# 238、pandas.Series.ravel方法

# Flattened data (C order): [1 2 3 4 5]

# Flattened data (F order): [1 2 3 4 5]

239、pandas.Series.repeat方法

239-1、语法

# 239、pandas.Series.repeat方法

pandas.Series.repeat(repeats, axis=None)

Repeat elements of a Series.

Returns a new Series where each element of the current Series is repeated consecutively a given number of times.

Parameters:

repeats

int or array of ints

The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty Series.

axis

None

Unused. Parameter needed for compatibility with DataFrame.

Returns:

Series

Newly created Series with repeated elements.

239-2、参数

239-2-1、repeats(必须)：整数或整数数组，如果是单个整数，则Series中的每个元素都会被重复该整数指定的次数；如果是一个与Series等长的整数数组，则每个元素会按照对应位置的整数进行重复。

239-2-2、axis(可选，默认值为None)：参数在Series中无效，因为Series是一维的，因此这个参数在这里不被使用。

239-3、功能

        用于将Series中的每个元素按指定的次数重复，该方法对于数据扩展或增加数据量非常有用。

239-4、返回值

        返回一个新的Pandas Series对象，其中每个元素按指定的次数进行了重复。

239-5、说明

        无

239-6、用法

239-6-1、数据准备

无

239-6-2、代码示例

# 239、pandas.Series.repeat方法

import pandas as pd

# 创建一个Pandas Series对象

data = pd.Series([1, 2, 3])

# 每个元素重复3次

repeated_data_1 = data.repeat(3)

# 每个元素根据给定的数组分别重复

repeated_data_2 = data.repeat([1, 2, 3])

print("Repeated data (3 times):")

print(repeated_data_1)

print("\nRepeated data (1, 2, 3 times respectively):")

print(repeated_data_2)

239-6-3、结果输出

# 239、pandas.Series.repeat方法

# Repeated data (3 times):

# 0 1

# 0 1

# 0 1

# 1 2

# 1 2

# 1 2

# 2 3

# 2 3

# 2 3

# dtype: int64

#

# Repeated data (1, 2, 3 times respectively):

# 0 1

# 1 2

# 1 2

# 2 3

# 2 3

# 2 3

# dtype: int64

240、pandas.Series.squeeze方法

240-1、语法

# 240、pandas.Series.squeeze方法

pandas.Series.squeeze(axis=None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don’t know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters:

axis

{0 or ‘index’, 1 or ‘columns’, None}, default None

A specific axis to squeeze. By default, all length-1 axes are squeezed. For Series this parameter is unused and defaults to None.

Returns:

DataFrame, Series, or scalar

The projection after squeezing axis or all the axes.

240-2、参数

240-2-1、axis(可选，默认值为None)：{None, 0, 1}，选项有：

None：默认值，自动删除长度为1的维度。0或index：如果Series或DataFrame在索引轴上只有一个值，则压缩该维度。1或columns：如果Series或DataFrame在列轴上只有一个值，则压缩该维度。

240-3、功能

        用于去除Series中长度为1的维度，它常用于处理从DataFrame中提取的单列或单行结果，使得返回的结果更加简洁。

240-4、返回值

        返回一个去除了长度为1的维度后的对象，如果没有长度为1的维度，则返回原对象。

240-5、说明

        无

240-6、用法

240-6-1、数据准备

无

240-6-2、代码示例

# 240、pandas.Series.squeeze方法

# 240-1、从DataFrame提取单行或单列

import pandas as pd

# 创建一个DataFrame

df = pd.DataFrame({

'A': [10, 20, 30],

'B': [15, 25, 35]

})

# 提取单列

single_column = df[['A']]

squeezed_column = single_column.squeeze()

# 提取单行

single_row = df.iloc[[0]]

squeezed_row = single_row.squeeze()

print("Original single column DataFrame:")

print(single_column)

print("Squeezed Series from single column:")

print(squeezed_column)

print("Original single row DataFrame:")

print(single_row)

print("Squeezed Series from single row:")

print(squeezed_row, end='\n\n')code>

# 240-2、数据分组后的操作

import pandas as pd

# 创建一个DataFrame

df = pd.DataFrame({

'Category': ['A', 'A', 'B'],

'Value': [10, 20, 30]

})

# 按'Category'分组并计算均值

grouped = df.groupby('Category').mean()

# 获取特定类别的数据并使用squeeze

single_category_mean = grouped.loc[['A']]

squeezed_category_mean = single_category_mean.squeeze()

print("Grouped mean DataFrame:")

print(single_category_mean)

print("Squeezed mean for single category:")

print(squeezed_category_mean, end='\n\n')code>

# 240-3、提高内存效率和性能

import pandas as pd

# 创建一个大型DataFrame

large_df = pd.DataFrame({'Value': range(1000000)})

# 提取单列并使用squeeze

squeezed_series = large_df[['Value']].squeeze()

# 检查内存使用

print("Memory usage of original DataFrame:", large_df.memory_usage(deep=True).sum())

print("Memory usage of squeezed Series:", squeezed_series.memory_usage(deep=True), end='\n\n')code>

# 240-4、与函数进行交互

import matplotlib.pyplot as plt

# 定义一个只接受 Series 的绘图函数

def plot_series(series):

series.plot(kind='line', title='Series Plot')code>

plt.show()

# 提取数据并传递给函数

data = df[['Value']].iloc[0:3] # 提取单列

plot_series(data.squeeze())

# 240-5、简化输出

# 计算平均值并使用squeeze

processed_result = df[['Value']].mean().squeeze()

def display_result(result):

print(f"Processed Result: {result}")

# 使用squeeze简化输出

display_result(processed_result)

# 240-6、数据清洗与转换

import pandas as pd

# 创建一个包含冗余维度的DataFrame

redundant_df = pd.DataFrame({'Value': [[10], [20], [30]]})

# 使用apply和squeeze清理数据

cleaned_series = redundant_df['Value'].apply(lambda x: pd.Series(x).squeeze())

print("Original DataFrame with redundant dimension:")

print(redundant_df)

print("Cleaned Series:")

print(cleaned_series, end='\n\n')code>

# 240-7、数学与统计计算

import pandas as pd

# 创建一个DataFrame

df = pd.DataFrame({'Value': [10, 20, 30]})

# 计算总和并使用squeeze

total_sum = df[['Value']].sum().squeeze()

print("Total sum of values:", total_sum)

240-6-3、结果输出