pandas常用函数,个人常用的

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1.创建DataFrame import numpy as np import pandas as pd da…

1.创建DataFrame

import numpy as np import  pandas as pd data = {"姓名":["张三","小明","李强","汪小小","李四","王麻子","余总","肖强"],        "score":[85,90,70,67,68,np.nan,100,86]}  data=pd.DataFrame(data) print(data)  

2.数据提取
提取姓名为余总的行

result=data[data['姓名'].str.contains("余总")] print(result) 

3.提取列名

print(data.columns) 

4.修改列名
修改第二列名字

data.rename(columns={'score':'分数'}, inplace = True) 

5.缺失值处理
前向插值

data['score'] = data['score'].fillna(data['score'].interpolate()) 

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6.数据提取

print(data[data['score']>90]) 

7.数据求重

data.drop_duplicates(['姓名']) 

8.数据计算
求平均值

data['score'].mean() 

9。数据范围提取

c=data[(data['score']>70) & (data['score']<90)] print(c) 

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9.数据查看

data.head()#查看前5行 data.tail()#查看后5行 

10.数据整理排序

c=data.sort_values('score') print(c) 

11.数据拼接

pd.concat([df1, df2],axis=1

12.数据计算

c=data[['score']].apply(lambda x: x.max() - x.min()) print(c) 

12:时间数据特征构造
数据为其他数据,非上文数据,就不展示啦
localminute为数据的时间列

use_data.loc[:, 'localminute'] = pd.to_datetime(use_data['localminute'],format='%Y-%m-%dT%H:%M:%S') # 几时 use_data.loc[:, 'hour'] = use_data['localminute'].apply(lambda x: x.hour) # 几号 use_data.loc[:, 'dom'] = use_data['localminute'].apply(lambda x: x.day) # 几月 use_data.loc[:, 'month'] = use_data['localminute'].apply(lambda x: x.month) 

13.新列添加方法1:

 ```python use_data['State']='' use_data.loc[use_data['refrigerator']==0,'State']=0 use_data.loc[use_data['refrigerator']>0,'State']=1 use_data.loc[use_data['refrigerator']>0.07,'State']=2  

14.新列添加方法2

use_data.loc[:,'State']=use_data['furnace1'].apply(lambda x: 0 if x<=0.1 else 1)# 

15.某一列向下平移 并去掉平移后的空值

data['use1']=data.groupby('dataid')['use'].shift(1) data['use2']=data.groupby('dataid')['use'].shift(2) data['use3']=data.groupby('dataid')['use'].shift(3)#平移 data['use1差值']=data.groupby('dataid')['use'].diff(1)#差值 data['use2差值']=data.groupby('dataid')['use'].diff(2) data['use3差值']=data.groupby('dataid')['use'].diff(3) data= data.dropna()#去均值 

15 修改列名:

#df=traindf1[['timestamp','value']]#原始列名 df = df.rename(columns={'timestamp':'时间'})#修改列名代码 

16 按条件删除行

df = df.drop(df[<some boolean condition>].index) #index 行 例子: df_clear = df.drop(df[df['x']<0.01].index) # 也可以使用多个条件 df_clear = df.drop(df[(df['x']<0.01) | (df['x']>10)].index) #删除x小于0.01或大于10的行 

十分钟入门 pandas
以下内容来源官方中文文档
本节以下列方式导入 Pandas 与 NumPy:

import numpy as np
import pandas as pd

生成对象
详见数据结构简介文档。
用值列表生成 Series 时,Pandas 默认自动生成整数索引:

In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
In [4]: s
Out[4]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64

用含日期时间索引与标签的 NumPy 数组生成 DataFrame

In [5]: dates = pd.date_range(‘20130101’, periods=6)
In [6]: dates
Out[6]:
DatetimeIndex([‘2013-01-01’, ‘2013-01-02’, ‘2013-01-03’, ‘2013-01-04’,
‘2013-01-05’, ‘2013-01-06’],
dtype=‘datetime64[ns]’, freq=‘D’)
In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list(‘ABCD’))
In [8]: df
Out[8]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988

用 Series 字典对象生成 DataFrame:

In [9]: df2 = pd.DataFrame({‘A’: 1.,
…: ‘B’: pd.Timestamp(‘20130102’),
…: ‘C’: pd.Series(1, index=list(range(4)), dtype=‘float32’),
…: ‘D’: np.array([3] * 4, dtype=‘int32’),
…: ‘E’: pd.Categorical([“test”, “train”, “test”, “train”]),
…: ‘F’: ‘foo’})
…:
In [10]: df2
Out[10]:
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo

DataFrame 的列有不同数据类型

In [11]: df2.dtypes
Out[11]:
A float64
B datetime64[ns] C float32
D int32
E category
F object
dtype: object

查看数据

In [13]: df.head()
Out[13]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401

In [14]: df.tail(3)
Out[14]:
A B C D
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988

显示索引与列名:

In [15]: df.index
Out[15]:
DatetimeIndex([‘2013-01-01’, ‘2013-01-02’, ‘2013-01-03’, ‘2013-01-04’,
‘2013-01-05’, ‘2013-01-06’],
dtype=‘datetime64[ns]’, freq=‘D’)

In [16]: df.columns
Out[16]: Index([‘A’, ‘B’, ‘C’, ‘D’], dtype=‘object’)

DataFrame.to_numpy() 输出底层数据的 NumPy 对象。注意,DataFrame 的列由多种数据类型组成时,该操作耗费系统资源较大,这也是 Pandas 和 NumPy 的本质区别:NumPy 数组只有一种数据类型,DataFrame 每列的数据类型各不相同。调用 DataFrame.to_numpy() 时,Pandas 查找支持 DataFrame 里所有数据类型的 NumPy 数据类型。还有一种数据类型是 object,可以把 DataFrame 列里的值强制转换为 Python 对象。
下面的 df 这个 DataFrame 里的值都是浮点数,DataFrame.to_numpy() 的操作会很快,而且不复制数据。

In [17]: df.to_numpy()
Out[17]:
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
[ 1.2121, -0.1732, 0.1192, -1.0442],
[-0.8618, -2.1046, -0.4949, 1.0718],
[ 0.7216, -0.7068, -1.0396, 0.2719],
[-0.425 , 0.567 , 0.2762, -1.0874],
[-0.6737, 0.1136, -1.4784, 0.525 ]])

df2 这个 DataFrame 包含了多种类型,DataFrame.to_numpy() 操作就会耗费较多资源。

In [18]: df2.to_numpy()
Out[18]:
array([[1.0, Timestamp(‘2013-01-02 00:00:00’), 1.0, 3, ‘test’, ‘foo’],
[1.0, Timestamp(‘2013-01-02 00:00:00’), 1.0, 3, ‘train’, ‘foo’],
[1.0, Timestamp(‘2013-01-02 00:00:00’), 1.0, 3, ‘test’, ‘foo’],
[1.0, Timestamp(‘2013-01-02 00:00:00’), 1.0, 3, ‘train’, ‘foo’]], dtype=object)

DataFrame.to_numpy() 的输出不包含行索引和列标签。

describe() 可以快速查看数据的统计摘要:

In [19]: df.describe()
Out[19]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.073711 -0.431125 -0.687758 -0.233103
std 0.843157 0.922818 0.779887 0.973118
min -0.861849 -2.104569 -1.509059 -1.135632
25% -0.611510 -0.600794 -1.368714 -1.076610
50% 0.022070 -0.228039 -0.767252 -0.386188
75% 0.658444 0.041933 -0.034326 0.461706
max 1.212112 0.567020 0.276232 1.071804

转置数据:

In [20]: df.T
Out[20]:
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988

按轴排序:

In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 -1.135632 -1.509059 -0.282863 0.469112
2013-01-02 -1.044236 0.119209 -0.173215 1.212112
2013-01-03 1.071804 -0.494929 -2.104569 -0.861849
2013-01-04 0.271860 -1.039575 -0.706771 0.721555
2013-01-05 -1.087401 0.276232 0.567020 -0.424972
2013-01-06 0.524988 -1.478427 0.113648 -0.673690

按值排序:

In [22]: df.sort_values(by=‘B’)
Out[22]:
A B C D
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
2013-01-05 -0.424972 0.567020 0.276232 -1.087401

选择

选择、设置标准 Python / Numpy 的表达式已经非常直观,交互也很方便,但对于生产代码,我们还是推荐优化过的 Pandas 数据访问方法:.at、.iat、.loc 和 .iloc。

详见索引与选择数据、多层索引与高级索引文档。

获取数据
选择单列,产生 Series,与 df.A 等效:

In [23]: df[‘A’] Out[23]:
2013-01-01 0.469112
2013-01-02 1.212112
2013-01-03 -0.861849
2013-01-04 0.721555
2013-01-05 -0.424972
2013-01-06 -0.673690
Freq: D, Name: A, dtype: float64

用 [ ] 切片行:

In [24]: df[0:3] Out[24]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804

In [25]: df[‘20130102’:‘20130104’] Out[25]:
A B C D
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860

按标签选择
详见按标签选择。

用标签提取一行数据:

In [26]: df.loc[dates[0]] Out[26]:
A 0.469112
B -0.282863
C -1.509059
D -1.135632
Name: 2013-01-01 00:00:00, dtype: float64

用标签选择多列数据:

In [27]: df.loc[:, [‘A’, ‘B’]] Out[27]:
A B
2013-01-01 0.469112 -0.282863
2013-01-02 1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04 0.721555 -0.706771
2013-01-05 -0.424972 0.567020
2013-01-06 -0.673690 0.113648

用标签切片,包含行与列结束点:

In [28]: df.loc[‘20130102’:‘20130104’, [‘A’, ‘B’]] Out[28]:
A B
2013-01-02 1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04 0.721555 -0.706771

返回对象降维:

In [29]: df.loc[‘20130102’, [‘A’, ‘B’]] Out[29]:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64

提取标量值:

In [30]: df.loc[dates[0], ‘A’] Out[30]: 0.46911229990718628
快速访问标量,与上述方法等效:
In [31]: df.at[dates[0], ‘A’] Out[31]: 0.46911229990718628

按位置选择
用整数位置选择:

In [32]: df.iloc[3] Out[32]:
A 0.721555
B -0.706771
C -1.039575
D 0.271860
Name: 2013-01-04 00:00:00, dtype: float64

用整数切片:

In [33]: df.iloc[3:5, 0:2] Out[33]:
A B
2013-01-04 0.721555 -0.706771
2013-01-05 -0.424972 0.567020

用整数列表按位置切片:

In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]:
A C
2013-01-02 1.212112 0.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.424972 0.276232

显式整行切片:

In [35]: df.iloc[1:3, :] Out[35]:
A B C D
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804

显式整列切片:

In [36]: df.iloc[:, 1:3] Out[36]:
B C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.173215 0.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-05 0.567020 0.276232
2013-01-06 0.113648 -1.478427

显式提取值:

In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858

布尔索引
用单列的值选择数据:

In [39]: df[df.A > 0] Out[39]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-04 0.721555 -0.706771 -1.039575 0.271860

选择 DataFrame 里满足条件的值:

In [40]: df[df > 0] Out[40]:
A B C D
2013-01-01 0.469112 NaN NaN NaN
2013-01-02 1.212112 NaN 0.119209 NaN
2013-01-03 NaN NaN NaN 1.071804
2013-01-04 0.721555 NaN NaN 0.271860
2013-01-05 NaN 0.567020 0.276232 NaN
2013-01-06 NaN 0.113648 NaN 0.524988

用 isin() 筛选:

In [41]: df2 = df.copy()
In [42]: df2[‘E’] = [‘one’, ‘one’, ‘two’, ‘three’, ‘four’, ‘three’]

In [43]: df2
Out[43]:
A B C D E
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one
2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three

In [44]: df2[df2[‘E’].isin([‘two’, ‘four’])] Out[44]:
A B C D E
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four

赋值
用索引自动对齐新增列的数据:

In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range(‘20130102’, periods=6))
In [46]: s1
Out[46]:
2013-01-02 1
2013-01-03 2
2013-01-04 3
2013-01-05 4
2013-01-06 5
2013-01-07 6
Freq: D, dtype: int64

In [47]: df[‘F’] = s1

按标签赋值:

In [48]: df.at[dates[0], ‘A’] = 0

按位置赋值:

In [49]: df.iat[0, 1] = 0

按 NumPy 数组赋值:

In [50]: df.loc[:, ‘D’] = np.array([5] * len(df))

上述赋值结果

In [51]: df
Out[51]:
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 5 NaN
2013-01-02 1.212112 -0.173215 0.119209 5 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0
2013-01-05 -0.424972 0.567020 0.276232 5 4.0
2013-01-06 -0.673690 0.113648 -1.478427 5 5.0

用 where 条件赋值

In [52]: df2 = df.copy()
In [53]: df2[df2 > 0] = -df2
In [54]: df2
Out[54]:
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 -5 NaN
2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0
2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0
2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0
2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0
2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0

缺失值
Pandas 主要用 np.nan 表示缺失数据。 计算时,默认不包含空值。详见缺失数据。

重建索引(reindex)可以更改、添加、删除指定轴的索引,并返回数据副本,即不更改原数据。

In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + [‘E’])
In [56]: df1.loc[dates[0]:dates[1], ‘E’] = 1
In [57]: df1
Out[57]:
A B C D F E
2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN

删除所有含缺失值的行:

In [58]: df1.dropna(how=‘any’)
Out[58]:
A B C D F E
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0

填充缺失值:

In [59]: df1.fillna(value=5)
Out[59]:
A B C D F E
2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0

提取 nan 值的布尔掩码:

In [60]: pd.isna(df1)
Out[60]:
A B C D F E
2013-01-01 False False False False True False
2013-01-02 False False False False False False
2013-01-03 False False False False False True
2013-01-04 False False False False False True

运算
描述性统计:

In [61]: df.mean()
Out[61]:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64

在另一个轴(即,行)上执行同样的操作:

In [62]: df.mean(1)
Out[62]:
2013-01-01 0.872735
2013-01-02 1.431621
2013-01-03 0.707731
2013-01-04 1.395042
2013-01-05 1.883656
2013-01-06 1.592306
Freq: D, dtype: float64

不同维度对象运算时,要先对齐。 此外,Pandas 自动沿指定维度广播。

In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2)
In [64]: s
Out[64]:
2013-01-01 NaN
2013-01-02 NaN
2013-01-03 1.0
2013-01-04 3.0
2013-01-05 5.0
2013-01-06 NaN
Freq: D, dtype: float64

In [65]: df.sub(s, axis=‘index’)
Out[65]:
A B C D F
2013-01-01 NaN NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN NaN
2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0
2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0
2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0
2013-01-06 NaN NaN NaN NaN NaN

Apply 函数
Apply 函数处理数据:

In [66]: df.apply(np.cumsum)
Out[66]:
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 5 NaN
2013-01-02 1.212112 -0.173215 -1.389850 10 1.0
2013-01-03 0.350263 -2.277784 -1.884779 15 3.0
2013-01-04 1.071818 -2.984555 -2.924354 20 6.0
2013-01-05 0.646846 -2.417535 -2.648122 25 10.0
2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0

In [67]: df.apply(lambda x: x.max() – x.min())
Out[67]:
A 2.073961
B 2.671590
C 1.785291
D 0.000000
F 4.000000
dtype: float64

画图
链接:

合并(Merge)
结合(Concat)

Pandas 提供了多种将 Series、DataFrame 对象组合在一起的功能,用索引与关联代数功能的多种设置逻辑可执行连接(join)与合并(merge)操作。

concat() 用于连接 Pandas 对象:

In [73]: df = pd.DataFrame(np.random.randn(10, 4))
In [74]: df
Out[74]:
0 1 2 3
0 -0.548702 1.467327 -1.015962 -0.483075
1 1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952 0.991460 -0.919069 0.266046
3 -0.709661 1.669052 1.037882 -1.705775
4 -0.919854 -0.042379 1.247642 -0.009920
5 0.290213 0.495767 0.362949 1.548106
6 -1.131345 -0.089329 0.337863 -0.945867
7 -0.932132 1.956030 0.017587 -0.016692
8 -0.575247 0.254161 -1.143704 0.215897
9 1.193555 -0.077118 -0.408530 -0.862495

分解为多组

n [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces)
Out[76]:
0 1 2 3
0 -0.548702 1.467327 -1.015962 -0.483075
1 1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952 0.991460 -0.919069 0.266046
3 -0.709661 1.669052 1.037882 -1.705775
4 -0.919854 -0.042379 1.247642 -0.009920
5 0.290213 0.495767 0.362949 1.548106
6 -1.131345 -0.089329 0.337863 -0.945867
7 -0.932132 1.956030 0.017587 -0.016692
8 -0.575247 0.254161 -1.143704 0.215897
9 1.193555 -0.077118 -0.408530 -0.862495

连接(join)

In [77]: left = pd.DataFrame({‘key’: [‘foo’, ‘foo’], ‘lval’: [1, 2]})
In [78]: right = pd.DataFrame({‘key’: [‘foo’, ‘foo’], ‘rval’: [4, 5]})
In [79]: left
Out[79]:
key lval
0 foo 1
1 foo 2
In [80]: right
Out[80]:
key rval
0 foo 4
1 foo 5
In [81]: pd.merge(left, right, on=‘key’)
Out[81]:
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5

In [82]: left = pd.DataFrame({‘key’: [‘foo’, ‘bar’], ‘lval’: [1, 2]})
In [83]: right = pd.DataFrame({‘key’: [‘foo’, ‘bar’], ‘rval’: [4, 5]})
In [84]: left
Out[84]:
key lval
0 foo 1
1 bar 2
In [85]: right
Out[85]:
key rval
0 foo 4
1 bar 5
In [86]: pd.merge(left, right, on=‘key’)
Out[86]:
key lval rval
0 foo 1 4
1 bar 2 5

追加(Append)

In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=[‘A’, ‘B’, ‘C’, ‘D’])
In [88]: df
Out[88]:
A B C D
0 1.346061 1.511763 1.627081 -0.990582
1 -0.441652 1.211526 0.268520 0.024580
2 -1.577585 0.396823 -0.105381 -0.532532
3 1.453749 1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346 0.339969 -0.693205
5 -0.339355 0.593616 0.884345 1.591431
6 0.141809 0.220390 0.435589 0.192451
7 -0.096701 0.803351 1.715071 -0.708758

In [89]: s = df.iloc[3] In [90]: df.append(s, ignore_index=True)
Out[90]:
A B C D
0 1.346061 1.511763 1.627081 -0.990582
1 -0.441652 1.211526 0.268520 0.024580
2 -1.577585 0.396823 -0.105381 -0.532532
3 1.453749 1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346 0.339969 -0.693205
5 -0.339355 0.593616 0.884345 1.591431
6 0.141809 0.220390 0.435589 0.192451
7 -0.096701 0.803351 1.715071 -0.708758
8 1.453749 1.208843 -0.080952 -0.264610

分组(Grouping)
group by” 指的是涵盖下列一项或多项步骤的处理流程:

分割:按条件把数据分割成多组;
应用:为每组单独应用函数;
组合:将处理结果组合成一个数据结构。

In [91]: df = pd.DataFrame({‘A’: [‘foo’, ‘bar’, ‘foo’, ‘bar’,
…: ‘foo’, ‘bar’, ‘foo’, ‘foo’],
…: ‘B’: [‘one’, ‘one’, ‘two’, ‘three’,
…: ‘two’, ‘two’, ‘one’, ‘three’],
…: ‘C’: np.random.randn(8),
…: ‘D’: np.random.randn(8)})
…:
In [92]: df
Out[92]:
A B C D
0 foo one -1.202872 -0.055224
1 bar one -1.814470 2.395985
2 foo two 1.018601 1.552825
3 bar three -0.595447 0.166599
4 foo two 1.395433 0.047609
5 bar two -0.392670 -0.136473
6 foo one 0.007207 -0.561757
7 foo three 1.928123 -1.623033

先分组,再用 sum()函数计算每组的汇总数据:

In [93]: df.groupby(‘A’).sum()
Out[93]:
C D
A
bar -2.802588 2.42611
foo 3.146492 -0.63958

多列分组后,生成多层索引,也可以应用 sum 函数:

In [94]: df.groupby([‘A’, ‘B’]).sum()
Out[94]:
C D
A B
bar one -1.814470 2.395985
three -0.595447 0.166599
two -0.392670 -0.136473
foo one -1.195665 -0.616981
three 1.928123 -1.623033
two 2.414034 1.600434

重塑(Reshaping)
堆叠(Stack)

In [95]: tuples = list(zip(*[[‘bar’, ‘bar’, ‘baz’, ‘baz’,
…: ‘foo’, ‘foo’, ‘qux’, ‘qux’],
…: [‘one’, ‘two’, ‘one’, ‘two’,
…: ‘one’, ‘two’, ‘one’, ‘two’]]))
…:
In [96]: index = pd.MultiIndex.from_tuples(tuples, names=[‘first’, ‘second’])
In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=[‘A’, ‘B’])
In [98]: df2 = df[:4] In [99]: df2
Out[99]:
A B
first second
bar one 0.029399 -0.542108
two 0.282696 -0.087302
baz one -1.575170 1.771208
two 0.816482 1.100230

stack()方法把 DataFrame 列压缩至一层:

In [100]: stacked = df2.stack()
In [101]: stacked
Out[101]:
first second
B -0.542108
two A 0.282696
B -0.087302
baz one A -1.575170
B 1.771208
two A 0.816482
B 1.100230
dtype: float64

压缩后的 DataFrame 或 Series 具有多层索引, stack() 的逆操作是 unstack(),默认为拆叠最后一层:

In [102]: stacked.unstack()
Out[102]:
A B
first second
bar one 0.029399 -0.542108
two 0.282696 -0.087302
baz one -1.575170 1.771208
two 0.816482 1.100230

In [103]: stacked.unstack(1)
Out[103]:
second one two
first
bar A 0.029399 0.282696
B -0.542108 -0.087302
baz A -1.575170 0.816482
B 1.771208 1.100230

In [104]: stacked.unstack(0)
Out[104]:
first bar baz
second
one A 0.029399 -1.575170
B -0.542108 1.771208
two A 0.282696 0.816482
B -0.087302 1.100230

数据透视表(Pivot Tables)

In [105]: df = pd.DataFrame({‘A’: [‘one’, ‘one’, ‘two’, ‘three’] * 3,
…: ‘B’: [‘A’, ‘B’, ‘C’] * 4,
…: ‘C’: [‘foo’, ‘foo’, ‘foo’, ‘bar’, ‘bar’, ‘bar’] * 2,
…: ‘D’: np.random.randn(12),
…: ‘E’: np.random.randn(12)})
…:

In [106]: df
Out[106]:
A B C D E
0 one A foo 1.418757 -0.179666
1 one B foo -1.879024 1.291836
2 two C foo 0.536826 -0.009614
3 three A bar 1.006160 0.392149
4 one B bar -0.029716 0.264599
5 one C bar -1.146178 -0.057409
6 two A foo 0.100900 -1.425638
7 three B foo -1.035018 1.024098
8 one C foo 0.314665 -0.106062
9 one A bar -0.773723 1.824375
10 two B bar -1.170653 0.595974
11 three C bar 0.648740 1.167115

用上述数据生成数据透视表非常简单:

In [107]: pd.pivot_table(df, values=‘D’, index=[‘A’, ‘B’], columns=[‘C’])
Out[107]:
C bar foo
A B
one A -0.773723 1.418757
B -0.029716 -1.879024
C -1.146178 0.314665
three A 1.006160 NaN
B NaN -1.035018
C 0.648740 NaN
two A NaN 0.100900
B -1.170653 NaN
C NaN 0.536826

数据输入 / 输出
CSV

写入 CSV 文件

df.to_csv(‘foo.csv’)

读取 CSV 文件数据:

pd.read_csv(‘foo.csv’)

写入 HDF5 Store

df.to_hdf(‘foo.h5’, ‘df’)

读取 HDF5 Store:

pd.read_hdf(‘foo.h5’, ‘df’)

写入 Excel 文件:

df.to_excel(‘foo.xlsx’, sheet_name=‘Sheet1’)

读取 Excel 文件:
pd.read_excel(‘foo.xlsx’, ‘Sheet1’, index_col=None, na_values=[‘NA’])

pandas常用函数,个人常用的

拾荒的老头

关于作者: 拾荒的老头

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