标签：数据分析 ... Task3 NaN 笔记 result female Mr male
复习：在前面我们已经学习了Pandas基础，第二章我们开始进入数据分析的业务部分，在第二章第一节的内容中，我们学习了数据的清洗，这一部分十分重要，只有数据变得相对干净，我们之后对数据的分析才可以更有力。而这一节，我们要做的是数据重构，数据重构依旧属于数据理解（准备）的范围。
开始之前，导入numpy、pandas包和数据
# 导入基本库
import numpy as np
import pandas as pd
# 载入data文件中的:train-left-up.csv
data=pd.read_csv("data/train-left-up.csv")
2 第二章：数据重构
2.4 数据的合并
2.4.1 任务一：将data文件夹里面的所有数据都载入，观察数据的之间的关系
#写入代码
dleftup=pd.read_csv('data/train-left-up.csv')
dleftdown=pd.read_csv('data/train-left-down.csv')
drightup=pd.read_csv('data/train-right-up.csv')
drightdown=pd.read_csv('data/train-right-down.csv')
drightup
| Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|
| 0 | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 434 | male | 50.0 | 1 | 0 | 13507 | 55.9000 | E44 | S |
| 435 | female | 14.0 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S |
| 436 | female | 21.0 | 2 | 2 | W./C. 6608 | 34.3750 | NaN | S |
| 437 | female | 24.0 | 2 | 3 | 29106 | 18.7500 | NaN | S |
| 438 | male | 64.0 | 1 | 4 | 19950 | 263.0000 | C23 C25 C27 | S |
439 rows × 8 columns
【提示】结合之前我们加载的train.csv数据，大致预测一下上面的数据是什么
2.4.2：任务二：使用concat方法：将数据train-left-up.csv和train-right-up.csv横向合并为一张表，并保存这张表为result_up
#写入代码
result_up=pd.concat([dleftup,drightup],axis=1)
result_up
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 434 | 435 | 0 | 1 | Silvey, Mr. William Baird | male | 50.0 | 1 | 0 | 13507 | 55.9000 | E44 | S |
| 435 | 436 | 1 | 1 | Carter, Miss. Lucile Polk | female | 14.0 | 1 | 2 | 113760 | 120.0000 | B96 B98 | S |
| 436 | 437 | 0 | 3 | Ford, Miss. Doolina Margaret "Daisy" | female | 21.0 | 2 | 2 | W./C. 6608 | 34.3750 | NaN | S |
| 437 | 438 | 1 | 2 | Richards, Mrs. Sidney (Emily Hocking) | female | 24.0 | 2 | 3 | 29106 | 18.7500 | NaN | S |
| 438 | 439 | 0 | 1 | Fortune, Mr. Mark | male | 64.0 | 1 | 4 | 19950 | 263.0000 | C23 C25 C27 | S |
439 rows × 12 columns
2.4.3 任务三：使用concat方法：将train-left-down和train-right-down横向合并为一张表，并保存这张表为result_down。然后将上边的result_up和result_down纵向合并为result。
#写入代码
result_down=pd.concat([dleftdown,drightdown],axis=1)
result=pd.concat([result_up,result_down])
result
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 447 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 448 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 449 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 450 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 451 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
pandas.concat(objs, # 合并对象
axis=0, # 合并方向，默认是0纵轴方向
join='outer', # 合并取的是交集inner还是并集outer
ignore_index=False, # 合并之后索引是否重新
keys=None, # 在行索引的方向上带上原来数据的名字；主要是用于层次化索引，可以是任意的列表或者数组、元组数据或者列表数组
levels=None, # 指定用作层次化索引各级别上的索引，如果是设置了keys
names=None, # 行索引的名字，列表形式
verify_integrity=False, # 检查行索引是否重复；有则报错
sort=False, # 对非连接的轴进行排序
copy=True # 是否进行深拷贝
)
2.4.4 任务四：使用DataFrame自带的方法join方法和append：完成任务二和任务三的任务
#写入代码
result_up_test=dleftup.join(drightup)
result_down_test=dleftdown.join(drightdown)
result_2=result_up_test.append(result_down_test,ignore_index=True)
result_2
C:\Users\ThinkPad\AppData\Local\Temp\ipykernel_4824\2842206337.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
result_2=result_up_test.append(result_down_test,ignore_index=True)
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 886 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 888 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 890 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
dataframe.join(other, # 待合并的另一个数据框
on=None, # 连接的键
how='left', # 连接方式：‘left’, ‘right’, ‘outer’, ‘inner’ 默认是left
lsuffix='', # 左边（第一个）数据框相同键的后缀
rsuffix='', # 第二个数据框的键的后缀
sort=False) # 是否根据连接的键进行排序；默认False
DataFrame.append(other,
ignore_index=False,
verify_integrity=False,
sort=False)
参数解释：
other：待合并的数据。可以是pandas中的DataFrame、series，或者是Python中的字典、列表这样的数据结构
ignore_index：是否忽略原来的索引，生成新的自然数索引
verify_integrity：默认是False，如果值为True，创建相同的index则会抛出异常的错误
sort：boolean，默认是None。如果self和other的列没有对齐，则对列进行排序，并且属性只在版本0.23.0中出现。
2.4.5 任务五：使用Panads的merge方法和DataFrame的append方法：完成任务二和任务三的任务
#写入代码
dup=dleftup.merge(drightup,left_index=True,right_index=True)
ddown=dleftdown.merge(drightdown,left_index=True,right_index=True)
result_3=dup.append(ddown)
result_3
C:\Users\ThinkPad\AppData\Local\Temp\ipykernel_4824\3296784267.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
result_3=dup.append(ddown)
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 447 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 448 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 449 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 450 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 451 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
merge(
left,
right,
how="inner",
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=("_x", "_y"),
copy=True,
indicator=False,
validate=None,
)
【思考】对比merge、join以及concat的方法的不同以及相同。思考一下在任务四和任务五的情况下，为什么都要求使用DataFrame的append方法，如何只要求使用merge或者join可不可以完成任务四和任务五呢？
DataFrame有一个实例方法join，相当于merge方法的参数left_index=True和right_index=True
append为添加行数，join可以通过axis设置左右合并
merge可以通过index设置，来实现左右合并和上下合并
join可以通过axis设置，来实现左右合并和上下合并。
2.4.6 任务六：完成的数据保存为result.csv
#写入代码
result_3.to_csv('data/result.csv')
2.5 换一种角度看数据
2.5.1 任务一：将我们的数据变为Series类型的数据
#写入代码
result_stack=result_3.stack()
result_stack
0 PassengerId 1
Survived 0
Pclass 3
Name Braund, Mr. Owen Harris
Sex male
...
451 SibSp 0
Parch 0
Ticket 370376
Fare 7.75
Embarked Q
Length: 9826, dtype: object
stack()即“堆叠”，作用是将列旋转到行
unstack()即stack()的反操作，将行旋转到列
result_3
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 447 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
| 448 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
| 449 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
| 450 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
| 451 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
#写入代码
type(result_stack)
pandas.core.series.Series标签：数据分析,...,Task3,NaN,笔记,result,female,Mr,male 来源： https://www.cnblogs.com/demimute/p/16295858.html
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