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pymongo

Example of operation by Python(pymongo)

About pymongo

Official Documentation (https://pymongo.readthedocs.io/en/stable/) Quote.

PyMongo is a Python distribution that includes tools for manipulating MongoDB and is the recommended way to manipulate MongoDB from Python.

Official MongoDB (https://www.mongodb.com/docs/drivers/pymongo/) Quote.

PyMong is the official MongoDB driver for synchronous Python applications.

Example

Info

The following includes personally created code.
Please check the official documentation for usage.

Write CSV

Add the following csv files in bulk.

data.csv 
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2023-03-03T00:05:14.071359,ALONE,414.0,1.0,87.99,221.0,135.0,92.04,224.0,125.0,90.19,214.0,130.0,90.72,234.0,130.0,89.26,208.0,140.0,77.73,262.0,160.0,86.77,205.0,185.0,85.84,281.0,210.0,88.13,208.0,240.0,89.21,288.0,260.0,89.16,214.0,280.0,70.95,269.0,270.0,73.1,240.0,285.0,76.71,288.0,340.0,79.44,259.0,345.0,78.61,307.0,395.0,83.3,281.0,395.0
2023-03-04T00:05:14.487122,ALONE,415.0,1.0,87.26,221.0,135.0,90.62,224.0,125.0,89.26,214.0,130.0,88.96,234.0,130.0,87.7,208.0,140.0,77.78,262.0,160.0,86.47,205.0,185.0,85.11,281.0,210.0,85.35,208.0,240.0,87.94,285.0,260.0,87.84,214.0,280.0,72.22,269.0,270.0,72.02,237.0,285.0,76.71,288.0,340.0,81.84,259.0,345.0,79.39,304.0,395.0,83.35,281.0,395.0
2023-03-05T00:05:14.890360,ALONE,416.0,1.0,85.99,221.0,136.0,90.48,224.0,126.0,87.3,214.0,131.0,88.62,234.0,131.0,90.82,205.0,141.0,76.61,262.0,161.0,84.18,202.0,186.0,85.99,281.0,211.0,88.82,208.0,241.0,89.21,288.0,261.0,89.5,214.0,281.0,71.53,269.0,271.0,73.19,240.0,286.0,77.05,288.0,336.0,82.23,259.0,346.0,79.83,304.0,396.0,81.1,281.0,391.0
2023-03-06T00:05:14.524698,ALONE,417.0,1.0,86.62,223.0,137.0,88.82,226.0,126.0,86.96,216.0,132.0,91.06,238.0,132.0,86.13,213.0,143.0,75.15,264.0,161.0,83.89,207.0,184.0,86.18,283.0,208.0,78.08,200.0,225.0,89.16,289.0,260.0,87.55,197.0,248.0,72.71,270.0,272.0,74.07,238.0,284.0,77.59,283.0,342.0,83.84,261.0,348.0,84.33,296.0,406.0,82.32,280.0,395.0
2023-03-07T00:05:14.930110,ALONE,418.0,1.0,88.04,230.0,134.0,90.72,237.0,124.0,86.87,227.0,129.0,89.65,249.0,129.0,90.19,221.0,139.0,82.71,278.0,163.0,84.52,214.0,178.0,83.15,297.0,216.0,82.96,202.0,221.0,88.09,300.0,264.0,89.5,211.0,211.0,69.19,281.0,279.0,71.63,246.0,289.0,83.5,288.0,342.0,76.71,262.0,347.0,79.93,294.0,400.0,76.81,281.0,395.0
2023-03-08T00:05:14.358364,ALONE,419.0,1.0,88.62,241.0,131.0,87.94,248.0,126.0,87.99,238.0,131.0,85.84,255.0,126.0,82.57,231.0,136.0,78.37,285.0,161.0,84.86,224.0,171.0,85.55,302.0,216.0,86.57,197.0,201.0,89.31,306.0,266.0,84.38,207.0,156.0,73.68,282.0,276.0,73.68,244.0,281.0,81.98,289.0,346.0,85.84,255.0,351.0,81.1,295.0,406.0,81.35,272.0,401.0
2023-03-09T00:05:14.762575,ALONE,420.0,1.0,85.45,245.0,138.0,85.11,248.0,127.0,82.08,238.0,132.0,87.3,258.0,127.0,83.84,231.0,138.0,78.27,285.0,163.0,83.5,225.0,168.0,84.67,302.0,220.0,80.81,201.0,204.0,88.28,302.0,266.0,83.45,204.0,168.0,73.58,282.0,276.0,71.63,245.0,286.0,81.59,289.0,343.0,84.28,255.0,348.0,81.4,295.0,409.0,81.59,272.0,399.0
2023-03-10T00:05:14.193706,ALONE,421.0,1.0,85.84,237.0,135.0,86.82,243.0,124.0,86.91,231.0,129.0,92.77,253.0,129.0,88.18,225.0,140.0,74.32,277.0,167.0,82.76,216.0,177.0,83.64,296.0,215.0,81.84,213.0,231.0,89.6,299.0,263.0,86.77,216.0,268.0,68.36,274.0,274.0,72.71,240.0,284.0,82.96,287.0,343.0,85.74,256.0,354.0,80.81,296.0,402.0,84.28,271.0,402.0
2023-03-11T00:05:14.594412,ALONE,422.0,1.0,83.54,228.0,129.0,88.67,234.0,124.0,81.88,222.0,124.0,87.94,246.0,124.0,85.99,216.0,135.0,75.88,271.0,166.0,83.69,210.0,181.0,82.62,286.0,218.0,86.28,207.0,233.0,89.01,289.0,265.0,86.18,213.0,275.0,68.9,271.0,275.0,71.09,237.0,291.0,79.83,283.0,343.0,83.59,255.0,353.0,81.25,295.0,405.0,81.69,274.0,400.0
2023-03-12T00:05:14.234126,ALONE,423.0,1.0,87.74,227.0,131.0,92.63,230.0,121.0,89.84,221.0,126.0,87.35,243.0,126.0,88.53,214.0,137.0,79.54,269.0,163.0,85.35,208.0,179.0,84.96,285.0,216.0,86.43,208.0,232.0,90.23,291.0,263.0,87.21,211.0,274.0,69.38,269.0,279.0,72.31,237.0,285.0,83.15,282.0,343.0,82.23,256.0,353.0,82.62,295.0,401.0,83.79,272.0,401.0
2023-03-13T00:05:14.634114,ALONE,424.0,1.0,86.18,225.0,131.0,91.55,228.0,121.0,85.11,218.0,126.0,88.57,240.0,126.0,83.25,212.0,137.0,76.76,266.0,163.0,83.15,205.0,184.0,85.16,285.0,216.0,85.3,209.0,237.0,89.99,288.0,263.0,86.28,215.0,274.0,69.43,269.0,279.0,70.12,234.0,290.0,82.23,282.0,343.0,82.86,256.0,353.0,80.27,295.0,401.0,83.64,272.0,401.0
2023-03-14T00:05:14.066018,ALONE,425.0,1.0,88.48,224.0,130.0,90.38,227.0,119.0,91.7,218.0,130.0,89.11,240.0,130.0,91.6,208.0,140.0,76.56,266.0,161.0,85.64,205.0,183.0,85.21,285.0,215.0,87.6,205.0,236.0,90.97,288.0,262.0,87.65,211.0,278.0,71.39,269.0,273.0,70.46,234.0,289.0,84.18,282.0,342.0,80.27,256.0,353.0,81.05,295.0,400.0,84.38,272.0,400.0
2023-03-15T00:05:14.466080,ALONE,426.0,1.0,86.08,221.0,135.0,90.48,227.0,125.0,87.65,218.0,130.0,92.04,237.0,130.0,89.6,208.0,141.0,76.37,265.0,162.0,85.45,205.0,183.0,84.81,284.0,215.0,89.16,205.0,236.0,90.04,287.0,262.0,89.75,212.0,278.0,70.21,268.0,278.0,71.34,234.0,289.0,82.23,281.0,342.0,81.88,256.0,353.0,78.56,293.0,405.0,83.64,271.0,400.0
2023-03-16T00:05:14.898491,ALONE,427.0,1.0,90.28,218.0,135.0,88.77,221.0,124.0,81.79,212.0,130.0,90.19,234.0,130.0,86.23,208.0,145.0,77.15,262.0,161.0,85.6,202.0,183.0,85.3,281.0,215.0,88.09,205.0,236.0,91.41,287.0,262.0,89.75,212.0,278.0,71.24,268.0,273.0,71.34,234.0,289.0,81.79,281.0,342.0,80.37,256.0,353.0,79.1,293.0,400.0,83.25,271.0,400.0
2023-03-17T00:05:14.298281,ALONE,428.0,1.0,89.11,223.0,139.0,90.43,227.0,129.0,88.72,220.0,134.0,92.87,241.0,129.0,87.16,213.0,144.0,76.12,268.0,160.0,87.65,213.0,187.0,84.42,293.0,218.0,88.09,217.0,239.0,91.31,296.0,266.0,87.7,220.0,276.0,74.12,279.0,276.0,71.92,244.0,287.0,79.05,303.0,345.0,78.17,258.0,350.0,82.62,331.0,402.0,87.3,275.0,402.0
2023-03-18T00:05:14.938566,ALONE,429.0,1.0,82.57,251.0,140.0,81.35,254.0,134.0,79.59,245.0,140.0,87.21,269.0,128.0,74.56,242.0,147.0,75.1,297.0,165.0,84.57,251.0,184.0,71.68,318.0,209.0,84.23,251.0,228.0,89.11,330.0,271.0,90.28,254.0,271.0,68.7,309.0,271.0,74.41,281.0,271.0,83.79,321.0,340.0,78.52,278.0,334.0,79.69,336.0,402.0,85.69,288.0,396.0
2023-03-19T00:05:14.338086,ALONE,430.0,1.0,84.52,253.0,129.0,89.21,256.0,124.0,74.32,247.0,129.0,91.6,274.0,129.0,38.45,271.0,129.0,76.37,301.0,162.0,80.03,262.0,179.0,80.13,322.0,218.0,83.11,268.0,229.0,87.6,319.0,273.0,86.62,271.0,267.0,55.62,319.0,267.0,66.5,295.0,278.0,72.41,322.0,345.0,79.15,310.0,345.0,77.15,334.0,406.0,71.39,328.0,395.0
2023-03-20T00:05:14.767194,ALONE,431.0,1.0,84.38,257.0,158.0,89.75,260.0,147.0,86.04,251.0,152.0,88.13,274.0,147.0,70.07,248.0,158.0,76.32,294.0,179.0,82.81,266.0,194.0,77.05,323.0,226.0,86.62,274.0,242.0,91.65,323.0,284.0,87.06,277.0,278.0,63.67,335.0,268.0,59.67,306.0,278.0,88.33,317.0,347.0,86.77,297.0,336.0,80.32,352.0,394.0,82.37,329.0,394.0
2023-03-21T00:05:14.170348,ALONE,432.0,1.0,88.96,264.0,212.0,93.16,267.0,200.0,93.26,257.0,208.0,92.58,284.0,200.0,83.69,254.0,212.0,74.51,308.0,232.0,69.38,277.0,236.0,80.18,332.0,287.0,68.16,288.0,287.0,88.28,332.0,338.0,74.61,288.0,323.0,61.23,332.0,326.0,63.57,305.0,319.0,85.45,339.0,397.0,88.53,281.0,366.0,63.13,390.0,371.0,77.78,328.0,397.0
2023-03-22T00:05:14.598332,ALONE,433.0,1.0,87.21,273.0,235.0,92.58,280.0,227.0,88.67,270.0,235.0,93.07,297.0,227.0,86.62,263.0,239.0,74.61,321.0,263.0,73.29,287.0,263.0,77.54,345.0,316.0,63.87,297.0,316.0,90.09,345.0,364.0,52.54,297.0,340.0,54.88,345.0,344.0,60.25,317.0,336.0,85.01,348.0,417.0,91.11,280.0,364.0,75.78,392.0,377.0,79.54,331.0,397.0
2023-03-23T00:05:14.002020,ALONE,434.0,1.0,91.36,285.0,240.0,93.7,292.0,228.0,90.58,278.0,232.0,91.7,310.0,232.0,75.1,278.0,240.0,76.27,338.0,268.0,72.22,303.0,264.0,80.76,363.0,325.0,40.62,310.0,305.0,88.77,363.0,373.0,43.7,306.0,333.0,62.6,349.0,345.0,61.72,324.0,337.0,85.79,349.0,418.0,88.67,285.0,365.0,58.69,398.0,382.0,78.56,334.0,386.0
2023-03-24T00:05:14.424593,ALONE,435.0,1.0,89.5,283.0,232.0,91.36,290.0,224.0,90.33,280.0,228.0,94.34,310.0,232.0,69.82,280.0,232.0,75.73,337.0,269.0,75.49,303.0,265.0,72.46,358.0,325.0,50.73,307.0,305.0,87.26,358.0,373.0,55.86,307.0,333.0,60.84,351.0,349.0,63.23,324.0,337.0,83.74,348.0,417.0,87.7,286.0,365.0,53.47,395.0,377.0,77.2,337.0,385.0
2023-03-25T00:05:14.042123,ALONE,436.0,1.0,89.79,284.0,233.0,95.41,291.0,225.0,91.8,280.0,229.0,97.46,307.0,229.0,85.69,272.0,233.0,78.96,331.0,266.0,73.49,299.0,262.0,82.23,354.0,322.0,59.33,307.0,302.0,87.74,354.0,374.0,55.57,303.0,334.0,63.57,354.0,346.0,57.81,323.0,334.0,82.08,347.0,418.0,88.43,288.0,366.0,58.25,398.0,378.0,79.0,339.0,386.0
2023-03-26T00:05:14.474056,ALONE,437.0,1.0,86.28,278.0,238.0,90.77,285.0,229.0,88.53,271.0,233.0,92.72,303.0,233.0,82.47,268.0,238.0,76.03,327.0,270.0,70.46,292.0,266.0,77.64,348.0,322.0,47.63,306.0,306.0,85.89,351.0,374.0,43.99,306.0,338.0,51.27,358.0,346.0,60.01,320.0,338.0,85.11,344.0,418.0,86.18,285.0,362.0,60.16,396.0,378.0,81.25,337.0,390.0
2023-03-27T00:05:14.874079,ALONE,438.0,1.0,87.26,271.0,242.0,92.87,278.0,234.0,91.36,267.0,238.0,93.99,296.0,234.0,79.88,264.0,242.0,77.93,322.0,270.0,65.58,296.0,262.0,76.07,348.0,323.0,35.74,318.0,307.0,88.09,348.0,376.0,26.98,311.0,332.0,56.3,358.0,348.0,53.47,326.0,340.0,84.47,344.0,421.0,88.04,286.0,364.0,73.58,395.0,381.0,79.83,337.0,393.0
2023-03-28T00:05:14.302648,ALONE,439.0,1.0,86.62,270.0,252.0,89.45,274.0,244.0,90.33,266.0,248.0,89.01,292.0,240.0,52.49,296.0,236.0,73.44,314.0,284.0,68.55,318.0,248.0,71.73,333.0,339.0,18.09,314.0,303.0,90.38,333.0,386.0,19.02,307.0,307.0,56.64,358.0,354.0,41.99,362.0,335.0,86.33,347.0,421.0,80.96,296.0,343.0,40.11,400.0,407.0,36.5,333.0,398.0
2023-03-29T00:05:14.706294,ALONE,440.0,1.0,83.2,282.0,242.0,89.94,285.0,234.0,76.81,279.0,234.0,88.04,304.0,227.0,61.04,310.0,223.0,74.85,320.0,271.0,77.78,338.0,246.0,77.54,326.0,331.0,34.23,351.0,290.0,82.52,320.0,379.0,17.36,357.0,320.0,53.03,351.0,353.0,41.38,363.0,342.0,81.98,348.0,420.0,46.22,357.0,394.0,68.31,410.0,405.0,81.3,410.0,386.0
2023-03-30T00:05:14.134645,ALONE,441.0,1.0,90.38,274.0,254.0,91.02,278.0,247.0,85.4,270.0,247.0,96.39,299.0,239.0,56.84,299.0,239.0,81.49,319.0,273.0,60.21,315.0,265.0,80.03,340.0,325.0,66.16,336.0,321.0,84.52,340.0,381.0,82.67,332.0,355.0,63.77,369.0,355.0,42.31,365.0,351.0,85.89,352.0,418.0,57.08,365.0,403.0,59.28,414.0,399.0,58.54,414.0,388.0
csv_data_incert.py 
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import csv
import pymongo
import dateutil.parser

# Connect to MongoDB
#client = pymongo.MongoClient("mongodb://localhost:27017/")
#client = pymongo.MongoClient("mongodb://admin:[pw]@localhost:27017/")
client = pymongo.MongoClient("mongodb://[username]:[pw]@localhost:27017/")
# Select Database
db = client["[DB_name]"]
# Select Collection
collection = db["Collection_name"]

# Open CSV file
with open("data.csv", "r") as f:
    # Create CSV reader
    reader = csv.reader(f)
    # Create a list of documents
    docs = []
    # Create a list of column names (for example, name them col1 through col55)
    col_names = ["col" + str(i) for i in range(1, 56)]
    # For each row of the CSV file
    for row in reader:
        # Create Documentation
        doc = {}
        # Mapping column names to values and adding them to the document
        for col_name, value in zip(col_names, row):
            # If the value is a number, convert to float
            try:
                value = float(value)
            # If the value is a date, convert to Date type
            except ValueError:
                try:
                    value = dateutil.parser.parse(value)
                # If the value is a string, leave it as it is.
                except ValueError:
                    pass
            # Add to Documentation
            doc[col_name] = value
        # Add to list of documents
        docs.append(doc)
    # Execute bulk insert
    collection.insert_many(docs)

Data acquisition and data visualization with Dash

get_data_show_dash.py 
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from dash import Dash, Input, Output, State, dcc, html
import plotly.graph_objects as go
import dash_bootstrap_components as dbc
from pymongo import MongoClient
from datetime import datetime, timedelta

# Connect to MongoDB
client = MongoClient("mongodb://[user_name]:[pw]@localhost:27017/")
# Select Database
db = client.[db_name]
# Select Collection
collection = db.[collection_name]
# Det data
min_date = collection.find().sort("col1", 1).limit(1)[0]["col1"]
max_date = collection.find().sort("col1", -1).limit(1)[0]["col1"]

# Create a Dash application
app = Dash(external_stylesheets=[dbc.themes.FLATLY])

# Defines the layout
app.layout = html.Div([
    dbc.Row(style={'margin-top': '10px'}, children=[
        dbc.Col(dcc.DatePickerRange(
            id='date-picker',
            min_date_allowed=min_date,
            max_date_allowed=max_date,
            start_date=min_date,
            end_date=max_date), width=3,style={"margin-top": "2px","margin-left": "5px"}),
        dbc.Col(dcc.Dropdown(
            id='yaxis-column1',
            options=[{'label': i, 'value': i} for i in ["col" + str(j) for j in range(4, 56)]],
            value='col4'), width=1,style={"margin-top": "5px"}),
        dbc.Col(dcc.Dropdown(
            id='yaxis-column2',
            options=[{'label': i, 'value': i} for i in ["col" + str(j) for j in range(4, 56)]],
            value='col5'), width=1,style={"margin-top": "5px"}),
        dbc.Col(dbc.Button('Update Graph', id='update-button', n_clicks=0), width=2, style={"margin-top": "5px"})
    ]),
    html.Div(id='graph-container')
])

# Define callbacks
@app.callback(
    Output('graph-container', 'children'),
    Input('update-button', 'n_clicks'),
    State('date-picker', 'start_date'),
    State('date-picker', 'end_date'),
    State('yaxis-column1', 'value'),
    State('yaxis-column2', 'value')
)
def update_graph(n_clicks, start_date, end_date, yaxis_column_name1, yaxis_column_name2):
    # Fetches data only when the button is clicked.
    if n_clicks > 0:
        # Convert string to a datetime object (ignoring time information)
        start_date = datetime.strptime(start_date.split("T")[0], "%Y-%m-%d")
        end_date = datetime.strptime(end_date.split("T")[0], "%Y-%m-%d")

        # In end_date, add one day to include data recorded in YYYYY-MM-DD
        end_date += timedelta(days=1)

        # Get data in the range of start_date and end_date.
        data = list(collection.find({"col1": {"$gte": start_date, "$lte": end_date}}))
        col1_dates = [row["col1"].date() for row in data]
        yaxis_data1 = [row[yaxis_column_name1] for row in data]
        yaxis_data2 = [row[yaxis_column_name2] for row in data]

        # Create graphs
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=col1_dates, y=yaxis_data1, mode="lines", name=yaxis_column_name1))
        fig.add_trace(go.Scatter(x=col1_dates, y=yaxis_data2, mode="lines", name=yaxis_column_name2))
        fig.update_layout(showlegend=True)

        # Returns a dcc.Graph component
        return dcc.Graph(figure=fig)
    else:
        # If the button is not clicked, nothing is returned
        return None


# Run the application
if __name__ == "__main__":
    app.run_server(debug=True)

Data acquisition and data visualization with Dash (DataFrame version)

get_data_show_dash_df.py 
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from dash import Dash, Input, Output, State, dcc, html
import plotly.graph_objects as go
import dash_bootstrap_components as dbc
from pymongo import MongoClient
from datetime import datetime, timedelta
import pandas as pd
#import numpy as np

# Connect to MongoDB
client = MongoClient("mongodb://[user_name]:[pw]@localhost:27017/")
# Select Database
db = client.[db_name]
# Select Collection
collection = db.[collection_name]
# Det data
min_date = collection.find().sort("col1", 1).limit(1)[0]["col1"]
max_date = collection.find().sort("col1", -1).limit(1)[0]["col1"]

# Create a Dash application
app = Dash(external_stylesheets=[dbc.themes.FLATLY])

# Defines the layout
app.layout = html.Div([
    dbc.Row(style={'margin-top': '10px'}, children=[
        dbc.Col(dcc.DatePickerRange(
            id='date-picker',
            min_date_allowed=min_date,
            max_date_allowed=max_date,
            start_date=min_date,
            end_date=max_date), width=3,style={"margin-top": "2px","margin-left": "5px"}),
        dbc.Col(dcc.Dropdown(
            id='yaxis-column1',
            options=[{'label': i, 'value': i} for i in ["col" + str(j) for j in range(4, 56)]],
            value='col4'), width=1,style={"margin-top": "5px"}),
        dbc.Col(dcc.Dropdown(
            id='yaxis-column2',
            options=[{'label': i, 'value': i} for i in ["col" + str(j) for j in range(4, 56)]],
            value='col5'), width=1,style={"margin-top": "5px"}),
        dbc.Col(dbc.Button('Update Graph', id='update-button', n_clicks=0), width=2, style={"margin-top": "5px"})
    ]),
    html.Div(id='graph-container')
])

#  Define callbacks
@app.callback(
    Output('graph-container', 'children'),
    Input('update-button', 'n_clicks'),
    State('date-picker', 'start_date'),
    State('date-picker', 'end_date'),
    State('yaxis-column1', 'value'),
    State('yaxis-column2', 'value')
)
def update_graph(n_clicks, start_date, end_date, yaxis_column_name1, yaxis_column_name2):
    # Fetches data only when the button is clicked.
    if n_clicks > 0:
        # Convert string to a datetime object (ignoring time information)
        start_date = datetime.strptime(start_date.split("T")[0], "%Y-%m-%d")
        end_date = datetime.strptime(end_date.split("T")[0], "%Y-%m-%d")

        # In end_date, add one day to include data recorded in YYYYY-MM-DD
        end_date += timedelta(days=1)

        # Get data in the range of start_date and end_date
        data = list(collection.find({"col1": {"$gte": start_date, "$lte": end_date}}))

        # Converts to a DataFrame
        df = pd.DataFrame(data)

        # Create graphs
        fig = go.Figure()

        # The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray.
        # To retain the old behavior, call `np.array` on the result
        #fig.add_trace(go.Scatter(x=df["col1"], y=df[yaxis_column_name1], mode="lines", name=yaxis_column_name1))
        #fig.add_trace(go.Scatter(x=df["col1"], y=df[yaxis_column_name2], mode="lines", name=yaxis_column_name2))

        # Convert date data to a NumPy array
        #col1_dates = np.array(df["col1"].dt.to_pydatetime())
        #fig.add_trace(go.Scatter(x=col1_dates, y=df[yaxis_column_name1], mode="lines", name=yaxis_column_name1))
        #fig.add_trace(go.Scatter(x=col1_dates, y=df[yaxis_column_name2], mode="lines", name=yaxis_column_name2))

        # Instead of plotting date data directly, convert the date data to a string before plotting.
        fig.add_trace(go.Scatter(x=df["col1"].dt.strftime('%Y-%m-%d'), y=df[yaxis_column_name1], mode="lines", name=yaxis_column_name1))
        fig.add_trace(go.Scatter(x=df["col1"].dt.strftime('%Y-%m-%d'), y=df[yaxis_column_name2], mode="lines", name=yaxis_column_name2))
        fig.update_layout(showlegend=True)

        # Returns a dcc.Graph component
        return dcc.Graph(figure=fig)
    else:
        # If the button is not clicked, nothing is returned
        return None


# Run the application
if __name__ == "__main__":
    app.run_server(debug=True)

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