numpy 模块

numpy 模块主要用来做数据分析，对numpy数组 进行科学运算

主要方法和常用属性，都是用numpy 生成的对象.出来的

import numpy as np

以下代码具体解释

lt1 = [1,2,3]
lt2 = [4,5,6]

lt = []
# 如果我们想要对这两个列表内数据相乘，我们可以用for循环
for i in range(len(lt1)):
    lt.append(lt1[i] * lt2[i])
print(lt)


import numpy as np

# 利用numpy 进行矩阵计算 更方便
arr1 = np.array([1,3])
arr2 = np.array([4,6])
print(arr1 * arr2)
## [ 4 10 18]




# numpy 创建 numpy 数组   --》 可变的数据类型
# 一维数组  通常不使用，创建的数组没有,arr = np.array([1,3])
print(arr)
# [1 2 3]

# 二维数组
arr = np.array([
    [1,3],[4,6]
])
print(arr)
# [[1 2 3]
#  [4 5 6]]

# 三维数组      通常不使用
arr = np.array([
    [1,6],[7,8,9]
])
print(arr)
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]


# numpy 数组的属性 特性
arr = np.array([
    [1,6]
])

# T数组的转置，行列互换
print(arr,"n",arr.T)
# [[1 4]
#  [2 5]
# [3 6]]

# 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数组
print(arr.dtype)
# int32

# size 数组元素的个数
print(arr.size)
# 6

# ndim 数据的维数
print(arr.ndim)
# 2

# shape 数据的纬度大小（以元组形式）
print(arr.shape)
# (2,3)

# astype 类型转换 为int32
arr = arr.astype(np.float64)
print(arr)
# [[1. 2. 3.]
#  [4. 5. 6.]]

# 切片numpy数组
arr = np.array([
    [1,6]
])

print(arr[:,:])     # ：行,：列
# [[1 2 3]
#  [4 5 6]]
print(arr[0,0])
# 1
print(arr[1,2])
# 6
print(arr[:,-2:])
# [[2 3]
#  [5 6]]

# 逻辑取值
print(arr[arr > 4])
# [[2 3]
#  [5 6]]
#  [5 6]

# 赋值
arr[0,0] = 0
print(arr)
# [[0 2 3]
#  [4 5 6]]

# 数组合并
arr1 = np.array([
    [1,6]
])

arr2 = np.array([
    [7,9],['a','b','c']
])

# 横向合并
print(np.hstack((arr1,arr2)))
# [['1' '2' '3' '7' '8' '9']
#  ['4' '5' '6' 'a' 'b' 'c']]

# 垂直合并
print(np.vstack((arr1,arr2)))
# [['1' '2' '3']
#  ['4' '5' '6']
#  ['7' '8' '9']
#  ['a' 'b' 'c']]

# 默认以列合并  #axis = 0    0表示列，1表示行
print(np.concatenate((arr1,arr2),axis=1))
# [['1' '2' '3' '7' '8' '9']
#  ['4' '5' '6' 'a' 'b' 'c']]

# 通过函数创建numpy数组

print(np.ones((2,3)))
# [[1. 1. 1.]
#  [1. 1. 1.]]

print(np.zeros((2,3)))
# [[0. 0. 0.]
#  [0. 0. 0.]]

print(np.eye(3,3))
# [0. 1. 0.]
# [0. 0. 1.]]

print(np.linspace(1,100,10))
# [  1.  12.  23.  34.  45.  56.  67.  78.  89. 100.]

print(np.arange(2,10))
# [2 3 4 5 6 7 8 9]

# 重构形状
arr1 = np.zeros((2,6))      #
print(arr1.reshape((3,4)))      # 重构形状必须相乘的 相等
# [[0. 0. 0. 0.]
#  [0. 0. 0. 0.]
#  [0. 0. 0. 0.]]


# numpy 数组运算
# +-*/
arr = np.ones((3,4)) * 4
print(arr)
# [[4. 4. 4. 4.]
#  [4. 4. 4. 4.]
#  [4. 4. 4. 4.]]

arr = np.ones((3,4)) + 4
print(arr)
# [[5. 5. 5. 5.]
#  [5. 5. 5. 5.]
#  [5. 5. 5. 5.]]

# numpy 数组运算函数      了解——————-
print(np.sin(arr))
# [[-0.95892427 -0.95892427 -0.95892427 -0.95892427]
#  [-0.95892427 -0.95892427 -0.95892427 -0.95892427]
#  [-0.95892427 -0.95892427 -0.95892427 -0.95892427]]

# 矩阵运算 --  点乘
arr1 = np.array([
    [1,6]
])

arr2 = np.array([
    [1,2],5],[6,7]
])
print(np.dot(arr1,arr2))
# [[27 33]
#  [60 75]]

# 求逆
arr = np.array([[1,[9,9]])
print(np.linalg.inv(arr))
# [[ 0.5        -1.          0.5       ]
#  [-3.          3.         -1.        ]
#  [ 2.16666667 -1.66666667  0.5       ]]





# numpy 数组数学和统计方法

arr = np.array([
    [1,6]
])
print(np.sum(arr[:,:]))
# 21

# 生成随机数
print(np.random.rand(3,4))
# [[0.76654824 0.23510842 0.79989748 0.93094884]
#  [0.97155472 0.29956374 0.27754847 0.91103403]
#  [0.43714323 0.7549109  0.14547903 0.20511579]]

print(np.random.random((3,4)))
# [[0.91673193 0.15218486 0.32976182 0.41812734]
#  [0.33360061 0.20190749 0.48689467 0.46679115]
#  [0.12490532 0.50441629 0.95525997 0.5402791 ]]


# 针对一维 随机选择数字
print(np.random.choice([1,1))
# [1]

# 追对某一范围
print(np.random.randint(1,(3,4)))
# [[33 40 93 18]
#  [80 65 64 51]
#  [66  6 83 10]]

matplotlib 模块

matplotlib 模块 就是用来画图的

# 条形图

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

# 设置字体，不然画出来会乱码
font = FontProperties(fname=r"C:WindowsFontssimsun.ttc")

# 设置背景
plt.style.use("ggplot")

# 定义 行 列 信息
clas = ["3班","4班","5班","6班"]
students = [50,55,45,60]
clas_index = range(len(clas))

# 开始画
plt.bar(clas_index,students,color="darkblue")

plt.xlabel("学生",FontProperties=font)
plt.xlabel("学生人数",FontProperties=font)
plt.title("班级-学生人数",FontProperties=font,Fontsize=25,fontweight=20)
plt.xticks(clas_index,clas,FontProperties=font)

# 展示
plt.show()

# 直方图
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.font_manager import FontProperties

# 设置字体，不然画出来会乱码
font = FontProperties(fname=r"C:WindowsFontssimsun.ttc")
plt.style.use("ggplot")

# 生成随机数对象
x1 = np.random.randn(10000)
x2 = np.random.randn(10000)

# 生成画布
fig = plt.figure()

# 每行每列
ax1 = fig.add_subplot(1,1)
ax2 = fig.add_subplot(1,2)

ax1.hist(x1,bins=50,color="darkblue")
ax2.hist(x2,color="y")

fig.suptitle("两个正太分布",fontsize=20)
ax1.set_title("x1的正态分布",FontProperties=font)
ax2.set_title("x2的正态分布",FontProperties=font)

# 展示
plt.show()

# 折线图

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

# 设置字体，不然画出来会乱码
font = FontProperties(fname=r"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,color="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=":",label="黑方线")
plt.legend(loc="best",prop=font)

# 展示
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

# 设置字体，不然画出来会乱码
font = FontProperties(fname='C:WindowsFontssimsun.ttc')
plt.style.use('ggplot')

fig = plt.figure()
ax1 = fig.add_subplot(1,2)


x = np.arange(20)
y = x ** 2
x2 = np.arange(20)
y2 = x2 ** 2

ax1.scatter(x,y,label="红")
ax2.scatter(x2,y2,label="蓝")

ax1.plot(x,y)
ax2.plot(x2,y2)

fig.suptitle("两张图",fontsize=15)
ax1.set_title("散点图",FontProperties=font)
ax2.set_title("折线图",FontProperties=font)
ax1.legend(prop=font)

# 展示
plt.show()

pandas 模块

pandas 模块 操作excel/json/sql/ini/csv文件的

import pandas as pd
import numpy as np

np.random.seed(10)

# 生成6个月份
index = pd.date_range("2019-01-01",periods=6,freq="M")
print(index)
columns = ["c1","c2","c3","c4"]

# 生成随机数
val = np.random.randn(6,4)


df = pd.DataFrame(index=index,columns=columns,data=val)
print(df)
#                   c1        c2        c3        c4
# 2019-01-31  1.331587  0.715279 -1.545400 -0.008384
# 2019-02-28  0.621336 -0.720086  0.265512  0.108549
# 2019-03-31  0.004291 -0.174600  0.433026  1.203037
# 2019-04-30 -0.965066  1.028274  0.228630  0.445138
# 2019-05-31 -1.136602  0.135137  1.484537 -1.079805
# 2019-06-30 -1.977728 -1.743372  0.266070  2.384967


# 保存成 xlsx 文件
df.to_excel("date_c.xlsx")
# 读出文件
df = pd.read_excel("date_c.xlsx",index_col=[0])
print(df)
#                   c1        c2        c3        c4
# 2019-01-31  1.331587  0.715279 -1.545400 -0.008384
# 2019-02-28  0.621336 -0.720086  0.265512  0.108549
# 2019-03-31  0.004291 -0.174600  0.433026  1.203037
# 2019-04-30 -0.965066  1.028274  0.228630  0.445138
# 2019-05-31 -1.136602  0.135137  1.484537 -1.079805
# 2019-06-30 -1.977728 -1.743372  0.266070  2.384967



###############
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
print(df)