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HomeBlogData SciencePandas vs NumPy in Data Science: Top 15 Differences
The most popular programming language nowadays is Python. It never fails to astound users when it comes to handling jobs and problems related to Data Science. The majority of data scientists already use Python's power daily. It is a popular, object-oriented, open-source, high-performance language that is simple to learn and easy to debug, among many other advantages. Python was created with outstanding data science packages, modules and libraries that programmers utilize daily to solve challenges.
A python library is a collection of methods and functions belonging to a related module that aid in completing specific tasks by saving considerable time and lines of code. The use of these libraries also helps us to avoid writing repeated codes. Most of the libraries are open source and maintained by a community of developers spread across geographical locations. At the same time, for building data science applications, Pandas and NumPy libraries are most widely used due to their easy performance of powerful computations.
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In this section, let us look at the 13 key differences between Python Pandas vs NumPy. Since both are widely used across Data Science applications, it becomes important to understand the Pandas and NumPy differences. It enables us to use the appropriate library concerning the problem statement.
| Criteria | Pandas | NumPy |
|---|---|---|
| Fundamental Data Object | Series and DataFrames | N-dimensional array or ndarray |
| Memory Consumption | More | Less |
| Performance on smaller datasets | Slower | Faster |
| Performance on larger datasets | Faster | Slower |
| Data Object Type | Heterogeneous | Homogeneous |
| Access Methods | Index positions and index labels | Index positions |
| Indexing | Slower | Faster |
| Core language | Python, Cython, and C language | C language |
| External Data | Pandas objects are created from external data such as CSV, Excel or SQL | NumPy generally uses data created by user or built-in functions |
| Application | Pandas objects are primarily used for data manipulation and data wrangling | NumPy objects are used to create matrices or arrays, which are used in creating ML or DL models |
| Operations | Pandas provide special utilities such as groupby, loc, iloc & which apply to access and manipulate different subsets of data | NumPy doesn’t provide any such functionalities, however, subset can be selected using indexes or conditional formatting |
| Speed | DataFrames are relatively slower than Array | NumPy arrays are faster than DataFrames |
| Usage | Commonly used for holding external user data and performing analysis on it to understand the data well | Commonly used for building components for ML or DL models |
In this section, we will check the differences between Pandas and NumPy. Both libraries form the basics of Python programming regarding data science. To know more about Data Science and its related fields, you can explore best Data Science course certifications that can help you sharpen your skills with Data Science Training from expert Trainers.
Since both Pandas and NumPy are open-source libraries, it becomes important to have active contributors to these libraries. These contributors actively maintain the library by suggesting and implementing enhancements and fixing bugs or issues raised by users. If a library does not have active contributors or maintainers, you will not get updates or resolutions to any issue faced by the library.
Healthy contributors are a testament that there are a lot of active users for the library, which also enables regular discussions on multiple platforms like StackOverflow over queries regarding the usage of these libraries.
| Parameter | Pandas | NumPy |
|---|---|---|
| Current Version | v1.4.4 | v1.23.3 |
| Releases | 88 | 90 |
| Contributors | 2,671 | 1,368 |
| Commits | 30,095 | 30,451 |
| Used By | 7,79,000 + | 12,00,000 + |
| Stars | 35,100 + | 21,400 + |
| Forks | 14,900 + | 7,300 + |
| Watched By | 1,100 + | 568 |
With the above stats, we can clearly say that a group of open-source developers actively maintains both libraries.
The fundamental data structure which powers Pandas library is ‘Data Frames’. A data frame with a single column is referred to as a ‘Series’. The fundamental data structure that powers the NumPy library is an n-dimensional array also referred to as ‘ndarray’.
The memory consumption for NumPy is less than that of Pandas. The primary reason for this is the extra overhead created in Pandas data frames for storing data types as objects and the setting of the index that takes place while creating a data frame.
Pandas is preferred while working with tabular data and is built on top of NumPy. Whereas, NumPy is preferred for performing various numerical computations and processing single or multi-dimensional arrays like matrices.
As per reports, the performance test of NumPy vs Pandas speed was done on the iris dataset. According to the test, NumPy is found to perform better than Pandas when the number of records or rows is less than or equal to 50k. For 500k or more records, Pandas performed better than NumPy.
Between 50k to 500k records, we cannot say conclusively which of them is better than the other. With these results, we can say that NumPy seems to provide better performance for smaller datasets, and Pandas can be preferred when the dataset is large.
Pandas DataFrames represent a tabular format consisting of rows and columns, which makes it a 2-dimensional data object. NumPy’s ndarray or n-dimensional array, as the name suggests, can create n-dimensional data objects.
NumPy arrays and Pandas DataFrames can store string, integer, float, list, etc., values. In the case of Pandas, DataFrames can store heterogeneous data types. Each column can be represented as a different data type. In the case of NumPy arrays, one single data type is associated with the array, making it a homogenous data type.
To access a data point or a group of data points in Pandas DataFrames, we can use index positions (represented using whole numbers) or index labels, that is, using column names and index names. For NumPy arrays, we can only use index position again represented as whole numbers.
Indexing operation is slower in Pandas DataFrames or series when compared with that of NumPy arrays. This is because Pandas is built on top of NumPy and therefore, Pandas adds its layer of indexing to the underlying array. This layer of indexing includes column and row labels.
Pandas is capable of performing complex operations like group by, multi-level sorting, etc in addition to the functionalities that we also see in NumPy. NumPy, on the other hand, does not include additional functions apart from the mathematical or matrix operations that can be performed on its array data structure.
Both libraries are capable of reading data from external files such as CSV formats. But in the case of Pandas, it has more powerful functionality in terms of reading external data. It can read data from different file formats like CSV, Excel, Parquet, and even databases.
Both NumPy and Pandas for Data Science are widely used across Industries. According to StackShare, 198 companies reportedly use Pandas in their tech stacks compared to 169 companies that use NumPy in their tech stacks. Also, 1107 and 751 developers on StackShare have stated that they use Pandas and NumPy, respectively.
Pandas is a popular library when it comes to data analysis, data manipulation and visualizations. It is extensively used during the exploratory data analysis phase of a Data Science project. NumPy is usually preferred when we need to perform mathematical calculations. It has inbuilt functionalities which can handle matrix computations with ease.
To understand when to use NumPy vs Pandas in Python, we must know that Pandas is widely used in Machine Learning use-cases where exploratory data analysis is involved before the model-building step. In AI applications where images and videos are involved, NumPy arrays are used to represent images and videos in the form of a matrix. However, for any AI or ML model training, the input data is in the form of NumPy arrays.
Pandas is written in Python, Cython, and C language, whereas NumPy is written in C.
Pandas is an open-source python library released under the BSD License. It is a fast and powerful library for data manipulation and analysis. Pandas use an expressive data structure called ‘Data Frames’ that represents data in a tabular format.
1. Pandas Series
2. Pandas DataFrame
Pandas provide the below special functions (this list is not exhaustive), which help the user to know data better.
1. Info: This method allows the user to access various useful information about data such as:
2. Describe: This method generates a 5-point data summary for ONLY numerical columns, which include: -
3. Shape: This method returns the number of rows and columns in the DataFrame.
4. Isnull(col): This method helps determine whether the supplied column has any NULL value or not.
Just like Pandas, NumPy is also an open-source python library released under the BSD license. NumPy or Numerical Python is a package that consists of high-level mathematical functions for performing scientific computing in Python. The basic difference between Pandas and NumPy is the fundamental data structure that they use. NumPy makes use of multi-dimensional arrays, which are fast in terms of computation speed as compared to Pandas data frames.
Let us decompose and understand this complicated introduction:
Some notable features of Pandas include:
Some notable features of NumPy include:
Pandas can be installed using Python’s PIP package using the following command:
>>> pip install Pandas
For the following examples, assume Pandas library has already been imported using:
import Pandas as pd
We will use the same dataset for all the below examples.
1. Reading Input Data
df = pd.read_csv(‘ds_salaries.csv’)
2. Performing Group by Operation
We will perform group by operation using the job title column to get the mean salary corresponding to each job title.
salary = df.groupby(by='job_title')[[ 'job_title', 'salary' ]].mean().reset_index()
Output (first five records shown):
3. Performing Sorting Operation
We will sort the above DataFrame ‘salary’ in descending order of ‘job_title’ column.
salary = salary.sort_values(by='job_title', ascending=False)
Output:
4. Creating Visualizations
Pandas is capable of providing powerful analysis with the in-built method ‘plot()’ to create visualizations. We will create a bar chart representing the mean salary information for the first five job titles.
salary[:5].plot(kind='bar', x='job_title', y='salary')
Output:
5. Joining Two Data Sets
The ‘join()’ method can be used to join two datasets. It works similarly to the joins in SQL. Consider the DataFrames ‘x1’ and ‘x2’ having a common column as ‘id’. We can perform an inner join on both these DataFrames using the column ‘id’ as shown below:
x3 = x1.join(other=x2, on='id', how='inner')
The ‘merge()’ method can also be used to join two datasets. The key difference between join() and merge() methods is that join() by default performs left join, whereas merge() by default performs inner join. In the join() method, DataFrames are joined on row indices whereas in merge() method, DataFrames can be joined on indices as well as columns.
x3= pd.merge(x1, x2, on='id')
6. Merging Two Data Sets
We can merge two or more datasets using the ‘append()’ method of DataFrames. Consider DataFrames ‘x1’ and ‘x2’ with the same set of columns. We can merge both these DataFrames to create one DataFrame with all the rows from both ‘x1’ and ‘x2’.
x4 = x1.append(other=x2, ignore_index=True)
NumPy can be installed using Python’s PIP package using the following command:
>>> pip install NumPy
For the following examples, assume Pandas library has already been imported using:
import NumPy as np
1. Creating a NumPy n-dimensional Array
We will create a 2-D NumPy array, known as ndarray, using the below code. The array contains 4 rows and 3 columns.
arr = np.array([[1, 2, 3], [4, 5, 6], [6, 5, 4], [3, 2, 1]])
Output:
2. Selecting Data Using Indexing
Indexing in NumPy is similar to what we do in Python list data type. The indexing starts with ‘0’ and is mentioned within the square brackets. In the below example, we are accessing the item present in the third row (represented as index value 2) and second column (represented as index value 1).
arr[2][1]
The above code returns the value 5 (refer to the output of example 1).
3. Selecting Data Using Slicing
The slicing operation helps to select more than one value. During slicing, we need to provide the range for rows to be selected as the first parameter and the range of columns to be selected as the second parameter. The below code returns the first row (represented as index value 0) and second row (represented as index value 1) along with the second column (represented as index value 1) and third column (represented as index value 2).
Please note that when we provide a slicing range as ‘1:4’, it implies that the selection should be made for indexes 1, 2 and 3 where 4 is exclusive of the range.
arr[0:2, 1:3]
4. Transposing an Array
As mentioned in this article, NumPy has in-built methods that help perform matrix operations. One such method is ‘transpose()’, which returns the transpose of a given matrix.
arr.transpose()
Output:
5. Array Building Using User Defined Values
We can create an array with user-defined values using the built-in syntax.
In the very first line, we are importing the NumPy library and using an alias as np for easy access at a later time. In the second line, we are defining an array using the built-in function array and passing a list of numbers as the argument.
Upon printing, we should see the array printed on the screen.
Some of the fundamental attributes of a NumPy object are:
NumPy provides various built-in stationary functions, which demonstrate meta-data about an array object.
We can access any element of an array using the "index" mechanism. Indexes represent the address or position of elements in an array. In Python, the index position starts from 0.
As seen in the above image, accessing an array object with 0 index (enclosed in square bracket) returns 1 (which is the first element of an array).
6. Array Building From Existing (other) Data Objects
We can choose to create an array from existing data structures such as List or Tuple.
As we can see, the built-in function to create an array (np.array) remained the same and only the passed argument changed. In the first instance, we passed an object of List and in the second instance we passed an object of Tuple.
7. Array Building Using in-built Functions
Lastly, we have the option to create an array using alternative or built-in methods. This option provides a great variety of variations to the user.
Here, we are creating an array with range of values using built-in function np.arange
We can also create an array with all elements initialized to either 0 or 1.
We can create an array that follows specific data distributions. This is especially helpful in initializing weights in neural networks.
In this article, we examined what the difference between Pandas and NumPy, two widely used Python data science tools is. In data science applications like numerical computations, data manipulation, data analysis, data visualizations, etc., both libraries are typically used in tandem. As we have seen, the task itself determines whether Pandas or NumPy should be used. For mathematical and scientific calculations, NumPy is used, but Pandas is chosen for data manipulation and analysis. This article's main lesson is that since NumPy is the foundation for Pandas, it is wise to consider each library's unique capabilities.
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In terms of speed, NumPy and Pandas difference is that numerous C or Cython-optimized functions that are available in Pandas may be quicker than their NumPy equivalents. Pandas DataFrames are typically going to be slower than a NumPy array if you want to perform mathematical operations like computing the mean, the dot product, and other similar tasks.
The ndarrays in NumPy are used in Pandas DataFrames and learning operations like indexing, slicing, etc. in ndarrays can prove to be useful while exploring Pandas.
No, NumPy is required for Pandas to work since Pandas is built on top of NumPy and other libraries.
Pandas make use of a single core of CPU to perform operations. Libraries such as Dask, PySpark, PyPolars, cuDF, Modin, etc. take advantage of multi-cores of CPU and therefore, are faster than Pandas.
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