Add_trace to plotly.subplots overwrites previous traces on that same cell

altatke · April 24, 2021, 2:27am

Hey. I’ve been struggling with this for a bit. From what I’ve seen nornally adding traces on the same cell (row and column) should just superpose the different traces, but mine overwrites the previous ones. This is part of the code I am using :

        fig4 = go.FigureWidget(plotly.subplots.make_subplots(rows=5, cols=1, shared_xaxes=True, print_grid = False))
        fig4['layout']['margin'] = {
            'l': 30, 'r': 10, 'b': 30, 't': 10}

        fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraph_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature today',
                  showlegend= True), row=1, col=1),

        fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraphpastday_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature one day ago',
                  showlegend= True), row=1, col=1),

        fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraphpastyear_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature one year ago',
                  showlegend= True), row=1, col=1),

And a sample of weathergraph_data, weathergraphpastday_data and weathergraphpastyear_data (I apologize for the formatting of the post, I just created an account on here, but thank you to whoever gets the chance to look at this):

{‘Sample Time’: [datetime.time(0, 0), datetime.time(0, 10), datetime.time(0, 20), datetime.time(0, 30), datetime.time(0, 40), datetime.time(0, 50), datetime.time(1, 0), datetime.time(1, 10), datetime.time(1, 20), datetime.time(1, 30), datetime.time(1, 40), datetime.time(1, 50), datetime.time(2, 0), datetime.time(2, 10), datetime.time(2, 20), datetime.time(2, 30), datetime.time(2, 40), datetime.time(2, 50), datetime.time(3, 0), datetime.time(3, 10), datetime.time(3, 20), datetime.time(3, 30), datetime.time(3, 40), datetime.time(3, 50), datetime.time(4, 0), datetime.time(4, 10), datetime.time(4, 20), datetime.time(4, 30), datetime.time(4, 40), datetime.time(4, 50), datetime.time(5, 0), datetime.time(5, 10), datetime.time(5, 20), datetime.time(5, 30), datetime.time(5, 40), datetime.time(5, 50), datetime.time(6, 0), datetime.time(6, 10), datetime.time(6, 20), datetime.time(6, 30), datetime.time(6, 40), datetime.time(6, 50), datetime.time(7, 0), datetime.time(7, 10), datetime.time(7, 20), datetime.time(7, 30), datetime.time(7, 40), datetime.time(7, 50), datetime.time(8, 0), datetime.time(8, 10), datetime.time(8, 20), datetime.time(8, 30), datetime.time(8, 40), datetime.time(8, 50), datetime.time(9, 0), datetime.time(9, 10), datetime.time(9, 20), datetime.time(9, 30), datetime.time(9, 40), datetime.time(9, 50), datetime.time(10, 0), datetime.time(10, 10), datetime.time(10, 20), datetime.time(10, 30), datetime.time(10, 40), datetime.time(10, 50), datetime.time(11, 0), datetime.time(11, 10), datetime.time(11, 20), datetime.time(11, 30), datetime.time(11, 40), datetime.time(11, 50), datetime.time(12, 0), datetime.time(12, 10), datetime.time(12, 20), datetime.time(12, 30), datetime.time(12, 40), datetime.time(12, 50), datetime.time(13, 0), datetime.time(13, 10), datetime.time(13, 20), datetime.time(13, 30), datetime.time(13, 40), datetime.time(13, 50), datetime.time(14, 0), datetime.time(14, 10), datetime.time(14, 20), datetime.time(14, 30), datetime.time(14, 40), datetime.time(14, 50), datetime.time(15, 0), datetime.time(15, 10), datetime.time(15, 20), datetime.time(15, 30), datetime.time(15, 40), datetime.time(15, 50), datetime.time(16, 0), datetime.time(16, 10), datetime.time(16, 20), datetime.time(16, 30), datetime.time(16, 40), datetime.time(16, 50), datetime.time(17, 0), datetime.time(17, 10), datetime.time(17, 20), datetime.time(17, 30), datetime.time(17, 40), datetime.time(17, 50), datetime.time(18, 0), datetime.time(18, 10), datetime.time(18, 20), datetime.time(18, 30), datetime.time(18, 40), datetime.time(18, 50), datetime.time(19, 0), datetime.time(19, 10), datetime.time(19, 20), datetime.time(19, 30), datetime.time(19, 40), datetime.time(19, 50), datetime.time(20, 0), datetime.time(20, 10), datetime.time(20, 20), datetime.time(20, 30), datetime.time(20, 40), datetime.time(20, 50), datetime.time(21, 0), datetime.time(21, 10), datetime.time(21, 20), datetime.time(21, 30), datetime.time(21, 40), datetime.time(21, 50), datetime.time(22, 0), datetime.time(22, 10), datetime.time(22, 20)], ‘Temperature’: [66.02, 65.05, 65.17, 65.21, 64.89, 65.16, 68.02, 64.74, 64.26, 64.78, 64.71, 64.45, 64.29, 63.79, 64.51, 64.85, 64.53, 64.27, 64.26, 64.09, 63.84, 63.91, 63.43, 63.41, 62.22, 61.95, 61.84, 61.65, 61.59, 61.9, 61.09, 60.58, 60.39, 60.39, 60.04, 60.26, 59.99, 59.86, 59.72, 59.74, 59.2, 58.59, 58.32, 58.1, 57.96, 58.23, 57.99, 57.88, 57.83, 59.94, 62.11, 64.72, 67.57, 71.17, 72.48, 78.3, 85.84, 86.88, 85.77, 86.25, 89.08, 92.35, 91.76, 94.21, 98.11, 96.3, 94.69, 91.22, 96.33, 100.04, 103.66, 101.95, 96.35, 98.1, 98.92, 96.31, 95.04, 95.72, 96.8, 95.52, 93.45, 95.49, 94.59, 100.26, 97.61, 96.1, 92.88, 94.64, 99.68, 97.34, 91.71, 90.81, 93.43, 94.26, 93.33, 95.99, 97.07, 99.39, 96.51, 94.57, 92.01, 93.76, 97.36, 96.06, 92.68, 94.48, 94.8, 90.63, 91.8, 93.97, 89.01, 87.75, 87.8, 88.5, 87.13, 86.65, 85.59, 84.9, 83.8, 84.09, 83.35, 82.2, 81.32, 79.66, 79.05, 77.99, 77.02, 76.14, 75.42, 75.4, 74.62, 74.1, 73.2, 73.04, 72.61], ‘Wind’: [2.49, 0.49, 1.16, 1.2, 1.62, 1.53, 2.68, 1.95, 1.21, 2.05, 2.82, 1.32, 1.76, 0.94, 2.47, 3.22, 3.2, 3.15, 3.41, 3.47, 3.27, 3.7, 3.1, 2.3, 0.67, 0.06, 1.62, 1.55, 0.0, 0.73, 2.06, 1.34, 0.38, 0.7, 0.5, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.39, 1.59, 0.01, 0.0, 0.0, 0.0, 0.0, 0.51, 1.32, 0.99, 0.8, 1.21, 1.09, 2.47, 1.09, 0.86, 1.2, 0.69, 1.78, 2.18, 2.01, 2.12, 3.07, 2.84, 2.24, 3.93, 3.47, 3.98, 3.5, 1.93, 3.44, 2.89, 3.17, 3.32, 1.86, 0.8, 3.93, 4.66, 3.84, 3.19, 2.88, 2.16, 1.81, 1.63, 3.36, 3.75, 5.57, 3.77, 1.97, 2.28, 4.43, 3.01, 3.23, 2.73, 3.6, 3.31, 7.74, 9.71, 8.58, 7.46, 8.02, 7.52, 7.64, 7.89, 8.21, 6.59, 5.37, 6.47, 6.02, 7.68, 5.25, 4.67, 4.17, 4.29, 3.86, 5.59, 4.5, 4.15, 2.81, 3.92, 4.39], ‘Wind Direction’: [43.74, 169.4, 34.69, 35.06, 33.69, 38.18, 125.4, 41.79, 26.92, 44.61, 34.71, 41.11, 40.76, 37.38, 48.76, 71.8, 64.73, 66.03, 68.62, 63.18, 72.0, 70.1, 72.4, 67.55, 59.7, 67.8, 320.9, 324.1, 317.5, 274.4, 231.3, 210.3, 192.2, 193.6, 246.0, 226.5, 200.9, 157.3, 152.0, 152.3, 159.5, 161.5, 160.8, 200.3, 242.3, 248.9, 207.4, 93.2, 113.4, 144.6, 145.2, 148.7, 160.6, 148.7, 161.8, 151.0, 150.6, 168.0, 158.8, 161.8, 168.3, 149.8, 144.8, 141.6, 134.8, 176.2, 129.8, 136.2, 157.7, 148.4, 109.5, 141.3, 153.0, 154.4, 131.5, 152.6, 165.1, 140.3, 106.5, 137.9, 141.7, 157.4, 123.0, 192.4, 122.6, 115.7, 134.5, 136.1, 183.3, 205.2, 132.5, 151.2, 155.3, 158.2, 186.3, 127.9, 227.5, 177.2, 189.9, 135.2, 152.7, 129.8, 99.8, 117.7, 157.4, 159.1, 133.0, 144.4, 111.9, 124.5, 179.8, 77.1, 84.8, 110.1, 93.8, 39.0, 67.58, 52.91, 44.9, 46.58, 83.6, 54.47, 60.82, 58.18, 63.12, 61.34, 66.8, 45.16, 48.61, 62.3, 61.73, 57.48, 63.29, 76.3, 77.3], ‘Humidity’: [30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.0, 30.03, 30.0, 30.0, 30.0, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06], ‘Precipitation’: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

altatke · April 26, 2021, 6:23pm

This is the full code I am running to test if anyone wants to take a look at it.


import datetime
import random
import csv
import numpy as np
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_daq as daq
import dash_table as dt
from dash.dependencies import Input, Output
import plotly
import plotly.graph_objects as go
import plotly.figure_factory as ff
import plotly.express as px
import plotly.subplots
import pandas as pd
import time, threading
import time as tt
import mysql.connector
from mysql.connector import Error

weathergraph_data = {'Sample Time': [datetime.time(0, 0), datetime.time(0, 10), datetime.time(0, 20), datetime.time(0, 30), datetime.time(0, 40), datetime.time(0, 50), datetime.time(1, 0), datetime.time(1, 10), datetime.time(1, 20), datetime.time(1, 30), datetime.time(1, 40), datetime.time(1, 50), datetime.time(2, 0), datetime.time(2, 10), datetime.time(2, 20), datetime.time(2, 30), datetime.time(2, 40), datetime.time(2, 50), datetime.time(3, 0), datetime.time(3, 10), datetime.time(3, 20), datetime.time(3, 30), datetime.time(3, 40), datetime.time(3, 50), datetime.time(4, 0), datetime.time(4, 10), datetime.time(4, 20), datetime.time(4, 30), datetime.time(4, 40), datetime.time(4, 50), datetime.time(5, 0), datetime.time(5, 10), datetime.time(5, 20), datetime.time(5, 30), datetime.time(5, 40), datetime.time(5, 50), datetime.time(6, 0), datetime.time(6, 10), datetime.time(6, 20), datetime.time(6, 30), datetime.time(6, 40), datetime.time(6, 50), datetime.time(7, 0), datetime.time(7, 10), datetime.time(7, 20), datetime.time(7, 30), datetime.time(7, 40), datetime.time(7, 50), datetime.time(8, 0), datetime.time(8, 10), datetime.time(8, 20), datetime.time(8, 30), datetime.time(8, 40), datetime.time(8, 50), datetime.time(9, 0), datetime.time(9, 10), datetime.time(9, 20), datetime.time(9, 30), datetime.time(9, 40), datetime.time(9, 50), datetime.time(10, 0), datetime.time(10, 10), datetime.time(10, 20), datetime.time(10, 30), datetime.time(10, 40), datetime.time(10, 50), datetime.time(11, 0), datetime.time(11, 10), datetime.time(11, 20), datetime.time(11, 30), datetime.time(11, 40), datetime.time(11, 50), datetime.time(12, 0), datetime.time(12, 10), datetime.time(12, 20), datetime.time(12, 30), datetime.time(12, 40), datetime.time(12, 50), datetime.time(13, 0), datetime.time(13, 10), datetime.time(13, 20), datetime.time(13, 30), datetime.time(13, 40), datetime.time(13, 50), datetime.time(14, 0), datetime.time(14, 10), datetime.time(14, 20), datetime.time(14, 30), datetime.time(14, 40), datetime.time(14, 50), datetime.time(15, 0), datetime.time(15, 10), datetime.time(15, 20), datetime.time(15, 30), datetime.time(15, 40), datetime.time(15, 50), datetime.time(16, 0), datetime.time(16, 10), datetime.time(16, 20), datetime.time(16, 30), datetime.time(16, 40), datetime.time(16, 50), datetime.time(17, 0), datetime.time(17, 10), datetime.time(17, 20), datetime.time(17, 30), datetime.time(17, 40), datetime.time(17, 50), datetime.time(18, 0), datetime.time(18, 10), datetime.time(18, 20), datetime.time(18, 30), datetime.time(18, 40), datetime.time(18, 50), datetime.time(19, 0), datetime.time(19, 10), datetime.time(19, 20), datetime.time(19, 30), datetime.time(19, 40), datetime.time(19, 50), datetime.time(20, 0), datetime.time(20, 10), datetime.time(20, 20), datetime.time(20, 30), datetime.time(20, 40), datetime.time(20, 50), datetime.time(21, 0), datetime.time(21, 10), datetime.time(21, 20), datetime.time(21, 30), datetime.time(21, 40), datetime.time(21, 50), datetime.time(22, 0), datetime.time(22, 10), datetime.time(22, 20)], 'Temperature': [66.02, 65.05, 65.17, 65.21, 64.89, 65.16, 68.02, 64.74, 64.26, 64.78, 64.71, 64.45, 64.29, 63.79, 64.51, 64.85, 64.53, 64.27, 64.26, 64.09, 63.84, 63.91, 63.43, 63.41, 62.22, 61.95, 61.84, 61.65, 61.59, 61.9, 61.09, 60.58, 60.39, 60.39, 60.04, 60.26, 59.99, 59.86, 59.72, 59.74, 59.2, 58.59, 58.32, 58.1, 57.96, 58.23, 57.99, 57.88, 57.83, 59.94, 62.11, 64.72, 67.57, 71.17, 72.48, 78.3, 85.84, 86.88, 85.77, 86.25, 89.08, 92.35, 91.76, 94.21, 98.11, 96.3, 94.69, 91.22, 96.33, 100.04, 103.66, 101.95, 96.35, 98.1, 98.92, 96.31, 95.04, 95.72, 96.8, 95.52, 93.45, 95.49, 94.59, 100.26, 97.61, 96.1, 92.88, 94.64, 99.68, 97.34, 91.71, 90.81, 93.43, 94.26, 93.33, 95.99, 97.07, 99.39, 96.51, 94.57, 92.01, 93.76, 97.36, 96.06, 92.68, 94.48, 94.8, 90.63, 91.8, 93.97, 89.01, 87.75, 87.8, 88.5, 87.13, 86.65, 85.59, 84.9, 83.8, 84.09, 83.35, 82.2, 81.32, 79.66, 79.05, 77.99, 77.02, 76.14, 75.42, 75.4, 74.62, 74.1, 73.2, 73.04, 72.61], 'Wind': [2.49, 0.49, 1.16, 1.2, 1.62, 1.53, 2.68, 1.95, 1.21, 2.05, 2.82, 1.32, 1.76, 0.94, 2.47, 3.22, 3.2, 3.15, 3.41, 3.47, 3.27, 3.7, 3.1, 2.3, 0.67, 0.06, 1.62, 1.55, 0.0, 0.73, 2.06, 1.34, 0.38, 0.7, 0.5, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.39, 1.59, 0.01, 0.0, 0.0, 0.0, 0.0, 0.51, 1.32, 0.99, 0.8, 1.21, 1.09, 2.47, 1.09, 0.86, 1.2, 0.69, 1.78, 2.18, 2.01, 2.12, 3.07, 2.84, 2.24, 3.93, 3.47, 3.98, 3.5, 1.93, 3.44, 2.89, 3.17, 3.32, 1.86, 0.8, 3.93, 4.66, 3.84, 3.19, 2.88, 2.16, 1.81, 1.63, 3.36, 3.75, 5.57, 3.77, 1.97, 2.28, 4.43, 3.01, 3.23, 2.73, 3.6, 3.31, 7.74, 9.71, 8.58, 7.46, 8.02, 7.52, 7.64, 7.89, 8.21, 6.59, 5.37, 6.47, 6.02, 7.68, 5.25, 4.67, 4.17, 4.29, 3.86, 5.59, 4.5, 4.15, 2.81, 3.92, 4.39], 'Wind Direction': [43.74, 169.4, 34.69, 35.06, 33.69, 38.18, 125.4, 41.79, 26.92, 44.61, 34.71, 41.11, 40.76, 37.38, 48.76, 71.8, 64.73, 66.03, 68.62, 63.18, 72.0, 70.1, 72.4, 67.55, 59.7, 67.8, 320.9, 324.1, 317.5, 274.4, 231.3, 210.3, 192.2, 193.6, 246.0, 226.5, 200.9, 157.3, 152.0, 152.3, 159.5, 161.5, 160.8, 200.3, 242.3, 248.9, 207.4, 93.2, 113.4, 144.6, 145.2, 148.7, 160.6, 148.7, 161.8, 151.0, 150.6, 168.0, 158.8, 161.8, 168.3, 149.8, 144.8, 141.6, 134.8, 176.2, 129.8, 136.2, 157.7, 148.4, 109.5, 141.3, 153.0, 154.4, 131.5, 152.6, 165.1, 140.3, 106.5, 137.9, 141.7, 157.4, 123.0, 192.4, 122.6, 115.7, 134.5, 136.1, 183.3, 205.2, 132.5, 151.2, 155.3, 158.2, 186.3, 127.9, 227.5, 177.2, 189.9, 135.2, 152.7, 129.8, 99.8, 117.7, 157.4, 159.1, 133.0, 144.4, 111.9, 124.5, 179.8, 77.1, 84.8, 110.1, 93.8, 39.0, 67.58, 52.91, 44.9, 46.58, 83.6, 54.47, 60.82, 58.18, 63.12, 61.34, 66.8, 45.16, 48.61, 62.3, 61.73, 57.48, 63.29, 76.3, 77.3], 'Humidity': [30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.0, 30.03, 30.0, 30.0, 30.0, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06], 'Precipitation': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

weathergraphpastday_data = {'Sample Time': [datetime.time(0, 0), datetime.time(0, 10), datetime.time(0, 20), datetime.time(0, 30), datetime.time(0, 40), datetime.time(0, 50), datetime.time(1, 0), datetime.time(1, 10), datetime.time(1, 20), datetime.time(1, 30), datetime.time(1, 40), datetime.time(1, 50), datetime.time(2, 0), datetime.time(2, 10), datetime.time(2, 20), datetime.time(2, 30), datetime.time(2, 40), datetime.time(2, 50), datetime.time(3, 0), datetime.time(3, 10), datetime.time(3, 20), datetime.time(3, 30), datetime.time(3, 40), datetime.time(3, 50), datetime.time(4, 0), datetime.time(4, 10), datetime.time(4, 20), datetime.time(4, 30), datetime.time(4, 40), datetime.time(4, 50), datetime.time(5, 0), datetime.time(5, 10), datetime.time(5, 20), datetime.time(5, 30), datetime.time(5, 40), datetime.time(5, 50), datetime.time(6, 0), datetime.time(6, 10), datetime.time(6, 20), datetime.time(6, 30), datetime.time(6, 40), datetime.time(6, 50), datetime.time(7, 0), datetime.time(7, 10), datetime.time(7, 20), datetime.time(7, 30), datetime.time(7, 40), datetime.time(7, 50), datetime.time(8, 0), datetime.time(8, 10), datetime.time(8, 20), datetime.time(8, 30), datetime.time(8, 40), datetime.time(8, 50), datetime.time(9, 0), datetime.time(9, 10), datetime.time(9, 20), datetime.time(9, 30), datetime.time(9, 40), datetime.time(9, 50), datetime.time(10, 0), datetime.time(10, 10), datetime.time(10, 20), datetime.time(10, 30), datetime.time(10, 40), datetime.time(10, 50), datetime.time(11, 0), datetime.time(11, 10), datetime.time(11, 20), datetime.time(11, 30), datetime.time(11, 40), datetime.time(11, 50), datetime.time(12, 0), datetime.time(12, 10), datetime.time(12, 20), datetime.time(12, 30), datetime.time(12, 40), datetime.time(12, 50), datetime.time(13, 0), datetime.time(13, 10), datetime.time(13, 20), datetime.time(13, 30), datetime.time(13, 40), datetime.time(13, 50), datetime.time(14, 0), datetime.time(14, 10), datetime.time(14, 20), datetime.time(14, 30), datetime.time(14, 40), datetime.time(14, 50), datetime.time(15, 0), datetime.time(15, 10), datetime.time(15, 20), datetime.time(15, 30), datetime.time(15, 40), datetime.time(15, 50), datetime.time(16, 0), datetime.time(16, 10), datetime.time(16, 20), datetime.time(16, 30), datetime.time(16, 40), datetime.time(16, 50), datetime.time(17, 0), datetime.time(17, 10), datetime.time(17, 20), datetime.time(17, 30), datetime.time(17, 40), datetime.time(17, 50), datetime.time(18, 0), datetime.time(18, 10), datetime.time(18, 20), datetime.time(18, 30), datetime.time(18, 40), datetime.time(18, 50), datetime.time(19, 0), datetime.time(19, 10), datetime.time(19, 20), datetime.time(19, 30), datetime.time(19, 40), datetime.time(19, 50), datetime.time(20, 0), datetime.time(20, 10), datetime.time(20, 20), datetime.time(20, 30), datetime.time(20, 40), datetime.time(20, 50), datetime.time(21, 0), datetime.time(21, 10), datetime.time(21, 20), datetime.time(21, 30), datetime.time(21, 40), datetime.time(21, 50), datetime.time(22, 0), datetime.time(22, 10), datetime.time(22, 20)], 'Temperature': [66.02, 65.05, 65.17, 65.21, 64.89, 65.16, 68.02, 64.74, 64.26, 64.78, 64.71, 64.45, 64.29, 63.79, 64.51, 64.85, 64.53, 64.27, 64.26, 64.09, 63.84, 63.91, 63.43, 63.41, 62.22, 61.95, 61.84, 61.65, 61.59, 61.9, 61.09, 60.58, 60.39, 60.39, 60.04, 60.26, 59.99, 59.86, 59.72, 59.74, 59.2, 58.59, 58.32, 58.1, 57.96, 58.23, 57.99, 57.88, 57.83, 59.94, 62.11, 64.72, 67.57, 71.17, 72.48, 78.3, 85.84, 86.88, 85.77, 86.25, 89.08, 92.35, 91.76, 94.21, 98.11, 96.3, 94.69, 91.22, 96.33, 100.04, 103.66, 101.95, 96.35, 98.1, 98.92, 96.31, 95.04, 95.72, 96.8, 95.52, 93.45, 95.49, 94.59, 100.26, 97.61, 96.1, 92.88, 94.64, 99.68, 97.34, 91.71, 90.81, 93.43, 94.26, 93.33, 95.99, 97.07, 99.39, 96.51, 94.57, 92.01, 93.76, 97.36, 96.06, 92.68, 94.48, 94.8, 90.63, 91.8, 93.97, 89.01, 87.75, 87.8, 88.5, 87.13, 86.65, 85.59, 84.9, 83.8, 84.09, 83.35, 82.2, 81.32, 79.66, 79.05, 77.99, 77.02, 76.14, 75.42, 75.4, 74.62, 74.1, 73.2, 73.04, 72.61], 'Wind': [2.49, 0.49, 1.16, 1.2, 1.62, 1.53, 2.68, 1.95, 1.21, 2.05, 2.82, 1.32, 1.76, 0.94, 2.47, 3.22, 3.2, 3.15, 3.41, 3.47, 3.27, 3.7, 3.1, 2.3, 0.67, 0.06, 1.62, 1.55, 0.0, 0.73, 2.06, 1.34, 0.38, 0.7, 0.5, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.39, 1.59, 0.01, 0.0, 0.0, 0.0, 0.0, 0.51, 1.32, 0.99, 0.8, 1.21, 1.09, 2.47, 1.09, 0.86, 1.2, 0.69, 1.78, 2.18, 2.01, 2.12, 3.07, 2.84, 2.24, 3.93, 3.47, 3.98, 3.5, 1.93, 3.44, 2.89, 3.17, 3.32, 1.86, 0.8, 3.93, 4.66, 3.84, 3.19, 2.88, 2.16, 1.81, 1.63, 3.36, 3.75, 5.57, 3.77, 1.97, 2.28, 4.43, 3.01, 3.23, 2.73, 3.6, 3.31, 7.74, 9.71, 8.58, 7.46, 8.02, 7.52, 7.64, 7.89, 8.21, 6.59, 5.37, 6.47, 6.02, 7.68, 5.25, 4.67, 4.17, 4.29, 3.86, 5.59, 4.5, 4.15, 2.81, 3.92, 4.39], 'Wind Direction': [43.74, 169.4, 34.69, 35.06, 33.69, 38.18, 125.4, 41.79, 26.92, 44.61, 34.71, 41.11, 40.76, 37.38, 48.76, 71.8, 64.73, 66.03, 68.62, 63.18, 72.0, 70.1, 72.4, 67.55, 59.7, 67.8, 320.9, 324.1, 317.5, 274.4, 231.3, 210.3, 192.2, 193.6, 246.0, 226.5, 200.9, 157.3, 152.0, 152.3, 159.5, 161.5, 160.8, 200.3, 242.3, 248.9, 207.4, 93.2, 113.4, 144.6, 145.2, 148.7, 160.6, 148.7, 161.8, 151.0, 150.6, 168.0, 158.8, 161.8, 168.3, 149.8, 144.8, 141.6, 134.8, 176.2, 129.8, 136.2, 157.7, 148.4, 109.5, 141.3, 153.0, 154.4, 131.5, 152.6, 165.1, 140.3, 106.5, 137.9, 141.7, 157.4, 123.0, 192.4, 122.6, 115.7, 134.5, 136.1, 183.3, 205.2, 132.5, 151.2, 155.3, 158.2, 186.3, 127.9, 227.5, 177.2, 189.9, 135.2, 152.7, 129.8, 99.8, 117.7, 157.4, 159.1, 133.0, 144.4, 111.9, 124.5, 179.8, 77.1, 84.8, 110.1, 93.8, 39.0, 67.58, 52.91, 44.9, 46.58, 83.6, 54.47, 60.82, 58.18, 63.12, 61.34, 66.8, 45.16, 48.61, 62.3, 61.73, 57.48, 63.29, 76.3, 77.3], 'Humidity': [30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.0, 30.03, 30.0, 30.0, 30.0, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06], 'Precipitation': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

weathergraphpastyear_data = {'Sample Time': [datetime.time(0, 0), datetime.time(0, 10), datetime.time(0, 20), datetime.time(0, 30), datetime.time(0, 40), datetime.time(0, 50), datetime.time(1, 0), datetime.time(1, 10), datetime.time(1, 20), datetime.time(1, 30), datetime.time(1, 40), datetime.time(1, 50), datetime.time(2, 0), datetime.time(2, 10), datetime.time(2, 20), datetime.time(2, 30), datetime.time(2, 40), datetime.time(2, 50), datetime.time(3, 0), datetime.time(3, 10), datetime.time(3, 20), datetime.time(3, 30), datetime.time(3, 40), datetime.time(3, 50), datetime.time(4, 0), datetime.time(4, 10), datetime.time(4, 20), datetime.time(4, 30), datetime.time(4, 40), datetime.time(4, 50), datetime.time(5, 0), datetime.time(5, 10), datetime.time(5, 20), datetime.time(5, 30), datetime.time(5, 40), datetime.time(5, 50), datetime.time(6, 0), datetime.time(6, 10), datetime.time(6, 20), datetime.time(6, 30), datetime.time(6, 40), datetime.time(6, 50), datetime.time(7, 0), datetime.time(7, 10), datetime.time(7, 20), datetime.time(7, 30), datetime.time(7, 40), datetime.time(7, 50), datetime.time(8, 0), datetime.time(8, 10), datetime.time(8, 20), datetime.time(8, 30), datetime.time(8, 40), datetime.time(8, 50), datetime.time(9, 0), datetime.time(9, 10), datetime.time(9, 20), datetime.time(9, 30), datetime.time(9, 40), datetime.time(9, 50), datetime.time(10, 0), datetime.time(10, 10), datetime.time(10, 20), datetime.time(10, 30), datetime.time(10, 40), datetime.time(10, 50), datetime.time(11, 0), datetime.time(11, 10), datetime.time(11, 20), datetime.time(11, 30), datetime.time(11, 40), datetime.time(11, 50), datetime.time(12, 0), datetime.time(12, 10), datetime.time(12, 20), datetime.time(12, 30), datetime.time(12, 40), datetime.time(12, 50), datetime.time(13, 0), datetime.time(13, 10), datetime.time(13, 20), datetime.time(13, 30), datetime.time(13, 40), datetime.time(13, 50), datetime.time(14, 0), datetime.time(14, 10), datetime.time(14, 20), datetime.time(14, 30), datetime.time(14, 40), datetime.time(14, 50), datetime.time(15, 0), datetime.time(15, 10), datetime.time(15, 20), datetime.time(15, 30), datetime.time(15, 40), datetime.time(15, 50), datetime.time(16, 0), datetime.time(16, 10), datetime.time(16, 20), datetime.time(16, 30), datetime.time(16, 40), datetime.time(16, 50), datetime.time(17, 0), datetime.time(17, 10), datetime.time(17, 20), datetime.time(17, 30), datetime.time(17, 40), datetime.time(17, 50), datetime.time(18, 0), datetime.time(18, 10), datetime.time(18, 20), datetime.time(18, 30), datetime.time(18, 40), datetime.time(18, 50), datetime.time(19, 0), datetime.time(19, 10), datetime.time(19, 20), datetime.time(19, 30), datetime.time(19, 40), datetime.time(19, 50), datetime.time(20, 0), datetime.time(20, 10), datetime.time(20, 20), datetime.time(20, 30), datetime.time(20, 40), datetime.time(20, 50), datetime.time(21, 0), datetime.time(21, 10), datetime.time(21, 20), datetime.time(21, 30), datetime.time(21, 40), datetime.time(21, 50), datetime.time(22, 0), datetime.time(22, 10), datetime.time(22, 20)], 'Temperature': [66.02, 65.05, 65.17, 65.21, 64.89, 65.16, 68.02, 64.74, 64.26, 64.78, 64.71, 64.45, 64.29, 63.79, 64.51, 64.85, 64.53, 64.27, 64.26, 64.09, 63.84, 63.91, 63.43, 63.41, 62.22, 61.95, 61.84, 61.65, 61.59, 61.9, 61.09, 60.58, 60.39, 60.39, 60.04, 60.26, 59.99, 59.86, 59.72, 59.74, 59.2, 58.59, 58.32, 58.1, 57.96, 58.23, 57.99, 57.88, 57.83, 59.94, 62.11, 64.72, 67.57, 71.17, 72.48, 78.3, 85.84, 86.88, 85.77, 86.25, 89.08, 92.35, 91.76, 94.21, 98.11, 96.3, 94.69, 91.22, 96.33, 100.04, 103.66, 101.95, 96.35, 98.1, 98.92, 96.31, 95.04, 95.72, 96.8, 95.52, 93.45, 95.49, 94.59, 100.26, 97.61, 96.1, 92.88, 94.64, 99.68, 97.34, 91.71, 90.81, 93.43, 94.26, 93.33, 95.99, 97.07, 99.39, 96.51, 94.57, 92.01, 93.76, 97.36, 96.06, 92.68, 94.48, 94.8, 90.63, 91.8, 93.97, 89.01, 87.75, 87.8, 88.5, 87.13, 86.65, 85.59, 84.9, 83.8, 84.09, 83.35, 82.2, 81.32, 79.66, 79.05, 77.99, 77.02, 76.14, 75.42, 75.4, 74.62, 74.1, 73.2, 73.04, 72.61], 'Wind': [2.49, 0.49, 1.16, 1.2, 1.62, 1.53, 2.68, 1.95, 1.21, 2.05, 2.82, 1.32, 1.76, 0.94, 2.47, 3.22, 3.2, 3.15, 3.41, 3.47, 3.27, 3.7, 3.1, 2.3, 0.67, 0.06, 1.62, 1.55, 0.0, 0.73, 2.06, 1.34, 0.38, 0.7, 0.5, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.39, 1.59, 0.01, 0.0, 0.0, 0.0, 0.0, 0.51, 1.32, 0.99, 0.8, 1.21, 1.09, 2.47, 1.09, 0.86, 1.2, 0.69, 1.78, 2.18, 2.01, 2.12, 3.07, 2.84, 2.24, 3.93, 3.47, 3.98, 3.5, 1.93, 3.44, 2.89, 3.17, 3.32, 1.86, 0.8, 3.93, 4.66, 3.84, 3.19, 2.88, 2.16, 1.81, 1.63, 3.36, 3.75, 5.57, 3.77, 1.97, 2.28, 4.43, 3.01, 3.23, 2.73, 3.6, 3.31, 7.74, 9.71, 8.58, 7.46, 8.02, 7.52, 7.64, 7.89, 8.21, 6.59, 5.37, 6.47, 6.02, 7.68, 5.25, 4.67, 4.17, 4.29, 3.86, 5.59, 4.5, 4.15, 2.81, 3.92, 4.39], 'Wind Direction': [43.74, 169.4, 34.69, 35.06, 33.69, 38.18, 125.4, 41.79, 26.92, 44.61, 34.71, 41.11, 40.76, 37.38, 48.76, 71.8, 64.73, 66.03, 68.62, 63.18, 72.0, 70.1, 72.4, 67.55, 59.7, 67.8, 320.9, 324.1, 317.5, 274.4, 231.3, 210.3, 192.2, 193.6, 246.0, 226.5, 200.9, 157.3, 152.0, 152.3, 159.5, 161.5, 160.8, 200.3, 242.3, 248.9, 207.4, 93.2, 113.4, 144.6, 145.2, 148.7, 160.6, 148.7, 161.8, 151.0, 150.6, 168.0, 158.8, 161.8, 168.3, 149.8, 144.8, 141.6, 134.8, 176.2, 129.8, 136.2, 157.7, 148.4, 109.5, 141.3, 153.0, 154.4, 131.5, 152.6, 165.1, 140.3, 106.5, 137.9, 141.7, 157.4, 123.0, 192.4, 122.6, 115.7, 134.5, 136.1, 183.3, 205.2, 132.5, 151.2, 155.3, 158.2, 186.3, 127.9, 227.5, 177.2, 189.9, 135.2, 152.7, 129.8, 99.8, 117.7, 157.4, 159.1, 133.0, 144.4, 111.9, 124.5, 179.8, 77.1, 84.8, 110.1, 93.8, 39.0, 67.58, 52.91, 44.9, 46.58, 83.6, 54.47, 60.82, 58.18, 63.12, 61.34, 66.8, 45.16, 48.61, 62.3, 61.73, 57.48, 63.29, 76.3, 77.3], 'Humidity': [30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.15, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.12, 30.09, 30.09, 30.09, 30.09, 30.09, 30.09, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.06, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.0, 30.03, 30.0, 30.0, 30.0, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.03, 30.06, 30.06, 30.06, 30.06, 30.06], 'Precipitation': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}

fig4 = go.FigureWidget(plotly.subplots.make_subplots(rows=1, cols=1, shared_xaxes=True, print_grid = False))
fig4['layout']['margin'] = {
            'l': 30, 'r': 10, 'b': 30, 't': 10}

fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraph_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature today',
                  showlegend= True), row=1, col=1),

fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraphpastday_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature one day ago',
                  showlegend= True), row=1, col=1),

fig4.add_trace(go.Scatter(
                  x= weathergraph_data['Sample Time'],
                  y= weathergraphpastyear_data['Temperature'],
                  mode = 'lines',
                  hoverinfo= 'x+y',
                  legendgroup= 'Temperature',
                  name= 'Temperature one year ago',
                  showlegend= True), row=1, col=1)

fig4.show('browser')

Topic		Replies	Views
Adding traces to subplots 📊 Plotly Python	6	33923	January 3, 2019
Add the same trace to multiple subplots? 📊 Plotly Python	1	2778	January 15, 2019
Multiple traces sub plots with common trace names 📊 Plotly Python	12	6168	November 6, 2019
Plotly Subplots using add_trace Dash Python	1	1475	April 25, 2020
Add trace to subplot created with low-level API 📊 Plotly Python	1	509	March 12, 2020

Add_trace to plotly.subplots overwrites previous traces on that same cell

Related topics