Filtering with Conditions (and, or, isin, query)

Setup

Setup DataFrame

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera", "Zoya", "Kabir"],
    "age": [23, 28, 26, 31, 22],
    "city": ["Pune", "Delhi", "Delhi", "Pune", "Mumbai"],
    "score": [88, 91, 77, 95, 60],
})
 
print(df)

Setup DataFrame

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Asha", "Ravi", "Meera", "Zoya", "Kabir"],
    "age": [23, 28, 26, 31, 22],
    "city": ["Pune", "Delhi", "Delhi", "Pune", "Mumbai"],
    "score": [88, 91, 77, 95, 60],
})
 
print(df)

Basic boolean filtering

Single condition

print(df[df["age"] >= 25])

Single condition

print(df[df["age"] >= 25])

Multiple conditions: AND / OR

Pandas uses bitwise operators:

AND: &&
OR: ||

Always use parentheses:

AND filter

mask = (df["city"] == "Delhi") & (df["score"] >= 80)
print(df[mask])

AND filter

mask = (df["city"] == "Delhi") & (df["score"] >= 80)
print(df[mask])

OR filter

mask = (df["city"] == "Mumbai") | (df["score"] >= 90)
print(df[mask])

OR filter

mask = (df["city"] == "Mumbai") | (df["score"] >= 90)
print(df[mask])

`isin()isin()` for membership checks

isin

mask = df["city"].isin(["Pune", "Delhi"])
print(df[mask])

isin

mask = df["city"].isin(["Pune", "Delhi"])
print(df[mask])

`between()between()` for range filters

between

mask = df["age"].between(23, 30, inclusive="both")
print(df[mask])

between

mask = df["age"].between(23, 30, inclusive="both")
print(df[mask])

`query()query()` for readable filters

query()query() can be easier to read, especially in notebooks.

query

result = df.query('city == "Delhi" and score >= 80')
print(result)

query

result = df.query('city == "Delhi" and score >= 80')
print(result)

Variables inside query

query with variable

min_score = 85
result = df.query('score >= @min_score')
print(result)

query with variable

min_score = 85
result = df.query('score >= @min_score')
print(result)

Common mistakes

Using andand / oror instead of && / || (will error)
Forgetting parentheses around conditions
Comparing strings with extra spaces (clean using .str.strip().str.strip())

🧪 Try It Yourself

Exercise 1 – Create a DataFrame

Exercise 2 – Select a Column

Exercise 3 – Filter Rows

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