Category : seaborn

I created a program which generates a large number of chart using seaborn catplot. This is the example of my code with the illustration of how the final chart looks like. plot= sns.catplot(data=df3, x="solutions", y="score", col="subject", col_wrap=3, hue="Value",height=3, aspect=1.5,legend=False, sharex=False, sharey=False) plt.legend(loc=’upper left’) plot.set_xticklabels(rotation=90) plt.tight_layout() #Create output plot.savefig("output3.pdf") However since the plot may extend up ..

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# Applying Ridge Regression with varying the hyperparameter ‘lambda’ Applying Ridge Regression with varying the hyperparameter ‘lambda’ X_seq = np.linspace(X_train_RFE.min(),X_train_RFE.max(),300).reshape(-1,1) # values to be considered for predictor variable lambdas = [0, 0.001, 0.01, 0.1, 1, 10, 100, 1000] # Higher the value of lambda, # more the regularization for i in lambdas: # for each ..

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I have a simple df with the Income Category and Credit Limit. When plotting using seaborn, I keep getting an unwanted order in the x-axis. What is the simplest option to "force" the plot to be in the order defined in my new_order? new_order = [’40k’,’40k-60k’,’60k-80k’,’80k-120k’,’120k’] sns.lineplot(data=dff, x="Income_Category", y="Credit_Limit") Source: Python-3x..

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I made a line plot using seaborn’s relplot and I wanted to customize my legend labels. For some reason when I do this, It creates another legend with out deleting the old one. How do I get rid of the initial legend (The legend with title "Sex")? Also how do I add a legend title ..

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I am trying to create a seaborn lineplot with sns.relplot using markers to distinguish between state changes over time, along with some arbitrary value on the y-axis. Using the dataset below: df = pd.DataFrame({ ‘NAME’:[‘29078719′,’29078719′,’29078719′,’29078719′,’29078719′,’29078719′,’29078719’], ‘DIAGNOSIS’:[‘negative’, ‘negative’, ‘positive’, ‘positive’, ‘positive’, ‘negative’, ‘negative’], ‘ENTRY_DATE’: [‘2014-01-23 15:13:54’, ‘2015-03-06 15:57:16’, ‘2016-02-26 14:40:53’, ‘2016-02-26 14:40:53’, ‘2017-11-24 15:20:38’, ‘2017-11-24 15:20:38’, ..

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