day 18 小结

目录

• numpy模块
• matplotlib模块
• pandas模块

numpy模块

numpy模块:用来做数据分析,对numpy数组(既有行又有列)--> 矩阵进行科学运算

lt1 = [1,2,3]  # n个元素
lt2 = [4,5,6]

lt = []
for i in reange(len(lt1)):  # 0(n)
    lt.append(lt1[i]*lt2[i])
    
print(lt)

import numpy as np  # 约定俗成

arr1 = np.array([1,2,3])
arr2 = np.array([4,5,6])
print(arr1 * arr2)

# 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)

# 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)))

# sklearn会对numpy封装

matplotlib模块

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()

pandas模块

pandas模块: 操作excel/json/sql/ini/csv(配置文件)

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['2017-01-31'])
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)