1. What is a CSV file?

CSV means Comma-Separated Values. It is a simple text file used to store information in rows and columns. Each line usually represents one record, and commas separate the values.

For example, a file named students.csv might contain:

Roll,Name,Age,Class,City,Marks
101,ram,16,10,Kathmandu,85
102,Sita,15,10,Pokhara,92
103,HARI,17,10,Lalitpur,68
104,ram,15,10,Bhaktapur,74
105,Gita,16,10,Kathmandu,90

The first line contains the column headings. The lines below it contain student records. CSV files are useful for moving data between programs and keeping simple records.

2. What is a Pandas DataFrame?

Pandas is a Python library for working with structured data. A DataFrame is a table made of rows and columns—similar to a spreadsheet.

Before using Pandas, import it. The common short name is pd:

import pandas as pd

Here, import makes the library available, and as pd gives it a shorter name so we can write pd.read_csv() instead of typing the full library name.

3. Read a CSV file into Python

Use pd.read_csv() to load a CSV file into a DataFrame.

import pandas as pd

df = pd.read_csv("students.csv")
print(df.head())

4. Filter rows to find information

Filtering means selecting only the rows that match a condition. In Pandas, a condition produces True or False values. Pandas keeps the rows where the condition is True.

Example A: Find marks greater than 80

high_scorers = df[df["Marks"] > 80]
print(high_scorers)

Read it from the inside out: df["Marks"] > 80 checks each student's marks. The outer df[ ... ] keeps only matching rows.

In the sample dataset, Sita (92), ram (85), and Gita (90) match this condition.

Example B: Match exact text

ktm_students = df[df["City"] == "Kathmandu"]
print(ktm_students)

The operator == asks whether a value is equal to the text "Kathmandu". It is different from =, which assigns a value to a variable. Text matching is case-sensitive, so "Kathmandu" and "kathmandu" are not identical strings.

Example C: Match a name regardless of letter case

ram_students = df[df["Name"].str.upper() == "RAM"]
print(ram_students)

.str.upper() converts text values in the Name column to uppercase for comparison. This allows names such as ram, Ram, and RAM to match "RAM".

Example D: Search for part of a word

result = df[df["Name"].str.contains("ra", case=False, na=False)]
print(result)

.str.contains("ra") searches within each name instead of requiring the whole name to match. case=False ignores letter case, and na=False treats missing values as non-matches.

5. Combine more than one condition

Sometimes one condition is not enough. Pandas lets you combine conditions using these operators:

Example: Kathmandu students with marks above 80

filtered_df = df[
    (df["City"] == "Kathmandu") & (df["Marks"] > 80)
]
print(filtered_df)

The brackets around each condition are important. With &, a student must be from Kathmandu and have more than 80 marks. In our sample, ram (85) and Gita (90) match.

Example using OR

selected = df[
    (df["City"] == "Pokhara") | (df["Marks"] > 90)
]
print(selected)

This keeps students who live in Pokhara or scored more than 90 (or both).

6. Save filtered data to a new CSV file

After filtering, you may want to save the selected rows so you can use them later. Pandas uses .to_csv().

top_students = df[df["Marks"] >= 85]
top_students.to_csv("top_students.csv", index=False)

print("Filtered data saved!")

>= 85 means 85 or higher. The selected records are saved in top_students.csv.

What does index=False mean?

A DataFrame has a row index used by Pandas. Setting index=False tells Pandas not to add that index as an extra column in the exported CSV. If you use index=True (the default), the index is written to the file too.

7. Create CSV data, take input, and combine records

You can also build a DataFrame directly in Python instead of reading an existing CSV file. A dictionary can store column names as keys and lists of values as the column data.

Type 1: Static data, without index

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Score": [85, 90, 78]
}
df = pd.DataFrame(data)
df.to_csv("students_scores.csv", index=False)
print(df)

Static data means the values are already written in the program. pd.DataFrame(data) turns the dictionary into a table.

Type 2: Static data, with index

df.to_csv("students_scores_with_index.csv", index=True)

This writes the DataFrame index into the CSV file. Run it after creating df as shown above.

Type 3: Get a record from the user

import pandas as pd

user_name = input("Enter student name: ")
user_score = int(input("Enter student score: "))

data = {
    "Name": [user_name],
    "Score": [user_score]
}
df = pd.DataFrame(data)
df.to_csv("new_student.csv", index=False)
print(df)

input() collects text typed by the user. int() converts the score to an integer so it can be stored as a number.

Type 4: Combine existing records with a new record

import pandas as pd

static_data = {
    "Name": ["Alice", "Bob"],
    "Score": [85, 90]
}
df_static = pd.DataFrame(static_data)

user_name = input("Enter new student name: ")
user_score = int(input("Enter new student score: "))

df_user = pd.DataFrame({
    "Name": [user_name],
    "Score": [user_score]
})

df_combined = pd.concat([df_static, df_user], ignore_index=True)
df_combined.to_csv("all_students.csv", index=True)
print(df_combined)

pd.concat() joins DataFrames together. Here, it places the new student's row after Alice and Bob. ignore_index=True creates a fresh sequence of row indexes in the combined DataFrame. Notice that to_csv(..., index=True) then saves that index to the file.

8. Common mistakes to avoid

• Using = instead of ==: use == to compare a column value with a target.
• Forgetting parentheses: write (condition1) & (condition2) when combining filters.
• Wrong column name: use the exact heading from the CSV, including spelling and capitalization, such as "Marks".
• Wrong file location: check that the CSV file exists where Python is looking for it.
• Unexpected index column: use index=False if you do not want the DataFrame index included in the saved file.
• Converting text to a number: use int(input(...)) when the input should be a whole number, such as a score.