{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![title](header.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn is a module that builds off matplotlib's functionality to let us create great looking graphs easily, usually within one function call. In a data science context, it's used to make very quick and rough judgements by eye, before going on to test them using statistical methods. By the end of this guide you should have a strong grip on how to use seaborn, and be comfortable with representing data of different types using seaborn.\n", "\n", "As always, we start by importing our modules for this guide:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "%matplotlib inline \n", "#For notebooks, lets us show plots straight away without using plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of using numbers without context, seaborn comes with some datasets that we can use for practice. Let's load the tips dataset using the load_dataset method:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tips = sns.load_dataset('tips')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n" ] } ], "source": [ "print(tips.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great - this dataset is a list of tips and other associated data from a restaurant - let's get started learning how to plot different types of data:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Categorical data, such as days of the week, or sex, can be represented using an number of plots in seaborn. The simplest is the countplot function, which will return a barplot of bars with height corresponding to how often each x value occurs in the dataset. For example, if we want to count how many men and women are in our dataset, we can use:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='sex',data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The syntax of the function is different from what we've worked with before with matplotlib, but it's consistent throughout the seaborn library. Each function has numerous optional variables, so we have to explicitly state which is which by saying \"x=sex\" and \"data = tips\". Notice how we use the entire dataset of tips as an arguement, then select the column from within the function, rather than indexing the column using pandas first.\n", "\n", "If we want to find out the relationship between a categorical variable like sex, and a continuous variable like the total bill, we can use a number of different methods.\n", "\n", "The first is the bar plot, which shows one metric very clearly. By default, this metric is set to the mean (or average), but we can change it by passing another arguement. It also gives us a confidence interval for the statistic in question by showing a black line at the top. Let's start by graphing the sex versus the mean total bill:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='sex',y='total_bill',data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change what statistic we use for the height of the bar,s we just pass another arguement - estimator. Here we are using the standard deviation function from numpy to graph how spread out the tips are from the mean." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Iz2wHbArc11oeALYCsomCNe4sN7wTEWcCO7d69lDte5u1Xi8b+3+FKuwBhoB1gP8GTouI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\nUkEMfUkqiKEvSQUx9CWpIP8L4T+roNHwpRkAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"estimator\" arguement can be any aggregate function, that is, any function that takes a number of arguements and returns one, like the mean or the variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get rid of the error lines on the bar plots, we can pass the argument - ci as False:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='sex',y='total_bill',data=tips, ci=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bar plots are the simplest, and most striking visualisation of data, but leave out some important information. From these two graphs we know that men tip more on average, and have a wider spread, but we can't really draw any more conclusions at a glance. Thankfully, there are some more functions we can run for different graphs that show more information, starting with the boxplot function:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gm186yyusZzXdczxk6Eul6IY+aOiffmh1jn36klQjhr4k1YihL0k10vN9+o3Dk5VOrdxo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\nMfQlqUYMfUmqEUNfkmrE0JekGvl/TYUFpQX7p4wAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also split this data by gender using the \"hue\" arguement:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips, hue=\"sex\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot shows use even more information - the quartiles as well as any outliers we have, and gives us a good general idea of how our data is distributed. This of course comes at a price of clarity - it takes more time to see the important information. If we want even more detail on the distribution of our data we can use a violin plot, which shows the exact distribution of the dataset in a nice way, along with quartiles, means and interquartile ranges:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TNIdiptOFUxBYtuwdIpGIJWsUmzVrViIgUF8g0QdorL04tfaqgq05FLboj8Ce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\nGKETa3HIMl/+8jf7OzZazUc/ei2zZs0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HYViw4EamTZtOtLUv7R788d4IkcM+xo1rumBq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\nCn0RkQT5L27xKRyUu8N8AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.violinplot(x=\"day\", y=\"total_bill\", data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly to the box plot, we can split this on gender, and for comparison, we can present it neatly as a split violin plot:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ki66WOthk7IZ3iz7eTEvAeWP7LRs0DW+9adjxgrEWV4bT/9v31TaybN2XaR8n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\nc0CnCPoApRf/EHdJF8OO57Q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.violinplot(x=\"day\", y=\"total_bill\", data=tips,hue='sex',split=True,palette='Set1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's up to you to decide what plot is the right plot to use based on context: it's pointless using a violin plot when arguing that one mean is larger than another, and it's pointless using a barplot when discussing the distributions of two variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far we've seen plots using categorical data, but more often than not, both of the variables we want to look at will be continuous - like time, or distance. For this, we are interested in distribution plots. The simplest of these is the distplot function, which gives us a histogram, as well as a kernel density estimation (the blue line) of one continuous variable:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\stmba\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(tips['total_bill']) #You might get an error here, but you can ignore it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of the time we will want to look at the relationship between two sets of continuous data, for example, the total bill and the tips associated with them. Seaborn comes with a neat method of plotting these through the joinplot function - that takes a \"kind\" arguement for the kind of plot we want. This can be any of the following:\n", "* “scatter” \n", "* “reg” \n", "* “resid” \n", "* “kde” \n", "* “hex”\n", "\n", "Here are graphs of the total bill versus the tip size with a few of these types of points:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7AUREeojF41i7YR+293gxNBJGa5MDC7s8WLmiExYzr9n1xMBERDVp7YZ9WLelN/394Eg4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\nwERERIbCwERERIby/wO8yOH/vSWgKwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x='total_bill',y='tip',data=tips,kind='scatter')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tIJB3YjsMc+Umsq1Zp9zDJg9tnbb62RuE74a1FfQ+72+ZtIJ0WkG1ZqAR0C4+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\nJX7lht/lp3Z/kLRP2i6sSnmVZ578Loef/T433vQx8vmCb9o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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\stmba\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gFogCf+pyuc6kcgxCCJEokahKnydIJMkzGpkxJdfbAYPL5doG/DXw7RQ/vhBC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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the categorical data, you should try and use the plot that is appropriate for the point you are making - if you are trying to show the relationship between two variables, the \"reg\" type might be the best - but if you are trying to show where clusters of data lie - you might be better off with the \"hex\" type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It should be noted if we want to quickly get insight into the relationships between our continuous (and discrete) data, we can use the pairplot function, which plots every column versus every other column in a grid:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ulwzbT2RNRI/28nbnSHSzpvdujbLHaWf/XhNvdxZG0mwmq0Amm2J4MdkDVVX9QlpnFmKH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\n4WBQIBiNa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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(data=tips)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Palettes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn also comes with a plethora of ways to make our data look nice, the easiest being the \"palette\" arguement - using this for our boxplots from earlier we get:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HDbB3fD8AywarC91lQwMdczxk6Eul6IQ+aOidfmi1j336klQjhr4k1YihL0k10vV9+osO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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips, palette=\"rainbow\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GT05WfuesyTNTAAytqW4k0vjJSTYOO59+pzD0pRJ0Qh80wNTJ0wAMra8udDcOr+mY4yFD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\n+pJUI4a+JNXI/wNwofmx4hD/nQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips, palette=\"coolwarm\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More information on this can be found in the documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Worked Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"titanic\" dataset contains information about the passengers on the titanic, including if they survived " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "attn = sns.load_dataset('titanic')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 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 \n" ] } ], "source": [ "print(attn.head())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's look at the distribution of genders:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Acsycz7w98B1Vf4y45U9g/HvmfLfXcD2zOyLiB5gMvAq8FcR8TJwPDCp5rjTgBMprtQA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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "\n", "%matplotlib inline \n", "\n", "sns.countplot(x='sex',data=attn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So about two thirds of passengers were men - what if we break this down by class?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B8sN/ZUQsBPYEHursxEOGDKJfv76VFS6pd2pqaqx3CXVTWRhk5o8jYkSHVfOBazLz4YiY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\nJOD/A413pcjCV1SUAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='class',data=attn, hue='sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that this imbalance of gender comes mainly from third class.\n", "\n", "What's the age distribution of the different classes, split by genders?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='class',y='age',data=attn, hue='sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So it seems that older people are more likely to be in first class - interesting!\n", "\n", "What happens if we graph age versus fare?" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KVvlZvXRuRs8thxOSl2zmBxXzXKKLzz6R9afPZ2ZDNRU+mNlQzfrT53Px2Sd6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\ntHd92rb9c2If4ASfbduJOle9QOM0xdFn23avZVn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x='age',y='fare',data=attn,kind='scatter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Later we will look at regression in more detail, but \"pearsonr\" is a measure of how strongly the data is correlated - another way of checking this is with corr(), or correlation method, on the dataframe we want to use. Here we run a list of columns as an index (hence the double square brackets) to avoid getting all the other correlations. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agefare
age1.0000000.096067
fare0.0960671.000000
\n", "
" ], "text/plain": [ " age fare\n", "age 1.000000 0.096067\n", "fare 0.096067 1.000000" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "attn[['age', 'fare']].corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like there isn't much correlation between the age of a passenger and the fare they paid.\n", "\n", "Let's take a look at who survived, starting with a simple count plot:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='class',data=attn, hue='survived')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So it looks like the majority of deaths came from third class. If we want to split this by gender, it's helpful to create a new dataframe with only those who survived in, and perform a countplot on that:\n", "\n", "What's going on here is that attn['survived'] == 1 returns a list of True and False values for each row, and then we only filter by those rows which are true:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " survived pclass sex age sibsp parch fare embarked class \\\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", "8 1 3 female 27.0 0 2 11.1333 S Third \n", "9 1 2 female 14.0 1 0 30.0708 C Second \n", "\n", " who adult_male deck embark_town alive alone \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", "8 woman False NaN Southampton yes False \n", "9 child False NaN Cherbourg yes False \n" ] } ], "source": [ "attnGender = attn[attn['survived'] == 1]\n", "\n", "print(attnGender.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the makeup out of those who survived:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='class',data=attnGender, hue='sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a load of different directions we can go with this - as well as using analytical techniques to improve our process. For now, it's about asking and answering the right questions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mini Project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load in the titanic dataset, and do the following:\n", "\n", "* Create a countplot of the towns the passengers embarked from.\n", "* Using the hue arguement, split this into gender.\n", "* Find the distribution of ages using distplot\n", "* Create a boxplot of age versus the town the passenger embarked from." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources and Further Reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Seaborn Documentation https://seaborn.pydata.org/\n", "* Pandas Documentation http://pandas.pydata.org/pandas-docs/stable/\n", "* Matplotlib Documentation https://matplotlib.org/contents.html\n", "* List of datasets that come with Seaborn https://github.com/mwaskom/seaborn-data " ] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }