复习:数据分析的第一步,加载数据我们已经学习完毕了。当数据展现在我们面前的时候,我们所要做的第一步就是认识他,今天我们要学习的就是了解字段含义以及初步观察数据。
我们学习pandas的基础操作,那么上一节通过pandas加载之后的数据,其数据类型是什么呢?
import numpy as np
import pandas as pd
sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
example_1 = pd.Series(sdata)
example_1
Ohio 35000 Texas 71000 Oregon 16000 Utah 5000 dtype: int64
data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002, 2003],'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
example_2 = pd.DataFrame(data)
example_2
state | year | pop | |
---|---|---|---|
0 | Ohio | 2000 | 1.5 |
1 | Ohio | 2001 | 1.7 |
2 | Ohio | 2002 | 3.6 |
3 | Nevada | 2001 | 2.4 |
4 | Nevada | 2002 | 2.9 |
5 | Nevada | 2003 | 3.2 |
df = pd.read_csv('/Users/chenandong/Documents/datawhale数据分析每个人题目设计/titanic/train.csv')
df.head(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 |
也可以加载上一节课保存的"train_chinese.csv"文件。
df.columns
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype='object')
df['Cabin'].head(3)
0 NaN 1 C85 2 NaN 3 C123 4 NaN Name: Cabin, dtype: object
df.Cabin.head(3)
0 NaN 1 C85 2 NaN 3 C123 4 NaN Name: Cabin, dtype: object
经过我们的观察发现一个测试集test_1.csv有一列是多余的,我们需要将这个多余的列删去
test_1 = pd.read_csv('test_1.csv')
test_1.head(3)
Unnamed: 0 | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | a | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 100 |
1 | 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 100 |
2 | 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 100 |
# 删除多余的列
del test_1['a']
test_1.head(3)
Unnamed: 0 | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
1 | 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
2 | 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
【思考】还有其他的删除多余的列的方式吗?
#思考回答
df.drop(['PassengerId','Name','Age','Ticket'],axis=1).head(3)
Survived | Pclass | Sex | SibSp | Parch | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|
0 | 0 | 3 | male | 1 | 0 | 7.2500 | NaN | S |
1 | 1 | 1 | female | 1 | 0 | 71.2833 | C85 | C |
2 | 1 | 3 | female | 0 | 0 | 7.9250 | NaN | S |
【思考】对比任务五和任务六,是不是使用了不一样的方法(函数),如果使用一样的函数如何完成上面的不同的要求呢?
【思考回答】
如果想要完全的删除你的数据结构,使用inplace=True,因为使用inplace就将原数据覆盖了,所以这里没有用
# 思考回答
df.head(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 |
表格数据中,最重要的一个功能就是要具有可筛选的能力,选出我所需要的信息,丢弃无用的信息。
下面我们还是用实战来学习pandas这个功能。
df[df["Age"]<10].head(3)
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
7 | 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2.0 | 3 | 1 | 349909 | 21.075 | NaN | S |
10 | 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4.0 | 1 | 1 | PP 9549 | 16.700 | G6 | S |
16 | 17 | 0 | 3 | Rice, Master. Eugene | male | 2.0 | 4 | 1 | 382652 | 29.125 | NaN | Q |
midage = df[(df["Age"]>10)& (df["Age"]<50)]
midage.head(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 |
【提示】了解pandas的条件筛选方式以及如何使用交集和并集操作
midage = midage.reset_index(drop=True)
midage.head(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 |
【思考】这个reset_index()函数的作用是什么?如果不用这个函数,下面的任务会出现什么情况?
midage.loc[[100],['Pclass','Sex']]
Pclass | Sex | |
---|---|---|
100 | 2 | male |
midage.loc[[100,105,108],['Pclass','Name','Sex']] #因为你主动的延长了行的距离,所以会产生表格形式
Pclass | Name | Sex | |
---|---|---|---|
100 | 2 | Byles, Rev. Thomas Roussel Davids | male |
105 | 3 | Cribb, Mr. John Hatfield | male |
108 | 3 | Calic, Mr. Jovo | male |
【提示】使用pandas提出的简单方式,你可以看看loc方法
对比整体的数据位置,你有发现什么问题吗?那么如何解决?
midage.iloc[[100,105,108],[2,3,4]]
Pclass | Name | Sex | |
---|---|---|---|
100 | 2 | Byles, Rev. Thomas Roussel Davids | male |
105 | 3 | Cribb, Mr. John Hatfield | male |
108 | 3 | Calic, Mr. Jovo | male |