Python for Data Analysts

Here's a list of important Pandas functions along with brief descriptions:
pd.read_csv() – Reads a CSV file into a DataFrame.
pd.DataFrame() – Creates a DataFrame from various input formats (e.g., lists, dictionaries).
df.head() – Displays the first few rows of the DataFrame.
df.tail() – Displays the last few rows of the DataFrame.
df.info() – Provides a concise summary of the DataFrame (data types, non-null counts).
df.describe() – Provides descriptive statistics for numerical columns.
df.columns – Returns the column labels of the DataFrame.
df.index – Returns the index (row labels) of the DataFrame.
df.shape – Returns the dimensions of the DataFrame (rows, columns).
df.dtypes – Returns the data types of each column.
df.isnull() – Detects missing values (returns Boolean values).
df.fillna() – Fills missing values with a specified value.
df.dropna() – Removes missing values from the DataFrame.
df.drop() – Drops specified labels from rows or columns.
df.duplicated() – Returns Boolean Series denoting duplicate rows.
df.drop_duplicates() – Removes duplicate rows from the DataFrame.
df.sort_values() – Sorts the DataFrame by the values of one or more columns.
df.groupby() – Groups data by one or more columns for aggregation.
df.apply() – Applies a function along an axis of the DataFrame.
df.loc[] – Accesses a group of rows and columns by labels or Boolean arrays.
df.iloc[] – Accesses rows and columns by index position.
df.merge() – Merges two DataFrames on common columns or indices.
df.join() – Joins two DataFrames based on their index.
df.concat() – Concatenates multiple DataFrames along a particular axis.
df.pivot_table() – Creates a pivot table for summarizing data.
df.melt() – Unpivots the DataFrame from wide to long format.
df.rename() – Renames columns or index labels of the DataFrame.
df.set_index() – Sets a column as the index of the DataFrame.
df.reset_index() – Resets the index to a default integer index.
pd.to_datetime() – Converts a column or series to datetime format.
pd.cut() – Bins continuous data into discrete intervals.
df.value_counts() – Returns a Series of counts for unique values in a column.
df.corr() – Computes the pairwise correlation between columns.
df.to_csv() – Writes the DataFrame to a CSV file.
df.plot() – Creates basic plots from DataFrame data using Matplotlib.

These functions cover essential operations in data handling, cleaning, analysis, and visualization using Pandas.

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Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

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𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

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Python Interview Questions for data analyst interview

Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.

Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)

Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.

Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.

Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.

Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average

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#Python

Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:

1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.

4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.

5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.

6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.

7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.

8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.

9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.

10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.

By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.

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#Python

Don't Confuse to learn Python.

Learn This Concept to be proficient in Python.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages

𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy

𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas

𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files

𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing

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#Python

Python CheatSheet 📚 ✅

1. Basic Syntax
- Print Statement: print("Hello, World!")
- Comments: # This is a comment

2. Data Types
- Integer: x = 10
- Float: y = 10.5
- String: name = "Alice"
- List: fruits = ["apple", "banana", "cherry"]
- Tuple: coordinates = (10, 20)
- Dictionary: person = {"name": "Alice", "age": 25}

3. Control Structures
- If Statement:

if x > 10:
print("x is greater than 10")

- For Loop:

for fruit in fruits:
print(fruit)

- While Loop:

while x < 5:
x += 1

4. Functions
- Define Function:

def greet(name):
return f"Hello, {name}!"

- Lambda Function: add = lambda a, b: a + b

5. Exception Handling
- Try-Except Block:

try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero.")

6. File I/O
- Read File:

with open('file.txt', 'r') as file:
content = file.read()

- Write File:

with open('file.txt', 'w') as file:
file.write("Hello, World!")

7. List Comprehensions
- Basic Example: squared = [x**2 for x in range(10)]
- Conditional Comprehension: even_squares = [x**2 for x in range(10) if x % 2 == 0]

8. Modules and Packages
- Import Module: import math
- Import Specific Function: from math import sqrt

9. Common Libraries
- NumPy: import numpy as np
- Pandas: import pandas as pd
- Matplotlib: import matplotlib.pyplot as plt

10. Object-Oriented Programming
- Define Class:

class Dog:
def __init__(self, name):
self.name = name
def bark(self):
return "Woof!"


11. Virtual Environments
- Create Environment: python -m venv myenv
- Activate Environment:
- Windows: myenv\Scripts\activate
- macOS/Linux: source myenv/bin/activate

12. Common Commands
- Run Script: python script.py
- Install Package: pip install package_name
- List Installed Packages: pip list

This Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!

Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data

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𝗧𝗼𝗽 𝟴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

1. NumPy
→ Fundamental library for numerical computing.
→ Used for array operations, linear algebra, and random number generation.

2. Pandas
→ Best for data manipulation and analysis.
→ Offers DataFrame and Series structures for handling tabular data.

3. Matplotlib
→ Creates static, animated, and interactive visualizations.
→ Ideal for line charts, scatter plots, and bar graphs.

4. Seaborn
→ Built on Matplotlib for statistical data visualization.
→ Supports heatmaps, violin plots, and pair plots for deeper insights.

5. Scikit-Learn
→ Essential for machine learning tasks.
→ Provides tools for regression, classification, clustering, and preprocessing.

6. TensorFlow
→ Used for deep learning and neural networks.
→ Supports distributed computing for large-scale models.

7. SciPy
→ Extends NumPy with advanced scientific computations.
→ Useful for optimization, signal processing, and integration.

8. Statsmodels
→ Designed for statistical modeling and hypothesis testing.
→ Great for linear models, time series analysis, and statistical tests.

𝗧𝗶𝗽: Start with NumPy and Pandas to build your foundation, then explore others as per your data science needs!

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#Python

Python (Pandas) interview questions for Data analyst role(entry level): ⬇️

1. What is Python Pandas and what is it used for?

2. Different types of Data Structures in Pandas?

3. Significant features of Pandas Library?

4. Time series in Pandas?

5. Reindexing in pandas along with its parameters?

6. Data Frames in Pandas?

7. MultiIndexing in Pandas?

8. Operation on Series in Pandas?

9. Different ways of creating Data Frames in Pandas?

10. Categorical Data in Pandas?

11. How to Read Text Files with Pandas?

12. How are iloc() and loc() different?

13. Difference between join() and merge() in Pandas?

14. How to add a row/column to a Pandas DataFrame?

15.GroupBy function in Pandas?

16.Use of pandas.Dataframe.aggregate() function?

17. Statistical functions in Python Pandas?

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#Python

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.

2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.

3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.

4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.

5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.

6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.

7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.

8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.

9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.

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#Python

Want to analyse data with Python?

Pandas is a must-know tool for data analysts:

- start with pandas
- read csv files
- check basic statistics
- group data
- pivot data
- sort data
- create a bar chart

Don't Confuse to learn Python.

Learn This Concept to be proficient in Python.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages

𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy

𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas

𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files

𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing

btw, do you use guys use medium being a data enthusiast - I would definitely recommend it for you.

I myself started creating content on medium from last few months : https://medium.com/@data_analyst