Pandas 是一个开源的 Python 库，专为数据处理和分析任务而设计；它提供了高性能、易用的数据结构和数据分析工具，使得在 Python 中进行数据科学变得简单高效；Pandas 基于 NumPy，因此可以与许多其他基于 NumPy 的库（如 SciPy 和 scikit-learn）无缝集成；
Pandas 中的两个主要数据结构
-
Series，一个一维带标签的数组，可存储整数、浮点数、字符串等不同类型的数据；Series 具有索引（index），这使得它类似于 Python 字典，但具有更多的特性和功能； -
DataFrame，一个二维带标签的数据结构，类似于表格或电子表格；它由一系列具有相同索引的列组成，每列可以具有不同的数据类型；DataFrame 提供了各种功能，如筛选、排序、分组、合并和聚合，以便在大型数据集上进行高效操作；
支持的数据处理和分析任务
- 数据导入和导出
- 数据清洗和预处理
- 数据过滤和选择
- 数据排序、排名和聚合
- 缺失值处理
- 分组操作
- 数据透视表
- 时间序列分析
- 合并和连接多个数据集
Pandas 提供了丰富的功能，使得它成为 Python 数据科学生态系统中最受欢迎和广泛使用的库之一；
import pandas as pd
import numpy as np
文章目录
- 1. Series
- 1. 构造与初始化
- 2. 选择数据
- 3. 元素赋值
- 4. 数学运算
- 5. 数据缺失
- 2. DataFrame
- 1. 构造和选择数据
- 2. 元素赋值
- 3. Index
- 1. Index 对象
- 2. Index 索引和切片
- 3. reindex
- 4. drop
- 5. Hierarchical Indexing
- 4. Concatenate 与 Append
- 1. 纵向 concat
- 2. 横向 concat
- 3. join concat
- 4. append（deprecated）
- 5. Series 与 DataFrame 进行 concatenate
- 5. Merge
- 1. 按指定列进行 inner join
- 2. 按指定列进行 outer join
- 3. 按指定列进行 right join
- 4. 按指定列进行 left join
- 6. Join
- 1. 按 index 进行 inner join
- 2. 按 index 进行 outer join
- 7. Group By
- 1. 分组求和
- 2. 展示分组的各种统计信息
- 8. 应用案例（计算每个工作日的骑车人数之和）
- 1. 读取 bike.csv 到 DataFrame
- 2. 查看数据样例
- 3. dropna
- 3. fillna
- 4. 计算单条路线每个工作日的骑车人数之和
- 5. 计算所有路线每个工作日的骑车人数之和
1. Series
1. 构造与初始化
- Series 是一个一维数据结构，Pandas 会默认用 0 到 n 来作为 Series 的 index；
>>> s = pd.Series([1, 3, 'Beijing', 3.14, -123, 'Year!'])
>>> s
0 1
1 3
2 Beijing
3 3.14
4 -123
5 Year!
dtype: object
- 自行指定 index；
>>> s = pd.Series([1, 3, 'Beijing', 3.14, -123, 'Year!'], index=['A', 'B', 'C', 'D', 'E','G'])
>>> s
A 1
B 3
C Beijing
D 3.14
E -123
G Year!
dtype: object
- 直接使用 dictionary 构造 Series, 因为 Series 本身就是 keyvalue pairs；
>>> cities = {'Beijing': 55000, 'Shanghai': 60000, 'Shenzhen': 50000,
>>> 'Hangzhou': 20000, 'Guangzhou': 25000, 'Suzhou': None}
>>> apts = pd.Series(cities)
>>> apts
Beijing 55000.0
Shanghai 60000.0
Shenzhen 50000.0
Hangzhou 20000.0
Guangzhou 25000.0
Suzhou NaN
dtype: float64
2. 选择数据
- 通过 index 选择数据
>>> apts['Hangzhou']
20000.0
>>> apts[['Hangzhou', 'Beijing', 'Shenzhen']]
Hangzhou 20000.0
Beijing 55000.0
Shenzhen 50000.0
dtype: float64
>>> # boolean indexing
>>> apts[apts < 50000]
Hangzhou 20000.0
Guangzhou 25000.0
dtype: float64
>>> # boolean indexing 的工作方式
>>> less_than_50000 = apts < 50000
>>> less_than_50000
Beijing False
Shanghai False
Shenzhen False
Hangzhou True
Guangzhou True
Suzhou False
dtype: bool
>>> apts[less_than_50000]
Hangzhou 20000.0
Guangzhou 25000.0
dtype: float64
3. 元素赋值
>>> print("Old value: ", apts['Shenzhen'])
Old value: 50000.0
>>> apts['Shenzhen'] = 55000
>>> print("New value: ", apts['Shenzhen'])
New value: 55000.0
>>> print(apts[apts < 50000])
Hangzhou 20000.0
Guangzhou 25000.0
dtype: float64
>>> apts[apts <= 50000] = 40000
>>> print(apts[apts < 50000])
angzhou 40000.0
Guangzhou 40000.0
dtype: float64
4. 数学运算
>>> apts / 2
Beijing 27500.0
Shanghai 30000.0
Shenzhen 27500.0
Hangzhou 20000.0
Guangzhou 20000.0
Suzhou NaN
dtype: float64
>>> np.square(apts)
Beijing 3.025000e+09
Shanghai 3.600000e+09
Shenzhen 3.025000e+09
Hangzhou 1.600000e+09
Guangzhou 1.600000e+09
Suzhou NaN
dtype: float64
>>> cars = pd.Series({'Beijing': 300000, 'Shanghai': 400000, 'Shenzhen': 300000,
>>> 'Tianjin': 200000, 'Guangzhou': 200000, 'Chongqing': 150000})
>>> cars
Beijing 300000
Shanghai 400000
Shenzhen 300000
Tianjin 200000
Guangzhou 200000
Chongqing 150000
dtype: int64
>>> # 按 index 运算
>>> cars + apts * 100
Beijing 5800000.0
Chongqing NaN
Guangzhou 4200000.0
Hangzhou NaN
Shanghai 6400000.0
Shenzhen 5800000.0
Suzhou NaN
Tianjin NaN
dtype: float64
5. 数据缺失
>>> print('Hangzhou' in apts)
True
>>> print('Hangzhou' in cars)
False
>>> apts.notnull()
Beijing True
Shanghai True
Shenzhen True
Hangzhou True
Guangzhou True
Suzhou False
dtype: bool
>>> print(apts.isnull())
Beijing False
Shanghai False
Shenzhen False
Hangzhou False
Guangzhou False
Suzhou True
dtype: bool
>>> print(apts[apts.isnull()])
Suzhou NaN
dtype: float64
2. DataFrame
一个 DataFrame 就是一张表格，Series 表示的是一维数组，DataFrame 则是一个二维数组，可以类比成一张 excel 的表格；也可以把 DataFrame 当做一个一组 Series 的集合；
1. 构造和选择数据
- 由一个 dictionary 构造；
>>> data = {'city': ['Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen', 'Hangzhou', 'Chongqing'],
>>> 'year': [2016, 2017, 2016, 2017, 2016, 2016],
>>> 'population': [2100, 2300, 1000, 700, 500, 500]}
>>> d1 = pd.DataFrame(data)
>>> print(d1)
city year population
0 Beijing 2016 2100
1 Shanghai 2017 2300
2 Guangzhou 2016 1000
3 Shenzhen 2017 700
4 Hangzhou 2016 500
5 Chongqing 2016 500
- 全列遍历
>>> for row in d1.values:
>>> print(row)
['Beijing' 2016 2100]
['Shanghai' 2017 2300]
['Guangzhou' 2016 1000]
['Shenzhen' 2017 700]
['Hangzhou' 2016 500]
['Chongqing' 2016 500]
- 选择列并合并遍历
>>> for row in zip(d1['city'], d1['year'], d1['population']):
>>> print(row)
('Beijing', 2016, 2100)
('Shanghai', 2017, 2300)
('Guangzhou', 2016, 1000)
('Shenzhen', 2017, 700)
('Hangzhou', 2016, 500)
('Chongqing', 2016, 500)
>>> print(d1.columns)
Index(['city', 'year', 'population'], dtype='object')
- 列序重组
>>> print(pd.DataFrame(data, columns=['year', 'city', 'population']))
year city population
0 2016 Beijing 2100
1 2017 Shanghai 2300
2 2016 Guangzhou 1000
3 2017 Shenzhen 700
4 2016 Hangzhou 500
5 2016 Chongqing 500
- 行与列索引
>>> frame2 = pd.DataFrame(data,
>>> columns=['year', 'city', 'population', 'debt'],
>>> index=['one', 'two', 'three', 'four', 'five', 'six'])
>>> print(frame2)
year city population debt
one 2016 Beijing 2100 NaN
two 2017 Shanghai 2300 NaN
three 2016 Guangzhou 1000 NaN
four 2017 Shenzhen 700 NaN
five 2016 Hangzhou 500 NaN
six 2016 Chongqing 500 NaN
>>> print(frame2['city'])
one Beijing
two Shanghai
three Guangzhou
four Shenzhen
five Hangzhou
six Chongqing
Name: city, dtype: object
>>> print(frame2.year)
one 2016
two 2017
three 2016
four 2017
five 2016
six 2016
Name: year, dtype: int64
>>> # loc 取 label based indexing or iloc 取 positional indexing
>>> print(frame2.loc['three'])
year 2016
city Guangzhou
population 1000
debt NaN
Name: three, dtype: object
>>> print(frame2.iloc[2].copy())
year 2016
city Guangzhou
population 1000
debt NaN
Name: three, dtype: object
2. 元素赋值
- 整列赋值（单值）；
>>> frame2['debt'] = 100
>>> print(frame2)
year city population debt
one 2016 Beijing 2100 100
two 2017 Shanghai 2300 100
three 2016 Guangzhou 1000 100
four 2017 Shenzhen 700 100
five 2016 Hangzhou 500 100
six 2016 Chongqing 500 100
- 整列赋值（列表值）；
>>> frame2.debt = np.arange(6)
>>> print(frame2)
year city population debt
one 2016 Beijing 2100 0
two 2017 Shanghai 2300 1
three 2016 Guangzhou 1000 2
four 2017 Shenzhen 700 3
five 2016 Hangzhou 500 4
six 2016 Chongqing 500 5
- 用 Series 来指定需要修改的 index 以及相对应的 value，没有指定的默认用 NaN；
>>> val = pd.Series([100, 200, 300], index=['two', 'three', 'five'])
>>> frame2['debt'] = val
>>> print(frame2)
year city population debt
one 2016 Beijing 2100 NaN
two 2017 Shanghai 2300 100.0
three 2016 Guangzhou 1000 200.0
four 2017 Shenzhen 700 NaN
five 2016 Hangzhou 500 300.0
six 2016 Chongqing 500 NaN
- 用存在的列赋值（创建新列）；
>>> frame2['western'] = (frame2.city == 'Chongqing')
>>> print(frame2)
year city population debt western
one 2016 Beijing 2100 NaN False
two 2017 Shanghai 2300 100.0 False
three 2016 Guangzhou 1000 200.0 False
four 2017 Shenzhen 700 NaN False
five 2016 Hangzhou 500 300.0 False
six 2016 Chongqing 500 NaN True
- DataFrame 的转置；
>>> pop = {'Beijing': {2016: 2100, 2017: 2200},
>>> 'Shanghai': {2015: 2400, 2016: 2500, 2017: 2600}}
>>> frame3 = pd.DataFrame(pop)
>>> print(frame3)
Beijing Shanghai
2016 2100.0 2500
2017 2200.0 2600
2015 NaN 2400
>>> print(frame3.T)
2016 2017 2015
Beijing 2100.0 2200.0 NaN
Shanghai 2500.0 2600.0 2400.0
- 行序重组；
>>> pd.DataFrame(pop, index=[2015, 2016, 2017])
Beijing Shanghai
2015 NaN 2400
2016 2100.0 2500
2017 2200.0 2600
- 使用切片初始化数据；
>>> pdata = {'Beijing': frame3['Beijing'][:-1],
>>> 'Shanghai': frame3['Shanghai'][:-1]}
>>> pd.DataFrame(pdata)
Beijing Shanghai
2016 2100.0 2500
2017 2200.0 2600
- 指定 index 的名字和列的名字；
>>> frame3.index.name = 'year'
>>> frame3.columns.name = 'city'
>>> print(frame3)
city Beijing Shanghai
year
2016 2100.0 2500
2017 2200.0 2600
2015 NaN 2400
>>> print(frame2.values)
[[2016 'Beijing' 2100 nan False]
[2017 'Shanghai' 2300 100.0 False]
[2016 'Guangzhou' 1000 200.0 False]
[2017 'Shenzhen' 700 nan False]
[2016 'Hangzhou' 500 300.0 False]
[2016 'Chongqing' 500 nan True]]
>>> print(frame2)
year city population debt western
one 2016 Beijing 2100 NaN False
two 2017 Shanghai 2300 100.0 False
three 2016 Guangzhou 1000 200.0 False
four 2017 Shenzhen 700 NaN False
five 2016 Hangzhou 500 300.0 False
six 2016 Chongqing 500 NaN True
>>> print(type(frame2.values))
<class 'numpy.ndarray'>
3. Index
1. Index 对象
>>> obj = pd.Series(range(3), index=['a', 'b', 'c'])
>>> index = obj.index
>>> index
Index(['a', 'b', 'c'], dtype='object')
>>> index[1:]
Index(['b', 'c'], dtype='object')
>>> # index 不能被动态改动
>>> # index[1]='d'
>>> index = pd.Index(np.arange(3))
>>> obj2 = pd.Series([2, 5, 7], index=index)
>>> print(obj2)
0 2
1 5
2 7
dtype: int64
>>> print(obj2.index is index)
True
>>> 2 in obj2.index
True
>>> pop = {'Beijing': {2016: 2100, 2017: 2200},
>>> 'Shanghai': {2015: 2400, 2016: 2500, 2017: 2600}}
>>> frame3 = pd.DataFrame(pop)
>>> print('Shanghai' in frame3.columns)
True
>>> print('2015' in frame3.index)
False
>>> print(2015 in frame3.index)
True
2. Index 索引和切片
>>> obj = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])
>>> print(obj)
a 0
b 1
c 2
d 3
dtype: int64
>>> print(obj['b'])
1
- 使用默认的数字 index；
>>> print(obj[3])
3
>>> print(obj[[1, 3]])
b 1
d 3
dtype: int64
>>> print(obj[['b', 'd']])
b 1
d 3
dtype: int64
- 条件筛选；
>>> print(obj[obj < 2])
a 0
b 1
dtype: int32
- 切片筛选与赋值
>>> print(obj['b':'c'])
b 1
c 2
dtype: int64
>>> obj['b':'c'] = 5
>>> print(obj)
a 0
b 5
c 5
d 3
dtype: int64
- DataFrame 的 Indexing
>>> a = np.arange(9).reshape(3, 3)
>>> print(a)
[[0 1 2]
[3 4 5]
[6 7 8]]
>>> frame = pd.DataFrame(a,
>>> index=['a', 'c', 'd'],
>>> columns=['Hangzhou', 'Shenzhen', 'Nanjing'])
>>> frame
Hangzhou Shenzhen Nanjing
a 0 1 2
c 3 4 5
d 6 7 8
>>> frame['Hangzhou']
a 0
c 3
d 6
Name: Hangzhou, dtype: int64
>>> frame[['Shenzhen', 'Nanjing']]
Shenzhen Nanjing
a 1 2
c 4 5
d 7 8
>>> frame[:2]
Hangzhou Shenzhen Nanjing
a 0 1 2
c 3 4 5
>>> frame.loc['a']
Hangzhou 0
Shenzhen 1
Nanjing 2
Name: a, dtype: int64
>>> frame.loc[['a', 'd'], ['Shenzhen', 'Nanjing']]
Shenzhen Nanjing
a 1 2
d 7 8
>>> frame.loc[:'c', 'Hangzhou']
a 0
c 3
Name: Hangzhou, dtype: int64
- DataFrame 的 condition selection
>>> frame[frame.Hangzhou > 1]
Hangzhou Shenzhen Nanjing
c 3 4 5
d 6 7 8
>>> frame < 5
Hangzhou Shenzhen Nanjing
a True True True
c True True False
d False False False
>>> frame[frame < 5] = 0
>>> frame
Hangzhou Shenzhen Nanjing
a 0 0 0
c 0 0 5
d 6 7 8
3. reindex
- 按照新的 index 顺序进行重排；
>>> obj = pd.Series([4.5, 7.2, -5.3, 3.2], index=['d', 'b', 'a', 'c'])
>>> print(obj)
d 4.5
b 7.2
a -5.3
c 3.2
dtype: float64
>>> obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])
>>> obj2
a -5.3
b 7.2
c 3.2
d 4.5
e NaN
dtype: float64
- 在新的 index 上填充指定值；
>>> obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)
a -5.3
b 7.2
c 3.2
d 4.5
e 0.0
dtype: float64
- 在新的 index 上填充前面最近的值；
>>> obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])
>>> obj3
0 blue
2 purple
4 yellow
dtype: object
>>> # forward
>>> obj3.reindex(range(6), method='ffill')
0 blue
1 blue
2 purple
3 purple
4 yellow
5 yellow
dtype: object
- 在新的 index 上填充后面最近的值；
>>> # backward
>>> obj3.reindex(range(6), method='bfill')
0 blue
1 purple
2 purple
3 yellow
4 yellow
5 NaN
dtype: object
- 对 DataFrame 进行 reindex；
>>> frame = pd.DataFrame(np.arange(9).reshape(3, 3),
index=['a', 'c', 'd'],
columns=['Hangzhou', 'Shenzhen', 'Nanjing'])
>>> frame
Hangzhou Shenzhen Nanjing
a 0 1 2
c 3 4 5
d 6 7 8
>>> frame2 = frame.reindex(['a', 'b', 'c', 'd'])
>>> frame2
Hangzhou Shenzhen Nanjing
a 0.0 1.0 2.0
b NaN NaN NaN
c 3.0 4.0 5.0
d 6.0 7.0 8.0
- 重新指定 columns；
>>> frame.reindex(columns=['Shenzhen', 'Hangzhou', 'Chongqing'])
Shenzhen Hangzhou Chongqing
a 1 0 NaN
c 4 3 NaN
d 7 6 NaN
>>> frame3 = frame.reindex(index=['a', 'b', 'c', 'd'], method='ffill').reindex(
>>> columns=['Shenzhen', 'Hangzhou', 'Chongqing'])
>>> print(frame3)
Shenzhen Hangzhou Chongqing
a 1 0 NaN
b 1 0 NaN
c 4 3 NaN
d 7 6 NaN
>>> print(frame3.loc[['a', 'b', 'd', 'c'],
>>> ['Shenzhen', 'Hangzhou', 'Chongqing']])
Shenzhen Hangzhou Chongqing
a 1 0 NaN
b 1 0 NaN
d 7 6 NaN
c 4 3 NaN
4. drop
- 删除 Series 和 DataFrame 中的 index；
>>> print(obj3)
0 blue
2 purple
4 yellow
dtype: object
>>> obj4 = obj3.drop(2)
>>> print(obj4)
0 blue
4 yellow
dtype: object
>>> print(obj3.drop([2, 4]))
0 blue
dtype: object
>>> print(frame)
Hangzhou Shenzhen Nanjing
a 0 1 2
c 3 4 5
d 6 7 8
>>> print(frame.drop(['a', 'c']))
Hangzhou Shenzhen Nanjing
d 6 7 8
- 删除 DataFrame 中的 columns；
>>> print(frame.drop('Shenzhen', axis=1))
Hangzhou Nanjing
a 0 2
c 3 5
d 6 8
>>> print(frame.drop(['Shenzhen', 'Hangzhou'], axis=1))
Nanjing
a 2
c 5
d 8
5. Hierarchical Indexing
- Series 的 hierarchical indexing；
>>> data = pd.Series(np.random.randn(10),
>>> index=[['a', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'd', 'd'],
>>> [1, 2, 3, 1, 2, 1, 2, 3, 1, 2]])
>>> data
a 1 -0.587772
2 0.597073
3 -2.354382
b 1 1.403719
2 -0.612704
c 1 -1.409393
2 2.098933
3 0.076322
d 1 0.295683
2 1.188039
>>> data.index
MultiIndex([('a', 1),
('a', 2),
('a', 3),
('b', 1),
('b', 2),
('c', 1),
('c', 2),
('c', 3),
('d', 1),
('d', 2)],
)
>>> data.b
1 1.403719
2 -0.612704
dtype: float64
>>> data['b':'c']
b 1 1.403719
2 -0.612704
c 1 -1.409393
2 2.098933
3 0.076322
dtype: float64
>>> data[:2]
a 1 -0.587772
2 0.597073
dtype: float64
- unstack 和 stack；
>>> # 将 hierarchical indexing 的 Series 转换为 DataFrame
>>> data.unstack()
1 2 3
a -0.587772 0.597073 -2.354382
b 1.403719 -0.612704 NaN
c -1.409393 2.098933 0.076322
d 0.295683 1.188039 NaN
>>> type(data.unstack())
pandas.core.frame.DataFrame
>>> # 将 DataFrame 转换为 hierarchical indexing 的 Series
>>> data.unstack().stack()
a 1 -0.587772
2 0.597073
3 -2.354382
b 1 1.403719
2 -0.612704
c 1 -1.409393
2 2.098933
3 0.076322
d 1 0.295683
2 1.188039
dtype: float64
- DataFrame 的 hierarchical indexing；
>>> frame = pd.DataFrame(np.arange(12).reshape((4, 3)),
>>> index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
>>> columns=[['Beijing', 'Beijing', 'Shanghai'],
>>> ['apts', 'cars', 'apts']])
>>> frame
Beijing Shanghai
apts cars apts
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> frame.index.names = ['key1', 'key2']
>>> frame.columns.names = ['city', 'type']
>>> frame
city Beijing Shanghai
type apts cars apts
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
>>> frame.loc['a', 1]
city type
Beijing apts 0
cars 1
Shanghai apts 2
Name: (a, 1), dtype: int64
>>> frame.loc['a', 2]['Beijing']
type
apts 3
cars 4
Name: (a, 2), dtype: int64
>>> frame.loc['a', 2]['Beijing']['apts'] # 等价：frame.loc['a', 2]['Beijing', 'apts']
3
4. Concatenate 与 Append
>>> df1 = pd.DataFrame({'apts': [55000, 60000],
>>> 'cars': [200000, 300000], },
>>> index=['Shanghai', 'Beijing'])
>>> print(df1)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
>>> df2 = pd.DataFrame({'apts': [25000, 20000],
>>> 'cars': [150000, 120000], },
>>> index=['Hangzhou', 'Najing'])
>>> print(df2)
apts cars
Hangzhou 25000 150000
Najing 20000 120000
>>> df3 = pd.DataFrame({'apts': [30000, 10000],
>>> 'cars': [180000, 100000], },
>>> index=['Guangzhou', 'Chongqing'])
>>> print(df3)
apts cars
Guangzhou 30000 180000
Chongqing 10000 100000
1. 纵向 concat
>>> frames = [df1, df2, df3]
>>> print(frames)
[ apts cars
Shanghai 55000 200000
Beijing 60000 300000, apts cars
Hangzhou 25000 150000
Najing 20000 120000, apts cars
Guangzhou 30000 180000
Chongqing 10000 100000]
>>> result = pd.concat(frames)
>>> print(result)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
Hangzhou 25000 150000
Najing 20000 120000
Guangzhou 30000 180000
Chongqing 10000 100000
>>> # 给 concat 的每一个部分加上一个 Key
>>> result2 = pd.concat(frames, keys=['x', 'y', 'z'])
>>> print(result2)
apts cars
x Shanghai 55000 200000
Beijing 60000 300000
y Hangzhou 25000 150000
Najing 20000 120000
z Guangzhou 30000 180000
Chongqing 10000 100000
>>> result2.loc['y']
apts cars
Hangzhou 25000 150000
Najing 20000 120000
2. 横向 concat
>>> df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000]},
>>> index=['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin'])
>>> print(df4)
salaries
Suzhou 10000
Beijing 30000
Shanghai 30000
Guangzhou 20000
Tianjin 15000
>>> result3 = pd.concat([result, df4], axis=1, sort=True)
>>> print(result3)
apts cars salaries
Beijing 60000.0 300000.0 30000.0
Chongqing 10000.0 100000.0 NaN
Guangzhou 30000.0 180000.0 20000.0
Hangzhou 25000.0 150000.0 NaN
Najing 20000.0 120000.0 NaN
Shanghai 55000.0 200000.0 30000.0
Suzhou NaN NaN 10000.0
Tianjin NaN NaN 15000.0
>>> # DataFrame 转化为 hierarchical indexing 的 Series
>>> print(result3.stack())
Beijing apts 60000.0
cars 300000.0
salaries 30000.0
Chongqing apts 10000.0
cars 100000.0
Guangzhou apts 30000.0
cars 180000.0
salaries 20000.0
Hangzhou apts 25000.0
cars 150000.0
Najing apts 20000.0
cars 120000.0
Shanghai apts 55000.0
cars 200000.0
salaries 30000.0
Suzhou salaries 10000.0
Tianjin salaries 15000.0
dtype: float64
3. join concat
- 按 index 进行 inner join；
>>> print(result)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
Hangzhou 25000 150000
Najing 20000 120000
Guangzhou 30000 180000
Chongqing 10000 100000
>>> print(df4)
salaries
Suzhou 10000
Beijing 30000
Shanghai 30000
Guangzhou 20000
Tianjin 15000
>>> result3 = pd.concat([result, df4], axis=1, join='inner')
>>> print(result3)
apts cars salaries
Shanghai 55000 200000 30000
Beijing 60000 300000 30000
Guangzhou 30000 180000 20000
4. append（deprecated）
- 纵向 concat；
>>> print(df1)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
>>> print(df2)
apts cars
Hangzhou 25000 150000
Najing 20000 120000
>>> df1.append(df2)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
Hangzhou 25000 150000
Najing 20000 120000
- 横向 concat；
>>> print(df1)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
>>> print(df4)
salaries
Suzhou 10000
Beijing 30000
Shanghai 30000
Guangzhou 20000
Tianjin 15000
>>> df1.append(df4, sort=True)
apts cars salaries
Shanghai 55000.0 200000.0 NaN
Beijing 60000.0 300000.0 NaN
Suzhou NaN NaN 10000.0
Beijing NaN NaN 30000.0
Shanghai NaN NaN 30000.0
Guangzhou NaN NaN 20000.0
Tianjin NaN NaN 15000.0
5. Series 与 DataFrame 进行 concatenate
- Series 作为 Column 进行 concat；
>>> s1 = pd.Series([60, 50], index=['Shanghai', 'Beijing'], name='meal')
>>> s1
Shanghai 60
Beijing 50
Name: meal, dtype: int64
>>> print(df1)
apts cars
Shanghai 55000 200000
Beijing 60000 300000
>>> print(s1)
Shanghai 60
Beijing 50
Name: meal, dtype: int64
>>> print(pd.concat([df1, s1], axis=1))
apts cars meal
Shanghai 55000 200000 60
Beijing 60000 300000 50
- Series 作为 Row 进行 concat；
>>> s2 = pd.Series([18000, 12000], index=['apts', 'cars'], name='Xiamen')
>>> s2
apts 18000
cars 12000
Name: Xiamen, dtype: int64
>>> print(df1.append(s2))
apts cars
Shanghai 55000 200000
Beijing 60000 300000
Xiamen 18000 12000
5. Merge
1. 按指定列进行 inner join
>>> df1 = pd.DataFrame({'apts': [55000, 60000, 58000],
>>> 'cars': [200000, 300000, 250000],
>>> 'cities': ['Shanghai', 'Beijing', 'Shenzhen']})
>>> df1
apts cars cities
0 55000 200000 Shanghai
1 60000 300000 Beijing
2 58000 250000 Shenzhen
>>> df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000],
>>> 'cities': ['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin']})
>>> df4
salaries cities
0 10000 Suzhou
1 30000 Beijing
2 30000 Shanghai
3 20000 Guangzhou
4 15000 Tianjin
>>> pd.merge(df1, df4, on='cities')
apts cars cities salaries
0 55000 200000 Shanghai 30000
1 60000 300000 Beijing 30000
2. 按指定列进行 outer join
>>> pd.merge(df1, df4, on='cities', how='outer')
apts cars cities salaries
0 55000.0 200000.0 Shanghai 30000.0
1 60000.0 300000.0 Beijing 30000.0
2 58000.0 250000.0 Shenzhen NaN
3 NaN NaN Suzhou 10000.0
4 NaN NaN Guangzhou 20000.0
5 NaN NaN Tianjin 15000.0
3. 按指定列进行 right join
>>> pd.merge(df1, df4, on='cities', how='right')
apts cars cities salaries
0 NaN NaN Suzhou 10000
1 60000.0 300000.0 Beijing 30000
2 55000.0 200000.0 Shanghai 30000
3 NaN NaN Guangzhou 20000
4 NaN NaN Tianjin 15000
4. 按指定列进行 left join
>>> pd.merge(df1, df4, on='cities', how='left')
apts cars cities salaries
0 55000 200000 Shanghai 30000.0
1 60000 300000 Beijing 30000.0
2 58000 250000 Shenzhen NaN
6. Join
>>> df1 = pd.DataFrame({'apts': [55000, 60000, 58000],
>>> 'cars': [200000, 300000, 250000]},
>>> index=['Shanghai', 'Beijing', 'Shenzhen'])
>>> df1
apts cars
Shanghai 55000 200000
Beijing 60000 300000
Shenzhen 58000 250000
>>> df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000]},
>>> index=['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin'])
>>> df4
salaries
Suzhou 10000
Beijing 30000
Shanghai 30000
Guangzhou 20000
Tianjin 15000
1. 按 index 进行 inner join
>>> df1.join(df4)
apts cars salaries
Shanghai 55000 200000 30000.0
Beijing 60000 300000 30000.0
Shenzhen 58000 250000 NaN
2. 按 index 进行 outer join
>>> df1.join(df4, how='outer')
apts cars salaries
Beijing 60000.0 300000.0 30000.0
Guangzhou NaN NaN 20000.0
Shanghai 55000.0 200000.0 30000.0
Shenzhen 58000.0 250000.0 NaN
Suzhou NaN NaN 10000.0
Tianjin NaN NaN 15000.0
>>> pd.merge(df1, df4, left_index=True, right_index=True, how='outer')
apts cars salaries
Beijing 60000.0 300000.0 30000.0
Guangzhou NaN NaN 20000.0
Shanghai 55000.0 200000.0 30000.0
Shenzhen 58000.0 250000.0 NaN
Suzhou NaN NaN 10000.0
Tianjin NaN NaN 15000.0
7. Group By
1. 分组求和
>>> salaries = pd.DataFrame({
>>> 'Name': ['July', 'Chu', 'Chu', 'Lin', 'July', 'July', 'Chu', 'July'],
>>> 'Year': [2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017],
>>> 'Salary': [10000, 2000, 4000, 5000, 18000, 25000, 3000, 4000],
>>> 'Bonus': [3000, 1000, 1000, 1200, 4000, 2300, 500, 1000]
>>> })
>>> salaries
Name Year Salary Bonus
0 July 2016 10000 3000
1 Chu 2016 2000 1000
2 Chu 2016 4000 1000
3 Lin 2016 5000 1200
4 July 2017 18000 4000
5 July 2017 25000 2300
6 Chu 2017 3000 500
7 July 2017 4000 1000
>>> group_by_name = salaries.groupby('Name')
>>> group_by_name
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x11c48e550>
>>> group_by_name.aggregate(sum)
Year Salary Bonus
Name
Chu 6049 9000 2500
July 8067 57000 10300
Lin 2016 5000 1200
>>> group_by_name.sum()
Year Salary Bonus
Name
Chu 6049 9000 2500
July 8067 57000 10300
Lin 2016 5000 1200
>>> group_by_name_year = salaries.groupby(['Name', 'Year'])
>>> group_by_name_year.sum()
Salary Bonus
Name Year
Chu 2016 6000 2000
2017 3000 500
July 2016 10000 3000
2017 47000 7300
Lin 2016 5000 1200
>>> group_by_name_year.size()
Name Year
Chu 2016 2
2017 1
July 2016 1
2017 3
Lin 2016 1
dtype: int64
2. 展示分组的各种统计信息
>>> group_by_name_year.describe()
Salary \
count mean std min 25% 50%
Name Year
Chu 2016 2.0 3000.000000 1414.213562 2000.0 2500.0 3000.0
2017 1.0 3000.000000 NaN 3000.0 3000.0 3000.0
July 2016 1.0 10000.000000 NaN 10000.0 10000.0 10000.0
2017 3.0 15666.666667 10692.676622 4000.0 11000.0 18000.0
Lin 2016 1.0 5000.000000 NaN 5000.0 5000.0 5000.0
Bonus \
75% max count mean std min 25%
Name Year
Chu 2016 3500.0 4000.0 2.0 1000.000000 0.00000 1000.0 1000.0
2017 3000.0 3000.0 1.0 500.000000 NaN 500.0 500.0
July 2016 10000.0 10000.0 1.0 3000.000000 NaN 3000.0 3000.0
2017 21500.0 25000.0 3.0 2433.333333 1504.43788 1000.0 1650.0
Lin 2016 5000.0 5000.0 1.0 1200.000000 NaN 1200.0 1200.0
50% 75% max
Name Year
Chu 2016 1000.0 1000.0 1000.0
2017 500.0 500.0 500.0
July 2016 3000.0 3000.0 3000.0
2017 2300.0 3150.0 4000.0
Lin 2016 1200.0 1200.0 1200.0
8. 应用案例（计算每个工作日的骑车人数之和）
1. 读取 bike.csv 到 DataFrame
read_csv API reference
bike.csv 记录了 Montreal 自行车路线的数据，具体有 7 条路线，数据记录了每条自行车路线每天分别有多少人；
>>> pd.set_option('display.max_columns', 60)
>>> bikes = pd.read_csv('data/bikes.csv', encoding='latin1')
>>> bikes
Date;Berri 1;Brébeuf (données non disponibles);Côte-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (données non disponibles)
0 01/01/2012;35;;0;38;51;26;10;16;
1 02/01/2012;83;;1;68;153;53;6;43;
2 03/01/2012;135;;2;104;248;89;3;58;
3 04/01/2012;144;;1;116;318;111;8;61;
4 05/01/2012;197;;2;124;330;97;13;95;
.. ...
305 01/11/2012;2405;;1208;1701;3082;2076;165;2461
306 02/11/2012;1582;;737;1109;2277;1392;97;1888
307 03/11/2012;844;;380;612;1137;713;105;1302
308 04/11/2012;966;;446;710;1277;692;197;1374
309 05/11/2012;2247;;1170;1705;3221;2143;179;2430
[310 rows x 1 columns]
按分割好的列装载 bike.csv 到 DataFrame；
>>> bikes = pd.read_csv('data/bikes.csv', sep=';',
>>> parse_dates=['Date'], encoding='latin1', dayfirst=True, index_col='Date')
>>> bikes
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \
Date
2012-01-01 35 NaN 0
2012-01-02 83 NaN 1
2012-01-03 135 NaN 2
2012-01-04 144 NaN 1
2012-01-05 197 NaN 2
... ... ... ...
2012-11-01 2405 NaN 1208
2012-11-02 1582 NaN 737
2012-11-03 844 NaN 380
2012-11-04 966 NaN 446
2012-11-05 2247 NaN 1170
Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \
Date
2012-01-01 38 51 26 10 16
2012-01-02 68 153 53 6 43
2012-01-03 104 248 89 3 58
2012-01-04 116 318 111 8 61
2012-01-05 124 330 97 13 95
... ... ... ... ... ...
2012-11-01 1701 3082 2076 165 2461
2012-11-02 1109 2277 1392 97 1888
2012-11-03 612 1137 713 105 1302
...
2012-11-03 NaN
2012-11-04 NaN
2012-11-05 NaN
[310 rows x 9 columns]
2. 查看数据样例
- 使用 head 与切片取前 5 行；
>>> bikes.head(5)
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \
Date
2012-01-01 35 NaN 0
2012-01-02 83 NaN 1
2012-01-03 135 NaN 2
2012-01-04 144 NaN 1
2012-01-05 197 NaN 2
Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \
Date
2012-01-01 38 51 26 10 16
2012-01-02 68 153 53 6 43
2012-01-03 104 248 89 3 58
2012-01-04 116 318 111 8 61
2012-01-05 124 330 97 13 95
St-Urbain (données non disponibles)
Date
2012-01-01 NaN
2012-01-02 NaN
2012-01-03 NaN
2012-01-04 NaN
2012-01-05 NaN
>>> bikes[:5]
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \
Date
2012-01-01 35 NaN 0
2012-01-02 83 NaN 1
2012-01-03 135 NaN 2
2012-01-04 144 NaN 1
2012-01-05 197 NaN 2
Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \
Date
2012-01-01 38 51 26 10 16
2012-01-02 68 153 53 6 43
2012-01-03 104 248 89 3 58
2012-01-04 116 318 111 8 61
2012-01-05 124 330 97 13 95
St-Urbain (données non disponibles)
Date
2012-01-01 NaN
2012-01-02 NaN
2012-01-03 NaN
2012-01-04 NaN
2012-01-05 NaN
- 使用 copy 复制选取的部分；
>>> berri_bikes = bikes[['Berri 1']].copy()
>>> berri_bikes.head()
Berri 1
Date
2012-01-01 35
2012-01-02 83
2012-01-03 135
2012-01-04 144
2012-01-05 197
>>> berri_bikes.index
DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',
'2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',
'2012-01-09', '2012-01-10',
...
'2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30',
'2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03',
'2012-11-04', '2012-11-05'],
dtype='datetime64[ns]', name='Date', length=310, freq=None)
>>> # 查看日期是每月的第几天
>>> berri_bikes.index.day
Int64Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
...
27, 28, 29, 30, 31, 1, 2, 3, 4, 5],
dtype='int64', name='Date', length=310)
>>> # 查看日期是星期几
>>> berri_bikes.index.weekday
Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,
...
5, 6, 0, 1, 2, 3, 4, 5, 6, 0],
dtype='int64', name='Date', length=310)
3. dropna
- 删除所有带 NaN 的行；
>>> bikes.dropna()
Empty DataFrame
Columns: [Berri 1, Brébeuf (données non disponibles), Côte-Sainte-Catherine, Maisonneuve 1, Maisonneuve 2, du Parc, Pierre-Dupuy, Rachel1, St-Urbain (données non disponibles)]
Index: []
- 删除整行都为 NaN 的行；
>>> bikes.dropna(how='all').head()
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \
Date
2012-01-01 35 NaN 0
2012-01-02 83 NaN 1
2012-01-03 135 NaN 2
2012-01-04 144 NaN 1
2012-01-05 197 NaN 2
Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \
Date
2012-01-01 38 51 26 10 16
2012-01-02 68 153 53 6 43
2012-01-03 104 248 89 3 58
2012-01-04 116 318 111 8 61
2012-01-05 124 330 97 13 95
St-Urbain (données non disponibles)
Date
2012-01-01 NaN
2012-01-02 NaN
2012-01-03 NaN
2012-01-04 NaN
2012-01-05 NaN
- 删除整列都为 NaN 的列；
>>> bikes.dropna(axis=1, how='all').head()
Berri 1 Côte-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \
Date
2012-01-01 35 0 38 51
2012-01-02 83 1 68 153
2012-01-03 135 2 104 248
2012-01-04 144 1 116 318
2012-01-05 197 2 124 330
du Parc Pierre-Dupuy Rachel1
Date
2012-01-01 26 10 16
2012-01-02 53 6 43
2012-01-03 89 3 58
2012-01-04 111 8 61
2012-01-05 97 13 95
3. fillna
- 填充缺失的数据（单行）；
>>> row = bikes.iloc[0].copy()
>>> print(row)
Berri 1 35.0
Brébeuf (données non disponibles) NaN
Côte-Sainte-Catherine 0.0
Maisonneuve 1 38.0
Maisonneuve 2 51.0
du Parc 26.0
Pierre-Dupuy 10.0
Rachel1 16.0
St-Urbain (données non disponibles) NaN
Name: 2012-01-01 00:00:00, dtype: float64
>>> # 平均值
>>> print(row.mean())
25.142857142857142
>>> print(row.fillna(row.mean()))
Berri 1 35.000000
Brébeuf (données non disponibles) 25.142857
Côte-Sainte-Catherine 0.000000
Maisonneuve 1 38.000000
Maisonneuve 2 51.000000
du Parc 26.000000
Pierre-Dupuy 10.000000
Rachel1 16.000000
St-Urbain (données non disponibles) 25.142857
Name: 2012-01-01 00:00:00, dtype: float64
- 填充缺失数据（全量）；
>>> # 求所有行的行平均值；
>>> m = bikes.mean(axis=1)
>>> print(m)
Date
2012-01-01 25.142857
2012-01-02 58.142857
2012-01-03 91.285714
2012-01-04 108.428571
2012-01-05 122.571429
...
2012-11-01 1871.142857
2012-11-02 1297.428571
2012-11-03 727.571429
2012-11-04 808.857143
2012-11-05 1870.714286
Length: 310, dtype: float64
>>> # 将行中缺失的部分用其行均值填充：遍历各列，将各列的所有行中为 NaN 的部分用 m 的对应位填充
>>> for i, col in enumerate(bikes):
>>> bikes.iloc[:, i] = bikes.iloc[:, i].fillna(m)
>>> print(i, col)
0 Berri 1
1 Brébeuf (données non disponibles)
2 Côte-Sainte-Catherine
3 Maisonneuve 1
4 Maisonneuve 2
5 du Parc
6 Pierre-Dupuy
7 Rachel1
8 St-Urbain (données non disponibles)
>>> bikes.head()
Berri 1 Brébeuf (données non disponibles) Côte-Sainte-Catherine \
Date
2012-01-01 35 25.142857 0
2012-01-02 83 58.142857 1
2012-01-03 135 91.285714 2
2012-01-04 144 108.428571 1
2012-01-05 197 122.571429 2
Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \
Date
2012-01-01 38 51 26 10 16
2012-01-02 68 153 53 6 43
2012-01-03 104 248 89 3 58
2012-01-04 116 318 111 8 61
2012-01-05 124 330 97 13 95
St-Urbain (données non disponibles)
Date
2012-01-01 25.142857
2012-01-02 58.142857
2012-01-03 91.285714
2012-01-04 108.428571
2012-01-05 122.571429
4. 计算单条路线每个工作日的骑车人数之和
- 新增一个 weekday 列；
>>> berri_bikes.loc[:, 'weekday'] = berri_bikes.index.weekday
>>> berri_bikes[:5]
Berri 1 weekday
Date
2012-01-01 35 6
2012-01-02 83 0
2012-01-03 135 1
2012-01-04 144 2
2012-01-05 197 3
- 按 weekday 分组求和；
>>> weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)
>>> weekday_counts
Berri 1
weekday
0 134298
1 135305
2 152972
3 160131
4 141771
5 101578
6 99310
- 使用星期名词替换默认 index；
>>> weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday',
>>> 'Thursday', 'Friday', 'Saturday', 'Sunday']
>>> weekday_counts
Berri 1
Monday 134298
Tuesday 135305
Wednesday 152972
Thursday 160131
Friday 141771
Saturday 101578
Sunday 99310
5. 计算所有路线每个工作日的骑车人数之和
- 按日期求和（所有路线每天的人数之和）；
>>> bikes = pd.read_csv('data/bikes.csv', sep=';',
>>> parse_dates=['Date'], encoding='latin1', dayfirst=True, index_col='Date')
>>> bikes_sum = bikes.sum(axis=1).to_frame()
>>> print(bikes_sum.head())
0
Date
2012-01-01 176.0
2012-01-02 407.0
2012-01-03 639.0
2012-01-04 759.0
2012-01-05 858.0
- 增加 weekday 列；
>>> print(bikes_sum.index)
DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',
'2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',
'2012-01-09', '2012-01-10',
...
'2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30',
'2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03',
'2012-11-04', '2012-11-05'],
dtype='datetime64[ns]', name='Date', length=310, freq=None)
>>> bikes_sum.loc[:, 'weekday'] = bikes_sum.index.weekday
>>> bikes_sum.head()
0 weekday
Date
2012-01-01 176.0 6
2012-01-02 407.0 0
2012-01-03 639.0 1
2012-01-04 759.0 2
2012-01-05 858.0 3
- 按工作日分钟求和（每个工作日骑车的人数之和）；
>>> weekday_counts = bikes_sum.groupby('weekday').aggregate(sum)
>>> weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday',
>>> 'Thursday', 'Friday', 'Saturday', 'Sunday']
>>> weekday_counts
0
Monday 714963.0
Tuesday 698582.0
Wednesday 789722.0
Thursday 829069.0
Friday 738772.0
Saturday 516701.0
Sunday 518047.0
- 上一篇：「Python 机器学习」Numpy 矩阵运算
- 下一篇：「Python 机器学习」Matplotlib 数据探索
PS：欢迎各路道友阅读与评论，感谢道友点赞、关注、收藏！
文章出处登录后可见！
已经登录？立即刷新
