data-science

I'm trying to calculate the Davies-Bouldin Index in Python. Here are the steps the code below tries to reproduce. 5 Steps : For each cluster, compute euclidean distances between each point to the centroid For each cluster, compute the average of these distances For each pair of clusters, compute the euclidean distance between their centroids Then, For each pair of clusters, make the sum of the average distances to their respective centroid (computed at step 2) and divide it by the distance separating them (computed at step 3). Finally, Compute the mean of all these divisions (= all indexes) to

Closed . This question is opinion-based. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it can be answered with facts and citations by editing this post . Hey I'm new to Pandas and I just came across df.query() . Why people would use df.query() when you can directly filter your Dataframes using brackets notation ? The official pandas tutorial also seems to prefer the latter approach. With brackets notation : df[df['age'] <= 21] With pandas query method : df.query('age <= 21') Besides some of the stylistic or flexibility differences

I am trying to groupby a column and compute value counts on another column. import pandas as pd dftest = pd.DataFrame({'A':[1,1,1,1,1,1,1,1,1,2,2,2,2,2], 'Amt':[20,20,20,30,30,30,30,40, 40,10, 10, 40,40,40]}) print(dftest) dftest looks like A Amt 0 1 20 1 1 20 2 1 20 3 1 30 4 1 30 5 1 30 6 1 30 7 1 40 8 1 40 9 2 10 10 2 10 11 2 40 12 2 40 13 2 40 perform grouping grouper = dftest.groupby('A') df_grouped = grouper['Amt'].value_counts() which gives A Amt 1 30 4 20 3 40 2 2 40 3 10 2 Name: Amt, dtype: int64 what I want is to keep top two rows of each group Also, I was perplexed by an error when I

I am building a model for binary classification problem where each of my data points is of 300 dimensions (I am using 300 features). I am using a PassiveAggressiveClassifier from sklearn . The model is performing really well. I wish to plot the decision boundary of the model. How can I do so ? To get a sense of the data, I am plotting it in 2D using TSNE. I reduced the dimensions of the data in 2 steps - from 300 to 50, then from 50 to 2 (this is a common recomendation). Below is the code snippet for the same : from sklearn.manifold import TSNE from sklearn.decomposition import TruncatedSVD X

I am trying to use a log scale as the margin plots for my seaborn jointplot. I am usings set_xticks() and set_yticks(), but my changes do not appear. Here is my code below and the resulting graph: import matplotlib.pyplot as plt %matplotlib inline import numpy as np import seaborn as sns import pandas as pd tips = sns.load_dataset('tips') female_waiters = tips[tips['sex']=='Female'] def graph_joint_histograms(df1): g=sns.jointplot(x = 'total_bill',y = 'tip', data = tips, space = 0.3,ratio = 3) g.ax_joint.cla() g.ax_marg_x.cla() g.ax_marg_y.cla() for xlabel_i in g.ax_marg_x.get_xticklabels():