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Python散點(diǎn)圖與折線圖繪制過(guò)程解析

更新時(shí)間：2019年11月30日 08:55:30 作者：wangxiaobei2019

這篇文章主要介紹了Python散點(diǎn)圖與折線圖繪制過(guò)程解析,文中通過(guò)示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友可以參考下

在數(shù)據(jù)分析的過(guò)程中，經(jīng)常需要將數(shù)據(jù)可視化，目前常使用的：散點(diǎn)圖折線圖

需要import的外部包一個(gè)是繪圖一個(gè)是字體導(dǎo)入

import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

在數(shù)據(jù)處理前需要獲取數(shù)據(jù)，從TXT XML csv excel 等文本中獲取需要的數(shù)據(jù)，保存到list

def GetFeatureList(full_path_file):
  file_name = full_path_file.split('\\')[-1][0:4]
  # print(file_name)
  # print(full_name)
  K0_list = []
  Area_list = []
  all_lines = []
  f = open(full_path_file,'r')
  all_lines = f.readlines()
  lines_num = len(all_lines)
  # 數(shù)據(jù)清洗
  if lines_num > 5000:
    for i in range(3,lines_num-1):
      temp_k0 = int(all_lines[i].split('\t')[1])
      if temp_k0 == 0:
        K0_list.append(ComputK0(all_lines[i]))
      else:
        K0_list.append(temp_k0)
      Area_list.append(float(all_lines[i].split('\t')[15]))
    # K0_Scatter(K0_list,Area_list,file_name)
  else:
    print('{} 該樣本量少于5000'.format(file_name))
  return K0_list, Area_list,file_name

繪制兩組數(shù)據(jù)的散點(diǎn)圖，同時(shí)繪制兩個(gè)散點(diǎn)圖，上下分布在同一個(gè)圖片中

def K0_Scatter(K0_list, area_list, pic_name):
  plt.figure(figsize=(25, 10), dpi=300)
  # 導(dǎo)入中文字體，及字體大小
  zhfont = FontProperties(fname='C:/Windows/Fonts/simsun.ttc', size=16)
  ax = plt.subplot(211)
  # print(K0_list)
  ax.scatter(range(len(K0_list)), K0_list, c='r', marker='o')
  plt.title(u'散點(diǎn)圖', fontproperties=zhfont)
  plt.xlabel('Sampling point', fontproperties=zhfont)
  plt.ylabel('K0_value', fontproperties=zhfont)
  ax = plt.subplot(212)
  ax.scatter(range(len(area_list)), area_list, c='b', marker='o')
  plt.xlabel('Sampling point', fontproperties=zhfont)
  plt.ylabel(u'大小', fontproperties=zhfont)
  plt.title(u'散點(diǎn)圖', fontproperties=zhfont)
  # imgname = 'E:\\' + pic_name + '.png'
  # plt.savefig(imgname, bbox_inches = 'tight')
  plt.show()

散點(diǎn)圖顯示

繪制一個(gè)折線圖每個(gè)數(shù)據(jù)增加標(biāo)簽

def K0_Plot(X_label, Y_label, pic_name):
  plt.figure(figsize=(25, 10), dpi=300)
  # 導(dǎo)入中文字體，及字體大小
  zhfont = FontProperties(fname='C:/Windows/Fonts/simsun.ttc', size=16)
  ax = plt.subplot(111)
  # print(K0_list)
  ax.plot(X_label, Y_label, c='r', marker='o')
  plt.title(pic_name, fontproperties=zhfont)
  plt.xlabel('coal_name', fontproperties=zhfont)
  plt.ylabel(pic_name, fontproperties=zhfont)
  # ax.xaxis.grid(True, which='major')
  ax.yaxis.grid(True, which='major')
  for a, b in zip(X_label, Y_label):
    str_label = a + str(b) + '%'
    plt.text(a, b, str_label, ha='center', va='bottom', fontsize=10)
  imgname = 'E:\\' + pic_name + '.png'
  plt.savefig(imgname, bbox_inches = 'tight')
  # plt.show()

繪制多條折線圖

def K0_MultPlot(dis_name, dis_lsit, pic_name):
  plt.figure(figsize=(80, 10), dpi=300)
  # 導(dǎo)入中文字體，及字體大小
  zhfont = FontProperties(fname='C:/Windows/Fonts/simsun.ttc', size=16)
  ax = plt.subplot(111)
  X_label = range(len(dis_lsit[1]))
  p1 = ax.plot(X_label, dis_lsit[1], c='r', marker='o',label='Euclidean Distance')
  p2 = ax.plot(X_label, dis_lsit[2], c='b', marker='o',label='Manhattan Distance')
  p3 = ax.plot(X_label, dis_lsit[4], c='y', marker='o',label='Chebyshev Distance')
  p4 = ax.plot(X_label, dis_lsit[5], c='g', marker='o',label='weighted Minkowski Distance')
  plt.legend()
  plt.title(pic_name, fontproperties=zhfont)
  plt.xlabel('coal_name', fontproperties=zhfont)
  plt.ylabel(pic_name, fontproperties=zhfont)
  # ax.xaxis.grid(True, which='major')
  ax.yaxis.grid(True, which='major')
  for a, b,c in zip(X_label, dis_lsit[5],dis_name):
    str_label = c + '_'+ str(b)
    plt.text(a, b, str_label, ha='center', va='bottom', fontsize=5)
  imgname = 'E:\\' + pic_name + '.png'
  plt.savefig(imgname,bbox_inches = 'tight')
  # plt.show()