ggplot plots

#-*-Coding:utf-8-*-"""Created on Sat 18 11:08:38 2018@author: Acadsoc"""Import Pandas as PDImport NumPy as NPImport MatplotlibImport Matplotlib.pyplot as PltFrom pyecharts import Bar, line, Page, overlapImport Statsmodels.api as SMFrom sklearn.preprocessing import Standardscaler# import PymssqlFrom Dateutil Import ParserImport CopyImport OSImport SysFrom featureselection import featureselectionPlt.style.use (' Ggplot ') # set GGPLOT2 paint style# Set

two kinds and the population obeys multivariate normal distribution. code example: NBSP; > if (Require (MASS) == FALSE) + " mass " ) +} > > Model1=lda (Species~.,data=iris) > table class ) > table Setosa versicolor virginica setosa 50 0 0 versicolor 0 2 virginica 0 1 49> sum (diag (prop.table (table))) ## #判对率 [1] 0.98 as a result, only three of the samples were observed to be judged incorrectly. After the discriminant function is established, the discriminant score can be plott

Related parameters in the code The main object is to use the Add_axes method, note that the coordinates of the three graphs correspond, draw the scatter chart, in the other two sub graphs to draw the corresponding column chart #!/usr/bin/python #coding: Utf-8 import numpy as NP import Matplotlib.pyplot as Plt plt.style.use ("ggplot") FIG = Plt.f Igure () # Draw the main figure bottom_x_1 = 0.15 Bottom_y_1 = 0.15 width = 0.5 height = 0.5 Rect1 = [Bot

1. You could be the same test_prediction = svc.predict (hog_features.reshape) modified before modification 2. This possibility Import NumPy as NP import Matplotlib.pyplot as plt from matplotlib import style style.use ("Ggplot") From Sklearn import SVM x = [1, 5, 1.5, 8, 1, 9] y = [2, 8, 1.8, 8, 0.6, one] plt.scatter (x,y) plt.show () C11/>x = Np.array ([[[1,2], [5,8], [1.5,1.8], [8,8],

(y~x1*x2), y = a*x1+b*x2+c*x1*x2+d Lm (Y~X1*X2*X3) Y =a*x1+b*x2+c*x3+d*x1*x2+e*x1*x2+f*x2*x3+g*x1*x2*x3+h Lm (Y~X1+X2+X3+X1:X2:X3) y = a*x1+b*x2+c*x3+d*x1*x2*x3+e SETP stepwise regression, you can remove the meaningless variable backwards, you can add a new variable to the forward regression Lm (y~x1, subset=1:100) selects only the first 100 data for regression Lm (Y~i (X1+X2)) to (X1+X2) regression Lm (Y~ploy (x,3,raw=true)) Y is the three-quadratic polynomial regression of x Lm (log (y) ~ x1

and less wind; a person in Suzhou, Shanghai, Hangzhou, or Xiamen, Hong Kong, Guangzhou, the public, muddy chaos chaos past, can only feel a little cool, autumn flavor, autumn color, autumn mood and posture, total look not full, taste , not a full-on. Autumn in Beijing: Cold, cool fast?Autumn in Shanghai: cool, cool and slow? in[11]:bj=pd.read_csv ( " beijing2004 , Parse_dates=true,index_col= CST " 12]:matplotlib.style.use ( " ggplot " ) in

not very reassuring. Like what The shiny in R has a partial problem. In the field of data science, R Studio is the best IDE, and even Microsoft's VS has to mimic a data sciences model, which shows that the model of the Editor + console + data panel + plot panel is the most suitable for the analysis. Python is not easy to use Ide,spyder package management and console is not good, Anaconda just put the Spyder into a bag. Rodeo is closest to R Studio, but it doesn't work with Node's front-end ca

First, the questionThere was a problem that bothered me for a long time.I have the following data, the first column is the Hour + minute, the second column is the value:0000 1120001 1230002 122...0059 1230100 120...2359 156How can I draw this into a time sequence diagram of minute granularity? The horizontal axis that is drawn directly using the Ggplot function is a number, not a time.Second, the answerThe essence of the problem is actually to unify t

Plotting data(1) Bar Chart (hist)1 ImportMatplotlib.pyplot as Plt2Plt.style.use ('Ggplot')3 #%matplotlib Inline4 ImportNumPy as NP5 6Fig,ax = Plt.subplots (figsize= (6,4))7Ax.hist (df['Petal Width'], color='Black')8Ax.set_ylabel ('Count', fontsize=12)9Ax.set_xlabel ('Width', fontsize=12)TenPlt.title ('Iris Petal Width', fontsize=14, y=1.01) onePlt.show ()Note:%matplotlib Inline is Ipython in the statement, first temporarily sealed off, figsize= (6,4)

data accuracy = Clf.score (X_test, y_test) # make predictions Forecast_set = Clf.predict (x_lately ) print (Forecast_set, accuracy)The preceding lines of code are scikit-learn the training and forecasting process using linear regression. We can calculate the accuracy of the model by testing the data accuracy and provide the prediction results by providing the model X_lately forecast_set .I run the resulting results as follows:This accuracy that needs to be noted accuracy does not indicate that

In Ggplot, the future of better data visualization, we may sometimes need to use some coordinate transformation operations, such as to draw a horizontal bar chart or spider chart.Coord_cartesian (Xlim = null, Ylim = NULL)Cartesian coordinates: From the point of view Coord_cartesian parameters are relatively simple, x and y data limitsCoord_flip (...)Horizontal transition coordinates: Swap x and y axes without special parametersCoord_trans (x = "Identi

Install.packages ("Ggplot2")Library (GGPLOT2)# WINDROSE.R Http://stackoverflow.com/questions/17266780/wind-rose-with-ggplot-rRequire (GGPLOT2)Require (Rcolorbrewer)Plot.windrose SpdDirSpdres = 10,Dirres = 30,Spdmin = 0,Spdmax = 90,Spdseq = NULL,palette = "Ylgnbu",Countmax = NA,debug = 0) {# look-to-see-what data is passed in to the functionif (Is.numeric (SPD) Is.numeric (dir)) {# Assume that we ' ve been given vectors of the speed and direction vect

Regression Model performance evaluation series 1-QQ chart, regression model evaluation 1-qq(Erbqi) the QQ plot is the Quantile-Quantile diagram, that is, the Quantile-Quantile diagram. A simple understanding is to plot the values of the two same Quantile distributions into points (x, y; if the two distributions are very close, the vertex (x, y) will be distributed near the y = x straight line; otherwise, no; the prediction result of the regression model can be evaluated from the QQ plot. There a

drop and you want T o Make sure everyone gets their own promo code or ID number?And if you ' re worried on running out of IDs, then fear not! The number of UUIDs you can generate are comparable to the number of atoms in the universe.import uuidprint uuid.uuid4()# e7bafa3d-274e-4b0a-b9cc-d898957b4b61 Well if you were a uuid probably would is. One) BashplotlibShameless self-promotion, is one of bashplotlib my creations. It lets you plot histograms and scatterplots using stdin. So

R Language Data Analysis series nine--by Comaple.zhangIn this section, logical regression and R language implementations, logistic regression (lr,logisticregression) is actually a generalized regression model, according to the types of dependent variables and the distribution can be divided into the common multivariate linear regression model, and logistic regression, the logistic regression is that the dependent variable is discrete and the value range is { 0,1} Two classes, if the discrete var

1) Bar chartBar charts are perhaps the most commonly used graphics, often used to show the relationship between categories (different categories on the x-axis) and values (numeric values on the y-axis). Sometimes the bar heights represent counts of cases in the data set, and sometimes they represent values in the data set (with The bar chart height represents the frequency (count) in the dataset, and sometimes represents the values in the dataset, which should be kept firmly in mind, otherwise c

(), and Dcast (), but they can "knead" the data into various shapes. Melt itself means dissolution, decomposition, and its role in a data set is actually split data, its object can be an array, a data frame or a list. > Library (RESHAPE2) > Data (airquality) > str (airquality) ' Data.frame ': 153obs. of 6 variables: $Ozone: int 41 36 12 Na 8 na ... $ solar.r:int 118 149 313 na Na 299 ... $Wind: num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ... $Temp: int .... $Month: int 5 5 5 5 5 5 5 5 5 5 .

scientific computing use Networkx-is an efficient software used for complex networks. pandas-This library provides high-performance, easy-to-use data structures and data analysis tools. The Business Intelligence tool (Pandas Web interface) in Open Mining-python. PYMC-MCMC Sampling Toolkit. Zipline-python's algorithmic Trading library. pydy-full name Python dynamics, assisting with dynamic modeling workflows based on NumPy, SciPy, Ipython, and Matplotlib. sympy-symbol

,coverage, etc. Lint Tools Libraries for static code analysis Flake8,pylint, etc. Debugging Tools Libraries for Debug ipdb,wdb, etc. Performance Tools libraries that assist in determining performance bottlenecks profiling,memory Profiler, etc. High Performance make Python a faster library Cython,pypy, etc. DevOps software and libraries for DevOps Ansible,fabric,pexpect, etc.

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