目录

一、用法精讲

55、pandas.lreshape函数

55-1、语法

55-2、参数

55-3、功能

55-4、返回值

55-5、说明

55-6、用法

55-6-1、数据准备

55-6-2、代码示例

55-6-3、结果输出

56、pandas.wide_to_long函数

56-1、语法

56-2、参数

56-3、功能

56-4、返回值

56-5、说明

56-6、用法

56-6-1、数据准备

56-6-2、代码示例

56-6-3、结果输出 

57、pandas.isna函数

57-1、语法

57-2、参数

57-3、功能

57-4、返回值

57-5、说明

57-6、用法

57-6-1、数据准备

57-6-2、代码示例

57-6-3、结果输出

二、推荐阅读

1、Python筑基之旅

2、Python函数之旅

3、Python算法之旅

4、Python魔法之旅

5、博客个人主页

一、用法精讲

55、pandas.lreshape函数

55-1、语法

<code># 55、pandas.lreshape函数

pandas.lreshape(data, groups, dropna=True)

Reshape wide-format data to long. Generalized inverse of DataFrame.pivot.

Accepts a dictionary, groups, in which each key is a new column name and each value is a list of old column names that will be “melted” under the new column name as part of the reshape.

Parameters:

data

DataFrame

The wide-format DataFrame.

groups

dict

{new_name : list_of_columns}.

dropna

bool, default True

Do not include columns whose entries are all NaN.

Returns:

DataFrame

Reshaped DataFrame.

55-2、参数

55-2-1、data(必须)：要进行重塑的Pandas数据框。

55-2-2、groups(必须)：一个字典，用于指定要重塑的列组，字典的键是新列的名称，值是要重塑的列列表，例如：{'A': ['A1', 'A2'], 'B': ['B1', 'B2']}。

55-2-3、dropna(可选，默认值为True)：指定是否在重塑过程中丢弃包含NaN的行，如果设置为True，则会丢弃包含NaN的行；如果设置为False，则保留NaN。

55-3、功能

        根据指定的列组对数据框进行重塑，将宽格式的数据转换为长格式。

55-4、返回值

        返回一个重塑后的Pandas数据框，其中包含从宽格式转换为长格式的数据。

55-5、说明

        Pandas.Ireshape是一个强大的工具，可以根据指定的列组对数据框进行宽转长的重塑，它具有三个参数：

55-5-1、data：要重塑的DataFrame。

55-5-2、groups：一个字典，定义新的列组。

55-5-3、dropna：指定是否丢弃包含NaN的行。

        通过理解和正确使用这些参数，可以灵活地重塑数据框，从而更好地组织和分析数据。

55-6、用法

55-6-1、数据准备

无

55-6-2、代码示例

# 55、pandas.lreshape函数

import pandas as pd

# 创建示例数据框

data = pd.DataFrame({

'A1': [1, 2, 3],

'A2': [4, 5, 6],

'B1': ['a', 'b', 'c'],

'B2': ['d', 'e', 'f']

})

# 定义列组

groups = {

'A': ['A1', 'A2'],

'B': ['B1', 'B2']

}

# 使用pandas.lreshape进行重塑

reshaped = pd.lreshape(data, groups)

print(reshaped)

55-6-3、结果输出

# 55、pandas.lreshape函数

# A B

# 0 1 a

# 1 2 b

# 2 3 c

# 3 4 d

# 4 5 e

# 5 6 f

56、pandas.wide_to_long函数

56-1、语法

# 56、pandas.wide_to_long函数

pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')code>

Unpivot a DataFrame from wide to long format.

Less flexible but more user-friendly than melt.

With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix1, B-suffix2,… You specify what you want to call this suffix in the resulting long format with j (for example j=’year’)

Each row of these wide variables are assumed to be uniquely identified by i (can be a single column name or a list of column names)

All remaining variables in the data frame are left intact.

Parameters:

df

DataFrame

The wide-format DataFrame.

stubnames

str or list-like

The stub name(s). The wide format variables are assumed to start with the stub names.

i

str or list-like

Column(s) to use as id variable(s).

j

str

The name of the sub-observation variable. What you wish to name your suffix in the long format.

sep

str, default “”

A character indicating the separation of the variable names in the wide format, to be stripped from the names in the long format. For example, if your column names are A-suffix1, A-suffix2, you can strip the hyphen by specifying sep=’-’.

suffix

str, default ‘\d+’

A regular expression capturing the wanted suffixes. ‘\d+’ captures numeric suffixes. Suffixes with no numbers could be specified with the negated character class ‘\D+’. You can also further disambiguate suffixes, for example, if your wide variables are of the form A-one, B-two,.., and you have an unrelated column A-rating, you can ignore the last one by specifying suffix=’(!?one|two)’. When all suffixes are numeric, they are cast to int64/float64.

Returns:

DataFrame

A DataFrame that contains each stub name as a variable, with new index (i, j).

56-2、参数