在上一篇文章中,介绍了如何使用python
导入数据,导入数据后的第二步往往就是数据清洗,下面我们来看看如何使用pandas
进行数据清洗工作
import pandas as pd
dataframe = pd.read_csv(r'C:/Users/DELL/data-science-learning/python数据分析笔记/探索性数据分析/train.csv')
dataframe.head(5)
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 |
dataframe.shape
(891, 12)
dataframe.describe()
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PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | |
---|---|---|---|---|---|---|---|
count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 |
mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 |
std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 |
min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 |
25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 |
50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 |
75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 |
max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 |
dataframe[(dataframe['Sex'] == 'female') & (dataframe['Age']>=60)]
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PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
275 | 276 | 1 | 1 | Andrews, Miss. Kornelia Theodosia | female | 63.0 | 1 | 0 | 13502 | 77.9583 | D7 | S |
366 | 367 | 1 | 1 | Warren, Mrs. Frank Manley (Anna Sophia Atkinson) | female | 60.0 | 1 | 0 | 110813 | 75.2500 | D37 | C |
483 | 484 | 1 | 3 | Turkula, Mrs. (Hedwig) | female | 63.0 | 0 | 0 | 4134 | 9.5875 | NaN | S |
829 | 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62.0 | 0 | 0 | 113572 | 80.0000 | B28 | NaN |
可以看出,一共有四名年龄大于60岁的女性乘客
female
换成woman
,将male
换成man
dataframe['Sex'].replace(['female','male'],['woman','man']).head(5)
0 man
1 woman
2 woman
3 woman
4 man
Name: Sex, dtype: object
dataframe.columns
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')
PassengerId | Survived | Passenger Class | 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 |
dataframe.rename(columns={'Pclass':'Passenger Class','Sex':'Gender'}).head()
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PassengerId | Survived | Passenger Class | Name | Gender | 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 |
在pandas
中,我们可以使用unique()
查找唯一值
# 查找唯一值
dataframe['Sex'].unique()
array(['male', 'female'], dtype=object)
# 显示唯一值出现的个数
dataframe['Sex'].value_counts()
male 577
female 314
Name: Sex, dtype: int64
# 查找类型票的数量
dataframe['Pclass'].value_counts()
3 491
1 216
2 184
Name: Pclass, dtype: int64
# 查找唯一值的种类
dataframe['Pclass'].nunique()
3
# 查找空数据
dataframe[dataframe['Age'].isnull()].head()
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PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q |
17 | 18 | 1 | 2 | Williams, Mr. Charles Eugene | male | NaN | 0 | 0 | 244373 | 13.0000 | NaN | S |
19 | 20 | 1 | 3 | Masselmani, Mrs. Fatima | female | NaN | 0 | 0 | 2649 | 7.2250 | NaN | C |
26 | 27 | 0 | 3 | Emir, Mr. Farred Chehab | male | NaN | 0 | 0 | 2631 | 7.2250 | NaN | C |
28 | 29 | 1 | 3 | O’Dwyer, Miss. Ellen “Nellie” | female | NaN | 0 | 0 | 330959 | 7.8792 | NaN | Q |
pandas
没有NaN
如果想要处理的话必须导入numpy
包
import numpy as np
dataframe['Sex'].replace('male',np.nan).head()
0 NaN
1 female
2 female
3 female
4 NaN
Name: Sex, dtype: object
# 删除一列,采用drop方法,并传入参数axis
dataframe.drop('Age',axis=1).head()
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PassengerId | Survived | Pclass | Name | Sex | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th… | female | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 1 | 0 | 113803 | 53.1000 | C123 | S |
4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 0 | 0 | 373450 | 8.0500 | NaN | S |
#删除行
dataframe.drop(1)
# 删除重复行 使用subset参数指明要删除的列
dataframe.drop_duplicates(subset='Sex').head()
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Name | PClass | Age | Sex | Survived | SexCode | |
---|---|---|---|---|---|---|
0 | Allen, Miss Elisabeth Walton | 1st | 29.0 | female | 1 | 1 |
2 | Allison, Mr Hudson Joshua Creighton | 1st | 30.0 | male | 0 | 0 |
==思路一==,将所有男性和女性的条件进行选取分别计算
man = dataframe[dataframe['Sex']=='male']
woman = dataframe[dataframe['Sex']=='female']
print(man.mean())
print(woman.mean())
Age 31.014338
Survived 0.166863
SexCode 0.000000
dtype: float64
Age 29.396424
Survived 0.666667
SexCode 1.000000
dtype: float64
==思路二==,用groupby方法简化
dataframe.groupby('Sex').mean()
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Age | Survived | SexCode | |
---|---|---|---|
Sex | |||
— | — | — | — |
female | 29.396424 | 0.666667 | 1.0 |
male | 31.014338 | 0.166863 | 0.0 |
# 按行分组,计算行数
dataframe.groupby('Sex')['Name'].count()
Sex
female 462
male 851
Name: Name, dtype: int64
dataframe.groupby(['Sex','Survived']).mean()
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PassengerId | Pclass | Age | SibSp | Parch | Fare | ||
---|---|---|---|---|---|---|---|
Sex | Survived | ||||||
— | — | — | — | — | — | — | — |
female | 0 | 434.851852 | 2.851852 | 25.046875 | 1.209877 | 1.037037 | 23.024385 |
1 | 429.699571 | 1.918455 | 28.847716 | 0.515021 | 0.515021 | 51.938573 | |
male | 0 | 449.121795 | 2.476496 | 31.618056 | 0.440171 | 0.207265 | 21.960993 |
1 | 475.724771 | 2.018349 | 27.276022 | 0.385321 | 0.357798 | 40.821484 |
# 创建时期范围
time_index = pd.date_range('06/06/2017', periods=100000, freq='30S') # periods表示有多少数据,freq表示步长
dataframe = pd.DataFrame(index=time_index)
# 创建一个随机变量
dataframe['Sale_Amout'] = np.random.randint(1, 10, 100000)
# resample 参数,按周对行分组,计算每一周的总和
dataframe.resample('W').sum()
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Sale_Amout | |
---|---|
2017-06-11 | 86292 |
2017-06-18 | 100359 |
2017-06-25 | 100907 |
2017-07-02 | 100868 |
2017-07-09 | 100522 |
2017-07-16 | 10478 |
# 使用resample可以按一组时间间隔来进行分组,然后计算每一个时间组的某个统计量
dataframe.resample('2W').mean()
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Sale_Amout | |
---|---|
2017-06-11 | 4.993750 |
2017-06-25 | 4.991716 |
2017-07-09 | 4.994792 |
2017-07-23 | 5.037500 |
dataframe.resample('M').count()
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Sale_Amout | |
---|---|
2017-06-30 | 72000 |
2017-07-31 | 28000 |
# resample默认是以最后一个数据作 使用label参数可以进行调整
dataframe.resample('M', label='left').count()
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Sale_Amout | |
---|---|
2017-05-31 | 72000 |
2017-06-30 | 28000 |
dataframe = pd.read_csv(url)
# 以大写的形势打印前两行的名字
for name in dataframe['Name'][0:2]:
print(name.upper())
ALLEN, MISS ELISABETH WALTON
ALLISON, MISS HELEN LORAINE
def uppercase(x):
return x.upper()
dataframe['Name'].apply(uppercase)[0:2]
0 ALLEN, MISS ELISABETH WALTON
1 ALLISON, MISS HELEN LORAINE
Name: Name, dtype: object
dataframe.groupby('Sex').apply(lambda x:x.count())
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Name | PClass | Age | Sex | Survived | SexCode | |
---|---|---|---|---|---|---|
Sex | ||||||
— | — | — | — | — | — | — |
female | 462 | 462 | 288 | 462 | 462 | 462 |
male | 851 | 851 | 468 | 851 | 851 | 851 |
通过联合使用groupby
和apply
,我们就能计算自定义的统计量 例如上面我们发现age
、cabin
具有大量的缺失值
data_a = {'id':['1', '2', '3'],
'first': ['Alex', 'Amy', 'Allen'],
'last': ['Anderson', 'Ackerman', 'Ali']}
dataframe_a = pd.DataFrame(data_a, columns=['id','first', 'last'])
data_b = {'id':['4', '5', '6'],
'first': ['Billy', 'Brian', 'Bran'],
'last': ['Bonder', 'Black', 'Balwner']}
dataframe_b = pd.DataFrame(data_b, columns=['id','first', 'last'])
pd.concat([dataframe_a, dataframe_b], axis=0)#在行的方向进行
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id | first | last | |
---|---|---|---|
0 | 1 | Alex | Anderson |
1 | 2 | Amy | Ackerman |
2 | 3 | Allen | Ali |
0 | 4 | Billy | Bonder |
1 | 5 | Brian | Black |
2 | 6 | Bran | Balwner |
pd.concat([dataframe_a, dataframe_b], axis=1)#在列的方向进行
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id | first | last | id | first | last | |
---|---|---|---|---|---|---|
0 | 1 | Alex | Anderson | 4 | Billy | Bonder |
1 | 2 | Amy | Ackerman | 5 | Brian | Black |
2 | 3 | Allen | Ali | 6 | Bran | Balwner |
# 也可以用append方法进行添加
c = pd.Series([10, 'Chris', 'Chillon'], index=['id','first','last'])
dataframe.append(c, ignore_index=True)#如果c原来有名字忽略
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id | first | last | |
---|---|---|---|
0 | 1 | Alex | Anderson |
1 | 2 | Amy | Ackerman |
2 | 3 | Allen | Ali |
3 | 10 | Chris | Chillon |
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