Summary of the use of pandas Series name attribute

Pandas2.2 Series

Attributes

method	describe
	Label or index for each data point
	The underlying data array of objects
	Access data values in Series as NumPy array
	Type (dtype) used to obtain data in Pandas Series
	Used to obtain the shape of Pandas Series, i.e. its dimension information
	The number of bytes required to store data in a Series object
	Get the number of dimensions of a Pandas Series object
	Returns the number of elements in the underlying data of a given Series object
	Used to return transposed data
Series.memory_usage([index, deep])	Used to return the memory usage of the Series object
	Used to check whether NaN exists in Series objects
	Used for inspection`Series`Is the object empty?
	Used to obtain`Series`Medium element data type
	Used to giveObject naming

Attributes are used to giveObject naming. This attribute is very important for the readability and management of data, especially when dealing with multipleSeriesWhen analyzing an object or data, naming can help us identify each one more clearly.SeriesThe data represented by the object.

Properties introduction

name: name
type: string (str）
use: forSeriesObject provides a tag or name.
default value: Default isNone。

Examples and results

Here are some usesExamples of properties:

Example 1: Create and name Series

import pandas as pd

# Create an unnamed Seriesdata = ([1, 2, 3, 4, 5])
print("Unnamed Series:")
print(data)

# Create a named Seriesnamed_data = ([1, 2, 3, 4, 5], name='Numbers')
print("\nName Series:")
print(named_data)

result:

Unnamed Series:
0 1
1 2
2 3
3 4
4 5
dtype: int64

Named Series:
0 1
1 2
2 3
3 4
4 5
Name: Numbers, dtype: int64

You can see that after the nameSeriesOutput,NameThe line showsSeriesThe name of .

Example 2: Modify the name of Series

# Create an unnamed Seriesdata = ([10, 20, 30, 40, 50])

# Modify the name of the Series = 'High Numbers'

print("Modified Series:")
print(data)

result:

Modified Series:
0 10
1 20
2 30
3 40
4 50
Name: High Numbers, dtype: int64

You can see,SeriesThe name of has been successfully modified.

Example 3: Using named Series in DataFrame

# Create two named Seriesseries1 = ([1, 2, 3], name='A')
series2 = ([4, 5, 6], name='B')

# Merge named Series into DataFramedf = ({'A': series1, 'B': series2})

print("DataFrame uses named Series:")
print(df)

result:

DataFrame uses named Series:
A B
0 1 4
1 2 5
2 3 6

In this example,DataFrameThe column names are automatically usedSeriesofnameAttributes.

Summarize

Attributes are a very useful feature that can help us better manage and identifySeriesObject. By givingSeriesNaming, we can improve the readability and maintenance of our code, especially when dealing with complex data analysis tasks.

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