💡 Problem Formulation: When plotting financial time series in Python using Matplotlib, the graph often shows gaps for weekends or public holidays where the financial markets are closed. This can result in misleading visuals as it seems like no data exists for these periods. This article discusses methods to handle and remove these empty date gaps to create a continuous time series visualization.
Method 1: Use Matplotlib Finance (mplfinance) Library
The mplfinance library, formerly known as matplotlib.finance, provides a function that automatically consolidates and skips non-trading days. It takes a Pandas DataFrame as input, where the index is a Pandas DateTimeIndex.
Here’s an example:
import mplfinance as mpf
import pandas as pd
# Your DataFrame should have dates as index and ohlc data
data = pd.read_csv('financial_data.csv', index_col='Date', parse_dates=True)
mpf.plot(data, type='line', show_nontrading=False)Output: A line graph with gaps for weekends and holidays removed.
This code snippet uses mplfinance to plot financial data while skipping non-trading days, ensuring that weekends and holidays do not appear in the graph, providing a continuous financial time series.
Method 2: Custom Business Day Frequency
By using the Pandas bdate_range function and specifying a custom frequency, you can filter out weekends when creating a date range for your financial data before plotting.
Here’s an example:
import pandas as pd
import matplotlib.pyplot as plt
dates = pd.bdate_range(start='2020-01-01', end='2020-12-31', freq='C')
data = pd.DataFrame({'Value': values}, index=dates)
plt.figure(figsize=(10,5))
plt.plot(data.index, data['Value'])
plt.show()Output: A plot showing financial data for business days only, with weekends omitted.
This code snippet creates a date range for business days only using pd.bdate_range with custom frequency. When plotted, this ensures that weekends are skipped and the financial graph does not display any gaps for those days.
Method 3: Resample and Forward Fill
To avoid weekends, you can resample the data to business days and then use forward-fill to handle empty data points. This doesn’t remove the actual dates but rather ensures there’s no gap in the data.
Here’s an example:
import pandas as pd
import matplotlib.pyplot as plt
# Assuming 'data' is a DataFrame with a DateTime index
data = data.resample('B').ffill()
plt.plot(data.index, data['Value'])
plt.show()Output: A plot that shows a continuous line over the business days, forward-filling data for the weekends.
The resample method in Pandas is used here to align the time series data to business days. Forward fill is then used to fill the missing values, allowing the graph to appear continuous over weekends.
Method 4: Filter Out Weekends from DataFrame Before Plotting
Directly filter out weekend dates from your DataFrame by using datetime properties before plotting. This method removes the weekends from the data set itself.
Here’s an example:
import pandas as pd import matplotlib.pyplot as plt # Assuming 'data' is a DataFrame with a DateTime index data = data[data.index.dayofweek < 5] plt.plot(data.index, data['Value']) plt.show()
Output: A plot of the data with weekends completely removed.
This snippet filters out the DataFrame rows where the day of the week is Saturday or Sunday (dayofweek >= 5), ensuring that weekends are not included in the plot.
Bonus One-Liner Method 5: Use the plotly Library
plotly, an interactive graphing library, allows for easy handling of non-trading days with built-in business day conventions.
Here’s an example:
import plotly.graph_objs as go
import pandas as pd
data = pd.read_csv('financial_data.csv', index_col='Date', parse_dates=True)
fig = go.Figure(data=[go.Scatter(x=data.index, y=data['Value'])])
fig.update_xaxes(type='date', dtick="M1", tickformat='%b\n%Y')
fig.show()Output: An interactive graph with weekends and holidays automatically skipped.
The plotly example provided creates an interactive financial chart that gracefully handles dates, including business days handling. The dtick parameter can be adjusted to change the x-axis tick frequency.
Summary/Discussion
- Method 1: mplfinance Library. Specifically designed for financial time series data. It provides functionality to exclude non-trading days natively. The main downside is it might not offer as much customization as pure Matplotlib.
- Method 2: Custom Business Day Frequency. It’s a powerful method when your data aligns with standard business days. Its limitation is that it may not account for non-standard holidays without additional custom settings.
- Method 3: Resample and Forward Fill. It creates a continuous dataset by forward-filling data points for the weekends but does not remove them from the dataset, which could be useful or misleading depending on the analysis required.
- Method 4: Filter Out Weekends Before Plotting. This method gives you full control over which data points are included in your plot. The trade-off is the manual effort required to determine which dates to exclude.
- Method 5: plotly Library. Provides an interactive and visually pleasing alternative with built-in business day handling. While powerful, it might be overkill for simple or static plots and has a learning curve for those familiar with Matplotlib.