Understanding Seaborn’s Distplot and Working with Pandas Datetime Series

Seaborn’s distplot is a versatile plotting function that can be used to visualize the distribution of various types of data. However, when working with pandas datetime series, we often encounter issues due to the inherent structure of these series.

In this article, we’ll delve into the world of Seaborn’s distplot, explore the limitations it poses when dealing with pandas datetime series, and discuss two potential workarounds: converting dates to numbers and using histograms directly without Seaborn’s distplot.

Introduction to Pandas Datetime Series

Before diving into the details, let’s take a brief look at what pandas datetime series are and how they’re represented in our data. A pandas datetime series is a collection of date values, which can be thought of as a combination of year, month, day, hour, minute, and second components.

In Python, dates are typically represented using the datetime module, which provides classes for representing dates and times. When working with pandas, we often use the pd.to_datetime() function to convert columns or rows into datetime series.

The Issue with Seaborn’s Distplot

Seaborn’s distplot is a wrapper around matplotlib’s histogram function. While it can be used to visualize various types of data, including numerical and categorical variables, it poses some challenges when dealing with pandas datetime series.

When we try to plot a datetime series using distplot, we often encounter the following error:

TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype '<M8[ns]'

This error occurs because Seaborn’s distplot expects numerical data, but we’re trying to plot a datetime series.

Option 1: Converting Dates to Numbers

One way to overcome this issue is to convert the dates to numbers, which can be done using various scaling techniques. For example, we can use MinMax Scaler to map dates to values in the range [0,1]. Here’s an example:

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

# Create a sample dataframe with datetime series
original_dates = ["2016-03-05", "2016-03-05", "2016-02-05", "2016-02-05", "2016-02-05", "2014-03-05"]
dates_list = [pd.to_datetime(date, format='%Y-%m-%d').date() for date in original_dates]

df = pd.DataFrame({"Date": dates_list})

# Create a new column with the difference in days between minimum date
min_date = df["Date"].min()
df["NewDate"] = df["Date"] - min_date

# Apply MinMax Scaler to map dates to values in range [0,1]
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df["ScaledDate"] = scaler.fit_transform(df["NewDate"].values.reshape(-1, 1))

# Plot the scaled dates using distplot
sns.set()
ax = sns.distplot(df['ScaledDate'])
plt.show()

In this example, we create a new column NewDate by subtracting the minimum date from each original date. We then use MinMax Scaler to map these values to the range [0,1]. Finally, we plot the scaled dates using distplot.

Option 2: Using Histogram Directly without Seaborn’s Distplot

Another way to visualize a datetime series is to use matplotlib’s histogram function directly, rather than relying on Seaborn’s distplot. We can group by day of the year and plot the histogram:

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

# Create a sample dataframe with datetime series
original_dates = ["2016-03-05", "2016-03-05", "2016-02-05", "2016-02-05", "2016-02-05", "2014-03-05"]
dates_list = [pd.to_datetime(date, format='%Y-%m-%d').date() for date in original_dates]

df = pd.DataFrame({"Date": dates_list})

# Group by day of the year and plot histogram
days_of_year = df["Date"].dt.dayofyear.unique()
for i, day in enumerate(days_of_year):
    day_df = df[df["Date"].dt.dayofyear == day]
    plt.plot(day_df["Date"], np.zeros(len(day_df)), label=f'Day {day}')

plt.xlabel('Date')
plt.ylabel('Frequency')
plt.title('Histogram of Dates')
plt.legend()
plt.show()

In this example, we group the dataframe by day of the year and plot a histogram for each group. This approach allows us to visualize the distribution of dates without relying on Seaborn’s distplot.

Conclusion

While Seaborn’s distplot is a powerful tool for visualizing data, it poses challenges when dealing with pandas datetime series. By converting dates to numbers using MinMax Scaler or by using histogram functions directly, we can overcome these limitations and gain insights into the distribution of our data.

Last modified on 2024-01-21