距离Pandas10分钟车程#
这是对Pandas的简短介绍，主要面向新用户。中您可以看到更复杂的食谱 Cookbook 。
通常，我们按如下方式导入：
In [1]: import numpy as np
In [2]: import pandas as pd
对象创建#
请参阅 Intro to data structures section 。
创建一个 Series 通过传递一个值列表，让Pandas创建一个默认的整数索引：
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
创建一个 DataFrame 通过传递NumPy数组，并使用 date_range() 和带标签的列：
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
创建一个 DataFrame 通过传递可转换为类似序列的结构的对象的字典：
In [9]: df2 = pd.DataFrame(
...: {
...: "A": 1.0,
...: "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
In [11]: df2.dtypes
Out[11]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
如果您使用的是IPython，则会自动启用列名(以及公共属性)的制表符完成功能。以下是将完成的属性的子集：
In [12]: df2.<TAB> # noqa: E225, E999
df2.A df2.bool
df2.abs df2.boxplot
df2.add df2.C
df2.add_prefix df2.clip
df2.add_suffix df2.columns
df2.align df2.copy
df2.all df2.count
df2.any df2.combine
df2.append df2.D
df2.apply df2.describe
df2.applymap df2.diff
df2.B df2.duplicated
如你所见，这些柱子 A ， B ， C ，以及 D 会自动按Tab键完成。 E 和 F 也存在；为简洁起见，其余属性已被截断。
查看数据#
请参阅 Basics section 。
使用 DataFrame.head() 和 DataFrame.tail() 要分别查看框架的顶行和底行，请执行以下操作：
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 DataFrame.index 或 DataFrame.columns ：
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() 给出基础数据的NumPy表示形式。请注意，这可能是一个昂贵的操作，当您 DataFrame 具有不同数据类型的列，这归根结底是Pandas和NumPy之间的根本区别： NumPy数组的整个数组都有一个数据类型，而PandasDataFrame的每列有一个数据类型 。当你打电话的时候 DataFrame.to_numpy() ，Pandas会发现NumPy dtype可以 all DataFrame中的数据类型。这可能最终会是 object ，这需要将每个值强制转换为一个Python对象。
为 df 我们的 DataFrame 在所有浮点值中，以及 DataFrame.to_numpy() 速度快，不需要复制数据：
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 ]])
为 df2 ，即 DataFrame 利用多个数据类型， DataFrame.to_numpy() 相对较贵：
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)
备注
DataFrame.to_numpy() 会吗？ not 在输出中包括索引或列标签。
describe() 显示数据的快速统计摘要：
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
调换您的数据：
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
DataFrame.sort_index() 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
DataFrame.sort_values() 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#
备注
虽然用于选择和设置的标准Python/NumPy表达式很直观，并且在交互工作中很方便，但对于生产代码，我们推荐优化的Pandas数据访问方法， DataFrame.at() ， DataFrame.iat() ， DataFrame.loc() 和 DataFrame.iloc() 。
请参阅索引文档 Indexing and Selecting Data 和 MultiIndex / Advanced Indexing 。
vbl.得到，得到#
选择单个列，这将生成 Series ，相当于 df.A ：
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
Selecting via [] (__getitem__), which slices the rows:
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#
有关详细信息，请参阅 Selection by Label 使用 DataFrame.loc() 或 DataFrame.at() 。
要使用标签获取横截面，请执行以下操作：
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
按标签在多轴上选择：
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
显示标签切片，两个端点都是 包括在内 ：
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
降低返回对象的维度：
In [29]: df.loc["20130102", ["A", "B"]]
Out[29]:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64
要获取标量值，请执行以下操作：
In [30]: df.loc[dates[0], "A"]
Out[30]: 0.4691122999071863
为了快速访问标量(等同于前面的方法)：
In [31]: df.at[dates[0], "A"]
Out[31]: 0.4691122999071863
按位置选择#
有关详细信息，请参阅 Selection by Position 使用 DataFrame.iloc() 或 DataFrame.at() 。
通过传递的整数的位置进行选择：
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
按整数切片，类似于NumPy/Python：
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
按整数位置位置列表，类似于NumPy/Python样式：
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
对于显式切片行：
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
对于显式切片列：
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
要显式获取值，请执行以下操作：
In [37]: df.iloc[1, 1]
Out[37]: -0.17321464905330858
为了快速访问标量(等同于前面的方法)：
In [38]: df.iat[1, 1]
Out[38]: -0.17321464905330858
布尔索引#
使用单个列的值选择数据：
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
从满足布尔条件的DataFrame选择值：
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
使用 isin() 过滤方法：
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
设置#
设置新列会自动按索引对齐数据：
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
按标签设置值：
In [48]: df.at[dates[0], "A"] = 0
按位置设置值：
In [49]: df.iat[0, 1] = 0
通过使用NumPy数组赋值进行设置：
In [50]: df.loc[:, "D"] = np.array([5] * len(df))
先前设置操作的结果：
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
A where 带设置的操作：
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
缺少数据#
Pandas主要使用的是价值 np.nan 来表示丢失的数据。默认情况下，它不包括在计算中。请参阅 Missing Data section 。
重建索引允许您更改/添加/删除指定轴上的索引。这将返回数据的副本：
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
DataFrame.dropna() 删除缺少数据的所有行：
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
DataFrame.fillna() 填充缺失的数据：
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
isna() 获取值所在位置的布尔掩码 nan ：
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
运营#
请参阅 Basic section on Binary Ops 。
统计数据#
总体运营情况 排除 缺少数据。
执行描述性统计：
In [61]: df.mean()
Out[61]:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64
在另一个轴上执行相同的操作：
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
操作具有不同维度且需要对齐的对象。此外，Pandas还会自动沿指定维度进行广播：
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
应用#
DataFrame.apply() 将用户定义的函数应用于数据：
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
组织构图#
见更多 Histogramming and Discretization 。
In [68]: s = pd.Series(np.random.randint(0, 7, size=10))
In [69]: s
Out[69]:
0 4
1 2
2 1
3 2
4 6
5 4
6 4
7 6
8 4
9 4
dtype: int64
In [70]: s.value_counts()
Out[70]:
4 5
2 2
6 2
1 1
dtype: int64
字符串方法#
系列中配备了一组字符串处理方法。 str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions 默认情况下(在某些情况下总是使用它们)。见更多 Vectorized String Methods 。
In [71]: s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"])
In [72]: s.str.lower()
Out[72]:
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype: object
合并#
合并#
Pandas提供了各种工具，可以轻松地将Series和DataFrame对象与索引的各种集合逻辑组合在一起，并在连接/合并类型的操作中使用关系代数功能。
请参阅 Merging section 。
将Pandas物体沿着一条轴连接在一起 concat() ：
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
# break it into pieces
In [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
会合#
merge() 启用沿特定列的SQL样式联接类型。请参阅 Database style joining 部分。
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
另一个可以举的例子是：
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
分组#
我们所说的“分组依据”是指涉及以下一个或多个步骤的过程：
拆分 根据某些标准将数据分组
施药 独立于每一组的函数
组合 将结果转换为数据结构
请参阅 Grouping section 。
In [87]: 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 [88]: df
Out[88]:
A B C D
0 foo one 1.346061 -1.577585
1 bar one 1.511763 0.396823
2 foo two 1.627081 -0.105381
3 bar three -0.990582 -0.532532
4 foo two -0.441652 1.453749
5 bar two 1.211526 1.208843
6 foo one 0.268520 -0.080952
7 foo three 0.024580 -0.264610
分组，然后应用 sum() 函数应用于所产生的组：
In [89]: df.groupby("A").sum()
Out[89]:
C D
A
bar 1.732707 1.073134
foo 2.824590 -0.574779
按多列分组形成了分层索引，我们可以再次应用 sum() 功能：
In [90]: df.groupby(["A", "B"]).sum()
Out[90]:
C D
A B
bar one 1.511763 0.396823
three -0.990582 -0.532532
two 1.211526 1.208843
foo one 1.614581 -1.658537
three 0.024580 -0.264610
two 1.185429 1.348368
重塑#
请参阅上的部分 Hierarchical Indexing 和 Reshaping 。
栈#
In [91]: tuples = list(
....: zip(
....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
....: ["one", "two", "one", "two", "one", "two", "one", "two"],
....: )
....: )
....:
In [92]: index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
In [93]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"])
In [94]: df2 = df[:4]
In [95]: df2
Out[95]:
A B
first second
bar one -0.727965 -0.589346
two 0.339969 -0.693205
baz one -0.339355 0.593616
two 0.884345 1.591431
这个 stack() 方法“压缩”DataFrame列中的一个级别：
In [96]: stacked = df2.stack()
In [97]: stacked
Out[97]:
first second
bar one A -0.727965
B -0.589346
two A 0.339969
B -0.693205
baz one A -0.339355
B 0.593616
two A 0.884345
B 1.591431
dtype: float64
具有“堆叠”的DataFrame或Series(具有 MultiIndex 作为 index )的逆运算。 stack() 是 unstack() ，这在默认情况下会将 最后一关 ：
In [98]: stacked.unstack()
Out[98]:
A B
first second
bar one -0.727965 -0.589346
two 0.339969 -0.693205
baz one -0.339355 0.593616
two 0.884345 1.591431
In [99]: stacked.unstack(1)
Out[99]:
second one two
first
bar A -0.727965 0.339969
B -0.589346 -0.693205
baz A -0.339355 0.884345
B 0.593616 1.591431
In [100]: stacked.unstack(0)
Out[100]:
first bar baz
second
one A -0.727965 -0.339355
B -0.589346 0.593616
two A 0.339969 0.884345
B -0.693205 1.591431
数据透视表#
请参阅 Pivot Tables 。
In [101]: 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 [102]: df
Out[102]:
A B C D E
0 one A foo -1.202872 0.047609
1 one B foo -1.814470 -0.136473
2 two C foo 1.018601 -0.561757
3 three A bar -0.595447 -1.623033
4 one B bar 1.395433 0.029399
5 one C bar -0.392670 -0.542108
6 two A foo 0.007207 0.282696
7 three B foo 1.928123 -0.087302
8 one C foo -0.055224 -1.575170
9 one A bar 2.395985 1.771208
10 two B bar 1.552825 0.816482
11 three C bar 0.166599 1.100230
pivot_table() pivots a DataFrame specifying the values, index and columns
In [103]: pd.pivot_table(df, values="D", index=["A", "B"], columns=["C"])
Out[103]:
C bar foo
A B
one A 2.395985 -1.202872
B 1.395433 -1.814470
C -0.392670 -0.055224
three A -0.595447 NaN
B NaN 1.928123
C 0.166599 NaN
two A NaN 0.007207
B 1.552825 NaN
C NaN 1.018601
时间序列#
Pandas具有简单、强大和高效的功能，可以在频率转换期间执行重采样操作(例如，将第二个数据转换为5分钟的数据)。这在金融应用程序中非常常见，但不限于此。请参阅 Time Series section 。
In [104]: rng = pd.date_range("1/1/2012", periods=100, freq="S")
In [105]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
In [106]: ts.resample("5Min").sum()
Out[106]:
2012-01-01 24182
Freq: 5T, dtype: int64
Series.tz_localize() 将时间序列本地化到时区：
In [107]: rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D")
In [108]: ts = pd.Series(np.random.randn(len(rng)), rng)
In [109]: ts
Out[109]:
2012-03-06 1.857704
2012-03-07 -1.193545
2012-03-08 0.677510
2012-03-09 -0.153931
2012-03-10 0.520091
Freq: D, dtype: float64
In [110]: ts_utc = ts.tz_localize("UTC")
In [111]: ts_utc
Out[111]:
2012-03-06 00:00:00+00:00 1.857704
2012-03-07 00:00:00+00:00 -1.193545
2012-03-08 00:00:00+00:00 0.677510
2012-03-09 00:00:00+00:00 -0.153931
2012-03-10 00:00:00+00:00 0.520091
Freq: D, dtype: float64
Series.tz_convert() 将时区感知的时间序列转换为另一个时区：
In [112]: ts_utc.tz_convert("US/Eastern")
Out[112]:
2012-03-05 19:00:00-05:00 1.857704
2012-03-06 19:00:00-05:00 -1.193545
2012-03-07 19:00:00-05:00 0.677510
2012-03-08 19:00:00-05:00 -0.153931
2012-03-09 19:00:00-05:00 0.520091
Freq: D, dtype: float64
在时间跨度表示法之间转换：
In [113]: rng = pd.date_range("1/1/2012", periods=5, freq="M")
In [114]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [115]: ts
Out[115]:
2012-01-31 -1.475051
2012-02-29 0.722570
2012-03-31 -0.322646
2012-04-30 -1.601631
2012-05-31 0.778033
Freq: M, dtype: float64
In [116]: ps = ts.to_period()
In [117]: ps
Out[117]:
2012-01 -1.475051
2012-02 0.722570
2012-03 -0.322646
2012-04 -1.601631
2012-05 0.778033
Freq: M, dtype: float64
In [118]: ps.to_timestamp()
Out[118]:
2012-01-01 -1.475051
2012-02-01 0.722570
2012-03-01 -0.322646
2012-04-01 -1.601631
2012-05-01 0.778033
Freq: MS, dtype: float64
在PERIOD和TIMESTAMP之间进行转换可以使用一些方便的算术函数。在下面的示例中，我们将年度结束时间为11月的季度频率转换为季度结束后的下一个月的上午9点：
In [119]: prng = pd.period_range("1990Q1", "2000Q4", freq="Q-NOV")
In [120]: ts = pd.Series(np.random.randn(len(prng)), prng)
In [121]: ts.index = (prng.asfreq("M", "e") + 1).asfreq("H", "s") + 9
In [122]: ts.head()
Out[122]:
1990-03-01 09:00 -0.289342
1990-06-01 09:00 0.233141
1990-09-01 09:00 -0.223540
1990-12-01 09:00 0.542054
1991-03-01 09:00 -0.688585
Freq: H, dtype: float64
类别词#
Pandas可以将分类数据包含在 DataFrame 。有关完整的文档，请参阅 categorical introduction 以及 API documentation 。
In [123]: df = pd.DataFrame(
.....: {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
.....: )
.....:
将原始分数转换为分类数据类型：
In [124]: df["grade"] = df["raw_grade"].astype("category")
In [125]: df["grade"]
Out[125]:
0 a
1 b
2 b
3 a
4 a
5 e
Name: grade, dtype: category
Categories (3, object): ['a', 'b', 'e']
将类别重命名为更有意义的名称(分配给 Series.cat.categories() 已就位！)：
In [126]: df["grade"].cat.categories = ["very good", "good", "very bad"]
重新排序类别并同时添加缺少的类别(方法位于 Series.cat() 返回一个新的 Series 默认情况下)：
In [127]: df["grade"] = df["grade"].cat.set_categories(
.....: ["very bad", "bad", "medium", "good", "very good"]
.....: )
.....:
In [128]: df["grade"]
Out[128]:
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']
排序是按类别的顺序进行的，而不是按词汇顺序：
In [129]: df.sort_values(by="grade")
Out[129]:
id raw_grade grade
5 6 e very bad
1 2 b good
2 3 b good
0 1 a very good
3 4 a very good
4 5 a very good
按分类列分组还会显示空类别：
In [130]: df.groupby("grade").size()
Out[130]:
grade
very bad 1
bad 0
medium 0
good 2
very good 3
dtype: int64
标绘#
请参阅 Plotting 医生。
我们使用标准约定来引用matplotlib API：
In [131]: import matplotlib.pyplot as plt
In [132]: plt.close("all")
这个 plt.close method is used to close 图形窗口：
In [133]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
In [134]: ts = ts.cumsum()
In [135]: ts.plot();
如果在Jupyter Notebook下运行，曲线图将显示在 plot() 。否则请使用 matplotlib.pyplot.show 展示它，或者 matplotlib.pyplot.savefig 将其写入文件。
In [136]: plt.show();
在DataFrame上， plot() 方法可以方便地绘制带有标签的所有列：
In [137]: df = pd.DataFrame(
.....: np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"]
.....: )
.....:
In [138]: df = df.cumsum()
In [139]: plt.figure();
In [140]: df.plot();
In [141]: plt.legend(loc='best');
导入和导出数据#
CSV#
Writing to a csv file: using DataFrame.to_csv()
In [142]: df.to_csv("foo.csv")
Reading from a csv file: using read_csv()
In [143]: pd.read_csv("foo.csv")
Out[143]:
Unnamed: 0 A B C D
0 2000-01-01 0.350262 0.843315 1.798556 0.782234
1 2000-01-02 -0.586873 0.034907 1.923792 -0.562651
2 2000-01-03 -1.245477 -0.963406 2.269575 -1.612566
3 2000-01-04 -0.252830 -0.498066 3.176886 -1.275581
4 2000-01-05 -1.044057 0.118042 2.768571 0.386039
.. ... ... ... ... ...
995 2002-09-22 -48.017654 31.474551 69.146374 -47.541670
996 2002-09-23 -47.207912 32.627390 68.505254 -48.828331
997 2002-09-24 -48.907133 31.990402 67.310924 -49.391051
998 2002-09-25 -50.146062 33.716770 67.717434 -49.037577
999 2002-09-26 -49.724318 33.479952 68.108014 -48.822030
[1000 rows x 5 columns]
HDF5#
读取和写入到 HDFStores 。
使用以下命令写入HDF5存储 DataFrame.to_hdf() ：
In [144]: df.to_hdf("foo.h5", "df")
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/compat/_optional.py:139, in import_optional_dependency(name, extra, errors, min_version)
138 try:
--> 139 module = importlib.import_module(name)
140 except ImportError:
File /usr/lib/python3.10/importlib/__init__.py:126, in import_module(name, package)
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
File <frozen importlib._bootstrap>:1050, in _gcd_import(name, package, level)
File <frozen importlib._bootstrap>:1027, in _find_and_load(name, import_)
File <frozen importlib._bootstrap>:1004, in _find_and_load_unlocked(name, import_)
ModuleNotFoundError: No module named 'tables'
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
Input In [144], in <cell line: 1>()
----> 1 df.to_hdf("foo.h5", "df")
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/core/generic.py:2655, in NDFrame.to_hdf(self, path_or_buf, key, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding)
2651 from pandas.io import pytables
2653 # Argument 3 to "to_hdf" has incompatible type "NDFrame"; expected
2654 # "Union[DataFrame, Series]" [arg-type]
-> 2655 pytables.to_hdf(
2656 path_or_buf,
2657 key,
2658 self, # type: ignore[arg-type]
2659 mode=mode,
2660 complevel=complevel,
2661 complib=complib,
2662 append=append,
2663 format=format,
2664 index=index,
2665 min_itemsize=min_itemsize,
2666 nan_rep=nan_rep,
2667 dropna=dropna,
2668 data_columns=data_columns,
2669 errors=errors,
2670 encoding=encoding,
2671 )
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/pytables.py:312, in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding)
310 path_or_buf = stringify_path(path_or_buf)
311 if isinstance(path_or_buf, str):
--> 312 with HDFStore(
313 path_or_buf, mode=mode, complevel=complevel, complib=complib
314 ) as store:
315 f(store)
316 else:
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/pytables.py:573, in HDFStore.__init__(self, path, mode, complevel, complib, fletcher32, **kwargs)
570 if "format" in kwargs:
571 raise ValueError("format is not a defined argument for HDFStore")
--> 573 tables = import_optional_dependency("tables")
575 if complib is not None and complib not in tables.filters.all_complibs:
576 raise ValueError(
577 f"complib only supports {tables.filters.all_complibs} compression."
578 )
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/compat/_optional.py:142, in import_optional_dependency(name, extra, errors, min_version)
140 except ImportError:
141 if errors == "raise":
--> 142 raise ImportError(msg)
143 else:
144 return None
ImportError: Missing optional dependency 'pytables'. Use pip or conda to install pytables.
使用以下命令从HDF5商店读取 read_hdf() ：
In [145]: pd.read_hdf("foo.h5", "df")
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Input In [145], in <cell line: 1>()
----> 1 pd.read_hdf("foo.h5", "df")
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/pytables.py:428, in read_hdf(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)
425 exists = False
427 if not exists:
--> 428 raise FileNotFoundError(f"File {path_or_buf} does not exist")
430 store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
431 # can't auto open/close if we are using an iterator
432 # so delegate to the iterator
FileNotFoundError: File foo.h5 does not exist
Excel#
读取和写入到 Excel 。
使用写入到Excel文件 DataFrame.to_excel() ：
In [146]: df.to_excel("foo.xlsx", sheet_name="Sheet1")
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Input In [146], in <cell line: 1>()
----> 1 df.to_excel("foo.xlsx", sheet_name="Sheet1")
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/core/generic.py:2237, in NDFrame.to_excel(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes, storage_options)
2224 from pandas.io.formats.excel import ExcelFormatter
2226 formatter = ExcelFormatter(
2227 df,
2228 na_rep=na_rep,
(...)
2235 inf_rep=inf_rep,
2236 )
-> 2237 formatter.write(
2238 excel_writer,
2239 sheet_name=sheet_name,
2240 startrow=startrow,
2241 startcol=startcol,
2242 freeze_panes=freeze_panes,
2243 engine=engine,
2244 storage_options=storage_options,
2245 )
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/formats/excel.py:896, in ExcelFormatter.write(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options)
892 need_save = False
893 else:
894 # error: Cannot instantiate abstract class 'ExcelWriter' with abstract
895 # attributes 'engine', 'save', 'supported_extensions' and 'write_cells'
--> 896 writer = ExcelWriter( # type: ignore[abstract]
897 writer, engine=engine, storage_options=storage_options
898 )
899 need_save = True
901 try:
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/excel/_openpyxl.py:55, in OpenpyxlWriter.__init__(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)
42 def __init__(
43 self,
44 path: FilePath | WriteExcelBuffer | ExcelWriter,
(...)
53 ) -> None:
54 # Use the openpyxl module as the Excel writer.
---> 55 from openpyxl.workbook import Workbook
57 engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
59 super().__init__(
60 path,
61 mode=mode,
(...)
64 engine_kwargs=engine_kwargs,
65 )
ModuleNotFoundError: No module named 'openpyxl'
使用从Excel文件中读取 read_excel() ：
In [147]: pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"])
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Input In [147], in <cell line: 1>()
----> 1 pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"])
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/util/_decorators.py:317, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
311 if len(args) > num_allow_args:
312 warnings.warn(
313 msg.format(arguments=arguments),
314 FutureWarning,
315 stacklevel=stacklevel,
316 )
--> 317 return func(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/excel/_base.py:458, in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)
456 if not isinstance(io, ExcelFile):
457 should_close = True
--> 458 io = ExcelFile(io, storage_options=storage_options, engine=engine)
459 elif engine and engine != io.engine:
460 raise ValueError(
461 "Engine should not be specified when passing "
462 "an ExcelFile - ExcelFile already has the engine set"
463 )
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/excel/_base.py:1482, in ExcelFile.__init__(self, path_or_buffer, engine, storage_options)
1480 ext = "xls"
1481 else:
-> 1482 ext = inspect_excel_format(
1483 content_or_path=path_or_buffer, storage_options=storage_options
1484 )
1485 if ext is None:
1486 raise ValueError(
1487 "Excel file format cannot be determined, you must specify "
1488 "an engine manually."
1489 )
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/excel/_base.py:1355, in inspect_excel_format(content_or_path, storage_options)
1352 if isinstance(content_or_path, bytes):
1353 content_or_path = BytesIO(content_or_path)
-> 1355 with get_handle(
1356 content_or_path, "rb", storage_options=storage_options, is_text=False
1357 ) as handle:
1358 stream = handle.handle
1359 stream.seek(0)
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/io/common.py:795, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
786 handle = open(
787 handle,
788 ioargs.mode,
(...)
791 newline="",
792 )
793 else:
794 # Binary mode
--> 795 handle = open(handle, ioargs.mode)
796 handles.append(handle)
798 # Convert BytesIO or file objects passed with an encoding
FileNotFoundError: [Errno 2] No such file or directory: 'foo.xlsx'
我明白了#
如果您正尝试对 Series 或 DataFrame 您可能会看到一个例外，如：
In [148]: if pd.Series([False, True, False]):
.....: print("I was true")
.....:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [148], in <cell line: 1>()
----> 1 if pd.Series([False, True, False]):
2 print("I was true")
File /usr/local/lib/python3.10/dist-packages/pandas-1.5.0.dev0+697.gf9762d8f52-py3.10-linux-x86_64.egg/pandas/core/generic.py:1417, in NDFrame.__nonzero__(self)
1415 @final
1416 def __nonzero__(self):
-> 1417 raise ValueError(
1418 f"The truth value of a {type(self).__name__} is ambiguous. "
1419 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
1420 )
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
看见 Comparisons 和 Gotchas 想要一个解释和该怎么做。