学ぶ Data Cleaning and Handling Missing Values | Financial Data Manipulation with Python

Python for Financial Analysts

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Financial datasets often contain imperfections that can significantly affect the quality of your analysis. Common issues include missing prices, outliers, and inconsistent data entries. Missing values might occur due to market holidays, trading suspensions, or data recording errors. Outliers, such as sudden spikes or drops in price, may result from erroneous trades or reporting mistakes. Inconsistent data, like mismatched date formats or duplicate entries, can arise when merging data from multiple sources. These issues can distort summary statistics, lead to misleading visualizations, and compromise the reliability of any models or forecasts built on the data. Addressing these problems is essential before conducting any meaningful analysis.


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import pandas as pd
import numpy as np

# Create a DataFrame with missing values in stock prices
dates = pd.date_range("2023-01-01", periods=7, freq="D")
data = {
    "AAPL": [150, np.nan, 152, np.nan, 155, 156, np.nan],
    "MSFT": [300, 301, np.nan, 303, np.nan, 306, 307]
}
prices = pd.DataFrame(data, index=dates)
print("Original DataFrame with Missing Values:")
print(prices)

When you encounter missing data in a financial time series, there are several techniques you can use to address the gaps. Using the DataFrame above as a reference, the most common methods are forward fill, backward fill, and interpolation.

Forward fill replaces each missing value with the last known valid value. This is particularly useful in financial time series where it is reasonable to assume that the most recent price remains valid until a new one is recorded;
Backward fill does the opposite, filling missing values with the next available value in the series;
Interpolation, on the other hand, estimates missing values based on surrounding data points, often using linear interpolation to assume a straight-line change between known values.

Choosing the right method depends on the nature of your data and the context of your analysis. For instance, forward fill is commonly used for stock prices, while interpolation might be more appropriate when you expect smooth changes between values.


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# Forward fill missing values
forward_filled = prices.ffill()
print("\nDataFrame after Forward Fill:")
print(forward_filled)

# Interpolate missing values linearly
interpolated = prices.interpolate(method="linear")
print("\nDataFrame after Linear Interpolation:")
print(interpolated)

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セクション 1. 章 2

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セクション 1. 章 2

Data Cleaning and Handling Missing Values

1. What is the purpose of forward filling missing values in financial time series?

2. When might interpolation be preferred over forward fill for missing financial data?

3. How can missing data affect financial analysis results?