seaborn

问题 I have some trouble understanding seaborns FacetGrids. I have 16 cotegorical columns in my data and I want to use countplot to show the distribution over the values. In addition i have one target variable Y which have to classes. Now I want the calculations for the countplot to be class-dependet. So for each of my 16 input variables I want to have to subplots ( for class 0 and class 1). I can achieve this by (not sure if this is the best solution): for col in df: g = sbn.FacetGrid(df, col="Y"

问题 I have the following view: but I'd like to have the y-axis here ordered so the num_engagements field is increasing as the y-axis goes up instead of the reverse case here. I've tried playing with the order field in the seaborn options, but if I set order=['num_engagements'] then I just get a blank plot as a result. Any thoughts? 回答1: You need to pass in a list of all the y-axis labels to your order keyword. The following would achieve what you want: sns.boxplot(y='num_engagements', x='channel

问题 I am trying to make a heatmap with logarithmic colorbar. But it keeps generating its own ticks and ticklabels along with the ones I input. I originally posted this to reformat the tick labels from scientific notation to plain but then ran into this problem. import numpy as np import seaborn as sns from matplotlib.colors import LogNorm import matplotlib.ticker as tkr matrix = np.random.rand(10, 10)/0.4 vmax=2 vmin=0.5 cbar_ticks = [0.5, 0.75, 1, 1.33, 2] formatter = tkr.ScalarFormatter

问题 I am trying to create a factor plot but I am not able to change the kind of it from point to bar. How do we do that? The codes used are import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline sns.catplot('Sex',kind="bar",data=titanic_df) 回答1: The seaborn documentation has the exact example you are looking for. Following the documentation, if you run the below lines, it should generate the bar plot shown. import matplotlib.pyplot as plt import seaborn as sns

问题 Is there a way to hatch particular 'cells' in a seaborn heatmap, which e.g. fullfill a condition? I already tried it with masked arrays and matplotlib pcolor, but it turned out that it hatched the wrong cells. import numpy as np import seaborn as sns import matplotlib.pyplot as plt flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") zm = np.ma.masked_less(flights.values, 200) x= np.arange(0,12) y= np.arange(0,12) sns.heatmap(flights,linewidth=.1) plt

问题 I want to plot multiple seaborn distplot under a same window, where each plot has the same x and y grid. My attempt is shown below, which does not work. # function to plot the density curve of the 200 Median Stn. MC-losses def make_density(stat_list,color, layer_num): num_subplots = len(stat_list) ncols = 3 nrows = (num_subplots + ncols - 1) // ncols fig, axes = plt.subplots(ncols=ncols, nrows=nrows, figsize=(ncols * 6, nrows * 5)) for i in range(len(stat_list)): # Plot formatting plt.title(

问题 I have plotted my data into a confusion matrix using seaborn but I ran into a problem. The problem is that it is only showing numbers from 0 to 11, on both axes, because I have 12 different labels. My code looks as follows: cf_matrix = confusion_matrix(y_test, y_pred) fig, ax = plt.subplots(figsize=(15,10)) sns.heatmap(cf_matrix, linewidths=1, annot=True, ax=ax, fmt='g') Here you can see my confusion matrix: I am getting the confusion matrix as I should. The only problem is the names of the

问题 I am working on a fairly large dataset (~40m rows). I have found that if I call sns.countplot() directly then my visualisation plots really quickly: %%time ax = sns.countplot(x="age_band",data=acme) However if I do the same visualisation using catplot(kind="count") then the speed of execution slows down dramatically: %%time g = sns.catplot(x="age_band",data=acme,kind="count") Is there a reason for such a large performance difference? Is catplot() doing some sort of conversion on my data

问题 I have just recently started to use matplotlib and seaborn to plot my graphs. This is the code that I wrote so far count = 1 l=[13,0,47,29,10] plt.figure(figsize=(30,40)) for ww in l: temp_dict = defaultdict(list) entropies = list() for k,v in df.ix[ww].iteritems(): e = 0 for i in v: temp_dict[k].append(float(i)) if not float(i) == 0: e += -1.0*float(i)*log10(float(i)) entropies.append(e) y = entropies x=(range(len(entropies))) slope, intercept, r_value, p_value, std_err = stats.linregress(x

问题 I have a factorplot that I have generated from a summary table, rather than raw data: Using the following code: sns.factorplot(col="followup", y="probability", hue="next intervention", x="age", data=table_flat[table_flat['next intervention']!='none'], facet_kws={'ylim':(0,0.6)}) Plotted here are the mean values from the summary table, but I would also like to plot the credible interval, whose upper and lower bounds are specified in two other columns. The table looks like this: Is there a way,