graphs_plot_examp

#https://www.cian.ru/sale/flat/281946132/
#https://www.cian.ru/sale/flat/281271977/
#https://www.cian.ru/sale/flat/281748776/


import pyvis
from uuid import uuid4

from pyvis.network import Network

net = Network()  # создаём объект графа
import pandas as pd

df = pd.DataFrame({'ID':[1,2,3,4,5,6],
                   'First Name':['Felix', 'Jean', 'James', 'Daphne', 'James', 'Peter'],
                   'Family Name': ['Revert', 'Durand', 'Wright', 'Hull', 'Conrad', 'Donovan'],
                   'Phone number': ['+33 6 12 34 56 78', '+33 7 00 00 00 00', '+33 6 12 34 56 78', '+33 6 99 99 99 99', '+852 0123 4567', '+852 0123 4567'],
                   'Email': ['felix.revert@gmail.com', 'jean.durand@gmail.com', 'j.custom@gmail.com', pd.np.nan, 'j.custom@gmail.com', pd.np.nan]})


column_edge = 'Phone number'
column_ID = 'ID'

data_to_merge = df[[column_ID, column_edge]].dropna(subset=[column_edge]).drop_duplicates() # select columns, remove NaN

# To create connections between people who have the same number,
# join data with itself on the 'ID' column.
data_to_merge = data_to_merge.merge(
    data_to_merge[[column_ID, column_edge]].rename(columns={column_ID:column_ID+"_2"}),
    on=column_edge
)

# By joining the data with itself, people will have a connection with themselves.
# Remove self connections, to keep only connected people who are different.
d = data_to_merge[~(data_to_merge[column_ID] == data_to_merge[column_ID + "_2"])] \
    .dropna()[[column_ID, column_ID + "_2", column_edge]]

# To avoid counting twice the connections (person 1 connected to person 2 and person 2 connected to person 1)
# we force the first ID to be "lower" then ID_2
d.drop(d.loc[d[column_ID + "_2"] < d[column_ID]].index.tolist(), inplace=True)


# добавление узлов
net.add_nodes(
    df,  # node ids
    label=['Table1', 'Table2', 'Table3', 'Table4', 'TAble5'],  # node labels
    # node titles (display on mouse hover)
    title=['Main node', 'Just node', 'Just node', 'Just node', 'Node with self-loop'],
    color=['#d47415', '#22b512', '#42adf5', '#4a21b0', '#e627a3']  # node colors (HEX)
)
# добавляем тот же список узлов, что и в предыдущем примере
net.add_edges([])

net.show('graph.html')

"""import plotly.express as px
import datapane as dp

location_df = dp.Blob.get(name='location_df', owner='khuyentran1401').download_df()

m = px.scatter_geo(location_df, lat='latitude', lon='longitude',
                 color='total_stars', size='forks',
                 hover_data=['user_name','followers'],
                 title='Locations of Top Users')

m.show()"""
"""import datapane as dp
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        "A": np.random.normal(-1, 1, 5),
        "B": np.random.normal(1, 2, 5),
    }
)

table = dp.DataTable(df,)
app = dp.App(table)
app.save(path="simple-table.html")"""


import networkx as nx

G = nx.from_pandas_edgelist(df=d, source=column_ID, target=column_ID+'_2', edge_attr=column_edge)

G.add_nodes_from(nodes_for_adding=df.ID.tolist())
nx.draw(G)



import matplotlib.pyplot as plt
import pyvis
from uuid import uuid4
import networkx as nx
from pyvis.network import Network

#net = Network()  # создаём объект графа
df = pd.read_excel("example_uuid.xlsx")
G = nx.Graph()  # создаём объект графа
fig = plt.figure()

# определяем список узлов (ID узлов)
nodes = []
for col in df:
    for i in df[col].unique():
        nodes.append(i)
    """for val in col[1]:
        print(val)"""
        #nodes.append(val)

stop = 0

# определяем список рёбер
# список кортежей, каждый из которых представляет ребро
# кортеж (id_1, id_2) означает, что узлы id_1 и id_2 соединены ребром
#edges = [(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (5, 5)]
edges = []
for i in range(len(nodes) - 1):
    edges.append((nodes[i], nodes[i + 1]))
# добавляем информацию в объект графа
G.add_nodes_from(nodes)
G.add_edges_from(edges)

# рисуем граф и отображаем его
nx.draw(G, with_labels=True, font_weight='bold')

plt.show()
stop = 0

"""***HELPFUL LINKS***

https://habr.com/ru/company/vk/blog/522094/
https://habr.com/ru/company/ruvds/blog/705368/
https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py
https://machinelearningmastery.ru/getting-started-with-graph-analysis-in-python-with-pandas-and-networkx-5e2d2f82f18e/
https://github.com/FelixChop/MediumArticles/blob/master/Graph_analysis_Python.ipynb"""