Impara Challenge: Clean and Prepare Price Data | Financial Data Analysis with Python

In trading, you often encounter price data with missing values. Properly cleaning and preparing this data is crucial before any analysis or modeling. You will work with a DataFrame containing daily closing prices, some of which are missing. Your task is to identify missing values, fill them using linear interpolation, and then compute basic statistics on the cleaned data. This process ensures your analysis reflects the true nature of the underlying price series, free from gaps that could distort calculations.


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import pandas as pd

# Hardcoded DataFrame of closing prices with missing values
data = {
    "Date": [
        "2024-01-01", "2024-01-02", "2024-01-03",
        "2024-01-04", "2024-01-05", "2024-01-06",
        "2024-01-07"
    ],
    "Close": [
        101.5, None, 102.7, None, 104.2, 105.0, None
    ]
}
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df.set_index("Date", inplace=True)

# Detect missing values
print("Missing values per day:")
print(df.isnull())

# Fill missing values using linear interpolation
df_filled = df.interpolate(method="linear")

# Calculate mean and standard deviation of the cleaned price series
mean_price = df_filled["Close"].mean()
std_price = df_filled["Close"].std()

# Print cleaned DataFrame and statistics
print("\nCleaned closing prices (after interpolation):")
print(df_filled)
print("\nMean closing price:", mean_price)
print("Standard deviation of closing price:", std_price)

Compito

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Use the provided DataFrame with missing values.
Identify which entries are missing.
Fill missing values using linear interpolation.
Calculate and print the mean and standard deviation of the cleaned price series.
Print the cleaned DataFrame.

Your solution must use pandas. Do not modify the original data structure.

Soluzione

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Sezione 1. Capitolo 7

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              123456789101112131415161718192021222324252627282930313233
            
import pandas as pd

# Hardcoded DataFrame of closing prices with missing values
data = {
    "Date": [
        "2024-01-01", "2024-01-02", "2024-01-03",
        "2024-01-04", "2024-01-05", "2024-01-06",
        "2024-01-07"
    ],
    "Close": [
        101.5, None, 102.7, None, 104.2, 105.0, None
    ]
}
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df.set_index("Date", inplace=True)

# Detect missing values
print("Missing values per day:")
print(df.isnull())

# Fill missing values using linear interpolation
df_filled = df.interpolate(method="linear")

# Calculate mean and standard deviation of the cleaned price series
mean_price = df_filled["Close"].mean()
std_price = df_filled["Close"].std()

# Print cleaned DataFrame and statistics
print("\nCleaned closing prices (after interpolation):")
print(df_filled)
print("\nMean closing price:", mean_price)
print("Standard deviation of closing price:", std_price)