Selecting and Filtering Data

Data analysis often requires extracting specific columns or identifying rows that meet certain conditions. Pandas provides several methods for selecting the required information.

Selecting Columns

Use a column name in square brackets to select one column.

salary = df["Salary"]

print(salary)

To select multiple columns, pass a list of column names.

employee_data = df[["Employee", "Salary"]]

print(employee_data)

Selecting only relevant columns can make subsequent calculations clearer and reduce unnecessary processing.

Filtering Rows with Conditions

Suppose you want to identify employees earning more than ₹60,000.

high_salary = df[df["Salary"] > 60000]

print(high_salary)

You can combine conditions to apply more specific rules.

it_high_salary = df[

(df["Department"] == "IT") &

(df["Salary"] > 60000)

]

print(it_high_salary)

Use parentheses around individual comparisons when combining conditions with & or |.

Using loc and iloc

Pandas offers two important accessors:

• loc selects rows and columns using labels.
• iloc selects rows and columns using integer positions.

print(df.loc[0, "Employee"])

print(df.loc[0:2, ["Employee", "Salary"]])

print(df.iloc[0, 1])

print(df.iloc[0:2, 0:2])

With loc, a label-based slice includes the ending label when it exists. With iloc, the ending position is excluded, following Python's usual slicing convention.