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
#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
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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
🔹 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
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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
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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
#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
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Mastering pandas%22.pdf
1.6 MB
🌟 A new and comprehensive book "Mastering pandas"
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
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