Plotting Datasets in Pandas based on Different Thresholds in Python

Introduction

In this article, we will explore how to create a line graph using pandas and matplotlib libraries in python, which is useful for visualizing data where there are fluctuations. We’ll start by understanding the basics of these two popular libraries and then move on to creating a custom plot based on different thresholds.

Installing Required Libraries

Before we begin with our example, you need to have pandas and matplotlib.pyplot installed in your Python environment. You can install them via pip:

pip install pandas matplotlib numpy scipy

Converting ‘Date’ Column to Datetime Format

The first step is to convert the ‘date’ column to datetime format, assuming it’s in string format. This will allow us to easily sort and plot data based on dates.

# Convert 'date' column to datetime format.
dataframe['date'] = pd.to_datetime(dataframe['date'])

Sorting Data by Date

Next, we need to sort the dataframe by date in ascending order.

# Sort by date and change indices to match.
dataframe = dataframe.sort_values(by = 'date', ascending = True).reset_index(drop=True)

Calculating Differences Between Consecutive Incomes

We calculate differences between consecutive incomes. This will be useful later for identifying whether the income is increasing or fluctuating.

# Get differences between consecutive incomes, with 0 as the income_diff for the very first row.
income = dataframe["income"].to_numpy().astype(float)
income_diffs = np.insert(np.diff(income), 0, 0)

# Add this to a new column in dataframe.
dataframe["income_diffs"] = income_diffs

Identifying Increasing and Fluctuating Values

We identify rows where income_diffs is positive (i.e., the value has increased) and those where it’s negative or zero (fluctuation).

# Rows with 0 or positive income diffs are stored in pos_diff.
pos_diff = dataframe[dataframe["income_diffs"] >= 0]
idx = pos_diff.index.values

# All other rows are stored in neg_diff.
neg_diff = dataframe.drop(idx, axis=0)

Plotting Data Points from pos_diff and neg_diff

We create two scatter plots. One for the increasing values (in blue) and another for fluctuating values (in red).

# Plot the dates and incomes from pos_diff in blue.
plt.scatter(pos_diff["date"], pos_diff["income"], color="b", label="Increase")

# Plot the dates and incomes from neg_diff in red. These are the fluctuating values.
plt.scatter(neg_diff["date"], neg_diff["income"], color="r", label="Fluctuation")

Customizing the Plot

Finally, we customize our plot to make it more visually appealing.

# Some stuff to prettify the plot.
plt.xlabel("Date", labelpad = 15)
plt.ylabel("Income ($)", labelpad = 10)
plt.title("Income Fluctuations Over Time")

plt.xticks(rotation = 45)
plt.legend(loc = "lower left", frameon=False)

Conclusion

In this article, we have learned how to create a custom line graph in pandas and matplotlib libraries using Python. We discussed the importance of converting date columns to datetime format and calculating differences between consecutive incomes to identify trends in data.

This example showcases an approach that can be used for many different types of datasets where there are fluctuations. By following these steps, you should now have a good understanding of how to create such plots and apply them to your own work with pandas and matplotlib libraries.

Last modified on 2024-12-07