将 matplotlib 绘图轴设置为数据框列名称 [英] Set matplotlib plot axis to be the dataframe column name

问题描述

我有一个像这样的数据框:

I have a dataframe like so:

data = DataFrame({'Sbet': [1,2,3,4,5], 'Length' : [2,4,6,8,10])

然后我有一个函数来绘制和拟合这些数据

Then I have a function that plots and fits this data

def lingregress(x,y):
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    r_sq = r_value ** 2

    plt.scatter(x,y)
    plt.plot(x,intercept + slope * x,c='r')

    print 'The slope is %.2f with R squared of %.2f' % (slope, r_sq)

然后我将在数据框上调用该函数:

Then I would call the function on the dataframe:

 linregress(data['Sbet'],data['Length'])

我的问题是如何在函数中将x轴标签和y轴标签设为 Sbet 和 Length ，并将图标题设为Sbet vs Length 我已经尝试了一些东西，但是当我使用 plt.xlabel(data['Sbet']) 和 plt 时，我倾向于恢复整列.title .

My question is how do I get the x axis label and y axis label to be Sbet and Length within the function as well as the plot title to be Sbet vs Length I've tried a few things but I tend to get the whole column back when I use plt.xlabel(data['Sbet']) and plt.title.

推荐答案

有序列

使用定义顺序的列构建数据框:

Ordered columns

Build your dataframe with the columns in a defined order:

data = DataFrame.from_items([('Sbet', [1,2,3,4,5]), ('Length', [2,4,6,8,10])])

现在您可以将第一列用作 x，将第二列用作 y:

Now you can use the first column as x and the second column as y:

def lingregress(data):
    x_name = data.columns[0]
    y_name = data.columns[1]
    x = data[x_name]
    y = data[y_name]
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    r_sq = r_value ** 2

    plt.scatter(x,y)
    plt.xlabel(x_name)
    plt.ylabel(y_name)
    plt.title('{x_name} vs. {y_name}'.format(x_name=x_name, y_name=y_name))
    plt.plot(x,intercept + slope * x,c='r')

    print('The slope is %.2f with R squared of %.2f' % (slope, r_sq))


lingregress(data)

明确的列名

字典没有有用的顺序.因此，您不知道列顺序，而需要显式提供名称顺序.

Explicit column names

Dictionaries have not useful order. Therefore, you don't know the column order and you need to supply the order of names explicitly.

这将起作用:

def lingregress(data, x_name, y_name):
    x = data[x_name]
    y = data[y_name]
    slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
    r_sq = r_value ** 2

    plt.scatter(x,y)
    plt.xlabel(x_name)
    plt.ylabel(y_name)
    plt.title('{x_name} vs. {y_name}'.format(x_name=x_name, y_name=y_name))
    plt.plot(x,intercept + slope * x,c='r')

    print('The slope is %.2f with R squared of %.2f' % (slope, r_sq))


lingregress(data, 'Sbet', 'Length')

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