i have 8 csv files the have the same x,y axis with different values. i would like to plot them all on the same plot to compare between them. this is a snap from a ploty code

import pandas as pd
import plotly.express as px

 df = pd.read_csv("file1.csv")
 df2 = pd.read_csv('file2.csv')
 fig = px.line(df, x = 'values', y = 'time')
 fig1 = px.line(df2, x = 'values', y = 'time')
 fig.show()
 fig1.show() 

but every csv file is plotted individually i want them to be on the same plot. like this plot

CodePudding user response：

You need to create a unique dataframe with df3=pd.concat([df,df2],axis=0) and use it to create your plot.

If you want to distinguish your two tables in your plot. You can create a new columns in your dataframe: df["Frame"]="df" df["Frame"]="df2" df3=pd.concat([df,df2],axis=0)

And add color option in px.line

fig = px.line(df2, x = 'values', y = 'time',color='Frame')

fig.show()

CodePudding user response：

• have synthesized CSV files to make MWE
• have presented two approaches
  1. concat all into one data frame then create figure
  2. create figure and add a trace for each CSV
• in both have used glob() to get all CSVs in a directory

from pathlib import Path
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

# setup env
f = Path.cwd().joinpath("SO71123005")
if not f.is_dir():
    f.mkdir()

# generate some CSVs
for i in range(10):
    pd.DataFrame(
        {
            "time": pd.date_range("14-Feb-2022", freq="1H", periods=48),
            "values": np.random.uniform(1, 20, 48),
        }
    ).to_csv(f.joinpath(f"{i}.csv"), index=False)

# create a line trace per CSV file in directory by concatenating all CSVs into data frame
px.line(
    pd.concat([pd.read_csv(csv).assign(file=csv.name) for csv in f.glob("*.csv")]),
    x="time",
    y="values",
    color="file",
).show()

# create a figure then add a line for each CSV in directory
fig = go.Figure()
for csv in f.glob("*.csv"):
    fig.add_traces(
        px.line(pd.read_csv(csv), x="time", y="values")
        .update_traces(line_color=None, showlegend=True, name=csv.name)
        .data
    )
fig