df.plot(kind='scatter', x='Height', y='Weight', title='Height vs Weight', xlabel='Height (cm)', ylabel='Weight (kg)', figsize=(8, 5)) plt.show()输出:4. 直方图 (Histogram)直方图用于显示数据的分布,特别是用于描述数据的频率分布。实例 import
- 'bar' : vertical bar plot - 'barh' : horizontal bar plot - 'hist' : histogram - 'box' : boxplot - 'kde' : Kernel Density Estimation plot - 'density' : same as 'kde' - 'area' : area plot - 'pie' : pie plot - 'scatter' : scatter plot (DataFrame only) - 'hexbin' :...
>>> pandas_ai.run(df, prompt='What is the sum of the GDPs of the 2 unhappiest countries?') >>> 19012600725504 >>> pandas_ai.run( df, "Plot the histogram of countries showing for each the gpd, using different colors for each bar", ) 赞叹之余,不免好奇这样集成是如何实现的呢?随手查...
# Quick examples of pandas histogram# Example 1: Plot the histogram from DataFramedf.hist()# Example 2: Customize the bins of histogramdf.hist(bins=3)# Example 3: create histogram of specified columndf.hist(column='Maths')# Example 4: Plot the histogram# Using plot()df.plot(kind='hist'...
• ‘hist’ for histogram #频率柱状图(计算某些值出现的频率) • ‘box’ for boxplot #箱线图() • ‘kde’ or ‘density’ for density plots #密度图(需要scipy这个包) • ‘area’ for area plots #区域图(不同域的面积占比)
‘hist’for histogram ‘box’for boxplot ‘kde’or‘density’for density plots。即Kernel Density Estimate 和密度估计,常常与hist一起混合使用。 ‘area’for area plots ‘scatter’for scatter plots ‘hexbin’for hexagonal bin plots ‘pie’for pie plots ...
1>>>df.plot(kind='bar',stacked=True,alpha=0.5)2<matplotlib.axes._subplots.AxesSubplotobjectat0x0000024F3A2F2048>3>>>plt.show() 2.3 直方图和密度图 直方图(histogram)是一种可以对值频率进行离散化显示的柱状图。数据点被拆分到离散的、间隔均匀的面元中,绘制的是各面元中数据点的数量。
plot.density(**kwds) #核密度Kernel Density Estimate plot DataFrame.plot.hexbin(x, y[, C,…]) #Hexbin plot DataFrame.plot.hist([by, bins]) #直方图Histogram DataFrame.plot.kde(**kwds) #核密度Kernel Density Estimate plot DataFrame.plot.line([x, y]) #线图Line plot DataFrame.plot.pie(...
df.plot.kde() 1. 05、密度图-density df.plot.density() 1. 06、直方图 换个数据集 df=pd.DataFrame({'A':np.random.randn(1000)+1, 'B':np.random.randn(1000), 'C':np.random.randn(1000)-1}, columns=['A','B','C']) df.plot.hist(bins=200) ...
要创建绘图，请对数据调用.plot（kind = ），如下所示：np.exp(data[data['Year']==2018]['Log GDP per capita']).plot( kind='hist' )运行上面的命令将产生以下图表。2018: Histogram of the number of countries per GDP per Capita bucket. Not surprisingly, most countri 使用Pandas进行绘图时，有五...