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5.7.11. API-DataFrame

Constructor

简介:

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

结构:

DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)[source]

Parameters:

1. data: ndarray (structured or homogeneous), Iterable, dict, or DataFrame
2. index: Index or array-like
3. columns: Index or array-like
4. dtype: dtype, default None
5. copy: bool, default False

备注

DataFrame的创建主要有三种方式

  1. 通过二维数组创建数据框:

    In [42]: arr2 = np.array(np.arange(12)).reshape(4,3)
  ...: print(arr2)
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]
In [43]: df1 = pd.DataFrame(arr2)
    ...: print(df1)
   0   1   2
0  0   1   2
1  3   4   5
2  6   7   8
3  9  10  11

  2. 通过字典的方式创建数据框:

    In [44]: dic2 = {'a':[1,2,3,4],'b':[5,6,7,8],'c':[9,10,11,12],'d':[13,14,15,16]}
    ...: print(dic2)
{'a': [1, 2, 3, 4],
 'b': [5, 6, 7, 8],
 'c': [9, 10, 11, 12],
 'd': [13, 14, 15, 16]}
In [47]: df2 = pd.DataFrame(dic2)
    ...: print(df2)
   a  b   c   d
0  1  5   9  13
1  2  6  10  14
2  3  7  11  15
3  4  8  12  16

  3. 通过数据框的方式创建数据框:

    In [49]: df4 = df2[['a','b']]
    ...: print(df4)
   a  b
0  1  5
1  2  6
2  3  7
3  4  8

Reshaping, sorting, transposing

DataFrame.pivot_table函数

说明:

Create a spreadsheet-style pivot table as a DataFrame

结构:

DataFrame.pivot_table(
    values=None,          # column to aggregate, optional
    index=None,           # Keys to group by on the pivot table index
    columns=None,         # Keys to group by on the pivot table column.
    aggfunc='mean',       # the key is column to aggregate
    fill_value=None,      # Value to replace missing values
    margins=False,        # Add all row / columns
    dropna=True,          # Do not include columns whose entries are all NaN
    margins_name='All',   #
    observed=False
)

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