大学python笔记_Introduction to Python课程笔记

首先恭喜自己终于通过了DATACAMP的第一个课程Introduction to Python，

课程讲义也上传到了百度云里，链接7天有效，需要的小伙伴们请提前保存。

提取码：0hr3

该课程主要分为四个章节：

Python Basics

Python Lists

Functions and Packages

NumPy

卡的比较久的几个代码主要是在没看清题目，又或者网络不好导致视频没仔细看，直接刷题。

重点如下：python区分大小写

变量不可以数字开头

python数据选取从0开始

[including:excluding]左闭右开，即最左边的区间选取，右边的区间不选取

python 不支持直接对list进行运算，所以需要用到np.array数组对列表数据进行运算。

即标准的列表list[1,2,3]+list[1,2,3]=list[1,2,3,1,2,3]，而numpy中的列表可以通过运算符进行数学运算np_list[1,2,3]+np_list[1,2,3]=np_list[2,4,6]

因此多数列表操作使用要调用numpy数组，比如np.mean(),np.median()

2维列表2dnumpyarray，即list of list 中的数据选取规则如下，逗号分隔，：代表全选

data[：，0]标识第一列全选，data[0,:]标识第一行全选

部分通过代码如下：

List操作方法

# string to experiment with: place

place = "poolhouse"

# Use upper() on place: place_up

place_up=place.upper()

# Print out place and place_up

print(place,place_up)

# Print out the number of o's in place

print(place.count('o'))

list.append()一次只能添加一个元素

import math

math.pi即为常数π

也可以只加载包中的一个函数使得运行更快。

from math import pi

一般会用ticks 缩写包名，

如import numpy as np

由于python不支持列的操作，所以一般用numpy包来对列表（数组）进行运算。

python的列表进行+运算会相连

Numpy的列表进行+运算会求和

可以使用np.array(list)来得到一个numpy列表。

# Create list baseball

baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np

import numpy as np

# Create a numpy array from baseball: np_baseball

np_baseball=np.array(baseball)

# Print out type of np_baseball

print(type(np_baseball))

# height is available as a regular list

# Import numpy

import numpy as np

# Create a numpy array from height_in: np_height_in

np_height_in=np.array(height_in)

# Print out np_height_in

print(np_height_in)

# Convert np_height_in to m: np_height_m

np_height_m=np_height_in*0.0254

# Print np_height_m

print(np_height_m)

# height and weight are available as regular lists

# Import numpy

import numpy as np

# Create array from height_in with metric units: np_height_m

np_height_m=np.array(height_in)*0.0254

# Create array from weight_lb with metric units: np_weight_kg

np_weight_kg=np.array(weight_lb)*0.453592

# Calculate the BMI: bmi

bmi=np_weight_kg/(np_height_m**2)

# Print out bmi

print(bmi)

选取列表元素

# height and weight are available as a regular lists

# Import numpy

import numpy as np

# Calculate the BMI: bmi

np_height_m = np.array(height_in) * 0.0254

np_weight_kg = np.array(weight_lb) * 0.453592

bmi = np_weight_kg / np_height_m ** 2

# Create the light array

light=bmi<21

# Print out light

print(light)

# Print out BMIs of all baseball players whose BMI is below 21

print(bmi[light])

# height and weight are available as a regular lists

# Import numpy

import numpy as np

# Store weight and height lists as numpy arrays

np_weight_lb = np.array(weight_lb)

np_height_in = np.array(height_in)

# Print out the weight at index 50

print(weight_lb[50])

# Print out sub-array of np_height_in: index 100 up to and including index 110

print(np_height_in[100:111])

2d numpy arrays 可以看做List of list，最多可以有7维的列表

数据选取类似R语言中的DATAFRAME，都可以通过逗号分隔来选取，但是需要加 :

# Create baseball, a list of lists

baseball = [[180, 78.4],

[215, 102.7],

[210, 98.5],

[188, 75.2]]

# Import numpy

import numpy as np

# Create a 2D numpy array from baseball: np_baseball

np_baseball=np.array(baseball)

# Print out the type of np_baseball

print(type(np_baseball))

# Print out the shape of np_baseball

print(np_baseball.shape)

# baseball is available as a regular list of lists

# Import numpy package

import numpy as np

# Create np_baseball (2 cols)

np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball

print(np_baseball[49,:])

# Select the entire second column of np_baseball: np_weight_lb

np_weight_lb=np_baseball[:,1]

# Print out height of 124th player

print(np_baseball[0:124,1])You managed to get hold of the changes in height, weight and age of all baseball players. It is available as a 2D numpy array, updated. Add np_baseball and updated and print out the result.

You want to convert the units of height and weight to metric (meters and kilograms respectively). As a first step, create a numpy array with three values: 0.0254, 0.453592 and 1. Name this array conversion.

Multiply np_baseball with conversion and print out the result.

# baseball is available as a regular list of lists

# updated is available as 2D numpy array

# Import numpy package

import numpy as np

# Create np_baseball (3 cols)

np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated

print(np_baseball+updated)

# Create numpy array: conversion

conversion=np.array([0.0254,0.453592,1])

# Print out product of np_baseball and conversion

print(np_baseball*conversion)

# np_baseball is available

# Import numpy

import numpy as np

# Create np_height_in from np_baseball

np_height_in=np_baseball[:,0]

# Print out the mean of np_height_in

print(np.mean(np_height_in))

# Print out the median of np_height_in

print(np.median(np_height_in))

# np_baseball is available

# Import numpy

import numpy as np

# Print mean height (first column)

avg = np.mean(np_baseball[:,0])

print("Average: " + str(avg))

# Print median height. Replace 'None'

med = np.median(np_baseball[:,0])

print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'

stddev = np.std(np_baseball[:,0])

print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'

corr = np.corrcoef(np_baseball[:,0],np_baseball[:,1])

print("Correlation: " + str(corr))

nparray选取子集

# heights and positions are available as lists

# Import numpy

import numpy as np

# Convert positions and heights to numpy arrays: np_positions, np_heights

np_heights=np.array(heights)

np_positions=np.array(positions)

# Heights of the goalkeepers: gk_heights

gk_heights=np_heights[np_positions=="GK"]

# Heights of the other players: other_heights

other_heights=np_heights[np_positions!="GK"]

# Print out the median height of goalkeepers. Replace 'None'

print("Median height of goalkeepers: " + str(np.median(gk_heights)))

# Print out the median height of other players. Replace 'None'

print("Median height of other players: " + str(np.median(other_heights)))

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