data-visualization

I have a data set with huge number of features, so analysing the correlation matrix has become very difficult. I want to plot a correlation matrix which we get using dataframe.corr() function from pandas library. Is there any built-in function provided by the pandas library to plot this matrix? jrjc You can use pyplot.matshow() from matplotlib : import matplotlib.pyplot as plt plt.matshow(dataframe.corr()) plt.show() Edit: In the comments was a request for how to change the axis tick labels. Here's a deluxe version that is drawn on a bigger figure size, has axis labels to match the dataframe,

Is there an easy way to increase the space between the plot title and the plot area below it (the box with the data). Similarly, I'd prefer to have some space between the axis title and axis labels. In other words, is there a way to "move the title a bit up, the y axis title a bit left, and the x axis title a bit down"? juba You can adjust the plot margins with plot.margin in theme() and then move your axis labels and title with the vjust argument of element_text() . For example : library(ggplot2) library(grid) qplot(rnorm(100)) + ggtitle("Title") + theme(axis.title.x=element_text(vjust=-2)) +

Using the charting control from System.Windows.Forms.DataVisualization.Charting.Chart , I am making a scatter plot. How can I constrain it so that the scale of the X axis is the same as the scale of the Y axis? Simply setting the control itself to be square is insufficient, because it has internal margins for drawing and labeling the axes which are not equal. I could pick a specific size and tweak it to be square, but it needs to be both square and resizable. I've searched high and low in the documentation and in the property browser, but I can't find anything or think of any ways to do it in

This question already has an answer here: How can I change the x axis in matplotlib so there is no white space? 1 answer Just noticed this nuance when I editing my works. Previously, the matplotlib would look like this: x=[1,2,3,4,5] y=[4,5,5,2,1] plot(x,y,'-') But after recent upgrade I believe, the there are offset, which would return like this It's a little bit unncessary from what I seen now. I want to know If this offset is a good practice in data visualization? If so, I'll leave it as it is. How to cancel out this offset? I can manually restore the limit by plt.gca().set_xlim([1, 5]) ,