A csv file "States.csv" contains some data about all the states of India. Each record of the file contains the

Question

Q. A csv file "States.csv" contains some data about all the states of India. Each record of the file contains the following data : - Name of the State - Capital of the State - Population of the State - Official Language of the State For example, a sample record in the file is : ['Andhra Pradesh','Amaravati',52221000,'Telugu'] Write a Python program which reads the data from this file and appends all those records where population is more than 10000000 into another csv file 'More.csv'. Note : "States.csv" also contains the Header row. The Header row should NOT be copied to "More.csv".

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Concept understanding — File Input Output Filtering

File Input Output Filtering

Think of a water filter. You pour in tap water, and what comes out is cleaner, purer, and ready to drink. The filter doesn't create new water — it takes what's already there and transforms it by removing impurities or adding something useful.

File Input Output Filtering works on the same principle. It is a way of processing data as it moves from one place to another — typically from an input source (like a file or keyboard) to an output destination (like a file or screen). The "filter" sits in between, reading the incoming data, modifying it in some way, and then writing the modified version out.

The Core Idea

At its simplest, a filter takes a stream of data, performs a transformation on it, and produces a new stream. The transformation could be anything: converting all letters to uppercase, removing blank lines, replacing one word with another, or rearranging the order of records.

The key insight is that the filter does not care where the data came from or where it is going. It only cares about what to do with the data as it passes through. This makes filters reusable — the same filter can work on data from a file, from a user typing at a keyboard, or from the output of another program.

Why It Matters

In the real world, data rarely arrives in the exact format you need. A bank might receive transaction records with dates written as "2024-03-15" but their system expects "15 March 2024". A school might get student lists with names in lowercase but needs them capitalised. A business might export a report with extra blank lines that clutter the final document.

Instead of rewriting entire programs for each of these situations, you can use filters. You take the raw data, run it through the appropriate filter, and get exactly what you need on the other side.

How It Works in Practice

A filter typically follows a simple pattern:

Read one piece of data (a line, a character, a record)
Process it according to the rule
Write the result
Repeat until there is no more data

This is called a "loop" — the filter keeps doing the same thing over and over until the input runs out. Because it processes data piece by piece rather than loading everything into memory at once, it can handle enormous files without slowing down.

Note

This "read-process-write" cycle is why filters are so efficient. A filter that converts a million-line file to uppercase never holds more than one line in memory at a time. It reads a line, converts it, writes it, and moves on.

Common Types of Filters

Filters can be grouped by what they do to the data:

Transformation filters change the content itself. Converting text to uppercase, replacing British spellings with American ones, or encrypting sensitive information are all transformations.

Selection filters choose which pieces of data to keep and which to discard. A filter that removes all lines containing the word "ERROR" is a selection filter — it lets some data through and blocks the rest.

Formatting filters change the appearance or structure without altering the meaning. Adding line numbers to a document, aligning columns, or inserting page breaks are formatting operations.

Combination filters do more than one thing. A filter might remove blank lines, convert everything to lowercase, and then add a timestamp to each line — all in a single pass.

The Chain Concept

The real power of filtering emerges when you connect filters together. The output of one filter becomes the input to the next. This is called a "pipeline" or "chain."

Imagine you have a raw data file with messy entries. You could:

First filter: remove blank lines
Second filter: convert all text to uppercase
Third filter: sort the lines alphabetically
Fourth filter: add line numbers

Each filter does one simple job. Together, they produce a clean, numbered, sorted, uppercase document from the original mess. You can rearrange, add, or remove filters without changing any of them individually.

Important

The principle of "one job, done well" is central to filtering. A filter that tries to do everything becomes complex and brittle. A filter that does one thing can be combined with others to achieve almost any result.

A Concrete Example Without Numbers …

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You need to read "States.csv" row by row, check each state's population, and write only the qualifying records (population > 10000000) to "More.csv" while skipping the header.

Open "States.csv" in read mode using the csv module's reader. The first row is the header, so read it separately to move past it without writing it anywhere. Then loop through the remaining rows: for each row, the population is at index 2 (third element). Convert that string to an integer and test whether it exceeds ten million. If it does, append the entire row to "More.csv" using a csv writer in append mode—or more cleanly, open "More.csv" once in write mode before the loop and write all qualifying rows in one pass.

A typical structure looks like this:

import csv

with open('States.csv', 'r') as infile, open('More.csv', 'w', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    
    next(reader)  # skip the header row
    
    for row in reader:
        population = int(row[2])
        if population > 10000000:
            writer.writerow(row)
``` …

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Read "States.csv", filter records where population exceeds 10,000,000, and write only those data rows (excluding the header) to "More.csv".

The task is a classic file input-output operation with filtering — a fundamental pattern in data processing. You have a source file containing structured information about Indian states, and you need to extract a subset of that data based on a criterion (population threshold) and write it to a new file. The challenge lies in correctly reading CSV data, applying the filter, and ensuring the header row is handled properly.

Python's csv module is purpose-built for this. When you open "States.csv", the first row is a header describing the columns. You need to skip copying this header to the output file, but you still need to read past it in the input file. Each subsequent row is a list of values: state name, capital, population (as a string that must be converted to an integer for comparison), and language.

The filtering condition is straightforward: if the population (third element, index 2) is greater than 10,000,000, write that entire record to "More.csv". Notice that the population in the file is stored as a string like '52221000', so you must convert it to an integer before comparing.

Here's how the program unfolds. Open the input file in read mode and the output file in write mode. Create a CSV reader for the input and a CSV writer for the output. Read the header from the input file first — this advances the file pointer past it, so subsequent reads give you only data rows. Then iterate through each remaining row, check the population, and if it exceeds the threshold, write that row to the output file.

import csv

# Open both files: input for reading, output for writing
with open('States.csv', 'r') as infile, open('More.csv', 'w', newline='') as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    
    # Read and discard the header row
    header = next(reader)
    
    # Process each data row
    for row in reader:
        # row[2] is the population (as a string)
        population = int(row[2])
        
        # Filter: write only if population > 10,000,000
        if population > 10000000:
            writer.writerow(row)

Note

The newline='' parameter when opening the output file in write mode is important on Windows — it prevents extra blank lines between rows in the CSV. On Unix-like systems it's harmless, so it's good practice to include it always. …

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Same / Similar Concept — real previous-year questions on the same or a closely similar concept, not this exact question.

CBSE 2020Set 91/D2 marks
$Q. Write a function Show_words() in Python to read the content of a text file 'NOTES.TXT' and display the entire content in capital letters. Example, if the file contains: "This is a test file" Then the function should display the output as: THIS IS A TEST FILE$
(OR)
$Write a function Show_words() in Python to read the content of a text file 'NOTES.TXT' and display only such lines of the file which have exactly 5 words in them. Example, if the file contains: This is a sample file. The file contains many sentences. But needs only sentences which have only 5 words. Then the function should display the output as: This is a sample file. The file contains many sentences :$
›Reveal solutionSolution
Part (a): read the whole file and print it in uppercase (upper()).

Part (b): print only the lines that contain exactly 5 words (len(line.split())==5).

Part (a)

Open NOTES.TXT in read mode, read its entire content with read(), convert to capitals with the string method upper(), and print it. The with block closes the file automatically.

def Show_words(): with open('NOTES.TXT', 'r') as f: content = f.read() print(content.upper())

For This is a test file:

THIS IS A TEST FILE …
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CBSE 2019Set 91/42 marks
$Q. Write a user-defined function TotalPrice() in C++ to read each object of a binary file STOCK.DAT, and display the Name from all such records whose Price is above 150. Assume that the file STOCK.DAT is created with the help of objects of class Stock, which is defined below: class Stock { char Name[20]; float Price; public: char* RName() { return Name; } float RPrice() { return Price; } };$
(OR)
$A binary file DOCTORS.DAT contains records stored as objects of the following class: class Doctor { int DNo; char Name[20]; float Fees; public: int *GetNo() { return DNo; } void Show() { cout<<Dno<<" * " <<Name<< " * " <<Fees<<endl;} }; Write definition for function Details(int N) in C++, which displays the details of the Doctor from the file DOCTORS.DAT, whose DNo matches with the parameter N passed to the function.$
›Reveal solutionSolution
Part (a): TotalPrice() reads STOCK.DAT and prints the Name of every item with Price > 150.

Part (b): Details(int N) reads DOCTORS.DAT and shows the doctor whose DNo equals N.

Both functions use binary file input with filtering: open with ios::binary, read whole objects with read((char*)&obj, sizeof(obj)) in a while loop (the stream is false at end of file), test a condition, and output the matches.

Part (a)

The Stock class exposes RName() and RPrice(). Read each object and print the name when the price is above 150.

void TotalPrice() { ifstream fin("STOCK.DAT", ios::binary); Stock S; while (fin.read((char*)&S, sizeof(S))) { if (S.RPrice() > 150) cout << S.RName() << endl; } fin.close(); } ``` …
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CBSE 2019Set 91/42 marks
$Q. Write a method/function ISTOUPCOUNT() in python to read contents from a text file WRITER.TXT, to count and display the occurrence of the word "IS" or "TO" or "UP". For example : If the content of the file is IT IS UP TO US TO TAKE CARE OF OUR SURROUNDING. IT IS NOT POSSIBLE ONLY FOR THE GOVERNMENT TO TAKE RESPONSIBILITY The method/function should display Count of IS TO and UP is 6$
(OR)
$Write a method/function AEDISP() in python to read lines from a text file WRITER.TXT, and display those lines, which are starting either with A or starting with E. For example : If the content of the file is A CLEAN ENVIRONMENT IS NECESSARY FOR OUR GOOD HEALTH. WE SHOULD TAKE CARE OF OUR ENVIRONMENT. EDUCATIONAL INSTITUTIONS SHOULD TAKE THE LEAD. The method should display A CLEAN ENVIRONMENT IS NECESSARY FOR OUR GOOD HEALTH. EDUCATIONAL INSTITUTIONS SHOULD TAKE THE LEAD.$
›Reveal solutionSolution
Part (a): ISTOUPCOUNT() counts words equal to "IS"/"TO"/"UP" and prints Count of IS TO and UP is 6 for the sample.

Part (b): AEDISP() prints only the lines that begin with 'A' or 'E'.

Part (a)

Read the whole file, break it into words with split(), and test each word against the set IS, TO, UP. (The check is on whole words, so UPWARD would not count as UP.)

def ISTOUPCOUNT(): f = open("WRITER.TXT", "r") data = f.read() f.close() count = 0 for word in data.split(): if word in ("IS", "TO", "UP"): count += 1 print("Count of IS TO and UP is", count)

For the sample content IT IS UP TO US TO TAKE CARE ... GOVERNMENT TO TAKE RESPONSIBILITY, the matches are IS (2), TO (3), UP (1) = 6:

Count of IS TO and UP is 6 …
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