烹饪指南
本节列出了一些短小精悍的 Pandas 实例与链接。
我们希望 Pandas 用户能积极踊跃地为本文档添加更多内容。为本节添加实用示例的链接或代码，是 Pandas 用户提交第一个 Pull Request 最好的选择。
本节列出了简单、精练、易上手的实例代码，以及 Stack Overflow 或 GitHub 上的链接，这些链接包含实例代码的更多详情。
pd 与 np 是 Pandas 与 Numpy 的缩写。为了让新手易于理解，其它模块是显式导入的。
下列实例均为 Python 3 代码，简单修改即可用于 Python 早期版本。
惯用语
以下是 Pandas 的惯用语。
对一列数据执行 if-then / if-then-else 操作，把计算结果赋值给一列或多列： (opens new window)
In [1]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
...: 'BBB': [10, 20, 30, 40],
...: 'CCC': [100, 50, -30, -50]})
...:
In [2]: df
Out[2]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
if-then…
在一列上执行 if-then 操作：
In [3]: df.loc[df.AAA >= 5, 'BBB'] = -1
In [4]: df
Out[4]:
AAA BBB CCC
0 4 10 100
1 5 -1 50
2 6 -1 -30
3 7 -1 -50
在两列上执行 if-then 操作：
In [5]: df.loc[df.AAA >= 5, ['BBB', 'CCC']] = 555
In [6]: df
Out[6]:
AAA BBB CCC
0 4 10 100
1 5 555 555
2 6 555 555
3 7 555 555
再添加一行代码，执行 -else 操作：
In [7]: df.loc[df.AAA < 5, ['BBB', 'CCC']] = 2000
In [8]: df
Out[8]:
AAA BBB CCC
0 4 2000 2000
1 5 555 555
2 6 555 555
3 7 555 555
或用 Pandas 的 where 设置掩码（mask）：
In [9]: df_mask = pd.DataFrame({'AAA': [True] * 4,
...: 'BBB': [False] * 4,
...: 'CCC': [True, False] * 2})
...:
In [10]: df.where(df_mask, -1000)
Out[10]:
AAA BBB CCC
0 4 -1000 2000
1 5 -1000 -1000
2 6 -1000 555
3 7 -1000 -1000
用 NumPy where() 函数实现 if-then-else (opens new window)
In [11]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [12]: df
Out[12]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [13]: df['logic'] = np.where(df['AAA'] > 5, 'high', 'low')
In [14]: df
Out[14]:
AAA BBB CCC logic
0 4 10 100 low
1 5 20 50 low
2 6 30 -30 high
3 7 40 -50 high
切割
用布尔条件切割 DataFrame (opens new window)
In [15]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [16]: df
Out[16]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [17]: df[df.AAA <= 5]
Out[17]:
AAA BBB CCC
0 4 10 100
1 5 20 50
In [18]: df[df.AAA > 5]
Out[18]:
AAA BBB CCC
2 6 30 -30
3 7 40 -50
设置条件
In [19]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [20]: df
Out[20]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
和（&），不赋值，直接返回 Series：
In [21]: df.loc[(df['BBB'] < 25) & (df['CCC'] >= -40), 'AAA']
Out[21]:
0 4
1 5
Name: AAA, dtype: int64
或（|），不赋值，直接返回 Series：
In [22]: df.loc[(df['BBB'] > 25) | (df['CCC'] >= -40), 'AAA']
Out[22]:
0 4
1 5
2 6
3 7
Name: AAA, dtype: int64
或（|），赋值，修改 DataFrame：
In [23]: df.loc[(df['BBB'] > 25) | (df['CCC'] >= 75), 'AAA'] = 0.1
In [24]: df
Out[24]:
AAA BBB CCC
0 0.1 10 100
1 5.0 20 50
2 0.1 30 -30
3 0.1 40 -50
用 argsort 选择最接近指定值的行 (opens new window)
In [25]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [26]: df
Out[26]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [27]: aValue = 43.0
In [28]: df.loc[(df.CCC - aValue).abs().argsort()]
Out[28]:
AAA BBB CCC
1 5 20 50
0 4 10 100
2 6 30 -30
3 7 40 -50
用二进制运算符动态减少条件列表 (opens new window)
In [29]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [30]: df
Out[30]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [31]: Crit1 = df.AAA <= 5.5
In [32]: Crit2 = df.BBB == 10.0
In [33]: Crit3 = df.CCC > -40.0
硬编码方式为：
In [34]: AllCrit = Crit1 & Crit2 & Crit3
生成动态条件列表：
In [35]: import functools
In [36]: CritList = [Crit1, Crit2, Crit3]
In [37]: AllCrit = functools.reduce(lambda x, y: x & y, CritList)
In [38]: df[AllCrit]
Out[38]:
AAA BBB CCC
0 4 10 100
选择
DataFrames
更多信息，请参阅索引 (opens new window)文档。
In [39]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [40]: df
Out[40]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [41]: df[(df.AAA <= 6) & (df.index.isin([0, 2, 4]))]
Out[41]:
AAA BBB CCC
0 4 10 100
2 6 30 -30
标签切片用 loc，位置切片用 iloc (opens new window)
In [42]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]},
....: index=['foo', 'bar', 'boo', 'kar'])
....:
前 2 个是显式切片方法，第 3 个是通用方法：
- 位置切片，Python 切片风格，不包括结尾数据；
- 标签切片，非 Python 切片风格，包括结尾数据；
- 通用切片，支持两种切片风格，取决于切片用的是标签还是位置。
In [43]: df.loc['bar':'kar'] # Label
Out[43]:
AAA BBB CCC
bar 5 20 50
boo 6 30 -30
kar 7 40 -50
# Generic
In [44]: df.iloc[0:3]
Out[44]:
AAA BBB CCC
foo 4 10 100
bar 5 20 50
boo 6 30 -30
In [45]: df.loc['bar':'kar']
Out[45]:
AAA BBB CCC
bar 5 20 50
boo 6 30 -30
kar 7 40 -50
包含整数，且不从 0 开始的索引，或不是逐步递增的索引会引发歧义。
In [46]: data = {'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]}
....:
In [47]: df2 = pd.DataFrame(data=data, index=[1, 2, 3, 4]) # Note index starts at 1.
In [48]: df2.iloc[1:3] # Position-oriented
Out[48]:
AAA BBB CCC
2 5 20 50
3 6 30 -30
In [49]: df2.loc[1:3] # Label-oriented
Out[49]:
AAA BBB CCC
1 4 10 100
2 5 20 50
3 6 30 -30
用逆运算符 (~)提取掩码的反向内容 (opens new window)
In [50]: df = pd.DataFrame({'AAA': [4, 5, 6, 7],
....: 'BBB': [10, 20, 30, 40],
....: 'CCC': [100, 50, -30, -50]})
....:
In [51]: df
Out[51]:
AAA BBB CCC
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
In [52]: df[~((df.AAA <= 6) & (df.index.isin([0, 2, 4])))]
Out[52]:
AAA BBB CCC
1 5 20 50
3 7 40 -50
生成新列
用 applymap 高效动态生成新列 (opens new window)
In [53]: df = pd.DataFrame({'AAA': [1, 2, 1, 3],
....: 'BBB': [1, 1, 2, 2],
....: 'CCC': [2, 1, 3, 1]})
....:
In [54]: df
Out[54]:
AAA BBB CCC
0 1 1 2
1 2 1 1
2 1 2 3
3 3 2 1
In [55]: source_cols = df.columns # Or some subset would work too
In [56]: new_cols = [str(x) + "_cat" for x in source_cols]
In [57]: categories = {1: 'Alpha', 2: 'Beta', 3: 'Charlie'}
In [58]: df[new_cols] = df[source_cols].applymap(categories.get)
In [59]: df
Out[59]:
AAA BBB CCC AAA_cat BBB_cat CCC_cat
0 1 1 2 Alpha Alpha Beta
1 2 1 1 Beta Alpha Alpha
2 1 2 3 Alpha Beta Charlie
3 3 2 1 Charlie Beta Alpha
In [60]: df = pd.DataFrame({'AAA': [1, 1, 1, 2, 2, 2, 3, 3],
....: 'BBB': [2, 1, 3, 4, 5, 1, 2, 3]})
....:
In [61]: df
Out[61]:
AAA BBB
0 1 2
1 1 1
2 1 3
3 2 4
4 2 5
5 2 1
6 3 2
7 3 3
方法1：用 idxmin() 提取每组最小值的索引
In [62]: df.loc[df.groupby("AAA")["BBB"].idxmin()]
Out[62]:
AAA BBB
1 1 1
5 2 1
6 3 2
方法 2：先排序，再提取每组的第一个值
In [63]: df.sort_values(by="BBB").groupby("AAA", as_index=False).first()
Out[63]:
AAA BBB
0 1 1
1 2 1
2 3 2
注意，提取的数据一样，但索引不一样。
多层索引
更多信息，请参阅多层索引 (opens new window)文档。
用带标签的字典创建多层索引 (opens new window)
In [64]: df = pd.DataFrame({'row': [0, 1, 2],
....: 'One_X': [1.1, 1.1, 1.1],
....: 'One_Y': [1.2, 1.2, 1.2],
....: 'Two_X': [1.11, 1.11, 1.11],
....: 'Two_Y': [1.22, 1.22, 1.22]})
....:
In [65]: df
Out[65]:
row One_X One_Y Two_X Two_Y
0 0 1.1 1.2 1.11 1.22
1 1 1.1 1.2 1.11 1.22
2 2 1.1 1.2 1.11 1.22
# 设置索引标签
In [66]: df = df.set_index('row')
In [67]: df
Out[67]:
One_X One_Y Two_X Two_Y
row
0 1.1 1.2 1.11 1.22
1 1.1 1.2 1.11 1.22
2 1.1 1.2 1.11 1.22
# 多层索引的列
In [68]: df.columns = pd.MultiIndex.from_tuples([tuple(c.split('_'))
....: for c in df.columns])
....:
In [69]: df
Out[69]:
One Two
X Y X Y
row
0 1.1 1.2 1.11 1.22
1 1.1 1.2 1.11 1.22
2 1.1 1.2 1.11 1.22
# 先 stack，然后 Reset 索引
In [70]: df = df.stack(0).reset_index(1)
In [71]: df
Out[71]:
level_1 X Y
row
0 One 1.10 1.20
0 Two 1.11 1.22
1 One 1.10 1.20
1 Two 1.11 1.22
2 One 1.10 1.20
2 Two 1.11 1.22
# 修整标签，注意自动添加了标签 `level_1`
In [72]: df.columns = ['Sample', 'All_X', 'All_Y']
In [73]: df
Out[73]:
Sample All_X All_Y
row
0 One 1.10 1.20
0 Two 1.11 1.22
1 One 1.10 1.20
1 Two 1.11 1.22
2 One 1.10 1.20
2 Two 1.11 1.22
运算
多层索引运算要用广播机制 (opens new window)
In [74]: cols = pd.MultiIndex.from_tuples([(x, y) for x in ['A', 'B', 'C']
....: for y in ['O', 'I']])
....:
In [75]: df = pd.DataFrame(np.random.randn(2, 6), index=['n', 'm'], columns=cols)
In [76]: df
Out[76]:
A B C
O I O I O I
n 0.469112 -0.282863 -1.509059 -1.135632 1.212112 -0.173215
m 0.119209 -1.044236 -0.861849 -2.104569 -0.494929 1.071804
In [77]: df = df.div(df['C'], level=1)
In [78]: df
Out[78]:
A B C
O I O I O I
n 0.387021 1.633022 -1.244983 6.556214 1.0 1.0
m -0.240860 -0.974279 1.741358 -1.963577 1.0 1.0
切片
用 xs 切片多层索引 (opens new window)
In [79]: coords = [('AA', 'one'), ('AA', 'six'), ('BB', 'one'), ('BB', 'two'),
....: ('BB', 'six')]
....:
In [80]: index = pd.MultiIndex.from_tuples(coords)
In [81]: df = pd.DataFrame([11, 22, 33, 44, 55], index, ['MyData'])
In [82]: df
Out[82]:
MyData
AA one 11
six 22
BB one 33
two 44
six 55
提取第一层与索引第一个轴的交叉数据：
# 注意：level 与 axis 是可选项，默认为 0
In [83]: df.xs('BB', level=0, axis=0)
Out[83]:
MyData
one 33
two 44
six 55
……现在是第 1 个轴的第 2 层
In [84]: df.xs('six', level=1, axis=0)
Out[84]:
MyData
AA 22
BB 55
用 xs 切片多层索引，方法 #2 (opens new window)
In [85]: import itertools
In [86]: index = list(itertools.product(['Ada', 'Quinn', 'Violet'],
....: ['Comp', 'Math', 'Sci']))
....:
In [87]: headr = list(itertools.product(['Exams', 'Labs'], ['I', 'II']))
In [88]: indx = pd.MultiIndex.from_tuples(index, names=['Student', 'Course'])
In [89]: cols = pd.MultiIndex.from_tuples(headr) # Notice these are un-named
In [90]: data = [[70 + x + y + (x * y) % 3 for x in range(4)] for y in range(9)]
In [91]: df = pd.DataFrame(data, indx, cols)
In [92]: df
Out[92]:
Exams Labs
I II I II
Student Course
Ada Comp 70 71 72 73
Math 71 73 75 74
Sci 72 75 75 75
Quinn Comp 73 74 75 76
Math 74 76 78 77
Sci 75 78 78 78
Violet Comp 76 77 78 79
Math 77 79 81 80
Sci 78 81 81 81
In [93]: All = slice(None)
In [94]: df.loc['Violet']
Out[94]:
Exams Labs
I II I II
Course
Comp 76 77 78 79
Math 77 79 81 80
Sci 78 81 81 81
In [95]: df.loc[(All, 'Math'), All]
Out[95]:
Exams Labs
I II I II
Student Course
Ada Math 71 73 75 74
Quinn Math 74 76 78 77
Violet Math 77 79 81 80
In [96]: df.loc[(slice('Ada', 'Quinn'), 'Math'), All]
Out[96]:
Exams Labs
I II I II
Student Course
Ada Math 71 73 75 74
Quinn Math 74 76 78 77
In [97]: df.loc[(All, 'Math'), ('Exams')]
Out[97]:
I II
Student Course
Ada Math 71 73
Quinn Math 74 76
Violet Math 77 79
In [98]: df.loc[(All, 'Math'), (All, 'II')]
Out[98]:
Exams Labs
II II
Student Course
Ada Math 73 74
Quinn Math 76 77
Violet Math 79 80
用 xs 设置多层索引比例 (opens new window)
排序
用多层索引按指定列或列序列表排序x (opens new window)
In [99]: df.sort_values(by=('Labs', 'II'), ascending=False)
Out[99]:
Exams Labs
I II I II
Student Course
Violet Sci 78 81 81 81
Math 77 79 81 80
Comp 76 77 78 79
Quinn Sci 75 78 78 78
Math 74 76 78 77
Comp 73 74 75 76
Ada Sci 72 75 75 75
Math 71 73 75 74
Comp 70 71 72 73
层级
缺失数据
向前填充逆序时间序列。
In [100]: df = pd.DataFrame(np.random.randn(6, 1),
.....: index=pd.date_range('2013-08-01', periods=6, freq='B'),
.....: columns=list('A'))
.....:
In [101]: df.loc[df.index[3], 'A'] = np.nan
In [102]: df
Out[102]:
A
2013-08-01 0.721555
2013-08-02 -0.706771
2013-08-05 -1.039575
2013-08-06 NaN
2013-08-07 -0.424972
2013-08-08 0.567020
In [103]: df.reindex(df.index[::-1]).ffill()
Out[103]:
A
2013-08-08 0.567020
2013-08-07 -0.424972
2013-08-06 -0.424972
2013-08-05 -1.039575
2013-08-02 -0.706771
2013-08-01 0.721555
空值时重置为 0，有值时累加 (opens new window)
替换
分组
用 apply 执行分组基础操作 (opens new window)
与聚合不同，传递给 DataFrame 子集的 apply 可回调，可以访问所有列。
In [104]: df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
.....: 'size': list('SSMMMLL'),
.....: 'weight': [8, 10, 11, 1, 20, 12, 12],
.....: 'adult': [False] * 5 + [True] * 2})
.....:
In [105]: df
Out[105]:
animal size weight adult
0 cat S 8 False
1 dog S 10 False
2 cat M 11 False
3 fish M 1 False
4 dog M 20 False
5 cat L 12 True
6 cat L 12 True
# 提取 size 列最重的动物列表
In [106]: df.groupby('animal').apply(lambda subf: subf['size'][subf['weight'].idxmax()])
Out[106]:
animal
cat L
dog M
fish M
dtype: object
使用 get_group (opens new window)
In [107]: gb = df.groupby(['animal'])
In [108]: gb.get_group('cat')
Out[108]:
animal size weight adult
0 cat S 8 False
2 cat M 11 False
5 cat L 12 True
6 cat L 12 True
为同一分组的不同内容使用 Apply 函数 (opens new window)
In [109]: def GrowUp(x):
.....: avg_weight = sum(x[x['size'] == 'S'].weight * 1.5)
.....: avg_weight += sum(x[x['size'] == 'M'].weight * 1.25)
.....: avg_weight += sum(x[x['size'] == 'L'].weight)
.....: avg_weight /= len(x)
.....: return pd.Series(['L', avg_weight, True],
.....: index=['size', 'weight', 'adult'])
.....:
In [110]: expected_df = gb.apply(GrowUp)
In [111]: expected_df
Out[111]:
size weight adult
animal
cat L 12.4375 True
dog L 20.0000 True
fish L 1.2500 True
In [112]: S = pd.Series([i / 100.0 for i in range(1, 11)])
In [113]: def cum_ret(x, y):
.....: return x * (1 + y)
.....:
In [114]: def red(x):
.....: return functools.reduce(cum_ret, x, 1.0)
.....:
In [115]: S.expanding().apply(red, raw=True)
Out[115]:
0 1.010000
1 1.030200
2 1.061106
3 1.103550
4 1.158728
5 1.228251
6 1.314229
7 1.419367
8 1.547110
9 1.701821
dtype: float64
用分组里的剩余值的平均值进行替换 (opens new window)
In [116]: df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, -1, 1, 2]})
In [117]: gb = df.groupby('A')
In [118]: def replace(g):
.....: mask = g < 0
.....: return g.where(mask, g[~mask].mean())
.....:
In [119]: gb.transform(replace)
Out[119]:
B
0 1.0
1 -1.0
2 1.5
3 1.5
In [120]: df = pd.DataFrame({'code': ['foo', 'bar', 'baz'] * 2,
.....: 'data': [0.16, -0.21, 0.33, 0.45, -0.59, 0.62],
.....: 'flag': [False, True] * 3})
.....:
In [121]: code_groups = df.groupby('code')
In [122]: agg_n_sort_order = code_groups[['data']].transform(sum).sort_values(by='data')
In [123]: sorted_df = df.loc[agg_n_sort_order.index]
In [124]: sorted_df
Out[124]:
code data flag
1 bar -0.21 True
4 bar -0.59 False
0 foo 0.16 False
3 foo 0.45 True
2 baz 0.33 False
5 baz 0.62 True
In [125]: rng = pd.date_range(start="2014-10-07", periods=10, freq='2min')
In [126]: ts = pd.Series(data=list(range(10)), index=rng)
In [127]: def MyCust(x):
.....: if len(x) > 2:
.....: return x[1] * 1.234
.....: return pd.NaT
.....:
In [128]: mhc = {'Mean': np.mean, 'Max': np.max, 'Custom': MyCust}
In [129]: ts.resample("5min").apply(mhc)
Out[129]:
Mean 2014-10-07 00:00:00 1
2014-10-07 00:05:00 3.5
2014-10-07 00:10:00 6
2014-10-07 00:15:00 8.5
Max 2014-10-07 00:00:00 2
2014-10-07 00:05:00 4
2014-10-07 00:10:00 7
2014-10-07 00:15:00 9
Custom 2014-10-07 00:00:00 1.234
2014-10-07 00:05:00 NaT
2014-10-07 00:10:00 7.404
2014-10-07 00:15:00 NaT
dtype: object
In [130]: ts
Out[130]:
2014-10-07 00:00:00 0
2014-10-07 00:02:00 1
2014-10-07 00:04:00 2
2014-10-07 00:06:00 3
2014-10-07 00:08:00 4
2014-10-07 00:10:00 5
2014-10-07 00:12:00 6
2014-10-07 00:14:00 7
2014-10-07 00:16:00 8
2014-10-07 00:18:00 9
Freq: 2T, dtype: int64
为 DataFrame 创建值计数列 (opens new window)
In [131]: df = pd.DataFrame({'Color': 'Red Red Red Blue'.split(),
.....: 'Value': [100, 150, 50, 50]})
.....:
In [132]: df
Out[132]:
Color Value
0 Red 100
1 Red 150
2 Red 50
3 Blue 50
In [133]: df['Counts'] = df.groupby(['Color']).transform(len)
In [134]: df
Out[134]:
Color Value Counts
0 Red 100 3
1 Red 150 3
2 Red 50 3
3 Blue 50 1
基于索引唯一某列不同分组的值 (opens new window)
In [135]: df = pd.DataFrame({'line_race': [10, 10, 8, 10, 10, 8],
.....: 'beyer': [99, 102, 103, 103, 88, 100]},
.....: index=['Last Gunfighter', 'Last Gunfighter',
.....: 'Last Gunfighter', 'Paynter', 'Paynter',
.....: 'Paynter'])
.....:
In [136]: df
Out[136]:
line_race beyer
Last Gunfighter 10 99
Last Gunfighter 10 102
Last Gunfighter 8 103
Paynter 10 103
Paynter 10 88
Paynter 8 100
In [137]: df['beyer_shifted'] = df.groupby(level=0)['beyer'].shift(1)
In [138]: df
Out[138]:
line_race beyer beyer_shifted
Last Gunfighter 10 99 NaN
Last Gunfighter 10 102 99.0
Last Gunfighter 8 103 102.0
Paynter 10 103 NaN
Paynter 10 88 103.0
Paynter 8 100 88.0
In [139]: df = pd.DataFrame({'host': ['other', 'other', 'that', 'this', 'this'],
.....: 'service': ['mail', 'web', 'mail', 'mail', 'web'],
.....: 'no': [1, 2, 1, 2, 1]}).set_index(['host', 'service'])
.....:
In [140]: mask = df.groupby(level=0).agg('idxmax')
In [141]: df_count = df.loc[mask['no']].reset_index()
In [142]: df_count
Out[142]:
host service no
0 other web 2
1 that mail 1
2 this mail 2
Python itertools.groupby 式分组 (opens new window)
In [143]: df = pd.DataFrame([0, 1, 0, 1, 1, 1, 0, 1, 1], columns=['A'])
In [144]: df.A.groupby((df.A != df.A.shift()).cumsum()).groups
Out[144]:
{1: Int64Index([0], dtype='int64'),
2: Int64Index([1], dtype='int64'),
3: Int64Index([2], dtype='int64'),
4: Int64Index([3, 4, 5], dtype='int64'),
5: Int64Index([6], dtype='int64'),
6: Int64Index([7, 8], dtype='int64')}
In [145]: df.A.groupby((df.A != df.A.shift()).cumsum()).cumsum()
Out[145]:
0 0
1 1
2 0
3 1
4 2
5 3
6 0
7 1
8 2
Name: A, dtype: int64
扩展数据
Alignment and to-date (opens new window)
基于计数值进行移动窗口计算 (opens new window)
按时间间隔计算滚动平均 (opens new window)
分割
分割 DataFrame (opens new window)
按指定逻辑，将不同的行，分割成 DataFrame 列表。
In [146]: df = pd.DataFrame(data={'Case': ['A', 'A', 'A', 'B', 'A', 'A', 'B', 'A',
.....: 'A'],
.....: 'Data': np.random.randn(9)})
.....:
In [147]: dfs = list(zip(*df.groupby((1 * (df['Case'] == 'B')).cumsum()
.....: .rolling(window=3, min_periods=1).median())))[-1]
.....:
In [148]: dfs[0]
Out[148]:
Case Data
0 A 0.276232
1 A -1.087401
2 A -0.673690
3 B 0.113648
In [149]: dfs[1]
Out[149]:
Case Data
4 A -1.478427
5 A 0.524988
6 B 0.404705
In [150]: dfs[2]
Out[150]:
Case Data
7 A 0.577046
8 A -1.715002
透视表
In [151]: df = pd.DataFrame(data={'Province': ['ON', 'QC', 'BC', 'AL', 'AL', 'MN', 'ON'],
.....: 'City': ['Toronto', 'Montreal', 'Vancouver',
.....: 'Calgary', 'Edmonton', 'Winnipeg',
.....: 'Windsor'],
.....: 'Sales': [13, 6, 16, 8, 4, 3, 1]})
.....:
In [152]: table = pd.pivot_table(df, values=['Sales'], index=['Province'],
.....: columns=['City'], aggfunc=np.sum, margins=True)
.....:
In [153]: table.stack('City')
Out[153]:
Sales
Province City
AL All 12.0
Calgary 8.0
Edmonton 4.0
BC All 16.0
Vancouver 16.0
... ...
All Montreal 6.0
Toronto 13.0
Vancouver 16.0
Windsor 1.0
Winnipeg 3.0
[20 rows x 1 columns]
类似 R 的 plyr 频率表 (opens new window)
In [154]: grades = [48, 99, 75, 80, 42, 80, 72, 68, 36, 78]
In [155]: df = pd.DataFrame({'ID': ["x%d" % r for r in range(10)],
.....: 'Gender': ['F', 'M', 'F', 'M', 'F',
.....: 'M', 'F', 'M', 'M', 'M'],
.....: 'ExamYear': ['2007', '2007', '2007', '2008', '2008',
.....: '2008', '2008', '2009', '2009', '2009'],
.....: 'Class': ['algebra', 'stats', 'bio', 'algebra',
.....: 'algebra', 'stats', 'stats', 'algebra',
.....: 'bio', 'bio'],
.....: 'Participated': ['yes', 'yes', 'yes', 'yes', 'no',
.....: 'yes', 'yes', 'yes', 'yes', 'yes'],
.....: 'Passed': ['yes' if x > 50 else 'no' for x in grades],
.....: 'Employed': [True, True, True, False,
.....: False, False, False, True, True, False],
.....: 'Grade': grades})
.....:
In [156]: df.groupby('ExamYear').agg({'Participated': lambda x: x.value_counts()['yes'],
.....: 'Passed': lambda x: sum(x == 'yes'),
.....: 'Employed': lambda x: sum(x),
.....: 'Grade': lambda x: sum(x) / len(x)})
.....:
Out[156]:
Participated Passed Employed Grade
ExamYear
2007 3 2 3 74.000000
2008 3 3 0 68.500000
2009 3 2 2 60.666667
按年生成 DataFrame (opens new window)
跨列表创建年月：
In [157]: df = pd.DataFrame({'value': np.random.randn(36)},
.....: index=pd.date_range('2011-01-01', freq='M', periods=36))
.....:
In [158]: pd.pivot_table(df, index=df.index.month, columns=df.index.year,
.....: values='value', aggfunc='sum')
.....:
Out[158]:
2011 2012 2013
1 -1.039268 -0.968914 2.565646
2 -0.370647 -1.294524 1.431256
3 -1.157892 0.413738 1.340309
4 -1.344312 0.276662 -1.170299
5 0.844885 -0.472035 -0.226169
6 1.075770 -0.013960 0.410835
7 -0.109050 -0.362543 0.813850
8 1.643563 -0.006154 0.132003
9 -1.469388 -0.923061 -0.827317
10 0.357021 0.895717 -0.076467
11 -0.674600 0.805244 -1.187678
12 -1.776904 -1.206412 1.130127
Apply 函数
把嵌入列表转换为多层索引 DataFrame (opens new window)
In [159]: df = pd.DataFrame(data={'A': [[2, 4, 8, 16], [100, 200], [10, 20, 30]],
.....: 'B': [['a', 'b', 'c'], ['jj', 'kk'], ['ccc']]},
.....: index=['I', 'II', 'III'])
.....:
In [160]: def SeriesFromSubList(aList):
.....: return pd.Series(aList)
.....:
In [161]: df_orgz = pd.concat({ind: row.apply(SeriesFromSubList)
.....: for ind, row in df.iterrows()})
.....:
In [162]: df_orgz
Out[162]:
0 1 2 3
I A 2 4 8 16.0
B a b c NaN
II A 100 200 NaN NaN
B jj kk NaN NaN
III A 10 20 30 NaN
B ccc NaN NaN NaN
Rolling Apply to multiple columns where function calculates a Series before a Scalar from the Series is returned
In [163]: df = pd.DataFrame(data=np.random.randn(2000, 2) / 10000,
.....: index=pd.date_range('2001-01-01', periods=2000),
.....: columns=['A', 'B'])
.....:
In [164]: df
Out[164]:
A B
2001-01-01 -0.000144 -0.000141
2001-01-02 0.000161 0.000102
2001-01-03 0.000057 0.000088
2001-01-04 -0.000221 0.000097
2001-01-05 -0.000201 -0.000041
... ... ...
2006-06-19 0.000040 -0.000235
2006-06-20 -0.000123 -0.000021
2006-06-21 -0.000113 0.000114
2006-06-22 0.000136 0.000109
2006-06-23 0.000027 0.000030
[2000 rows x 2 columns]
In [165]: def gm(df, const):
.....: v = ((((df.A + df.B) + 1).cumprod()) - 1) * const
.....: return v.iloc[-1]
.....:
In [166]: s = pd.Series({df.index[i]: gm(df.iloc[i:min(i + 51, len(df) - 1)], 5)
.....: for i in range(len(df) - 50)})
.....:
In [167]: s
Out[167]:
2001-01-01 0.000930
2001-01-02 0.002615
2001-01-03 0.001281
2001-01-04 0.001117
2001-01-05 0.002772
...
2006-04-30 0.003296
2006-05-01 0.002629
2006-05-02 0.002081
2006-05-03 0.004247
2006-05-04 0.003928
Length: 1950, dtype: float64
Rolling Apply to multiple columns where function returns a Scalar (Volume Weighted Average Price) 对多列执行滚动 Apply，函数返回标量值（成交价加权平均价）
In [168]: rng = pd.date_range(start='2014-01-01', periods=100)
In [169]: df = pd.DataFrame({'Open': np.random.randn(len(rng)),
.....: 'Close': np.random.randn(len(rng)),
.....: 'Volume': np.random.randint(100, 2000, len(rng))},
.....: index=rng)
.....:
In [170]: df
Out[170]:
Open Close Volume
2014-01-01 -1.611353 -0.492885 1219
2014-01-02 -3.000951 0.445794 1054
2014-01-03 -0.138359 -0.076081 1381
2014-01-04 0.301568 1.198259 1253
2014-01-05 0.276381 -0.669831 1728
... ... ... ...
2014-04-06 -0.040338 0.937843 1188
2014-04-07 0.359661 -0.285908 1864
2014-04-08 0.060978 1.714814 941
2014-04-09 1.759055 -0.455942 1065
2014-04-10 0.138185 -1.147008 1453
[100 rows x 3 columns]
In [171]: def vwap(bars):
.....: return ((bars.Close * bars.Volume).sum() / bars.Volume.sum())
.....:
In [172]: window = 5
In [173]: s = pd.concat([(pd.Series(vwap(df.iloc[i:i + window]),
.....: index=[df.index[i + window]]))
.....: for i in range(len(df) - window)])
.....:
In [174]: s.round(2)
Out[174]:
2014-01-06 0.02
2014-01-07 0.11
2014-01-08 0.10
2014-01-09 0.07
2014-01-10 -0.29
...
2014-04-06 -0.63
2014-04-07 -0.02
2014-04-08 -0.03
2014-04-09 0.34
2014-04-10 0.29
Length: 95, dtype: float64
时间序列
删除指定时间之外的数据 (opens new window)
用 indexer 提取在时间范围内的数据 (opens new window)
创建不包括周末，且只包含指定时间的日期时间范围 (opens new window)
把以小时为列，天为行的矩阵转换为连续的时间序列。 如何重排 DataFrame？ (opens new window)
重建索引为指定频率时，如何处理重复值 (opens new window)
为 DatetimeIndex 里每条记录计算当月第一天
In [175]: dates = pd.date_range('2000-01-01', periods=5)
In [176]: dates.to_period(freq='M').to_timestamp()
Out[176]:
DatetimeIndex(['2000-01-01', '2000-01-01', '2000-01-01', '2000-01-01',
'2000-01-01'],
dtype='datetime64[ns]', freq=None)
重采样
用 Grouper 代替 TimeGrouper 处理时间分组的值 (opens new window)
Grouper 的有效时间频率参数 (opens new window)
用 TimeGrouper 与另一个分组创建子分组，再 Apply 自定义函数 (opens new window)
不添加新日期，重采样某日数据 (opens new window)
合并
连接 (opens new window) docs. The Join (opens new window)文档。
模拟 R 的 rbind：追加两个重叠索引的 DataFrame (opens new window)
In [177]: rng = pd.date_range('2000-01-01', periods=6)
In [178]: df1 = pd.DataFrame(np.random.randn(6, 3), index=rng, columns=['A', 'B', 'C'])
In [179]: df2 = df1.copy()
基于 df 构建器，需要ignore_index。
In [180]: df = df1.append(df2, ignore_index=True)
In [181]: df
Out[181]:
A B C
0 -0.870117 -0.479265 -0.790855
1 0.144817 1.726395 -0.464535
2 -0.821906 1.597605 0.187307
3 -0.128342 -1.511638 -0.289858
4 0.399194 -1.430030 -0.639760
5 1.115116 -2.012600 1.810662
6 -0.870117 -0.479265 -0.790855
7 0.144817 1.726395 -0.464535
8 -0.821906 1.597605 0.187307
9 -0.128342 -1.511638 -0.289858
10 0.399194 -1.430030 -0.639760
11 1.115116 -2.012600 1.810662
自连接 DataFrame (opens new window)
In [182]: df = pd.DataFrame(data={'Area': ['A'] * 5 + ['C'] * 2,
.....: 'Bins': [110] * 2 + [160] * 3 + [40] * 2,
.....: 'Test_0': [0, 1, 0, 1, 2, 0, 1],
.....: 'Data': np.random.randn(7)})
.....:
In [183]: df
Out[183]:
Area Bins Test_0 Data
0 A 110 0 -0.433937
1 A 110 1 -0.160552
2 A 160 0 0.744434
3 A 160 1 1.754213
4 A 160 2 0.000850
5 C 40 0 0.342243
6 C 40 1 1.070599
In [184]: df['Test_1'] = df['Test_0'] - 1
In [185]: pd.merge(df, df, left_on=['Bins', 'Area', 'Test_0'],
.....: right_on=['Bins', 'Area', 'Test_1'],
.....: suffixes=('_L', '_R'))
.....:
Out[185]:
Area Bins Test_0_L Data_L Test_1_L Test_0_R Data_R Test_1_R
0 A 110 0 -0.433937 -1 1 -0.160552 0
1 A 160 0 0.744434 -1 1 1.754213 0
2 A 160 1 1.754213 0 2 0.000850 1
3 C 40 0 0.342243 -1 1 1.070599 0
KDB 式的 asof 连接 (opens new window)
基于符合条件的值进行连接 (opens new window)
基于范围里的值，用 searchsorted 合并 (opens new window)
可视化
让 Matplotlib 看上去像 R (opens new window)
设置 x 轴的主次标签 (opens new window)
在 iPython Notebook 里创建多个可视图 (opens new window)
用 Pandas、Vincent、xlsxwriter 生成 Excel 文件里的嵌入可视图 (opens new window)
为分层变量的每个四分位数绘制箱型图 (opens new window)
In [186]: df = pd.DataFrame(
.....: {'stratifying_var': np.random.uniform(0, 100, 20),
.....: 'price': np.random.normal(100, 5, 20)})
.....:
In [187]: df['quartiles'] = pd.qcut(
.....: df['stratifying_var'],
.....: 4,
.....: labels=['0-25%', '25-50%', '50-75%', '75-100%'])
.....:
In [188]: df.boxplot(column='price', by='quartiles')
Out[188]: <matplotlib.axes._subplots.AxesSubplot at 0x7efff077f910>
数据输入输出
SQL 与 HDF5 性能对比 (opens new window)
CSV
read_csv 函数实战 (opens new window)
把 DataFrame 追加到 CSV 文件 (opens new window)
只读取 DataFrame 的前几列 (opens new window)
读取不是 gzip 或 bz2 压缩（read_csv 可识别的内置压缩格式）的文件。本例在介绍如何读取 WinZip 压缩文件的同时，还介绍了在环境管理器里打开文件，并读取内容的通用操作方式。详见本链接 (opens new window)
用 Unix 时间戳读取 CSV，并转为本地时区 (opens new window)
写入多行索引 CSV 时，不写入重复值 (opens new window)
从多个文件读取数据，创建单个 DataFrame
最好的方式是先一个个读取单个文件，然后再把每个文件的内容存成列表，再用 pd.concat() 组合成一个 DataFrame:
In [189]: for i in range(3):
.....: data = pd.DataFrame(np.random.randn(10, 4))
.....: data.to_csv('file_{}.csv'.format(i))
.....:
In [190]: files = ['file_0.csv', 'file_1.csv', 'file_2.csv']
In [191]: result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
还可以用同样的方法读取所有匹配同一模式的文件，下面这个例子使用的是glob：
In [192]: import glob
In [193]: import os
In [194]: files = glob.glob('file_*.csv')
In [195]: result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
最后，这种方式也适用于 io 文档 (opens new window) 介绍的其它 pd.read_* 函数。
解析多列里的日期组件
用一种格式解析多列的日期组件，速度更快。
In [196]: i = pd.date_range('20000101', periods=10000)
In [197]: df = pd.DataFrame({'year': i.year, 'month': i.month, 'day': i.day})
In [198]: df.head()
Out[198]:
year month day
0 2000 1 1
1 2000 1 2
2 2000 1 3
3 2000 1 4
4 2000 1 5
In [199]: %timeit pd.to_datetime(df.year * 10000 + df.month * 100 + df.day, format='%Y%m%d')
.....: ds = df.apply(lambda x: "%04d%02d%02d" % (x['year'],
.....: x['month'], x['day']), axis=1)
.....: ds.head()
.....: %timeit pd.to_datetime(ds)
.....:
10.6 ms +- 698 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
3.21 ms +- 36.4 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
跳过标题与数据之间的行
In [200]: data = """;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: ;;;;
.....: date;Param1;Param2;Param4;Param5
.....: ;m²;°C;m²;m
.....: ;;;;
.....: 01.01.1990 00:00;1;1;2;3
.....: 01.01.1990 01:00;5;3;4;5
.....: 01.01.1990 02:00;9;5;6;7
.....: 01.01.1990 03:00;13;7;8;9
.....: 01.01.1990 04:00;17;9;10;11
.....: 01.01.1990 05:00;21;11;12;13
.....: """
.....:
选项 1：显式跳过行
In [201]: from io import StringIO
In [202]: pd.read_csv(StringIO(data), sep=';', skiprows=[11, 12],
.....: index_col=0, parse_dates=True, header=10)
.....:
Out[202]:
Param1 Param2 Param4 Param5
date
1990-01-01 00:00:00 1 1 2 3
1990-01-01 01:00:00 5 3 4 5
1990-01-01 02:00:00 9 5 6 7
1990-01-01 03:00:00 13 7 8 9
1990-01-01 04:00:00 17 9 10 11
1990-01-01 05:00:00 21 11 12 13
选项 2：读取列名，然后再读取数据
In [203]: pd.read_csv(StringIO(data), sep=';', header=10, nrows=10).columns
Out[203]: Index(['date', 'Param1', 'Param2', 'Param4', 'Param5'], dtype='object')
In [204]: columns = pd.read_csv(StringIO(data), sep=';', header=10, nrows=10).columns
In [205]: pd.read_csv(StringIO(data), sep=';', index_col=0,
.....: header=12, parse_dates=True, names=columns)
.....:
Out[205]:
Param1 Param2 Param4 Param5
date
1990-01-01 00:00:00 1 1 2 3
1990-01-01 01:00:00 5 3 4 5
1990-01-01 02:00:00 9 5 6 7
1990-01-01 03:00:00 13 7 8 9
1990-01-01 04:00:00 17 9 10 11
1990-01-01 05:00:00 21 11 12 13
SQL
用 SQL 读取数据库 (opens new window)
Excel
用 XlsxWriter 修改输出格式 (opens new window)
HTML
从不能处理默认请求 header 的服务器读取 HTML 表格 (opens new window)
HDFStore
HDFStores (opens new window)文档
用链式多表架构管理异构数据 (opens new window)
在硬盘上合并数百万行的表格 (opens new window)
避免多进程/线程存储数据出现不一致 (opens new window)
按块对大规模数据存储去重的本质是递归还原操作。这里 (opens new window)介绍了一个函数，可以从 CSV 文件里按块提取数据，解析日期后，再按块存储。
按块读取 CSV 文件，并保存 (opens new window)
追加到已存储的文件，且确保索引唯一 (opens new window)
读取一系列文件，追加时采用全局唯一索引 (opens new window)
用低分组密度分组 HDFStore 文件 (opens new window)
用高分组密度分组 HDFStore 文件 (opens new window)
HDFStore 文件结构化查询 (opens new window)
HDFStore 计数 (opens new window)
HDFStore 异常解答 (opens new window)
用字符串设置 min_itemsize (opens new window)
用 ptrepack 创建完全排序索引 (opens new window)
把属性存至分组节点
In [206]: df = pd.DataFrame(np.random.randn(8, 3))
In [207]: store = pd.HDFStore('test.h5')
In [208]: store.put('df', df)
# 用 pickle 存储任意 Python 对象
In [209]: store.get_storer('df').attrs.my_attribute = {'A': 10}
In [210]: store.get_storer('df').attrs.my_attribute
Out[210]: {'A': 10}
二进制文件
读取 C 结构体数组组成的二进制文件，Pandas 支持 NumPy 记录数组。 比如说，名为 main.c 的文件包含下列 C 代码，并在 64 位机器上用 gcc main.c -std=gnu99 进行编译。
#include <stdio.h>
#include <stdint.h>
typedef struct _Data
{
int32_t count;
double avg;
float scale;
} Data;
int main(int argc, const char *argv[])
{
size_t n = 10;
Data d[n];
for (int i = 0; i < n; ++i)
{
d[i].count = i;
d[i].avg = i + 1.0;
d[i].scale = (float) i + 2.0f;
}
FILE *file = fopen("binary.dat", "wb");
fwrite(&d, sizeof(Data), n, file);
fclose(file);
return 0;
}
下列 Python 代码读取二进制二建 binary.dat，并将之存为 pandas DataFrame，每个结构体的元素对应 DataFrame 里的列：
names = 'count', 'avg', 'scale'
# 注意：因为结构体填充，位移量比类型尺寸大
offsets = 0, 8, 16
formats = 'i4', 'f8', 'f4'
dt = np.dtype({'names': names, 'offsets': offsets, 'formats': formats},
align=True)
df = pd.DataFrame(np.fromfile('binary.dat', dt))
注意
不同机器上创建的文件因其架构不同，结构化元素的位移量也不同，原生二进制格式文件不能跨平台使用，因此不建议作为通用数据存储格式。建议用 Pandas IO 功能支持的 HDF5 或 msgpack 文件。
计算
基于采样的时间序列数值整合 (opens new window)
相关性
用 DataFrame.corr() (opens new window) 计算得出的相关矩阵的下（或上）三角形式一般都非常有用。下例通过把布尔掩码传递给 where 可以实现这一功能：
In [211]: df = pd.DataFrame(np.random.random(size=(100, 5)))
In [212]: corr_mat = df.corr()
In [213]: mask = np.tril(np.ones_like(corr_mat, dtype=np.bool), k=-1)
In [214]: corr_mat.where(mask)
Out[214]:
0 1 2 3 4
0 NaN NaN NaN NaN NaN
1 -0.018923 NaN NaN NaN NaN
2 -0.076296 -0.012464 NaN NaN NaN
3 -0.169941 -0.289416 0.076462 NaN NaN
4 0.064326 0.018759 -0.084140 -0.079859 NaN
除了命名相关类型之外，DataFrame.corr 还接受回调，此处计算 DataFrame 对象的距离相关矩阵 (opens new window)。
In [215]: def distcorr(x, y):
.....: n = len(x)
.....: a = np.zeros(shape=(n, n))
.....: b = np.zeros(shape=(n, n))
.....: for i in range(n):
.....: for j in range(i + 1, n):
.....: a[i, j] = abs(x[i] - x[j])
.....: b[i, j] = abs(y[i] - y[j])
.....: a += a.T
.....: b += b.T
.....: a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
.....: b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
.....: A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
.....: B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
.....: cov_ab = np.sqrt(np.nansum(A * B)) / n
.....: std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
.....: std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
.....: return cov_ab / std_a / std_b
.....:
In [216]: df = pd.DataFrame(np.random.normal(size=(100, 3)))
In [217]: df.corr(method=distcorr)
Out[217]:
0 1 2
0 1.000000 0.199653 0.214871
1 0.199653 1.000000 0.195116
2 0.214871 0.195116 1.000000
时间差
In [218]: import datetime
In [219]: s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
In [220]: s - s.max()
Out[220]:
0 -2 days
1 -1 days
2 0 days
dtype: timedelta64[ns]
In [221]: s.max() - s
Out[221]:
0 2 days
1 1 days
2 0 days
dtype: timedelta64[ns]
In [222]: s - datetime.datetime(2011, 1, 1, 3, 5)
Out[222]:
0 364 days 20:55:00
1 365 days 20:55:00
2 366 days 20:55:00
dtype: timedelta64[ns]
In [223]: s + datetime.timedelta(minutes=5)
Out[223]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
dtype: datetime64[ns]
In [224]: datetime.datetime(2011, 1, 1, 3, 5) - s
Out[224]:
0 -365 days +03:05:00
1 -366 days +03:05:00
2 -367 days +03:05:00
dtype: timedelta64[ns]
In [225]: datetime.timedelta(minutes=5) + s
Out[225]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
dtype: datetime64[ns]
In [226]: deltas = pd.Series([datetime.timedelta(days=i) for i in range(3)])
In [227]: df = pd.DataFrame({'A': s, 'B': deltas})
In [228]: df
Out[228]:
A B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days
In [229]: df['New Dates'] = df['A'] + df['B']
In [230]: df['Delta'] = df['A'] - df['New Dates']
In [231]: df
Out[231]:
A B New Dates Delta
0 2012-01-01 0 days 2012-01-01 0 days
1 2012-01-02 1 days 2012-01-03 -1 days
2 2012-01-03 2 days 2012-01-05 -2 days
In [232]: df.dtypes
Out[232]:
A datetime64[ns]
B timedelta64[ns]
New Dates datetime64[ns]
Delta timedelta64[ns]
dtype: object
与 datetime 类似，用 np.nan 可以把值设为 NaT。
In [233]: y = s - s.shift()
In [234]: y
Out[234]:
0 NaT
1 1 days
2 1 days
dtype: timedelta64[ns]
In [235]: y[1] = np.nan
In [236]: y
Out[236]:
0 NaT
1 NaT
2 1 days
dtype: timedelta64[ns]
轴别名
设置全局轴别名，可以定义以下两个函数：
In [237]: def set_axis_alias(cls, axis, alias):
.....: if axis not in cls._AXIS_NUMBERS:
.....: raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
.....: cls._AXIS_ALIASES[alias] = axis
.....:
In [238]: def clear_axis_alias(cls, axis, alias):
.....: if axis not in cls._AXIS_NUMBERS:
.....: raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
.....: cls._AXIS_ALIASES.pop(alias, None)
.....:
In [239]: set_axis_alias(pd.DataFrame, 'columns', 'myaxis2')
In [240]: df2 = pd.DataFrame(np.random.randn(3, 2), columns=['c1', 'c2'],
.....: index=['i1', 'i2', 'i3'])
.....:
In [241]: df2.sum(axis='myaxis2')
Out[241]:
i1 -0.461013
i2 2.040016
i3 0.904681
dtype: float64
In [242]: clear_axis_alias(pd.DataFrame, 'columns', 'myaxis2')
创建示例数据
类似 R 的 expand.grid() 函数，用不同类型的值组生成 DataFrame，需要创建键是列名，值是数据值列表的字典：
In [243]: def expand_grid(data_dict):
.....: rows = itertools.product(*data_dict.values())
.....: return pd.DataFrame.from_records(rows, columns=data_dict.keys())
.....:
In [244]: df = expand_grid({'height': [60, 70],
.....: 'weight': [100, 140, 180],
.....: 'sex': ['Male', 'Female']})
.....:
In [245]: df
Out[245]:
height weight sex
0 60 100 Male
1 60 100 Female
2 60 140 Male
3 60 140 Female
4 60 180 Male
5 60 180 Female
6 70 100 Male
7 70 100 Female
8 70 140 Male
9 70 140 Female
10 70 180 Male
11 70 180 Female
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