Pandas中dff的示例分析
這篇文章主要介紹Pandas中dff的示例分析，文中介紹的非常詳細，具有一定的參考價值，感興趣的小伙伴們一定要看完！
數據分析處理庫
import pandas as pd
df=pd.read_csv("./pandas/data/titanic.csv")df.head(N) 讀取數據的前N行
df.head(6)
df.info() 獲取DataFrame的簡要摘要
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
df.index 查看索引
df.index
RangeIndex(start=0, stop=891, step=1)
df.columns 查看所有列名
df.columns
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')
df.dtypes 查看每一列的字段類型
df.dtypes
PassengerId int64
Survived int64
Pclass int64
Name object
Sex object
Age float64
SibSp int64
Parch int64
Ticket object
Fare float64
Cabin object
Embarked object
dtype: object
df.values查看所有數據
df.values
array([[1, 0, 3, ..., 7.25, nan, 'S'],
[2, 1, 1, ..., 71.2833, 'C85', 'C'],
[3, 1, 3, ..., 7.925, nan, 'S'],
...,
[889, 0, 3, ..., 23.45, nan, 'S'],
[890, 1, 1, ..., 30.0, 'C148', 'C'],
[891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)
df['Name']
0 Braund, Mr. Owen Harris
1 Cumings, Mrs. John Bradley (Florence Briggs Th...
2 Heikkinen, Miss. Laina
3 Futrelle, Mrs. Jacques Heath (Lily May Peel)
4 Allen, Mr. William Henry
...
886 Montvila, Rev. Juozas
887 Graham, Miss. Margaret Edith
888 Johnston, Miss. Catherine Helen "Carrie"
889 Behr, Mr. Karl Howell
890 Dooley, Mr. Patrick
Name: Name, Length: 891, dtype: object
df=df.set_index('Name')
df查詢Age列的前8列數據
df['Age'][:8]
Name
Braund, Mr. Owen Harris 22.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38.0
Heikkinen, Miss. Laina 26.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0
Allen, Mr. William Henry 35.0
Moran, Mr. James NaN
McCarthy, Mr. Timothy J 54.0
Palsson, Master. Gosta Leonard 2.0
Name: Age, dtype: float64
對單列數據的操作
age=df['Age']
age
Name
Braund, Mr. Owen Harris 22.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38.0
Heikkinen, Miss. Laina 26.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0
Allen, Mr. William Henry 35.0
...
Montvila, Rev. Juozas 27.0
Graham, Miss. Margaret Edith 19.0
Johnston, Miss. Catherine Helen "Carrie" NaN
Behr, Mr. Karl Howell 26.0
Dooley, Mr. Patrick 32.0
Name: Age, Length: 891, dtype: float64
# 每一個Age統(tǒng)一加10
age=age+10
age
Name
Braund, Mr. Owen Harris 32.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 48.0
Heikkinen, Miss. Laina 36.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 45.0
Allen, Mr. William Henry 45.0
...
Montvila, Rev. Juozas 37.0
Graham, Miss. Margaret Edith 29.0
Johnston, Miss. Catherine Helen "Carrie" NaN
Behr, Mr. Karl Howell 36.0
Dooley, Mr. Patrick 42.0
Name: Age, Length: 891, dtype: float64
# Age的最大值
age.max()
90.0
# Age的最小值
age.min()
10.42
# Age的平均值
age.mean()
39.69911764705882
describe得到數據的基本統(tǒng)計特征
df.describe()
只查詢某集幾列
df[['Age','Fare']][:5]
通過索引或者標簽查詢數據
# 通過索引查看某一行的數據
df.iloc[0]
# 查詢前4行數據
df.iloc[0:5]
# 查詢前4行前3列的數據
df.iloc[0:5,1:3]
# 通過索引列值讀取某一行的數據
df.loc['Futrelle, Mrs. Jacques Heath (Lily May Peel)']
# 查詢某行某列的某個值
df.loc['Futrelle, Mrs. Jacques Heath (Lily May Peel)','Age']
# 查詢某幾行的數某幾列的數據
df.loc['Braund, Mr. Owen Harris':'Graham, Miss. Margaret Edith','Sex':'Age']
# 修改某個值
df.loc['Heikkinen, Miss. Laina','Age']=2000
bool運算
# 查詢Age大于50的前5行數據
df[df['Age']>50][:5]
# 查詢Sex為female的數據
df[df['Sex']=='female']
# 計算Sex為male,Age的平均值
df.loc[df['Sex']=='male','Age'].mean()
# 計算Age大于50的年齡和
(df['Age']>50).sum()
65
DataFrame groupby數據分組
dff=pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],'value':[0,5,10,5,10,15,10,15,20]})
dff按照key分組求和
dff.groupby('key').sum()import numpy as np
dff.groupby('key').aggregate(np.mean)# 按照Sex分組,計算Age的平均值
df.groupby('Sex')['Age'].mean()Sex
female 35.478927
male 30.726645
Name: Age, dtype: float64
數值運算
df1=pd.DataFrame([[1,2,3,4],[3,4,5,6]],index=['a','b'],columns=['A','B','C','D'])
df1
# 每一列求值
df1.sum()
df1.sum(axis=0)
A 4
B 6
C 8
D 10
dtype: int64
# 每一行求和
df1.sum(axis=1)
a 10
b 18
dtype: int64
# 每一列求平均值
df1.mean(axis=0)
A 2.0
B 3.0
C 4.0
D 5.0
dtype: float64
# 每一行求平均值
df1.mean(axis=1)
a 2.5
b 4.5
dtype: float64
df
# 協(xié)方差
df.cov()
# 相關性
df.corr()
# 統(tǒng)計某一個每一個值出現的次數
df['Age'].value_counts()
24.00 30
22.00 27
18.00 26
28.00 25
19.00 25
..
53.00 1
55.50 1
70.50 1
23.50 1
0.42 1
Name: Age, Length: 89, dtype: int64
# 統(tǒng)計某一個每一個值出現的次數,次數由少到多排列
df['Age'].value_counts(ascending=True)
0.42 1
23.50 1
70.50 1
55.50 1
53.00 1
..
19.00 25
28.00 25
18.00 26
22.00 27
24.00 30
Name: Age, Length: 89, dtype: int64
對象操作(Series一行或者一列)
data=[1,2,3,4]
index=['a','b','c','d']
s=pd.Series(index=index,data=data)
# 查詢第一行
s[0]
# 查詢1到3行
s[1:3]
# 掩碼操作 只顯示a c行
mask=[True,False,True,False]
s[mask]
#修改某個值
s['a']=200
# 值替換將3替換為300
s.replace(to_replace=3,value=300,inplace=True)
# 修改列名
s.rename(index={'a':'A'},inplace=True)
# 添加數據
s1=pd.Series(index=['e','f'],data=[5,6])
s3=s.append(s1)
# 刪除A行數據
del s3['A']
# 一次刪除多行數據
s3.drop(['c','d'],inplace=True)
s3b 2
e 5
f 6
dtype: int64
DataFrame的增刪改查操作
# 構造一個DataFrame
data=[[1,2,3,4],[5,6,7,8]]
index=['a','b']
columns=['A','B','C','D']
dff=pd.DataFrame(data=data,index=index,columns=columns)
# 通過loc(‘索引值’)和iloc(索引數值)查詢
dff1=dff.iloc[1]
dff1=dff.loc['a']
dff1
A 1
B 2
C 3
D 4
Name: a, dtype: int64
# 修改值
dff.loc['a']['A']=1000
dff
# 修改索引
dff.index=['m','n']
dff
# 添加一行數據
dff.loc['o']=[10,11,12,13]
dff
| A | B | C | D |
|---|
| m | 1000 | 2 | 3 | 4 |
| n | 5 | 6 | 7 | 8 |
| o | 10 | 11 | 12 | 13 |
# 添加一列數據
dff['E']=[5,9,14]
dff
| A | B | C | D | E |
|---|
| m | 1000 | 2 | 3 | 4 | 5 |
| n | 5 | 6 | 7 | 8 | 9 |
| o | 10 | 11 | 12 | 13 | 14 |
# 批量添加多列數據
df4=pd.DataFrame([[6,10,15],[7,11,16],[8,12,17]],index=['m','n','o'],columns=['F','M','N'])
df5=pd.concat([dff,df4],axis=1)
df5
| A | B | C | D | E | F | M | N |
|---|
| m | 1000 | 2 | 3 | 4 | 5 | 6 | 10 | 15 |
| n | 5 | 6 | 7 | 8 | 9 | 7 | 11 | 16 |
| o | 10 | 11 | 12 | 13 | 14 | 8 | 12 | 17 |
# 刪除一行數據
df5.drop(['o'],axis=0,inplace=True)
df5
| A | B | C | D | E | F | M | N |
|---|
| m | 1000 | 2 | 3 | 4 | 5 | 6 | 10 | 15 |
| n | 5 | 6 | 7 | 8 | 9 | 7 | 11 | 16 |
# 刪除列
df5.drop(['E','F'],axis=1,inplace=True)
df5
| A | B | C | D | M | N |
|---|
| m | 1000 | 2 | 3 | 4 | 10 | 15 |
| n | 5 | 6 | 7 | 8 | 11 | 16 |
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