{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seaborn | Part-2: Controlling Aesthetics of Seaborn Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the biggest advantages of Seaborn over Matplotlib is that its default aesthetics are visually far more appealing. Undoubtedly Matplotlib is highly customizable, but sometimes it may get difficult to know exact settings to tweak in order to achieve an attractive plot. Whereas, Seaborn comes with a number of customized themes and a high-level interface for controlling the look of same matplotlib figures.\n", "\n", "Seaborn splits Matplotlib parameters into two independent groups: *First* group sets the aesthetic style of the plot; and *second* scales various elements of the figure to get easily incorporated into different contexts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn doesn’t take away any of Matplotlib credits, but rather adds some nice default aesthetics and built-in plots that complement and sometimes replace the complicated Matplotlib code professionals needed to write. *Facet plots* and *Regression plots* are an example of that. In this lecture I shall show how easy it is to build a *Regression plot* using Seaborn and then let us compare it by building something similar in Matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quick Note (for non-native English speakers): The term *Aesthetics* just refers to the appearance of the figure/plot." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Importing intrinsic libraries:\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, once we have imported necessary libraries, let us begin talking about aesthetics with those 2 special lines of code we had added in our previous lecture. Our first line of code was:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`%matplotlib` is one of the *magic function* of Jupyter Notebook that along with `inline` [sets the backend of matplotlib](http://ipython.readthedocs.io/en/stable/interactive/plotting.html) to the *inline* backend within Jupyter notebook. If we look into relevant documentation, it says:\n", "\n", ">IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. There are two kinds of magics, line-oriented and cell-oriented. Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Lines magics can return results and can be used in the right hand side of an assignment. Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.\n", "\n", ">With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document.<\n", "\n", "Furthermore, if we want interactivity as well, we can use `nbagg` backend with `%matplotlib` notebook (in IPython 3.x). Though we won't be getting into that extremely advanced level of Matplotlib specific presentation here, but if that interest you, please check [here](https://matplotlib.org/users/whats_new.html#the-nbagg-backend).\n", "\n", "Let us now move on to our second line of code:\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "sns.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn runs on top of Matplotlib and with that as a default comes a *White* background for each of our plot, that looks pretty dull. With this line of code, we switch our plot to Seaborn default using `.set()` function. Let us look into what it yields, using one of the built-in dataset:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tips = sns.load_dataset(\"tips\")\n", "\n", "sns.barplot(x = \"day\", y = \"total_bill\", data = tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quick Note: `sns.set()` needs to be explicitly called ONLY in Seaborn v0.8 and above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With both the lines of code explained, let us now look into what else can we do to vary appearance of our plot. Earlier we spoke about Seaborn segregating Matplotlib parameters into 2 independent groups: *Styling* and *Scaling* of figures.\n", "\n", "Let us begin by delving into *Styling* aspect, that is controlled by using `sns.axes_style()` and `sns.set_style()` functions." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"whitegrid\")\n", "sns.boxplot(x = \"day\", y = \"total_bill\", data = tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Few common background options include: `whitegrid` (as shown above), `dark` (for solid grey color), `white` (this is default) and `ticks`. Let us explore an example with `ticks` as background and then try to remove top and right axes spines:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"ticks\")\n", "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above plot we observe 2 changes:\n", "\n", "- There are no horizontal line/grid in the background, that we had in previous figure.\n", "- There are `ticks` on X and Y axes, representing the axis interval.\n", "\n", "We still have top and right axes spine so let's get rid of that now:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"ticks\")\n", "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we also want to despine left axis, we just need to alter our last line of code by adding a parameter: `(left=True)`, and it shall fetch:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"ticks\")\n", "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)\n", "sns.despine(left=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us try to temporarily visualize two types of background in a single plot. For this, we shall use `.axes_style()` as follows:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This function shall help us plot some offset sine waves:\n", "def sinplot(flip=1):\n", " x = np.linspace(0, 14, 100)\n", " for i in range(1, 7):\n", " plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)\n", "\n", "with sns.axes_style(\"darkgrid\"):\n", " plt.subplot(211)\n", " sinplot()\n", "plt.subplot(212)\n", "sinplot(-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now look into other independent group of Matplotlib parameters created by Seaborn, i.e. *Scaling* of plot elements; that is controlled using `.plotting_context()` and `.set_context()` functions.\n", "\n", "The four preset contexts, in order of relative size, are `paper`, `notebook`, `talk`, and `poster`. The `notebook` style is default, and was used in the plots above. At first let’s reset the default parameters by calling `sns.set()` & then play around with other contexts for scaling, starting with `paper`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "sns.set()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_context(\"paper\")\n", "sns.set_style(\"whitegrid\")\n", "\n", "sns.boxplot(x = \"day\", y = \"total_bill\", data = tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am pretty sure you must be thinking that this figure/plot in no ways is scaled as it looks similar to our previous plot outputs. So, I shall clarify that right away:\n", "\n", "Jupyter Notebook scales down large images in the notebook cell output. This is generally done because past a certain size, we get automatic figure scaling. For exploratory analysis, we prefer iterating quickly over a number of different analyses and it's more useful to have [facets](https://dictionary.cambridge.org/dictionary/english/facet) that are of similar size; than to have overall figures that are same size in a particular context.\n", "\n", "When we're in a situation where we need to have something that's exactly a certain size overall; ideally we:\n", "- know precisely what you want and\n", "- can afford to take off some time and work through the calculations.\n", "\n", "With all that being said, if we plot the same figure in an Editor like *Anaconda Spyder* or *JetBrains' PyCharm* or *IntelliJ*, we shall be able to visualize them in their original size. Hence what needs to be our take-away from scaling segment, is that an addition of a line of code can fetch the size of image as per our requirement and we may experiment accordingly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practical world, we also add a dictionary of parameters using `rc` to have a finer control over the aesthetics. Let me show you an example with the same _sinplot_ function we defined earlier:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(style='whitegrid', rc={\"grid.linewidth\": 1.5})\n", "sns.set_context(\"poster\", font_scale=2.5, rc={\"lines.linewidth\": 5.0})\n", "sinplot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Though our Notebook didn't display enlarged (scaled) plot, we may notice how in the backend (in memory) it has created the figure as per our instructions. We have *thick lines* now in our plot because I set `linewidth` to `5`, *font size* on axes have thickened because of `font_scale`. Generally we don't use anything more than that during data analysis although exceptional scenarios may demand few more parameters as per requirement. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">IMPORTANT NOTE: `%matplotlib inline` just needs to be called once in a kernel unless we refresh/restart the kernel. This is a function that works only in Jupyter Notebook. In other IDE, you will have to explicitly mention either `sns.plt.show()` or just `plt.show()` at the end of your code for plot/figure to be displayed.\n", "Also, style and context once set remains defined unless we explicitly call for a change. If never called in a kernel, Seaborn will work with it's defaults: Matplotlib 'white' background without grids, and 'notebook' scale size." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next lecture, we shall see the difference in coding between matplotlib and Seaborn to achieve a certain goal (Regression plot illustration). Till then, happy visualizing!" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }