sns | AngularJS based open source Instagram clone , Dribbble clone | Plugin library

While, regplot returns one ax (subplot), relplot returns a complete grid of subplots (a FacetGrid). Often, the return value is grabbed into a variable named g (calling it cookie can make things very confusing when comparing with code from the documents).

You can loop through the individual axes of the FacetGrid and make the change for each of them:

You could use collections.Counter to count the words:

• Calculate the mean for each group, and then add them to the existing ax with a seaborn.lineplot
• Set dodge=False in the seaborn.boxplot
• Remember that the line in the boxplot is the median, not the mean.
  • Add the means to boxplot with showmeans=True, and then remove marker='o' from the lineplot, if desired.
• As pointed out JohanC's answer:
  • sns.pointplot(data=dfm, x='variable', y='value', hue='parametrized_factor', ax=ax) can be used without the need for calculating dfm_mean, however there isn't a legend=False parameter, which then requires manually managing the legend.
  • Also, I think it's more straightforward to use dodge=False than to calculate the offsets.
  • Either answer is viable, depending on your requirements.

You are missing the col_order=cols argument to the grid = sns.FacetGrid(...) call.

In a histogram, a "rectangle"'s height represents how many values are in the given range which is in turn described by the width of the rectangle. You can get the width of each rectangle by (max - min) / number_of_rectangles.

For example, in the matplotlib's output, there are 10 rectangles (bins). Since your data has a minimum around 3 and maximum around 50, each width is around 4.7 units wide. Now, to get the 3rd rectangles range, for example, we start from minimum and add this width until we get there, i.e., 3 + 4.7*2 = 12.4. It then ends at 12.4 + 4.7 = 17.1. So, the counts corresponding to 3rd bin is the number of values in tips_df.total_bill that fall in this range. Let's find it manually:

After some trials and errors I figured out the solution for my own question

1- The GCM part of the payload should be a string not a json 2- The message parameter should have an attribute that explicitly sets the mime type of the payload to Json

Taking all that into consideration

You need to set self.theta to be an array, not a scalar (at least in this specific problem).

In your case, (intercepted-augmented) X is a '3 by n' array, so try self.theta = [0, 0, 0] for example. This will correct the specific error 'bool' object has no attribute 'mean'. Still, this will just produce preds as a zero vector; you haven't fit the model yet.

To let you know how I approached the error, I first went to the exact line the error message was pointing to, and put print(preds == y) before the line, and it printed out False. I guess what you expected was a vector of True and Falses. Your y seemed okay; it was a vector (a list to be specific). So I tried print(pred), which showed me a '3 by n' array, which is weird. Now going up from that line, I found out that pred comes from predict_prob(), especially np.dot(X, self.theta). Here, when X is a '3 by n' array and self.theta is a scalar, numpy seems to multiply the scalar to each item in the array and return the array (having the same dimension as the original array), instead of doing matrix multiplication! So you need to explicitly provide self.theta as an array (conforming to the dimension of X).

Hope the answer and the reasoning behind it helped.

As for the red line you mentioned in the comment, I guess it is also because you are not fitting the model. (To see the problem, put print(probs) before plt.countour(...). You'll see an array with 0.5 only.)

So try putting model.fit(X, y) before preds = model.predict(X). (You'll also need to put self.verbose = verbose in the __init__().)

After that, I get the following:

You could get back ax from your plot call, use labels=ax.get_xticklabels() to get them in order, and then ax.set_xticklabels(labels=labels,rotation=90)
However, with thousands of points, readability would still be a challenge. If you can skip labels, you could replace them with empty strings except 1 in [how many makes it work].
Or maybe keep only those with 0-value as the others seem quite uniformly distributed. Only you know.
Of course resizing could also help.

When calculating an eigenvector you may change its sign and the solution will also be a valid one.

So any PCA axis can be reversed and the solution will be valid.

Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.