Lernen Handling Missing Data in Research | Data Manipulation for Research

Missing data is a common challenge in research datasets and can arise from various sources. Frequently, missing values occur when participants skip survey questions, sensors fail to record a measurement, or data entry errors happen during collection. Sometimes, certain variables may not be applicable to all cases, leading to intentional gaps. If left unaddressed, missing data can bias your analyses, reduce statistical power, and lead to misleading conclusions. Therefore, detecting and handling missing values is a crucial step in preparing your data for research.


              12345678910111213141516171819
            
import pandas as pd

# Example DataFrame with missing values
data = {
    "participant": [1, 2, 3, 4],
    "age": [25, None, 28, 30],
    "score": [88, 92, None, 85]
}
df = pd.DataFrame(data)

# Detect missing values in the DataFrame
missing = df.isnull()
print("Missing values (True indicates missing):")
print(missing)

# Count missing values per column
missing_count = df.isnull().sum()
print("\nNumber of missing values per column:")
print(missing_count)

Once you have identified missing data, you need to decide how to handle it. The main strategies are:

Dropping: remove rows or columns containing missing values, which is useful if only a small portion of the dataset is affected;
Filling: replace missing values with a specific value, such as zero or the mean of the column;
Imputing: estimate missing values based on other data, using methods like interpolation or model-based prediction.

The choice of strategy depends on the research context, the amount of missing data, and the importance of the affected variables.


              1234567891011
            
# Fill missing values in 'age' with the mean of the column
mean_age = df["age"].mean()
df_filled = df.copy()
df_filled["age"] = df_filled["age"].fillna(mean_age)
print("DataFrame after filling missing 'age' values with the mean:")
print(df_filled)

# Drop rows with any missing data
df_dropped = df.dropna()
print("\nDataFrame after dropping rows with missing data:")
print(df_dropped)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 6

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

Suggested prompts:

What are the pros and cons of dropping versus filling missing data?

Can you explain how to choose the best strategy for handling missing values?

Are there more advanced methods for imputing missing data?

Swipe um das Menü anzuzeigen


              12345678910111213141516171819
            
import pandas as pd

# Example DataFrame with missing values
data = {
    "participant": [1, 2, 3, 4],
    "age": [25, None, 28, 30],
    "score": [88, 92, None, 85]
}
df = pd.DataFrame(data)

# Detect missing values in the DataFrame
missing = df.isnull()
print("Missing values (True indicates missing):")
print(missing)

# Count missing values per column
missing_count = df.isnull().sum()
print("\nNumber of missing values per column:")
print(missing_count)

Once you have identified missing data, you need to decide how to handle it. The main strategies are:

Dropping: remove rows or columns containing missing values, which is useful if only a small portion of the dataset is affected;
Filling: replace missing values with a specific value, such as zero or the mean of the column;
Imputing: estimate missing values based on other data, using methods like interpolation or model-based prediction.

The choice of strategy depends on the research context, the amount of missing data, and the importance of the affected variables.


              1234567891011
            
# Fill missing values in 'age' with the mean of the column
mean_age = df["age"].mean()
df_filled = df.copy()
df_filled["age"] = df_filled["age"].fillna(mean_age)
print("DataFrame after filling missing 'age' values with the mean:")
print(df_filled)

# Drop rows with any missing data
df_dropped = df.dropna()
print("\nDataFrame after dropping rows with missing data:")
print(df_dropped)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 6

Handling Missing Data in Research

1. What method can you use to detect missing values in a pandas DataFrame?

2. Name one strategy for handling missing data in research.

3. What is the effect of using dropna on a DataFrame?

Handling Missing Data in Research

1. What method can you use to detect missing values in a pandas DataFrame?

2. Name one strategy for handling missing data in research.

3. What is the effect of using dropna on a DataFrame?