spark and python for big data with pyspark

Big Data-Hash Teach you how to quickly kill: 99% of the massive data processing surface test http://blog.csdn.net/v_july_v/article/details/7382693 1: operator 2: import HEAPQ 3: 4: def hashfiles (): 5: 6: files = [] 7: for in range (0, 10): 8: '. txt 'w ') 9: Ten: queryfile = File ('./

278446Graphics outputIn [71]: import matplotlib.pyplot as plt #使ipython-notebook支持matplotlib绘图 %matplotlib inlineIn [74]: df = data #绘图 df[u"业绩"].plot() MaxValue = df[u"业绩"].max() MaxName = df[u"姓名"][df[u"业绩"] == df[u"业绩"].max()].values Text = str(MaxValue) + " - " + MaxName #给图添加文本标注 plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0), xycoords=(‘axes fraction‘, ‘

different. 5-hour-regestered.png 5-hour-casual.png 4-boxplot-day.png Next, the correlation coefficient cor is used to test the relationship between the user, temperature, body sense temperature, humidity and wind speed. Correlation coefficient: The linear correlation measure between variables to test the correlation degree of different data.The value range [ -1,1], the closer 0 the more irrelevant. It can be seen from the operation results that the use of the population and wind speed

. ') Os.chdir (PATH) def request (self, url): R = Requests.get (URL, headers=self.headers) return R def Get_de Tail (self, page): R = self.request (Self.base_url + page) ul = BeautifulSoup (R.text, ' lxml '). Find (' UL ', class _= ' sojob-list ') plist = Ul.find_all (' li ') self.makedir (' job_data ') rows = [] for item in plist : Job_info = item.find (' div ', class_= ' Sojob-item-main clearfix '). Find (' div ', class_= ' job-info ') posi tion = job_info.find (' h3 '). Get (' titl

fake_func (P, x):f = np.poly1d (P) #多项式分布的函数return f (x)#残差函数def residuals (p, y, x):Return Y-fake_func (p, x)#随机选了9个点, as Xx = Np.linspace (0, 1, 9)A lot of points #画图的时候需要的 "continuous"X_show = Np.linspace (0, 1, 1000)y0 = Real_func (x)#加入正态分布噪音后的yy1 = [Np.random.normal (0, 0.1) + Y for y in y0]#先随机产生一组多项式分布的参数P0 = Np.random.randn (M)PLSQ = LEASTSQ (residuals, P0, args= (y1, x))Print (' Fitting Parameters: ', plsq[0]) #输出拟合参数Pl.plot (X_show, Real_func (x_show), label= ' real ')Pl.plot (X_show

The TOPK problem, which is finding the largest number of K, is very common, such as finding the hottest 10 keywords from 10 million search records.Method One:First, then the number of the first k is truncated.Time complexity: O (N*logn) +o (k) =o (N*LOGN).Method Two:Minimum heap.Maintain the smallest heap with a capacity of K. According to the minimum heap nature, the heap top must be the smallest, if smaller than the heap top, then the direct pass, if greater than the heap top, then replace the

If you call the Read () method directly on a large file object, it causes unpredictable memory consumption. A good approach is to use fixed-length buffers to continuously read the contents of the file. That is through yield. When using Python to read a two multi-g txt text, naïve direct use of the ReadLines method, the result of a running memory will be collapsed. Fortunately colleagues to the next, with yield method, tested under no pressure. The re

At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online every time shopping, to driverless cars, smart homes, intelligent robots, etc., have machine learning shadow.facebook Open source AI system tensorflow,2015 year 11 month, Google, Mi