[ad_1] As explained in matplotlib blog, the benefits of OO approach are scalability and control. The functional interface (pyplot) was build to reproduce MATLAB’s method of generating figures, which is a very popular propetary programing language in engeenering departments. Pyplot works as a state-based interface, where the states are figures that can be changed by … Read more
[ad_1] The part I am getting hung up on is how I can loop through the values for each key and get to the individual data in each value. I can do the rest after that So if I understand you correctly then you want to iterate through a dictionary which has lists as its … Read more
[ad_1] If you are using Julia’s Polynomials package then something like this pattern will work: p = Poly(reverse(ais)) # Poly wants [a0, a1, …, an] xs = linspace(a, b, 100) # fill in a, b plot(xs, p.(xs)) If you are using Plots, the last two lines could be plot(x->p(x), a, b). [ad_2] solved Plotting polynomial … Read more
[ad_1] You should create a list Z in which you are going to append the Z values computed at each iteration, like this: import numpy as np import pandas as pd import matplotlib.pyplot as plt K = 1 Rmv = 26 SigS = 111.7 M = 2.050 N = 2 SigD = (-249.4) def Mittelspannung(): … Read more
[ad_1] You can display subscripts by writing your column names using LaTex: import pandas as pd import matplotlib.pyplot as plt df = pd.DataFrame( { 0: { “Method 1”: 31.7, “Method 2”: 44.2, “Method 3”: 75.6, “Method 4”: 87.5, “Method 5”: 88.6, “Method 6”: 100.0, }, 1: { “Method 1”: 32.9, “Method 2”: 45.4, “Method 3”: … Read more
[ad_1] import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv(‘dataset.csv’) data[‘sample_date’] = pd.to_datetime(data[‘sample_date’]) data[‘sample_date’].value_counts().sort_index().plot(kind=’bar’) # Use sort_index() plt.tight_layout() plt.show() 0 [ad_2] solved FREQUENCY BAR CHART OF A DATE COLUMN IN AN ASCENDING ORDER OF DATES
[ad_1] The following could be used to plot your data. The main point is that you need to specify the (rather unusual) format of the datetimes (“%Y/%m/%d/%H/%M”), such that it can be converted to a datetime object. import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv(“data/minuteData.csv”) df[“minute”] = pd.to_datetime(df[“minute”], format=”%Y/%m/%d/%H/%M”) plt.plot(df[“minute”],df[“spreadprice”], label=”spreadprice” ) … Read more
[ad_1] I think there is confusion here! a confusion matrix is set(y_test) + set(y_pred). so if this comes to be 8. then the matrix will be 8 X 8. Now, if you have more labels you want to print even though it does not matter if it’s all zero. then while building the matrix , … Read more
[ad_1] This is how I would handle this: def recolorBars(self, event): y = event.ydata for i, rect in enumerate(self.rects): t, p, _ = sms.DescrStatsW(self.df[self.df.columns[i]]).ttest_mean(y) rect.set_color(self.cpick.to_rgba((1 – p) * t / abs(t))) When iterating through the bars, first test the value against the sample mean, then set the color based on p-value and test statistic t: … Read more
[ad_1] x and y are single floats, not arrays as indicated by the output of your print statement. plt.plot() tries to draw lines between points, but since there is only one point, there can be no line. I would assume you want to somehow make x and y into arrays of values perhaps something like … Read more
[ad_1] You should use im = ax.imshow() when you want add something to the existing axis, for example, a colorbar. If you don’t need to add anything to the axis, then it is fine to just use ax.imshow(). See this: https://www.geeksforgeeks.org/matplotlib-axes-axes-imshow-in-python/ Example from the link: c = ax.imshow(z, cmap =’Greens’, vmin = z_min, vmax = … Read more
[ad_1] Because you change the question here is the updated answer: See comments in code import pandas as pd import matplotlib.pyplot as plt from matplotlib import style style.use(‘ggplot’) %matplotlib inline # read your dataframe and sort df = pd.read_clipboard() df.drop(columns=[‘length’], inplace=True) df.rename(columns={‘Text.1’: ‘Text length’}, inplace=True) df.sort_values([‘Text’, ‘Tag’, ‘Time’], inplace=True) x = list(df[‘Time’]) # set x … Read more
[ad_1] Best way to visualize a dependent variable as a function of two other independent variables, each of them is a column of a datafarme? [ad_2] solved Best way to visualize a dependent variable as a function of two other independent variables, each of them is a column of a datafarme?
[ad_1] If you want to colorize each point on a usual HD screen that would result in ~2 million points. It does not seem to make sense to plot more than 100 times that much data. Apart this is surely a question of available memory and computing time. So I ran the experiment. After 15 … Read more
[ad_1] You can turn a string into code by using pythons eval function, but this is dangerous and generally considered bad style, See this: https://stackoverflow.com/a/661128/3838691. If user can input the string, they could input something like import subprocess; subprocess.check_call([‘rm’, ‘-rf’, ‘*’], shell=True). So be sure that you build in reasonable security into this. You can … Read more