Pandas入门教程(二)

merge合并DataFrame

import pandas as pdleft=pd.DataFrame({'key':['k0','k1','k2','k3','k4','k5'],'A':['A0','A1','A2','A3','A4','A5'],'B':['B0','B1','B2','B3','B4','B5']
})right=pd.DataFrame({'key':['k0','k1','k2','k3','k4','k5'],'C':['C0','C1','C2','C3','C4','C5'],'D':['D0','D1','D2','D3','D4','D5']
})
print(left)
print('-'*20)
print(right)

  key   A   B
0  k0  A0  B0
1  k1  A1  B1
2  k2  A2  B2
3  k3  A3  B3
4  k4  A4  B4
5  k5  A5  B5
--------------------key   C   D
0  k0  C0  D0
1  k1  C1  D1
2  k2  C2  D2
3  k3  C3  D3
4  k4  C4  D4
5  k5  C5  D5

# 合并
res=pd.merge(left,right)
print(res)print('-'*20)
# 指定合并的key
res=pd.merge(left,right,on='key')
print(res)

  key   A   B   C   D
0  k0  A0  B0  C0  D0
1  k1  A1  B1  C1  D1
2  k2  A2  B2  C2  D2
3  k3  A3  B3  C3  D3
4  k4  A4  B4  C4  D4
5  k5  A5  B5  C5  D5
--------------------key   A   B   C   D
0  k0  A0  B0  C0  D0
1  k1  A1  B1  C1  D1
2  k2  A2  B2  C2  D2
3  k3  A3  B3  C3  D3
4  k4  A4  B4  C4  D4
5  k5  A5  B5  C5  D5

left = pd.DataFrame({'key1': ['K0', 'K1', 'K2', 'K3'],'key2': ['K0', 'K1', 'K2', 'K3'],'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K2', 'K3'],'key2': ['K0', 'K1', 'K2', 'K4'],'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']})
print(left)
print('-'*20)
print(right)

  key1 key2   A   B
0   K0   K0  A0  B0
1   K1   K1  A1  B1
2   K2   K2  A2  B2
3   K3   K3  A3  B3
--------------------key1 key2   C   D
0   K0   K0  C0  D0
1   K1   K1  C1  D1
2   K2   K2  C2  D2
3   K3   K4  C3  D3

# 默认取交集 how='inner'
res=pd.merge(left,right,on=['key1','key2'])
print(res)

  key1 key2   A   B   C   D
0   K0   K0  A0  B0  C0  D0
1   K1   K1  A1  B1  C1  D1
2   K2   K2  A2  B2  C2  D2

# how='outer' 取并集
res=pd.merge(left,right,on=['key1','key2'],how='outer')
print(res)

  key1 key2    A    B    C    D
0   K0   K0   A0   B0   C0   D0
1   K1   K1   A1   B1   C1   D1
2   K2   K2   A2   B2   C2   D2
3   K3   K3   A3   B3  NaN  NaN
4   K3   K4  NaN  NaN   C3   D3

# 显示合并数据中数据来自哪个表
res=pd.merge(left,right,on=['key1','key2'],how='outer',indicator=True)
print(res)

  key1 key2    A    B    C    D      _merge
0   K0   K0   A0   B0   C0   D0        both
1   K1   K1   A1   B1   C1   D1        both
2   K2   K2   A2   B2   C2   D2        both
3   K3   K3   A3   B3  NaN  NaN   left_only
4   K3   K4  NaN  NaN   C3   D3  right_only

# 左链接
res=pd.merge(left,right,on=['key1','key2'],how='left')
print(res)
print('-'*30)
# 右链接
res=pd.merge(left,right,on=['key1','key2'],how='right')
print(res)

  key1 key2   A   B    C    D
0   K0   K0  A0  B0   C0   D0
1   K1   K1  A1  B1   C1   D1
2   K2   K2  A2  B2   C2   D2
3   K3   K3  A3  B3  NaN  NaN
------------------------------key1 key2    A    B   C   D
0   K0   K0   A0   B0  C0  D0
1   K1   K1   A1   B1  C1  D1
2   K2   K2   A2   B2  C2  D2
3   K3   K4  NaN  NaN  C3  D3

join 拼接列，主要用于索引上的合并

left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],'B': ['B0', 'B1', 'B2', 'B3'],'key': ['K0', 'K1', 'K0', 'K1']})right = pd.DataFrame({'C': ['C0', 'C1'],'D': ['D0', 'D1']},index=['K0', 'K1'])print(left)
print('-'*15)
print(right)

    A   B key
0  A0  B0  K0
1  A1  B1  K1
2  A2  B2  K0
3  A3  B3  K1
---------------C   D
K0  C0  D0
K1  C1  D1

res=left.join(right,on='key')
print(res)

    A   B key   C   D
0  A0  B0  K0  C0  D0
1  A1  B1  K1  C1  D1
2  A2  B2  K0  C0  D0
3  A3  B3  K1  C1  D1

Pandas数据透视表

df = pd.DataFrame({'Month': ["January", "January", "January", "January", "February", "February", "February", "February", "March", "March", "March", "March"],'Category': ["Transportation", "Grocery", "Household", "Entertainment","Transportation", "Grocery", "Household", "Entertainment","Transportation", "Grocery", "Household", "Entertainment"],'Amount': [74., 235., 175., 100., 115., 240., 225., 125., 90., 260., 200., 120.]})
print(df)

       Month        Category  Amount
0    January  Transportation    74.0
1    January         Grocery   235.0
2    January       Household   175.0
3    January   Entertainment   100.0
4   February  Transportation   115.0
5   February         Grocery   240.0
6   February       Household   225.0
7   February   Entertainment   125.0
8      March  Transportation    90.0
9      March         Grocery   260.0
10     March       Household   200.0
11     March   Entertainment   120.0

# 构造一个索引为Category 列为Month 值为Amount的表
res=df.pivot(index='Category',columns='Month',values='Amount')
print(res)

Month           February  January  March
Category
Entertainment      125.0    100.0  120.0
Grocery            240.0    235.0  260.0
Household          225.0    175.0  200.0
Transportation     115.0     74.0   90.0

# 按列求和
res.sum(axis=0)

Month
February    705.0
January     584.0
March       670.0
dtype: float64

# 按行求和
res.sum(axis=1)

Category
Entertainment     345.0
Grocery           735.0
Household         600.0
Transportation    279.0
dtype: float64

pivot_table

df=pd.read_csv('./pandas/data/titanic.csv')
df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],dtype='object')

#默认求平均值
res=df.pivot_table(index='Sex',columns='Pclass',values='Fare')
print(res)

Pclass           1          2          3
Sex
female  106.125798  21.970121  16.118810
male     67.226127  19.741782  12.661633

# 求最大值
res=df.pivot_table(index='Sex',columns='Pclass',values='Fare',aggfunc='max')
print(res)

Pclass         1     2      3
Sex
female  512.3292  65.0  69.55
male    512.3292  73.5  69.55

# 统计个数
res=df.pivot_table(index='Sex',columns='Pclass',values='Fare',aggfunc='count')
print(res)
print('-'*20)
# crosstab 统计个数
res=pd.crosstab(index=df['Sex'],columns=df['Pclass'])
print(res)

Pclass    1    2    3
Sex
female   94   76  144
male    122  108  347
--------------------
Pclass    1    2    3
Sex
female   94   76  144
male    122  108  347

# 求平均值
res=df.pivot_table(index='Sex',columns='Pclass',values='Fare',aggfunc='mean')
print(res)

Pclass           1          2          3
Sex
female  106.125798  21.970121  16.118810
male     67.226127  19.741782  12.661633

# 计算未成年男女存活率
df['minor']=df['Age']<=18
res=df.pivot_table(index='minor',columns='Sex',values='Survived',aggfunc='mean')
print(res)

Sex      female      male
minor
False  0.760163  0.167984
True   0.676471  0.338028

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Pandas入门教程(二)

merge合并DataFrame

join 拼接列，主要用于索引上的合并

Pandas数据透视表

pivot_table

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