numpy模块、matplotlib模块、pandas模块

目录

• 1. numpy模块
• 2. matplotlib模块
• 3. pandas模块

1. numpy模块

• numpy模块的作用

  用来做数据分析，对numpy数组（既有行又有列）——矩阵进行科学计算

• 实例

  lt1 = [1, 2, 3]  # n个元素
  lt2 = [4, 5, 6]
  
  lt = []
  for i in range(len(lt1)):  # O(n)
      lt.append(lt1[i] * lt2[i])
  
  print(lt)
  
  import numpy as np  # 约定俗成的
  
  arr1 = np.array([1, 2, 3])
  arr2 = np.array([4, 5, 6])
  print(arr1 * arr2)
  
  # gpu --> 图形显卡
  
  # 创建numpy数组 --> 可变
  
  # 一维数组(不在讨论范围内)
  arr = np.array([1, 2, 4])
  print(type(arr), arr)
  
  # 二维数组（******）
  arr = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  print(arr)
  
  # 三维数组（不在讨论范围内）--》tensorflow
  arr3 = np.array([
      [[1, 2, 3],
       [4, 5, 6]],
      [[1, 2, 3],
       [4, 5, 6]],
  ])
  print(arr)
  
  # numpy数组的属性
  
  arr = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  
  # T	数组的转置（对高维数组而言） --> 行列互换，转置
  print(arr, '
  ', arr.T)
  
  # dtype	数组元素的数据类型,numpy数组是属于python解释器的；int32/float64属于numpy的
  print(arr.dtype)
  '''
  # 定制化的科学计算机
  11111111111111111111111111111111111111111
  '''
  # size	数组元素的个数
  print(arr.size)
  # ndim	数组的维数
  print(arr.ndim)
  print(arr3.ndim)
  # shape	数组的维度大小（以元组形式）
  print(arr.shape[0])
  print(arr.shape[1])
  # astype	类型转换
  arr = arr.astype(np.float64)
  print(arr)
  
  # 切片numpy数组
  lt = [1, 2, 3]
  
  print(lt[:])
  
  arr = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  
  print(arr[:, :])  # 行，列
  
  print(arr[0, 0])
  
  print(arr[0, :])
  
  print(arr[:, -2:])
  
  # 逻辑取值
  print(arr[arr > 4])
  
  # 赋值
  lt = [1, 2, 3]
  
  lt[:] = [0, 0, 0]
  print(lt)
  
  arr = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  
  arr[0, 0] = 0
  print(arr)
  
  arr[0, :] = 0
  print(arr)
  
  arr[:, :] = 0
  print(arr)
  
  # 数组的合并
  
  arr1 = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  
  arr2 = np.array([
      [7, 8, 9],
      ['a', 'b', 'c']
  ])
  
  print(np.hstack((arr1, arr2)))  # 只能放元组
  
  print(np.vstack((arr1, arr2)))
  
  print(np.concatenate((arr1, arr2), axis=1))  # 默认以列合并 # 0表示列，1表示行
  
  # 通过函数创建numpy数组
  
  print(np.ones((2, 3)))
  
  print(np.zeros((2, 3)))
  
  print(np.eye(3, 3))
  
  print(np.linspace(1, 100, 10))
  
  print(np.arange(2, 10))
  
  arr1 = np.zeros((1, 12))
  print(arr1.reshape((3, 4)))  # 重构形状
  
  # numpy数组运算
  
  # +-*'
  arr1 = np.ones((3, 4)) * 4
  print(arr1)
  
  # numpy数组运算函数
  
  print(np.sin(arr1))
  
  # 矩阵运算--点乘
  
  arr1 = np.array([
      [1, 2, 3],
      [4, 5, 6]
  ])
  
  arr2 = np.array([
      [1, 2],
      [4, 5],
      [6, 7]
  ])
  # 2* 3 3*2
  print(np.dot(arr1, arr2))
  
  # 求逆
  arr = np.array([[1, 2, 3], [4, 5, 6], [9, 8, 9]])
  print(np.linalg.inv(arr))
  
  # numpy数组数学和统计方法
  print(np.sum(arr[0, :]))
  
  # numpy.random生成随机数(******)
  print(np.random.rand(3, 4))
  
  print(np.random.random((3, 4)))
  
  # np.random.seed(1)
  print(np.random.random((3, 4)))
  
  s = np.random.RandomState(1)
  print(s.random((3, 4)))
  
  arr = np.array([[1, 2, 3], [4, 5, 6], [9, 8, 9]])
  np.random.shuffle(arr)
  print(arr)
  
  # 针对一维
  print(np.random.choice([1, 2, 3], 1))
  
  # 针对某一个范围
  print(np.random.randint(1, 100, (3, 4)))
  

2. matplotlib模块

• matplotlib模块的作用

  画图（画各种与数据相关的图）

• 实例

  # 条形图
  # from matplotlib import pyplot as plt  # 约定俗成
  # from matplotlib.font_manager import FontProperties  # 修改字体
  #
  # font = FontProperties(fname='C:WindowsFontssimsun.ttc')
  #
  # plt.style.use('ggplot')  # 设置背景
  #
  # clas = ['3班', '4班', '5班', '6班']
  # students = [50, 55, 45, 60]
  # clas_index = range(len(clas))
  #
  # # [0,1,2,3] [50,55,45,60]
  # plt.bar(clas_index,students,color='darkblue')
  #
  # plt.xlabel('学生',fontproperties=font)
  # plt.ylabel('学生人数',fontproperties=font)
  # plt.title('班级-学生人数',fontproperties=font,fontsize=20,fontweight=25)
  # plt.xticks(clas_index,clas,fontproperties=font)
  #
  # plt.show()
  
  
  # # 直方图
  # import numpy as np
  # from matplotlib import pyplot as plt  # 约定俗成
  # from matplotlib.font_manager import FontProperties  # 修改字体
  #
  # font = FontProperties(fname='C:WindowsFontssimsun.ttc')
  #
  # plt.style.use('ggplot')
  #
  # x1 = np.random.randn(10000)
  #
  # x2 = np.random.randn(10000)
  #
  # fig = plt.figure()  # 生成一张画布
  # ax1 = fig.add_subplot(1, 2, 1)  # 1行2列取第一个
  # ax2 = fig.add_subplot(1, 2, 2)
  #
  # ax1.hist(x1, bins=50,color='darkblue')
  # ax2.hist(x2, bins=50,color='y')
  #
  # fig.suptitle('两个正太分布',fontproperties=font,fontsize=20)
  # ax1.set_title('x1的正太分布',fontproperties=font)  # 加子标题
  # ax2.set_title('x2的正太分布',fontproperties=font)
  # plt.show()
  
  
  # 折线图
  #
  # import numpy as np
  # from matplotlib import pyplot as plt  # 约定俗成
  # from matplotlib.font_manager import FontProperties  # 修改字体
  #
  # font = FontProperties(fname='C:WindowsFontssimsun.ttc')
  #
  # plt.style.use('ggplot')
  #
  # np.random.seed(10)
  # x1 = np.random.randn(40).cumsum()
  # x2 = np.random.randn(40).cumsum()
  # x3 = np.random.randn(40).cumsum()
  # x4 = np.random.randn(40).cumsum()
  #
  # plt.plot(x1, c='r', linestyle='-', marker='o', label='红圆线')
  # plt.plot(x2, color='y', linestyle='--', marker='*', label='黄虚线')
  # plt.plot(x3, color='b', linestyle='-.', marker='s', label='蓝方线')
  # plt.plot(x4, color='black', linestyle=':', marker='s', label='黑方线')
  # plt.legend(loc='best', prop=font)  # 显示label
  # plt.show()
  
  
  # 散点图+直线图
  import numpy as np
  from matplotlib import pyplot as plt  # 约定俗成
  from matplotlib.font_manager import FontProperties  # 修改字体
  
  font = FontProperties(fname='C:WindowsFontssimsun.ttc')
  
  plt.style.use('ggplot')
  
  fig = plt.figure()
  ax1 = fig.add_subplot(1, 2, 1)
  ax2 = fig.add_subplot(1, 2, 2)
  
  x = np.arange(20)
  y = x ** 2
  
  x2 = np.arange(20)
  y2 = x2
  
  ax1.scatter(x, y, c='r', label='红')
  ax1.scatter(x2, y2, c='b', label='蓝')
  
  ax2.plot(x, y)
  ax2.plot(x2, y2)
  
  fig.suptitle('两张图', fontproperties=font, fontsize=15)
  ax1.set_title('散点图', fontproperties=font)
  ax2.set_title('折线图', fontproperties=font)
  ax1.legend(prop=font)
  plt.show()
  

3. pandas模块

• pandas模块的作用

  操作各种文本文件（如 excel / json / sql / ini / csv 等）

• 实例

  # import pandas as pd
  #
  # df = pd.read_csv('test.csv',header=None)
  # df.to_excel('test.xls')
  
  
  # pd从excel中读取 DataFrame数据类型
  import numpy as np
  import pandas as pd
  
  np.random.seed(10)
  
  index = pd.date_range('2019-01-01', periods=6, freq='M')
  print(index)
  columns = ['c1', 'c2', 'c3', 'c4']
  print(columns)
  val = np.random.randn(6, 4)
  print(val)
  
  df = pd.DataFrame(index=index, columns=columns, data=val)
  print(df)
  
  # 保存文件，读出成文件
  df.to_excel('date_c.xlsx')
  
  # 读出文件
  df = pd.read_excel('date_c.xlsx', index_col=[0])
  print(df)
  
  print(df.index)
  print(df.columns)
  print(df.values)
  
  print(df[['c1', 'c2']])
  
  # 按照index取值
  # print(df['2019-01-31'])
  print(df.loc['2019-01-31'])
  print(df.loc['2019-01-31':'2019-05-31'])
  
  # 按照values取值
  print(df)
  print(df.iloc[0, 0])
  
  df.iloc[0, :] = 0