🐍Accessing columns and rows of Data frame

Once you have a DataFrame, you often need to access specific columns, rows, or subsets of data. Pandas provides multiple methods for this, including label-based and position-based indexing.

🔹 Accessing Columns

Columns can be accessed like dictionary keys or using dot notation.

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "London", "Paris"]
}
df = pd.DataFrame(data)

# Using dictionary-style
print(df["Name"])

# Using dot notation
print(df.Age)
    

✅ Dot notation works only if column names are valid Python identifiers.

🔹 Accessing Multiple Columns

# Select multiple columns by passing a list
print(df[["Name", "City"]])
    

🔹 Accessing Rows

Rows can be accessed by index labels (loc) or integer positions (iloc).

# Using loc (label-based)
print(df.loc[0])       # first row
print(df.loc[1:2])     # rows 1 to 2

# Using iloc (position-based)
print(df.iloc[0])      # first row
print(df.iloc[1:3])    # rows 1 and 2
    

🔹 Accessing Specific Cells

Combine row and column selection to access a single cell.

# Using loc
print(df.loc[0, "Name"])    # Alice

# Using iloc
print(df.iloc[1, 2])        # London
    

🔹 Conditional Row Selection

You can filter rows using conditions.

# Rows where Age > 28
print(df[df["Age"] > 28])

# Rows where City is London
print(df[df["City"] == "London"])
    

⚠️ Common Mistakes

• Using dot notation for column names with spaces or special characters (df.Age works but df.Age(Years) does not).
• Confusing loc (label-based) with iloc (integer-based) indexing.
• Trying to access rows by number using df[0] — always use loc or iloc.
• For conditional selection, forgetting to use brackets around the condition: df[df["Age"] > 28] is correct, df["Age"] > 28 returns a boolean Series only.

Next Steps

Next, we will learn how to filter data efficiently in Pandas using multiple conditions and methods.

➡️ Next: Filtering Data

⬅️ Back to Pandas main page

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