Introduction to DataFrames
Research data is often organized in tables, whether you are analyzing survey responses, experimental results, or observational records. In Python, the DataFrame is the core tool for handling such tabular data. DataFrames are essential for researchers because they make it easy to store, explore, and manipulate structured datasets, much like spreadsheets or database tables but with the power and flexibility of Python. They provide a familiar format for representing rows and columns, helping you perform everything from simple summaries to complex analyses with ease.
1234567891011import pandas as pd # Create a small research dataset representing experiment results data = { "Participant": ["A", "B", "C", "D"], "Group": ["Control", "Treatment", "Control", "Treatment"], "Score": [88, 92, 85, 95] } df = pd.DataFrame(data) print(df)
A DataFrame is structured with rows and columns, similar to a table in a research paper or spreadsheet. Each row typically represents an observation or a record, such as an individual participant's result. Each column holds a variable or measurement, such as group assignment or test score. The index labels each row, which can be the default integer sequence or custom labels. This structure allows you to easily map your research data into a DataFrame, making it straightforward to analyze, filter, and visualize your results.
12345678# Accessing columns by name print(df["Score"]) # Accessing rows by label (using .loc) print(df.loc[1]) # Accessing rows by integer position (using .iloc) print(df.iloc[2])
1. What is a DataFrame and why is it useful for research data?
2. Which method allows you to access a column by its name in pandas?
3. What is the difference between .loc and .iloc in pandas?
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Research data is often organized in tables, whether you are analyzing survey responses, experimental results, or observational records. In Python, the DataFrame is the core tool for handling such tabular data. DataFrames are essential for researchers because they make it easy to store, explore, and manipulate structured datasets, much like spreadsheets or database tables but with the power and flexibility of Python. They provide a familiar format for representing rows and columns, helping you perform everything from simple summaries to complex analyses with ease.
1234567891011import pandas as pd # Create a small research dataset representing experiment results data = { "Participant": ["A", "B", "C", "D"], "Group": ["Control", "Treatment", "Control", "Treatment"], "Score": [88, 92, 85, 95] } df = pd.DataFrame(data) print(df)
A DataFrame is structured with rows and columns, similar to a table in a research paper or spreadsheet. Each row typically represents an observation or a record, such as an individual participant's result. Each column holds a variable or measurement, such as group assignment or test score. The index labels each row, which can be the default integer sequence or custom labels. This structure allows you to easily map your research data into a DataFrame, making it straightforward to analyze, filter, and visualize your results.
12345678# Accessing columns by name print(df["Score"]) # Accessing rows by label (using .loc) print(df.loc[1]) # Accessing rows by integer position (using .iloc) print(df.iloc[2])
1. What is a DataFrame and why is it useful for research data?
2. Which method allows you to access a column by its name in pandas?
3. What is the difference between .loc and .iloc in pandas?
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