Give the basic info, with a clear route for how to go further – update your browser! I think sacrificing functionality for 99% of users to accommodate 1% is sheer bloody mindedness. Sure, it is possible to allow for non-javascript enabled content for every aspect of a website, plus provide the optimal experience – but … Read more
Update: As folks have pointed out this answer is not correct. A probplot is different from a quantile-quantile plot. Please see those comments and other answers before you make an error in interpreting or conveying your distributions’ relationship. I think that scipy.stats.probplot will do what you want. See the documentation for more detail. import numpy … Read more
You can use the pandas.DataFrame.quantile() function, as shown below. import pandas as pd import random A = [ random.randint(0,100) for i in range(10) ] B = [ random.randint(0,100) for i in range(10) ] df = pd.DataFrame({ ‘field_A’: A, ‘field_B’: B }) df # field_A field_B # 0 90 72 # 1 63 84 # 2 … Read more
Maybe .value_counts()? >>> import pandas >>> my_series = pandas.Series([1,2,2,3,3,3, “fred”, 1.8, 1.8]) >>> my_series 0 1 1 2 2 2 3 3 4 3 5 3 6 fred 7 1.8 8 1.8 >>> counts = my_series.value_counts() >>> counts 3 3 2 2 1.8 2 fred 1 1 1 >>> len(counts) 5 >>> sum(counts) 9 >>> … Read more
Here are two ways you may try, assuming your model is an sklearn predictor: import sklearn.metrics as metrics # calculate the fpr and tpr for all thresholds of the classification probs = model.predict_proba(X_test) preds = probs[:,1] fpr, tpr, threshold = metrics.roc_curve(y_test, preds) roc_auc = metrics.auc(fpr, tpr) # method I: plt import matplotlib.pyplot as plt plt.title(‘Receiver … Read more
sweep() is typically used when you operate a matrix by row or by column, and the other input of the operation is a different value for each row / column. Whether you operate by row or column is defined by MARGIN, as for apply(). The values used for what I called “the other input” is … Read more
No, but there are a few people who have written one, such as here. Another possibility is to use this: exp(mean(log(x)))
There’s one in scipy.stats: >>> import scipy.stats >>> scipy.stats.norm(0, 1) <scipy.stats.distributions.rv_frozen object at 0x928352c> >>> scipy.stats.norm(0, 1).pdf(0) 0.3989422804014327 >>> scipy.stats.norm(0, 1).cdf(0) 0.5 >>> scipy.stats.norm(100, 12) <scipy.stats.distributions.rv_frozen object at 0x928352c> >>> scipy.stats.norm(100, 12).pdf(98) 0.032786643008494994 >>> scipy.stats.norm(100, 12).cdf(98) 0.43381616738909634 >>> scipy.stats.norm(100, 12).cdf(100) 0.5 [One thing to beware of — just a tip — is that the parameter … Read more
The exponential function is e^x where e is a mathematical constant called Euler’s number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array.
I have looked at R’s src/library/stats/src/Trunmed.c a few times as I wanted something similar too in a standalone C++ class / C subroutine. Note that this are actually two implementations in one, see src/library/stats/man/runmed.Rd (the source of the help file) which says \details{ Apart from the end values, the result \code{y = runmed(x, k)} simply … Read more