PYTHON — Plotting Data Using Pandas In Python

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# Plotting Data Using Pandas in Python

The pandas library is not only popular for enabling powerful data analysis but also for its convenient pre-canned plotting methods. In this tutorial, we will explore how to use pandas alongside matplotlib to produce sophisticated visualizations.

Working with Pandas in the Interactive Shell

Before we start scripting, let’s explore how Pandas works in the interactive shell. We import the required libraries and create a pandas.Series, a one-dimensional labeled array using np.arange() and a custom index.

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

s = pd.Series(np.arange(5), index=list('abcde'))
ax = s.plot()
type(ax)

Here, we create a pandas.Series and then use the plot() method to visualize the data. The plot() method implicitly tracks the current Axes, and we can obtain the Axes object by storing it in a variable.

Plotting Financial Data with Pandas and Matplotlib

In this example, we will plot the moving average of the CBOE market volatility index using financial data. We import the required libraries and then create a pandas.Series from a CSV file containing dates and their associated volatility. We then generate a Series of the 90-day rolling averages and split the data into bins using the pandas.cut() function. We also use a color map to visualize the data.

import matplotlib.transforms as mtransforms
import pandas as pd

# Read the CSV file
vix = pd.read_csv('path_to_file.csv', parse_dates=True)

# Generate a Series of the 90-day rolling averages
ma = vix.rolling(window=90).mean()

# Split the data into bins
state = pd.cut(ma, bins=[0, 14, 18, 24, ma.max()], labels=[0, 1, 2, 3])

# Create the plot
cmap = plt.get_cmap('RdYlGn_r')
ax = ma.plot(figsize=(8, 4))
ax = plt.gca()

# Set Axes properties
# Draw colored bars in the visualization based on the state bins
# Draw a horizontal dashed line at the mean of the VIX data

In this example, we use Pandas and Matplotlib to visualize financial data and create a sophisticated plot.

Conclusion

In this tutorial, we explored how to use Pandas alongside Matplotlib to visualize data and create sophisticated plots. By leveraging the powerful data analysis capabilities of Pandas and the visualization capabilities of Matplotlib, we can produce insightful visualizations for various applications.

For more in-depth learning about Pandas and its functionalities, dedicated tutorials are available at realpython.com.

Remember, Pandas and Matplotlib can be used in conjunction to unlock new opportunities for data analysis and visualization.

This tutorial provides a foundation for using Pandas and Matplotlib for plotting data and can serve as a starting point for further exploration in this subject.

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