udemy python data analysis

one-dimensional array in column form5. Row-StackedSplitting numpy arraysRelated functions Hsplit (), Vsplit (), Dsplit (), and split (). We can either divide the array into an array of the same shape, or we can begin to cut the array from the specified position.1. Split horizontallyThe split () function equivalent to calling the parameter Axis=1:2. Split verticallyWhen the parameter Axis=0,split () function also decomposes the array along the vertical axis3. Deep splitThe premise is that there

Objective Pandas is a data analysis package built on Numpy that contains more advanced structures and tools similar to the core of Numpy is the Ndarray,pandas also revolves around Series and DataFrame two core data structures. Series and DataFrame correspond to one-dimensional sequences and two-dimensional table structures, respectively. The following are the co

This time will be the next issue of SHUANGSE Qiu number forecast, think of a little excitement ah. The code uses the linear regression algorithm, which uses this algorithm to predict the effect, and you can consider using other algorithms to try the results. Before discovering a lot of code is repetitive work, in order to make the code look more elegant, define the function, to call, suddenly tall #!/usr/bin/python #-*-Coding:utf-8-*-#导入需要的包 Import p

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1.1. Pandas Analysis steps Loading data COUNT the date of the access_time. SQL similar to the following: SELECT date_format (access_time, '%H '), COUNT (*) from log GROUP by Date_format (access_time, '%H '); 1.2. Code Cat pd_ng_log_stat.py#!/usr/bin/env python#-*-Coding:utf-8-*-From Ng_line_parser import NglineparserImport Pandas as PDImport socketImport str

methodRanking:Rank ()Axis index with duplicate valuesThe Is_unique () property of the index can tell you if its value is uniqueSummary and calculation of descriptive statisticsSUM ()Mean ()Describe ()Describing and summarizing statistical functionscorrelation coefficients and covarianceThe series and Dataframe methods are computed for the parameter pairs.Unique value, value count, and membershipUnique value: Unique () methodValue count: The Value_counts () method calculates how often each value

specified axisTake and PutRemove a specific element from an arraybroadcasting/BroadcastSpreads along the x-axis and spreads along the y-axis with subtle differencesNp.newaxis () Add new axisAdvanced Ufunc UsageUfunc is the abbreviation for universal function, which is a function that can operate on each element of an array, numpy many of the Ufunc functions are implemented at the C level, so they are computationally fast Np.add.reduce: Add and operate Np.add.accumulate: Similar to

#-*-coding:utf-8-*-#analysis of food and beverage sales data statistics from __future__ Importprint_functionImportPandas as Pdcatering_sale='.. /data/catering_sale.xls' #Dining Datadata = Pd.read_excel (Catering_sale, Index_col = u'Date')#reading data, specifying "date" as index columndata =

1, NumpyInstallation: Pip Install NumPy[[email protected] work]#Cat numpy_test.py#!/usr/bin/env python#Coding:utf-8 from __future__ Importprint_function#import modules and add aliasesImportNumPy as NP#Create an arrayA = Np.array ([2,0,1,7])Print(a)Print(A[:3])Print(A.min ()) A.sort ()Print(a)#Create two-dimensional datab = Np.array ([[1,2,3],[4,5,6]])Print(b)Print(b*b) [[email protected] work]#python numpy_

Fourth NumPy basics: arrays and vector calculations To be honest, the main purpose of using NumPy is to apply vectorization operations. NumPy does not have much advanced data analysis capabilities, and understanding numpy and array-oriented computations can help to understand the pandas behind it. According to the textbook, the author's concern is mainly focused on: Fast vectorization operations f

variable (local).-python called namespaces-The following functiondef func(): a = [] for i in range(5): a.append(i)-After Func is called, the empty list A is created first, then 5 elements are added, and a is destroyed when the function exits-If we define a as followsa = []def func(): for i in range(5): a.append(i)-Although you can assign a global variable to a function, those variables must be declared as global variables with

The Ggplot library used in Python in data analysis can be applied to drawData, for example, using data from the course VII of the InstituteData is: https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/hr_year.csv Scatter plot: gp=pandas.read_csv (hr_year_csv) GG=ggplot (Gp,aes ('yearid','HR ')

pandas:powerful Python Data Analysis Toolkit Official document: http://pandas.pydata.org/pandas-docs/stable/1. Import Package PandasImport Pandas as PD　　2. Get the file name under the folderImport osfilenames=[]Path= "C:/users/forrest/pycharmprojects/test" for file in Os.listdir (path): filenames.append (file)　　3. Read the first few lines of files (. csv file)

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Pandas is the preferred library for subsequent content in this book. The pandas can meet the following requirements: Data structure with automatic or explicit data alignment by axis. This prevents many common errors caused by data misalignment and data from different data