matplotlib

最新推荐文章于 2024-09-07 00:11:42 发布

focus_reality

最新推荐文章于 2024-09-07 00:11:42 发布

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博客围绕matplotlib展开，但具体内容缺失。matplotlib是信息技术领域用于数据可视化的工具，可绘制多种图形，在数据分析等场景有重要应用。

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# line plot
import matplotlib.pyplot as plt
plt.plot(x,y)
plt.show()

# Import package
import matplotlib.pyplot as plt
# scatter plot
plt.scatter(x,y)
# Put the x-axis on a logarithmic scale
plt.xscale('log')
# Show plot
plt.show()

import matplotlib.pyplot as plt
# Build histogram with 5 bins
plt.hist(life_exp, bins = 5)
# Show and clean up plot
plt.show()
plt.clf()
# Build histogram with 20 bins
plt.hist(life_exp, bins = 20)
# Show and clean up again
plt.show()
plt.clf()

# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log') 

# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'
# Add axis labels
plt.xlabel(xlab)
plt.ylabel(ylab)
# Add title
plt.title(title)
# After customizing, display the plot
plt.show()

在这里插入图片描述

# Scatter plot
plt.scatter(gdp_cap, life_exp)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')

# Definition of tick_val and tick_lab
tick_val = [1000, 10000, 100000]
tick_lab = ['1k', '10k', '100k']

# Adapt the ticks on the x-axis
plt.xticks(tick_val, tick_lab)

# After customizing, display the plot
plt.show()

在这里插入图片描述

# Import numpy as np
import numpy as np
# Store pop as a numpy array: np_pop
np_pop = np.array(pop)
# Double np_pop
np_pop *= 2
# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = np_pop)
# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])
# Display the plot
plt.show()

在这里插入图片描述

# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c =col, alpha = 0.8)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000,10000,100000], ['1k','10k','100k'])
# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')
# Add grid() call
plt.grid(True)
# Show the plot
plt.show()

在这里插入图片描述