Aprenda Identifying Missing Values

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import pandas as pd

# Create a sample time series with missing values
dates = pd.date_range("2024-01-01", periods=7, freq="D")
data = [1.2, None, 2.5, 3.1, None, 4.7, 5.3]
ts = pd.Series(data, index=dates)

# Detect missing values
missing_mask = ts.isna()
print("Missing value mask:")
print(missing_mask)

# Count total missing values
missing_count = ts.isna().sum()
print("\nTotal missing values:", missing_count)

Missing values are common in time series datasets and can occur due to data collection errors, sensor malfunctions, or irregular reporting. If you do not address missing values, your analyses and models may produce biased or inaccurate results. For example, missing entries can distort trends, reduce the effectiveness of statistical methods, and cause errors in calculations like averages or rolling statistics. Handling missing data is crucial for ensuring the reliability of forecasts and insights derived from time series analysis.

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