Python的练手项目很多，特别是Github上，建议不管新手、老司机都去看看。这里推荐给大家两个Github上练习的项目，算法仓库-algorithms和脚本仓库-Python master。algorithms算法仓库
首先来看看算法仓库-algorithms。
这里面集合众多核心算法的Python实现，比如排序、图计算、回溯、队列、流计算、堆、搜索、压缩等等。
该仓库支持第三方库安装，在python中进行调用，非常方便。
首先使用pip进行安装：
pip3 install algorithms
然后导入相关模块进行调用，比如sort模块里的merge_sort归并排序算法。
from algorithms.sort import merge_sort
if __name__ == '__main__':
my_list = [1, 8, 3, 5, 6]
my_list = merge_sort(my_list)
print(my_list)
个人感觉这个仓库里的算法很齐全，适合做练习，小伙伴们可以试试。
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另外，@公众号：数据STUDIO 还为大家整理和筛选了大量火爆全网的Python数据科学学习资料，全部资料按需自助免费获取！直接点击👇链接： Python脚本仓库
另外还有一个很好的练手项目，脚本仓库-Python master。这个项目收集了作者平时工作用到的几千个实用小脚本，作者虽然不是程序员，但他这种用代码解决问题的习惯会极大的提升效率，也会迸发出更多的创新思维。我觉得这样的代码每个人都可以写出来，只要慢慢积累多练习就可以。举一个简单的例子，作者写了一个创建二维码的脚本，可以自动将url转化为二维码。import pyqrcode
import png
from pyqrcode import QRCode
# Text which is to be converted to QR code
print('Enter text to convert')
s = input(': ')
# Name of QR code png file
print('Enter image name to save')
n = input(': ')
# Adding extension as .pnf
d = n + '.png'
# Creating QR code
url = pyqrcode.create(s)
# Saving QR code as a png file
url.show()
url.png(d, scale=6)
除此之外，该仓库中还有很多这样实用的脚本文件。
▲点击关注「数据STUDIO」回复d
另外，@公众号：数据STUDIO 还为大家整理和筛选了大量火爆全网的Python数据科学学习资料，全部资料按需自助免费获取！直接点击👇链接： 接下来，展示一些更多的代码案例，供大家参考。
从图片中截取文字
# extract text from a img and its coordinates using the pytesseract module
import cv2
import pytesseract
# You need to add tesseract binary dependency to system variable for this to work
img = cv2.imread('img.png')
# We need to convert the img into RGB format
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
hI, wI, k = img.shape
print(pytesseract.image_to_string(img))
boxes = pytesseract.image_to_boxes(img)
for b in boxes.splitlines():
b = b.split(' ')
x, y, w, h = int(b[1]), int(b[2]), int(b[3]), int(b[4])
cv2.rectangle(img, (x, hI - y), (w, hI - h), (0, 0, 255), 0.2)
cv2.imshow('img', img)
cv2.waitKey(0)
判断闰年
def is_leap(year):
leap = False
if year % 4 == 0:
leap = True
if year % 100 == 0:
leap = False
if year % 400 == 0:
leap = True
return leap
year = int(input('Enter the year here: '))
print(is_leap(year))
打印图片分辨率
def jpeg_res(filename):
''''This function prints the resolution of the jpeg image file passed into it'''
# open image for reading in binary mode
with open(filename,'rb') as img_file:
# height of image (in 2 bytes) is at 164th position
img_file.seek(163)
# read the 2 bytes
a = img_file.read(2)
# calculate height
height = (a[0] << 8) + a[1]
# next 2 bytes is width
a = img_file.read(2)
# calculate width
width = (a[0] << 8) + a[1]
print('The resolution of the image is',width,'x',height)
jpeg_res('img1.jpg')
排序算法-桶排序
def bucket_sort(arr):
''' Bucket Sort
Complexity: O(n^2)
The complexity is dominated by nextSort
'''
# The number of buckets and make buckets
num_buckets = len(arr)
buckets = [[] for bucket in range(num_buckets)]
# Assign values into bucket_sort
for value in arr:
index = value * num_buckets // (max(arr) + 1)
buckets[index].append(value)
# Sort
sorted_list = []
for i in range(num_buckets):
sorted_list.extend(next_sort(buckets[i]))
return sorted_list
def next_sort(arr):
# We will use insertion sort here.
for i in range(1, len(arr)):
j = i - 1
key = arr[i]
while arr[j] > key and j >= 0:
arr[j+1] = arr[j]
j = j - 1
arr[j + 1] = key
return arr
机器学习-最近邻插值法
import math
def distance(x,y):
'''[summary]
HELPER-FUNCTION
calculates the (eulidean) distance between vector x and y.
Arguments:
x {[tuple]} -- [vector]
y {[tuple]} -- [vector]
'''
assert len(x) == len(y), 'The vector must have same length'
result = ()
sum = 0
for i in range(len(x)):
result += (x[i] -y[i],)
for component in result:
sum += component**2
return math.sqrt(sum)
def nearest_neighbor(x, tSet):
'''[summary]
Implements the nearest neighbor algorithm
Arguments:
x {[tupel]} -- [vector]
tSet {[dict]} -- [training set]
Returns:
[type] -- [result of the AND-function]
'''
assert isinstance(x, tuple) and isinstance(tSet, dict)
current_key = ()
min_d = float('inf')
for key in tSet:
d = distance(x, key)
if d < min_d:
min_d = d
current_key = key
return tSet[current_key]
符串解码编码
# Implement the encode and decode methods.
def encode(strs):
'''Encodes a list of strings to a single string.
:type strs: List[str]
:rtype: str
'''
res = ''
for string in strs.split():
res += str(len(string)) + ':' + string
return res
def decode(s):
'''Decodes a single string to a list of strings.
:type s: str
:rtype: List[str]
'''
strs = []
i = 0
while i < len(s):
index = s.find(':', i)
size = int(s[i:index])
strs.append(s[index+1: index+1+size])
i = index+1+size
return strs
直方分布
def get_histogram(input_list: list) -> dict:
'''
Get histogram representation
:param input_list: list with different and unordered values
:return histogram: dict with histogram of input_list
'''
# Create dict to store histogram
histogram = {}
# For each list value, add one to the respective histogram dict position
for i in input_list:
histogram[i] = histogram.get(i, 0) + 1
return histogram
个人感觉这两个仓库里的算法和脚本很齐全，适合做练习，小伙伴们可以试试。
