Category: seaborn

Seaborn – Visualizing Pairwise Relationship ”; Previous Next Datasets under real-time study contain many variables. In such cases, the relation between each and every variable should be analyzed. Plotting Bivariate Distribution for (n,2) combinations will be a very complex and time taking process. To plot multiple pairwise bivariate distributions in a dataset, you can use the pairplot() function. This shows the relationship for (n,2) combination of variable in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots. Axes In this section, we will learn what are Axes, their usage, parameters, and so on. Usage seaborn.pairplot(data,…) Parameters Following table lists down the parameters for Axes − Sr.No. Parameter & Description 1 data Dataframe 2 hue Variable in data to map plot aspects to different colors. 3 palette Set of colors for mapping the hue variable 4 kind Kind of plot for the non-identity relationships. {‘scatter’, ‘reg’} 5 diag_kind Kind of plot for the diagonal subplots. {‘hist’, ‘kde’} Except data, all other parameters are optional. There are few other parameters which pairplot can accept. The above mentioned are often used params. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.set_style(“ticks”) sb.pairplot(df,hue = ”species”,diag_kind = “kde”,kind = “scatter”,palette = “husl”) plt.show() Output We can observe the variations in each plot. The plots are in matrix format where the row name represents x axis and column name represents the y axis. The diagonal plots are kernel density plots where the other plots are scatter plots as mentioned. Print Page Previous Next Advertisements ”;

Seaborn – Linear Relationships ”; Previous Next Most of the times, we use datasets that contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. This can be done through the regression lines. While building the regression models, we often check for multicollinearity, where we had to see the correlation between all the combinations of continuous variables and will take necessary action to remove multicollinearity if exists. In such cases, the following techniques helps. Functions to Draw Linear Regression Models There are two main functions in Seaborn to visualize a linear relationship determined through regression. These functions are regplot() and lmplot(). regplot vs lmplot regplot lmplot accepts the x and y variables in a variety of formats including simple numpy arrays, pandas Series objects, or as references to variables in a pandas DataFrame has data as a required parameter and the x and y variables must be specified as strings. This data format is called “long-form” data Let us now draw the plots. Example Plotting the regplot and then lmplot with the same data in this example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”tips”) sb.regplot(x = “total_bill”, y = “tip”, data = df) sb.lmplot(x = “total_bill”, y = “tip”, data = df) plt.show() Output You can see the difference in the size between two plots. We can also fit a linear regression when one of the variables takes discrete values Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”tips”) sb.lmplot(x = “size”, y = “tip”, data = df) plt.show() Output Fitting Different Kinds of Models The simple linear regression model used above is very simple to fit, but in most of the cases, the data is non-linear and the above methods cannot generalize the regression line. Let us use Anscombe’s dataset with the regression plots − Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”anscombe”) sb.lmplot(x=”x”, y=”y”, data=df.query(“dataset == ”I””)) plt.show() In this case, the data is good fit for linear regression model with less variance. Let us see another example where the data takes high deviation which shows the line of best fit is not good. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”anscombe”) sb.lmplot(x = “x”, y = “y”, data = df.query(“dataset == ”II””)) plt.show() Output The plot shows the high deviation of data points from the regression line. Such non-linear, higher order can be visualized using the lmplot() and regplot().These can fit a polynomial regression model to explore simple kinds of nonlinear trends in the dataset − Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”anscombe”) sb.lmplot(x = “x”, y = “y”, data = df.query(“dataset == ”II””),order = 2) plt.show() Output Print Page Previous Next Advertisements ”;

Seaborn – Pair Grid ”; Previous Next PairGrid allows us to draw a grid of subplots using the same plot type to visualize data. Unlike FacetGrid, it uses different pair of variable for each subplot. It forms a matrix of sub-plots. It is also sometimes called as “scatterplot matrix”. The usage of pairgrid is similar to facetgrid. First initialise the grid and then pass the plotting function. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) g = sb.PairGrid(df) g.map(plt.scatter); plt.show() It is also possible to plot a different function on the diagonal to show the univariate distribution of the variable in each column. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) g = sb.PairGrid(df) g.map_diag(plt.hist) g.map_offdiag(plt.scatter); plt.show() Output We can customize the color of these plots using another categorical variable. For example, the iris dataset has four measurements for each of three different species of iris flowers so you can see how they differ. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) g = sb.PairGrid(df) g.map_diag(plt.hist) g.map_offdiag(plt.scatter); plt.show() Output We can use a different function in the upper and lower triangles to see different aspects of the relationship. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) g = sb.PairGrid(df) g.map_upper(plt.scatter) g.map_lower(sb.kdeplot, cmap = “Blues_d”) g.map_diag(sb.kdeplot, lw = 3, legend = False); plt.show() Output Print Page Previous Next Advertisements ”;

Seaborn – Importing Datasets and Libraries ”; Previous Next In this chapter, we will discuss how to import Datasets and Libraries. Let us begin by understanding how to import libraries. Importing Libraries Let us start by importing Pandas, which is a great library for managing relational (table-format) datasets. Seaborn comes handy when dealing with DataFrames, which is most widely used data structure for data analysis. The following command will help you import Pandas − # Pandas for managing datasets import pandas as pd Now, let us import the Matplotlib library, which helps us customize our plots. # Matplotlib for additional customization from matplotlib import pyplot as plt We will import the Seaborn library with the following command − # Seaborn for plotting and styling import seaborn as sb Importing Datasets We have imported the required libraries. In this section, we will understand how to import the required datasets. Seaborn comes with a few important datasets in the library. When Seaborn is installed, the datasets download automatically. You can use any of these datasets for your learning. With the help of the following function you can load the required dataset load_dataset() Importing Data as Pandas DataFrame In this section, we will import a dataset. This dataset loads as Pandas DataFrame by default. If there is any function in the Pandas DataFrame, it works on this DataFrame. The following line of code will help you import the dataset − # Seaborn for plotting and styling import seaborn as sb df = sb.load_dataset(”tips”) print df.head() The above line of code will generate the following output − total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 To view all the available data sets in the Seaborn library, you can use the following command with the get_dataset_names() function as shown below − import seaborn as sb print sb.get_dataset_names() The above line of code will return the list of datasets available as the following output [u”anscombe”, u”attention”, u”brain_networks”, u”car_crashes”, u”dots”, u”exercise”, u”flights”, u”fmri”, u”gammas”, u”iris”, u”planets”, u”tips”, u”titanic”] DataFrames store data in the form of rectangular grids by which the data can be over viewed easily. Each row of the rectangular grid contains values of an instance, and each column of the grid is a vector which holds data for a specific variable. This means that rows of a DataFrame do not need to contain, values of same data type, they can be numeric, character, logical, etc. DataFrames for Python come with the Pandas library, and they are defined as two-dimensional labeled data structures with potentially different types of columns. For more details on DataFrames, visit our tutorial on pandas. Print Page Previous Next Advertisements ”;

Seaborn – Figure Aesthetic ”; Previous Next Visualizing data is one step and further making the visualized data more pleasing is another step. Visualization plays a vital role in communicating quantitative insights to an audience to catch their attention. Aesthetics means a set of principles concerned with the nature and appreciation of beauty, especially in art. Visualization is an art of representing data in effective and easiest possible way. Matplotlib library highly supports customization, but knowing what settings to tweak to achieve an attractive and anticipated plot is what one should be aware of to make use of it. Unlike Matplotlib, Seaborn comes packed with customized themes and a high-level interface for customizing and controlling the look of Matplotlib figures. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip = 1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) sinplot() plt.show() This is how a plot looks with the defaults Matplotlib − To change the same plot to Seaborn defaults, use the set() function − Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip = 1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set() sinplot() plt.show() Output The above two figures show the difference in the default Matplotlib and Seaborn plots. The representation of data is same, but the representation style varies in both. Basically, Seaborn splits the Matplotlib parameters into two groups− Plot styles Plot scale Seaborn Figure Styles The interface for manipulating the styles is set_style(). Using this function you can set the theme of the plot. As per the latest updated version, below are the five themes available. Darkgrid Whitegrid Dark White Ticks Let us try applying a theme from the above-mentioned list. The default theme of the plot will be darkgrid which we have seen in the previous example. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip=1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set_style(“whitegrid”) sinplot() plt.show() Output The difference between the above two plots is the background color Removing Axes Spines In the white and ticks themes, we can remove the top and right axis spines using the despine() function. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip=1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set_style(“white”) sinplot() sb.despine() plt.show() Output In the regular plots, we use left and bottom axes only. Using the despine() function, we can avoid the unnecessary right and top axes spines, which is not supported in Matplotlib. Overriding the Elements If you want to customize the Seaborn styles, you can pass a dictionary of parameters to the set_style() function. Parameters available are viewed using axes_style() function. Example import seaborn as sb print sb.axes_style Output {”axes.axisbelow” : False, ”axes.edgecolor” : ”white”, ”axes.facecolor” : ”#EAEAF2”, ”axes.grid” : True, ”axes.labelcolor” : ”.15”, ”axes.linewidth” : 0.0, ”figure.facecolor” : ”white”, ”font.family” : [u”sans-serif”], ”font.sans-serif” : [u”Arial”, u”Liberation Sans”, u”Bitstream Vera Sans”, u”sans-serif”], ”grid.color” : ”white”, ”grid.linestyle” : u”-”, ”image.cmap” : u”Greys”, ”legend.frameon” : False, ”legend.numpoints” : 1, ”legend.scatterpoints”: 1, ”lines.solid_capstyle”: u”round”, ”text.color” : ”.15”, ”xtick.color” : ”.15”, ”xtick.direction” : u”out”, ”xtick.major.size” : 0.0, ”xtick.minor.size” : 0.0, ”ytick.color” : ”.15”, ”ytick.direction” : u”out”, ”ytick.major.size” : 0.0, ”ytick.minor.size” : 0.0} Altering the values of any of the parameter will alter the plot style. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip=1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set_style(“darkgrid”, {”axes.axisbelow”: False}) sinplot() sb.despine() plt.show() Output Scaling Plot Elements We also have control on the plot elements and can control the scale of plot using the set_context() function. We have four preset templates for contexts, based on relative size, the contexts are named as follows Paper Notebook Talk Poster By default, context is set to notebook; and was used in the plots above. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip = 1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set_style(“darkgrid”, {”axes.axisbelow”: False}) sinplot() sb.despine() plt.show() Output The output size of the actual plot is bigger in size when compared to the above plots. Note − Due to scaling of images on our web page, you might miss the actual difference in our example plots. Print Page Previous Next Advertisements ”;

Seaborn – Introduction ”; Previous Next In the world of Analytics, the best way to get insights is by visualizing the data. Data can be visualized by representing it as plots which is easy to understand, explore and grasp. Such data helps in drawing the attention of key elements. To analyse a set of data using Python, we make use of Matplotlib, a widely implemented 2D plotting library. Likewise, Seaborn is a visualization library in Python. It is built on top of Matplotlib. Seaborn Vs Matplotlib It is summarized that if Matplotlib “tries to make easy things easy and hard things possible”, Seaborn tries to make a well-defined set of hard things easy too.” Seaborn helps resolve the two major problems faced by Matplotlib; the problems are − Default Matplotlib parameters Working with data frames As Seaborn compliments and extends Matplotlib, the learning curve is quite gradual. If you know Matplotlib, you are already half way through Seaborn. Important Features of Seaborn Seaborn is built on top of Python’s core visualization library Matplotlib. It is meant to serve as a complement, and not a replacement. However, Seaborn comes with some very important features. Let us see a few of them here. The features help in − Built in themes for styling matplotlib graphics Visualizing univariate and bivariate data Fitting in and visualizing linear regression models Plotting statistical time series data Seaborn works well with NumPy and Pandas data structures It comes with built in themes for styling Matplotlib graphics In most cases, you will still use Matplotlib for simple plotting. The knowledge of Matplotlib is recommended to tweak Seaborn’s default plots. Print Page Previous Next Advertisements ”;

Seaborn – Color Palette ”; Previous Next Color plays an important role than any other aspect in the visualizations. When used effectively, color adds more value to the plot. A palette means a flat surface on which a painter arranges and mixes paints. Building Color Palette Seaborn provides a function called color_palette(), which can be used to give colors to plots and adding more aesthetic value to it. Usage seaborn.color_palette(palette = None, n_colors = None, desat = None) Parameter The following table lists down the parameters for building color palette − Sr.No. Palatte & Description 1 n_colors Number of colors in the palette. If None, the default will depend on how palette is specified. By default the value of n_colors is 6 colors. 2 desat Proportion to desaturate each color. Return Return refers to the list of RGB tuples. Following are the readily available Seaborn palettes − Deep Muted Bright Pastel Dark Colorblind Besides these, one can also generate new palette It is hard to decide which palette should be used for a given data set without knowing the characteristics of data. Being aware of it, we will classify the different ways for using color_palette() types − qualitative sequential diverging We have another function seaborn.palplot() which deals with color palettes. This function plots the color palette as horizontal array. We will know more regarding seaborn.palplot() in the coming examples. Qualitative Color Palettes Qualitative or categorical palettes are best suitable to plot the categorical data. Example from matplotlib import pyplot as plt import seaborn as sb current_palette = sb.color_palette() sb.palplot(current_palette) plt.show() Output We haven’t passed any parameters in color_palette(); by default, we are seeing 6 colors. You can see the desired number of colors by passing a value to the n_colors parameter. Here, the palplot() is used to plot the array of colors horizontally. Sequential Color Palettes Sequential plots are suitable to express the distribution of data ranging from relative lower values to higher values within a range. Appending an additional character ‘s’ to the color passed to the color parameter will plot the Sequential plot. Example from matplotlib import pyplot as plt import seaborn as sb current_palette = sb.color_palette() sb.palplot(sb.color_palette(“Greens”)) plt.show() Note −We need to append ‘s’ to the parameter like ‘Greens’ in the above example. Diverging Color Palette Diverging palettes use two different colors. Each color represents variation in the value ranging from a common point in either direction. Assume plotting the data ranging from -1 to 1. The values from -1 to 0 takes one color and 0 to +1 takes another color. By default, the values are centered from zero. You can control it with parameter center by passing a value. Example from matplotlib import pyplot as plt import seaborn as sb current_palette = sb.color_palette() sb.palplot(sb.color_palette(“BrBG”, 7)) plt.show() Output Setting the Default Color Palette The functions color_palette() has a companion called set_palette() The relationship between them is similar to the pairs covered in the aesthetics chapter. The arguments are same for both set_palette() and color_palette(), but the default Matplotlib parameters are changed so that the palette is used for all plots. Example import numpy as np from matplotlib import pyplot as plt def sinplot(flip = 1): x = np.linspace(0, 14, 100) for i in range(1, 5): plt.plot(x, np.sin(x + i * .5) * (7 – i) * flip) import seaborn as sb sb.set_style(“white”) sb.set_palette(“husl”) sinplot() plt.show() Output Plotting Univariate Distribution Distribution of data is the foremost thing that we need to understand while analysing the data. Here, we will see how seaborn helps us in understanding the univariate distribution of the data. Function distplot() provides the most convenient way to take a quick look at univariate distribution. This function will plot a histogram that fits the kernel density estimation of the data. Usage seaborn.distplot() Parameters The following table lists down the parameters and their description − Sr.No. Parameter & Description 1 data Series, 1d array or a list 2 bins Specification of hist bins 3 hist bool 4 kde bool These are basic and important parameters to look into. Print Page Previous Next Advertisements ”;

Seaborn – Kernel Density Estimates ”; Previous Next Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis. Setting the hist flag to False in distplot will yield the kernel density estimation plot. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.distplot(df[”petal_length”],hist=False) plt.show() Output Fitting Parametric Distribution distplot() is used to visualize the parametric distribution of a dataset. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.distplot(df[”petal_length”]) plt.show() Output Plotting Bivariate Distribution Bivariate Distribution is used to determine the relation between two variables. This mainly deals with relationship between two variables and how one variable is behaving with respect to the other. The best way to analyze Bivariate Distribution in seaborn is by using the jointplot() function. Jointplot creates a multi-panel figure that projects the bivariate relationship between two variables and also the univariate distribution of each variable on separate axes. Scatter Plot Scatter plot is the most convenient way to visualize the distribution where each observation is represented in two-dimensional plot via x and y axis. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.jointplot(x = ”petal_length”,y = ”petal_width”,data = df) plt.show() Output The above figure shows the relationship between the petal_length and petal_width in the Iris data. A trend in the plot says that positive correlation exists between the variables under study. Hexbin Plot Hexagonal binning is used in bivariate data analysis when the data is sparse in density i.e., when the data is very scattered and difficult to analyze through scatterplots. An addition parameter called ‘kind’ and value ‘hex’ plots the hexbin plot. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.jointplot(x = ”petal_length”,y = ”petal_width”,data = df,kind = ”hex”) plt.show() Kernel Density Estimation Kernel density estimation is a non-parametric way to estimate the distribution of a variable. In seaborn, we can plot a kde using jointplot(). Pass value ‘kde’ to the parameter kind to plot kernel plot. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.jointplot(x = ”petal_length”,y = ”petal_width”,data = df,kind = ”hex”) plt.show() Output Print Page Previous Next Advertisements ”;

Seaborn – Plotting Categorical Data ”; Previous Next In our previous chapters we learnt about scatter plots, hexbin plots and kde plots which are used to analyze the continuous variables under study. These plots are not suitable when the variable under study is categorical. When one or both the variables under study are categorical, we use plots like striplot(), swarmplot(), etc,. Seaborn provides interface to do so. Categorical Scatter Plots In this section, we will learn about categorical scatter plots. stripplot() stripplot() is used when one of the variable under study is categorical. It represents the data in sorted order along any one of the axis. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.stripplot(x = “species”, y = “petal_length”, data = df) plt.show() Output In the above plot, we can clearly see the difference of petal_length in each species. But, the major problem with the above scatter plot is that the points on the scatter plot are overlapped. We use the ‘Jitter’ parameter to handle this kind of scenario. Jitter adds some random noise to the data. This parameter will adjust the positions along the categorical axis. Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.stripplot(x = “species”, y = “petal_length”, data = df, jitter = Ture) plt.show() Output Now, the distribution of points can be seen easily. Swarmplot() Another option which can be used as an alternate to ‘Jitter’ is function swarmplot(). This function positions each point of scatter plot on the categorical axis and thereby avoids overlapping points − Example import pandas as pd import seaborn as sb from matplotlib import pyplot as plt df = sb.load_dataset(”iris”) sb.swarmplot(x = “species”, y = “petal_length”, data = df) plt.show() Output Print Page Previous Next Advertisements ”;

Seaborn – Environment Setup ”; Previous Next In this chapter, we will discuss the environment setup for Seaborn. Let us begin with the installation and understand how to get started as we move ahead. Installing Seaborn and getting started In this section, we will understand the steps involved in the installation of Seaborn. Using Pip Installer To install the latest release of Seaborn, you can use pip − pip install seaborn For Windows, Linux & Mac using Anaconda Anaconda (from https://www.anaconda.com/ is a free Python distribution for SciPy stack. It is also available for Linux and Mac. It is also possible to install the released version using conda − conda install seaborn To install the development version of Seaborn directly from github https://github.com/mwaskom/seaborn” Dependencies Consider the following dependencies of Seaborn − Python 2.7 or 3.4+ numpy scipy pandas matplotlib Print Page Previous Next Advertisements ”;