python for finance analyze big financial data

Python Big Data App IntroductionIntroduction: At present, the industry mainstream storage and analysis platform for the Hadoop-based open-source ecosystem, mapreduce as a data set of Hadoop parallel operation model, in addition to provide Java to write MapReduce task, but also compatible with the streaming way, You can

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‘, ‘

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

In daily life, we know that search engines such as Baidu, 360, Sogou, Google, and so on, search is the big data in the field of common needs. Splunk and elk are leaders in the field of non-open source and open source, respectively. This article uses very few Python code to implement a basic data search function, trying

format for writing only. Overwrite the file if it already exists. If the file does not exist, create a new file.w+ open a file for read-write. Overwrite the file if it already exists. If the file does not exist, create a new file.wb+ opens a file in binary format for read-write. Overwrite the file if it already exists. If the file does not exist, create a new file.A opens a file for appending. If the file already exists, the file pointer will be placed at the end of the file. In other words, th

#-*-coding:utf-8-*-#Data IntegrationImportCSVImportNumPy as NPImportPandas as PDImportMatplotlib.pyplot as Plt#Customer InformationBasicinfo = PD. Dataframe.from_csv ('Datas/basicinfo_train.csv', header=0, sep=',', Index_col=0, Parse_dates=true, Encoding=none, Tupleize_cols=false, infer_datetime_format=False)#Historical Repayment RecordsHistoryinfo = PD. Dataframe.from_csv ('Datas/history_train.csv', header=0, sep=',', Index_col=0, Parse_dates=true, E