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This article mainly explains "how to draw a lollipop chart with Python". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to draw a lollipop chart with Python".

The birth data of China from 1949 to 2019 are used, and the data are from the National Bureau of Statistics.

Read the data first.

Import pandas as pdimport matplotlib.pyplot as plt# read data df = pd.read_csv ('data.csv') print (df)

The dataset is simple, with only one year and one value per row.

First draw a bar chart with annual values.

# draw histogram plt.bar (df.Year, df.value) plt.show ()

You can get a bar chart with two lines of code, and it does look a little crowded.

Here is a distinction between the data for the last year, 2019.

The 2019 bar is colored black and the other years light gray.

And add a scatter chart to the chart to draw a circle at the top of the bar chart.

# New canvas fig, ax = plt.subplots (1, figsize= (12,8)) # years n = len (df) # Color setting colors = ['black'] + ((NME1) * [lightgrey']) plt.bar (df.Year, df.value, color=colors) plt.scatter (df.Year, df.value, color=colors) plt.show ()

The color has been modified successfully, and you need to adjust the width of the bar chart and the size of the circle at the top.

# width: bar chart width s: scatter chart circle size plt.bar (df.Year, df.value, color=colors, width=0.2) plt.scatter (df.Year, df.value, color=colors, plt.show 10) plt.show ()

Compared to the previous blue bar chart, the lollipop chart does look a lot better.

In addition to drawing lollipop charts with bar graphs, you can also use lines so that the overall width is more consistent.

X takes Year (year) data as the starting point and end point, and Y uses-20 and year data as the starting point and end point.

Import pandas as pdimport matplotlib.pyplot as plt# read data df = pd.read_csv ('data.csv') print (df) # New canvas fig, ax = plt.subplots (1, figsize= (12, 8)) # years n = len (df) # Color setting colors = [' black'] + ((NMMI 1) * ['lightgrey']) # use lines for idx, val in df.iterrows (): plt.plot ([val.Year, val.Year] [- 20, val.value], color= colors [IDX]) plt.show ()

Instead of generating scatter plots at the top, you can use parameter markers to draw circles at both ends.

You can then hide the innermost circle by changing the y-limit parameter.

# New canvas fig, ax = plt.subplots (1, figsize= (12,8)) # number of years n = len (df) # Color setting colors = ['black'] + ((NME1) * [' lightgrey']) # use lines, markersize to set marker point size for idx, val in df.iterrows (): plt.plot ([val.Year, val.Year], [- 20, val.value], color=colors [idx] Marker='o', markersize=3) # set the minimum y-axis value plt.ylim (0,) plt.show ()

In addition, you can adjust the lw and markersize parameters, define the thickness of the line and the size of the mark, and even draw the line twice to create an outline effect.

# New canvas fig, ax = plt.subplots (1, figsize= (12, 8)) color='b'# years n = len (df) # Color setting colors = ['black'] + ((nMuth1) * [' lightgrey']) # use lines for idx, val in df.iterrows (): plt.plot ([val.Year, val.Year], [- 20, val.value], color='black' Marker='o', lw=4, markersize=6) plt.plot ([val.Year, val.Year], [- 20, val.value], color=colors [idx], marker='o' Markersize=4) # remove the top border, right border ax.spines ['right']. Set_visible (False) ax.spines [' top']. Set_visible (False) # set x and y axis range plt.xlim (1948, 2020) plt.ylim (0,) # Chinese display plt.rcParams ['font.sans-serif'] = [' Songti SC'] plt.title ('Chinese birth data (10,000)', loc='left', fontsize=16) plt.text (2019) -220, 'Source: national Bureau of Statistics, ha='right') # number of people born in 2019 (shown) value_2019 = df [df [' Year'] = = 2019] .value.values [0] plt.text (2019, value_2019+80, value_2019, ha='center') # Save picture plt.savefig ('chart.png')

Black is not very good-looking, change the color to have a look.

# New canvas fig, ax = plt.subplots (1, figsize= (12,8)) # years n = len (df) # Color setting color= 'b'colors = [' # E74C3C'] + ((len (df)-1) * ['# F5B7B1']) # use lines for idx, val in df.iterrows (): plt.plot ([val.Year, val.Year], [- 20, val.value], color=colors [idx] Marker='o', lw=4, markersize=6, markerfacecolor='#E74C3C') # remove the top border, right border ax.spines ['right'] .set_visible (False) ax.spines [' top'] .set_visible (False) # set x, y axis range plt.xlim (1948, 2020) plt.ylim (0 ) # Chinese display plt.rcParams ['font.sans-serif'] = [' Songti SC'] plt.title ('Chinese birth data (10,000)', loc='left', fontsize=16) plt.text (2019,-220, 'Source: national Bureau of Statistics, ha='right') # number of births in 2019 (shown) value_2019 = df [df [' Year'] = 2019] .value.values [0] plt.text (2019, value_2019+80, value_2019) Ha='center') # Save picture plt.savefig ('chart.png') Thank you for reading The above is the content of "how to draw a lollipop chart with Python". After the study of this article, I believe you have a deeper understanding of how to draw a lollipop chart with Python, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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