In [1]:

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Energy Visualization Chart¶

Datascience Analysis Workflow Demonstration:

Using Panadas DataFrame library to import excel sheets
Transform & Manipulate data to uncover insight using pythob
Plot, visualize data using Plotly-Dash, Matplotlib, Seaborn
Interactive Hosted Dashboard for sharing with interal team and external clients

In [2]:

!pip install plotly-express -q

In [3]:

import pandas as pd; import numpy as np; import matplotlib.pyplot as plt

csv_file = '/content/drive/MyDrive/02_CONSULTING/ENERVA/enerva_plotly/Analysis 3 with calculation - Updated Hourly.csv'
data = pd.read_csv(csv_file)
data = data[['HDH','kW','CDH','kW.3']]
data

Out[3]:

	HDH	kW	CDH	kW.3
0	20.5	125.61	2.5	69.18
1	20.7	129.51	3.3	60.48
2	21.2	119.70	3.7	60.90
3	21.4	120.51	4.5	66.24
4	21.8	116.28	4.7	64.71
...	...	...	...	...
6606	26.6	212.16	NaN	NaN
6607	26.8	206.37	NaN	NaN
6608	26.6	197.10	NaN	NaN
6609	26.4	194.61	NaN	NaN
6610	26.2	191.28	NaN	NaN

6611 rows × 4 columns

In [4]:

import plotly.express as px

fig = px.scatter(data, x="HDH", y="kW", trendline="ols", title="kW vs HDH")
fig.show()

In [5]:

fig = px.scatter(data, x="CDH", y="kW.3", trendline="ols", title="kW vs CDH")
fig.show()

In [6]:

csv3d_file = '/content/drive/MyDrive/02_CONSULTING/ENERVA/enerva_plotly/Analysis 3 with calculation - 3D.csv'
data_3d = pd.read_csv(csv3d_file)
data_3d['size'] = 0.001
data_3d

Out[6]:

	Hour	kW	Temp (°C)	size
0	20	123.69	5.8	0.001
1	21	120.99	6.1	0.001
2	16	134.19	5.0	0.001
3	15	102.24	5.3	0.001
4	16	102.69	5.3	0.001
...	...	...	...	...
1939	1	127.20	5.2	0.001
1940	2	120.24	5.7	0.001
1941	3	119.61	5.5	0.001
1942	4	116.64	5.1	0.001
1943	5	122.34	5.5	0.001

1944 rows × 4 columns

In [7]:

fig = px.scatter_3d(data_3d, x='Hour', y='kW', z='Temp (°C)', size = 'size')
fig.show()

In [7]: