Lära Cleaning | Preparing Experiment Data

Before you analyze experimental data, you need to ensure the dataset is clean and reliable.

Common data cleaning steps for experiment data include:

Handling missing values;
Removing duplicates;
Correcting data types.

These steps help prevent misleading results and ensure your statistical tests are valid.

Missing values

Missing values can occur due to user drop-off, technical issues, or incomplete data collection. You must decide whether to remove rows with missing values or fill them in using a specific strategy.

Removing missing values is straightforward, but you might lose valuable information if too many rows are affected;
Filling in missing values (imputation) requires careful consideration to avoid introducing bias.

Duplicates

Duplicates can arise from errors in data collection or merging datasets. Duplicate records can inflate counts or skew summary statistics, so it is important to remove them before analysis.

Data types

Data types must be correct for each column in your dataset.

Numerical columns should not be stored as strings;
Date columns should be converted to datetime objects.

Incorrect data types can cause errors during analysis or lead to incorrect results.

You can use the pandas library in python to efficiently perform these cleaning steps on experiment data.


              12345678910111213141516171819202122
            
import pandas as pd

# Sample experiment data
data = {
    "user_id": [101, 102, 103, 104, 104, 105, 106],
    "group": ["control", "treatment", "control", "treatment", "treatment", None, "control"],
    "conversion": ["1", "0", None, "1", "1", "0", "1"],
    "timestamp": ["2024-06-01", "2024-06-02", "2024-06-02", "2024-06-03", "2024-06-03", "2024-06-04", "2024-06-05"]
}
df = pd.DataFrame(data)

# 1. Drop rows with missing values
df_clean = df.dropna()

# 2. Remove duplicate rows (e.g., duplicate user_id and timestamp)
df_clean = df_clean.drop_duplicates(subset=["user_id", "timestamp"])

# 3. Convert data types
df_clean["conversion"] = df_clean["conversion"].astype(int)
df_clean["timestamp"] = pd.to_datetime(df_clean["timestamp"])

print(df_clean)

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