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diff --git a/data/Employee_Bar_Graph.png b/data/Employee_Bar_Graph.png
deleted file mode 100644
index dc7b893..0000000
Binary files a/data/Employee_Bar_Graph.png and /dev/null differ
diff --git a/data/Employee_Pie_Chart.png b/data/Employee_Pie_Chart.png
deleted file mode 100644
index 5a66122..0000000
Binary files a/data/Employee_Pie_Chart.png and /dev/null differ
diff --git a/data/data_file.csv b/data/data_file.csv
deleted file mode 100644
index b9ec813..0000000
--- a/data/data_file.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-emp_id,emp_name,emp_address,proficiency
-1,John,Bangalore,"c,c++,python"
-2,Mary,Hyderabad,"java,react,python"
-3,Sherlock,Mumbai,"c++,java,python,django"
-4,Ross Geller,Bangalore,"c++,django"
-5,Monica Geller,Mumbai,"c++,python"
-6,Rachel Green,Mumbai,"mongo_db,java"
-7,Sherlock,Mumbai,"css,html,javascript,bootstrap"
-8,Sherlock,Mumbai,"c++,xml,bootstrap"
-9,Sherlock,Mumbai,"xml,django"
-10,Marie,Hyderabad,"react,html"
diff --git a/data/datafile.json b/data/datafile.json
deleted file mode 100644
index fc5ac32..0000000
--- a/data/datafile.json
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "employee_details":[
- {
- "emp_id":"1",
- "emp_name":"John",
- "emp_address":"Bangalore",
- "proficiency":["c","c++","python"]
- },
- {
- "emp_id":2,
- "emp_name":"Mary",
- "emp_address":"Hyderabad",
- "proficiency":["java","react","python"]
- },
- {
- "emp_id":"3",
- "emp_name":"Sherlock",
- "emp_address":"Mumbai",
- "proficiency":["c++","java","python","django"]
- },
- {
- "emp_id":"4",
- "emp_name":"Ross Geller",
- "emp_address":"Bangalore",
- "proficiency":["c++","django"]
- },
- {
- "emp_id":"5",
- "emp_name":"Monica Geller",
- "emp_address":"Mumbai",
- "proficiency":["c++","python"]
- },
- {
- "emp_id":"6",
- "emp_name":"Rachel Green",
- "emp_address":"Mumbai",
- "proficiency":["mongo_db","java"]
- },
- {
- "emp_id":"7",
- "emp_name":"Sherlock",
- "emp_address":"Mumbai",
- "proficiency":["css","html","javascript","bootstrap"]
- },
- {
- "emp_id":"8",
- "emp_name":"Sherlock",
- "emp_address":"Mumbai",
- "proficiency":["c++","xml","bootstrap"]
- },
- {
- "emp_id":"9",
- "emp_name":"Sherlock",
- "emp_address":"Mumbai",
- "proficiency":["xml","django"]
- },
- {
- "emp_id":"10",
- "emp_name":"Marie",
- "emp_address":"Hyderabad",
- "proficiency":["react","html"]
- }
- ]
-}
\ No newline at end of file
diff --git a/notebooks/json_to_csv.ipynb b/notebooks/json_to_csv.ipynb
deleted file mode 100644
index e47c83e..0000000
--- a/notebooks/json_to_csv.ipynb
+++ /dev/null
@@ -1,316 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Conversion from json to csv format using python and the analysis using charts"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step1: Importing the modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json \n",
- "import csv \n",
- "import os"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step2:Changing the directory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.chdir(\"../data\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step3:Accessing the json file "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'employee_details': [{'emp_id': '1', 'emp_name': 'John', 'emp_address': 'Bangalore', 'proficiency': ['c', 'c++', 'python']}, {'emp_id': 2, 'emp_name': 'Mary', 'emp_address': 'Hyderabad', 'proficiency': ['java', 'react', 'python']}, {'emp_id': '3', 'emp_name': 'Sherlock', 'emp_address': 'Mumbai', 'proficiency': ['c++', 'java', 'python', 'django']}, {'emp_id': '4', 'emp_name': 'Ross Geller', 'emp_address': 'Bangalore', 'proficiency': ['c++', 'django']}, {'emp_id': '5', 'emp_name': 'Monica Geller', 'emp_address': 'Mumbai', 'proficiency': ['c++', 'python']}, {'emp_id': '6', 'emp_name': 'Rachel Green', 'emp_address': 'Mumbai', 'proficiency': ['mongo_db', 'java']}, {'emp_id': '7', 'emp_name': 'Sherlock', 'emp_address': 'Mumbai', 'proficiency': ['css', 'html', 'javascript', 'bootstrap']}, {'emp_id': '8', 'emp_name': 'Sherlock', 'emp_address': 'Mumbai', 'proficiency': ['c++', 'xml', 'bootstrap']}, {'emp_id': '9', 'emp_name': 'Sherlock', 'emp_address': 'Mumbai', 'proficiency': ['xml', 'django']}, {'emp_id': '10', 'emp_name': 'Marie', 'emp_address': 'Hyderabad', 'proficiency': ['react', 'html']}]}\n"
- ]
- }
- ],
- "source": [
- "with open(\"datafile.json\") as json_file:\n",
- " data = json.load(json_file) \n",
- "\n",
- "print(data)\n",
- "employee_data = data[\"employee_details\"] "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step4:Opening a CSV file in write Mode"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_file = open(\"data_file.csv\", 'w') \n",
- "csv_writer = csv.writer(data_file) #csv writer object\n",
- " \n",
- "# Counter variable used for writing headers to the CSV file\n",
- "count = 0"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step5:Dictionary of tech stack analysis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "tech_stack={\n",
- "'c++':0,\n",
- "'c':0,\n",
- "'java':0,\n",
- "'react':0,\n",
- "'django':0,\n",
- "'python':0,\n",
- "'xml':0,\n",
- "'mongo_db':0,\n",
- "'html':0,\n",
- "'css':0,\n",
- "'javascript':0,\n",
- "'bootstrap':0\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Step6:Write into CSV and also analyse the data "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "for emp in employee_data: \n",
- " data=[]\n",
- " if count == 0: \n",
- " header = emp.keys() # Writing headers of CSV file \n",
- " csv_writer.writerow(header) \n",
- " \n",
- " count += 1 \n",
- " \n",
- " for val in emp.values():\n",
- " if(type(val)==list):\n",
- " s=','.join(val) #creating a comma seperated string for the tech_stack\n",
- " for v in val:\n",
- " tech_stack[v]+=1\n",
- " data.append(s)\n",
- " else:\n",
- " data.append(val)\n",
- " csv_writer.writerow(data) #writing rows into CSV file \n",
- " \n",
- "data_file.close() #closing the csv file"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Step6:No of Employees in total and tech stack preferences"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "10\n"
- ]
- }
- ],
- "source": [
- "Employee_count=count\n",
- "print(Employee_count)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of employees who know c++ 5\n",
- "Number of employees who know c 1\n",
- "Number of employees who know java 3\n",
- "Number of employees who know react 2\n",
- "Number of employees who know django 3\n",
- "Number of employees who know python 4\n",
- "Number of employees who know xml 2\n",
- "Number of employees who know mongo_db 1\n",
- "Number of employees who know html 2\n",
- "Number of employees who know css 1\n",
- "Number of employees who know javascript 1\n",
- "Number of employees who know bootstrap 2\n"
- ]
- }
- ],
- "source": [
- "for key in tech_stack.keys():\n",
- " print('Number of employees who know ',key,' ',tech_stack[key])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Figure size 800x400 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "fig = plt.figure(figsize=(8,4))\n",
- "ax = fig.add_axes([0,0,1,1])\n",
- "\n",
- "technology =tech_stack.keys()\n",
- "employees =tech_stack.values()\n",
- "ax.bar(technology ,employees)\n",
- "plt.title('Employee Tech stack Plot -Bar chart')\n",
- "plt.xlabel('Technology stack')\n",
- "plt.ylabel('No of Employees')\n",
- "plt.savefig('Employee_Bar_Graph.png',bbox_inches='tight')\n",
- "plt.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure()\n",
- "ax = fig.add_axes([0,0,1,1])\n",
- "ax.axis('equal')\n",
- "plt.title('Employee Tech stack Plot -Pie chart')\n",
- "ax.pie(employees, labels = technology ,autopct='%1.2f%%')\n",
- "plt.savefig('Employee_Pie_Chart.png',bbox_inches='tight')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of people to be given c++ training is 5\n",
- "Number of people to be given c training is 9\n",
- "Number of people to be given java training is 7\n",
- "Number of people to be given react training is 8\n",
- "Number of people to be given django training is 7\n",
- "Number of people to be given python training is 6\n",
- "Number of people to be given xml training is 8\n",
- "Number of people to be given mongo_db training is 9\n",
- "Number of people to be given html training is 8\n",
- "Number of people to be given css training is 9\n",
- "Number of people to be given javascript training is 9\n",
- "Number of people to be given bootstrap training is 8\n"
- ]
- }
- ],
- "source": [
- "for key in tech_stack.keys():\n",
- " print('Number of people to be given ',key,' training is ',Employee_count-tech_stack[key])"
- ]
- }
- ],
- "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": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}