![Line Graph
Ø Line Chart displays time-series relationships with continuous data.
Functions for plotting line graph :
1. Axes.plot(self, *args, scalex=True, scaley=True, data=None, **kwargs) : Plot y versus x as line and/or markers.
For details, click here.
Call signatures: Plot([x], y, [fmt], *, data=None, **kwargs)
2. Axes.tick_params(axis='x', direction='out', length=3, width=0.5, labelrotation=75.00): Decorate the appearance of
ticks, ticklabels, and gridlines. For more details, click here.
3. Axes.grid(color='lightgray', linestyle='--', linewidth='0.5'): Provides configurable grid lines. For details, click here.
Parameters: x, y : array-like or scalar; the coordinates of the points or line nodes are given by x, y.
fmt : str, optional; a format string, e.g., ‘ro’ for red circles.
data : indexable object, optional; An object with label data.
**kwargs Used to specify properties like line label (for auto legend), line width, antialiasing,
marker face color.](https://image.slidesharecdn.com/datavisualizationv3-a-191102061619/85/Data-Visualization-in-Python-15-320.jpg)
Data Visualization in Python
- 2. v Visualization Libraries And Modules v Version Overview v Visualization Plot Types Overview • Bar Chart • Stacked Area Graph • Scatter Plot • Horizontal Bar Chart • Pie Chart • Boxplot • Stacked Bar Chart • Histogram • Violin Plot • Grouped Bar Chart • Density Plot • Heatmap • Line Chart • Density Plot with Histogram • Time Series
- 3. Visualization Libraries And Modules Libraries and Modules Description Doc Link Installation Link Import Statement Pandas pandas is an open source software library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Link Installation import pandas as pd NumPy NumPy is the fundamental package for scientific computing with Python providing multidimensional array object, various derived objects such as masked arrays and matrices. Link Installation import numpy as np Matplotlib Matplotlib is a Python 2D plotting library. Link Installation import matplotlib matplotlib.pyplot matplotlib.pyplot is a state-based interface to matplotlib. It provides a MATLAB-like plotting. Link import matplotlib.pyplot as plt Seaborn Seaborn is a python data visualization library based on matplotlib. Link Installation import seaborn as sns Plotly Plotly python open source graphing library makes interactive graphs online Link Installation import plotly.express as px import plotly.graph_objects as go Bokeh Bokeh is a python based interactive visualization library that targets web browsers to present very large and streaming datasets. Link Installation from bokeh.plotting import figure, show
- 4. Version Overview Version What’s New in Each Version? 2015.09 • Basic visualization plots using matplotlib, seaborn, and pandas 2017.06 • Visualization plots using seaborn • Updates some graphs and charts with new functions and data 2018.03 • Data visualization with Plotly • 3D Visualization plots • Updates some graphs and charts with new functions and data 2018.12 • Network graph visualization with Bokeh • Interactive data visualization with Bokeh • Geo Data Mapping with Bokeh
- 6. Ø Bar Chart Shows comparisons between different categories, different parts of a whole. Ø Shows relationship between a numerical variable and a discrete variable. Variations of Bar Chart : Ø Vertical Bar Chart or Column Chart : Best used to visualize relationship or comparisons with chronological data. Ø Horizontal Bar Chart : Useful when category labels are long. Ø Stacked Bar Chart : Used to compare different parts of a whole using discrete or continuous variables. Ø Grouped Bar Chart : Used to compare multiple data series in a given category. Bar Chart
- 7. Vertical Bar Chart (Column Chart) Functions for plotting bar chart: 1. fig, ax = plt.subplots(nrows=no_of_row, ncols=no_of_col, figsize=(x , y)) : Creates an figure and a set of subplots and sets figure size. Returns a single Axes object or an array of axes objects if more than one subplot are created. For details click here. 2. matplotlib.pyplot.bar(x, height, width, bottom=None, align='center', data=None, **kwargs) Make a Bar Chart. For more details, click here. 3. matplotlib.pyplot.xticks(ticks=None, labels=None, **kwargs) : Get or set the current tick locations and labels of the x-axis. For more details, click here. Parameters: x : Sequence of scalars; the bars are positioned at x with the given alignment. height, width : Scalar or sequence of scalar or array like; the dimensions of bar are set by these parameters. bottom : Scalar or array like; the vertical baseline is bottom (default 0). align : Alignment of the bars to the x coordinates; {‘center’, ‘edge’}, default(‘center’). **kwargs color : Scalar or array-like; the color of the bar faces. For, e.g., we have set blue color as color=‘b’ in the given example. alpha : Float or None; set the alpha transparency of the patch (a 2D artist with a face color or an edge color). For, e.g., we have set alpha=0.6 in the given example.
- 9. Functions for plotting horizontal bar chart : 1. matplotlib.axes.Axes.barh(self, y, width, height=0.8, left=None, align='center', **kwargs) Make a horizontal bar. For more details click here. 2. matplotlib.ticker.FuncFormatter(func) : Uses user defined function for label formatting. For details click here. 3. ax.set_yticks(self, ticks, minor=False) : Set the y ticks with list of ticks. It the parameter minor is False sets major ticks, if True sets major ticks. Default is False. 4. ax.set_yticklabels(self, labels, fontdict=None, minor=False, **kwargs) : Sets the y-tick labels with list of strings labels. For details click here. 5. ax.invert_yaxis(self) : Inverts the y-axis. Horizontal Bar Chart Parameters: y : Scalar or array like; y coordinates of the bars. Width, height : Scalar or array like; sequence of scalars; the dimensions of bar are set by these parameters. left : Sequence of scalars; the x coordinates of the left sides of the bars (default 0). align : Alignment of the bars to the y coordinates; {‘center’, ‘edge’}, default(‘center’). **kwargs color : Scalar or array-like; the color of the bar faces. For details click here. alpha : Float or None; set the alpha transparency of the patch (a 2D artist with a face color or an edge color).
- 11. Stacked Bar Chart Functions for plotting stacked bar chart : 1. matplotlib.pyplot.bar(x, height, width, bottom=None, align='center', data=None, **kwargs) Make a Stacked Bar Chart. For more details, click here. 2. matplotlib.pyplot.xticks(ticks=None, labels=None, **kwargs) : Get or set the current tick locations and labels of the x-axis. For more details, click here. 3. ax.yaxis.set_major_formatter(formatter) : Provides Configurable tick locating and formatting. 4. matplotlib.ticker.FuncFormatter(func) : Use user defined function for label formatting. For details click here. Parameters: x : Sequence of scalars; the bars are positioned at x with the given alignment. height, width : Scalar or sequence of scalar or array like; the dimensions of bar are set by these parameters. bottom : Scalar or array like; the vertical baseline is bottom (default 0). In the given example, we have set bottom=revenue to plot stacked bar chart. align : Alignment of the bars to the x coordinates; {‘center’, ‘edge’}, default(‘center’).
- 13. Grouped Bar Chart Functions for plotting grouped bar chart : 1. matplotlib.pyplot.bar(x, height, width, bottom=None, align='center', data=None, **kwargs) Make a Stacked Bar Chart. For more details, click here. 2. matplotlib.pyplot.xticks(ticks=None, labels=None, **kwargs) : Get or set the current tick locations and labels of the x-axis. For more details, click here. 3. Axes.yaxis.set_major_formatter(formatter) : Provides Configurable tick locating and formatting. 4. matplotlib.ticker.FuncFormatter(func) : Use user defined function for label formatting. For details click here. 5. autolabel(bars) : Attach a text label above each bar, displaying its height. Parameters: x : Sequence of scalars; the bars are positioned at x with the given alignment. In the given example, for bar1, we have set (x = x - width/2 ) and for bar2 (x = x + width/2) to plot a grouped bar. height, width : Scalar or sequence of scalar or array like; the dimensions of bar are set by these parameters. bottom : Scalar or array like; the vertical baseline is bottom (default 0). align : Alignment of the bars to the x coordinates; {‘center’, ‘edge’}, default(‘center’).
- 15. Line Graph Ø Line Chart displays time-series relationships with continuous data. Functions for plotting line graph : 1. Axes.plot(self, *args, scalex=True, scaley=True, data=None, **kwargs) : Plot y versus x as line and/or markers. For details, click here. Call signatures: Plot([x], y, [fmt], *, data=None, **kwargs) 2. Axes.tick_params(axis='x', direction='out', length=3, width=0.5, labelrotation=75.00): Decorate the appearance of ticks, ticklabels, and gridlines. For more details, click here. 3. Axes.grid(color='lightgray', linestyle='--', linewidth='0.5'): Provides configurable grid lines. For details, click here. Parameters: x, y : array-like or scalar; the coordinates of the points or line nodes are given by x, y. fmt : str, optional; a format string, e.g., ‘ro’ for red circles. data : indexable object, optional; An object with label data. **kwargs Used to specify properties like line label (for auto legend), line width, antialiasing, marker face color.
- 17. Stacked Area Graph Ø Stacked Area Graph Displays the contribution of each data series to a cumulative total over time. Functions for plotting stacked area graph : 1. Axes.stackplot(axes, x, *args, labels=(), colors=None, baseline=‘zero’, data=None, **kwargs) : Draw a stacked area plot. For details, click here. 2. Axes.tick_params(axis='x', direction='out', length=3, width=0.5, labelrotation=75.00): Decorate the appearance of ticks, ticklabels, and gridlines. For more details, click here. Parameters: x, y : x : 1d array of dimension N; y: 2d array (MxN) or sequence of 1d arrays(each dimension 1xN) baseline : Calculate the baseline; {‘zero’, ‘sym’, ‘wiggle’, ‘weighted_wiggle’}. labels : Length N sequence of strings. Labels to assign to each data series. colors : Length N sequence of colors; a list or tuple of colors; used to color the stacked areas.
- 19. Pie Chart Ø Pie Chart is a circular statistical graphic, which is divided into slices to show part-to-whole relationships with continuous or discrete data. Ø It is best used with a small data. Functions for plotting Pie Chart : 1. Axes.pie(self, x, autopct=None, textprops=None, *args ) : Make a Pie chart of array x. For details, click here. 2. plt.setps(obj, *args, **kwargs) : set the property on an artist object. For more details, click here. Parameters: x : array-like; the wedge sizes. autopct : None (default), string, or function, optional. If not None, is a string or function used to label the wedges with their numeric value. textprops : dict, optional; default: None. Dict of arguments to pass to the text objects. Other parameters explode, labels, colors, pctdistance, shadow, labeldistance, radius, counterclock, wedgeprop, etc.
- 21. Histogram Ø Histogram displays the underlying frequency distribution of a set of continuous data (univariate data). Functions for plotting Histogram: 1. Axes.hist(self, x, bins=None, range=None, density=None, histtype=‘bar’, *args, **kwargs) : Plot a histogram. For details, click here. Parameters: x : (n,) array or sequence of (n,) arrays. Input values, this takes either a single array or a sequence of arrays which are not required to be of same length. bins : int or sequence of str, optional. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. density : Bool, optional; if True, the area under histogram will sum to 1. Other parameters: weights, cumulative, bottom, histtype, align, orientation, rwidth, log, label, stack, normed, data, **kwargs. Details here.
- 22. Example : Histogram of NBA player’s weight. See full code on github-histogram. Histogram : Example
- 23. Density Plot Ø Density Plot displays the univariate distribution of data. Ø Uses a kernel density estimate to show the probability density function (PDF) of the variable. Functions for plotting density plot: 1. seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel=‘gau’, … ) : Fit and plot a univariate or bivariate kernel density estimate. For details, click here. Parameters: data : 1d array-like; input data. data2 : 1d array-like, optional. Second input data; if present a bivariate kde will be estimated. shade : Bool, optional; if True, shade in the area under the kde curve. vertical : Bool, optional; if True, density is on x-axis. kernel : {‘gau’ | ‘cos’ | ‘biw’ | ‘epa’ | ‘tri’ | ‘triw’}, optional. Code for shape of kernel to fit with. Bivariate kde can only use Gaussian kernel.
- 26. Scatter Plot Ø Scatter Plot shows correlation between two sets of data (bivariate data). Ø Scatter plots are best used for large dataset. Functions for plotting Scatter plot: 1. DataFrame.plot.scatter(self, x, y, s=None, c=None, **kwargs) : Create a scatter plot with varying marker point size and color. Returns matplotlib.axes.Axes or numpy.ndarray of them. For details, click here. Parameters: x : int or str; the column name or column position to be used as horizontal coordinates for each point. y : int or str; the column name or column position to be used as vertical coordinates for each point. s : Scalar or array-like, optional; the size of each point. c : Scalar or array-like, optional; the color of each point. *kwargs : Keyword arguments to pass on to DataFrame.plot().
- 28. Boxplot Ø Box Plot displays the distribution of data through their quartiles (minimum, first quartile(Q1), median, third quartile(Q3), and maximum). Ø Displays outliers with their values. Functions for plotting Boxplot: 1. Axes.boxplot(self, x, notch=None, patch_artist=None, labels=None, flierprops=None, … ) : Makes a box and whisker plot for each column of x or each vector in sequence x. Returns matplotlib.axes.Axes or numpy.ndarray of them. For details, click here. Parameters: x : Array or a sequence of vectors; the input data. notch : bool, optional (False). If True, will produce a notched box plot. Otherwise, a rectangular boxplot is produced. labels : Sequence, optional. Labels for each dataset. Length must be compatible with dimensions of x. patch_artist : bool, optional (False). If True, produces boxes with the Line2D artist. Otherwise, boxes and drawn with patch artist. flierprops : dict, optional (None). Specifies the style of the fliers.
- 30. Violin Plot Ø Violin Plot plots numeric data with a rotated kernel density plot on each side. Ø Shows the probability density of the data at different values, usually smoothed by a kernel density estimator. Functions for plotting violin plot: 1. seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None…) : Draws a combination of boxplot and kernel density estimate. For details, click here. Parameters: x, y, hue : Names of variables in data or vector data; optional. data : DataFrame, array, or list of arrays; optional. Dataset for plotting. If x and y are absent, this is interpreted as wide-form. Otherwise, it is expected to be long- form. order, hue_order : Lists of strings; optional. Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.
- 32. Heatmap Ø Heatmap represents data where individual values contained in a matrix are represented as colors. Functions for plotting heatmap: 1. heatmap(data, row_labes, col_labels, ax=None, cbar_kw={}, cbarlabel=“”, **kwargs) : Create a heatmap from a numpy array and two lists of labels . For details, click here. Parameters: data : A 2D numpy array of shape (N, M). row_labels : A list or array of length N with the labels for the rows. col_labels : A list or array of length M with the labels for the columns. ax : A ‘matplotlib.axes.Axes’ instance to which the heatmap is plotted; optional. cbar_kw : A dictionary with arguments to matplotlib.Figure.colorbar’; optional. cbarlabel : The label for the colorbar; optional.
- 35. Summary Plot Types API Call Bar Chart, Stacked Bar Chart, Grouped Bar Chart matplotlib.pyplot.bar(x, height, width, bottom=None, align='center', data=None, …) Horizontal Bar Chart matplotlib.axes.Axes.barh(self, y, width, height=0.8, left=None, align='center', …) Line Chart matplotlib.axes.Axes.plot(self, *args, scalex=True, scaley=True, data=None, **kwargs) Stacked Area Graph matplotlib.axes.Axes.stackplot(axes, x, *args, labels=(), colors=None, baseline=‘zero’,…) Pie Chart matplotlib.axes.Axes.pie(self, x, autopct=None, textprops=None, *args ) Histogram matplotlib.axes.Axes.hist(self, x, bins=None, range=None, density=None, histtype=‘bar’, …) Density Plot seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel=‘gau’, … ) Histogram with Kde Plot seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False, hist_kws=None, … ) Scatter Plot DataFrame.plot.scatter(self, x, y, s=None, c=None, **kwargs) Box Plot matplotlib.axes.Axes.boxplot(self, x, notch=None, patch_artist=None, labels=None, … ) Violin Plot seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None…) Heatmap heatmap(data, row_labes, col_labels, ax=None, cbar_kw={}, cbarlabel=“”, **kwargs)
- 37. Visualization Plots Using Seaborn v Visualization Plot Types • Bar Plot • Density Plot with filled area • lmplot • Strip Plot • Histogram • Regplot • Swarm Plot • Histogram and Rug Plot • 2D Kde Plot • Boxplot • Density Plot with Rug Plot • Joint Plot • Boxen Plot • Histogram, Density and Rug Plot • Pair Plot
- 38. Barplot using catplot() Ø seaborn.catplot(x=None, y=None, hue=None, data=None, kind=‘bar’, … ) : Figure-level interface for drawing categorical plots onto a FacetGrid. This function provides access to several axes- level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations. For more details, click here. Ø The kind parameter in function catplot() selects the underlying functions to plot: Categorical scatterplots: kind=‘strip’ for stripplot(); kind=‘swarm’ for swarmplot() Categorical distribution plots: kind=‘box’ for boxplot(); kind=‘violin’ for violinplot(); kind=‘boxen’ for boxenplot() Categorical estimate plots: kind=‘point’ for pointplot(); kind=‘bar’ for barplot(); kind=‘count’ for countplot Parameters: x, y, hue: Name of variables in data; inputs for plotting long-form data. data : DataFrame; long-form (tidy) dataset. kind : String, optional; the kind of plot to draw (bar, strip, swarm, box, violin, boxen).
- 42. Histogram, Density Plot, Rug Plot using distplot() Ø seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False, hist_kws=None, kde_kws, rug_kws=None, … ) : Combines the matplotlib hist() function, with the seaborn kdeplot() and rugplot() functions and plot the estimate PDF over the data. For details, click here. Parameters: a : Series, 1d-array, or list. bins : Argument for matplotlib hist(), or None, optional; specification for hist bins. hist : Bool, optional; whether to plot a (normed) histogram kde : Bool, optional; whether to plot a Gaussian kernel density estimate. rug : Bool, optional; whether to plot a rugplot on the support axis. {hist, kde, rug, fit}_kws : Dictionaries, optional; keyword arguments for underlying plotting functions.
- 45. Implot() Ø seaborn.lmplot(x, y, data, hue=None, col=None, … ) : Plots data and regression model fits across a FacetGrid. Combines regplot() and FacetGrid. For details, click here. Parameters: x, y : Strings, optional data : DataFrame; Tidy (long-form) dataframe where each column is a variable and each row is an observation. hue, col, row : Strings; variables that define subsets of the data.
- 47. regplot() Ø seaborn.regplot(x, y, data=None, x_estimator=None, x_bins=None, fit_reg=True, … ) : Plot data and a linear regression model fit. For details, click here. Parameters: x, y : String, series, or vector array; input variables. data : DataFrame; Tidy (long-form) dataframe where each column is a variable and each row is an observation. x_estimator : Called that maps vector -> scalar, optional. x_bins : int or vector, optional. fit_reg : Bool, optional. If True, estimate and plot a regression model relating the x and y variables.
- 49. 2D Density Plot using kdeplot() Ø seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel=‘gau’, … ) : Fit and plot a univariate or bivariate kernel density estimate. For details, click here. Parameters: data : 1d array-like; input data. data2 : 1d array-like, optional. Second input data; if present a bivariate kde will be estimated. shade : Bool, optional; if True, shade in the area under the kde curve. vertical : Bool, optional; if True, density is on x-axis. kernel : {‘gau’ | ‘cos’ | ‘biw’ | ‘epa’ | ‘tri’ | ‘triw’}, optional. Code for shape of kernel to fit with. Bivariate kde can only use Gaussian kernel.
- 51. jointplot() Ø seaborn.jointplot(x, y, data=None, kind=‘scatter’, stat_func=None, color=None, … ) : Draw a plot of two variables with bivariate and univariate graphs. For details, click here. Parameters: x, y : Strings or vectors. Data or names of variables in data. data : DataFrame, optional. DataFrame when x and y are variable names. kind : {“scatter” | “reg” | “resid” | “kde” | “hex”}, optional. Kind of plot to draw. stat_func : Callable or None, optional. Deprecated. color : Matplotlib color, optional.
- 53. pairplot() Ø seaborn.pairplot(data, hue=None, hue_order=None, palette=None, vars=None, … ) : Draw a plot of two variables with bivariate and univariate graphs. For details, click here. Parameters: data : DataFrame; Tidy (long-form) dataframe where each column is a variable and each row is an observation. hue : String (variable name), optional. Variable in data to map plot aspects to different colors. hue_order : List of strings. Order for the levels of the hue variable in the palette. palette : dict or seaborn color palette. Set of colors for mapping the hue variable. If a dict, keys should be values in the hue variable. vars : List of variable names, optional. Variables within data to use, otherwise use every column with a numeric datatype.
- 55. Summary Plot Types API Call Categorical data Visualization: Bar plot Strip plot Swarm plot Count plot Boxplot Boxen plot Violin plot seaborn.catplot(x=None, y=None, hue=None, data=None, kind=‘bar’, … ) Distribution of data: Histogram Density plot Rug plot seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False, hist_kws=None, kde_kws, rug_kws=None, … ) Density plot seaborn.kdeplot(data, data2=None, shade=False, vertical=False, kernel=‘gau’, … ) Pairplot seaborn.pairplot(data, hue=None, hue_order=None, palette=None, vars=None, … ) Jointplot seaborn.jointplot(x, y, data=None, kind=‘scatter’, stat_func=None, color=None, … ) Linear relationship Scatter plot with regression line seaborn.regplot(x, y, data=None, x_estimator=None, x_bins=None, fit_reg=True, … ) seaborn.lmplot(x, y, data, hue=None, col=None, … )