Apprendre 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.


              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)

Tâche

Swipe to start coding

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.

Solution

Tout était clair ?

Merci pour vos commentaires !

Section 1. Chapitre 7

single

Demandez à l'IA

Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

Suggested prompts:

Can you explain how linear interpolation works in this context?

What would happen if there were missing values at the start or end of the series?

Can you show how to visualize the cleaned data?

Glissez pour afficher le menu


              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)