pandas速记手册

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导入包:

In [1]: import numpy as np

In [2]: import pandas as pd

创建序列：

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

使用date_range()和NumPy创建DataFrame：

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

通过dict创建DataFrame:

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

查看DataFrame每列的类型：

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

查看DataFrame的前后N行数据：

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

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