{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Exercises, Part 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wealth and Democracy\n", "\n", "Let's now pivot from working with example data to real data. Load the World Development Indicator data you worked with over the summer. This is country-level data that includes information on both countries' GDP per capita (a measure of wealth) and the Polity IV scores (a measure of how democratic a country is -- countries with higher scores are liberal democracies, countries with low scores are autocratic.). Use the code below to download the data. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "wdi = pd.read_csv(\n", " \"https://raw.githubusercontent.com/nickeubank/\"\n", " \"practicaldatascience/master/Example_Data/world-small.csv\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your data should look like this: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryregiongdppcap08polityIV
0AlbaniaC&E Europe771517.8
1AlgeriaAfrica803310.0
2AngolaAfrica58998.0
3ArgentinaS. America1433318.0
4ArmeniaC&E Europe607015.0
\n", "
" ], "text/plain": [ " country region gdppcap08 polityIV\n", "0 Albania C&E Europe 7715 17.8\n", "1 Algeria Africa 8033 10.0\n", "2 Angola Africa 5899 8.0\n", "3 Argentina S. America 14333 18.0\n", "4 Armenia C&E Europe 6070 15.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wdi.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1\n", "\n", "Let's being analyzing this data by estimating a simple linear model (\"ordinary least squares\") of the relationship between GDP per capita (`gdppcap08`) and democracy scores (`polityIV`). We will do so using the `statsmodel` package, which we'll discuss in detail later is this course. For the momement, just use this code:\n", "\n", "```python\n", "import statsmodels.formula.api as smf\n", "results = smf.ols('polityIV ~ gdppcap08',\n", " data=wdi).fit()\n", "print(results.summary())\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2\n", "\n", "Based on the results of this analysis, what would you conclude about about the relationship between `gdppcap08` and `polityIV`? \n", "\n", "(If you aren't familiar with Linear Models and aren't sure how to interprete this, you can also just look at the simple correlation between these two variables using `wdi[['polityIV', 'gdppcap08']].corr()`.)\n", "\n", "Write down your conclusions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 3\n", "\n", "Now let's plot the relationship you just estimated statistically. First, use seaborn to create a scatter plot of `polityIV` and `gdppcap08`. Include a title and label your axes (with formatted words, not variable names)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4\n", "\n", "Now add a linear regression (*not* a higher order polynomial, just linear) fit to the scatter plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5\n", "\n", "Does it seem like the linear model you estimated fits the data well?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 6\n", "\n", "Linear models impose a very strict functional form on the model they use: they try to draw a straight line through the data, no matter what. \n", "\n", "Can you think of a transform for your data that would make the data a little more sane?\n", "\n", "Apply the transformation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 7\n", "\n", "Once you've applied that transformation, let's re-fit our model. \n", "\n", "Rather than imposing linearity this time, however, let's fit a model with a flexible functional form. Using the recipe for a [lowess regression you can find here](https://nickeubank.github.io/practicaldatascience_book/notebooks/class_5/week_2/20_plotting_with_seaborn.html#a-little-bonus), see how well a lowess regression fits your updated data. This is a form of local polynomial regression that is designed to be flexible in how it fits the data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 8\n", "\n", "This does seem to fit the data better, but there seem to be quite a few outliers in the bottom right. Who is that? Add text labels to the points on your graph with country names. Make sure the size of your text labels leaves them legible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 9\n", "\n", "Interesting. It seems that there's are a lot of rich, undemocratic countries that all have something in common: they're oil-rich, small, Middle Eastern countries.\n", "\n", "Let's see what happens if we exclude the ten countries with the highest per-capita oil production from our data: Qatar, Kuwait, Equatorial Guinea, United Arab Emirates, Norway, Saudi Arabia, Libya, Oman, Gabon, and Angola. (Note this was in 2007, and excludes very small countries!) \n", "\n", "What does the relationship between Polity and GDP per capita look like for non-natural resource producers?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 10\n", "\n", "Let's make sure that you accurately identified all 10 of the oil producers. Write a line of code to count up how many big producers you have identified. If you do not get 10, can you figure out what you did wrong?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 11\n", "\n", "How does the relationship between GDP per capita and Polity look for the oil producers we dropped above?\n", "\n", "(note a Lowess line may not plot if you don't have enough data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 12\n", "\n", "Look back to your answer for Exercise 2. Do you still believe the result of your linear model? What did you learn from plotting. Write down your answers with your partner. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 13\n", "\n", "Finally, let's make a plot that color codes countries by whether they are big oil producers. Include separate linear regression fits for both groups." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Take-aways\n", "\n", "One of our main jobs as data scientists is to *summarize* data. In fact, its such an obvious part of our jobs we often don't think about it very much. In reality, however, this is one of the most difficult things we do. \n", "\n", "Summarization means taking rich, complex data and trying to tell readers about what is going on in that data using simple statistics. In the process of summarization, therefore, we must necessarily throw away much of the richness of the original data. When done well, this simplification makes data easier to understand, but only if we throw away the *right* data. You can *always* calulate the average value of a variable, or fit a linear model, but whether doing so generates a summary statistic that properly represents the essence of the data being studied depends on the data itself. \n", "\n", "Plotting is one fo the best tools we have as data scientists for evaluating whether we are throwing away the *right* data. As we learned from Part 1 of this exercise, just looking at means and standard deviations can mask tremendous variation. Each of our example datasets looked the same when we examined our summary statistics, but they were all radically different when plotted. \n", "\n", "Similarly, a simple linear model would \"tell\" us that if GDP per capita increases by \\$10,000, we would expect Polity scores to increase by about 1 (i.e. the coefficent on the linear model was 9.602e-05). But when we plot the data, not only can we that the data is definitely *not* linear (and so that slope doesn't really mean anything), but we can also see that oil producing countries seem to defy the overall trend, and so should maybe be studied separately. \n", "\n", "Moreover, we can see that if we just look at oil producers, there is no clear story: some are rich and democratic, while others are rich and autocratic (indeed, [this observation is the foundation of some great research on the political consequences of resource wealth](https://www.jstor.org/stable/41480824)!)\n", "\n", "So remember this: tools for summarizing data will always give you an answer, but it's up to you as a data scientist to make sure that the summaries you pass on to other people properly represent the data you're using. And there is perhaps no better way to do this than with plotting!\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overlaying Data Series with matplotlib\n", "\n", "In our last plotting exercises, you were asked to make a paired plot in which different data series were plotted next to one another with a shared x-axis. Presumably that resulted in a figure that looked something like this:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 3, "metadata": { "image/png": { "height": 378.25, "width": 509.15 } }, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "pd.set_option(\"mode.copy_on_write\", True)\n", "\n", "import seaborn.objects as so\n", "from matplotlib import style\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", "\n", "wdi = pd.read_csv(\n", " \"https://raw.githubusercontent.com/nickeubank/\"\n", " \"practicaldatascience/master/Example_Data/wdi_plotting.csv\"\n", ")\n", "\n", "india = wdi[wdi[\"Country Name\"] == \"India\"]\n", "\n", "india = india.rename(\n", " columns={\n", " \"CO2 emissions (metric tons per capita)\": \"Metric Tons Per Cap CO2\",\n", " \"GDP per capita (constant 2010 US$)\": \"GDP per capita (US$)\",\n", " }\n", ")\n", "p = (\n", " so.Plot(\n", " india,\n", " x=\"Year\",\n", " )\n", " .add(so.Dots())\n", " .pair(\n", " y=[\n", " \"Metric Tons Per Cap CO2\",\n", " \"GDP per capita (US$)\",\n", " ]\n", " )\n", ")\n", "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often times, however, it's more interesting to directly overlay data series on the same plot to make a figure like this:\n", "\n", "![two series sample plot](images/two_series_overlay.png)\n", "\n", "So let's do that here!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 14\n", "\n", "Making this work will require two new tricks: \n", "\n", "- using the `.twinx()` method from matplotlib, and \n", "- suing the `.on()` method from seaborn.objects.\n", "\n", "How? Great question! I'm going to leave it to you to figure that out using the documentation for these methods. But here's a start — you can find the `.on()` [method for seaborn.objects here](https://seaborn.pydata.org/generated/seaborn.objects.Plot.on.html), and the `.twinx()` for matplotlib [method demonstrated here](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/two_scales.html#sphx-glr-gallery-subplots-axes-and-figures-two-scales-py)\n", "\n", "Oh, and you may note use these two variables as your two. :) \n", "\n", "Good luck! \n", "\n", "Also, if you want to, feel free to add any extra bells and whistles as part of your exploration (like a legend, or colored y-axis labels)." ] } ], "metadata": { "interpreter": { "hash": "f06fa9c80cc08d4d343f66ad24a278ad0285590eac640a80c32c9d748f33a802" }, "kernelspec": { "display_name": "Python 3.9.7 64-bit ('base': conda)", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 4 }