learning python for data analysis and visualization github

reports that generate data across all databases and keys(2) Convert the dump file to JSON(3) Comparison of two dump files using standard diff toolSpecific source GitHub Link: https://github.com/sripathikrishnan/redis-rdb-tools/MySQL: An open-source and relatively lightweight relational database. This article uses Rdbtools to parse out a redis dump.rdb file and generate a memory report *.csv file (PS: The f

:#!/usr/bin/python# Filename: reference.pyprint ‘Simple Assignment‘shoplist = [‘apple‘, ‘mango‘, ‘carrot‘, ‘banana‘]mylist = shoplist # mylist is just another name pointing to the same object!del shoplist[0]print ‘shoplist is‘, shoplistprint ‘mylist is‘, mylist# notice that both shoplist and mylist both print the same list without# the ‘apple‘ confirming that they point to the same objectprint ‘Copy by making a full slice‘mylist = shoplist[:] # make a

‘) #颜色深蓝cup_style = bra.groupby(‘cup‘)[‘cup‘].count() #cup列唯一值得数量cup_styleplt.figure(figsize=(8,6),dpi=80)labels = list(cup_style.index)plt.xlabel(‘cup‘) #x轴为cupplt.ylabel(‘count‘) #y轴为count数量plt.bar(range(len(labels)),cup_style,color=‘royalblue‘,alpha=0.7) #alpha为透明度plt.xticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘y‘,alpha=0.6)plt.legend([‘user-count‘])for x,y in zip(range(len(labels)),cup_style):plt.text(x,y,y,ha=‘center‘,va=‘bottom‘)co

') # Adjust data trend show subplot_arr[1, 0].plot (adj_cliton_sum, color= ' R ') subplot_arr[1, 0].plot (adj_trump_sum, color= ' g ') width = 0.25 x = Np.arange (len (months)) subplot_arr[1, 1] . Bar (x, Adj_cliton_sum, Width, color= ' R ') subplot_arr[1, 1].bar (x + width, adj_trump_sum, width, color= ' g ') subplot_ Arr[1, 1].set_xticks (x + width) subplot_arr[1, 1].set_xticklabels (months, rotation= ' vertical ') plt.subplots_adjust (w space=0.2)

the data after dimensionality reduction -Pca_svc =linearsvc () the #Learning - Pca_svc.fit (Pca_x_train, Y_train)WuyiPca_y_predict =pca_svc.predict (pca_x_test) the - #4 Model Evaluation Wu Print("accuracy of raw data:", Svc.score (X_test, y_test)) - Print("other ratings: \ n", Classification_report (Y_test, Y_predict, Target_names=np.arange (10). Astype (str )

In computer science, algorithmic analysis (analyst ofalgorithm) is the process of analyzing the amount of computing resources (such as compute time, memory usage, etc.) that are consumed by executing a given algorithm. The efficiency or complexity of an algorithm is theoretically represented as a function. The defined field is the length of the input data, which is usually the number of steps (time complexi

references: The reference is the low-dimensional matrix returned. corresponding to the input parameters of two.The number of references two corresponds to the matrix after the axis is moved.The previous picture. Green is the raw data. Red is a 2-dimensional feature of extraction.3. Code Download:Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the source : Http://blog.csdn.net/bu

(Np.mean (A)) -7.5Wuyi Print(Np.average (A)) the7.5 - Print(A.mean ()) Wu7.5# cumsum Iteration Add the A -Out[24]: inArray ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])Bayi Print(A.cumsum ()) the[2 5 9 14 20 27 35 44 54 65 77 90] the A -Out[27]: -Array ([[[2, 3, 4, 5], the[6, 7, 8, 9], the[10, 11, 12, 13]])# Clip (A, a_min, A_max) will determine the data in the Ndarray, the value of less than A_min is assigned to A_min, is greater than the

It 's written in front . After learning the Python basics, start with this section to formally learn about data structure and algorithm related content. This is a more complex topic, generally divided into the primary, advanced, and specialized algorithm analysis three stages to learn, so we also need to be gradual. T

Returns a Series that contains only non-empty data and index valuesRemove the missing field first: Cframe=frame[frame.a.notnull ()]Second, it calculates whether the rows are Windows based on the value of a, #np. The WHERE function is a vectorization ifelse functionOperating_system=np.where (cframe[' a '].str.contains (' windows '), ' windows ', ' no windows ')Next, the data is grouped according to the time