The next step for your bar charting journey is the need to compare series from a different set of samples. To start, prepare your data for the line chart. https://www.shanelynn.ie/bar-plots-in-python-using-pandas-dataframes 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. A bar chart is a great way to compare categorical data across one or two dimensions. Make live graphs with dynamic line, scatter and bar plots. 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. The next dimension to play with on bar charts is different categories of bar. Let us see how we will do so. 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') No chart is complete without a labelled x and y axis, and potentially a title and/or caption. Suppose if we have a data frame, we can directly create different types of plots like scatter, bar, line using a single function. A great place to start is the plotting section of the pandas DataFrame documentation. Using the plot instance various diagrams for visualization can be drawn including the Bar Chart. Now define a dictionary that maps the gender values to colours, and use the Pandas “replace” function to insert these into the plotting command. 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. 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. The default look and feel for the Matplotlib plots produced with the Pandas library are sometimes not aesthetically amazing for those with an eye for colour or design. The choice of chart depends on the story you are telling or point being illustrated. Let’s first understand what is a bar graph. With multiple series in the DataFrame, a legend is automatically added to the plot to differentiate the colours on the resulting plot. matplotlib.pyplot.bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs) [source] ¶. Unfortunately, this is another area where Pandas default plotting is not as friendly as it could be. 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. Example 1: (Simple grouped bar plot) 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') 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). Luckily, the ‘PyPlot’ module from Matplotlib has a readily available bar plot function. 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