当前的学习也是调参的过程
matplotlib
plot
# Print the last item of gdp_cap and life_exp
print(gdp_cap)
print(life_exp)
# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap,life_exp)
# Display the plot
plt.show()Scatter Plot
# Change the line plot below to a scatter plot
plt.plot(gdp_cap, life_exp)
# Put the x-axis on a logarithmic scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log')
# Show plot
plt.show()histogram
# Create histogram of life_exp data
plt.hist(life_exp)
# Display histogram
plt.show()
# Build histogram with 5 bins
plt.hist(life_exp,bins=5)
# Show and clean up plot
plt.show()
plt.clf()
Customization
自定义绘图
# 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()# 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()# 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'])
# Show the plot
plt.show()