{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#\n", "# Import packages needed for processing\n", "#\n", "import numpy as np # import the numpy...

Hi, could you explain the code with #comments& make corrections if anything is wrong..



{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#\n", "# Import packages needed for processing\n", "#\n", "import numpy as np # import the numpy package\n", "import csv # this package needed for processing csv file\n", "from collections import Counter # this is for dictionary construction with counting functionality\n", "import matplotlib.pyplot as plt # this is for plotting and other descriptive statistics\n", "import datetime # this package is for handling time\n", "#\n", "# If you need add any additional packages, then add them below this line\n", "#\n", "\n", "import pandas as pd\n", "import timeit\n", "import matplotlib as mpl\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: you must place your data file in the same folder with your python notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Declare the path to the data file\n", "DATA_FILE = \"owid-covid-data.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**. The following segment of codes is to load the data from the .csv file using csv package. It has been provided for you to get started. Please **do not** change this piece of code as well the variable names as we will need these variables to complete subsequent tasks. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases', 'new_cases_smoothed', 'total_deaths', 'new_deaths', 'new_deaths_smoothed', 'total_cases_per_million', 'new_cases_per_million', 'new_cases_smoothed_per_million', 'total_deaths_per_million', 'new_deaths_per_million', 'new_deaths_smoothed_per_million', 'reproduction_rate', 'icu_patients', 'icu_patients_per_million', 'hosp_patients', 'hosp_patients_per_million', 'weekly_icu_admissions', 'weekly_icu_admissions_per_million', 'weekly_hosp_admissions', 'weekly_hosp_admissions_per_million', 'new_tests', 'total_tests', 'total_tests_per_thousand', 'new_tests_per_thousand', 'new_tests_smoothed', 'new_tests_smoothed_per_thousand', 'positive_rate', 'tests_per_case', 'tests_units', 'total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'new_vaccinations_smoothed', 'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred', 'new_vaccinations_smoothed_per_million', 'stringency_index', 'population', 'population_density', 'median_age', 'aged_65_older', 'aged_70_older', 'gdp_per_capita', 'extreme_poverty', 'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers', 'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand', 'life_expectancy', 'human_development_index'])\n" ] } ], "source": [ "# Load CSV file using DictReader\n", "input_file = csv.DictReader(open(DATA_FILE))\n", "fieldnames = input_file.fieldnames\n", "data_dict = {fn: [] for fn in fieldnames}\n", "print(data_dict.keys())\n", "for line in input_file:\n", " for k, v in line.items():\n", " if (v == ''): #quick fix for missing values\n", " v=0\n", " try:\n", " data_dict[k].append(int(v))\n", " except ValueError:\n", " try:\n", " data_dict[k].append(float(v))\n", " except ValueError:\n", " data_dict[k].append(v)\n", " \n", "for k, v in data_dict.items():\n", " data_dict[k] = np.array(v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## **Instruction 1**\n", "We first examine the variables and the overall data size. The tasks:\n", "\n", "1. Write your code to print the type for the following variables (**4 marks**):\n", " * input_file\n", " * data_dict\n", " * data_dict['iso_code']\n", " * data_dict['reproduction_rate']\n", "\n", "\n", "2. Write your code to print out the number of data records (**1 mark**):\n", "\n", "[**Total mark: 5**]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n" ] } ], "source": [ "# INSERT YOUR CODE HERE\n", "#\n", "#1. Write you code to print the type for the following variables:\n", "# * input_file\n", "# * data_dict\n", "# * data_dict['iso_code']\n", "# * data_dict['reproduction_rate']\n", "#\n", "\n", "print(type(input_file))\n", "print(type(data_dict))\n", "print(type(data_dict['iso_code']))\n", "print(type(data_dict['reproduction_rate']))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79668\n" ] } ], "source": [ "# INSERT YOUR CODE HERE\n", "#\n", "# 2. Write your code to print out the number of data records\n", "#\n", "\n", "file = open(\"owid-covid-data.csv\")\n", "numline = len(file.readlines())\n", "print (numline)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Instruction 2\n", "The following code segment is designed to calculate some basic statistics from the data for a recent date. The data is stored in **data_filtered_by_date** dictionary.\n", "\n", "**Your task:**\n", "1. Learn how the code, which extracts the data for all countries at a required date, works. Why **try-except** construction is required in this code? Provide the answer. (**2 marks**)\n", "1. Write your code to find and print the minimum and the maximum values of mortality per million in the **total_deaths_per_million** column. (**1 mark**)\n", "2. Write your code to find and print the minimum and the maximum values of cases per million in the **total_cases_per_million** column. (**1 mark**)\n", "3. Write your code to find and print the mean and median mortality per million, and the standard deviation from the **total_deaths_per_million** column. (**2 marks**)\n", "4. Write your code to construct a box plot for the **total_deaths_per_million**. (**2 marks**) \n", "4. You will find that mean and median values for mortality are quite different. Briefly describe the meaning of the difference between the mean and median mortality per million. Why are they different? (**2 marks**)\n", "\n", "**[Total mark: 10]**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# THIS PART EXTRACTS THE DATA FOR ALL COUNTRIES AT A REQUIRED DATE.\n", "# LEARN HOW IT WORKS.\n", "# data_filtered_by_date DICTIONARY CONTAINS ALL DATA FOR ALL COUNTRIES AT required_date\n", "\n", "required_date = '2021-03-09'\n", "\n", "index_for_date = (data_dict['date'] == required_date)\n", "data_filtered_by_date = {}\n", "for key in data_dict.keys():\n", " try:\n", " data_filtered_by_date[key] = np.float_(data_dict[key][index_for_date])\n", " except:\n", " data_filtered_by_date[key] = data_dict[key][index_for_date]" ] }, {