Python基础（十一） | 超详细的Pandas库三万字总结（上）

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timerring 发表于 2022/10/07 09:26:49 2022/10/07
【摘要】 ⭐本专栏旨在对Python的基础语法进行详解，精炼地总结语法中的重点，详解难点，面向零基础及入门的学习者，通过专栏的学习可以熟练掌握python编程，同时为后续的数据分析，机器学习及深度学习的代码能力打下坚实的基础。🔥本文已收录于Python基础系列专栏： Python基础系列教程 欢迎订阅，持续更新。 引子Numpy 在向量化的数值计算中表现优异但是在处理更灵活、复杂的数据任务：如为数据...

⭐本专栏旨在对Python的基础语法进行详解，精炼地总结语法中的重点，详解难点，面向零基础及入门的学习者，通过专栏的学习可以熟练掌握python编程，同时为后续的数据分析，机器学习及深度学习的代码能力打下坚实的基础。

🔥本文已收录于Python基础系列专栏： Python基础系列教程 欢迎订阅，持续更新。

image-20221002210956895

image-20221002211020709

引子

Numpy 在向量化的数值计算中表现优异

但是在处理更灵活、复杂的数据任务：

如为数据添加标签、处理缺失值、分组和透视表等方面

Numpy显得力不从心

而基于Numpy构建的Pandas库，提供了使得数据分析变得更快更简单的高级数据结构和操作工具

11.1 对象创建

11.1.1 Pandas Series对象

Series 是带标签数据的一维数组

Series对象的创建

通用结构: pd.Series(data, index=index, dtype=dtype)

data：数据，可以是列表，字典或Numpy数组

index：索引，为可选参数

dtype: 数据类型，为可选参数

1、用列表创建

  • index缺省，默认为整数序列
import pandas as pd
​
data = pd.Series([1.5, 3, 4.5, 6])
data

0    1.5
1    3.0
2    4.5
3    6.0
dtype: float64
  • 增加index
data = pd.Series([1.5, 3, 4.5, 6], index=["a", "b", "c", "d"])
data

a    1.5
b    3.0
c    4.5
d    6.0
dtype: float64

  • 增加数据类型

    缺省则从传入的数据自动判断

data = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"])    
data

a    1
b    2
c    3
d    4
dtype: int64

data = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"], dtype="float")
data

a    1.0
b    2.0
c    3.0
d    4.0
dtype: float64

注意：数据支持多种类型

  • 混合后数据类型变为object
data = pd.Series([1, 2, "3", 4], index=["a", "b", "c", "d"])
data

a    1
b    2
c    3
d    4
dtype: object

data["a"]

1

data["c"]

'3'

数据类型可被强制改变

data = pd.Series([1, 2, "3", 4], index=["a", "b", "c", "d"], dtype=float)
data

a    1.0
b    2.0
c    3.0
d    4.0
dtype: float64

data["c"]

3.0

不能转为浮点数则会报错

data = pd.Series([1, 2, "a", 4], index=["a", "b", "c", "d"], dtype=float)
data

---------------------------------------------------------------------------
​
NameError                                 Traceback (most recent call last)
​
~\AppData\Local\Temp/ipykernel_9236/4046912764.py in <module>
----> 1 data = pd.Series([1, 2, "a", 4], index=["a", "b", "c", "d"], dtype=float)
      2 data

NameError: name 'pd' is not defined

2、用一维numpy数组创建

import numpy as np
​
x = np.arange(5)
pd.Series(x)

0    0
1    1
2    2
3    3
4    4
dtype: int32

3、用字典创建

  • 默认以键为index 值为data
population_dict = {"BeiJing": 2154,
                   "ShangHai": 2424,
                   "ShenZhen": 1303,
                   "HangZhou": 981 }
population = pd.Series(population_dict)    
population

BeiJing     2154
ShangHai    2424
ShenZhen    1303
HangZhou     981
dtype: int64
  • 字典创建，如果指定index，则会到字典的键中筛选，找不到的，值设为NaN
population = pd.Series(population_dict, index=["BeiJing", "HangZhou", "c", "d"])    
population

BeiJing     2154.0
HangZhou     981.0
c              NaN
d              NaN
dtype: float64

4、data为标量的情况

pd.Series(5, index=[100, 200, 300])

100    5
200    5
300    5
dtype: int64

11.1.2 Pandas DataFrame对象

DataFrame 是带标签数据的多维数组

DataFrame对象的创建

通用结构: pd.DataFrame(data, index=index, columns=columns)

data：数据，可以是列表，字典或Numpy数组

index：索引，为可选参数

columns: 列标签，为可选参数

1、通过Series对象创建

population_dict = {"BeiJing": 2154,
                   "ShangHai": 2424,
                   "ShenZhen": 1303,
                   "HangZhou": 981 }
​
population = pd.Series(population_dict)    
pd.DataFrame(population)

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0
BeiJing 2154
ShangHai 2424
ShenZhen 1303
HangZhou 981

​

pd.DataFrame(population, columns=["population"])

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population
BeiJing 2154
ShangHai 2424
ShenZhen 1303
HangZhou 981

​

2、通过Series对象字典创建

GDP_dict = {"BeiJing": 30320,
            "ShangHai": 32680,
            "ShenZhen": 24222,
            "HangZhou": 13468 }
​
GDP = pd.Series(GDP_dict)
GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
dtype: int64

pd.DataFrame({"population": population,
              "GDP": GDP})

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population GDP
BeiJing 2154 30320
ShangHai 2424 32680
ShenZhen 1303 24222
HangZhou 981 13468

​

注意：数量不够的会自动补齐

pd.DataFrame({"population": population,
              "GDP": GDP,
              "country": "China"})

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population GDP country
BeiJing 2154 30320 China
ShangHai 2424 32680 China
ShenZhen 1303 24222 China
HangZhou 981 13468 China

​

3、通过字典列表对象创建

  • 字典索引作为index，字典键作为columns
import numpy as np
import pandas as pd
​
data = [{"a": i, "b": 2*i} for i in range(3)]
data

[{'a': 0, 'b': 0}, {'a': 1, 'b': 2}, {'a': 2, 'b': 4}]

data = pd.DataFrame(data)
data

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a b
0 0 0
1 1 2
2 2 4

​

行的标签没有排，因此行从0开始，列的标签延续。

  • 从中取出一列数据
data1 = data["a"].copy()
data1

0    0
1    1
2    2
Name: a, dtype: int64

data1[0] = 10
data1

0    10
1     1
2     2
Name: a, dtype: int64

data

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​

a b
0 0 0
1 1 2
2 2 4

​

  • 不存在的键，会默认值为NaN
data = [{"a": 1, "b":1},{"b": 3, "c":4}]
data

[{'a': 1, 'b': 1}, {'b': 3, 'c': 4}]

pd.DataFrame(data)

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a b c
0 1.0 1 NaN
1 NaN 3 4.0

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4、通过Numpy二维数组创建

data = np.random.randint(10, size=(3, 2))
data

array([[1, 6],
       [2, 9],
       [4, 0]])

pd.DataFrame(data, columns=["foo", "bar"], index=["a", "b", "c"])

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foo bar
a 1 6
b 2 9
c 4 0

​

11.2 DataFrame性质

1、属性

data = pd.DataFrame({"pop": population, "GDP": GDP})
data

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pop GDP
BeiJing 2154 30320
ShangHai 2424 32680
ShenZhen 1303 24222
HangZhou 981 13468

​

（1）df.values 返回numpy数组表示的数据

data.values

array([[ 2154, 30320],
       [ 2424, 32680],
       [ 1303, 24222],
       [  981, 13468]], dtype=int64)

（2）df.index 返回行索引

data.index

Index(['BeiJing', 'ShangHai', 'ShenZhen', 'HangZhou'], dtype='object')

（3）df.columns 返回列索引

data.columns

Index(['pop', 'GDP'], dtype='object')

（4）df.shape 形状

data.shape

(4, 2)

（5） pd.size 大小

data.size

8

（6）pd.dtypes 返回每列数据类型

data.dtypes

pop    int64
GDP    int64
dtype: object

2、索引

data

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pop GDP
BeiJing 2154 30320
ShangHai 2424 32680
ShenZhen 1303 24222
HangZhou 981 13468

​

（1）获取列

  • 字典式
data["pop"]

BeiJing     2154
ShangHai    2424
ShenZhen    1303
HangZhou     981
Name: pop, dtype: int64

data[["GDP", "pop"]]

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GDP pop
BeiJing 30320 2154
ShangHai 32680 2424
ShenZhen 24222 1303
HangZhou 13468 981

​

  • 对象属性式
data.GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
Name: GDP, dtype: int64

（2）获取行

  • 绝对索引 df.loc
data.loc["BeiJing"]

pop     2154
GDP    30320
Name: BeiJing, dtype: int64

data.loc[["BeiJing", "HangZhou"]]

​

pop GDP
BeiJing 2154 30320
HangZhou 981 13468

​

  • 相对索引 df.iloc
data

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pop GDP
BeiJing 2154 30320
ShangHai 2424 32680
ShenZhen 1303 24222
HangZhou 981 13468

​

data.iloc[0]

pop     2154
GDP    30320
Name: BeiJing, dtype: int64

data.iloc[[1, 3]]

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pop GDP
ShangHai 2424 32680
HangZhou 981 13468

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（3）获取标量

data

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pop GDP
BeiJing 2154 30320
ShangHai 2424 32680
ShenZhen 1303 24222
HangZhou 981 13468

​

data.loc["BeiJing", "GDP"]

30320

data.iloc[0, 1]

30320

data.values[0][1]

30320

（4）Series对象的索引

type(data.GDP)

pandas.core.series.Series

GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
dtype: int64

GDP["BeiJing"]

30320

3、切片

dates = pd.date_range(start='2019-01-01', periods=6)
dates

DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06'],
              dtype='datetime64[ns]', freq='D')

df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=["A", "B", "C", "D"])
df

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726
2019-01-04 0.305088 0.535920 -0.978434 0.177251
2019-01-05 0.313383 0.234041 0.163155 -0.296649
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342

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（1）行切片

df["2019-01-01": "2019-01-03"]

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726

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df.loc["2019-01-01": "2019-01-03"]

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726

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df.iloc[0: 3]

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726

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注意：这里的3是取不到的。

（2）列切片

df

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726
2019-01-04 0.305088 0.535920 -0.978434 0.177251
2019-01-05 0.313383 0.234041 0.163155 -0.296649
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342

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df.loc[:, "A": "C"]

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A B C
2019-01-01 -0.935378 -0.190742 0.925984
2019-01-02 -0.234414 -1.194674 1.080779
2019-01-03 -0.141572 0.058118 1.102248
2019-01-04 0.305088 0.535920 -0.978434
2019-01-05 0.313383 0.234041 0.163155
2019-01-06 0.250613 -0.904400 -0.858240

​

df.iloc[:, 0: 3]

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A B C
2019-01-01 -0.935378 -0.190742 0.925984
2019-01-02 -0.234414 -1.194674 1.080779
2019-01-03 -0.141572 0.058118 1.102248
2019-01-04 0.305088 0.535920 -0.978434
2019-01-05 0.313383 0.234041 0.163155
2019-01-06 0.250613 -0.904400 -0.858240

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（3）多种多样的取值

df

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726
2019-01-04 0.305088 0.535920 -0.978434 0.177251
2019-01-05 0.313383 0.234041 0.163155 -0.296649
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342

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  • 行、列同时切片
df.loc["2019-01-02": "2019-01-03", "C":"D"]

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C D
2019-01-02 1.080779 -2.294395
2019-01-03 1.102248 1.207726

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df.iloc[1: 3, 2:]

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C D
2019-01-02 1.080779 -2.294395
2019-01-03 1.102248 1.207726

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  • 行切片，列分散取值
df.loc["2019-01-04": "2019-01-06", ["A", "C"]]

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A C
2019-01-04 0.305088 -0.978434
2019-01-05 0.313383 0.163155
2019-01-06 0.250613 -0.858240

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df.iloc[3:, [0, 2]]

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A C
2019-01-04 0.305088 -0.978434
2019-01-05 0.313383 0.163155
2019-01-06 0.250613 -0.858240

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  • 行分散取值，列切片
df.loc[["2019-01-02", "2019-01-06"], "C": "D"]

上面这种方式是行不通的。

df.iloc[[1, 5], 0: 3]

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A B C
2019-01-02 -0.234414 -1.194674 1.080779
2019-01-06 0.250613 -0.904400 -0.858240

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  • 行、列均分散取值
df.loc[["2019-01-04", "2019-01-06"], ["A", "D"]]

同样，上面这种方式是行不通的。

df.iloc[[1, 5], [0, 3]]

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A D
2019-01-02 -0.234414 -2.294395
2019-01-06 0.250613 -1.573342

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4、布尔索引

相当于numpy当中的掩码操作。

df

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A B C D
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395
2019-01-03 -0.141572 0.058118 1.102248 1.207726
2019-01-04 0.305088 0.535920 -0.978434 0.177251
2019-01-05 0.313383 0.234041 0.163155 -0.296649
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342

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df > 0

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A B C D
2019-01-01 False False True False
2019-01-02 False False True False
2019-01-03 False True True True
2019-01-04 True True False True
2019-01-05 True True True False
2019-01-06 True False False False

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df[df > 0]

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A B C D
2019-01-01 NaN NaN 0.925984 NaN
2019-01-02 NaN NaN 1.080779 NaN
2019-01-03 NaN 0.058118 1.102248 1.207726
2019-01-04 0.305088 0.535920 NaN 0.177251
2019-01-05 0.313383 0.234041 0.163155 NaN
2019-01-06 0.250613 NaN NaN NaN

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可以观察到，为true的部分都被取到了，而false没有。

df.A > 0

2019-01-01    False
2019-01-02    False
2019-01-03    False
2019-01-04     True
2019-01-05     True
2019-01-06     True
Freq: D, Name: A, dtype: bool

df[df.A > 0]

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A B C D
2019-01-04 0.305088 0.535920 -0.978434 0.177251
2019-01-05 0.313383 0.234041 0.163155 -0.296649
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342

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  • isin（）方法
df2 = df.copy()
df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
df2

​​

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A B C D E
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969 one
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395 one
2019-01-03 -0.141572 0.058118 1.102248 1.207726 two
2019-01-04 0.305088 0.535920 -0.978434 0.177251 three
2019-01-05 0.313383 0.234041 0.163155 -0.296649 four
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342 three

​

ind = df2["E"].isin(["two", "four"])
ind     

2019-01-01    False
2019-01-02    False
2019-01-03     True
2019-01-04    False
2019-01-05     True
2019-01-06    False
Freq: D, Name: E, dtype: bool

df2[ind]

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A B C D E
2019-01-03 -0.141572 0.058118 1.102248 1.207726 two
2019-01-05 0.313383 0.234041 0.163155 -0.296649 four

​

（5）赋值

df
  • DataFrame 增加新列
s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20190101', periods=6))
s1

2019-01-01    1
2019-01-02    2
2019-01-03    3
2019-01-04    4
2019-01-05    5
2019-01-06    6
Freq: D, dtype: int64

df["E"] = s1
df

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A B C D E
2019-01-01 -0.935378 -0.190742 0.925984 -0.818969 1
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395 2
2019-01-03 -0.141572 0.058118 1.102248 1.207726 3
2019-01-04 0.305088 0.535920 -0.978434 0.177251 4
2019-01-05 0.313383 0.234041 0.163155 -0.296649 5
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342 6

​

  • 修改赋值
df.loc["2019-01-01", "A"] = 0
df

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A B C D E
2019-01-01 0.000000 -0.190742 0.925984 -0.818969 1
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395 2
2019-01-03 -0.141572 0.058118 1.102248 1.207726 3
2019-01-04 0.305088 0.535920 -0.978434 0.177251 4
2019-01-05 0.313383 0.234041 0.163155 -0.296649 5
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342 6

​

df.iloc[0, 1] = 0
df

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A B C D E
2019-01-01 0.000000 0.000000 0.925984 -0.818969 1
2019-01-02 -0.234414 -1.194674 1.080779 -2.294395 2
2019-01-03 -0.141572 0.058118 1.102248 1.207726 3
2019-01-04 0.305088 0.535920 -0.978434 0.177251 4
2019-01-05 0.313383 0.234041 0.163155 -0.296649 5
2019-01-06 0.250613 -0.904400 -0.858240 -1.573342 6

​

df["D"] = np.array([5]*len(df))   # 可简化成df["D"] = 5
df

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A B C D E
2019-01-01 0.000000 0.000000 0.925984 5 1
2019-01-02 -0.234414 -1.194674 1.080779 5 2
2019-01-03 -0.141572 0.058118 1.102248 5 3
2019-01-04 0.305088 0.535920 -0.978434 5 4
2019-01-05 0.313383 0.234041 0.163155 5 5
2019-01-06 0.250613 -0.904400 -0.858240 5 6

​

  • 修改index和columns
df.index = [i for i in range(len(df))]
df

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A B C D E
0 0.000000 0.000000 0.925984 5 1
1 -0.234414 -1.194674 1.080779 5 2
2 -0.141572 0.058118 1.102248 5 3
3 0.305088 0.535920 -0.978434 5 4
4 0.313383 0.234041 0.163155 5 5
5 0.250613 -0.904400 -0.858240 5 6

​

df.columns = [i for i in range(df.shape[1])]
df

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0 1 2 3 4
0 0.000000 0.000000 0.925984 5 1
1 -0.234414 -1.194674 1.080779 5 2
2 -0.141572 0.058118 1.102248 5 3
3 0.305088 0.535920 -0.978434 5 4
4 0.313383 0.234041 0.163155 5 5
5 0.250613 -0.904400 -0.858240 5 6

​

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Numpy Python
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