learning python for data analysis and visualization github

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[Reading notes] Python Data Analysis (11) Economic and financial data applications

resample: resampling function that can increase or decrease the sampling frequency by time, Fill_method can use different filling methods.Freq parameter enumeration for Pandas.data_range: Alias Description B Business Day Frequency C Custom Business Day Frequency D Calendar Day Frequency W Weekly frequency M Month End Frequency Sm Semi-month End Frequency (1

Data Analysis---Data normalization using python

','a','b','a'],'data1': Range (6)}) DF2=PD. DataFrame ({'Key':['a','a','C','b','D'],'data2': Range (5)}) Pd.merge (Df1,df2,on='Key', how=' Right') back to key data1 data20B0.0 31B2.0 32B4.0 33C1.0 24A3.0 05A5.0 06A3.0 17A5.0 18D NaN4Many-to-many merges produce a Cartesian product of rows, that is, DF1 has 2 a,df2 with 2 A, and rallies produce 4 aWhen you need to merge from multiple keys, simply pass in a list of column names.When merging operations, you need to handle dup

Data type accuracy issues in Python learning python

Python is really amazing ... Magic to no direct data type concept, and precision can be arbitrary precision . Think originally, first contact Oi algorithm, write the first algorithm is high-precision addition, tinkering for a half day. All in Python's view, just three lines of code can be done.#!/usr/bin/python3a=int (Input ()) B=int (input ()) print (A+B)Not only the integer type, but also the floating-poi

[Deep Learning] Python/theano Code Analysis of implementing logistic regression Network

First the PO on the main Python code (2.7), this code can be found on the deep learning. 1 # Allocate symbolic variables for the data 2 index = T.lscalar () # Index to a [mini]batch 3 x = T.matrix (' x ') # The data is presented as rasterized images 4 y = t.ivector (' y ') # The labels is pre

Learning programs for Python, data analysts, algorithmic engineers

1. PrefaceRecently (2018.4.1) in the busy schedule to open a blog, like to be able to learn what they want to precipitate down, this is my system to learn python, called the data Analyst and algorithm engineer Road plan, hope to be interested in the same goal struggle data ape together to communicate and learn.2. Python

[Python Machine learning and Practice (6)] Sklearn Implementing principal component Analysis (PCA)

factors other than the data set.2) orthogonal between the main components, can eliminate the interaction between the original data components of the factors.3) Calculation method is simple, the main operation is eigenvalue decomposition, easy to achieve.The main drawbacks of PCA algorithms are:1) The meaning of each characteristic dimension of principal component has certain fuzziness, which is not better

Python VS R language? Data analysis and mining which one should I choose?

packages are written by the R language, LaTeX, Java, and the most commonly used C language and Fortran. The version of the executable that you download will be accompanied by a batch of core features, and there are thousands of different packages based on the Cran record. Several of them are more commonly used, such as economic metrology, financial analysis, humanities research, and artificial intelligence. The common features of

Learning data sharing: What can python do?

is the cloud5 , Ai – who will become the first language of development in the AI and Big data era? This is a question that is not to be debated. If there were opportunities for Matlab, Scala, R, Java, and Python three years ago, the situation is unclear, and three years later, the trend is very clear, especially after the first two days of Facebook open source Pytorch,

Data analysis using Python-data normalization: cleanup, transformation, merging, reshaping (vii) (1)

A lot of programming in data analysis and modeling is used for data preparation: onboarding, cleanup, transformation, and remodeling. Sometimes, the data stored in a file or database does not meet the requirements of your data processing application. Many people choose to sp

Python Data Analysis 1

Summary of this section  Basic EnvironmentIpython FoundationObjectiveThis is the first blog in 18, because boss for some of my job expectations, need to start doing some data analysis work, so began to write this series of blog. The main content of the classification is basically the landlord in view of the reading "Data anal

Simple analysis of Redis cache consumption memory data based on Python project (with detailed procedure)

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

Python Learning Path-fifth day-python data structure

:#!/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

Python Data Analysis Instance operations

‘) #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

Python data analysis U.S. election Project Combat (iii)

') # 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 path of machine learning: The main component analysis of the Python feature reduced dimension PCA

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 )

Python data structure and algorithm--algorithm analysis

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

"Machine Learning Algorithm-python realization" PCA principal component analysis, dimensionality reduction

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

Python data analysis Numpy (numerical python Basic)

(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

Data structure and algorithm (Python)-General concepts and algorithm efficiency analysis

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

Introduction to the second chapter, "Data analysis using Python" study notes _1

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

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