`FillNa`#

Description#

The FillNa class replaces any NaN values in a data sequence with a specified fill value. This function is useful for handling missing values by substituting them with a constant, such as 0 or a mean value, which can improve the continuity of data for certain analyses.

Parameters:

fill: The value to replace NaN entries with. This can be any numeric value, allowing customization to fit the context of the data.

NaN handling: All NaN values are replaced with the specified fill value, ensuring no NaN values remain in the output data.

NaN handling#

Policy: nan-aware. This function is designed to consume NaN inputs; see the description above for its specific behavior.

Examples#

Usage example#

 import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from screamer import FillNa

# Generate example data with NaN values
data = np.random.normal(size=30)
data[[3, 6, 10, 15, 21]] = np.nan  # Introduce NaN values

# Apply forward fill
filled_data = FillNa(0)(data)

# Create subplots with specified row heights and shared x-axis
fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    row_heights=[1/2, 1/2],
    vertical_spacing=0.1
)

# Add traces for original data and filled data
fig.add_trace(go.Scatter(y=data, mode='lines+markers', name='Original Data'), row=1, col=1)
fig.add_trace(go.Scatter(y=filled_data, mode='lines+markers', name='Filled Data', line=dict(color='red')), row=2, col=1)

# Update layout with individual y-axis titles
fig.update_layout(
    title="FillNa(0.0) on Data with NaNs",
    xaxis_title="Index",
    yaxis_title="Original Data",
    yaxis2_title="Filled Data",
    margin=dict(l=20, r=20, t=80, b=20),
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)

fig.show()

Implementation Details#

Algorithm for `FillNa`#

The FillNa function checks each data point for NaN values and substitutes any found with the specified fill value, providing a straightforward and efficient method to eliminate NaNs from the data.

Complexity#

Time Complexity: O(1)
Space Complexity: O(1)

Performance#

Ffill is a lightweight operation that process data efficiently. They are suitable for real-time or streaming data applications where missing values need to be managed with minimal overhead.

FillNa#