Understanding Pandas and Matplotlib for Multiple Markers in Pyplot

As a data analyst or scientist, working with pandas and matplotlib is crucial when it comes to creating informative and visually appealing plots. In this article, we will delve into the world of multiple markers on the same line using pyplot.

Introduction

Pyplot is a powerful plotting library that allows users to create high-quality 2D and 3D plots for visualizing data. When working with pandas, which provides data structures such as Series and DataFrames, it’s not uncommon to encounter situations where you need to plot multiple markers on the same line.

In this article, we’ll explore a common scenario: plotting a line with two different markers in the same syntax, along with a dotted line connecting certain points. We’ll break down the code, discussing each step and explaining how matplotlib handles various marker styles.

Setting Up Pandas and Matplotlib

To begin, ensure you have pandas and matplotlib installed. You can install them using pip:

pip install pandas matplotlib numpy

Creating the DataFrame

We start by creating a sample DataFrame df with columns “time”, “c_1”, “c_2”, and “c_3”. The values in these columns will serve as input for our plot.

import pandas as pd
import numpy as np; np.random.seed(0)

# Create a sample DataFrame
time = np.arange(1,7)
c_1 = np.random.rand(6)
c_2 = time*np.array([np.nan, np.nan, 1, np.nan, 1, np.nan])
c_3 = time*np.array([1, np.nan, np.nan, np.nan, np.nan, 1])

df = pd.DataFrame({"time":time, "c_1":c_1,"c_2":c_2,"c_3":c_3 })

Plotting the Line with Markers

The next step is to plot a line using df.plot(). This function will create a basic line plot of our data.

ax = df.plot("time", "c_1")

To add markers, we use the plot() method again, this time specifying the marker style and color. We’ll create two markers: one for column “c_2” in lime green (marker="s"), and another for column “c_3” in crimson (marker="o").

ax.plot(df["c_2"], df["c_1"], marker="s", color="limegreen",  linestyle="")
ax.plot(df["c_3"], df["c_1"], marker="o", color="crimson", linestyle="")

Notice that we’re using the linestyle="" argument to remove the line connecting these markers.

Connecting Points with a Dotted Line

To draw a dotted line between specific points, we need to join them manually. We create two new DataFrames, df2 and df3, which contain only the relevant columns (c_1 and c_2). We then concatenate these DataFrames using pd.concat() and sort them by “c_2” before plotting.

# Create a new DataFrame for connecting points
df2 = df[["c_1","c_2"]].dropna()
df3 = df[["c_1","c_3"]].dropna().rename(columns = {'c_3':'c_2'}, inplace = False)
df4 = pd.concat([df2, df3]).sort_values(by=["c_2"])

# Plot the dotted line
ax.plot(df4["c_2"], df4["c_1"], color="burlywood",  linestyle=":", lw=2.5)

In this code snippet, we’re creating a new DataFrame df4 that contains only the points to be connected. We sort it by “c_2” before plotting.

Customizing the Plot

Finally, we customize our plot by setting the x-axis limits using ax.set_xlim() and displaying the plot with plt.show().

# Customize the plot
ax.set_xlim(0,7)
plt.show()

Conclusion

In this article, we explored how to create a line plot with multiple markers on the same line using pyplot. We discussed how to add custom markers, connect specific points, and customize our plot for better visualization.

By following these steps and understanding how matplotlib handles different marker styles, you can now tackle common scenarios in data analysis and visualization.

Last modified on 2023-06-29