Indexing and Selecting Data (loc, iloc)

Why selection matters

Most Pandas bugs come from:

Selecting the wrong rows
Accidentally getting a copy instead of a view
Confusing label-based vs position-based indexing

Pandas provides two primary tools:

locloc → label-based
ilociloc → position-based

Setup example DataFrame

Sample DataFrame

import pandas as pd
 
df = pd.DataFrame(
    {
        "name": ["Asha", "Ravi", "Meera", "Zoya"],
        "age": [23, 28, 26, 31],
        "city": ["Pune", "Delhi", "Delhi", "Pune"],
        "score": [88, 91, 77, 95],
    },
    index=["u1", "u2", "u3", "u4"],
)
 
print(df)

Sample DataFrame

import pandas as pd
 
df = pd.DataFrame(
    {
        "name": ["Asha", "Ravi", "Meera", "Zoya"],
        "age": [23, 28, 26, 31],
        "city": ["Pune", "Delhi", "Delhi", "Pune"],
        "score": [88, 91, 77, 95],
    },
    index=["u1", "u2", "u3", "u4"],
)
 
print(df)

Selecting columns

One column

print(df["age"])  # returns a Series

One column

print(df["age"])  # returns a Series

Multiple columns

print(df[["name", "score"]])

Multiple columns

print(df[["name", "score"]])

Selecting rows with `locloc` (labels)

loc by label

print(df.loc["u2"])          # one row (Series)
print(df.loc[["u1", "u4"]]) # multiple rows (DataFrame)

loc by label

print(df.loc["u2"])          # one row (Series)
print(df.loc[["u1", "u4"]]) # multiple rows (DataFrame)

Selecting rows + columns together:

loc rows + cols

print(df.loc[["u1", "u3"], ["name", "score"]])

loc rows + cols

print(df.loc[["u1", "u3"], ["name", "score"]])

Selecting rows with `ilociloc` (positions)

iloc by position

print(df.iloc[0])      # first row
print(df.iloc[0:2])    # first two rows
print(df.iloc[:, 0:2]) # first two cols

iloc by position

print(df.iloc[0])      # first row
print(df.iloc[0:2])    # first two rows
print(df.iloc[:, 0:2]) # first two cols

Boolean filtering (most common)

Filter rows

filtered = df[df["city"] == "Delhi"]
print(filtered)

Filter rows

filtered = df[df["city"] == "Delhi"]
print(filtered)

Combine conditions:

Multiple conditions

filtered = df[(df["city"] == "Pune") & (df["score"] >= 90)]
print(filtered)

Multiple conditions

filtered = df[(df["city"] == "Pune") & (df["score"] >= 90)]
print(filtered)

Safer assignment after filtering

When you filter and then assign, prefer .loc.loc:

Safe assignment with loc

df.loc[df["city"] == "Delhi", "is_delhi"] = True
 
df["is_delhi"] = df["is_delhi"].fillna(False)
print(df)

Safe assignment with loc

df.loc[df["city"] == "Delhi", "is_delhi"] = True
 
df["is_delhi"] = df["is_delhi"].fillna(False)
print(df)

Note on `&&` and `||`

In Pandas boolean filters:

Use && for AND, || for OR
Always wrap sub-conditions in parentheses

This avoids operator precedence bugs.

🧪 Try It Yourself

Exercise 1 – Create a DataFrame

Exercise 2 – Select a Column

Exercise 3 – Filter Rows

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