Infosys Python - Pandas Interview Q & A.pdf
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Essential Python Libraries for Data Analytics ππ
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
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Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
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Python Cheat Sheet.pdf
677.7 KB
This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries
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Python Roadmap
https://www.linkedin.com/posts/sql-analysts_python-learning-plan-in-2024-week-1-activity-7222465692049018880-ltJm
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Python from scratch
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
π15β€6π₯°4
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
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Stars βοΈ: 33.2k
Forked By: 6.7k
https://github.com/Azure/azure-sdk-for-python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
Creator: Asabeneh
Stars βοΈ: 33.2k
Forked By: 6.7k
https://github.com/Azure/azure-sdk-for-python
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Free Resources for Python
Codebasics python tutorials (first 16) β
https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0
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https://dabeaz-course.github.io/practical-python/Notes/Contents.html
Codebasics python HINDI tutorials β
https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Useful Python resources for beginners
https://t.iss.one/programming_guide/8
Python 3 Book for beginners
https://t.iss.one/pythondevelopersindia/272?single
Codebasics python tutorials (first 16) β
https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0
Practice Python course
https://dabeaz-course.github.io/practical-python/Notes/Contents.html
Codebasics python HINDI tutorials β
https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Useful Python resources for beginners
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Python 3 Book for beginners
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π Essential Python snippets to explore data:
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
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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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Always remember this one thing in your life β€οΈπ
https://youtube.com/shorts/ir-4oBR9ec0?feature=share
https://youtube.com/shorts/ir-4oBR9ec0?feature=share
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Easy Python scenarios for everyday data tasks
Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.
Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)
def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)
Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.
Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)
def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales
total_sales = total_sales_per_region(df)
print(total_sales)
Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns βCustomerIDβ, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.
Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)
def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)
Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.
Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)
def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)
Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.
Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)
def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales
total_sales = total_sales_per_region(df)
print(total_sales)
Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns βCustomerIDβ, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.
Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)
def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)
π25β€8
5 essential Python functions for handling missing data:
πΉ isna(): Detects missing values in your DataFrame. Identifies NaNs
πΉ notna(): Detects non-missing values. Filters out the NaNs.
πΉ interpolate(): Fills missing values using interpolation
πΉ bfill(): Backward fill. Fills missing values with the next valid observation.
πΉ ffill(): Forward fill. Fills missing values with the previous valid observation.
πΉ isna(): Detects missing values in your DataFrame. Identifies NaNs
πΉ notna(): Detects non-missing values. Filters out the NaNs.
πΉ interpolate(): Fills missing values using interpolation
πΉ bfill(): Backward fill. Fills missing values with the next valid observation.
πΉ ffill(): Forward fill. Fills missing values with the previous valid observation.
π14β€10
7 level of writing Python Dictionary
Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
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Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
I have curated the best interview resources to crack Python Interviews ππ
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this πβ€οΈ
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ππ²ππ‘π¨π§ ππ§πππ«π―π’ππ° ππ«ππ©:
Must practise the following questions for your next Python interview:
1. How would you handle missing values in a dataset?
2. Write a python code to merge datasets based on a common column.
3. How would you analyse the distribution of a continuous variable in dataset?
4. Write a python code to pivot an dataframe.
5. How would you handle categorical variables with many levels?
6. Write a python code to calculate the accuracy, precision, and recall of a classification model?
7. How would you handle errors when working with large datasets?
I have curated the best interview resources to crack Python Interviews ππ
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this πβ€οΈ
Must practise the following questions for your next Python interview:
1. How would you handle missing values in a dataset?
2. Write a python code to merge datasets based on a common column.
3. How would you analyse the distribution of a continuous variable in dataset?
4. Write a python code to pivot an dataframe.
5. How would you handle categorical variables with many levels?
6. Write a python code to calculate the accuracy, precision, and recall of a classification model?
7. How would you handle errors when working with large datasets?
I have curated the best interview resources to crack Python Interviews ππ
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this πβ€οΈ
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