Challenge: Clean Stock Price Data
Before diving into the challenge, recall that missing values are a common issue when working with financial time series data. These gaps can result from market holidays, data transmission errors, or other disruptions. Accurately handling missing values is essential because unaddressed gaps can distort calculations of returns, volatility, correlations, and other key metrics. Ensuring the completeness and consistency of your dataset is a foundational step in maintaining the integrity of your financial analyses.
12345678910111213import pandas as pd import numpy as np # Example DataFrame: daily closing prices for 4 stocks over 10 business days dates = pd.date_range("2024-06-03", periods=10, freq="B") data = { "AAPL": [189.9, np.nan, 190.5, 191.4, np.nan, 192.0, 191.7, np.nan, 192.5, 192.7], "GOOG": [np.nan, 2775.0, 2780.5, np.nan, 2788.0, 2790.3, np.nan, 2795.0, 2796.8, np.nan], "MSFT": [np.nan, np.nan, 329.5, 330.1, 330.8, np.nan, 332.0, 332.5, np.nan, 333.1], "AMZN": [129.7, 130.1, np.nan, 131.0, 131.5, 132.2, np.nan, np.nan, 133.0, 133.5], } df = pd.DataFrame(data, index=dates) print(df)
To clean this DataFrame, start by identifying how many missing values there are in each column. For each stock, you want to fill missing values using a forward fill, which propagates the last valid observation forward to the next missing value. However, if the first value in a column is missing, forward fill will leave it as NaN. In this case, you should use a backward fill to fill these initial gaps with the next available value. After filling, check again to confirm that all missing values have been addressed. This approach ensures no artificial discontinuities are introduced at the start of your data, which is critical for accurate subsequent analysis.
Swipe to start coding
You are given a DataFrame containing daily closing prices for four stocks, with some missing values. Your task is to clean the data and summarize the changes.
- Identify the total number of missing values in the DataFrame before cleaning.
- Fill missing values using forward fill for each column, but if the first value is missing, fill it with the next available value using backward fill.
- Create a new DataFrame containing the cleaned data.
- Identify the total number of missing values after cleaning.
- Print the number of missing values before cleaning, the cleaned DataFrame, and the number of missing values after cleaning.
Soluzione
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How do I count the missing values in each column?
Can you show me how to apply forward fill and backward fill to this DataFrame?
How do I verify that all missing values have been filled after cleaning?
Fantastico!
Completion tasso migliorato a 4.76Challenge: Clean Stock Price Data
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Before diving into the challenge, recall that missing values are a common issue when working with financial time series data. These gaps can result from market holidays, data transmission errors, or other disruptions. Accurately handling missing values is essential because unaddressed gaps can distort calculations of returns, volatility, correlations, and other key metrics. Ensuring the completeness and consistency of your dataset is a foundational step in maintaining the integrity of your financial analyses.
12345678910111213import pandas as pd import numpy as np # Example DataFrame: daily closing prices for 4 stocks over 10 business days dates = pd.date_range("2024-06-03", periods=10, freq="B") data = { "AAPL": [189.9, np.nan, 190.5, 191.4, np.nan, 192.0, 191.7, np.nan, 192.5, 192.7], "GOOG": [np.nan, 2775.0, 2780.5, np.nan, 2788.0, 2790.3, np.nan, 2795.0, 2796.8, np.nan], "MSFT": [np.nan, np.nan, 329.5, 330.1, 330.8, np.nan, 332.0, 332.5, np.nan, 333.1], "AMZN": [129.7, 130.1, np.nan, 131.0, 131.5, 132.2, np.nan, np.nan, 133.0, 133.5], } df = pd.DataFrame(data, index=dates) print(df)
To clean this DataFrame, start by identifying how many missing values there are in each column. For each stock, you want to fill missing values using a forward fill, which propagates the last valid observation forward to the next missing value. However, if the first value in a column is missing, forward fill will leave it as NaN. In this case, you should use a backward fill to fill these initial gaps with the next available value. After filling, check again to confirm that all missing values have been addressed. This approach ensures no artificial discontinuities are introduced at the start of your data, which is critical for accurate subsequent analysis.
Swipe to start coding
You are given a DataFrame containing daily closing prices for four stocks, with some missing values. Your task is to clean the data and summarize the changes.
- Identify the total number of missing values in the DataFrame before cleaning.
- Fill missing values using forward fill for each column, but if the first value is missing, fill it with the next available value using backward fill.
- Create a new DataFrame containing the cleaned data.
- Identify the total number of missing values after cleaning.
- Print the number of missing values before cleaning, the cleaned DataFrame, and the number of missing values after cleaning.
Soluzione
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