I have a heatmap image (correlation between all matrix columns) and I'm straggling to preform all the changes below within the same image: bar colors should be symmetric around zero (e.g., correlation of 1 and -1 should be with the same color) change the correlation matrix to a diagonal matrix, since correlation values are symmetric - and show only upper matrix triangle (mask out the lower triangle ) show the correlation values in every cell of the diagonal matrix x,y …
In the following code of the DBSCAN algorithm, as a beginner I need an explanation for what happens to the data in the bottom for loop and why ? Generate sample data import numpy as np from sklearn.cluster import DBSCAN from sklearn import metrics from sklearn.datasets import make_blobs from sklearn.preprocessing import StandardScaler centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4, random_state=0) X = StandardScaler().fit_transform(X) Compute DBSCAN db = DBSCAN(eps=0.3, min_samples=10).fit(X) core_samples_mask = np.zeros_like(db.labels_, dtype=bool) …
I am trying to plot some data so get statistics about it, but matplotlib simply can't plot it as boxplots. I tried with histograms and it workd well: But when i change the code to plot boxplots it just doesnt work: I know that the y axis is in the wrong place, but I even searched on where it should be (for example SAQRS in the range of -150 to 50) but even there there is nothing. The plotting code …
How can I change the legend as we can see now the legend has some cluster numbers missing. How can I adjust the legend so that it can show all the cluster numbers (such as Cluster 1, Cluster 2 etc, no it's only 0 3 6 9)? (codes I followed this link: Perform k-means clustering over multiple columns) kmeans = KMeans(n_clusters=10) y2 = kmeans.fit_predict(scaled_data) reduced_scaled_data = PCA(n_components=2).fit_transform(scaled_data) results = pd.DataFrame(reduced_scaled_data,columns=['pca1','pca2']) sns.scatterplot(x="pca1", y="pca2", hue=y2, data=results) #y2 is my cluster number plt.title('K-means …
cat = {'A':1, 'B':2, 'C':3} dog = {'A':2, 'B':2, 'C':4} owl = {'A':3, 'B':3, 'C':3} Suppose I have 3 dictionary, each containing pairs of (subcategory, count). How can I plot a segmented bar chart (i.e stacked bar graph) using Python with x being 3 categories (cat, dog, owl) and y being proportion (of each subcategory)? What I have in mind looks like this:
I have used 4 machine learning models on a task and now I am struggling to plot their bar charts just like shown below in the image. I am printing classification report to get precision, recall etc. My code is shown: def Statistics(data): # Classification Report print("Classification Report is shown below") print(classification_report(data['actual labels'],data['predicted labels'])) # Confusion matrix print("Confusion matrix is shown below") cm=confusion_matrix(data['actual labels'],data['predicted labels']) plt.figure(figsize=(10,7)) sn.heatmap(cm, annot=True,cmap='Blues', fmt='g') plt.xlabel('Predicted') plt.ylabel('Truth') Statistics(data) How can I plot this type of chart …
I have a bunch of plots as the one reported below. The data is from measurements performed on different times and different days. In the plot (which is a cumulative distribution function, if that matters), the colors differentiate data relevant to different days; the markers are used to further differentiate the data within each day. The problem is that the plot is very crowded and a bit ugly. Some markers can be barely seen. Question: Any idea how I can …
So for context, I have a massive dataset of over 2.7 million rows of average download/upload speeds of individuals in Canada, with province/city columns. I would like to plot a contour map of average down/up speed over a picture of the country Canada, kind of like this: https://www.floodmap.net/Elevation/ElevationMap/CountryMaps/?cz=US_1 But unfortunately I have no clue on how to make something like that. I would really appreciate it if someone could point me to the right direction.
I've created a histogram as well as a QQPlot from the residuals of my Regression Model: Mean: 0.35 Standard Deviation: 18.14 Judging from these plots, is it okay to say that my residuals are normally distributed? Or what else can I draw from these plots? Update: Created the Histogram using ns.distplot(x, hist=True) Here's the result:
I would like to visualize a large amount of events composed of time serie windows. A typical event would be: Problem is, my events are not synchronized, and so if I plot them all, it would look like: Question Is there any way to visualize all my events so I can see their original/"typical" shape (preferably in the time domain) despite their unsynchronization ? What I have tried so far: Visualize features: approach is good but I have to guess …
I am using matplotlib to generate a filled contour plot, please consider the below example as a sample contour plot. I want to read off the contour values from such a filled contour plot using opencv's mouse interaction modules. For example, if the uses hovers the mouse over this contour image, it should dynamically display the contour values as the mouse moves over the image. I have the opencv part figured out, but I am struggling to link the RGB …
I am trying to create a bar plot for a Pandas Series and the bar plot is not showing up in Jupyter notebook. When I run the cell, I only get the following and I do not see the bar plot. <matplotlib.figure.Figure at 0x7fa555abc080> Please advise.
im trying to add a dropdown menu on my tkinter popup window but when ever i run it on my visual studio code ide nothing displays but when i run the code by it self on jupyter everything work fine so what is going on def btn4(): newWindow4 = Toplevel(root) newWindow4.title("GOES NOAA V1.0 ") newWindow4.geometry("1620x1300") fig = plt.figure(figsize=(6, 6)) canvas = FigureCanvasTkAgg(fig, master=newWindow4) canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) channel_list = {u'1 - Blue Band 0.47 \u03BCm': 1, u'2 - Red Band 0.64 …
I have a dataframe with multiple time series and columns with labels. My goal is to plot all time series in a single plot, where the labels should be used in the legend of the plot. The important point is that the x-data of the time series do not match each other, only their ranges roughly do. See this example: import pandas as pd import matplotlib.pyplot as plt df = pd.DataFrame([[1, 2, "A", "A"], [2, 3, "A", "A"], [3, 1, …
I have a dataset containing three years of data which I would like to plot and compare by date and month; but, I am having a hard time with the final result. I am nearly there, but for some strange reason, while plotting I continue to get an annoying gap in between the data points, even if this does not seem to be included in the data series. The whole dataset is this: Day Visits 0 2018-04-01 1 1 2018-04-02 …
I have a curve and I want to create the confidence interval for the curve. Here, I provide a simple example: mean, lower, upper = [],[],[] ci = 0.2 for i in range (20): a = np.random.rand(100) MEAN = np.mean(a) mean.append(MEAN) std = np.std(a) Upper = MEAN+ci*std Lower = MEAN-ci*std lower.append(Lower) upper.append(Upper) plt.figure(figsize=(20,8)) plt.plot(mean,'-b', label='mean') plt.plot(upper,'-r', label='upper') plt.plot(lower,'-g', label='lower') plt.xlabel("Value", fontsize = 30) plt.ylabel("Loss", fontsize = 30) plt.xticks(fontsize= 30) plt.yticks(fontsize= 30) plt.legend(loc=4, prop={'size': 30}) In the above example, I drew …
I have a Dataframe which looks as shown below I am trying to make a line plot for looking at the peaks for both columns (a,b), I have gotten as far as sns.set_style("darkgrid") plt.plot(wr['a'][:100]) plt.show() but the plot looks shabby, wr.set_index(['Date_x'],inplace=True) wr['a'][:100].plot() wr['b'][:100].plot() I am looking to have something like this Any Help is Appreciated.
I have the a dataframe(df) which has the data of a Job being executed at different time intervals. It includes the following details about the execution of a job: Job Start Time (START) Job End Time (END) Time Interval (interval) i.e., END - START. A small part of dataframe is shown below. Dataframe(df): END | START | interval 1423.0 | 1357.0 | 66.0 33277.0 | 33325.0 | -48.0 42284.0 | 42250.0 | 34.0 53466.0 | 53218.0 | 248.0 62158.0 | …