Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries.
NumPy
SciPy
Matplotlib
TensorFlow
Seaborn
Scikit Learn
Keras
1. Pandas
Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provide fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very quickly and intuitively.
Features:
Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data
Enables you to create your function and run it across a series of data
High-level abstraction
Contains high-level data structures and manipulation tools
Applications:
General data wrangling and cleaning
ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format
Used in a variety of academic and commercial areas, including statistics, finance, and neuroscience
Time-series-specific functionality, such as date range generation, moving window, linear regression, and date shifting.
2. NumPy
NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
Features:
Provides fast, precompiled functions for numerical routines
Array-oriented computing for better efficiency
Supports an object-oriented approach
Compact and faster computations with vectorization
Applications:
Extensively used in data analysis
Creates a powerful N-dimensional array
Forms the base of other libraries, such as SciPy and scikit-learn
Replacement of MATLAB when used with SciPy and matplotlib
3. SciPy
SciPy (Scientific Python) is another free and open-source Python library extensively used in data science for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s widely used for scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.
Features:
Collection of algorithms and functions built on the NumPy extension of Python
High-level commands for data manipulation and visualization
Multidimensional image processing with the SciPy.ndimage submodule
Includes built-in functions for solving differential equations
Applications:
Multidimensional image operations
Solving differential equations and the Fourier transform
Optimization algorithms
Linear algebra
A simple demonstration of the functions of SciPy follows in the video of Python libraries for Data Science.
4. Matplotlib
This is undoubtedly my favorite and a quintessential Python library. You can create stories with the data visualized with Matplotlib. Another library from the SciPy Stack, Matplotlib plots 2D figures.
When to use? Matplotlib is the plotting library for Python that provides an object-oriented API for embedding plots into applications. It is a close resemblance to MATLAB embedded in Python programming language.
What can you do with Matplotlib?
Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of visualizations. With a bit of effort and tint of visualization capabilities, with Matplotlib, you can create just any visualizations:
Line plots
Scatter plots
Area plots
Bar charts and Histograms
Pie charts
Stem plots
Contour plots
Quiver plots
Spectrograms
Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib. Basically, everything that can be drawn!
5. TensorFlow
TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of about 1,500 contributors. It’s used across various scientific fields. TensorFlow is a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.
Features:
Better computational graph visualizations
Reduces error by 50 to 60 percent in neural machine learning
Parallel computing to execute complex models
Seamless library management backed by Google
Quicker updates and frequent new releases to provide you with the latest features
TensorFlow is particularly useful for the following applications:
Speech and image recognition
Text-based applications
Time-series analysis
Video detection
6. Seaborn
Seaborn is based on Matplotlib and serves as a useful Python machine learning tool for visualizing statistical models – heatmaps and other types of visualizations that summarize data and depict the overall distributions. When using this library, you get to benefit from an extensive gallery of visualizations (including complex ones like time series, joint plots, and violin diagrams).So, what is the difference between Matplotlib and Seaborn? Matplotlib is used for basic plotting; bars, pies, lines, scatter plots and stuff whereas, seaborn provides a variety of visualization patterns with less complex and fewer syntax.
What can you do with Seaborn?
Determine relationships between multiple variables (correlation)
Observe categorical variables for aggregate statistics
Analyze uni-variate or bi-variate distributions and compare them between different data subsets
Plot linear regression models for dependent variables
Provide high-level abstractions, multi-plot grids
Seaborn is a great second-hand for R visualization libraries like corrplot and ggplot.
7. SciKit-Learn
This is an industry-standard for data science projects based in Python. Scikits is a group of packages in the SciPy Stack that were created for specific functionalities – for example, image processing. Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms.
Data scientists use it for handling standard machine learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction, and classification. Another advantage? It comes with quality documentation and offers high performance.
What can you do with Scikit Learn?
Classification: Spam detection, image recognition
Clustering: Drug response, Stock price
Regression: Customer segmentation, Grouping experiment outcomes
Dimensionality reduction: Visualization, Increased efficiency
Model selection: Improved accuracy via parameter tuning
Pre-processing: Preparing input data as a text for processing with machine learning algorithms.
Scikit Learn focuses on modeling data; not manipulating data. We have NumPy and Pandas for summarizing and manipulation.
8.Keras
Keras is a great library for building neural networks and modeling. It's very straightforward to use and provides developers with a good degree of extensibility. The library takes advantage of other packages, (Theano or TensorFlow) as its backends. Moreover, Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend. It's a great pick if you want to experiment quickly using compact systems – the minimalist approach to design really pays off!
What is the difference between Keras and TensorFlow after all?
Keras is a neural network Python library while TensorFlow is an open-source library for various machine learning tasks. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Keras is built for Python which makes it way more user-friendly, modular and composable than TensorFlow.
What can you do with Keras?
Determine percentage accuracy
Compute loss function
Create custom function layers
Built-in data and image processing
Write functions with repeating code blocks: 20, 50, 100 layers deep
Important Python Libraries for Data Science:
PandasNumPy
SciPy
Matplotlib
TensorFlow
Seaborn
Scikit Learn
Keras
1. Pandas
Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provide fast, flexible data structures, such as data frame CDs, which are designed to work with structured data very quickly and intuitively.
Features:
Eloquent syntax and rich functionalities that gives you the freedom to deal with missing data
Enables you to create your function and run it across a series of data
High-level abstraction
Contains high-level data structures and manipulation tools
Applications:
General data wrangling and cleaning
ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format
Used in a variety of academic and commercial areas, including statistics, finance, and neuroscience
Time-series-specific functionality, such as date range generation, moving window, linear regression, and date shifting.
2. NumPy
NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
Features:
Provides fast, precompiled functions for numerical routines
Array-oriented computing for better efficiency
Supports an object-oriented approach
Compact and faster computations with vectorization
Applications:
Extensively used in data analysis
Creates a powerful N-dimensional array
Forms the base of other libraries, such as SciPy and scikit-learn
Replacement of MATLAB when used with SciPy and matplotlib
3. SciPy
SciPy (Scientific Python) is another free and open-source Python library extensively used in data science for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s widely used for scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.
Features:
Collection of algorithms and functions built on the NumPy extension of Python
High-level commands for data manipulation and visualization
Multidimensional image processing with the SciPy.ndimage submodule
Includes built-in functions for solving differential equations
Applications:
Multidimensional image operations
Solving differential equations and the Fourier transform
Optimization algorithms
Linear algebra
A simple demonstration of the functions of SciPy follows in the video of Python libraries for Data Science.
4. Matplotlib
This is undoubtedly my favorite and a quintessential Python library. You can create stories with the data visualized with Matplotlib. Another library from the SciPy Stack, Matplotlib plots 2D figures.
When to use? Matplotlib is the plotting library for Python that provides an object-oriented API for embedding plots into applications. It is a close resemblance to MATLAB embedded in Python programming language.
What can you do with Matplotlib?
Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of visualizations. With a bit of effort and tint of visualization capabilities, with Matplotlib, you can create just any visualizations:
Line plots
Scatter plots
Area plots
Bar charts and Histograms
Pie charts
Stem plots
Contour plots
Quiver plots
Spectrograms
Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib. Basically, everything that can be drawn!
5. TensorFlow
TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of about 1,500 contributors. It’s used across various scientific fields. TensorFlow is a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.
Features:
Better computational graph visualizations
Reduces error by 50 to 60 percent in neural machine learning
Parallel computing to execute complex models
Seamless library management backed by Google
Quicker updates and frequent new releases to provide you with the latest features
TensorFlow is particularly useful for the following applications:
Speech and image recognition
Text-based applications
Time-series analysis
Video detection
6. Seaborn
Seaborn is based on Matplotlib and serves as a useful Python machine learning tool for visualizing statistical models – heatmaps and other types of visualizations that summarize data and depict the overall distributions. When using this library, you get to benefit from an extensive gallery of visualizations (including complex ones like time series, joint plots, and violin diagrams).So, what is the difference between Matplotlib and Seaborn? Matplotlib is used for basic plotting; bars, pies, lines, scatter plots and stuff whereas, seaborn provides a variety of visualization patterns with less complex and fewer syntax.
What can you do with Seaborn?
Determine relationships between multiple variables (correlation)
Observe categorical variables for aggregate statistics
Analyze uni-variate or bi-variate distributions and compare them between different data subsets
Plot linear regression models for dependent variables
Provide high-level abstractions, multi-plot grids
Seaborn is a great second-hand for R visualization libraries like corrplot and ggplot.
7. SciKit-Learn
This is an industry-standard for data science projects based in Python. Scikits is a group of packages in the SciPy Stack that were created for specific functionalities – for example, image processing. Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms.
Data scientists use it for handling standard machine learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction, and classification. Another advantage? It comes with quality documentation and offers high performance.
What can you do with Scikit Learn?
Classification: Spam detection, image recognition
Clustering: Drug response, Stock price
Regression: Customer segmentation, Grouping experiment outcomes
Dimensionality reduction: Visualization, Increased efficiency
Model selection: Improved accuracy via parameter tuning
Pre-processing: Preparing input data as a text for processing with machine learning algorithms.
Scikit Learn focuses on modeling data; not manipulating data. We have NumPy and Pandas for summarizing and manipulation.
Keras is a great library for building neural networks and modeling. It's very straightforward to use and provides developers with a good degree of extensibility. The library takes advantage of other packages, (Theano or TensorFlow) as its backends. Moreover, Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend. It's a great pick if you want to experiment quickly using compact systems – the minimalist approach to design really pays off!
What is the difference between Keras and TensorFlow after all?
Keras is a neural network Python library while TensorFlow is an open-source library for various machine learning tasks. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Keras is built for Python which makes it way more user-friendly, modular and composable than TensorFlow.
What can you do with Keras?
Determine percentage accuracy
Compute loss function
Create custom function layers
Built-in data and image processing
Write functions with repeating code blocks: 20, 50, 100 layers deep
Comments
Post a Comment