{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization\n", "
\n", "\n", "> In good information visualization, there are no rules, no guidelines, no template, no standard technologies, no\n", "> stylebook... You must simply do whatever it takes\n", ">\n", "> ― Edward Tufte\n", "\n", "Data visualization is an essential tool for data science at every step of analysis, from data cleaning to EDA to communicating conclusions and predictions. Because human minds are highly developed for visual perception, a\n", "well-chosen plot can often reveal trends and anomalies in the data much more efficiently than a textual description.\n", "\n", "To effectively use data visualizations, you must be proficient with both the programming tools to generate plots and the principles of visualization. In this tutorial we will introduce `seaborn` and `matplotlib`, our tools of choice for creating plots. We will also learn how to spot misleading visualizations and how to improve visualizations.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing Quantitative Data\n", "
\n", "\n", "We generally use different types of charts to visualize quantitative (numerical) data and qualitative (ordinal or nominal) data.\n", "\n", "For quantitative data, we most often use histograms, box plots, and scatter plots.\n", "\n", "We can use the [seaborn plotting library](http://seaborn.pydata.org/) to create these plots in Python. We will use a dataset containing information about passengers aboard the Titanic." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Import seaborn and apply its plotting styles\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "sns.set()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(182, 15)\n" ] }, { "data": { "text/html": [ "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
011female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
111female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
201male54.00051.8625SFirstmanTrueESouthamptonnoTrue
313female4.01116.7000SThirdchildFalseGSouthamptonyesFalse
411female58.00026.5500SFirstwomanFalseCSouthamptonyesTrue
\n", "
" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 1 1 female 38.0 1 0 71.2833 C First \n", "1 1 1 female 35.0 1 0 53.1000 S First \n", "2 0 1 male 54.0 0 0 51.8625 S First \n", "3 1 3 female 4.0 1 1 16.7000 S Third \n", "4 1 1 female 58.0 0 0 26.5500 S First \n", "\n", " who adult_male deck embark_town alive alone \n", "0 woman False C Cherbourg yes False \n", "1 woman False C Southampton yes False \n", "2 man True E Southampton no True \n", "3 child False G Southampton yes False \n", "4 woman False C Southampton yes True " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the dataset and drop N/A values to make plot function calls simpler\n", "titanic = sns.load_dataset('titanic').dropna().reset_index(drop=True)\n", "\n", "print(titanic.shape)\n", "titanic.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the dataset contains one row for every passenger. Each row includes the age of the passenger and the amount the passenger paid for a ticket. Let's visualize the ages using a histogram. We can use seaborn's `distplot` function:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Adding a semi-colon at the end tells Jupyter not to output the\n", "# usual line\n", "sns.distplot(titanic['age']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, seaborn's `distplot` function will output a smoothed curve that roughly fits the distribution. We can also add a rugplot which marks each individual point on the x-axis:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(titanic['age'], rug=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the distribution itself. Adjusting the number of bins shows that there were a number of children on board." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(titanic['age'], kde=False, bins=30);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Box plots\n", "
\n", "\n", "Box plots are a convenient way to see where most of the data lie. Typically, we use the 25th and 75th percentiles of the data as the start and endpoints of the box and draw a line within the box for the 50th percentile (the median). We draw two \"whiskers\" that extend to show the the remaining data except outliers, which are marked as individual points outside the whiskers." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='fare', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We typically use the Inter-Quartile Range (IQR) to determine which points are considered outliers for the box plot. The IQR is the difference between the 75th percentile of the data and the 25th percentile." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "60.3" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lower, upper = np.percentile(titanic['fare'], [25, 75])\n", "iqr = upper - lower\n", "iqr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Values greater than 1.5 $\\times$ IQR above the 75th percentile and less than 1.5 $\\times$ IQR below the 25th percentile are considered outliers and we can see them marked indivdiually on the boxplot above:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(180.45, -60.749999999999986)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upper_cutoff = upper + 1.5 * iqr\n", "lower_cutoff = lower - 1.5 * iqr\n", "upper_cutoff, lower_cutoff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although histograms show the entire distribution at once, box plots are often easier to understand when we split the data by different categories. For example, we can make one box plot for each passenger type:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='fare', y='who', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The separate box plots are much easier to understand than the overlaid histogram below which plots the same data:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(titanic.loc[titanic['who'] == 'woman', 'fare'])\n", "sns.distplot(titanic.loc[titanic['who'] == 'man', 'fare'])\n", "sns.distplot(titanic.loc[titanic['who'] == 'child', 'fare']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Seaborn features\n", "
\n", "\n", "You may have noticed that the `boxplot` call to make separate box plots for the `who` column was simpler than the equivalent code to make an overlaid histogram. Although `sns.distplot` takes in an array or Series of data, most other seaborn functions allow you to pass in a DataFrame and specify which column to plot on the x and y axes. For example:\n", "\n", "```python\n", "# Plots the `fare` column of the `titanic` DF on the x-axis\n", "sns.boxplot(x='fare', data=titanic);\n", "```\n", "\n", "When the column is categorical (the `'who'` column contained `'woman'`, `'man'`, and `'child'`), seaborn will automatically split the data by category before plotting. This means we don't have to filter out each category ourselves like we did for `sns.distplot`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fare (numerical) on the x-axis,\n", "# who (nominal) on the y-axis\n", "sns.boxplot(x='fare', y='who', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatter Plots\n", "
\n", "Scatter plots are used to compare two quantitative variables. We can compare the `age` and `fare` columns of our Titanic dataset using a scatter plot." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='age', y='fare', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default seaborn will also fit a regression line to our scatterplot and bootstrap the scatterplot to create a 95% confidence interval around the regression line shown as the light blue shading around the line above. In this case, the regression line doesn't seem to fit the scatter plot very well so we can turn off the regression." ] }, { "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": [ "sns.lmplot(x='age', y='fare', data=ti, fit_reg=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can color the points using a categorical variable. Let's use the `who` column once more:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='age', y='fare', hue='who', data=ti, fit_reg=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this plot we can see that all passengers below the age of 18 or so were marked as `child`. There doesn't seem to be a noticable split between male and female passenger fares, although the two most expensive tickets were purchased by males." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing Qualitative Data\n", "
\n", "\n", "For qualitative or categorical data, we most often use bar charts and dot charts. We will show how to create these plots using `seaborn` and the Titanic survivors dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", "
" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 0 3 male 22.0 1 0 7.2500 S Third \n", "1 1 1 female 38.0 1 0 71.2833 C First \n", "2 1 3 female 26.0 0 0 7.9250 S Third \n", "3 1 1 female 35.0 1 0 53.1000 S First \n", "4 0 3 male 35.0 0 0 8.0500 S Third \n", "\n", " who adult_male deck embark_town alive alone \n", "0 man True NaN Southampton no False \n", "1 woman False C Cherbourg yes False \n", "2 woman False NaN Southampton yes True \n", "3 woman False C Southampton yes False \n", "4 man True NaN Southampton no True " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "sns.set()\n", "\n", "# Load the dataset\n", "titanic = sns.load_dataset('titanic').reset_index(drop=True)\n", "\n", "# This table is too large to fit onto a page so we'll output sliders to\n", "# pan through different sections.\n", "titanic.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Charts\n", "
\n", "\n", "In `seaborn`, there are two types of bar charts. The first type uses the `countplot` method to count up the number of times each category appears in a column." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Counts how many passengers survived and didn't survive and\n", "# draws bars with corresponding heights\n", "sns.countplot(x='alive', data=titanic);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='class', data=titanic);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# As with box plots, we can break down each category further using color\n", "sns.countplot(x='alive', hue='class', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `barplot` method, on the other hand, groups the DataFrame by a categorical column and plots the height of the bars according to the average of a numerical column within each group." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For each set of alive/not alive passengers, compute and plot the average age.\n", "sns.barplot(x='alive', y='age', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The height of each bar can be computed by grouping the original DataFrame and averaging the `age` column:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " age\n", "alive \n", "no 30.626179\n", "yes 28.343690" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic[['alive', 'age']].groupby('alive').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the `barplot` method will also compute a bootstrap 95% confidence interval for each averaged value, marked as the black lines in the bar chart above. The confidence intervals show that if the dataset contained a random sample of Titanic passengers, the difference between passenger age for those that survived and those that didn't is not statistically significant at the 5% significance level.\n", "\n", "These confidence intervals take long to generate when we have larger datasets so it is sometimes useful to turn them off:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='alive', y='age', data=titanic, ci=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dot Charts\n", "
\n", "\n", "Dot charts are similar to bar charts. Instead of plotting bars, dot charts mark a single point at the end of where a bar would go. We use the `pointplot` method to make dot charts in seaborn. Like the `barplot` method, the pointplot method also automatically groups the DataFrame and computes the average of a separate numerical variable, marking 95% confidence intervals as vertical lines centered on each point." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For each set of alive/not alive passengers, compute and plot the average age.\n", "sns.pointplot(x='alive', y='age', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dot charts are most useful when comparing changes across categories:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Shows the proportion of survivors for each passenger class\n", "sns.pointplot(x='class', y='survived', data=titanic);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Shows the proportion of survivors for each passenger class,\n", "# split by whether the passenger was an adult male\n", "sns.pointplot(x='class', y='survived', hue='adult_male', data=titanic);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customizing Plots using matplotlib\n", "
\n", "\n", "Although `seaborn` allows us to quickly create many types of plots, it does not give us fine-grained control over the chart. For example, we cannot use seaborn to modify a plot's title, change x or y-axis labels, or add annotations to a plot. Instead, we must use the matplotlib library that seaborn is based off of.\n", "\n", "`matplotlib` provides basic building blocks for creating plots in Python. Although it gives great control, it is also more verbose — recreating the seaborn plots from the previous sections in matplotlib would take many lines of code. In fact, we can think of seaborn as a set of useful shortcuts to create matplotlib plots. Although we prefer to prototype plots in seaborn, in order to customize plots for communicating our findings we will need to learn basic pieces of matplotlib.\n", "\n", "Before we look at our first simple example, we must activate matplotlib support in the notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "sns.set()\n", "\n", "# This line allows matplotlib plots to appear as images in the notebook\n", "# instead of in a separate window.\n", "%matplotlib inline\n", "\n", "# plt is a commonly used shortcut for matplotlib\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing Figures and Axes\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to create a plot in `matplotlib`, we create a *figure*, then add an *axes* to the figure. In matplotlib, an axes is a single chart, and figures can contain multiple axes in a tablular layout. An axes contains *marks*, the lines or patches drawn on the plot." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a figure\n", "figure = plt.figure()\n", "\n", "# Add an axes to the figure. The second and third arguments create a table\n", "# with 1 row and 1 column. The first argument places the axes in the first\n", "# cell of the table.\n", "axis = figure.add_subplot(1, 1, 1)\n", "\n", "# Create a line plot on the axes\n", "axis.plot([0, 1, 2, 3], [1, 3, 4, 3])\n", "\n", "# Show the plot. This will automatically get called in a Jupyter notebook\n", "# so we'll omit it in future cells\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To customize the plot, we can use other methods on the axes object, below we will create some datapoints to plot using the numpy library we learnt in preceding section:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure = plt.figure()\n", "axis = figure.add_subplot(1, 1, 1)\n", "\n", "values = np.arange(0, 10, 0.1)\n", "\n", "# Setting the label kwarg lets us generate a legend\n", "axis.plot(values, np.sin(values), label='sin(values)')\n", "axis.plot(values, np.cos(values), label='cos(values)')\n", "axis.legend()\n", "\n", "axis.set_title('Sinusoids')\n", "axis.set_xlabel('x')\n", "axis.set_ylabel('y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plt` module has analogous methods to an axes, so we can recreate one of the plots above using `plt` shorthands." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(0, 10, 0.1)\n", "\n", "plt.plot(x, np.sin(x), label='sin(x)')\n", "plt.plot(x, np.cos(x), label='cos(x)')\n", "plt.legend()\n", "\n", "# Shorthand for ax.set_title\n", "plt.title('Sinusoids')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "\n", "# Set the x and y-axis limits\n", "plt.xlim(-1, 11)\n", "plt.ylim(-1.2, 1.2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing Marks\n", "
\n", "\n", "To change properties of the plot marks themselves (e.g. the lines in the plot above), we can pass additional arguments into `plt.plot`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x), linestyle='--', color='purple');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checking the `matplotlib` documentation is the easiest way to figure out which arguments are available for each method. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Annotating our charts\n", "
\n", "\n", "In matplotlib, text can be added either relative to an individual axis object or to the whole figure.\n", "\n", "These commands add text to the Axes:\n", "\n", "- `set_title()` - add a title\n", "- `set_xlabel()` - add an axis label to the x-axis\n", "- `set_ylabel()` - add an axis label to the y-axis\n", "- `text()` - add text at an arbitrary location\n", "- `annotate()` - add an annotation, with optional arrow\n", "\n", "And these act on the whole figure:\n", "\n", "- `figtext()` - add text at an arbitrary location\n", "- `suptitle()` - add a title\n", "\n", "And any text field can contain [LaTeX expressions](https://www.authorea.com/users/77723/articles/110898-how-to-write-mathematical-equations-expressions-and-symbols-with-latex-a-cheatsheet) for mathematics, as long as they are enclosed in `$` signs.\n", "\n", "This example illustrates all of them:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n", "\n", "ax = fig.add_subplot(1, 1, 1)\n", "fig.subplots_adjust(top=0.85)\n", "ax.set_title('axes title')\n", "\n", "ax.set_xlabel('xlabel')\n", "ax.set_ylabel('ylabel')\n", "\n", "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n", " bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})\n", "\n", "ax.text(2, 6, 'an equation: $E=mc^2$', fontsize=15)\n", "\n", "ax.text(3, 2, 'unicode: Institut für Festkörperphysik')\n", "\n", "ax.text(0.95, 0.01, 'colored text in axes coords',\n", " verticalalignment='bottom', horizontalalignment='right',\n", " transform=ax.transAxes,\n", " color='green', fontsize=15)\n", "\n", "\n", "ax.plot([2], [1], 'o')\n", "ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),\n", " arrowprops=dict(facecolor='black', shrink=0.05))\n", "\n", "ax.axis([0, 10, 0, 10]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing a `seaborn` plot using `matplotlib`\n", "
\n", "\n", "Now that we've seen how to use `matplotlib` to customize a plot, we can use the same methods to customize seaborn plots since seaborn creates plots using matplotlib behind-the-scenes." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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411female58.00026.5500SFirstwomanFalseCSouthamptonyesTrue
\n", "
" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 1 1 female 38.0 1 0 71.2833 C First \n", "1 1 1 female 35.0 1 0 53.1000 S First \n", "2 0 1 male 54.0 0 0 51.8625 S First \n", "3 1 3 female 4.0 1 1 16.7000 S Third \n", "4 1 1 female 58.0 0 0 26.5500 S First \n", "\n", " who adult_male deck embark_town alive alone \n", "0 woman False C Cherbourg yes False \n", "1 woman False C Southampton yes False \n", "2 man True E Southampton no True \n", "3 child False G Southampton yes False \n", "4 woman False C Southampton yes True " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load seaborn\n", "import seaborn as sns\n", "sns.set()\n", "sns.set_context('talk')\n", "\n", "# Load dataset\n", "titanic = sns.load_dataset('titanic').dropna().reset_index(drop=True)\n", "titanic.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll start with this plot:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='age', y='fare', hue='who', data=titanic, fit_reg=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the plot needs a title and better labels for the x and y-axes. In addition, the two people with the most expensive fares survived, so we can annotate them on our plot." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='age', y='fare', hue='who', data=titanic, fit_reg=False)\n", "\n", "plt.title('Fare Paid vs. Age of Passenger, Colored by Passenger Type')\n", "plt.xlabel('Age of Passenger')\n", "plt.ylabel('Fare in USD')\n", "\n", "plt.annotate('Both survived', xy=(35, 500), xytext=(35, 420),\n", " arrowprops=dict(facecolor='black', shrink=0.05));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practice, we use `seaborn` to quickly explore the data and then turn to `matplotlib` for fine-tuning once we decide on the plots to use in our reports or presentation." ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }