Pandas & Numpy Tutorials

Pandas is a popular Python library used to manipulate tabular data. It provides a versatile dataframe object that can read data from many popular formats, such as Excel, SQL, CSV and more. It provides an incredibly helpful methods to both reshape your data and analyze your data in different ways.

Featured Pandas Articles

Python Pivot Tables – The Ultimate Guide – Learn everything you need to know about Pandas pivot tables in this in-depth guide, covering off the versatile function. You’ll learn how to work with multi-index pivot tables and creating your own custom functions to analyze data with.

Exploring the Pandas Style API – Learn how to style your Pandas Dataframe in different ways, both using colour and value formatting to better illustrate the importance of what you’re presenting. The Pandas style API provides you with many different tools that makes working with styling tabular data much easier.

Pandas Tutorials

• Pandas GroupBy Multiple Columns Explained with Examples
  The Pandas groupby method is a powerful tool that allows you to aggregate data using a simple syntax, while abstracting away complex calculations. One of the strongest benefits of the groupby method is the ability to group by multiple columns, and even apply multiple transformations. By the end of this tutorial, you’ll have learned the
• Pandas Drop a Dataframe Index Column: Guide with Examples
  Learn how to use Pandas to drop a dataframe index column using the reset_index and set_index methods and how to read csv without an index.
• Pandas Quantile: Calculate Percentiles of a Dataframe
  Learn how to use the Pandas quantile method to calculate percentiles in Pandas including how to modify the interpolation of values.
• How to Calculate a Rolling Average (Mean) in Pandas
  In this post, you’ll learn how to calculate a rolling mean in Pandas using the rolling() function. Rolling averages are also known as moving averages. Creating a rolling average allows you to “smooth” out small fluctuations in datasets, while gaining insight into trends. It’s often used in macroeconomics, such as unemployment, gross domestic product, and stock prices.A moving
• Pandas fillna: A Guide for Tackling Missing Data in DataFrames
  Welcome to our comprehensive guide on using the Pandas fillna method! Handling missing data is an essential step in the data-cleaning process. It ensures that your analysis provides reliable, accurate, and consistent results. Luckily, using the Pandas .fillna() method can make dealing with those pesky “NaN” or “null” values a breeze. In this tutorial, we’ll
• Pandas unique(): Get Unique Values in a DataFrame
  Pandas provides a lot of different ways to interact with unique values. Learn how to get unique values as a list, get unique values across columns and more!
• Unpivot Your Data with the Pandas Melt Function
  You can easily unpivot and reshape data you with python by using Pandas and the Melt function! Find out how using this thorough overview!
• Pandas date_range: How to Create a Date Range in Pandas
  In this tutorial, we’re diving deep into one of the essential and versatile tools of the Pandas library—the date_range function. Whether you’re a beginner just starting to explore the power of Pandas or already an adept user, this function is one you’ll definitely want to have in your Python toolbox. This will open up your
• Pandas Reset Index: How to Reset a Pandas Index
  Learn how to use the Pandas reset index method to reset an index, including working with a multi-index and dropping the original index.
• Pandas replace() – Replace Values in Pandas Dataframe
  Learn how to use the Pandas replace method to replace values across columns and dataframes, including with regular expressions.
• Move a Pandas DataFrame Column to Position (Start and End)
  Being able to work with and manipulate Pandas DataFrames is an essential skill for anyone working in data analysis or data science. As Pandas becomes the ubiquitous tool for data analysts, being able to get DataFrames ready for presentation is important. In this tutorial, you’ll learn how to use Pandas to move a DataFrame column
• Python Pandas Tutorial: A Complete Guide
  In this guide, you’ll learn about the pandas library in Python! The library allows you to work with tabular data in a familiar and approachable format. pandas provides incredible simplicity when it’s needed but also allows you to dive deep into finding, manipulating, and aggregating data. pandas is one of the most valuable data-wrangling libraries
• Pandas: Replace NaN with Zeroes
  Working with missing data is an essential skill for any data analyst or data scientist! In many cases, you’ll want to replace your missing data, or NaN values, with zeroes. In this tutorial, you’ll learn how to use Pandas to replace NaN values with zeroes. This is a common skill that is part of better
• Convert a Pandas DataFrame to a Pickle File
  Pickle files are serialized data structures that allow you to maintain data state across sessions. Pickle files are incredibly common in data science. In this tutorial, you’ll learn how to serialize a Pandas DataFrame to a Pickle file. This is an important function to understand, given the prevalence of pickle files in data science workflows.
• Convert a Pandas DataFrame to JSON
  In this tutorial, you’ll learn how to convert a Pandas DataFrame to a JSON object and file using Python. Most programming languages can read, parse, and work with JSON. Because of this, knowing how to convert a Pandas DataFrame to JSON is an important skill. Pandas provides a lot of flexibility when converting a DataFrame
• Convert a Pandas DataFrame to a Dictionary
  In this post, you’ll learn how to use Python to convert a Pandas DataFrame into a dictionary. Because Pandas DataFrames are complex data structures, there are many different ways in which this can be done. This post explores all of the different options that Pandas makes available! For example, Pandas allows you to convert a
• Convert a Pandas DataFrame to a List
  In this post, you’ll learn how to convert a Pandas DataFrame to a list, including a list of lists, a list of tuples, and a list of dictionaries. Being able to convert a Pandas DataFrame to different formats allows you to work with different libraries that may not accept Pandas DataFrames. Pandas provides you with
• pd.read_parquet: Read Parquet Files in Pandas
  In this tutorial, you’ll learn how to use the Pandas read_parquet function to read parquet files in Pandas. While CSV files may be the ubiquitous file format for data analysts, they have limitations as your data size grows. This is where Apache Parquet files can help! By the end of this tutorial, you’ll have learned:
• Pandas dropna(): Drop Missing Records and Columns in DataFrames
  In this tutorial, you’ll learn how to use the Pandas dropna() method to drop missing values in a Pandas DataFrame. Working with missing data is one of the essential skills in cleaning your data before analyzing it. Because data cleaning can take up to 80% of a data analyst’s / data scientist’s time, being able
• How to Add a New Column to a Pandas DataFrame
  In this tutorial, you’ll learn how to add a new column to a Pandas DataFrame. The Pandas library provides a helpful way of working with tabular data. One of the most common tasks you’ll encounter is the need to add more data to a Pandas DataFrame. By the end of this tutorial, you’ll have learned:
• Count Unique Values in Pandas
  In this tutorial, you’ll learn how to use Pandas to count unique values. You’ll learn how to count unique values in a column, in multiple columns, and in an entire DataFrame. Being able to count unique values can have important use cases. For example, this can be used in testing your code. Similarly, it can
• Show All Columns and Rows in a Pandas DataFrame
  In this tutorial, you’ll learn how to change your display options in Pandas to display all columns, as well as all rows in your DataFrame. By default, Pandas will limit the number of columns and rows to display. While this can be helpful in terms of being able to read content, it can often lead
• Pandas to_excel: Writing DataFrames to Excel Files
  In this tutorial, you’ll learn how to save your Pandas DataFrame or DataFrames to Excel files. Being able to save data to this ubiquitous data format is an important skill in many organizations. In this tutorial, you’ll learn how to save a simple DataFrame to Excel, but also how to customize your options to create
• How to Use Pandas to Read Excel Files in Python
  In this tutorial, you’ll learn how to use Python and Pandas to read Excel files using the Pandas read_excel function. Excel files are everywhere – and while they may not be the ideal data type for many data scientists, knowing how to work with them is an essential skill. By the end of this tutorial,
• Convert a List of Dictionaries to a Pandas DataFrame
  In this tutorial, you’ll learn how to convert a list of Python dictionaries into a Pandas DataFrame. Pandas provides a number of different ways in which to convert dictionaries into a DataFrame. You’ll learn how to use the Pandas from_dict method, the DataFrame constructor, and the json_normalize function. By the end of this tutorial, you’ll
• How to Add / Insert a Row into a Pandas DataFrame
  In this tutorial, you’ll learn how to add (or insert) a row into a Pandas DataFrame. You’ll learn how to add a single row, multiple rows, and at specific positions. You’ll also learn how to add a row using a list, a Series, and a dictionary. By the end of this tutorial, you’ll have learned:
• Pandas Scatter Plot: How to Make a Scatter Plot in Pandas
  In this tutorial, you’ll learn how to use Pandas to make a scatter plot. Under the hood, Pandas uses Matplotlib, which can make customizing your plot a familiar experience. Pandas allows you to customize your scatter plot by changing colors, adding titles, and more. In more recent versions, Pandas included the ability to use different
• Pandas to_datetime: Convert a Pandas String Column to Date Time
  In this tutorial, you’ll learn how to use the Pandas to_datetime function to convert a Pandas column to date time. Pandas provides a huge number of methods and functions that make working with dates incredibly versatile. However, data aren’t always read correctly. By the end of this tutorial, you’ll have learned: How to use the
• Introduction to Pandas for Data Science
  In this tutorial, you’ll learn how to dive into the wonderful world of Pandas. Pandas is a Python package that provides fast and flexible data structures used for data manipulation and analysis. By the end of this tutorial, you’ll have learned how to: Install pandas for Python using pip or conda Understand the pandas series
• Indexing, Selecting, and Assigning Data in Pandas
  In this tutorial, you’ll learn how to index, select and assign data in a Pandas DataFrame. Understanding how to index and select data is an important first step in almost any exploratory work you’ll take on in data science. Similarly, knowing how to assign values in Pandas can open up a whole new world potential
• Summarizing and Analyzing a Pandas DataFrame
  In this tutorial, you’ll learn how to quickly summarize and analyze a Pandas DataFrame. By the end of this tutorial, you’ll have learned to take on some exploratory analysis of your dataset using pandas. You’ll learn how to calculate general attributes of your dataset, such as measures of central tendency or measures of dispersion. You’ll
• Transforming Pandas Columns with map and apply
  In this tutorial, you’ll learn how to transform your Pandas DataFrame columns using vectorized functions and custom functions using the map and apply methods. By the end of this tutorial, you’ll have a strong understanding of how Pandas applies vectorized functions and how these are optimized for performance. You’ll also learn how to use custom
• Binning Data in Pandas with cut and qcut
  In this tutorial, you’ll learn how to bin data in Python with the Pandas cut and qcut functions. You’ll learn why binning is a useful skill in Pandas and how you can use it to better group and distill information. By the end of this tutorial, you’ll have learned: How to use the cut and
• DateTime in Pandas and Python
  In this tutorial, you’ll learn how to work with dates, times, and DateTime in Pandas and Python. Working with DateTime in Python and Pandas can be a complicated thing. This guide aims to make the complicated, simple, by focusing on what you need to know to get started and to know enough to discover more
• Data Cleaning and Preparation in Pandas and Python
  In this tutorial, you’ll learn how to clean and prepare data in a Pandas DataFrame. You’ll learn how to work with missing data, how to work with duplicate data, and dealing with messy string data. Being able to effectively clean and prepare a dataset is an important skill. Many data scientists estimate that they spend
• Pandas GroupBy: Group, Summarize, and Aggregate Data in Python
  The Pandas groupby method is an incredibly powerful tool to help you gain effective and impactful insight into your dataset. In just a few, easy to understand lines of code, you can aggregate your data in incredibly straightforward and powerful ways. By the end of this tutorial, you’ll have learned how the Pandas .groupby() method
• Pandas Datetime to Date Parts (Month, Year, etc.)
  In this tutorial, you’ll learn how to use Pandas to extract date parts from a datetime column, such as to date, year, and month. Pandas provides a number of easy ways to extract parts from a datetime object, including using the .dt accessor. By the end of this tutorial, you’ll have learned how the dt
• Pandas: Get the Row Number from a Dataframe
  Learn how to use Pandas to get the row number of rows matching a condition or multiple conditions, and how to count rows matching conditions.
• Calculate a Weighted Average in Pandas and Python
  Learn how to use Pandas to calculate the weighted average in Python, using groupby, numpy, and the zip function between two lists.
• How to Shuffle Pandas Dataframe Rows in Python
  Learn how to shuffle a Pandas Dataframe using three different methods, including how to be able to reproduce your shuffle results.
• Pandas: Number of Columns (Count Dataframe Columns)
  Learn how to use Python and Pandas to count the number of columns in a dataframe, using counting the number of columns meeting a condition.
• Pandas Sum: Add Dataframe Columns and Rows
  Learn how to use Pandas to calculate a sum, including adding Pandas Dataframe columns and rows, and how to add columns conditionally.
• Pandas Diff: Calculate the Difference Between Pandas Rows
  Learn how to use the Pandas diff method to calculate the difference between dataframe rows and columns, including at defined intervals.
• Normalize a Pandas Column or Dataframe (w/ Pandas or sklearn)
  Learn how to normalize and standardize a Pandas Dataframe with sklearn, including max absolute scaling, min-max scaling and z-scoare scaling.
• Pandas Rank Function: Rank Dataframe Data (SQL row_number Equivalent)
  Learn how to use the Pandas rank method to rank you data, including how to rank a grouped dataframe using the groupby method.
• Pandas Describe: Descriptive Statistics on Your Dataframe
  Learn how to use the Pandas describe method to generate summary statistics on your Pandas Dataframe, including changing percentiles.
• Pandas Shift: Shift a Dataframe Column Up or Down
  Learn how to use the Python Pandas shift function to move a dataframe’s rows up or down, including working with time series and missing data.
• 7 Ways to Sample Data in Pandas
  Learn how to sample data in Pandas using Python, including how to use the sample function, reproduce results, and weighted samples of data.
• Pandas Dataframe to CSV File – Export Using .to_csv()
  Use Python and Pandas to export a dataframe to a CSV file, using .to_csv, including changing separators, encoding, and missing values.
• Pandas: Iterate over a Pandas Dataframe Rows
  Learn how to use Python and Pandas to iterate over rows of a dataframe, why vectorization is better, and how to use iterrows and itertuples.
• Pandas: Convert Column Values to Strings
  Learn how to use Python and Pandas to convert a dataframe column values to strings, including how to optimize for memory and efficiency.
• Pandas Variance: Calculating Variance of a Pandas Dataframe Column
  Learn how to calculate the variance of a variable in Pandas, including how to calculate for a single column, multiple or a whole dataframe.
• Pandas: Create a Dataframe from Lists (5 Ways!)
  Learn how to create a Pandas dataframe from lists, including using lists of lists, the zip() function, and ways to add columns and an index.
• Pandas Rename Index: How to Rename a Pandas Dataframe Index
  Learn how to rename a Pandas index, including a single index or multi-index, as well as how to drop an index name altogether.
• Pandas: Count Unique Values in a GroupBy Object
  Learn how to use Pandas to count unique values in a GroupBy object, allowing you to count distinct values using the popular groupby method.
• Pandas: Add Days to a Date Column
  Learn how to use Pandas to add days to a date column, both constant values and based off another column, using the Pandas timedelta function.
• Pandas Mean: Calculate Pandas Average for One or Multiple Columns
  Learn how to calculate the Pandas mean (or Pandas Average), including how to calculate it on a column, dataframe, and row, and with nulls.
• Pandas Column to List – Convert a Pandas Series to a List
  Learn how to convert a Pandas column to list with this tutorial. Learn three different ways to accomplish this, all very easy to follow!
• Transpose a Pandas Dataframe
  Learn how to transpose a pandas dataframe, including how to work with mixed datatypes and what their outputs may be.
• Python: Split a Pandas Dataframe
  Learn how to split a Pandas dataframe in Python. Split a dataframe by column value, by position, and by random values.
• How to Get Column Names in a Pandas DataFrame
  Learn how to get Pandas columns as a list, a sorted list and how to check if a column exists in a particular dataframe.
• Pandas: Number of Rows in a Dataframe (6 Ways)
  Learn how to count the number of rows in a Pandas Dataframe, including identifying how many rows contain a value or meet a condition.
• Create an Empty Pandas Dataframe and Append Data
  In this post, you’ll learn how to create empty pandas dataframes and how to add data to them row-by-row and add rows via a loop.
• How to Rename Pandas DataFrame Columns (with Examples)
  Learn the ways in which you can rename Pandas columns, using Pandas .rename() method. Learn how to rename one, all, including automatically.
• Reorder Pandas Columns: Pandas Reindex and Pandas insert
  Learn different ways to reorder Pandas columns, including the Pandas reindex function as well as a custom function.
• Pandas get_dummies (One-Hot Encoding) Explained
  The pandas get dummies function allows you to easily one-hot encode your data sets for use in machine learning algorithms.
• Relative Frequencies and Absolute Frequencies in Python and Pandas
  In this post, you’ll learn how to calculate relative frequencies and absolute frequencies using pure Python, as well as the popular data science library, Pandas. A relative frequency, measures how often a certain value occurs in a dataset, relative to the total number of values in that dataset. An absolute frequency, meanwhile, simply measures how
• Pandas Fiscal Year – Get Financial Year with Pandas
  Learn how to calculate custom a Pandas fiscal year and how to format these fiscal years in custom ways.
• How to Sort Data in a Pandas DataFrame
  Lean how to sort data in a Pandas dataframe, including how to sort in ascending or descending order, as well as sorting by multiple columns.
• Pandas Value_counts to Count Unique Values
  The Pandas value_counts functioncounts values in a Pandas dataframe. Learn to normalize, include missing values, and combine with groupby.
• How to Drop One or More Columns in Pandas
  Learn how to use Pandas to drop columns and rows in a dataframe, including how to drop columns or rows based on conditions.
• Create New Columns in Pandas
  Pandas is one of the quintessential libraries for data science in Python. A useful skill is the ability to create new columns, either by adding your own data or calculating data based on existing data. Video Tutorial Loading Dataset Let’s start off the tutorial by loading the dataset we’ll use throughout the tutorial. We can
• Pandas Crosstab – Everything You Need to Know
  The Pandas crosstab function is one of the many ways in which Pandas allows you to customize data. On the surface, it appears to be quite similar to the Pandas pivot table function, which I’ve covered extensively here. This post will give you a complete overview of how to best leverage the function. The crosstab
• Pandas drop_duplicates: Drop Duplicate Rows in Pandas
  Learn how to drop duplicates in Pandas, including keeping the first or last instance, and dropping duplicates based only on a subset of columns.
• Use Pandas & Python to Extract Tables from Webpages (read_html)
  Learn how to easily scrape data from the web without having to build a complex web scraping script!
• Plotting a Histogram in Python with Matplotlib and Pandas
  Learn what histograms are and how to create them in Python with Matplotlib and Pandas.
• All the Ways to Filter Pandas Dataframes
  Learn all the ways in which to filter pandas dataframes in this tutorial, including filtering dates, multiple columns, using the iloc, loc and query functions!
• Selecting Columns in Pandas: Complete Guide
  In this tutorial, you’ll learn how to select all the different ways you can select columns in Pandas, either by name or index. You’ll learn how to use the loc, iloc accessors and how to select columns directly. You’ll also learn how to select columns conditionally, such as those containing a specific substring. By the end of this tutorial,
• Exploring the Pandas Style API
  Explore the how to style Pandas dataframes and make them presentation ready, including how to add conditional formatting and data type labels!
• Pivot Tables in Pandas with Python
  You may be familiar with pivot tables in Excel to generate easy insights into your data. In this post, you’ll learn how to create pivot tables in Python and Pandas using the .pivot_table() method. This post will give you a complete overview of how to use the .pivot_table() function! Being able to quickly summarize data
• Binning Data in Python with Pandas’ cut()
  In this post, we’ll explore how binning data in Python works with the cut() method in Pandas. In the past, we’ve explored how to use the describe() method to generate some descriptive statistics. In particular, the describe method allows us to see the quarter percentiles of a numerical column. However, as we’re generating insight into
• VLOOKUP in Python and Pandas using .map() or .merge()
  In this tutorial, you’ll learn how to use Python and Pandas to VLOOKUP data in a Pandas DataFrame. VLOOKUPs are common functions in Excel that allow you to map data from one table to another. In many cases, this can be used to lookup data from a reference table, such as mapping in, say, a

Numpy Tutorials

Numpy is an incredible library used to work with arrays and matrices to calculate linear algebra problems and many other applications. The library provides list-like numpy arrays, which can be up to 50 times faster than Python lists. The library provides the basis for many other libraries.

• ReLU Activation Function for Deep Learning: A Complete Guide to the Rectified Linear Unit
  In the world of deep learning, activations breathe the life into neural networks by introducing non-linearity, enabling them to learn complex patterns. The Rectified Linear Unit (ReLU) function is a cornerstone activation function, enabling simple, neural efficiency for reducing the impact of the vanishing gradient problem. In this complete guide to the ReLU activation function,
• How to Normalize NumPy Arrays (Min-Max Scaling, Z-Score, L2)
  In this tutorial, you’ll learn how normalize NumPy arrays, including multi-dimensional arrays. Normalization is an important skill for any data analyst or data scientist. Normalization refers to the process of scaling data within a specific range or distribution to make it more suitable for analysis and model training. This is an important and common preprocessing
• How to Calculate the Cross Product in Python
  In this tutorial, you’ll learn how to use Python to calculate the cross-product. In particular, you’ll learn how to calculate the cross-product using the popular library, NumPy, and how to calculate it from scratch. The cross product is a common mathematical vector operation that takes two vectors as input and produces a third vector that
• NumPy split: Split a NumPy Array into Chunks
  In this tutorial, you’ll learn how to use the NumPy split function to split an array into chunks. Being able to work with and manipulate arrays in NumPy using Python is an important skill for anyone working with data. By the end of this tutorial, you’ll have learned how to: Want to learn how to
• NumPy: Best Ways to Map a Function Over an Array
  In this tutorial, you’ll learn how to use NumPy to map a function over an array using different methods such as NumPy vectorize. Being able to apply the same function to each element in an array is an important skill. However, because NumPy arrays can often be quite large, we need to consider performance when
• NumPy full: Create Arrays With a Fill Value
  NumPy arrays are essential to most data analysis and data science workflows in Python. Because of this, being able to generate arrays is an important skill. In this tutorial, you’ll learn how to use the NumPy full() function to generate arrays filled with a given value. This function is similar to the NumPy zeroes() and
• NumPy clip(): Limit Array Values with Min and Max
  NumPy arrays are essential to most data analysis and data science workflows. You may want to limit arrays to contain values within a minimum and maximum range – this is what the NumPy clip function accomplishes. In this tutorial, you’ll learn how to limit the values of a NumPy array by using the .clip() function.
• NumPy cumsum: Calculating Cumulative Sums of NumPy Arrays
  The NumPy cumsum function is used to calculate the cumulative sum of elements in a NumPy array across a specified axis. In this tutorial, you’ll learn how to use the NumPy cumsum function to calculate cumulative sums of arrays. The function allows you to specify the axis on which to calculate sums as well as
• NumPy Histogram: Understanding the np.histogram Function
  In this tutorial, you’ll learn how to use the NumPy histogram function to calculate a histogram of a given dataset. A histogram shows the frequency of numerical data in bins of grouped ranges. By using NumPy to calculate histograms, you can easily calculate and access the frequencies (relative or absolute) of different values. By the
• NumPy arange(): Complete Guide (w/ Examples)
  In this guide, you’ll learn how to use the NumPy arange function to create a sequence of numbers. This guide will teach you everything you need to know – including how the function can be customized to meet your needs. NumPy provides a number of different functions to create arrays, such as the np.linspace() function
• Indexing and Slicing NumPy Arrays: A Complete Guide
  This comprehensive guide will teach you all the different ways to index and slice NumPy arrays. NumPy is an essential library for any data analyst or data scientist using Python. Effectively indexing and slicing NumPy arrays can make you a stronger programmer. By the end of this tutorial, you’ll have learned: How NumPy array indexing
• NumPy argmin(): Get Index of the Min Value in Arrays
  In this tutorial, you’ll learn how to master the NumPy argmin() function to find the index position of the minimum value in a NumPy array. The np.argmin() function provides incredible functionality for working with one-dimensional and multi-dimensional arrays. You’ll also learn how to extend the functionality to Pandas DataFrames, allowing you to find values across
• Flatten an Array with NumPy flatten
  In this tutorial, you’ll learn how to flatten an array with NumPy flatten function, meaning that an array is collapsed to a single dimension. The NumPy flatten function allows you to turn a multi-dimensional array into a single-dimensional array. The function allows you to easily flatten arrays in different ways, including column-wise and row-wise. By
• NumPy Stack: Join NumPy Arrays Along Different Axes
  In this tutorial, you’ll learn how to use the NumPy stack() function to join NumPy arrays along various axes. NumPy is an essential Python library for anyone working with data in Python. The NumPy stack() function allows you to combine NumPy arrays in different ways, along NumPy axes. By the end of this tutorial, you’ll
• NumPy Squeeze: Removing Dimensionality from NumPy Arrays
  In this tutorial, you’ll learn how to use the NumPy squeeze() function. The np.squeeze() function allows you to remove single-dimensional entries from an array’s shape. This allows you to better transform arrays that aren’t shaped in the way that makes sense for the work that you’re doing. In short, the function returns the input array
• NumPy Repeat Function: Repeating NumPy Arrays
  In this tutorial, you’ll learn how to use the NumPy repeat() function. The np.repeat() function is used to repeat arrays and provides significant opportunity to customize how the arrays are repeated. Being able to work with NumPy arrays is an important skill for data analysis, data science, and deep learning. By the end of this
• NumPy Exponential: Using the NumPy.exp() Function
  In this tutorial, you’ll learn how to use the NumPy exponential function, np.exp(). The function raises the Euler’s constant, e, to a given power. Because Euler’s constant has many practical applications in science, math, and deep learning, being able to work with this function in meaningful ways is an asset for any Python user! By
• Implementing the Sigmoid Function in Python
  In this tutorial, you’ll learn how to implement the sigmoid activation function in Python. Because the sigmoid function is an activation function in neural networks, it’s important to understand how to implement it in Python. You’ll also learn some of the key attributes of the sigmoid function and why it’s such a useful function in
• NumPy Pad: Using np.pad() to Pad Arrays and Matrices
  In this tutorial, you’ll learn how to use the powerful NumPy pad function to pad arrays and matrices in NumPy and Python. The function is a powerful method that is often used in deep learning, especially in developing convolutional neural networks. Being able to master this function will allow you to build powerful deep learning
• NumPy where: Process Array Elements Conditionally
  In this tutorial, you’ll learn how to use the NumPy where() function to process or return elements based on a single condition or multiple conditions. The np.where() function is one of the most powerful functions available within NumPy. The function allows you to both return indices where a condition is met, or process array items
• np.argmax(): How to Use NumPy Argmax
  In this tutorial, you’ll learn how to use the NumPy argmax() function to find the index of the largest value in an array. The np.argmax() function can be used to find the maximum value across an array, as well as across axes of different dimensions. The function can often be confusing, though this tutorial should
• NumPy linspace: Creating Evenly Spaced Arrays with np.linspace
  In this tutorial, you’ll learn how to use the NumPy linspace function to create arrays of evenly spaced numbers. This can be incredibly helpful when you’re working with numerical applications. The NumPy linspace function allows you to create evenly spaced ranges of numbers and to customize these arrays using a wide assortment of parameters. By
• NumPy logspace: Understanding the np.logspace() Function
  In this tutorial, you’ll learn how to use the NumPy logspace function and how to use its different parameters. The np.logspace() function is used to return numbers that are evenly spaced on a log scale. The function allows you to specify many different attributes, including modifying the base of the log scale you want to
• NumPy Tile to Arrange Arrays
  In this tutorial, you’ll learn how to use the NumPy tile function to arrange arrays using Python. The NumPy tile() function takes an array as an input and creates a new array by repeating the input array in different ways. Being able to work with and manipulate NumPy arrays is an important skill for data
• Different Ways to Concatenate NumPy Arrays in Python
  In this tutorial, you’ll learn how to concatenate NumPy arrays in Python. Knowing how to work with NumPy arrays is an important skill as you progress in data science in Python. Because NumPy arrays can be 1-dimensional or 2-dimensional, it’s important to understand the many different ways in which to join NumPy arrays. By the
• NumPy Zeros: Create Zero Arrays and Matrix in NumPy
  In this tutorial, you’ll learn how to generate a zero matrix using the NumPy zeros function. Zero arrays and matrices have special purposes in machine learning. Being able to create them efficiently will allow you to become more capable in linear algebra and machine learning. By the end of this tutorial, you’ll have learned: Why
• Using Pi in Python (NumPy and Math)
  In this tutorial, you’ll learn how to get and use the value of pi in Python. We’ll explore a number of different ways in which you can get and store the value of pi in Python. First, we’ll look at the math library, followed by the NumPy library. We’ll explore why you may want to
• Numpy Normal (Gaussian) Distribution (Numpy Random Normal)
  In this tutorial, you’ll learn how to use the Numpy random.normal function to create normal (or Gaussian) distributions. The functions provides you with tools that allow you create distributions with specific means and standard distributions. Additionally, you can create distributions of different sizes. By the end of this tutorial, you’ll have learned: Let’s get started!
• NumPy for Data Science in Python
  In this tutorial, you’ll learn how to use Python’s NumPy library for data science. You’ll learn why the library matters in the realm of data science and how it’s foundational for many other libraries. You’ll learn about the NumPy ndarray data structure and how it works. By the end of the tutorial, you’ll have learned:
• Numpy Dot Product: Calculate the Python Dot Product
  Learn how to use Python and numpy to calculate the dot product, including between arrays of different dimensions and of scalars.
• Python Natural Log: Calculate ln in Python
  Learn how to use Python to calculate the natural logarithm, often referred to as ln, using the math and numpy libraries, and how to plot it.