plot several graphs panda code example

Example 1: create plots with multiple dataframes python

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

scenarios = ['scen-1', 'scen-2']

fig, ax = plt.subplots()

for index, item in enumerate(scenarios):
    df = pd.DataFrame({'A' : np.random.randn(4)})
    print df
    df.plot(ax=ax)

plt.ylabel('y-label')
plt.xlabel('x-label')
plt.title('Title')
plt.show()

Example 2: Graphing From Multiple DataFrames

import pandas as pd

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
from dash.dependencies import Input, Output, State

df1 = pd.DataFrame({'col1': ['a', 'b', 'c'], 'col2': ['d', 'e', 'f']})
df2 = pd.DataFrame({'col1': ['g', 'h', 'i'], 'col2': ['j', 'k', 'l']})
dict_main = {'df1': df1, 'df2': df2}

app = dash.Dash(__name__)
server = app.server

rpm = list(dict_main.keys())
channels = dict_main[rpm[0]]

app.layout = html.Div(
    [
        html.Div([
        dcc.Dropdown(
            id='rpm-dropdown',
            options=[{'label':speed, 'value':speed} for speed in rpm],
            value=list(dict_main.keys())[0],
            # I removed the multi=True because it requires a distinction between the columns in the next dropdown...
            searchable=False
            ),
            ],style={'width': '20%', 'display': 'inline-block'}),
        html.Div([
        dcc.Dropdown(
            id='channel-dropdown',
            multi=True
            ),
            ],style={'width': '20%', 'display': 'inline-block'}
        ),
		html.Div([
			dcc.Graph(
				id='Main-Graph' # the initial graph is in the callback
			),
			], style={'width': '98%', 'display': 'inline-block'}
		)
    ]
)

@app.callback(
    Output('channel-dropdown', 'options'),
    [Input('rpm-dropdown', 'value')])
def update_date_dropdown(speed):
    return [{'label': i, 'value': i} for i in dict_main[speed]]

@app.callback(
    Output('Main-Graph', 'figure'),
    [Input('channel-dropdown', 'value')],
    [State('rpm-dropdown', 'value')]) # This is the way to inform the callback which dataframe is to be charted
def updateGraph(channels, speed):
	if channels:
        # return the entire figure with the different traces
		return go.Figure(data=[go.Scatter(x=dict_main[speed].index, y=dict_main[speed][i]) for i in channels])
	else: 
	    # at initialization the graph is returned empty
        return go.Figure(data=[])
	
if __name__ == '__main__':
	app.run_server(debug=True)