Cleaning
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
datetimeobjects.
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.
12345678910111213141516171819202122import 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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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
datetimeobjects.
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.
12345678910111213141516171819202122import 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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