ailearning/docs/da/151.md

# 十分钟上手 Pandas

`pandas` 是一个 `Python Data Analysis Library`。

安装请参考官网的教程，如果安装了 `Anaconda`，则不需要安装 `pandas` 库。

In [1]:

```py
%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

```

## 产生 Pandas 对象

`pandas` 中有三种基本结构：

*   `Series`
    *   1D labeled homogeneously-typed array
*   `DataFrame`
    *   General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns
*   `Panel`
    *   General 3D labeled, also size-mutable array

### Series

一维 `Series` 可以用一维列表初始化：

In [2]:

```py
s = pd.Series([1,3,5,np.nan,6,8])

print s

```

```py
0     1
1     3
2     5
3   NaN
4     6
5     8
dtype: float64

```

默认情况下，`Series` 的下标都是数字（可以使用额外参数指定），类型是统一的。

### DataFrame

`DataFrame` 则是个二维结构，这里首先构造一组时间序列，作为我们第一维的下标：

In [3]:

```py
dates = pd.date_range('20130101', periods=6)

print dates

```

```py
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

```

然后创建一个 `DataFrame` 结构：

In [4]:

```py
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

df

```

Out[4]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 |

默认情况下，如果不指定 `index` 参数和 `columns`，那么他们的值将用从 `0` 开始的数字替代。

除了向 `DataFrame` 中传入二维数组，我们也可以使用字典传入数据：

In [5]:

```py
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' })

df2

```

Out[5]:

|  | A | B | C | D | E | F |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 1 | 2013-01-02 | 1 | 3 | test | foo |
| 1 | 1 | 2013-01-02 | 1 | 3 | train | foo |
| 2 | 1 | 2013-01-02 | 1 | 3 | test | foo |
| 3 | 1 | 2013-01-02 | 1 | 3 | train | foo |

字典的每个 `key` 代表一列，其 `value` 可以是各种能够转化为 `Series` 的对象。

与 `Series` 要求所有的类型都一致不同，`DataFrame` 值要求每一列数据的格式相同：

In [6]:

```py
df2.dtypes

```

Out[6]:

```py
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object
```

## 查看数据

### 头尾数据

`head` 和 `tail` 方法可以分别查看最前面几行和最后面几行的数据（默认为 5）：

In [7]:

```py
df.head()

```

Out[7]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |

最后 3 行：

In [8]:

```py
df.tail(3)

```

Out[8]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 |

### 下标，列标，数据

下标使用 `index` 属性查看：

In [9]:

```py
df.index

```

Out[9]:

```py
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')
```

列标使用 `columns` 属性查看：

In [10]:

```py
df.columns

```

Out[10]:

```py
Index([u'A', u'B', u'C', u'D'], dtype='object')
```

数据值使用 `values` 查看：

In [11]:

```py
df.values

```

Out[11]:

```py
array([[-0.60593585, -0.86165752, -1.00192387,  1.52858443],
       [-0.16540784,  0.38833783,  1.18718697,  1.81981793],
       [ 0.06525454, -1.60807414, -1.2823306 , -0.28606716],
       [ 1.28930486,  0.49711531, -0.22535143,  0.04023897],
       [ 0.03823179,  0.87505664, -0.0925258 ,  0.93443212],
       [-2.16345271, -0.01027865,  1.69988608,  1.29165337]])
```

### 统计数据

查看简单的统计数据：

In [12]:

```py
df.describe()

```

Out[12]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| count | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| mean | -0.257001 | -0.119917 | 0.047490 | 0.888110 |
| std | 1.126657 | 0.938705 | 1.182629 | 0.841529 |
| min | -2.163453 | -1.608074 | -1.282331 | -0.286067 |
| 25% | -0.495804 | -0.648813 | -0.807781 | 0.263787 |
| 50% | -0.063588 | 0.189030 | -0.158939 | 1.113043 |
| 75% | 0.058499 | 0.469921 | 0.867259 | 1.469352 |
| max | 1.289305 | 0.875057 | 1.699886 | 1.819818 |

### 转置

In [13]:

```py
df.T

```

Out[13]:

|  | 2013-01-01 00:00:00 | 2013-01-02 00:00:00 | 2013-01-03 00:00:00 | 2013-01-04 00:00:00 | 2013-01-05 00:00:00 | 2013-01-06 00:00:00 |
| --- | --- | --- | --- | --- | --- | --- |
| A | -0.605936 | -0.165408 | 0.065255 | 1.289305 | 0.038232 | -2.163453 |
| B | -0.861658 | 0.388338 | -1.608074 | 0.497115 | 0.875057 | -0.010279 |
| C | -1.001924 | 1.187187 | -1.282331 | -0.225351 | -0.092526 | 1.699886 |
| D | 1.528584 | 1.819818 | -0.286067 | 0.040239 | 0.934432 | 1.291653 |

## 排序

`sort_index(axis=0, ascending=True)` 方法按照下标大小进行排序，`axis=0` 表示按第 0 维进行排序。

In [14]:

```py
df.sort_index(ascending=False)

```

Out[14]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |

In [15]:

```py
df.sort_index(axis=1, ascending=False)

```

Out[15]:

|  | D | C | B | A |
| --- | --- | --- | --- | --- |
| 2013-01-01 | 1.528584 | -1.001924 | -0.861658 | -0.605936 |
| 2013-01-02 | 1.819818 | 1.187187 | 0.388338 | -0.165408 |
| 2013-01-03 | -0.286067 | -1.282331 | -1.608074 | 0.065255 |
| 2013-01-04 | 0.040239 | -0.225351 | 0.497115 | 1.289305 |
| 2013-01-05 | 0.934432 | -0.092526 | 0.875057 | 0.038232 |
| 2013-01-06 | 1.291653 | 1.699886 | -0.010279 | -2.163453 |

`sort_values(by, axis=0, ascending=True)` 方法按照 `by` 的值的大小进行排序，例如按照 `B` 列的大小：

In [16]:

```py
df.sort_values(by="B")

```

Out[16]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |

## 索引

虽然 `DataFrame` 支持 `Python/Numpy` 的索引语法，但是推荐使用 `.at, .iat, .loc, .iloc 和 .ix` 方法进行索引。

### 读取数据

选择单列数据：

In [17]:

```py
df["A"]

```

Out[17]:

```py
2013-01-01   -0.605936
2013-01-02   -0.165408
2013-01-03    0.065255
2013-01-04    1.289305
2013-01-05    0.038232
2013-01-06   -2.163453
Freq: D, Name: A, dtype: float64
```

也可以用 `df.A`：

In [18]:

```py
df.A

```

Out[18]:

```py
2013-01-01   -0.605936
2013-01-02   -0.165408
2013-01-03    0.065255
2013-01-04    1.289305
2013-01-05    0.038232
2013-01-06   -2.163453
Freq: D, Name: A, dtype: float64
```

使用切片读取多行：

In [19]:

```py
df[0:3]

```

Out[19]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |

`index` 名字也可以进行切片：

In [20]:

```py
df["20130101":"20130103"]

```

Out[20]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |

### 使用 `label` 索引

`loc` 可以方便的使用 `label` 进行索引：

In [21]:

```py
df.loc[dates[0]]

```

Out[21]:

```py
A   -0.605936
B   -0.861658
C   -1.001924
D    1.528584
Name: 2013-01-01 00:00:00, dtype: float64
```

多列数据：

In [22]:

```py
df.loc[:,['A','B']]

```

Out[22]:

|  | A | B |
| --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 |
| 2013-01-02 | -0.165408 | 0.388338 |
| 2013-01-03 | 0.065255 | -1.608074 |
| 2013-01-04 | 1.289305 | 0.497115 |
| 2013-01-05 | 0.038232 | 0.875057 |
| 2013-01-06 | -2.163453 | -0.010279 |

选择多行多列：

In [23]:

```py
df.loc['20130102':'20130104',['A','B']]

```

Out[23]:

|  | A | B |
| --- | --- | --- |
| 2013-01-02 | -0.165408 | 0.388338 |
| 2013-01-03 | 0.065255 | -1.608074 |
| 2013-01-04 | 1.289305 | 0.497115 |

数据降维：

In [24]:

```py
df.loc['20130102',['A','B']]

```

Out[24]:

```py
A   -0.165408
B    0.388338
Name: 2013-01-02 00:00:00, dtype: float64
```

得到标量值：

In [25]:

```py
df.loc[dates[0],'B']

```

Out[25]:

```py
-0.86165751902832299
```

不过得到标量值可以用 `at`，速度更快：

In [26]:

```py
%timeit -n100 df.loc[dates[0],'B']
%timeit -n100 df.at[dates[0],'B']

print df.at[dates[0],'B']

```

```py
100 loops, best of 3: 329 µs per loop
100 loops, best of 3: 31.1 µs per loop
-0.861657519028

```

### 使用位置索引

`iloc` 使用位置进行索引：

In [27]:

```py
df.iloc[3]

```

Out[27]:

```py
A    1.289305
B    0.497115
C   -0.225351
D    0.040239
Name: 2013-01-04 00:00:00, dtype: float64
```

连续切片：

In [28]:

```py
df.iloc[3:5,0:2]

```

Out[28]:

|  | A | B |
| --- | --- | --- |
| 2013-01-04 | 1.289305 | 0.497115 |
| 2013-01-05 | 0.038232 | 0.875057 |

索引不连续的部分：

In [29]:

```py
df.iloc[[1,2,4],[0,2]]

```

Out[29]:

|  | A | C |
| --- | --- | --- |
| 2013-01-02 | -0.165408 | 1.187187 |
| 2013-01-03 | 0.065255 | -1.282331 |
| 2013-01-05 | 0.038232 | -0.092526 |

索引整行：

In [30]:

```py
df.iloc[1:3,:]

```

Out[30]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |

整列：

In [31]:

```py
df.iloc[:, 1:3]

```

Out[31]:

|  | B | C |
| --- | --- | --- |
| 2013-01-01 | -0.861658 | -1.001924 |
| 2013-01-02 | 0.388338 | 1.187187 |
| 2013-01-03 | -1.608074 | -1.282331 |
| 2013-01-04 | 0.497115 | -0.225351 |
| 2013-01-05 | 0.875057 | -0.092526 |
| 2013-01-06 | -0.010279 | 1.699886 |

标量值：

In [32]:

```py
df.iloc[1,1]

```

Out[32]:

```py
0.3883378290420279
```

当然，使用 `iat` 索引标量值更快：

In [33]:

```py
%timeit -n100 df.iloc[1,1]
%timeit -n100 df.iat[1,1]

df.iat[1,1]

```

```py
100 loops, best of 3: 236 µs per loop
100 loops, best of 3: 14.5 µs per loop

```

Out[33]:

```py
0.3883378290420279
```

### 布尔型索引

所有 `A` 列大于 0 的行：

In [34]:

```py
df[df.A > 0]

```

Out[34]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 |

只留下所有大于 0 的数值：

In [35]:

```py
df[df > 0]

```

Out[35]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2013-01-01 | NaN | NaN | NaN | 1.528584 |
| 2013-01-02 | NaN | 0.388338 | 1.187187 | 1.819818 |
| 2013-01-03 | 0.065255 | NaN | NaN | NaN |
| 2013-01-04 | 1.289305 | 0.497115 | NaN | 0.040239 |
| 2013-01-05 | 0.038232 | 0.875057 | NaN | 0.934432 |
| 2013-01-06 | NaN | NaN | 1.699886 | 1.291653 |

使用 `isin` 方法做 `filter` 过滤：

In [36]:

```py
df2 = df.copy()
df2['E'] = ['one', 'one','two','three','four','three']

df2

```

Out[36]:

|  | A | B | C | D | E |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 | one |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 | one |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 | two |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 | three |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 | four |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 | three |

In [37]:

```py
df2[df2['E'].isin(['two','four'])]

```

Out[37]:

|  | A | B | C | D | E |
| --- | --- | --- | --- | --- | --- |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 | two |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 | four |

### 设定数据的值

In [38]:

```py
s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))

s1

```

Out[38]:

```py
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
```

像字典一样，直接指定 `F` 列的值为 `s1`，此时以 `df` 已有的 `index` 为标准将二者进行合并，`s1` 中没有的 `index` 项设为 `NaN`，多余的项舍去：

In [39]:

```py
df['F'] = s1

df

```

Out[39]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | -0.605936 | -0.861658 | -1.001924 | 1.528584 | NaN |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 | 2 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 | 3 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 | 4 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 | 5 |

或者使用 `at` 或 `iat` 修改单个值：

In [40]:

```py
df.at[dates[0],'A'] = 0

df

```

Out[40]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | -0.861658 | -1.001924 | 1.528584 | NaN |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 | 2 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 | 3 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 | 4 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 | 5 |

In [41]:

```py
df.iat[0, 1] = 0

df

```

Out[41]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | 1.528584 | NaN |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 1.819818 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | -0.286067 | 2 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 0.040239 | 3 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 0.934432 | 4 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 1.291653 | 5 |

设定一整列：

In [42]:

```py
df.loc[:,'D'] = np.array([5] * len(df))

df

```

Out[42]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | 5 | NaN |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 5 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | 5 | 2 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 5 | 3 |
| 2013-01-05 | 0.038232 | 0.875057 | -0.092526 | 5 | 4 |
| 2013-01-06 | -2.163453 | -0.010279 | 1.699886 | 5 | 5 |

设定满足条件的数值：

In [43]:

```py
df2 = df.copy()

df2[df2 > 0] = -df2

df2

```

Out[43]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | -5 | NaN |
| 2013-01-02 | -0.165408 | -0.388338 | -1.187187 | -5 | -1 |
| 2013-01-03 | -0.065255 | -1.608074 | -1.282331 | -5 | -2 |
| 2013-01-04 | -1.289305 | -0.497115 | -0.225351 | -5 | -3 |
| 2013-01-05 | -0.038232 | -0.875057 | -0.092526 | -5 | -4 |
| 2013-01-06 | -2.163453 | -0.010279 | -1.699886 | -5 | -5 |

## 缺失数据

In [44]:

```py
df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1.loc[dates[0]:dates[1],'E'] = 1

df1

```

Out[44]:

|  | A | B | C | D | F | E |
| --- | --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | 5 | NaN | 1 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 5 | 1 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | 5 | 2 | NaN |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 5 | 3 | NaN |

丢弃所有缺失数据的行得到的新数据：

In [45]:

```py
df1.dropna(how='any')

```

Out[45]:

|  | A | B | C | D | F | E |
| --- | --- | --- | --- | --- | --- | --- |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 5 | 1 | 1 |

填充缺失数据：

In [46]:

```py
df1.fillna(value=5)

```

Out[46]:

|  | A | B | C | D | F | E |
| --- | --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | 5 | 5 | 1 |
| 2013-01-02 | -0.165408 | 0.388338 | 1.187187 | 5 | 1 | 1 |
| 2013-01-03 | 0.065255 | -1.608074 | -1.282331 | 5 | 2 | 5 |
| 2013-01-04 | 1.289305 | 0.497115 | -0.225351 | 5 | 3 | 5 |

检查缺失数据的位置：

In [47]:

```py
pd.isnull(df1)

```

Out[47]:

|  | 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 |

## 计算操作

### 统计信息

每一列的均值：

In [48]:

```py
df.mean()

```

Out[48]:

```py
A   -0.156012
B    0.023693
C    0.047490
D    5.000000
F    3.000000
dtype: float64
```

每一行的均值：

In [49]:

```py
df.mean(1)

```

Out[49]:

```py
2013-01-01    0.999519
2013-01-02    1.482023
2013-01-03    0.834970
2013-01-04    1.912214
2013-01-05    1.964153
2013-01-06    1.905231
Freq: D, dtype: float64
```

多个对象之间的操作，如果维度不对，`pandas` 会自动调用 `broadcasting` 机制：

In [50]:

```py
s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)

print s

```

```py
2013-01-01   NaN
2013-01-02   NaN
2013-01-03     1
2013-01-04     3
2013-01-05     5
2013-01-06   NaN
Freq: D, dtype: float64

```

相减 `df - s`：

In [51]:

```py
df.sub(s, axis='index')

```

Out[51]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | NaN | NaN | NaN | NaN | NaN |
| 2013-01-02 | NaN | NaN | NaN | NaN | NaN |
| 2013-01-03 | -0.934745 | -2.608074 | -2.282331 | 4 | 1 |
| 2013-01-04 | -1.710695 | -2.502885 | -3.225351 | 2 | 0 |
| 2013-01-05 | -4.961768 | -4.124943 | -5.092526 | 0 | -1 |
| 2013-01-06 | NaN | NaN | NaN | NaN | NaN |

### apply 操作

与 `R` 中的 `apply` 操作类似，接收一个函数，默认是对将函数作用到每一列上：

In [52]:

```py
df.apply(np.cumsum)

```

Out[52]:

|  | A | B | C | D | F |
| --- | --- | --- | --- | --- | --- |
| 2013-01-01 | 0.000000 | 0.000000 | -1.001924 | 5 | NaN |
| 2013-01-02 | -0.165408 | 0.388338 | 0.185263 | 10 | 1 |
| 2013-01-03 | -0.100153 | -1.219736 | -1.097067 | 15 | 3 |
| 2013-01-04 | 1.189152 | -0.722621 | -1.322419 | 20 | 6 |
| 2013-01-05 | 1.227383 | 0.152436 | -1.414945 | 25 | 10 |
| 2013-01-06 | -0.936069 | 0.142157 | 0.284941 | 30 | 15 |

求每列最大最小值之差：

In [53]:

```py
df.apply(lambda x: x.max() - x.min())

```

Out[53]:

```py
A    3.452758
B    2.483131
C    2.982217
D    0.000000
F    4.000000
dtype: float64
```

### 直方图

In [54]:

```py
s = pd.Series(np.random.randint(0, 7, size=10))
print s

```

```py
0    2
1    5
2    6
3    6
4    6
5    3
6    5
7    0
8    4
9    4
dtype: int64

```

直方图信息：

In [55]:

```py
print s.value_counts()

```

```py
6    3
5    2
4    2
3    1
2    1
0    1
dtype: int64

```

绘制直方图信息：

In [56]:

```py
h = s.hist()

```

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)

### 字符串方法

当 `Series` 或者 `DataFrame` 的某一列是字符串时，我们可以用 `.str` 对这个字符串数组进行字符串的基本操作：

In [57]:

```py
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

print s.str.lower()

```

```py
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

```

## 合并

### 连接

In [58]:

```py
df = pd.DataFrame(np.random.randn(10, 4))

df

```

Out[58]:

|  | 0 | 1 | 2 | 3 |
| --- | --- | --- | --- | --- |
| 0 | -2.346373 | 0.105651 | -0.048027 | 0.010637 |
| 1 | -0.682198 | 0.943043 | 0.147312 | -0.657871 |
| 2 | 0.515766 | -0.768286 | 0.361570 | 1.146278 |
| 3 | -0.607277 | -0.003086 | -1.499001 | 1.165728 |
| 4 | -1.226279 | -0.177246 | -1.379631 | -0.639261 |
| 5 | 0.807364 | -1.855060 | 0.325968 | 1.898831 |
| 6 | 0.438539 | -0.728131 | -0.009924 | 0.398360 |
| 7 | 1.497457 | -1.506314 | -1.557624 | 0.869043 |
| 8 | 0.945985 | -0.519435 | -0.510359 | -1.077751 |
| 9 | 1.597679 | -0.285955 | -1.060736 | 0.608629 |

可以使用 `pd.concat` 函数将多个 `pandas` 对象进行连接：

In [59]:

```py
pieces = [df[:2], df[4:5], df[7:]]

pd.concat(pieces)

```

Out[59]:

|  | 0 | 1 | 2 | 3 |
| --- | --- | --- | --- | --- |
| 0 | -2.346373 | 0.105651 | -0.048027 | 0.010637 |
| 1 | -0.682198 | 0.943043 | 0.147312 | -0.657871 |
| 4 | -1.226279 | -0.177246 | -1.379631 | -0.639261 |
| 7 | 1.497457 | -1.506314 | -1.557624 | 0.869043 |
| 8 | 0.945985 | -0.519435 | -0.510359 | -1.077751 |
| 9 | 1.597679 | -0.285955 | -1.060736 | 0.608629 |

### 数据库中的 Join

`merge` 可以实现数据库中的 `join` 操作：

In [60]:

```py
left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

print left
print right

```

```py
   key  lval
0  foo     1
1  foo     2
   key  rval
0  foo     4
1  foo     5

```

In [61]:

```py
pd.merge(left, right, on='key')

```

Out[61]:

|  | key | lval | rval |
| --- | --- | --- | --- |
| 0 | foo | 1 | 4 |
| 1 | foo | 1 | 5 |
| 2 | foo | 2 | 4 |
| 3 | foo | 2 | 5 |

### append

向 `DataFrame` 中添加行：

In [62]:

```py
df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])

df

```

Out[62]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 0 | 1.587778 | -0.110297 | 0.602245 | 1.212597 |
| 1 | -0.551109 | 0.337387 | -0.220919 | 0.363332 |
| 2 | 1.207373 | -0.128394 | 0.619937 | -0.612694 |
| 3 | -0.978282 | -1.038170 | 0.048995 | -0.788973 |
| 4 | 0.843893 | -1.079021 | 0.092212 | 0.485422 |
| 5 | -0.056594 | 1.831206 | 1.910864 | -1.331739 |
| 6 | -0.487106 | -1.495367 | 0.853440 | 0.410854 |
| 7 | 1.830852 | -0.014893 | 0.254025 | 0.197422 |

将第三行的值添加到最后：

In [63]:

```py
s = df.iloc[3]

df.append(s, ignore_index=True)

```

Out[63]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 0 | 1.587778 | -0.110297 | 0.602245 | 1.212597 |
| 1 | -0.551109 | 0.337387 | -0.220919 | 0.363332 |
| 2 | 1.207373 | -0.128394 | 0.619937 | -0.612694 |
| 3 | -0.978282 | -1.038170 | 0.048995 | -0.788973 |
| 4 | 0.843893 | -1.079021 | 0.092212 | 0.485422 |
| 5 | -0.056594 | 1.831206 | 1.910864 | -1.331739 |
| 6 | -0.487106 | -1.495367 | 0.853440 | 0.410854 |
| 7 | 1.830852 | -0.014893 | 0.254025 | 0.197422 |
| 8 | -0.978282 | -1.038170 | 0.048995 | -0.788973 |

### Grouping

In [64]:

```py
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)})

df

```

Out[64]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 0 | foo | one | 0.773062 | 0.206503 |
| 1 | bar | one | 1.414609 | -0.346719 |
| 2 | foo | two | 0.964174 | 0.706623 |
| 3 | bar | three | 0.182239 | -1.516509 |
| 4 | foo | two | -0.096255 | 0.494177 |
| 5 | bar | two | -0.759471 | -0.389213 |
| 6 | foo | one | -0.257519 | -1.411693 |
| 7 | foo | three | -0.109368 | 0.241862 |

按照 `A` 的值进行分类：

In [65]:

```py
df.groupby('A').sum()

```

Out[65]:

|  | C | D |
| --- | --- | --- |
| A |  |  |
| --- | --- | --- |
| bar | 0.837377 | -2.252441 |
| foo | 1.274094 | 0.237472 |

按照 `A, B` 的值进行分类：

In [66]:

```py
df.groupby(['A', 'B']).sum()

```

Out[66]:

|  |  | C | D |
| --- | --- | --- | --- |
| A | B |  |  |
| --- | --- | --- | --- |
| bar | one | 1.414609 | -0.346719 |
| three | 0.182239 | -1.516509 |
| two | -0.759471 | -0.389213 |
| foo | one | 0.515543 | -1.205191 |
| three | -0.109368 | 0.241862 |
| two | 0.867919 | 1.200800 |

## 改变形状

### Stack

产生一个多 `index` 的 `DataFrame`：

In [67]:

```py
tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
                     'foo', 'foo', 'qux', 'qux'],
                    ['one', 'two', 'one', 'two',
                     'one', 'two', 'one', 'two']]))

index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])

df

```

Out[67]:

|  |  | A | B |
| --- | --- | --- | --- |
| first | second |  |  |
| --- | --- | --- | --- |
| bar | one | -0.109174 | 0.958551 |
| two | -0.254743 | -0.975924 |
| baz | one | -0.132039 | -0.119009 |
| two | 0.587063 | -0.819037 |
| foo | one | -0.754123 | 0.430747 |
| two | -0.426544 | 0.389822 |
| qux | one | -0.382501 | -0.562910 |
| two | -0.529287 | 0.826337 |

`stack` 方法将 `columns` 变成一个新的 `index` 部分：

In [68]:

```py
df2 = df[:4]

stacked = df2.stack()

stacked

```

Out[68]:

```py
first  second   
bar    one     A   -0.109174
               B    0.958551
       two     A   -0.254743
               B   -0.975924
baz    one     A   -0.132039
               B   -0.119009
       two     A    0.587063
               B   -0.819037
dtype: float64
```

可以使用 `unstack()` 将最后一级 `index` 放回 `column`：

In [69]:

```py
stacked.unstack()

```

Out[69]:

|  |  | A | B |
| --- | --- | --- | --- |
| first | second |  |  |
| --- | --- | --- | --- |
| bar | one | -0.109174 | 0.958551 |
| two | -0.254743 | -0.975924 |
| baz | one | -0.132039 | -0.119009 |
| two | 0.587063 | -0.819037 |

也可以指定其他的级别：

In [70]:

```py
stacked.unstack(1)

```

Out[70]:

|  | second | one | two |
| --- | --- | --- | --- |
| first |  |  |  |
| --- | --- | --- | --- |
| bar | A | -0.109174 | -0.254743 |
| B | 0.958551 | -0.975924 |
| baz | A | -0.132039 | 0.587063 |
| B | -0.119009 | -0.819037 |

## 时间序列

金融分析中常用到时间序列数据：

In [71]:

```py
rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
ts = pd.Series(np.random.randn(len(rng)), rng)

ts

```

Out[71]:

```py
2012-03-06    1.096788
2012-03-07    0.029678
2012-03-08    0.511461
2012-03-09   -0.332369
2012-03-10    1.720321
Freq: D, dtype: float64
```

标准时间表示：

In [72]:

```py
ts_utc = ts.tz_localize('UTC')

ts_utc

```

Out[72]:

```py
2012-03-06 00:00:00+00:00    1.096788
2012-03-07 00:00:00+00:00    0.029678
2012-03-08 00:00:00+00:00    0.511461
2012-03-09 00:00:00+00:00   -0.332369
2012-03-10 00:00:00+00:00    1.720321
Freq: D, dtype: float64
```

In [ ]:

改变时区表示：

In [73]:

```py
ts_utc.tz_convert('US/Eastern')

```

Out[73]:

```py
2012-03-05 19:00:00-05:00    1.096788
2012-03-06 19:00:00-05:00    0.029678
2012-03-07 19:00:00-05:00    0.511461
2012-03-08 19:00:00-05:00   -0.332369
2012-03-09 19:00:00-05:00    1.720321
Freq: D, dtype: float64
```

## Categoricals

In [74]:

```py
df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

df

```

Out[74]:

|  | id | raw_grade |
| --- | --- | --- |
| 0 | 1 | a |
| 1 | 2 | b |
| 2 | 3 | b |
| 3 | 4 | a |
| 4 | 5 | a |
| 5 | 6 | e |

可以将 `grade` 变成类别：

In [75]:

```py
df["grade"] = df["raw_grade"].astype("category")

df["grade"]

```

Out[75]:

```py
0    a
1    b
2    b
3    a
4    a
5    e
Name: grade, dtype: category
Categories (3, object): [a, b, e]
```

将类别的表示转化为有意义的字符：

In [76]:

```py
df["grade"].cat.categories = ["very good", "good", "very bad"]

df["grade"]

```

Out[76]:

```py
0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (3, object): [very good, good, very bad]
```

添加缺失的类别：

In [77]:

```py
df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
df["grade"]

```

Out[77]:

```py
0    very good
1         good
2         good
3    very good
4    very good
5     very bad
Name: grade, dtype: category
Categories (5, object): [very bad, bad, medium, good, very good]
```

使用 `grade` 分组：

In [78]:

```py
df.groupby("grade").size()

```

Out[78]:

```py
grade
very bad     1
bad          0
medium       0
good         2
very good    3
dtype: int64
```

## 绘图

使用 `ggplot` 风格：

In [79]:

```py
plt.style.use('ggplot')

```

`Series` 绘图：

In [80]:

```py
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

p = ts.cumsum().plot()

```

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`DataFrame` 按照 `columns` 绘图：

In [81]:

```py
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
                  columns=['A', 'B', 'C', 'D'])

df.cumsum().plot()
p = plt.legend(loc="best")

```

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AeLi4ggICPDIfQiuTleNs1arZcSIEYAzuSsmJgaz2eyRe+rLeNzGX1JSQk5ODvHx8VRWVsr/2AaD
QU7rtlgsmEwm+RiTyYTZbMZisWA0GuXtRqNR/Eh6MDcy1oLeQ1eNc21tLWlpaYwcObL7hO8neFTx
NzQ08PLLL7Ns2TJ8fHxcPusLiRyCK9zIWIvfQu+hq8bZbrezevVq5syZQ0hIiFtk7c94TPHbbDZe
fvllpk+fzsSJEwHnjKCiogJwzggMBgPgnMmXl5fLx5aXl2MymVrN8MvLy11WAIKewY2OtRjT3kFX
jvNbb71FeHg4d955ZzfeQf/BI4pfkiTefPNNIiIi+MlPfiJvT0pKkrvT7NmzhwkTJsjbDxw4gM1m
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fZoXX3yRqKgoeXn3wAMPEBcX127o1+eff86uXbtQqVQsW7bs/2/njmkgCIEwCv8WqDEBFtgGASig
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AGMIPwAY8wFNd7R2zEaFBAAAAABJRU5ErkJggg==
)

## 文件读写

### csv

写入文件：

In [82]:

```py
df.to_csv('foo.csv')

```

从文件中读取：

In [83]:

```py
pd.read_csv('foo.csv').head()

```

Out[83]:

|  | Unnamed: 0 | A | B | C | D |
| --- | --- | --- | --- | --- | --- |
| 0 | 2000-01-01 | -1.011554 | 1.200283 | -0.310949 | -1.060734 |
| 1 | 2000-01-02 | -1.030894 | 0.660518 | -0.214002 | -0.422014 |
| 2 | 2000-01-03 | -0.488692 | 1.709209 | -0.602208 | 1.115456 |
| 3 | 2000-01-04 | -0.440243 | 0.826692 | 0.321648 | -0.351698 |
| 4 | 2000-01-05 | -0.165684 | 1.297303 | 0.817233 | 0.174767 |

### hdf5

写入文件：

In [84]:

```py
df.to_hdf("foo.h5", "df")

```

读取文件：

In [85]:

```py
pd.read_hdf('foo.h5','df').head()

```

Out[85]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2000-01-01 | -1.011554 | 1.200283 | -0.310949 | -1.060734 |
| 2000-01-02 | -1.030894 | 0.660518 | -0.214002 | -0.422014 |
| 2000-01-03 | -0.488692 | 1.709209 | -0.602208 | 1.115456 |
| 2000-01-04 | -0.440243 | 0.826692 | 0.321648 | -0.351698 |
| 2000-01-05 | -0.165684 | 1.297303 | 0.817233 | 0.174767 |

### excel

写入文件：

In [86]:

```py
df.to_excel('foo.xlsx', sheet_name='Sheet1')

```

读取文件：

In [87]:

```py
pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']).head()

```

Out[87]:

|  | A | B | C | D |
| --- | --- | --- | --- | --- |
| 2000-01-01 | -1.011554 | 1.200283 | -0.310949 | -1.060734 |
| 2000-01-02 | -1.030894 | 0.660518 | -0.214002 | -0.422014 |
| 2000-01-03 | -0.488692 | 1.709209 | -0.602208 | 1.115456 |
| 2000-01-04 | -0.440243 | 0.826692 | 0.321648 | -0.351698 |
| 2000-01-05 | -0.165684 | 1.297303 | 0.817233 | 0.174767 |

清理生成的临时文件：

In [88]:

```py
import glob
import os

for f in glob.glob("foo*"):
    os.remove(f)

```