Programmer's Picnic · Python / Pandas · Lesson 1

Search, Select and Filter DataFrames

Find exactly the rows and values you need: by name, by score, by index position and by multiple conditions.

0 to infinity · Learn by making real DataFrames · No file required

Our sample data

All examples use the same students. Every code box begins with its own DataFrame, so you can copy and run it independently.

Row	Name	City	Marks	FeePaid
0	Asha	Varanasi	88	True
1	Ravi	Ranchi	42	False
2	Meera	Varanasi	76	True
3	Kabir	Patna	91	False

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

print(students)

Vocabulary: Selecting a column is different from filtering rows. Searching often combines both.

Step 1

Select one column, or several

A single column name returns a Series. A list of column names returns a DataFrame.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

print(students["Name"])
print(students[["Name", "Marks"]])

Remember: Use double square brackets for a DataFrame of multiple columns.

Step 2

Find a student by an exact name

Build a condition for matching names. Pandas uses == to test equality.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

match = students[students["Name"] == "Meera"]
print(match)

Remember: For a match, compare every row: the result is a Boolean mask (True or False for each row).

Step 3

Select with .loc and .iloc

Use .loc for row and column labels; .iloc for integer positions.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

print(students.loc[1, "Name"])        # Ravi
print(students.loc[0:2, ["Name", "Marks"]])
print(students.iloc[1, 2])             # 42
print(students.iloc[0:2, 0:3])

Remember: .loc[0:2] includes label 2; .iloc[0:2] stops before position 2.

Step 4

Search by a condition

Filter students who obtained at least 75 marks; choose only the columns relevant to the answer.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

high_scorers = students.loc[students["Marks"] >= 75, ["Name", "Marks"]]
print(high_scorers)

Remember: Use <, <=, >, >=, == and != to compare values.

Step 5

Search with two conditions

Each comparison needs its own parentheses. Use & for AND; | for OR; ~ to reverse a condition.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

varanasi_top = students.loc[
    (students["City"] == "Varanasi") & (students["Marks"] >= 75),
    ["Name", "City", "Marks"]
]
print(varanasi_top)

support_or_unpaid = students.loc[
    (students["Marks"] < 50) | (~students["FeePaid"]),
    ["Name", "Marks", "FeePaid"]
]
print(support_or_unpaid)

Remember: Do not use Python and/or directly between pandas Series; use & and |.

Step 6

Find items in a list or a range

Use isin() to match several cities and between() for a numerical range.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

print(students[students["City"].isin(["Ranchi", "Patna"])])
print(students[students["Marks"].between(70, 90)])

Remember: between(70, 90) includes both 70 and 90 by default.

Step 7

Search inside text

String methods help search text columns. Set regex=False for a literal substring and na=False to ignore missing names.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

found = students.loc[
    students["Name"].str.contains("a", case=False, regex=False, na=False),
    ["Name", "City"]
]
print(found)

Remember: String searches can be case-sensitive; case=False makes this search ignore letter case.

Step 8

Quickly count matching rows

Use a condition to count true values and see category frequencies.

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

print((students["Marks"] >= 75).sum())
print(students["City"].value_counts())
print(len(students))

Remember: True counts as 1 when summed; value_counts gives the frequency of each city.

Practice B · Locate records

Display only Name and Marks for students from Varanasi with marks of at least 80.

Reveal a possible solution

Python · pandas

import pandas as pd

students = pd.DataFrame({
    "Name": ["Asha", "Ravi", "Meera", "Kabir"],
    "City": ["Varanasi", "Ranchi", "Varanasi", "Patna"],
    "Marks": [88, 42, 76, 91],
    "FeePaid": [True, False, True, False]
})

answer = students.loc[
    (students["City"] == "Varanasi") & (students["Marks"] >= 80),
    ["Name", "Marks"]
]
print(answer)  # Asha

Checkpoint

Can you find Ravi, select a single cell, find students with marks below 50 and locate students from either Varanasi or Ranchi?

Official guide to indexing and selecting.

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