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Goal: Realtime data plot using Pandas.

Separate data and visualization.

3: Simple Dataframe Plot: Data

Tips: Do not mix stuff!

The main idea, is to get your data ready. so you can plot your chart easier.

The more mature, you build your dataframe, the easier to think about the visual representation.

Python Source Code

Baby crawl before you walk, matplotlib is a complex library.

Life is easier with source code. And I give you two.

The source code above is very similar, except one method.

The first is using matplotlib object directly, and the second one is using dataframe object to plot.

Required Package


I also read people’s blog. Sometimes I get lost, when people do not give the import stuff.

So I present you complete stuff.

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as md

import pandas as pd
import numpy as np
import random

from time import sleep

Panda Plot: Simple Dataframe Plot: Import


But complete code is confusing. So We need an overview.

This might looks scary at first. But don’t worry, most of the method is, verbatim from previous class.

class rndPlotter:
  def __init__(self):
  def chart_setup(self):
  def number_setup(self):
  def update_num(self):
  def print_new_pair(self, new_pair):
  def update_data(self):
  def generateSeries(self):
  def plotSeries(self):
  def __call__(self):

Panda Plot: Simple Dataframe Plot: Skeleton


We only add this chart_setup() line.

class rndPlotter:
  def __init__(self):
    # save initial parameter
    self.index = 0
    self.timeframe = pd.DataFrame({
      "time": [], "temp": [] })


Panda Plot: Simple Dataframe Plot: Constructor

We are going to discuss chart at the end of this article.

Setting up Number

The same initial setup as previous.

Panda Plot: Simple Dataframe Plot: Setup Number

Update Number

The same update number method as previous.

Panda Plot: Simple Dataframe Plot: Update Number

Update Data Frame

The same update dataframe method as previous.

Panda Plot: Simple Dataframe Plot: Update Data

Is it debugging stuff?

Why do we even’t need to print progress in console?

Most of the times, I need to know whether my script is freeze or not. That is all.

So this is not a debugging stuff, but rather a soft requirement. You can freely dispose this method, if you prefer clean console.

  def print_new_pair(self, new_pair):
    if self.index == 0:
      new_str = new_pair.to_string(
      new_str = new_pair.to_string(
        index=False, header=False)

Panda Plot: Simple Dataframe Plot: Print Pair

The result will be as below code:

Panda Plot: Simple Dataframe Plot: Output: Info


We can refactor the main method into two parts. Generatting the dataframe series, and plotting the data frame series.

  def __call__(self):

Panda Plot: Simple Dataframe Plot: Main Method

Where the data build in this generateSeries method.

  def generateSeries(self):
    for i in range(0, 40):


Panda Plot: Simple Dataframe Plot: Generate Series

Dataframe structure

I put timeframe.info() to check the structure of the dataframe. The output could be as below:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 40 entries, 0 to 39
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   time    40 non-null     datetime64[ns]
 1   temp    40 non-null     float64       
dtypes: datetime64[ns](1), float64(1)
memory usage: 960.0 bytes

Output in CLI

We can create class instance and run:

rndPlo = rndPlotter()

Panda Plot: Simple Dataframe Plot: Execute

Without the plot stuff, we can examine the data.

This is going to be a long output.

❯ python 03-plot.py
                      time     temp
2023-01-28 10:27:33.299987 19.58234
2023-01-28 10:27:33.554449 15.834969
2023-01-28 10:27:42.906653 28.701011
2023-01-28 10:27:43.159416 23.97157

Panda Plot: Simple Dataframe Plot: Output: Table

3: Simple Dataframe Plot: Chart

After the data ready, we can care about the chart.

In this simple example, we only need to represent X axis as time series.

Setting up Chart

  def chart_setup(self):
    self.fig, self.axes = plt.subplots()

    xfmt = md.DateFormatter('%H:%M:%S')

Panda Plot: Simple Dataframe Plot: Setup chart

Chart Preview

Now we can plot the data series in matplotlib.

  def plotSeries(self):
    plt.xticks(rotation=90, ha='right')
    plt.title('Value by Time')



Panda Plot: Simple Dataframe Plot: Plot Series

With the result similar as below figure:

Panda Plot: Simple Dataframe Plot: Chart

Plotting Using Dataframe

Alternatively, you may utilize dataframe method to plot data.

  def plotSeries(self):
      'time', 'temp', ax=self.axes)

    for tick in self.axes.get_xticklabels():


Panda Plot: Simple Dataframe Plot: Plot using Dataframe

With about the same result as previous chart.

Panda Plot: Simple Dataframe Plot: Chart Dataframe

It is up to you to choose whatever suit your case better.

What is Next 🤔?

We are done with simple example. But we need more than just simple example right?

How about smoothing the line? The issue with smoothing is, the interpolation method does not accept time series. So we have to keep the data in float instead, and cast into time series before we plot.

Consider continue reading [ Python - Panda - Time Series ].