#Pandas #DataAnalysis #Python #DataScience #Tutorial

Top 30 Pandas Functions & Methods

This lesson covers 30 essential Pandas functions for data manipulation and analysis, each with a standalone example and its output.

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1. pd.DataFrame()
Creates a new DataFrame (a 2D labeled data structure) from various inputs like dictionaries or lists.

import pandas as pd
data = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data)
print(df)

col1  col2
0     1     3
1     2     4

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2. pd.Series()
Creates a new Series (a 1D labeled array).

import pandas as pd
s = pd.Series([10, 20, 30, 40], name='MyNumbers')
print(s)

0    10
1    20
2    30
3    40
Name: MyNumbers, dtype: int64

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3. pd.read_csv()
Reads data from a CSV file into a DataFrame. (Assuming a file data.csv exists).

# Create a dummy csv file first
with open('data.csv', 'w') as f:
    f.write('Name,Age\nAlice,25\nBob,30')

df = pd.read_csv('data.csv')
print(df)

Name  Age
0  Alice   25
1    Bob   30

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4. df.to_csv()
Writes a DataFrame to a CSV file.

import pandas as pd
df = pd.DataFrame({'Name': ['Charlie'], 'Age': [35]})
# index=False prevents writing the DataFrame index to the file
df.to_csv('output.csv', index=False)
# You can check that 'output.csv' has been created.
print("File 'output.csv' created.")

File 'output.csv' created.

#PandasIO #DataFrame #Series

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5. df.head()
Returns the first n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.head(3))

Name  Value
0    A      1
1    B      2
2    C      3

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6. df.tail()
Returns the last n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.tail(2))

Name  Value
4    E      5
5    F      6

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7. df.info()
Provides a concise summary of the DataFrame, including data types and non-null values.

import pandas as pd
import numpy as np
data = {'col1': [1, 2, 3], 'col2': [4.0, 5.0, np.nan], 'col3': ['A', 'B', 'C']}
df = pd.DataFrame(data)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   col1    3 non-null      int64  
 1   col2    2 non-null      float64
 2   col3    3 non-null      object 
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes

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8. df.shape
Returns a tuple representing the dimensionality (rows, columns) of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
print(df.shape)

(2, 3)

#DataInspection #PandasBasics

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9. df.describe()
Generates descriptive statistics for numerical columns (count, mean, std, min, max, etc.).

import pandas as pd
df = pd.DataFrame({'Age': [22, 38, 26, 35, 29]})
print(df.describe())

Top 100 Data Analyst Interview Questions & Answers

#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience

Part 1: SQL Questions (Q1-30)

#1. What is the difference between DELETE, TRUNCATE, and DROP?
A:
• DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.
• TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.
• DROP is a DDL command that removes the entire table, including its structure, data, and indexes.

#2. Select all unique departments from the employees table.
A: Use the DISTINCT keyword.

SELECT DISTINCT department
FROM employees;

#3. Find the top 5 highest-paid employees.
A: Use ORDER BY and LIMIT.

SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

#4. What is the difference between WHERE and HAVING?
A:
• WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).
• HAVING is used to filter groups after aggregations (GROUP BY) have been performed.

-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;

#5. What are the different types of SQL joins?
A:
• (INNER) JOIN: Returns records that have matching values in both tables.
• LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.
• RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.
• FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.
• SELF JOIN: A regular join, but the table is joined with itself.

#6. Write a query to find the second-highest salary.
A: Use OFFSET or a subquery.

-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

#7. Find duplicate emails in a customers table.
A: Group by the email column and use HAVING to find groups with a count greater than 1.

SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;

#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.

#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.

-- Rank employees by salary within each department
SELECT
    name,
    department,
    salary,
    RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;

#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.