Question

我要在同一图中绘制3个数据矩阵。我的代码如下所示，为了使问题更清楚地显示，我不得不将数据帧制作得很长：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

profile_features = ['Col1']
question_features = ['Col2']

fig = plt.figure(figsize=(10,5))

count = 1
for pf in profile_features:
    for qf in question_features:

        data1 = pd.DataFrame(columns=['Col1', 'Col2'])
        data1['Col1'] = [0, 2, 1, 1, -1, 0, -2, 0, 0, 0, 0, -2, 0, 0, 0, 0, 1, -1, 1, 0, 1, -1, 1, 0, -1, 1, 2, 1, -2, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 1, 0, 0, -1, -1, -1, -2, 1, -2, -1, 0, 0, -2, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, -2, 1, -1, 0, 1, -1, 0, 0, 1, -1, 0, 0, 0, 1, 0, -1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 0, -2, 2, -1, 1, -2, 0, 0, 0, 1, 0, 0, 0, 1, -2, 0, 0, 0, 0, 0, 0, 0, 0, -1, -2, -1, 0, -1, 0, 1, -1, -1, -2, 0, -2, 0, 1, 0, -1, 1, 0, 0, 0, -2, -1, -2, -2, 0, 0, 1, 0, 0, 0, 1, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 1, 1, 0, 1, -1, -1, 0, 0, 0, 0, -2, 1, 1, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 2, -1, -2, 0, 0, 0, 1, 0, 1, 0, 0, -1, 0, 0, 1, 1, 0, -2, 0, 1, -1, -2, 0, 1, 0, 1, 0, 0, 1, -1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 1, 1, 1, -2, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, -2, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, -2, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, -2, 0, 0, 0, 0, -1, 1, -2, 1, 0, -1, 0, 0, -2, 2, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, -2, -1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, -2, 0, -1, 0, 0, 0, 0, 0, 1, 0, 1, -2, -2, 0, 1, -1, 0, 0, 1, 0, 1, -2, 0, 0, 0, -1, 0, 0, 0, -2, 0, -2, 0, -1, 0, 1, 1, -2, 1, 0, -2, 0, 0, 1, -2, -1, -2, -1, 0, 0, 1, 0, -1, 0, 0, 0, -2, -2, -1, 0, 0, -1, -1, 0, 1, -2, 0, 1, 0, 0, 0, 0, 0, -1, 1, 0, 0, -1, 0, 1, 0, 0, 0, 2, -1, 0, -1, -1, -1, 0, 0, 1, 0, 0, 0, 0, 0, -1, 1, 0, 1, 0, 2, -2, 2, 2, -2, 1, -2, 1]
        data1['Col2'] = [0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 0, 2, 0, 0, 1, 3, 1, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 6, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 7, 1, 0, 0, 0, 0, 0, 0, 5, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 5, 4, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 1, 1, 3, 0, 0, 4, 1, 0, 4, 0, 0, 1, 2, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4]

        data2 = pd.DataFrame(columns=['Col1', 'Col2'])
        data2['Col1'] = [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 1, 2, 0, 1, 1, 0, 0, 1, 1, 0, 1, 2, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 1, 2, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, -1, 1, 0, 1, 1, 0, 2, 1, 0, 0, 1, 0, 0, 0, 1, 2, 0, -1, 2, 1, 1, 1, 1, 1, 1, -1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, -1, 1, 0, 2, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 2, -1, 1, 0, 1, 0, 1, -1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 1, 1, 1, 1, -1, 0, 2, -1, 0, 1, 0, 0, 2, 0, 0, 2, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 0, 0, -1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, -1, 2, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 2, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, -1, 2, 2, 1, 1, 0, 0, 2, 1, 2, 1, 1, 0, 0, 0, 2, 0, 1, 1, 1, 0, 0, 1, 1, -1, 1, 1, 0, 1, 1, 0, -1, 1, 0, 1, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, -1, 1, -1, 0, -1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 2, 0, 0, 0, 2, 1, 1, 0, 0, 1, -1, 0, 1, 0, -1, 1, 1, 0, 1, 1, 0, 1, -2, 1, 1, 1, 2, 0, -2, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 2, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, -1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 2, 1, 0]
        data2['Col2'] = [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 3, 0, 0, 6, 0, 2, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 1, 0, 1, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 3, 2, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 1, 0, 1, 0, 0, 0, 4, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 3, 2, 0, 0]

        data3 = pd.DataFrame(columns=['Col1', 'Col2'])
        data3['Col1'] = [1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 1, 2, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, -1, 2, 2, 0, 1, 0, 1, 2, 2, 1, 0, 1, 1, 2, 0, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 0, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 2, 0, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 2, 2, 1, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 0, 1, 1, 2, 1, 2, 0, 1, -1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 1]
        data3['Col2'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 0, 0, 3, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 8, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 8, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 2, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 4, 1, 1]


        if len(data1[pf].unique())<10:
            nbins = len(data1[pf].unique())
        else:
            nbins = 10




        _, bins = np.histogram(data1[pf], nbins)
        for i in range(len(bins)):
            bins[i] = int(bins[i])
        labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])] #dynamically create labels
        labels[0] = '{}-{}'.format(bins[0], bins[1])
        if len(data1[pf].unique())<10:
            binned = pd.cut(data1[pf], bins=nbins)
        else:
            binned = pd.cut(data1[pf], bins=bins, labels=labels, include_lowest=True, precision=0)
        df1 = data1.groupby(binned)[qf].mean().reset_index()



        _, bins = np.histogram(data1[pf], nbins)
        for i in range(len(bins)):
            bins[i] = int(bins[i])
        labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])] #dynamically create labels
        labels[0] = '{}-{}'.format(bins[0], bins[1])
        if len(data2[pf].unique())<10:
            binned = pd.cut(data2[pf], bins=nbins)
        else:
            binned = pd.cut(data2[pf], bins=bins, labels=labels, include_lowest=True, precision=0)
        df2 = data2.groupby(binned)[qf].mean().reset_index()



        _, bins = np.histogram(data1[pf], nbins)
        for i in range(len(bins)):
            bins[i] = int(bins[i])
        labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])] #dynamically create labels
        labels[0] = '{}-{}'.format(bins[0], bins[1])
        if len(data3[pf].unique())<10:
            binned = pd.cut(data3[pf], bins=nbins)
        else:
            binned = pd.cut(data3[pf], bins=bins, labels=labels, include_lowest=True, precision=0)
        df3 = data3.groupby(binned)[qf].mean().reset_index()




        plt.subplot(len(profile_features),len(question_features),count)

        sns.set_style("darkgrid")
        plt.plot([str(i) for i in df1[pf]], df1[qf], 'o-')
        plt.plot([str(i) for i in df2[pf]], df2[qf], 'd-')
        plt.plot([str(i) for i in df3[pf]], df3[qf], 's-')

        plt.xticks(rotation=45)
        plt.xlabel(pf)
        plt.ylabel('Mean '+qf)

        plt.legend(['Plot 1', 'Plot 2', 'Plot 3'])

        fig.subplots_adjust(hspace=0.5)

        count += 1

这将产生以下情节：

图1和图2的x轴相同，但图3的x轴不同。

如果我按如下所示分别绘制3个图，则可以更清楚地理解这一点，您可以在其中看到第三个图具有不同的x轴：

fig = plt.figure(figsize=(5,10))

plt.subplot(3,1,1)
plt.plot([str(i) for i in df1[pf]], df1[qf], 'o-')

plt.subplot(3,1,2)
plt.plot([str(i) for i in df2[pf]], df2[qf], 'd-')

plt.subplot(3,1,3)
plt.plot([str(i) for i in df3[pf]], df3[qf], 's-')

可能是因为Col1，data1和data2中data3中的值范围不同：

print(max(data1['Col1']), max(data2['Col2']), max(data3['Col1']))
>>> 6 5 3

那么，如何使装箱保持一致，以便所有3个图在同一图中以相同的x轴显示？

在matplotlib（或seaborn）的同一图形中绘制多个子图时，如何使x轴一致？

0 个答案: