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This article mainly introduces how to use python to draw column chart related knowledge, the content is detailed and easy to understand, the operation is simple and fast, has a certain reference value, I believe you will have something to gain after reading this article on how to draw a column chart with python, let's take a look at it.

# column chart import pandasimport numpyimport matplotlib from matplotlib import pyplot as plt# import data data_columns=pandas.read_csv ('D://Python projects//reference data//6.4//data.csv') # define Chinese format font= {' family':'MicroSoft Yahei', 'weight':'bold',' size':12} matplotlib.rc ('font',**font) # use mobile phone brand as grouping column Monthly consumption as a statistical column result_columns=data_columns.groupby (by= ['mobile phone brand'], as_index=False) ['monthly consumption (yuan)'] .agg ({'monthly total consumption': numpy.sum}) # generates a sequence index=numpy.arange with an interval of 1 (result_columns. Total monthly consumption. Size) # draw a vertical bar chart plt.bar (index,result_columns ['monthly total consumption']) #% matplotlib qtplt.show () # configure color maincolor= (42go 256277) plt.bar (index,result_columns ['monthly total consumption']) plt.show () # configure X-axis label plt.bar (index,result_columns ['monthly total consumption']) plt.xticks (index,result_columns. Mobile brand) plt.show () # sorts the data in descending order and displays result_asd=result_columns.sort_values (by=' monthly consumption', ascending=False) plt.bar (index, result_asd. Total monthly consumption, color=maincolor) plt.xticks (index,result_asd. Mobile phone brand) plt.show ()

The result is:

# horizontal bar chart result_asd=result_columns.sort_values (by=' monthly total consumption', ascending=False) plt.barh (index, result_asd. Total monthly consumption, color=maincolor) plt.yticks (index,result_asd. Mobile phone brand) plt.show ()

The result is:

# calculate the data result=data_columns.pivot_table of crosstab (values=' monthly consumption (CNY)', index=' mobile brand', columns=' communication brand', aggfunc=numpy.sum)

The result is:

# define three colors index=numpy.arange (len (result)) mincolor= (42x256) plt.bar (index+1/4, result) plt.bar (index+1/4, result ['dynamic zone'], color=midcolor Width=1/4) plt.bar (index+1/2, result ['Shenzhouxing'], color=maxcolor, width=1/4) plt.xticks (index+1/3,result.index) # add legend plt.legend (['GSM', 'dynamic Zone', 'Shenzhouxing']) plt.show ()

The result is:

# reorder to draw result=result.sort_values (by=' Shenzhouxing', ascending=False) plt.bar (index, result ['GSM'], color=mincolor, width=1/4) plt.bar (index+1/4, result ['dynamic zone'], color=midcolor, width=1/4) plt.bar (index+1/2 Result ['Shenzhouxing'], color=maxcolor, width=1/4) plt.xticks (index+1/3,result.index) plt.legend (['GSM', 'dynamic Zone', 'Shenzhouxing']) plt.show ()

The result is:

# draw stacked bar chart result=result.sort_values (by=' Shenzhouxing', ascending=False) plt.bar (index, result ['GSM'], color=maxcolor) plt.bar (index, result ['dynamic Zone'], bottom=result ['GSM'], color=midcolor) plt.bar (index, result ['Shenzhouxing'] Bottom=result ['GSM'] + result ['dynamic zone'], color=mincolor) plt.xticks (index,result.index) plt.legend (['GSM', 'dynamic zone', 'Shenzhouxing']) plt.show ()

The result is:

# draw bi-directional bar chart plt.barh (index, result ['Shenzhouxing'], color=midcolor) plt.barh (index,-result ['dynamic zone'], color=maxcolor) plt.yticks (index, result.index) plt.legend (['dynamic zone', 'Shenzhouxing']) plt.show ()

The result is:

This is the end of the article on "how to draw a column chart with python". Thank you for reading! I believe you all have a certain understanding of "how to draw a bar chart with python". If you want to learn more, you are welcome to follow the industry information channel.

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