![]() You can either manually enter the list of color names or can use the color palettes available in matplotlib using a colormap. You can do it by specifying the value for the parameter color in the () function, which can accept the list of color names or color codes or color hash codes. You can specify different colors to different bars in a bar chart. Read: Matplotlib plot a line Matplotlib plot bar chart with different colors # Increase the size of the figure (chart) ![]() Plt.title('Top 10 football goal scorers of all time') 'Gerd Muller', 'Eusebio', 'Joe Bambrick'] 'Ferenc Puskas', 'Josef Bican', 'Jimmy Jones', Players = ['Cristiano Ronaldo', 'Pele', 'Romario', 'Lionel Messi', You can specify the size of the chart using matplotlib in python by using () function with parameter figsize, which can accept a list of two values representing the width and height of the figure/chart. Read: How to install matplotlib python Matplotlib plot bar chart size Plt.bar(blogs, posts, width=0.7, bottom=50, align='edge') # Creating a bar chart with the parameters Plt.title('The posts in different blogs') Show the graph/plot/chart defined above.Įxample : # Importing the required librariesīlogs =.Plot command to define the plot with the features (parameters to the command) you want to add on the plot (plot with diffrent color bars, labels, title, legend, etc).Define the data to be visualized (Define the x and y axis values).Import the libraries necessary to plot the graph (numpy or/and pandas for data inclusion or creation or manipulation, pyplot from the matplotlib for data visualization, etc.).You can follow the following mentioned general steps to create a bar chart: log to set the scale of the height axis to the log if set True.capsize to specify the length of the errorbar caps in points, the default is 0.0.ecolor to specify the line color of the errorbars, the default is ‘black’.yerr/xerr to specify the error bars at the tip of the bars.tick_label to specify the tick labels of the bars.linewidth to specify the width of the edge of the bars, if 0 no edges will be drawn.edgecolor to specify the color of the edges of the bars.color to specify the color of the bars.There are other parameters also that you can specify according to your needs like:.Set it ‘edge’ with the negative value of the width to align the bars so as the right edges of the bars get at the ticks positions.Set it ‘edge’ to align the bars so as the left edges of the bars get at the ticks positions.Set it ‘centre’ to align the bars so as the ticks positions get to the centre of the base of the bars.You can specify the alignment of the bars to the categories axis ticks.You can specify the bases of the bars (y-cordinate of the bars bases) in bottom.You can specify the width of the bars in width. ![]() ![]() The heights specify the value or list/array of the values against each respective categories.The categories specify the value or list/array of the categories to be compared.The syntax of the bar() function is as follows: (categories, heights ) You can use the function bar() of the submodule pyplot of module (library) matplotlib to create a bar plot/chart/graph in python. One of the axes shows the categories to be compared and the other one shows the values of those categories. You can plot bars vertically or horizontally according to your needs. It means that the bar plots are used to visualize the categorical data (compare the categories in a data). Pyplot provides a variety of plots and functions related to them.Ī bar chart is a type of figure that presents different categories in data as rectangular bars with the heights or lengths proportional to the values that these categories represent respectively. You can create bar plots or bar charts or bar graphs in python using the API pyplot provided by the matplotlib library as a submodule in matplotlib. This library is built on the Numpy arrays in python. It supports a wide variety of data visualization tools to make 2D plots from the data provided by different sources or of different types like from lists, arrays, dictionaries, DataFrames, JSON files, CSV files, etc. Matplotlib is the most commonly used data visualization tool-rich library in python. Matplotlib plot horizontal bar chart Matplotlib plot bar chart
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