EdMan1022

from flask import Blueprint, request

from ..helper_functions import cluster_analysis_function

analytics = Blueprint('analytics', __name__, template_folder='templates')


@analytics.route('/api/analytics/cluster_analysis', methods=['GET'])
def cluster_analysis_view():
    """

EdMan1022

{% extends 'utilities/base.html' %}
{% block title %}{{ analysis_group.name }}{% endblock %}
{% block content %}
    <div class="row justify-content-center">
        <div class="col-8">
            <div class="card border-primary mb-3">
                <div class="card-header">
                    <h3>{{ analysis_group.name }}</h3>
                    <h4>{{ analysis_group.description }}</h4>
                </div>

EdMan1022

*

EdMan1022

import pandas as pd

# Pandas dataframe to txt file with comma delimiter test

df = pd.DataFrame(index = [0,1,2,3,4])

df.loc[:, 'a'] = 'a'
df.loc[:, 'b'] = 2
df.loc[:, 'c'] = '   '
df.loc[:, 'd'] = None

EdMan1022

import pandas as pd
import numpy as np

df = pd.DataFrame(data=[[3., 1., np.NaN], [np.NaN, 3., 2.], [4., 1., 3.]], index=[0, 1, 2],
                  columns=['apple', 'carrot', 'pear'])


def null_only_categorical_func(data):
    """
    Take a series, and return values of 1. where the series is null

EdMan1022

from sklearn import decomposition
import numpy as np

pca = decomposition.PCA(n_components=2)

X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])

x = pca.fit_transform(X)