080RkQ5ZtgG84/HDnLRmkBUD6U43ZV6/ce5Gpgtlrr/niU43RUQ6ZNkG8Pa94zzrGd1Xfog87+TVnP2MEf7pBzspa3IekZ60LAP48HSRRw9Mc/bG7is/RMyMd19yGg+PTXH9zx7vdHNEpAOWZQDveDyo/3ZzDxjgFWcdxxkbsnzkpl+QK5Q73RwRabNlGcA/emiMZMI4Z9PKTjdlUYmE8aFXP4tH9k/zNzfe3+nmiEibLbsAdndu2P4kF52yhhWD3XkCrtpFp67hrRedxOf+Yzffulcn5ER6ybIL4If2TbJrbIpXnr2h002p2ft/5Zmcf+Iq3nPV3dx8/75ON0dE2iTV6Qa02g3bnwTgFWcf1+GW1G6gL8ln3/pc3vipn/CfP387733pGbzzxafSl1p2/z92RKlc4baHD3DXowd5cnyGUtnZuHKAC05axXNPXk26iyZrktapVJwdj4/z4L4JpvIlNq4a4PwTV7FysLMrpFdbVgFcLFe49q69nH/iSjaMdM8UlLVYMZjmmndexAeuvZe/+84v+Nqde3jHL53Mq895Rlf9gzmWPLRvkqvveIxr79zL6EQegDVDfZgZY5NH7r/1BZt5y4UnsWpIP+flYKZY5sqfPsoVt+ziicMzT3sunTReeuYG3vOy03nm8Z0fJWXu3TMGdevWrb5t27aGX//FW3fzP/5tB5/+3a287KxjpwQx1y2/GOXDNz7AvXsPk7DgcuotG4bZvHaITasGGO5PMdiXYqgvxUBfksG+JAPpJAN9STKpBGbW6UPomH0TM9y4/Umuu/tx7njkIMmEccmZ63ndBZu46NQ1jPQH5wUm8yV+/NAYX739Mb57/z4G0kle/9wTuPxFp/CMld01gZPUpliu8JXbH+Pj33uIJ8dneP7Jq/mdrSdw/kmrGOxLsntsipvue4qvbnuMiXyJ3zx3I3/w8jPatWjvvH+UyyaA90/mecXf3cJp67NcdfmFyyKEtu89zLfve4rbdu1n19jUbC9uMWYwkA5CuT+dJJtJBV/9wffh8Hs2kybbn2IgnQSg4o4TnMScmCkxPlNkPBd9LzI+U2IqX2Iy/D5dLOPumBkJA8PoSyXIpBL0p5Nk0gkyqST96QT9qeB+f3Q/nazaLridSSVIJoxkwkhY8D1pRiJhpBIWbDP7XsH7uMP+qQIHpvLs3DfFTx8+wO2PHMAdTl+f5be3buI3ztvI+uHFPw098OQEn7xlJ9+4+3HM4DfP28jbX3gKW47rvomc5GiVivP/7n2Cv/32Azyyf5oLTlrFH738DF5w2tp5tz80XeCffrCTz/14N+7wpgtP5Pdfchprspk4m9n+ADazS4GPAUng0+7+V4tt32gA7xuf4c2fuY1HD0xzzX99QVdOwNMKU/kSew/lmMqXyBXKTBXKTBeC27limelCmZnwe65YDrbJl5iMvmZKTITfc8XFxx33JROMDKQZGUgx0p9muD8I76G+IMwH+5IYhuO4Q8WDHshMscxMscJMqUy+WCFfCtqUL1U9V3U/X2rNlJzJhLFlwzAvP2sDr3rO8Q3Ngrfn4DSfumUXV93+GPlShbOfMcJLn7mBczat4LT1WTatGiSZOPb/Y18uDk4V+Nb2J/nCrbu5/8kJzjxumPdduoWXbFlfUwfsicM5PvadB/nqtscYSCd5y0WbedPzT4yrR9zeADazJPAL4OXAHuB24I3uft9Cr6k3gMdninzi5p188dbdOPCZtz6Xi05d02TLe0OpXGEqX2a6WCJhFvzrMEiYkc2k6A97xnFzd/KlCvlShUrFKbvPfi9XnEoFSpXK7DZBiAfBbcDqoT5WD/Vx/IoBBvpa0+b9k3n+7e7Huf5nj3P3Y4eIrhSP/lMaygQln1Qy6K1b+PNLhD+/4LHwdoLZbY48z5z7VdvbPNsngu1TCaMvmSCdSpBOJuhLWvA9/PRQ/Wkkei1P22f4GHP3S9UxBPsLfjfhV/h7guh2cCuKjugxP+oxn32f8BVPe8/o9+/RC3j6e0DQ5ihMJ2ZKHJjKMzqR5+dPTLDj8cNUPFjk4PIXncJrzt3Y0H+QD+2b4P9++xfcuONJKg7P3riCC05axZbjhjljQ5a12cxsJ6SJ1XXaHsAXAR9091eG9/8MwN3/z0KvqTeAJ2aK/PKHv8/Fp63lPS89ndPWZ5tttsjTjM8UefCpidnhjRNRCaYQlGAqHpRvKk54P/hPo+LRJwOf8/w82/s821fmbu+UKk6xVKFYdgo9Opl/wmD1UIYtx2W54MRVvOysDTx744qWlBz3Hsrxb3fv5eb797Hj8XGm57k69WvvfAEXnLSqkbdvewC/DrjU3d8R3n8L8Hx3//05210OXB7e3QI8EEuDlrYW6MV1gnTcvUXH3Rlj7n7p3Ac7PgzN3a8Aruh0O8xsm7tv7XQ72k3H3Vt03N0lzhHoe4ETqu5vCh8TERHiDeDbgdPN7GQz6wPeAHwjxv2JiBxTYitBuHvJzH4fuJFgGNpn3X1HXPtrgY6XQTpEx91bdNxdpKsuxBAR6SWahUREpEMUwCIiHdJzAWxml5rZA2b2kJm9f57nM2b2lfD528xscwea2XI1HPdlZjZqZneHX+/oRDtbycw+a2b7zGz7As+bmf19+DP5mZmd3+42xqGG436xmR2u+l3/RbvbGAczO8HMbjaz+8xsh5m9Z55tuut37u4980VwMnAncArQB9wDnDVnm/8G/HN4+w3AVzrd7jYd92XAxzvd1hYf94uA84HtCzz/q8C3CK5SuhC4rdNtbtNxvxi4vtPtjOG4jwfOD28PE0yFMPffeVf9znutB/w84CF33+XuBeAq4DVztnkN8Pnw9jXAS+3Yn1qtluNedtz9FuDAIpu8BviCB34CrDSz49vTuvjUcNzLkrs/4e53hrcngJ8DG+ds1lW/814L4I3AY1X393D0L2h2G3cvAYeBY32Gn1qOG+C3wo9l15jZCfM8v9zU+nNZji4ys3vM7FtmdnanG9NqYenwPOC2OU911e+81wJYFvbvwGZ3fw5wE0c+BcjycydwkrufA/wDcF1nm9NaZpYFvga8193HO92exfRaANdyefTsNmaWAlYA+9vSuvgsedzuvt/doxnfPw1c0Ka2dVJPXi7v7uPuPhne/iaQNrP5Zy8/xphZmiB8v+zu186zSVf9znstgGu5PPobwFvD268Dvudh9f4YtuRxz6mDvZqgfrbcfQP43fDM+IXAYXd/otONipuZHRed1zCz5xHkwLHeySA8ps8AP3f3jyywWVf9zjs+G1o7+QKXR5vZXwLb3P0bBL/AL5rZQwQnMt7QuRa3Ro3H/d/N7NVAieC4L+tYg1vEzK4kOOO/1sz2AP8TSAO4+z8D3yQ4K/4QMA28rTMtba0ajvt1wDvNrATkgDcsg04GwMXAW4B7zezu8LEPACdCd/7OdSmyiEiH9FoJQkSkayiARUQ6RAEsItIhCmARkQ5RAIuILGCpiY3m2f53qiYD+teltlcAS2zC8aZXmdlOM7vDzL5pZmd0oB2Xmdkz6nzN5uo/OjN7npndEs4od5eZfdrMBlvfWukynwOOWs14PmZ2OvBnwMXufjbw3qVeowCWWISD4r8OfN/dT3X3Cwj+cW5oczuSBGOa6wrgOe+xAbga+FN33+Lu5wE3EMy4JcvYfBMbmdmpZnZD2Kn4oZmdGT71e8A/uvvB8LX7lnp/BbDE5SVAMRz8DoC73wP8yMw+bGbbzexeM3s9zM5R+/1wIqD7zezL4dVKl5rZ1dF7hNtdH95+hZndamZ3mtnV4RwAmNluM/trM7sTeCOwFfhyOPftgJldYGY/CP+AboyuAgwfv8fM7gHeVXUs7wI+7+63Vh3LNe7+lJmtNrPrwkmMfmJmzwnf64Nm9vnwD/QRM3utmf1NeMw3hJfMRm2NHv+pmZ0Wxy9DWuoK4N1hp+KPgU+Ej58BnGFmPw7/LSzZc1YAS1yeBdwxz+OvBc4FzgFeBny46jLo8wg+tp1FMHfxxcB3gOeb2VC4zeuBqyyYu+DPgZe5+/nANuAPq/az393Pd/cvhc+9yd3PJbjS7x+A14V/QJ8F/nf4mn8h+MM6p8ZjAfgQcFc4idEHgC9UPXcqcAnBpd1fAm5292cTXH32qqrtDoePfxz46AL7kS4Q/if/AuDq8Gq7TxLMQwzBlcWnE1yF+EbgU2a2crH366lLkaUrvBC40t3LwFNm9gPgucA48FN33wMQ/uPe7O4/MrMbgF83s2sIgut9wC8TBPWPw2kN+oBbq/bzlQX2v4UgUG8KX5cEngj/UFaGHzkBvgj8So3H81sA7v49M1tjZiPhc99y96KZ3Rvu54bw8XuBzVXvcWXV97+rYZ/SOQngUPif+Vx7CCZ4LwIPm9kvCAL59sXeTCQOO6h/RrV81e0yRzoIVwG/Q9Cb3BZOtm3ATe5+bvh1lru/ver1Uwvsw4AdVa97tru/IoZjgfB43L1CUI6Jrvuv8PTOjy9wW7pMOL3lw2b22zC7xFH0iek6gt4v4Se0M4Bdi72fAlji8j0gY2aXRw+E9dFDwOvNLGlm6wiWz/npEu/1A4Ildn6PIIwBfgJcHNVMzWxokREWExw5YfYAsM7MLgpflzazs939EHDIzF4Ybvemqtd/HHirmT2/6lheG56c+2G0rZm9GBhrYA7a11d9v3WxDaW9LJjY6FZgi5ntMbO3E/y+3x6eK9jBkdVlbgT2m9l9wM3An7j7orPMqQQhsXB3N7PfBD5qZn8KzAC7CWq8WYJ16Rx4n7s/WXUmeb73Kocn3i4jnCrU3UfN7DLgSjPLhJv+OcE6YHN9DvhnM8sBFxHMBvb3ZraC4G/gowR/SG8DPmtmDny7av9PmdkbgL81s/UEPdhbCEoKHwxf8zOC2bWiqUzrsSp8fZ6gdihdwt0X+n0cdYIt/ITzhzz9XMSiNBuaSAeZ2W5gq7uPdbot0n4qQYiIdIh6wCIiHaIesIhIhyiARUQ6RAEsItIhCmARkQ5RAIuIdMj/BzJK53vd9sLiAAAAAElFTkSuQmCC","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# your code goes here\n","import seaborn as sns\n","\n","sns.displot(df['ConvertedComp'], kind = \"kde\")"]},{"cell_type":"markdown","metadata":{},"source":["Plot the histogram for the column `ConvertedComp`.\n"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":["(array([9659.,  238.,  115.,  125.,   99.,  131.,   34.,   15.,   15.,\n","         151.]),\n"," array([      0.,  200000.,  400000.,  600000.,  800000., 1000000.,\n","        1200000., 1400000., 1600000., 1800000., 2000000.]),\n"," <BarContainer object of 10 artists>)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# your code goes here\n","import matplotlib.pyplot as plt\n","\n","plt.hist(df['ConvertedComp'])"]},{"cell_type":"markdown","metadata":{},"source":["What is the median of the column `ConvertedComp`?\n"]},{"cell_type":"code","execution_count":112,"metadata":{},"outputs":[{"data":{"text/plain":["65537.5"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df['ConvertedComp'].median()"]},{"cell_type":"markdown","metadata":{},"source":["How many responders identified themselves only as a **Man**?\n"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/plain":["Man                                                            10480\n","Woman                                                            731\n","Non-binary, genderqueer, or gender non-conforming                 63\n","Man;Non-binary, genderqueer, or gender non-conforming             26\n","Woman;Non-binary, genderqueer, or gender non-conforming           14\n","Woman;Man                                                          9\n","Woman;Man;Non-binary, genderqueer, or gender non-conforming        2\n","Name: Gender, dtype: int64"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df['Gender'].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/plain":["57708.0"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df_woman = df[df['Gender'] == 'Woman']\n","df_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":32,"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":32,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","\n","df['Age'].describe()"]},{"cell_type":"markdown","metadata":{},"source":["Plot a histogram of the column `Age`.\n"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/plain":["(array([2.094e+03, 5.337e+03, 2.557e+03, 8.420e+02, 2.250e+02, 4.900e+01,\n","        6.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]),\n"," array([16. , 24.3, 32.6, 40.9, 49.2, 57.5, 65.8, 74.1, 82.4, 90.7, 99. ]),\n"," <BarContainer object of 10 artists>)"]},"execution_count":33,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# your code goes here\n","plt.hist(df['Age'])"]},{"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":52,"metadata":{},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":52,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# your code goes here\n","df.boxplot(column='Age')"]},{"cell_type":"markdown","metadata":{},"source":["Find out the Inter Quartile Range for the column `ConvertedComp`.\n"]},{"cell_type":"code","execution_count":83,"metadata":{},"outputs":[{"data":{"text/plain":["70842.5"]},"execution_count":83,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","import numpy as np\n","\n","df_cv = df['ConvertedComp'].dropna()\n","q75, q25 = np.percentile(df_cv, [75, 25])\n","iqr = q75 - q25\n","iqr"]},{"cell_type":"markdown","metadata":{},"source":["Find out the upper and lower bounds.\n"]},{"cell_type":"code","execution_count":86,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["211511.25\n","-71858.75\n"]}],"source":["# your code goes here\n","upper = q75+(iqr * 1.5)\n","lower = q25-(iqr * 1.5)\n","\n","print(upper)\n","print(lower)\n"]},{"cell_type":"markdown","metadata":{},"source":["Identify how many outliers are there in the `ConvertedComp` column.\n"]},{"cell_type":"code","execution_count":103,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["False    1008\n","Name: ConvertedComp, dtype: int64\n","False    937\n","True      71\n","Name: ConvertedComp, dtype: int64\n"]}],"source":["# your code goes here\n","lower_o = df['ConvertedComp'] < lower\n","upper_o = df['ConvertedComp'] > upper\n","\n","print(lower_o.value_counts())\n","print(upper_o.value_counts())\n"]},{"cell_type":"markdown","metadata":{},"source":["Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"]},{"cell_type":"code","execution_count":105,"metadata":{},"outputs":[{"data":{"text/plain":["6         41244.0\n","15       156000.0\n","17        87996.0\n","36       117540.0\n","47         2169.0\n","           ...   \n","11320     71085.0\n","11321     35518.0\n","11324     11004.0\n","11345     68705.0\n","11384     58024.0\n","Name: ConvertedComp, Length: 1008, dtype: float64"]},"execution_count":105,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df2 = df['ConvertedComp'].clip(upper, lower)\n","\n","df2"]},{"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":118,"metadata":{},"outputs":[{"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>Respondent</th>\n","      <th>CompTotal</th>\n","      <th>ConvertedComp</th>\n","      <th>WorkWeekHrs</th>\n","      <th>CodeRevHrs</th>\n","      <th>Age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Respondent</th>\n","      <td>1.000000</td>\n","      <td>-0.057366</td>\n","      <td>-0.065603</td>\n","      <td>-0.060420</td>\n","      <td>-0.038389</td>\n","      <td>0.027928</td>\n","    </tr>\n","    <tr>\n","      <th>CompTotal</th>\n","      <td>-0.057366</td>\n","      <td>1.000000</td>\n","      <td>-0.028490</td>\n","      <td>-0.006841</td>\n","      <td>-0.039165</td>\n","      <td>0.012503</td>\n","    </tr>\n","    <tr>\n","      <th>ConvertedComp</th>\n","      <td>-0.065603</td>\n","      <td>-0.028490</td>\n","      <td>1.000000</td>\n","      <td>0.102571</td>\n","      <td>-0.012538</td>\n","      <td>0.109655</td>\n","    </tr>\n","    <tr>\n","      <th>WorkWeekHrs</th>\n","      <td>-0.060420</td>\n","      <td>-0.006841</td>\n","      <td>0.102571</td>\n","      <td>1.000000</td>\n","      <td>0.082863</td>\n","      <td>0.040690</td>\n","    </tr>\n","    <tr>\n","      <th>CodeRevHrs</th>\n","      <td>-0.038389</td>\n","      <td>-0.039165</td>\n","      <td>-0.012538</td>\n","      <td>0.082863</td>\n","      <td>1.000000</td>\n","      <td>-0.007431</td>\n","    </tr>\n","    <tr>\n","      <th>Age</th>\n","      <td>0.027928</td>\n","      <td>0.012503</td>\n","      <td>0.109655</td>\n","      <td>0.040690</td>\n","      <td>-0.007431</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\n","Respondent       1.000000  -0.057366      -0.065603    -0.060420   -0.038389   \n","CompTotal       -0.057366   1.000000      -0.028490    -0.006841   -0.039165   \n","ConvertedComp   -0.065603  -0.028490       1.000000     0.102571   -0.012538   \n","WorkWeekHrs     -0.060420  -0.006841       0.102571     1.000000    0.082863   \n","CodeRevHrs      -0.038389  -0.039165      -0.012538     0.082863    1.000000   \n","Age              0.027928   0.012503       0.109655     0.040690   -0.007431   \n","\n","                    Age  \n","Respondent     0.027928  \n","CompTotal      0.012503  \n","ConvertedComp  0.109655  \n","WorkWeekHrs    0.040690  \n","CodeRevHrs    -0.007431  \n","Age            1.000000  "]},"execution_count":118,"metadata":{},"output_type":"execute_result"}],"source":["# your code goes here\n","df.corr()"]},{"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-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork21426264-2022-01-01&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":"3.7.6"}},"nbformat":4,"nbformat_minor":4}