Most Important Python Topics for Data Analyst Interview:

#Basics of Python:

1. Data Types

2. Lists

3. Dictionaries

4. Control Structures:

- if-elif-else

- Loops

5. Functions

6. Practice basic FAQs questions, below mentioned are few examples:

- How to reverse a string in Python?

- How to find the largest/smallest number in a list?

- How to remove duplicates from a list?

- How to count the occurrences of each element in a list?

- How to check if a string is a palindrome?

#Pandas:

1. Pandas Data Structures (Series, DataFrame)

2. Creating and Manipulating DataFrames

3. Filtering and Selecting Data

4. Grouping and Aggregating Data

5. Handling Missing Values

6. Merging and Joining DataFrames

7. Adding and Removing Columns

8. Exploratory Data Analysis (EDA):

- Descriptive Statistics

- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)

- Correlation and Covariance

- Handling Duplicates

- Data Transformation

#Numpy:

1. NumPy Arrays

2. Array Operations:

- Creating Arrays

- Slicing and Indexing

- Arithmetic Operations

#Integration with Other Libraries:

1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)

#Key Concepts to Revise:

1. Data Manipulation with Pandas and NumPy

2. Data Cleaning Techniques

3. File Handling (reading and writing CSV files, JSON files)

4. Handling Missing and Duplicate Values

5. Data Transformation (scaling, normalization)

6. Data Aggregation and Group Operations

7. Combining and Merging Datasets

5 Pandas Functions to Handle Missing Data

🔹 fillna() – Fill missing values with a specific value or method
🔹 interpolate() – Fill NaNs with interpolated values (e.g., linear, time-based)
🔹 ffill() – Forward-fill missing values with the previous valid entry
🔹 bfill() – Backward-fill missing values with the next valid entry
🔹 dropna() – Remove rows or columns with missing values

#Pandas

Python #Pandas Cheat Sheet 🐼

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

Most Important Python Topics for Data Analyst Interview:

#Basics of Python:

1. Data Types

2. Lists

3. Dictionaries

4. Control Structures:

- if-elif-else

- Loops

5. Functions

6. Practice basic FAQs questions, below mentioned are few examples:

- How to reverse a string in Python?

- How to find the largest/smallest number in a list?

- How to remove duplicates from a list?

- How to count the occurrences of each element in a list?

- How to check if a string is a palindrome?

#Pandas:

1. Pandas Data Structures (Series, DataFrame)

2. Creating and Manipulating DataFrames

3. Filtering and Selecting Data

4. Grouping and Aggregating Data

5. Handling Missing Values

6. Merging and Joining DataFrames

7. Adding and Removing Columns

8. Exploratory Data Analysis (EDA):

- Descriptive Statistics

- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)

- Correlation and Covariance

- Handling Duplicates

- Data Transformation

#Numpy:

1. NumPy Arrays

2. Array Operations:

- Creating Arrays

- Slicing and Indexing

- Arithmetic Operations

#Integration with Other Libraries:

1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)

#Key Concepts to Revise:

1. Data Manipulation with Pandas and NumPy

2. Data Cleaning Techniques

3. File Handling (reading and writing CSV files, JSON files)

4. Handling Missing and Duplicate Values

5. Data Transformation (scaling, normalization)

6. Data Aggregation and Group Operations

7. Combining and Merging Datasets