Python3, an article on the differences and application scenarios between Numpy and Pandas, I learned it properly.

1 Introduction

Xiao Diaosi : Brother Yu, you said that both Numpy and Pandas are used for data processing and analysis, so what is the difference between them?
Xiaoyu : This...the name is different.

Little Diaosi : Brother Yu, be serious.
Xiaoyu : Alright, then I will tell you a few words. Essential difference.
Little Diaosi : Don't just say a few words, it's a rare opportunity, you can say a few more words, anyway, Mr. Z will pay for it.
Xiaoyu : ...Okay, then for Mr. Z's sake, I'll just say a few more words.

2. Comparison between Numpy and Pandas

When it comes to Numpy and Pandas, everyone in the data processing and analysis world knows everyone.
But hey, why am I even blah?
Because today, we're going to talk about the other side of Numpy and Pandas.

2.1 Similarities

First of all, we all know the similarities between Numpy and Pandas, namely:

• Both Numpy and Pandas are third-party libraries for Python for data manipulation and analysis.
• They both provide efficient data structures and functions that can handle large-scale data.
• Both Numpy and Pandas support vectorized operations, which allow for fast calculations on entire arrays or data frames.
• They all have a wide range of functions and methods for reading, transforming, filtering, and aggregating data.

2.2 Differences

Secondly, the differences between Numpy and Pandas, such as:

• Application is different

  • Numpy is mainly used for numerical computing and scientific computing, providing multidimensional array objects (ndarray) and related mathematical functions;
  • Pandas is more suitable for data processing and analysis, providing data structures such as DataFrame and Series;

• different data structures

  • Numpy's data structure is a multidimensional array, suitable for processing numerical data;
  • The data structure of Pandas is more flexible and can handle different types of data, including numeric, string, time series, etc.;

• Operation is different

  • Numpy's operations are lower-level and more suitable for numerical calculations and array operations;
  • Pandas provides more advanced data manipulation and analysis functions, such as data merging, reshaping, grouping, perspective, etc.;

• different performance

  • Numpy has higher performance and is suitable for processing large-scale numerical calculations;
  • The performance of Pandas is relatively low, but it is more suitable for processing structured data and data analysis.

2.3 Application scenarios

2.3.1 Numpy application scenarios

Numpy application scenarios are as follows:

• Numerical calculation: numpy provides efficient array operation methods that can handle data sets containing a large number of numbers, such as matrix operations, vector operations, array addition, subtraction, multiplication, and division, and so on.

• Scientific computing: numpy provides a large number of methods and functions for processing data related to scientific computing, such as data visualization, integration, differentiation, signal processing, and so on.

• Machine learning: numpy provides a large number of methods and functions for processing data related to machine learning, such as neural network model training, autoencoders, conditional random fields, etc.

• Image processing: numpy provides a large number of methods and functions for processing image-related data, such as image filtering, image transformation, image segmentation, and so on.

• Scientific data analysis: numpy provides a large number of methods and functions for processing data related to scientific data, such as astronomy, physics, earth science, etc.

2.3.2 Pandas application scenarios

The application scenarios of pandas are as follows:

• Data analysis: pandas provides a large number of methods and functions for different aspects of processing data, such as data reading, data filtering, data grouping, data sorting, data summarization, data analysis, and so on.

• Financial analysis: pandas provides a large number of methods and functions for processing data related to financial data, such as stock data analysis, bond data analysis, futures data analysis, and so on.

• Business intelligence: pandas provides a large number of methods and functions for processing data related to business data, such as sales data analysis, customer data analysis, market data analysis, etc.

• Data visualization: pandas provides a large number of methods and functions for processing data related to data visualization, such as chart drawing, data map generation, and so on.

• Artificial intelligence: pandas provides a large number of methods and functions for processing data related to artificial intelligence, such as natural language processing, image recognition, speech recognition, and so on.

2.4 Code example

Next, I will use two pieces of code to see the difference between Pandas and Numpy.

2.4.1 Numpy

code example

import numpy as np

# 创建一个一维数组
arr = np.array([1, 2, 3, 4, 5])

# 对数组进行加法操作
result = arr + 1

print(result)  # 输出：[2 3 4 5 6]

2.4.2 Pandas

code example

import pandas as pd

# 创建一个数据框
data = {
    
    'Name': ['Tom', 'John', 'Mike'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}
df = pd.DataFrame(data)

# 筛选年龄大于30的数据
result = df[df['Age'] > 30]

print(result)

3. Summary

Seeing this, today's sharing is over.
Today, I mainly shared some differences and application scenarios between Numpy and Pandas.
In the previous article, Xiaoyu also said that if you learn Pandas well, you will not be afraid to go anywhere.

If you want to learn data analysis, you can jump directly to Xiaoyu's data analysis column ,

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