{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Fundamentals: Iteration and Visualization\n", "# SOLUTIONS" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Recall that pandas is frequently imported with the alias pd\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
See Full Dataframe in Mito
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countryyearpopcontinentlifeExpgdpPercapgniPercap
0Afghanistan196210267083.0Asia31.997853.100710NaN
1Afghanistan196711537966.0Asia34.020836.197138NaN
2Afghanistan197213079460.0Asia36.088739.981106NaN
3Afghanistan197714880372.0Asia38.438786.113360NaN
4Afghanistan198212881816.0Asia39.854978.011439NaN
" ], "text/plain": [ " country year pop continent lifeExp gdpPercap gniPercap\n", "0 Afghanistan 1962 10267083.0 Asia 31.997 853.100710 NaN\n", "1 Afghanistan 1967 11537966.0 Asia 34.020 836.197138 NaN\n", "2 Afghanistan 1972 13079460.0 Asia 36.088 739.981106 NaN\n", "3 Afghanistan 1977 14880372.0 Asia 38.438 786.113360 NaN\n", "4 Afghanistan 1982 12881816.0 Asia 39.854 978.011439 NaN" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('../data/gapminder_gni.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 🥊 Challenge 1: Fixing Loop Syntax\n", "\n", "The following block of code contains **three errors** that are preventing it from running properly. What are the errors? How would you fix them?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "3\n", "5\n" ] } ], "source": [ "# Add a colon, add indentation, match loop variable\n", "for number in [2, 3, 5]:\n", " print(number)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 🥊 Challenge 2: Aggregation Practice\n", "\n", "Below are a few examples showing the different types of quantities you might aggregate using a for loop. These loops are partially filled out. Finish them and test that they work!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Find the total length of the strings in the given list. Store this quantity in a variable called `total`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n" ] } ], "source": [ "total = 0\n", "words = ['red', 'green', 'blue']\n", "\n", "for w in words:\n", " total = total + len(w)\n", "\n", "print(total)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Find the length of each word in the list, and store these lengths in another list called `lengths`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 5, 4]\n" ] } ], "source": [ "lengths = []\n", "words = ['red', 'green', 'blue']\n", "\n", "for w in words:\n", " lengths.append(len(w))\n", "\n", "print(lengths)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Concatenate all words into a single string called `result`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "redgreenblue\n" ] } ], "source": [ "words = ['red', 'green', 'blue']\n", "result = ''\n", "\n", "for w in words:\n", " result += w\n", "\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Create an acronym, as a single string, representing the list of words. Each part of the acronym should consist of the first letter of each word, capitalized. For example, your loop should output `'RGB'` for the input `['red', 'green', 'blue']`. For this one, write the entire loop yourself!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RGB\n" ] } ], "source": [ "words = ['red', 'green', 'blue']\n", "\n", "up = ''\n", "# YOUR CODE HERE\n", "for each in words:\n", " up += each[0].upper()\n", "\n", "print(up)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ANOTHER OPTION, USING LIST COMPREHENSION\n", "\n", "''.join([each[0].upper() for each in words])" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## 🥊 Challenge 3: Get Vectorized\n", "\n", "Say our `year` column contains wrong information and we need to add one year to each value. Use a vectorized operation to get this done." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1963\n", "1 1968\n", "2 1973\n", "3 1978\n", "4 1983\n", " ... \n", "1315 1988\n", "1316 1993\n", "1317 1998\n", "1318 2003\n", "1319 2008\n", "Name: year, Length: 1320, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR CODE HERE\n", "df['year'] + 1\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## 🥊 Challenge 4: Check the Data Type\n", "\n", "What is the data type of the output of `describe()`?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(df.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 🥊 Challenge 5: Loops and Plots\n", "\n", "Let's say you have a list of countries you want to compare life expectancy for using a single lineplot. We will create a function for this.\n", "\n", "We have set up the list and function for you. Your goal is to:\n", "1. Add three country names in the DataFrame to `country_list`.\n", "2. Add two parameters to the function; one for a DataFrame, and one for the list of countries.\n", "3. Within the function block, loop over the list of countries. \n", "4. Within the for-loop, create a subset of the DataFrame using a comparison operator that sets `country_data` to the subset of the country you are looping over in the list.\n", "5. In the `label=` parameter of `plt.plot()`, fill in the loop variable name.\n", "\n", "\n", "Run the cell when you're done: if you've succeeded, you should see a single line plot with life expectancy for all of the countries in `country_list`.\n", "\n", "💡 **Tip**: If you have time left, try to add labels and title to the plot using `plt.xlabel()`, `plt.ylabel()`, and `plt.title()`. See [this resource](https://www.w3schools.com/python/matplotlib_labels.asp) for more information!\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# YOUR CODE HERE\n", "\n", "country_list = ['Afghanistan', 'Canada', 'Zimbabwe']\n", "\n", "def plot_life_expectancy(df, countries):\n", " for country in countries:\n", " country_data = df[df['country'] == country]\n", " plt.plot(country_data['year'], country_data['lifeExp'], label=country)\n", " plt.xlabel('Years')\n", " plt.ylabel('Life Expectancy')\n", " plt.title('Life Expectancy by Country')\n", " plt.legend()\n", " plt.show()\n", "\n", "plot_life_expectancy(df, country_list)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 4 }