NumPy vs. pandas: What’s the difference?

In the realm of data science and scientific computing, Python stands out as a powerful and versatile programming language. Python seems to have an expanse of libraries available for these use case, but two of the most widely used are NumPy and pandas.

If you’re stuck choosing between Numpy and pandas, it’s very understandable. Both libraries have become indispensable tools for data scientists, analysts, and engineers, providing robust functionality for numerical computations and data manipulation. However, that choice will be easier once you learn where each tool excels, and therefore: which is the best for your data.

Let’s dive in!

We’ll cover the following

What is NumPy?
- Strengths of NumPy
What is pandas?
- Strengths of pandas
NumPy vs. pandas: The core differences
- 1. Data structures
  - NumPy arrays
  - pandas Series and DataFrames
- 2. Indexing and selection
  - NumPy indexing
  - pandas indexing
NumPy and pandas functionality
- 1. Mathematical operations
  - NumPy: Mathematical operations
  - pandas Mathematical operations
- 2. Loading data from file/dataset
  - NumPy: Loading data
  - pandas: Loading data
- 3. Data manipulation
  - NumPy: Data manipulation
  - pandas: Data manipulation
Integration and ecosystem
- Interoperability
  - NumPy: Interoperability
  - pandas: Interoperability
Use cases of NumPy and pandas
Pros and cons of NumPy and pandas
Comparison between NumPy and pandas
Conclusion
Next steps

What is NumPy?#

NumPy, short for Numerical Python, is a library that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on the arrays. It was created in 2005 by Travis Oliphant, building on the earlier Numeric and Numarray libraries to create a more complete and efficient package for array computing.

NumPy’s core functionality revolves around the ndarray object, a powerful $n$ -dimensional array that allows for efficient storage and manipulation of large datasets. These arrays provide a high-performance alternative to Python’s built-in lists, especially for large-scale numerical data.

Strengths of NumPy#

NumPy is renowned for its efficiency in handling numerical computations and its ability to process large datasets swiftly. It's implemented in C, which gives NumPy a significant speed advantage over pure Python code.

Numerical computations: NumPy offers a comprehensive suite of mathematical functions for operations such as linear algebra, random number generation, Fourier transforms, and statistical computations. Its functions are implemented in C, providing a significant speed advantage over pure Python code.
Handling of n-dimensional arrays: The ndarray object is designed to handle a variety of data shapes and sizes, from simple 1-dimensional arrays to complex $n$ -dimensional datasets. This flexibility makes NumPy an essential tool for scientific computing, where data often comes in multi-dimensional forms.
Broadcasting: NumPy’s broadcasting feature allows arithmetic operations to be performed on arrays of different shapes and sizes without requiring explicit replication of data, making code more efficient and easier to write.

What is pandas?#

Pandas is a powerful data manipulation and analysis library for Python created by Wes McKinney in 2008. It was developed to address the need for a flexible, high-performance tool for working with structured data, which was lacking in the existing scientific Python ecosystem at the time.

The pandas library introduces two primary data structures:

Series
DataFrame.

A Series is a one-dimensional labeled array capable of holding any data type, while a DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Strengths of pandas#

pandas is highly regarded for its versatility in data manipulation and ability to easily handle complex transformations, thanks to its intuitive syntax and robust set of functions.

Data manipulation: It provides a wide range of functions for data manipulation, including filtering, merging, reshaping, and aggregation. Its intuitive syntax makes it easy to perform complex data transformations and cleaning tasks.
Handling tabular data: The DataFrame structure is particularly well-suited for working with tabular data, similar to the structure of a database table or an Excel spreadsheet. This makes pandas an ideal tool for data analysis tasks in domains such as finance, economics, statistics, and many others.
Data alignment: It excels in handling missing data and aligning data from different sources based on their indexes. This capability is crucial for real-world data analysis, where data often comes with gaps or needs to be integrated from multiple sources.
Time series analysis: It offers powerful tools for time series analysis, including date range generation, frequency conversion, moving window statistics, and more, making it an excellent choice for analyzing time-based data.

NumPy vs. pandas: The core differences#

Understanding the core differences between NumPy and pandas is crucial for determining which library to use for specific tasks. Here, we will dive into two key aspects:

Data structures
Indexing mechanisms

Data structures#

With NumPy, we get arrays, and pandas gives us Series and DataFrames. Depending on the data you're working with, data structures of each library may be your deciding factor.

Let's explore which use cases each data structure excels in.

NumPy arrays#

NumPy’s primary data structure is the array. This array object is homogeneous, meaning all elements are of the same type, and provides a range of functionalities for numerical computations.

Code explanation:

Line 1: We import the NumPy library and assign it the alias np.
Line 4: We create a 2D NumPy array (which can be thought of as a matrix) using np.array() function. The array is initialized with the values [[1, 2, 3], [4, 5, 6]]. This means it has 2 rows and 3 columns.
Line 5: We use print() to display a message "2D array (matrix):\n" followed by the contents of array_2d. The \n in the string is a newline character.
Line 8: We perform an element-wise operation on array_2d. In NumPy, operations like ** 2 on an array mean each element of the array is squared individually. So array_2d ** 2 squares each element of array_2d and stores the result in array_squared.
Line 9: We use print() to display a message "2D array (matrix) after performing element-wise operation:\n" followed by array_squared.

pandas Series and DataFrames#

The pandas library introduces two core data structures: Series and DataFrame. These structures are designed to handle labeled data intuitively and efficiently.

Code explanation:

Line 1: We import the pandas library and assign it the alias pd.
Line 4: We create a pandas Series using the pd.Series() function. Here, we pass a list [10, 20, 30] as data and specify index=['a', 'b', 'c'] to label each element in the Series.
Line 5: We use print() to display a message "Series:\n" followed by the contents of series. The \n in the string is a newline character.
Lines 8–9: We create a pandas DataFrame using the pd.DataFrame() function. Here, data is a dictionary where keys are column names ('Name' and 'Age') and values are lists representing the data in each column (['Alice', 'Bob', 'Charlie'] and [25, 30, 35], respectively).
Line 10: We use print() to display a message "DataFrame:\n" followed by the contents of df.

2. Indexing and selection#

IndexingIndexing in NumPy is the method used to access individual elements, slices, or a subset of elements within a NumPy array. and selectionIn NumPy, a selection refers to the process of retrieving a subset of elements from an array. are fundamental operations for both NumPy and pandas. However, they offer different methods and flexibilities for accessing and modifying data.

NumPy indexing#

NumPy arrays allow for both basic and advanced indexing techniques. Basic indexing involves using integers, slices, or boolean arrays to access elements.

The following code shows how to access and modify elements in a NumPy array using basic indexing techniques:

Code explanation:

Line 10: We print the message "Accessing the third element: " followed by array_1d[2]. This accesses and displays the third element (index 2) of array_1d.
Line 13: We print the message "Accessing elements from index 1 to 3: " followed by array_1d[1:4]. This performs slicing on array_1d, accessing elements from index 1 (inclusive) to index 4 (exclusive) and displaying them.
Line 16: We print the message "Accessing elements greater than 25: " followed by array_1d[array_1d > 25]. This uses boolean indexing to filter elements in array_1d that are greater than 25 and display them.

pandas indexing#

The pandas library provides more flexible and powerful indexing options. It supports both label-based and location-based indexing through .loc and .iloc.

Label-based indexing (.loc): Access elements by labels.
Location-based indexing (.iloc): Access elements by integer location.

The following code demonstrates accessing elements in a pandas DataFrame using both label-based and location-based indexing:

Python Data Analysis and Visualization

Python Data Analysis and Visualization

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16hrs

12 Challenges

24 Quizzes

NumPy vs. pandas functionality#

NumPy and pandas each provide a rich set of functionalities that cater to different needs in data science and analysis. As such, your specific needs will influence your choice of library.

We’ll dive into specific capabilities of each library, focusing on :

Mathematical operations
Loading data from file/dataset
Data manipulation

Mathematical operations#

Mathematical operations are fundamental in data analysis and scientific computing, enabling tasks like statistical calculations and modeling.

NumPy: Mathematical operations#

NumPy excels in numerical computations, providing a wide array of mathematical functions that are optimized for performance. These functions make it a powerful tool for tasks involving linear algebra, random sampling, and Fourier transforms.

Linear algebra#

NumPy offers comprehensive support for linear algebra operations, including:

Matrix multiplication
Decomposition
Inversion
Eigenvalue calculations

These functionalities are essential for solving systems of linear equations and performing various mathematical transformations.

Python 3.10.4

import numpy as np
# Define two matrices
matrix_a = np.array([[1, 2],
                     [3, 4]])
matrix_b = np.array([[5, 6],
                     [7, 8]])
# Print the original matrices
print("\nMatrix A:")
print(matrix_a)
print("\nMatrix B:")
print(matrix_b)
# Perform matrix operations
print("\nMatrix operations:")
print("Transpose of Matrix A:\n", np.transpose(matrix_a))
print("Determinant Matrix A:", np.linalg.det(matrix_a))
print("Inverse Matrix A:\n", np.linalg.inv(matrix_a))
print("Trace Matrix A:", np.trace(matrix_a))
# Perform matrix multiplication
print("\nMatrix Multiplication:")
result_mult = np.dot(matrix_a, matrix_b)
print(result_mult)
# Perform QR decomposition (alternative to LU decomposition)
print("\nQR Decomposition of Matrix A:")
q, r = np.linalg.qr(matrix_a)
print("Q Matrix:")
print(q)
print("R Matrix:")
print(r)
# Perform matrix inversion
print("\nInverse of Matrix A:")
result_inv = np.linalg.inv(matrix_a)
print(result_inv)
# Perform eigenvalue and eigenvector calculation
print("\nEigenvalues and Eigenvectors of Matrix A:")
eigenvalues, eigenvectors = np.linalg.eig(matrix_a)
print("Eigenvalues:")
print(eigenvalues)
print("Eigenvectors:")
print(eigenvectors)

Code explanation:

Lines 18–22: Perform various matrix operations on matrix_a:
- Transpose: np.transpose(matrix_a) calculates and prints the transpose of matrix_a.
- Determinant: np.linalg.det(matrix_a) computes and prints the determinant of matrix_a.
- Inverse: np.linalg.inv(matrix_a) computes and prints the inverse of matrix_a.
- Trace: np.trace(matrix_a) computes and prints the trace (sum of diagonal elements) of matrix_a.
Lines 25–27: We perform matrix multiplication using np.dot(matrix_a, matrix_b). Store the result in result_mult and print it.
Lines 30–35: We perform QR decomposition of matrix_a using np.linalg.qr(matrix_a). We store the matrices q (orthogonal/unitary matrix) and r (upper triangular matrix) and print them.
Lines 38–40: We compute the inverse of matrix_a using np.linalg.inv(matrix_a). We store the result in result_inv and print it.
Lines 43–48: We compute the eigenvalues and eigenvectors of matrix_a using np.linalg.eig(matrix_a). We store the eigenvalues in eigenvalues and eigenvectors in eigenvectors, and print them. This computes and prints the eigenvalues and corresponding eigenvectors of matrix_a.

Random sampling #

NumPy’s random module allows for generating random numbers, creating random samples, and performing random sampling from different distributions.

Python 3.10.4

import pandas as pd
# Creating a DataFrame
data = {'Name': ['Alice', 'Bob'], 'Math': [85, 90], 'Science': [88, 92]}
df = pd.DataFrame(data)
# Display DataFrame
print("Original DataFrame:\n", df)
# Melting the DataFrame to unpivot subjects into rows
melted = pd.melt(df, id_vars=['Name'], value_vars=['Math', 'Science'], var_name='Subject', value_name='Score')
print("\nMelted DataFrame (Subjects as rows):\n", melted)
# Using stack to pivot the DataFrame
stacked = df.set_index('Name').stack().reset_index(name='Score').rename(columns={'level_1': 'Subject'})
print("\nStacked DataFrame (Subjects as rows):\n", stacked)
# Pivoting the melted DataFrame back to original form using pivot
unmelted = melted.pivot(index='Name', columns='Subject', values='Score').reset_index()
print("\nPivoted DataFrame (Original form):\n", unmelted)

Code explanation:

Line 11: We use the pd.melt() function to melt (unpivot) the DataFrame df:
- id_vars=['Name']: Specifies the 'Name' column as the identifier variable (unchanged).
- value_vars=['Math', 'Science']: Specifies the 'Math' and 'Science' columns to melt.
- var_name='Subject': Renames the variable column to 'Subject'.
- value_name='Score': Renames the value column to 'Score'.
- The result is stored in melted.
Line 15: We use the stack() method to pivot the DataFrame df by stacking columns into rows:
- set_index('Name'): Sets the 'Name' column as the index.
- stack(): Pivots all remaining columns into rows.
- reset_index(name='Score'): Resets the index and renames the resulting stacked column to 'Score'.
- rename(columns={'level_1': 'Subject'}): Renames the column previously holding column names to 'Subject'.
- The result is stored in stacked.
Line 19: We use the pivot() method on the melted DataFrame to pivot it back to the original form:
- index='Name': Sets the 'Name' column as the index.
- columns='Subject': Specifies the 'Subject' column values to pivot.
- values='Score': Specifies the 'Score' column values to populate the pivoted DataFrame.
- reset_index(): Resets the index to convert 'Name' from the index back to a regular column.
- The result is stored in unmelted.

Handling missing data#

pandas provides functions to detect, remove, or fill missing data in DataFrames.

Code explanation:

Line 11: We use the fillna() method on the 'Score' column of df to fill missing values (None) with the mean of existing values in the column:
- df['Score'].mean(): Computes the mean of non-missing values in 'Score'.
- inplace=True: Modifies df in place rather than returning a new DataFrame.
- The filled DataFrame is stored back into df['Score'].

Loading data from file/dataset#

Loading data from external files or datasets is a fundamental operation in data analysis and scientific computing. Both NumPy and pandas provide capabilities to read data from various file formats, each tailored to different use cases.

NumPy: Loading data#

NumPy primarily deals with numerical data in the form of arrays. It provides basic functionalities to load data from text files, such as CSV files, but it stores the data in its own ndarray format, which is homogeneous and optimized for numerical computations.

The following is an example of loading data with NumPy:

Code explanation:

Line 4: We use the np.loadtxt() function to load data from a CSV file 'data.csv' into a NumPy ndarray data_np:
- 'data.csv': Specifies the path to the CSV file to be loaded.
- delimiter=',': Specifies that the data in the CSV file is separated by commas.

pandas: Loading data#

pandas excels in handling structured data, including loading data from various file formats such as CSV, Excel, SQL databases, and more. It stores the data in DataFrame objects, which are flexible and capable of handling heterogeneous data types.

The following is an example of loading data with pandas:

Code explanation:

Line 4: We use the pd.read_csv() function to load data from a CSV file 'data.csv' into a pandas DataFrame df:
- 'data.csv': Specifies the path to the CSV file to be loaded.

Data manipulation#

Effective data manipulation is crucial in preparing data for analysis and ensuring it meets the requirements of various computational tasks.

NumPy: Data manipulation#

NumPy offers a range of functionalities for basic data manipulation, including slicing, reshaping, and broadcasting.

Slicing #

Slicing in NumPy allows you to extract parts of an array.

Python 3.10.4

import numpy as np
# When one operand is N*N and other is 1*1
print("Case 1:")
Z1 = np.arange(9).reshape(3,3)
print("Z1:")
print(Z1)
Z2 = 1
print("Z2:")
print(Z2)
print("Z1 + Z2:")
print(Z1 + Z2)
# When one operand is N*N and other is N*1
print("\nCase 2:")
Z1 = np.arange(9).reshape(3,3)
print("Z1:")
print(Z1)
Z2 = np.arange(3)[::-1].reshape(3,1)
print("Z2:")
print(Z2)
print("Z1 + Z2:")
print(Z1 + Z2)
# When one operand is N*N and other is 1*N
print("\nCase 3:")
Z1 = np.arange(9).reshape(3,3)
print("Z1:")
print(Z1)
Z2 = np.arange(3)[::-1]
print("Z2:")
print(Z2)
print("Z1 + Z2:")
print(Z1 + Z2)

Code explanation:

The code demonstrates different scenarios of addition between NumPy arrays (Z1) and other operands (Z2) of different shapes:
- Lines 4–14 (case 1): Addition of a 3x3 array (Z1) and a scalar (Z2 = 1).
- Lines 17–27 (case 2): Addition of a 3x3 array (Z1) and a 3x1 array (Z2).
- Lines 30–40 (case 3): Addition of a 3x3 array (Z1) and a 1D array (Z2).

pandas: Data manipulation#

pandas offers advanced tools for data manipulation, including data cleaning, merging, grouping, and time series manipulation.

Data cleaning#

pandas provides functions to clean and preprocess data, such as dropna and fillna.

Python 3.10.4

import pandas as pd
# Creating a DataFrame with the missing values
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Score': [85, None, 78]}
df = pd.DataFrame(data)
# Display DataFrame
print("Original DataFrame:\n", df)
# Dropping rows with the missing values
cleaned_df = df.dropna()
print("\nDataFrame after dropping missing values:\n", cleaned_df)
# Filling the missing values with a specific value (e.g., 0)
filled_df = df.fillna(0)
print("\nDataFrame after filling missing values with 0:\n", filled_df)

Code explanation:

Line 12: We use df.resample('M').sum() to resample the DataFrame df to a monthly frequency and calculate the sum of sales for each month, resulting in a new DataFrame monthly_sales.

Integration and ecosystem#

Both NumPy and pandas are integral parts of the Python data science ecosystem. They are designed to seamlessly integrate with other libraries, enhancing their capabilities and providing a comprehensive toolkit for data analysis and scientific computing.

Interoperability#

Effective interoperability ensures that NumPy and pandas can collaborate seamlessly with other libraries, enhancing their utility in diverse analytical and scientific applications.

NumPy: Interoperability#

NumPy is designed to work well with other scientific libraries in Python. Its interoperability allows it to serve as the foundation for a wide range of scientific and analytical tools.

SciPy #

SciPy builds on NumPy to provide additional functionality for scientific and technical computing, including modules for optimization, integration, interpolation, eigenvalue problems, and other advanced mathematical tasks.

Code explanation:

Line 5: We use np.linspace(0, 2 * np.pi, 100) to create an array x of 100 evenly spaced values ranging from 0 to 2 * np.pi.
Line 6: We use np.sin(x) to compute the sine of each value in the array x, resulting in an array y.
Line 9: We use plt.plot(x, y) to create a plot of y vs. x.
Line 10: We use plt.title('Sine Wave') to set the title of the plot to 'Sine Wave'.
Line 11: We use plt.xlabel('x') to label the x-axis as 'x'.
Line 12: We use plt.ylabel('sin(x)') to label the y-axis as 'sin(x)'.
Line 13: We use plt.show() to display the plot.

pandas: Interoperability#

pandas is also highly interoperable with a variety of other data tools and libraries, making it a versatile choice for data manipulation and analysis.

SQL databases#

pandas can read from and write to SQL databases, allowing for efficient data retrieval and storage. The read_sql and to_sql functions facilitate this integration.

Code explanation:

Line 5: We use sqlite3.connect(':memory:') to create an in-memory SQLite database and establishes a connection to it, assigned to conn.
Line 8: We create a dictionary data with sample data.
Line 9: We use pd.DataFrame(data) to create a DataFrame df from the dictionary data.
Line 12: We use df.to_sql('people', conn, index=False) to write the DataFrame df to a SQL table named 'people' in the SQLite database connected to by conn.
Line 15: We use pd.read_sql('SELECT * FROM people', conn) to read the data back from the SQL table 'people' into a new DataFrame df_from_sql.

Matplotlib #

pandas integrates smoothly with Matplotlib, making it easy to generate plots directly from DataFrames.

Code explanation:

Line 5: We create a dictionary data with sample data.
Line 6: We use pd.DataFrame(data) to create a DataFrame df from the dictionary data.
Line 9: We use df.plot(x='Month', y='Sales', kind='bar') to create a bar plot with 'Month' on the x-axis and 'Sales' on the y-axis.
Line 10: We use plt.title('Monthly Sales') to set the title of the plot to 'Monthly Sales'.
Line 11: We use plt.xlabel('Month') to label the x-axis as 'Month'.
Line 12: We use plt.ylabel('Sales') to label the y-axis as 'Sales'.
Line 13: We use plt.show() to display the plot.

Seaborn#

Seaborn is a statistical data visualization library built on top of Matplotlib that works well with pandas DataFrames. It provides high-level interfaces for drawing attractive and informative statistical graphics.

Python 3.10.4

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Score': [85, 90, 78, 88]}
df = pd.DataFrame(data)
# Set up the plotting context and style
sns.set_context("talk")  
sns.set_style("whitegrid") 
# Create a bar plot using Seaborn
ax = sns.barplot(x='Name', y='Score', data=df)  
# Access the axis object to set labels and title using matplotlib
ax.set_xlabel('Name', fontsize=14)  
ax.set_ylabel('Score', fontsize=14)  
ax.set_title('Student Scores', fontsize=16)  
# Show the plot
sns.despine() 
plt.show()

Code explanation:

Line 6: We create a dictionary data with sample data.
Line 7: We use pd.DataFrame(data) to create a DataFrame df from the dictionary data.
Line 10: We use sns.set_context("talk") to set the plotting context to "talk", which adjusts the size of the plot elements.
Line 11: We use sns.set_style("whitegrid") to set the plot style to "whitegrid", which adds grid lines on a white background for aesthetics.
Line 14: We use sns.barplot(x='Name', y='Score', data=df) to create a bar plot (barplot) with 'Name' on the x-axis and 'Score' on the y-axis, using data from the DataFrame df. The resulting plot axis object is stored in ax.
Line 17: We use ax.set_xlabel('Name', fontsize=14) to set the x-axis label to 'Name' with a font size of 14.
Line 18: We use ax.set_ylabel('Score', fontsize=14) to set the y-axis label to 'Score' with a font size of 14.
Line 19: We use ax.set_title('Student Scores', fontsize=16) to set the plot title to 'Student Scores' with a font size of 16.
Line 22: We use sns.despine() to remove the top and right spines from the plot for better aesthetics.
Line 23: We use plt.show() to display the plot using Matplotlib’s show function.

Use cases of NumPy and pandas#

Understanding the specific use cases for NumPy and pandas helps in selecting the right tool for your data processing tasks. Here, we’ll outline the primary use cases for each library, providing a clear comparison of their strengths and applications.

Library	Use Cases	Description
NumPy	Scientific computing	NumPy is the preferred library for performing scientific calculations that require high precision and performance.
		Machine learning	It provides the foundational data structures and mathematical operations essential for machine learning algorithms.
		Numerical simulations	NumPy is used for creating simulations that require handling large amounts of numerical data efficiently.
pandas	Data analysis	pandas is particularly effective in handling and analyzing structured data, making it perfect for tasks like exploring data and creating reports.
		Data preprocessing for machine learning	It provides tools for cleaning and preparing data, including handling missing values and transforming data formats.
		Financial modeling	pandas’ robust data manipulation capabilities are perfect for building and analyzing financial models.

Library	Pros	Cons
NumPy	Speed: Highly optimized for numerical computations, offering superior performance compared to native Python.	Less intuitive for tabular data: Handling tabular data can be challenging and less straightforward compared to using pandas.
		Numerical operations: Extensive support for a wide range of mathematical functions and operations.	Limited data types: Primarily designed for numerical data, with less flexibility for heterogeneous data types.
		Memory efficiency: Efficiently handles large arrays and matrices, minimizing memory overhead.	Steep learning curve: Might have a steeper learning curve for users unfamiliar with array-based programming.
pandas	Data manipulation: Excellent tools for data manipulation, cleaning, and transformation, making it easy to handle complex datasets.	Performance: Can be slower and less efficient with very large datasets compared to NumPy.
		Intuitive syntax: User-friendly and intuitive syntax, especially for operations on tabular data.	Memory consumption: Higher memory usage when dealing with large DataFrames due to its rich feature set.
		Versatility: Supports various data formats and integrates well with other data analysis libraries.	Complexity for simple tasks: May introduce unnecessary complexity for tasks that are simple in NumPy.

NumPy vs. pandas

Feature	NumPy	pandas
Data structures	Homogeneous arrays (single data type)	Heterogeneous DataFrames (mixed data types)
Performance (Numerical)	Generally faster	Slower for raw calculations, but convenient functions
Memory usage	Memory efficient	Potentially higher memory usage
Strengths	Efficient numerical computations, vectorized operations	Data cleaning, manipulation, analysis, time series
Common use cases	Scientific computing, machine learning (numerical data), image processing	Data loading, cleaning, EDA, feature engineering, time series analysis
Indexing	Basic (integer-based, slices, and boolean indexing)	Advanced indexing (label-based, location-based)
Missing value handling	Limited (manual replacement)	Flexible (`fillna`, interpolation)
Data types	Supports various numerical data types (integer, float, complex) and boolean	Supports various numerical data types, strings, categorical data, and custom data types
Math functions	Rich collection of element-wise mathematical functions (arithmetic, trigonometric, linear algebra)	Offers functions for common data analysis tasks (e.g., mean, standard deviation, correlation)
Time series functionality	Limited	Specialized functionalities (date/time objects, resampling)
Multidimensional data	Efficient handling of n-dimensional arrays	Less efficient for high-dimensional data
Learning curve	Easier to learn due to simpler data structures	Steeper learning curve due to richer features and functionalities
Interoperability	Integrates seamlessly with other scientific Python libraries (SciPy, Matplotlib)	Integrates well with NumPy and other data science libraries (Matplotlib, scikit-learn, and Seaborn)

Conclusion#

Now that you know about both of Python data manipulation tools, we hope you feel ready to make a choice about which one to pick.

NumPy shines in numerical computations and high-performance scientific computing, making it the preferred choice for tasks involving large-scale numerical data and complex mathematical operations.

pandas, on the other hand, is particularly effective in data manipulation and analysis, providing intuitive tools for handling and transforming structured data, which is invaluable for data cleaning, exploration, and preprocessing in machine learning.

Python Data Analysis and Visualization

With over 400 billion gigabytes of data out there and more every day, companies are paying top dollar to those who can leverage it. Strong data skills are becoming increasingly valuable - even if you choose not to become a professional data scientist. This path will help you master the skills to extract insights from data using a powerful (and easy to use) assortment of popular Python libraries.

16hrs

12 Challenges

24 Quizzes

NumPy Data Structure	Properties	Use Cases
`ndarray`	Homogeneity: All elements in an `ndarray` are of the same type	Scientific computing and simulations
		n-dimensional: Can handle multidimensional data (e.g., 1D, 2D, 3D arrays)	Handling large numerical datasets
		Efficient: Optimized for performance, making it ideal for numerical computations	Machine learning algorithms

pandas Data Structure	Properties	Use Cases
Series	One-dimensional: Similar to a column in a table Labeled index: Each element is associated with an index Flexible: Can hold different data types	Data analysis and manipulation Handling and cleaning tabular data Time series analysis
DataFrame	Two-dimensional: Similar to a table with rows and columns Labeled axes: Both rows and columns are indexed Heterogeneous: Can hold different data types in different columns

Table of Contents

NumPy vs. pandas: What’s the difference?

What is NumPy?#

Strengths of NumPy#

What is pandas?#

Strengths of pandas#

NumPy vs. pandas: The core differences#

Data structures#

NumPy arrays#

pandas Series and DataFrames#

Series and DataFrames

2. Indexing and selection#

NumPy indexing#

pandas indexing#

NumPy vs. pandas functionality#

Mathematical operations#

NumPy: Mathematical operations#

Linear algebra#

Random sampling #

Fourier transforms #

pandas: Mathematical operations#

Data aggregation #

Merging#

Reshaping#

Handling missing data#

Loading data from file/dataset#

NumPy: Loading data#

pandas: Loading data#

Data manipulation#

NumPy: Data manipulation#

Slicing #

Reshaping#

Broadcasting #

pandas: Data manipulation#

Data cleaning#

Merging#

Grouping #

Time series manipulation#

Integration and ecosystem#

Interoperability#

NumPy: Interoperability#

SciPy #

Matplotlib#

pandas: Interoperability#

SQL databases#

Matplotlib #

Seaborn#

Use cases of NumPy and pandas#

Pros and cons: NumPy vs. pandas#

Comparison between NumPy and pandas#

NumPy vs. pandas

Conclusion#

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