Plotting two pd dataframes with n columns, each df representing an axis coordinate?

Question

I have two dataframes which each have n columns (changes depending input), where each dataframe represents an axis coordinate. Each column represents separate lines I want to plot.

Example dfs (1st being x coordinates, 2nd being y coordinates):

I've looked at multiple threads on here and have tried to df.concat, create a df3 with tuple coordinates. The problem is that all the solutions often require plotting each column separately. However, I need this to be variable to the number of columns I generate based on my inputs.

My current code:

#Using datapull
from jdK_RS import RelStr, rsMOM
rs= RelStr
rsMO = rsMOM

#Remove first row and column so all points are plottable
rs = rs.iloc[1:,:]
rsMO = rsMO.iloc[1:,:]

#Create legend from the column headers 
legend = rs.columns.values.tolist()

#Combine the matrices to develop plots
rrg = pd.DataFrame()
rrg.reindex_like(rs)
rrg = pd.DataFrame({x: zip(rs[x], rsMO[x]) for x in rs.columns})

#Plot RRG
plt.plot(rrg)
plt.figure(figsize=(8,8))
plt.legend(legend)
plt.show()

When I plot a scatter, it plots the points correctly, but unfortunately does not create the separate lines I need for my analysis. As an example of the type of visual I'd like...

Answer 1

This how a did it:

Replicating your data:

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

data1=[
["2022-03-23",101.309106,99.773478,101.172967],

["2022-03-24",100.153241,102.079970,99.553582],

["2022-03-25",101.454518,100.903563,101.097076],

["2022-03-28",99.821442,101.924765,102.355196]
]

data2=[
["2022-03-23",100.740636,100.392111,100.974014],

["2022-03-24",100.578897,100.216871,99.600151],

["2022-03-25",102.463595,100.002197,101.820720],

["2022-03-28",100.216871,101.387072,101.605115]
]

columns=['Date','XLF','XLK','XLRE']
rs=pd.DataFrame(data1,columns=columns)
rsMO=pd.DataFrame(data2,columns=columns)

Dropping the first column(in your original code you dropped the first row):

rs = rs.iloc[:,1:]
rsMO = rsMO.iloc[:,1:]

Using iteritems():

for (column_name, column_data) in rs.iteritems():
    plt.plot(column_data,rsMO[column_name],'-o',label=column_name)
    plt.legend()
plt.figure(figsize=(8,8))
plt.show()

So, we iterate through the n columns of the rs dataframe, we get the column_name that we use as label, then we plot the pair formed by the column_data we got from rs and the data of the same column_name from the rsMO dataframe.

Pandas iteritems()