### Export Company Problems to Markdown with Pandas

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

This Python function converts a company's LeetCode problems into a markdown-formatted study guide. It reads all problems from a CSV, sorts them by frequency, and then organizes them by difficulty (Easy, Medium, Hard) in the markdown file. Each problem entry includes its title, link, frequency, and acceptance rate. This is useful for creating shareable documentation.

```python
import pandas as pd

def export_to_markdown(company, output_file):
    """Export company problems to a markdown study guide."""

    df = pd.read_csv(f'{company}/5. All.csv')
    df = df.sort_values('Frequency', ascending=False)

    with open(output_file, 'w') as f:
        f.write(f"# {company} LeetCode Interview Questions\n\n")

        for difficulty in ['EASY', 'MEDIUM', 'HARD']:
            subset = df[df['Difficulty'] == difficulty]
            f.write(f"## {difficulty.title()} ({len(subset)} problems)\n\n")
            f.write("| Problem | Frequency | Acceptance | Topics |\n")
            f.write("|---------|-----------|------------|--------|\n")

            for _, row in subset.head(20).iterrows():
                title = f"[{row['Title']}]({row['Link']})"
                f.write(f"| {title} | {row['Frequency']:.1f} | {row['Acceptance Rate']:.1%} | {row['Topics']} |\n")

            f.write("\n")

export_to_markdown('Netflix', 'netflix_problems.md')
print("Exported to netflix_problems.md")
```

--------------------------------

### Analyze LeetCode Data with Bash Commands

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

This section demonstrates how to perform common data analysis tasks on LeetCode problem CSV files directly from the command line using standard Unix tools. It includes commands to count total problems, find the most common problems across all companies, list companies with a high number of problems, and search for specific problems across different company directories. These scripts offer a quick way to gain insights without writing Python code.

```bash
# Count total problems across all companies
find . -name "5. All.csv" -exec wc -l {} + | tail -1
# Output: 52847 total

# Find most common problem across all companies
cat */5.\ All.csv | cut -d',' -f2 | sort | uniq -c | sort -rn | head -10
# Output:
#  470 Two Sum
#  465 Add Two Numbers
#  458 Longest Substring Without Repeating Characters
#  ...

# List all companies with more than 100 problems
for dir in */; do
    count=$(wc -l < "${dir}5. All.csv" 2>/dev/null)
    if [ "$count" -gt 100 ]; then
        echo "${dir%/}: $count problems"
    fi
done

# Search for specific problem across companies
grep -l "Binary Tree Maximum Path Sum" */5.\ All.csv
# Output: Amazon/5. All.csv, Google/5. All.csv, Meta/5. All.csv, ...
```

--------------------------------

### Load and Analyze Company Data with Python

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

Demonstrates how to load LeetCode problem data for a specific company (e.g., Amazon) using pandas. It includes filtering problems by difficulty (e.g., HARD) and sorting them by frequency to identify the most asked questions. This snippet is useful for initial data exploration and understanding problem distribution.

```python
import pandas as pd
import os

# Load all Amazon problems
amazon_all = pd.read_csv('Amazon/5. All.csv')

# Filter by difficulty
hard_problems = amazon_all[amazon_all['Difficulty'] == 'HARD']
print(f"Total Amazon problems: {len(amazon_all)}")
print(f"Hard problems: {len(hard_problems)}")

# Sort by frequency (most asked first)
most_frequent = amazon_all.sort_values('Frequency', ascending=False).head(10)
print("\nTop 10 most frequent Amazon questions:")
for idx, row in most_frequent.iterrows():
    print(f"  [{row['Difficulty']}] {row['Title']} (Frequency: {row['Frequency']})")
```

--------------------------------

### Compare Company Problem Sets with Python

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

Provides a Python script to compare LeetCode problem sets across multiple companies (e.g., FAANG). It loads recent problems (last 30 days) for each company and identifies common questions asked by all of them. This is useful for understanding which problems are frequently asked across the industry.

```python
import pandas as pd
from collections import Counter

companies = ['Google', 'Amazon', 'Meta', 'Microsoft', 'Apple']
all_problems = {}

# Load recent problems for each company
for company in companies:
    df = pd.read_csv(f'{company}/1. Thirty Days.csv')
    all_problems[company] = set(df['Title'].tolist())

# Find problems asked by all FAANG companies
common_problems = set.intersection(*all_problems.values())
print(f"Problems asked by ALL companies in last 30 days: {len(common_problems)}")
for problem in list(common_problems)[:5]:
    print(f"  - {problem}")
```

--------------------------------

### Filter LeetCode Problems by Topic with Python

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

Shows how to filter LeetCode problems based on specific algorithm topics using pandas. This includes finding all problems related to 'Dynamic Programming' or problems that involve both 'Graph' and 'Depth-First Search'. It also demonstrates how to extract all unique topics present in a company's problem set.

```python
import pandas as pd

# Load Google problems
google_problems = pd.read_csv('Google/5. All.csv')

# Find all Dynamic Programming problems
dp_problems = google_problems[google_problems['Topics'].str.contains('Dynamic Programming', na=False)]

# Find problems with multiple specific topics
graph_dfs = google_problems[
    google_problems['Topics'].str.contains('Graph', na=False) &
    google_problems['Topics'].str.contains('Depth-First Search', na=False)
]

print(f"Dynamic Programming problems at Google: {len(dp_problems)}")
print(f"Graph + DFS problems at Google: {len(graph_dfs)}")

# Get unique topics across all problems
all_topics = set()
for topics in google_problems['Topics'].dropna():
    all_topics.update([t.strip() for t in topics.split(',')])
print(f"\nUnique topics covered: {len(all_topics)}")
```

--------------------------------

### Generate Prioritized Study Plan with Pandas

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

This Python function generates a personalized study plan based on company, available days, and desired problems per day. It uses pandas to read a CSV, sort problems by frequency, and select a prioritized list. The output is a structured plan detailing problems for each day, including difficulty, title, link, and topics. Dependencies include the pandas library.

```python
import pandas as pd

def create_study_plan(company, days_available, problems_per_day=3):
    """Generate a study plan prioritized by frequency and balanced by difficulty."""

    # Use recent questions for highest relevance
    df = pd.read_csv(f'{company}/2. Three Months.csv')

    # Sort by frequency
    df = df.sort_values('Frequency', ascending=False)

    total_problems = days_available * problems_per_day
    selected = df.head(total_problems)

    study_plan = []
    for day in range(days_available):
        start_idx = day * problems_per_day
        end_idx = start_idx + problems_per_day
        day_problems = selected.iloc[start_idx:end_idx]

        study_plan.append({
            'day': day + 1,
            'problems': day_problems[['Difficulty', 'Title', 'Link', 'Topics']].to_dict('records')
        })

    return study_plan

# Create 7-day study plan for Meta interview
plan = create_study_plan('Meta', days_available=7, problems_per_day=3)

print("7-Day Meta Interview Prep Plan:")
for day in plan[:2]:  # Show first 2 days
    print(f"\nDay {day['day']}:")
    for p in day['problems']:
        print(f"  [{p['Difficulty']}] {p['Title']}")
        print(f"    Topics: {p['Topics']}")
        print(f"    Link: {p['Link']}")
```

--------------------------------

### Count Problem Frequency with Python Counter

Source: https://context7.com/liquidslr/interview-company-wise-problems/llms.txt

This snippet counts the occurrences of each unique LeetCode problem across all companies using Python's Counter object. It then prints the top 10 most frequently asked problems and the number of companies that asked them. This is useful for identifying universally popular interview questions.

```python
from collections import Counter

problem_counter = Counter()
for company, problems in all_problems.items():
    for problem in problems:
        problem_counter[problem] += 1

print("\nMost universally asked problems:")
for problem, count in problem_counter.most_common(10):
    print(f"  {problem}: asked by {count} companies")
```

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