环境配置及基础实操

一：下载、安装 Python

二：下载、安装 Anaconda

三：新建开发环境、安装 Jupyter notebook

新建开发环境

  • 创建新环境：conda create -n env_name
  • 激活新环境：conda active env_name
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(base) C:\Users\lenovo>conda create -n imooc_ai
Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: Y:\SoftWare\anaconda\Anaconda3\envs\imooc_ai



Proceed ([y]/n)? y

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate imooc_ai
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Retrieving notices: ...working... done

(base) C:\Users\lenovo>conda activate imooc_ai

四：Jupyter notebook 界面优化

借助开源项目，对界面进行优化，

项目地址：https://github.com/dunovank/jupyter-themes

Install with pip

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# install jupyterthemes
pip install jupyterthemes

# upgrade to latest version
pip install --upgrade jupyterthemes

配置

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jt -t grade3 -f fira -fs 16 -cellw 90% -ofs 11 -dfs 11 -T

五：Python 语法实操

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# python 基本语法：基本运算、列表生成、函数、模块引入
a = 1 
b = 2
print(a,b)

1 2
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c = a + b
print(c)

3
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a = [1,2,3,4]
print(type(a),a)

<class 'list'> [1, 2, 3, 4]
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b = [x+10 for x in a]
print(type(b),b)

<class 'list'> [11, 12, 13, 14]
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# 创建加法运算函数
def plusFunction(x1,x2):
    x = x1 + x2
    return x
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a = 1
b = 2
c = plusFunction(a,b)
print(type(c),c)

<class 'int'> 3
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# 库模块引入
import random
m = random.random()
print(m)

0.21730372821275368
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for i in [1,2,3,4,5,6,7,8,9,10]:
    m_i = random.random()
    print(m_i)
    
0.22372759310304402
0.15114745033159727
0.18443572156105592
0.8748129421654894
0.532564243518567
0.27301908385530627
0.3783824153668166
0.7295293943105771
0.1718886842112275
0.45445079430510904

六：Matplotlib 实操

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import matplotlib
x = [1,2,3,4,5]
y = [2,3,4,5,6]
print(x,y)

[1, 2, 3, 4, 5] [2, 3, 4, 5, 6]
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from matplotlib import pyplot as plt
fig1 = plt.figure(figsize=(5,5))
plt.plot(x,y)
plt.show()
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fig2 = plt.figure(figsize=(5,5))
plt.scatter(x,y)
plt.title("x vs y")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

七：Numpy 实操

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import numpy as np
a = np.eye(5)
print(type(a))
print(a)

<class 'numpy.ndarray'>
[[1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1.]]
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b = np.ones([5,5])
print(type(b))
print(b)
print(b.shape)

<class 'numpy.ndarray'>
[[1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1.]]
(5, 5)
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c = a + b
print(type(c))
print(c.shape)
print(c)

<class 'numpy.ndarray'>
(5, 5)
[[2. 1. 1. 1. 1.]
 [1. 2. 1. 1. 1.]
 [1. 1. 2. 1. 1.]
 [1. 1. 1. 2. 1.]
 [1. 1. 1. 1. 2.]]

八：Pandas 实操

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import pandas as pd
data = pd.read_csv("Y:\\temp\\data\\date.csv")
print(type(data))
print(data)

<class 'pandas.core.frame.DataFrame'>
   x  y
0  1  2
1  2  3
2  3  4
3  5  4
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x = data.loc[:,'x']
print(type(x))
y = data.loc[:,'y']
print(y)

<class 'pandas.core.series.Series'>
0    2
1    3
2    4
3    4
Name: y, dtype: int64
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c = data.loc[:,'x'][y>3]
print(c)

2    3
3    5
Name: x, dtype: int64
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data_array = np.array(data)
print(type(data_array))
print(data_array)

<class 'numpy.ndarray'>
[[1 2]
 [2 3]
 [3 4]
 [5 4]]
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data_new = data + 10
data_new.head()


x	y
0	11	12
1	12	13
2	13	14
3	15	14
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# data to csv file 
data_new.to_csv('Y:\\temp\\data\\data.csv')