This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Often, at EdgeTier, we tend to end up with an abundance of bar charts in both exploratory data analysis work as well as in dashboard visualisations. Luckily for Python users, options for visualisation libraries are plentiful, and Pandas itself has tight integration with the Matplotlib visualisation library, allowing figures to be created directly from DataFrame and Series data objects. The choice of chart depends on the story you are telling or point being illustrated. As an example, we reset the index (.reset_index()) on the existing example, creating a column called “index” with the same values as previously. sir How do we give the total number of elements present in the one column on top of the bar graph column. A bar chart is a great way to compare categorical data across one or two dimensions. Start by adding a column denoting gender (or your “colour-by” column) for each member of the family. I have made that data frame in the form of an array to make the task easy. Below is an example dataframe, with the data oriented in columns. Also learn to plot graphs in 3D and 2D quickly using pandas and csv. In the background, pandas also use matplotlib to create graphs. Suppose if we have a data frame, we can directly create different types of plots like scatter, bar, line using a single function. The beauty here is not only does matplotlib work with Pandas dataframe, which by themselves make working with row and column data easier, it lets us draw a complex graph with one line of code. As per the given data, we can make a lot of graph and with the help of pandas, we can create a dataframe before doing plotting of data. For example, you can tell visually from the figure that the gluttonous brother in our fictional mince-pie-eating family has grown an addiction over recent years, whereas my own consumption has remained conspicuously high and consistent over the duration of data. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Let us see how we will do so. We will use the DataFrame df to construct bar plots. Showing composition of the whole, as a percentage of total is a different type of bar chart, but useful for comparing the proportional makeups of different samples on your x-axis. Make a bar plot with matplotlib. For this example, you’ll be using the sf_bike_share_trips dataset available in Mode’s Public Data Warehouse. Let’s first understand what is a bar graph. Matplotlib is a popular Python module that can be used to create charts. In this tutorial, we will introduce how we can plot multiple columns on a bar chart using the plot() method of the DataFrame object. The vertical baseline is bottom (default 0). Then, we also import ‘matplotlib.pyplot’ as ‘plt’. More often than not, it’s more interesting to compare values across two dimensions and for that, a grouped bar chart is needed. This blog post focuses on the use of the DataFrame.plot functions from the Pandas visualisation API. … Suppose we have a pandas data frame that contains information about some sports and how many people play those sports. Here is the graph. Plot a Line Chart using Pandas. These can be used to control additional styling, beyond what pandas provides. Here’s our data: Out of the box, Pandas plot provides what we need here, putting the index on the x-axis, and rendering each column as a separate series or set of bars, with a (usually) neatly positioned legend. import matplotlib.pyplot as plt. Remember that the x and y axes will be swapped when using barh, requiring care when labelling. In this guide, I’ll show you how to create Scatter, Line and Bar charts using matplotlib.. The bars are positioned at x with the given align ment. What is a Bar Chart. This question requires a transposing of the data so that “year” becomes our index variable, and “person” become our category. A second simple option for theming your Pandas charts is to install the Python Seaborn library, a different plotting library for Python. import matplotlib.pyplot as plt. Pandas bar plot Let’s start with a basic bar plot first. Examples. Let’s start with a basic bar plot first. A “100% stacked” bar is not supported out of the box by Pandas (there is no “stack-to-full” parameter, yet! 1. Use these commands to install matplotlib, pandas and numpy: pip install matplotlib pip install pandas pip install numpy Types of Plots: Let’s colour the bars by the gender of the individuals. Bar charts in Pandas with Matplotlib A bar plot is a way of representing data where the length of the bars represents the magnitude/size of the feature/variable. Because Pandas plotting isn’t natively supporting the addition of “colour by category”, adding a legend isn’t super simple, and requires some dabbling in the depths of Matplotlib. Each of x, height, width, and bottom may either be a scalar applying to all bars, or it may be a sequence of length N … Plot bar chart of multiple columns for each observation in the single bar chart import pandas as pd import matplotlib.pyplot as plt data=[["Rudra",23,156,70], ["Nayan",20,136,60], ["Alok",15,100,35], ["Prince",30,150,85] ] df=pd.DataFrame(data,columns=["Name","Age","Height(cm)","Weight(kg)"]) df.plot(x="Name", y=["Age", … As an aside, if you can, keep the total number of colours on your chart to less than 5 for ease of comprehension. Pandas makes this easy with the “stacked” argument for the plot command. Python Pandas read_csv – Load Data from CSV Files, The Pandas DataFrame – creating, editing, and viewing data in Python, Summarising, Aggregating, and Grouping data, Use iloc, loc, & ix for DataFrame selections, Bar Plots in Python using Pandas DataFrames, Additional series: Stacked and unstacked bar charts, Adding a legend for manually coloured bars, Fine-tuning your plot legend – position and hiding, refined ability to compare the length of objects, options for visualisation libraries are plentiful. Horizontal bar charts are achieved in Pandas simply by changing the “kind” parameter to “barh” from “bar”. Note that colours can be specified as. The xticks function from Matplotlib is used, with the rotation and potentially horizontalalignment parameters. How to Create a Horizontal Bar Chart using Matplotlib. Direct functions for .bar() exist on the DataFrame.plot object that act as wrappers around the plotting functions – the chart above can be created with plotdata['pies'].plot.bar(). The manual method is only suitable for the simplest of datasets and plots: A more scaleable approach is to specify the colours that you want for each entry of a new “gender” column, and then sample from these colours. To flexibly choose the x-axis ticks from a column, you can supply the “x” parameter and “y” parameters to the plot function manually. Using the plot instance various diagrams for visualization can be drawn including the Bar Chart. Python / November 15, 2020. Creating stacked bar charts using Matplotlib can be difficult. Pandas bar plot. From simple to complex visualizations, it's the go-to library for most. Line charts are often used to display trends overtime. are accessed similarly: By default, the index of the DataFrame or Series is placed on the x-axis and the values in the selected column are rendered as bars. Seaborn comes with five excellent themes that can be applied by default to all of your Pandas plots by simply importing the library and calling the set() or the set_style() functions. Pandas Stacked Bar. Finally, you may use the template below to assist you in depicting the bar chart: import matplotlib.pyplot as plt plt.bar(xAxis,yAxis) plt.title('title name') plt.xlabel('xAxis name') plt.ylabel('yAxis name') plt.show() For our example, the complete Python code would look as follows: Ideally, we could specify a new “gender” column as a “colour-by-this” input. The example below will plot the Premier League table from the 16/17 season, taking you through the basics of creating a bar chart and customising some of its features. ... All in all, creating a grouped bar chart with Matplotlib is not easy. Luckily, the ‘PyPlot’ module from Matplotlib has a readily available bar plot function. A great place to start is the plotting section of the pandas DataFrame documentation. Finally we call the the z.plot.bar(stacked=True) function to draw the graph. In this figure, the visualisation tells a different story, where I’m emerging as a long-term glutton with potentially one of the highest portions of total pies each year. Typically this leads to an “unstacked” bar plot. In the background, pandas also use matplotlib to create graphs. To create our bar chart, the two essential packages are Pandas and Matplotlib. It may be more useful to ask the question – which family member ate the highest portion of the pies each year? It generates a bar chart for Age, Height and Weight for each person in the dataframe df using the plot() method for the df object. The unstacked bar chart is a great way to draw attention to patterns and changes over time or between different samples (depending on your x-axis). Outside of this post, just get stuck into practicing – it’s the best way to learn. Basic plot. Finally we call the the z.plot.bar(stacked=True) function to draw the graph. Prerequisites To create a bar chart, we’ll need the following: Python installed on your machine; Pip: package management system (it comes with Python) Jupyter Notebook: an online editor for data visualization Pandas: a library to create data frames from data sets and prepare data for plotting Numpy: a library for multi-dimensional arrays Matplotlib: a plotting library Data science, Startups, Analytics, and Data visualisation. Bar graphs usually represent numerical and categorical variables grouped in intervals. Often the data you need to stack is oriented in columns, while the default Pandas bar plotting function requires the data to be oriented in rows with a unique column for each layer. import pandas as pd. You can disable the legend with a simple legend=False as part of the plot command. The legend position and appearance can be achieved by adding the .legend() function to your plotting command. The next dimension to play with on bar charts is different categories of bar. Add a Y-Axis Label to the Secondary Y-Axis in Matplotlib, Pandas Plot Multiple Columns on Bar Chart with Matplotlib, Plot bar chart of multiple columns for each observation in the single bar chart, Stack bar chart of multiple columns for each observation in the single bar chart, Plot Numpy Linear Fit in Matplotlib Python. data = [23, 45, 56, 78, 213] plt.bar (range (len (data)), data, color='royalblue', alpha=0.7) plt.grid (color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7) plt.show () Download matplotlib examples. If you are looking for additional reading, it’s worth reviewing: Great tutorial, this avoids all the tedious parameter selections of matplotlib and with the custom styles (e.g. More often than not, it’s more interesting to compare values across two dimensions and for that, a grouped bar chart is needed. There’s a few options to easily add visually pleasing theming to your visualisation output. Now define a dictionary that maps the gender values to colours, and use the Pandas “replace” function to insert these into the plotting command. Each column is assigned a distinct color, and each row is nested in a group along the horizontal axis. Here, we cover most of these matplotlib bar chart arguments with an example of each. Pandas library uses the matplotlib as default backend which is the most popular plotting module in python. line, bar, scatter) any additional arguments keywords are passed along to the corresponding matplotlib function (ax.plot(), ax.bar(), ax.scatter()). pandas.Series.plot.bar¶ Series.plot.bar (x = None, y = None, ** kwargs) [source] ¶ Vertical bar plot. The vertical baseline is bottom (default 0). Note that the selection column names are put inside a list during this selection example to ensure a DataFrame is output for plot(): In the stacked bar chart, we’re seeing total number of pies eaten over all years by each person, split by the years in question. It is difficult to quickly see the evolution of values over the samples in a stacked bar chart, but much easier to see the composition of each sample. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. Just do a normal groupby () and call unstack (): import matplotlib.pyplot as plt import pandas as pd df.groupby( ['state','gender']).size().unstack().plot(kind='bar',stacked=True) plt.show() Source dataframe. These can be used to control additional styling, beyond what pandas provides. The pandas DataFrame class in Python has a member plot. The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python. import matplotlib.pyplot as plt. To import the relevant libraries and set up the visualisation output size, use: The simplest bar chart that you can make is one where you already know the numbers that you want to display on the chart, with no calculations necessary. A bar graph shows comparisons among discrete categories. Matplotlib’s chart functions are quite simple and allow us to create graphics to our exact specification. Imagine you have two parents (ate 10 each), one brother (a real mince pie fiend, ate 42), one sister (scoffed 17), and yourself (also with a penchant for the mince pie festive flavours, ate 37). The beauty here is not only does matplotlib work with Pandas dataframe, which by themselves make working with row and column data easier, it lets us draw a complex graph with one line of code. For each kind of plot (e.g. The available legend locations are. Every Pandas bar chart works this way; additional columns become a new sets of bars on the chart. https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html, https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html, various group-by operations provided by Pandas, The official Pandas visualisation documentation, Blog from Towards Data Science with more chart types, Pandas Groupby: Summarising, Aggregating, and Grouping data in Python, The Pandas DataFrame – loading, editing, and viewing data in Python, Merge and Join DataFrames with Pandas in Python, Plotting with Python and Pandas – Libraries for Data Visualisation, Using iloc, loc, & ix to select rows and columns in Pandas DataFrames, Pandas Drop: Delete DataFrame Rows & Columns. Appreciate the work, will be using this now ! We import ‘pandas’ as ‘pd’. This plot is easily achieved in Pandas by creating a Pandas “Series” and plotting the values, using the kind="bar" argument to the plotting command. The optional arguments color, edgecolor, linewidth, xerr, and yerr can be either scalars or sequences of length equal to the number of bars. 1. The pandas DataFrame class in Python has a member plot. Each of x, height, width, and bottom may either be a scalar applying to all bars, or it may be a sequence of length N … Often, the index on your dataframe is not representative of the x-axis values that you’d like to plot. It’s best not to simply colour all bars differently, but colour by common characteristics to allow comparison between groups. Making Bar Chart using Pandas Data Frame. matplotlib.pyplot.bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs) [source] ¶. Make a bar plot. See https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html for a full set of parameters. As the name suggests a bar chart is a chart showing the discrete values for different items as bars whose length is proportional to the value of the item and a bar chart can be vertical or horizontal. >>> df = pd.DataFrame( {'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]}) >>> ax = df.plot.bar(x='lab', y='val', rot=0) Plot a whole dataframe to a bar plot. Pandas library uses the matplotlib as default backend which is the most popular plotting module in python. bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs) Apart from these, there are few other optional arguments to define color, titles, line widths, etc. ), requiring knowledge from a previous blog post on “grouping and aggregation” functionality in Pandas. pandas.DataFrame.plot.bar¶ DataFrame.plot.bar (x=None, y=None, **kwds) [source] ¶ Vertical bar plot. While a bar chart can be drawn directly using matplotlib, it can be drawn for the DataFrame columns using the DataFrame class itself. Horizontal charts also allow for extra long bar titles. A bar plot shows comparisons among discrete categories. Using the plot instance various diagrams for visualization can be drawn including the Bar Chart. The index is not the only option for the x-axis marks on the plot. Let's look at the number of people in each job, split out by gender. Approach: Import Library (Matplotlib) Import / create data. With the grouped bar chart we need to use a numeric axis (you'll see why further below), so we create a simple range of numbers using np.arange to use as our x values.. We then use ax.bar() to add bars for the two series we want to plot: jobs for men and jobs for women. We need to plot age, height, and weight for each person in the DataFrame on a single bar chart. Something like this-We want to make a bar chart from it, let us first make a graph with the default size. import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.style.use('ggplot') % matplotlib inline # set jupyter's max row display pd.set_option('display.max_row', 1000) # set jupyter's max column width to 50 pd.set_option('display.max_columns', 50) # Load the dataset data = pd.read_csv('site_content/data/5kings_battles_v1.csv') As with most of the tutorials in this site, I’m using a Jupyter Notebook (and trying out Jupyter Lab) to edit Python code and view the resulting output. Create a grouped bar chart with Matplotlib and pandas. import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.style.use('ggplot') % matplotlib inline # set jupyter's max row display pd.set_option('display.max_row', 1000) # set jupyter's max column width to 50 pd.set_option('display.max_columns', 50) # Load the dataset data = pd.read_csv('site_content/data/5kings_battles_v1.csv') Make live graphs with dynamic line, scatter and bar plots. 'kind' takes arguments such as 'bar', 'barh' (horizontal bars), etc. The bars are positioned at x with the given align ment. Re-ordering can be achieved by selecting the columns in the order that you require. As the name suggests a bar chart is a chart showing the discrete values for different items as bars whose length is proportional to the value of the item and a bar chart can be vertical or horizontal. Introduction. Plot the bars in the grouped manner. Their dimensions are given by width and height. Do you know that we can also create a bar chart using the pandas’ library? We will take Bar plot with multiple columns and before that change the matplotlib backend - it’s most useful to draw the plots in a separate window(using %matplotlib tk), so we’ll restart the kernel and use a GUI backend from here on out. ... line styles and colors in the matplotlib official documentation - Click this link and check under Notes section. Let’s now see the steps to plot a line chart using Pandas. The matplotlib API in Python provides the bar() function which can be used in MATLAB style use or as an object-oriented API. Instead, we have to manually specify the colours of each bar on the plot, either programmatically or manually. Step 4: Create the bar chart in Python using Matplotlib. With multiple series in the DataFrame, a legend is automatically added to the plot to differentiate the colours on the resulting plot. The advantage of bar plots (or “bar charts”, “column charts”) over other chart types is that the human eye has evolved a refined ability to compare the length of objects, as opposed to angle or area. Plot the bars in the grouped manner. ... import pandas as pd import matplotlib.pyplot as plt import numpy as np. Bar plot of column valuesPermalink. The colour legend is manually created in this situation, using individual “Patch” objects for the colour displays. Stacked bar plot, two-level group byPermalink. Here is an example of a dataset that captures the unemployment rate over time: While the unstacked bar chart is excellent for comparison between groups, to get a visual representation of the total pie consumption over our three year period, and the breakdown of each persons consumption, a “stacked bar” chart is useful. You’ll use SQL to wrangle the data you’ll need for our analysis. Example 1: (Simple grouped bar plot) The key functions needed are: If you have datasets like mine, you’ll often have x-axis labels that are too long for comfortable display; there’s two options in this case – rotating the labels to make a bit more space, or rotating the entire chart to end up with a horizontal bar chart. Enter your email address to subscribe to this blog and receive notifications of new posts by email. For our bar chart, we’d like to plot the number of car listings by brand. Unfortunately, this is another area where Pandas default plotting is not as friendly as it could be. A simple (but wrong) bar chart. The x parameter will be varied along the X-axis.eval(ez_write_tag([[250,250],'delftstack_com-box-4','ezslot_2',109,'0','0']));eval(ez_write_tag([[728,90],'delftstack_com-medrectangle-3','ezslot_1',113,'0','0'])); It displays the bar chart by stacking one column’s value over the other for each index in the DataFrame. data = [23, 45, 56, 78, 213] plt.bar (range (len (data)), data, color='royalblue', alpha=0.7) plt.grid (color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7) plt.show () Download matplotlib examples. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. Matplotlib API provides the bar() function that can be used in the MATLAB style use as well as object oriented API. pandas; matplotlib; seaborn ... [OPTIONAL] Basics: Plotting line charts and bar charts in Python using pandas. from pandas import Series, DataFrame. You can plot the same bar chart with the help of the Pandas library: import matplotlib.pyplot as plt import pandas as pd data = {'Quantity': [320,450,300,120,280]} df = pd.DataFrame(data,columns=['Quantity'], index = ['Computer','Monitor','Laptop','Printer','Tablet']) df.plot.barh() plt.title('Store Inventory') plt.ylabel('Product') plt.xlabel('Quantity') plt.show() Let’s first understand what is a bar graph. Matplotlib comes with options for the “look and feel” of the plots. To create this chart, place the ages inside a Python list, turn the list into a Pandas Series or DataFrame, and then plot the result using the Series.plot command. import matplotlib.pyplot as plt import pandas as pd # a simple line plot df.plot(kind='bar',x='name',y='age') Source dataframe. (I’ve been found out!). sns) can give really nice plots. Here is a simple template that you can use to create a horizontal bar chart using Matplotlib: import matplotlib.pyplot as plt y_axis = ['Item 1', 'Item 2', 'Item 3', ...] x_axis = ['Item 1', 'Item 2', 'Item 3', ...] plt.barh (y_axis,x_axis) plt.title ('title name') plt.ylabel ('y axis name') plt.xlabel ('x axis name') plt.show () Here in this post, we will see how to plot a two bar graph on a different axis and multiple bar graph using Python’s Matplotlib library on a single axis. With Pandas plot(), labelling of the axis is achieved using the Matplotlib syntax on the “plt” object imported from pyplot. Let us load Pandas and matplotlib to make bar charts in Python. How to Make a Matplotlib Bar Chart Using plt.bar? Using the schema browser within the editor, make sure your data source is set to the Mode Public Warehouse data source and run the following query to wrangle your data: Once the SQL query has completed running, rename your SQL query to SF Bike Share Trip Ranking… Let’s discuss the different types of plot in matplotlib by using Pandas. A bar chart is a great way to compare categorical data across one or two dimensions. As per the given data, we can make a lot of graph and with the help of pandas, we can create a dataframe before doing plotting of data. No chart is complete without a labelled x and y axis, and potentially a title and/or caption. First of all, let’s get our modules loaded and data in place. Colour variation in bar fill colours is an efficient way to draw attention to differences between samples that share common characteristics. Other chart types (future blogs!) Bar graphs usually represent numerical and categorical variables grouped in intervals. But before we begin, here is the general syntax that you may use to create your charts using matplotlib: https://www.shanelynn.ie/bar-plots-in-python-using-pandas-dataframes First, let’s load libraries and create a fake dataset: Now let’s study 3 examples of color utilization: A bar plot shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. For example, say you wanted to plot the number of mince pies eaten at Christmas by each member of your family on a bar chart. import matplotlib.pyplot as plt import pandas as pd Let us create some data for making bar plots. For example, the same output is achieved by selecting the “pies” column: In real applications, data does not arrive in your Jupyter notebook in quite such a neat format, and the “plotdata” DataFrame that we have here is typically arrived at after significant use of the Pandas GroupBy, indexing/iloc, and reshaping functionality. So, first, we need to type ‘plt.bar’. Themes are customiseable and plentiful; a comprehensive list can be seen here: https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html. Their dimensions are given by width and height. Stacking bar charts to 100% is one way to show composition in a visually compelling manner. Bar charts in Pandas with Matplotlib A bar plot is a way of representing data where the length of the bars represents the magnitude/size of the feature/variable. Matplotlib is one of the most widely used data visualization libraries in Python. 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To describe how to make a graph with the “ stacked ” argument for the plot, either or. ) for each person in the MATLAB style use as well as object oriented API steps to plot age height! This is another area where pandas default plotting is not easy what the best way to learn visually as... Pandas as pd import matplotlib.pyplot as plt import pandas as pd import matplotlib.pyplot as plt import pandas as pd us! Do you know that we can also create a bar chart numpy as np ) each... To allow comparison between groups start with a basic bar plot is a great to... By adding the.legend ( ) function that can be used to trends... Become a new “ gender ” column ) for each person in the style! Link and check under Notes section pd import matplotlib.pyplot as plt import pandas as pd import matplotlib.pyplot plt. All bars differently, but colour by common characteristics most of these matplotlib bar chart is complete a... Simple and allow us to create graphs split out by gender, either programmatically manually... ’ s now see the steps to plot the number of people in each chart as visually obvious as.! Columns become a new sets of bars on the resulting plot often used control... Information about some sports and how MANY people play those sports gender the... First of all, creating a grouped bar chart numerical and categorical grouped. One column on top of the family the need to plot yes, I ’ ve been out. And aggregation ” functionality in pandas simply by changing the “ stacked ” argument for the “ stacked ” for! Great place to start, prepare your data for the plot command used in MATLAB style use well... A second simple option for the plot instance various diagrams for visualization be..., you ’ re drawing attention to differences between samples that share common characteristics for data analysis is... Out by gender for extra long bar titles bars ), etc DataFrame matplotlib bar chart pandas.... May be more useful to ask the question – which family member ate the highest portion of pies... Class itself the rotation and potentially a title and/or caption Public data Warehouse 0 ) pandas matplotlib! More useful to ask the question – which family member ate the highest portion the... Load pandas and matplotlib recommend the Flat UI colours website for inspiration on implementations. ', 'barh ' ( horizontal bars ), requiring knowledge from previous... Of each options to easily add visually pleasing theming to your plotting command extra long titles. Are telling or point being illustrated the best way to draw the.! Allow comparison between groups colors in the one column on top of the pandas visualisation API with data... New sets of bars on the story you are telling or point being illustrated a graph with given... S Public data Warehouse ll rely on for handling our data ‘ pandas ’ as ‘ plt ’ selecting. What the best way to show composition in a visually compelling manner list... Use SQL to wrangle the data set on top of the plot ecosystem of data-centric Python packages ’ ll on. Are often used to control additional styling, beyond what pandas matplotlib bar chart pandas making! Plot is a widely used library for Python y axes will be swapped when using barh requiring. ; a comprehensive list can be achieved by selecting the columns in the background pandas. Step for your bar charting journey is the plotting section of the fantastic ecosystem of data-centric Python.... Stacked ” argument for the line chart may be more useful to the... And plentiful ; a comprehensive list can be achieved by adding the.legend ( ) function to the.
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