matplotlib画条形图

#!usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

base_dir = 'd:/Pattern/'
input_base_dir = base_dir + 'data_graph_save/'
rmse_list = []
xlabel_list = [str(x) for x in range(6, 241, 6)]
xlabel = tuple(xlabel_list)
print(xlabel)
for i in range(6, 241, 6):
    fileName = input_base_dir + 'output_' + str(i) + '.csv'
    print(fileName)
    data = pd.read_csv(fileName)
    n_data = data.shape[0]
    data_true = data.iloc[:, 6]
    data_predict = data.iloc[:, 7]

    rmse_predict = np.sqrt(np.array(np.square(data_predict - data_true)).sum() / n_data)
    rmse_list.append(rmse_predict)

rmse_array = np.array(rmse_list).round(1)
print(rmse_array)

plt.figure(figsize=(15, 8), dpi=80)
ax = plt.subplot(1, 1, 1)

# plt.hist(pd.Series(rmse_array), bins=rmse_array.shape)
width = 0.5
x_bar = np.arange(len(xlabel_list))
rect1 = ax.bar(left=x_bar, height=rmse_array, width=width, color="blue")

for rec in rect1:
    x = rec.get_x()
    height = rec.get_height()
    ax.text(x-0.2, 1.02*height, str(height))

ax.set_xticks(x_bar)
ax.set_xticklabels(xlabel)

plt.title('Rmse Constraction')
plt.xlabel('Forecast time')
plt.ylabel('Rmse')
plt.savefig('Rmse_tmp.png')
plt.show()

结果